From 24a08f3aa70475c998aecb34fa1137fa5e21af67 Mon Sep 17 00:00:00 2001 From: David Dotson Date: Tue, 14 Feb 2023 21:25:26 -0700 Subject: [PATCH 01/14] Added tutorial for building AlchemicalNetworks This was originally created for [`alchemiscale`](https://github.com/openforcefield/alchemiscale/pull/46), but is being contributed here instead, since it really is a `gufe` tutorial. --- networks/Preparing AlchemicalNetworks.ipynb | 976 ++++++++++++++++++++ 1 file changed, 976 insertions(+) create mode 100644 networks/Preparing AlchemicalNetworks.ipynb diff --git a/networks/Preparing AlchemicalNetworks.ipynb b/networks/Preparing AlchemicalNetworks.ipynb new file mode 100644 index 0000000..7be925f --- /dev/null +++ b/networks/Preparing AlchemicalNetworks.ipynb @@ -0,0 +1,976 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4ed54201-aa1f-4ba9-bdb7-1c7f5a2cbd05", + "metadata": {}, + "source": [ + "# Preparing `AlchemicalNetwork`s for use with `fah-alchemy`" + ] + }, + { + "cell_type": "markdown", + "id": "6da80e66-6e17-407f-9ba4-a25ded4777bc", + "metadata": {}, + "source": [ + "`fah-alchemy` is a platform for evluating the free energy differences between chemical systems in an alchemical network.\n", + "This notebook will illustrate how to build alchemical networks suitable for submission to a deployed `fah-alchemy` instance." + ] + }, + { + "cell_type": "markdown", + "id": "9d6eaa9e-daa0-4fee-8e72-d1116a1d4954", + "metadata": {}, + "source": [ + "`fah-alchemy` works in terms of `gufe` objects; the `gufe` module defines the data model for `AlchemicalNetwork`s and all objects they are composed of. We'll import the classes of objects we'll use in this tutorial here." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "70c6fe1e-3655-4189-bd97-94ba324cd159", + "metadata": {}, + "outputs": [], + "source": [ + "# suppress `numba` warnings, if present\n", + "from numba.core.errors import NumbaWarning\n", + "import warnings\n", + "\n", + "warnings.simplefilter('ignore', category=NumbaWarning)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6bb3f4a4-2135-494a-8365-9ef229dd124d", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "LICENSE: Could not open license file \"oe_license.txt\" in local directory\n", + "LICENSE: N.B. OE_LICENSE environment variable is not set\n", + "LICENSE: N.B. OE_DIR environment variable is not set\n", + "LICENSE: No product keys!\n", + "LICENSE: No product keys!\n", + "LICENSE: No product keys!\n", + "LICENSE: No product keys!\n" + ] + } + ], + "source": [ + "from gufe import AlchemicalNetwork, Transformation, ChemicalSystem\n", + "from gufe.components import ProteinComponent, SmallMoleculeComponent, SolventComponent\n", + "\n", + "from openff.units import unit" + ] + }, + { + "cell_type": "markdown", + "id": "99190957-890d-4729-ac72-bef35fbb212f", + "metadata": {}, + "source": [ + "## Sample network from `openfe-benchmark`" + ] + }, + { + "cell_type": "markdown", + "id": "c7362410-7045-42a6-8249-93a1e832d8c9", + "metadata": {}, + "source": [ + "We'll use a sample network in `openfe-benchmark` for demonstration purposes. The sources can be found here: https://github.com/OpenFreeEnergy/openfe-benchmarks\n", + "\n", + "In particular, we'll use the `tyk2` network. We'll extract ligands manually from the ligand SDF, and the protein target from its PDB." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "08f7ddfd-4fd5-4bef-8bf9-b2a286b094c6", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" + ] + } + ], + "source": [ + "from importlib import resources\n", + "from rdkit import Chem\n", + "\n", + "from openfe_benchmarks import tyk2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d3d7946f-6b31-4dfd-b337-7a41d717e391", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tyk2_system = tyk2.get_system()\n", + "tyk2_system" + ] + }, + { + "cell_type": "markdown", + "id": "7aff6317-abe0-40df-99db-9d1eb121b20e", + "metadata": {}, + "source": [ + "The connections for the network are defined here; we'll use these for building up our own `AlchemicalNetwork`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ec682c29-12cf-46dc-aea7-8b7e45324529", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('lig_ejm_31', 'lig_ejm_50'),\n", + " ('lig_ejm_46', 'lig_jmc_23'),\n", + " ('lig_ejm_31', 'lig_ejm_55'),\n", + " ('lig_ejm_31', 'lig_ejm_48'),\n", + " ('lig_ejm_31', 'lig_ejm_54'),\n", + " ('lig_ejm_31', 'lig_ejm_47'),\n", + " ('lig_ejm_31', 'lig_ejm_46'),\n", + " ('lig_ejm_46', 'lig_jmc_27'),\n", + " ('lig_ejm_46', 'lig_jmc_28'),\n", + " ('lig_ejm_42', 'lig_ejm_43'),\n", + " ('lig_ejm_31', 'lig_ejm_42'),\n", + " ('lig_ejm_45', 'lig_ejm_55')]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tyk2_system.connections" + ] + }, + { + "cell_type": "markdown", + "id": "5d3285c5-0b7e-461f-a761-dabd01182775", + "metadata": {}, + "source": [ + "## Define `ChemicalSystem`s for network nodes" + ] + }, + { + "cell_type": "markdown", + "id": "90ddf9d5-71d0-44ed-8eea-5ea4d512f615", + "metadata": {}, + "source": [ + "An `AlchemicalNetwork` features `ChemicalSystem`s as nodes and `Transformation`s as directed edges between nodes. We'll start by defining the nodes for our network.\n", + "\n", + "A `ChemicalSystem` is made of one or more `Component`s. These can be one of `ProteinComponent`, `SmallMoleculeComponent`, or `SolventComponent`, and potentially others as needed. This design allows for memory efficient representation of large networks with perhaps hundreds or thousands of nodes, but perhaps far fewer variants in proteins, ligands, etc." + ] + }, + { + "cell_type": "markdown", + "id": "0b64f1c8-0fa0-4b10-baba-7014f9de9440", + "metadata": {}, + "source": [ + "### Define `Component`s for a given `ChemicalSystem`" + ] + }, + { + "cell_type": "markdown", + "id": "90fd672d-ce78-48de-811a-7490ada1ea3b", + "metadata": {}, + "source": [ + "Let's start by assembling the ligands. These are defined as `SmallMoleculeComponent`s, and can be initialized with RDKit molecules. \n", + "\n", + "We'll read a multimolecule SDF from `openfe-benchmarks` and create a `SmallMoleculeComponent` for each ligand in the file:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "7880e75b-d28d-411c-b5c1-3aafbed31099", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[SmallMoleculeComponent(name=lig_ejm_31),\n", + " SmallMoleculeComponent(name=lig_ejm_42),\n", + " SmallMoleculeComponent(name=lig_ejm_43),\n", + " SmallMoleculeComponent(name=lig_ejm_45),\n", + " SmallMoleculeComponent(name=lig_ejm_46),\n", + " SmallMoleculeComponent(name=lig_ejm_47),\n", + " SmallMoleculeComponent(name=lig_ejm_48),\n", + " SmallMoleculeComponent(name=lig_ejm_50),\n", + " SmallMoleculeComponent(name=lig_ejm_54),\n", + " SmallMoleculeComponent(name=lig_ejm_55),\n", + " SmallMoleculeComponent(name=lig_jmc_23),\n", + " SmallMoleculeComponent(name=lig_jmc_27),\n", + " SmallMoleculeComponent(name=lig_jmc_28)]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with resources.path('openfe_benchmarks.data',\n", + " 'tyk2_ligands.sdf') as fn:\n", + " ligands_sdf = Chem.SDMolSupplier(str(fn), removeHs=False)\n", + " ligands = [SmallMoleculeComponent(rdkit_ligand) for rdkit_ligand in ligands_sdf]\n", + "\n", + "ligands" + ] + }, + { + "cell_type": "markdown", + "id": "d0f8e9a5-80a1-4eef-86e9-b47a9ba1f9c5", + "metadata": {}, + "source": [ + "We'll also load our protein into a `ProteinComponent`:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "8ffcd2d6-47ec-46ee-bfe8-8182915f01bd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "ProteinComponent(name=tyk2)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with resources.path('openfe_benchmarks.data',\n", + " 'tyk2_protein.pdb') as fn:\n", + " protein = ProteinComponent.from_pdb_file(str(fn), name='tyk2')\n", + "\n", + "protein" + ] + }, + { + "cell_type": "markdown", + "id": "e653c208-1f69-4f08-a38e-55b7123292d9", + "metadata": {}, + "source": [ + "We'll also need at least one `SolventComponent` to encode our choice of solvent and counterions, with concentration:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3c47166d-143b-41f0-9dce-7805e56d7191", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SolventComponent(name=O, Na+, Cl-)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solvent = SolventComponent(positive_ion='Na', \n", + " negative_ion='Cl',\n", + " neutralize=True, \n", + " ion_concentration=0.15*unit.molar)\n", + "solvent" + ] + }, + { + "cell_type": "markdown", + "id": "9306e96d-f257-4b71-83e1-9814701570cd", + "metadata": {}, + "source": [ + "The `SolventComponent` doesn't actually perform any actual solvation (packing water molecules, ions); that is performed just before simulation time during `Protocol` execution." + ] + }, + { + "cell_type": "markdown", + "id": "ea4dfd8d-4ab6-44c8-a268-abb3d1505803", + "metadata": {}, + "source": [ + "Each of the ligands have been pre-docked into the protein and aligned to their common scaffold. It is important to recognize that any processing required to prepare ligand and protein structures for alchemical free energy calculations should be done *before* the steps we are taking here." + ] + }, + { + "cell_type": "markdown", + "id": "b0b11357-2179-496b-9bff-89303e1c9c33", + "metadata": {}, + "source": [ + "### Build the `ChemicalSystem`s" + ] + }, + { + "cell_type": "markdown", + "id": "c314a1a2-afcc-4390-9ed0-16fae3d48c6e", + "metadata": {}, + "source": [ + "We can now construct the `ChemicalSystem`s we want represented in our network. Since we are planning to perform relative binding free energy (RBFE) calculations, we'll define both *complex* and *solvent* variants for each ligand." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "10edc8b3-5f45-4e37-b1d5-bd508601e352", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'lig_ejm_31': ChemicalSystem(name=lig_ejm_31_complex, components={'ligand': SmallMoleculeComponent(name=lig_ejm_31), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)}),\n", + " 'lig_ejm_42': ChemicalSystem(name=lig_ejm_42_complex, components={'ligand': SmallMoleculeComponent(name=lig_ejm_42), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)}),\n", + " 'lig_ejm_43': ChemicalSystem(name=lig_ejm_43_complex, components={'ligand': SmallMoleculeComponent(name=lig_ejm_43), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)}),\n", + " 'lig_ejm_45': ChemicalSystem(name=lig_ejm_45_complex, components={'ligand': SmallMoleculeComponent(name=lig_ejm_45), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)}),\n", + " 'lig_ejm_46': ChemicalSystem(name=lig_ejm_46_complex, components={'ligand': SmallMoleculeComponent(name=lig_ejm_46), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)}),\n", + " 'lig_ejm_47': ChemicalSystem(name=lig_ejm_47_complex, components={'ligand': SmallMoleculeComponent(name=lig_ejm_47), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)}),\n", + " 'lig_ejm_48': ChemicalSystem(name=lig_ejm_48_complex, components={'ligand': SmallMoleculeComponent(name=lig_ejm_48), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)}),\n", + " 'lig_ejm_50': ChemicalSystem(name=lig_ejm_50_complex, components={'ligand': SmallMoleculeComponent(name=lig_ejm_50), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)}),\n", + " 'lig_ejm_54': ChemicalSystem(name=lig_ejm_54_complex, components={'ligand': SmallMoleculeComponent(name=lig_ejm_54), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)}),\n", + " 'lig_ejm_55': ChemicalSystem(name=lig_ejm_55_complex, components={'ligand': SmallMoleculeComponent(name=lig_ejm_55), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)}),\n", + " 'lig_jmc_23': ChemicalSystem(name=lig_jmc_23_complex, components={'ligand': SmallMoleculeComponent(name=lig_jmc_23), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)}),\n", + " 'lig_jmc_27': ChemicalSystem(name=lig_jmc_27_complex, components={'ligand': SmallMoleculeComponent(name=lig_jmc_27), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)}),\n", + " 'lig_jmc_28': ChemicalSystem(name=lig_jmc_28_complex, components={'ligand': SmallMoleculeComponent(name=lig_jmc_28), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)})}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "complexed = {l.name: ChemicalSystem(components={'ligand': l, \n", + " 'solvent': solvent, \n", + " 'protein': protein}, \n", + " name=f\"{l.name}_complex\") \n", + " for l in ligands}\n", + "complexed" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c8a0b60d-bfae-4f99-9f4d-656c982433b9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'lig_ejm_31': ChemicalSystem(name=lig_ejm_31_water, components={'ligand': SmallMoleculeComponent(name=lig_ejm_31), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n", + " 'lig_ejm_42': ChemicalSystem(name=lig_ejm_42_water, components={'ligand': SmallMoleculeComponent(name=lig_ejm_42), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n", + " 'lig_ejm_43': ChemicalSystem(name=lig_ejm_43_water, components={'ligand': SmallMoleculeComponent(name=lig_ejm_43), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n", + " 'lig_ejm_45': ChemicalSystem(name=lig_ejm_45_water, components={'ligand': SmallMoleculeComponent(name=lig_ejm_45), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n", + " 'lig_ejm_46': ChemicalSystem(name=lig_ejm_46_water, components={'ligand': SmallMoleculeComponent(name=lig_ejm_46), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n", + " 'lig_ejm_47': ChemicalSystem(name=lig_ejm_47_water, components={'ligand': SmallMoleculeComponent(name=lig_ejm_47), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n", + " 'lig_ejm_48': ChemicalSystem(name=lig_ejm_48_water, components={'ligand': SmallMoleculeComponent(name=lig_ejm_48), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n", + " 'lig_ejm_50': ChemicalSystem(name=lig_ejm_50_water, components={'ligand': SmallMoleculeComponent(name=lig_ejm_50), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n", + " 'lig_ejm_54': ChemicalSystem(name=lig_ejm_54_water, components={'ligand': SmallMoleculeComponent(name=lig_ejm_54), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n", + " 'lig_ejm_55': ChemicalSystem(name=lig_ejm_55_water, components={'ligand': SmallMoleculeComponent(name=lig_ejm_55), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n", + " 'lig_jmc_23': ChemicalSystem(name=lig_jmc_23_water, components={'ligand': SmallMoleculeComponent(name=lig_jmc_23), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n", + " 'lig_jmc_27': ChemicalSystem(name=lig_jmc_27_water, components={'ligand': SmallMoleculeComponent(name=lig_jmc_27), 'solvent': SolventComponent(name=O, Na+, Cl-)}),\n", + " 'lig_jmc_28': ChemicalSystem(name=lig_jmc_28_water, components={'ligand': SmallMoleculeComponent(name=lig_jmc_28), 'solvent': SolventComponent(name=O, Na+, Cl-)})}" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "solvated = {l.name: ChemicalSystem(components={'ligand': l, \n", + " 'solvent': solvent}, \n", + " name=f\"{l.name}_water\") \n", + " for l in ligands}\n", + "solvated" + ] + }, + { + "cell_type": "markdown", + "id": "7566fda4-6c8d-4003-8601-45f05face65a", + "metadata": {}, + "source": [ + "We now have all our network nodes defined. Next, we need to define the `Transformation`s that we wish to perform between them." + ] + }, + { + "cell_type": "markdown", + "id": "7bc3a65a-979c-4114-b948-f7315796709d", + "metadata": {}, + "source": [ + "## Define `Transformation`s between `ChemicalSystem`s as network edges" + ] + }, + { + "cell_type": "markdown", + "id": "0a92b4c1-fffb-410c-9c76-177fe3ea0a87", + "metadata": {}, + "source": [ + "A `Transformation` is a directed edge between two `ChemicalSystem`s. It includes a `Protocol` parameterized with `Settings`, and if optionally a `ComponentMapping`. \n", + "\n", + "The `Protocol` defines the actual computational method used to evaluate the `Transformation` to yield estimates for the free energy difference between the `ChemicalSystem`s.\n", + "\n", + "The `ComponentMapping` defines the atom mapping(s) between corresponding `Component`s in the two `ChemicalSystem`s. This is often critical for relative binding free energy calculations, since the choice of mapping can heavily influence convergence of the resulting estimates." + ] + }, + { + "cell_type": "markdown", + "id": "ace9de53-2527-47f5-8aae-98d9adef2c1d", + "metadata": {}, + "source": [ + "### Define the `Protocol` used for `Transformation` evaluation" + ] + }, + { + "cell_type": "markdown", + "id": "0338be2a-b861-4534-bc78-49c2e00ca2ac", + "metadata": {}, + "source": [ + "For this example, we'll use the same `Protocol` for all our `Transformation`s, with identical `Settings` for each." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "183b23a6-1839-4586-bbbe-60b2defd2f01", + "metadata": {}, + "outputs": [], + "source": [ + "from perses.protocols.nonequilibrium_cycling import NonEquilibriumCyclingProtocol" + ] + }, + { + "cell_type": "markdown", + "id": "9e686c7c-9ef7-4e97-b384-63334eeb894e", + "metadata": {}, + "source": [ + "Any given `Protocol` features a `default_settings` method, which can be used to get the default settings that are specific to that `Protocol`:" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "d7bac455-ddc1-4735-96d7-5c6681ab3cbc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'lambda_functions': {'lambda_sterics_core': 'lambda',\n", + " 'lambda_electrostatics_core': 'lambda',\n", + " 'lambda_sterics_insert': 'select(step(lambda - 0.5), 1.0, 2.0 * lambda)',\n", + " 'lambda_sterics_delete': 'select(step(lambda - 0.5), 2.0 * (lambda - 0.5), 0.0)',\n", + " 'lambda_electrostatics_insert': 'select(step(lambda - 0.5), 2.0 * (lambda - 0.5), 0.0)',\n", + " 'lambda_electrostatics_delete': 'select(step(lambda - 0.5), 1.0, 2.0 * lambda)',\n", + " 'lambda_bonds': 'lambda',\n", + " 'lambda_angles': 'lambda',\n", + " 'lambda_torsions': 'lambda'},\n", + " 'softcore_LJ_v2': True,\n", + " 'interpolate_old_and_new_14s': False,\n", + " 'phase': 'vacuum',\n", + " 'forcefield_files': ['amber/ff14SB.xml',\n", + " 'amber/tip3p_standard.xml',\n", + " 'amber/tip3p_HFE_multivalent.xml',\n", + " 'amber/phosaa10.xml'],\n", + " 'small_molecule_forcefield': 'openff-2.0.0',\n", + " 'timestep': 4.0 ,\n", + " 'neq_splitting': 'V R H O R V',\n", + " 'eq_steps': 1000,\n", + " 'neq_steps': 100,\n", + " 'platform': 'CUDA',\n", + " 'save_frequency': 100}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "protocol_settings = NonEquilibriumCyclingProtocol.default_settings()\n", + "protocol_settings.dict()" + ] + }, + { + "cell_type": "markdown", + "id": "d7898feb-419a-45cc-8837-53cd583d130b", + "metadata": {}, + "source": [ + "These can be edited, e.g. with:" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "998ef869-7086-4c24-9c53-2d4e9553b6b9", + "metadata": {}, + "outputs": [ + { + "ename": "TypeError", + "evalue": "\"NonEqCyclingSettings\" is immutable and does not support item assignment", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mprotocol_settings\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave_frequency\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m200\u001b[39m\n", + "File \u001b[0;32m~/.conda/envs/fah-alchemy-client/lib/python3.9/site-packages/pydantic/main.py:360\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__setattr__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: \"NonEqCyclingSettings\" is immutable and does not support item assignment" + ] + } + ], + "source": [ + "protocol_settings.save_frequency = 200" + ] + }, + { + "cell_type": "markdown", + "id": "8af105c1-08fe-4f49-a422-a6d6307695d5", + "metadata": {}, + "source": [ + "We'll construct our full `Settings` for our chosen `NonEquilibriumCyclingProtocol`, which will include the more general `ThermoSettings` and `ForcefieldSettings` as well:" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "19abd504-1e44-413e-8740-42413163a25a", + "metadata": {}, + "outputs": [ + { + "ename": "ValidationError", + "evalue": "3 validation errors for ForcefieldSettings\nvdW\n field required (type=value_error.missing)\nelectrostatics\n field required (type=value_error.missing)\ngbsa\n field required (type=value_error.missing)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[24], line 12\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgufe\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msettings\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 3\u001b[0m Settings, \n\u001b[1;32m 4\u001b[0m ThermoSettings, \n\u001b[1;32m 5\u001b[0m ForcefieldSettings,\n\u001b[1;32m 6\u001b[0m )\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mperses\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprotocols\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msettings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m NonEqCyclingSettings\n\u001b[1;32m 9\u001b[0m settings \u001b[38;5;241m=\u001b[39m Settings(\n\u001b[1;32m 10\u001b[0m settings_version\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 11\u001b[0m forcefield_file\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfoobar.xml\u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[0;32m---> 12\u001b[0m forcefield_settings\u001b[38;5;241m=\u001b[39m\u001b[43mForcefieldSettings\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 13\u001b[0m thermo_settings\u001b[38;5;241m=\u001b[39mThermoSettings(temperature\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m300\u001b[39m\u001b[38;5;241m*\u001b[39munit\u001b[38;5;241m.\u001b[39mkelvin),\n\u001b[1;32m 14\u001b[0m protocol_settings\u001b[38;5;241m=\u001b[39mprotocol_settings,\n\u001b[1;32m 15\u001b[0m )\n", + "File \u001b[0;32m~/.conda/envs/fah-alchemy-client/lib/python3.9/site-packages/pydantic/main.py:342\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mValidationError\u001b[0m: 3 validation errors for ForcefieldSettings\nvdW\n field required (type=value_error.missing)\nelectrostatics\n field required (type=value_error.missing)\ngbsa\n field required (type=value_error.missing)" + ] + } + ], + "source": [ + "from openff.units import unit\n", + "from gufe.settings.models import (\n", + " Settings, \n", + " ThermoSettings, \n", + " ForcefieldSettings,\n", + ")\n", + "from perses.protocols.settings import NonEqCyclingSettings\n", + "\n", + "settings = Settings(\n", + " settings_version=0,\n", + " forcefield_file=\"foobar.xml\", \n", + " forcefield_settings=ForcefieldSettings(),\n", + " thermo_settings=ThermoSettings(temperature=300*unit.kelvin),\n", + " protocol_settings=protocol_settings,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "70e558b5-93e4-4c91-8ff0-bc5b99646a71", + "metadata": {}, + "source": [ + "We can now produce a parameterized `NonEquilibriumCyclingProtocol` instance:" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "bd1d0a0d-00c9-44a1-820c-186b4ee05334", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'settings' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m protocol \u001b[38;5;241m=\u001b[39m NonEquilibriumCyclingProtocol(\u001b[43msettings\u001b[49m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'settings' is not defined" + ] + } + ], + "source": [ + "protocol = NonEquilibriumCyclingProtocol(settings)" + ] + }, + { + "cell_type": "markdown", + "id": "57239762-43b9-4501-b3ad-9eebbcd64a20", + "metadata": {}, + "source": [ + "### Build the `Transformation`s" + ] + }, + { + "cell_type": "markdown", + "id": "8ccdec56-519e-4293-86c4-60b0255cfcad", + "metadata": {}, + "source": [ + "We can now construct the `Transformation`s we want represented in our network. We'll use the predefined connections from the `tyk2` system from above as the basis for our choices here, but you could use any network planner of your choice to generate connections and use those instead." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "abdcc8cb-6460-42f5-9a3b-f360041fd1e4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('lig_ejm_31', 'lig_ejm_50'),\n", + " ('lig_ejm_46', 'lig_jmc_23'),\n", + " ('lig_ejm_31', 'lig_ejm_55'),\n", + " ('lig_ejm_31', 'lig_ejm_48'),\n", + " ('lig_ejm_31', 'lig_ejm_54'),\n", + " ('lig_ejm_31', 'lig_ejm_47'),\n", + " ('lig_ejm_31', 'lig_ejm_46'),\n", + " ('lig_ejm_46', 'lig_jmc_27'),\n", + " ('lig_ejm_46', 'lig_jmc_28'),\n", + " ('lig_ejm_42', 'lig_ejm_43'),\n", + " ('lig_ejm_31', 'lig_ejm_42'),\n", + " ('lig_ejm_45', 'lig_ejm_55')]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tyk2_system.connections" + ] + }, + { + "cell_type": "markdown", + "id": "b1b65f19-15bb-49df-88e8-ddf8adccf8e1", + "metadata": {}, + "source": [ + "**TODO: need to add mappings for each edge; these would be included in the `Transformation` creations below.**" + ] + }, + { + "cell_type": "markdown", + "id": "cfd3fe16-1666-4b78-8263-becff81dfef1", + "metadata": {}, + "source": [ + "Since we are planning to perform relative binding free energy (RBFE) calculations, we'll define both *complex* and *solvent* variants for each `Transformation`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c1b4bf5-8fcb-4ce8-8eba-12ad3c4c93f8", + "metadata": {}, + "outputs": [], + "source": [ + "complexed_transformations = [Transformation(stateA=complexed[edge[0]], \n", + " stateB=complexed[edge[1]], \n", + " protocol=protocol) \n", + " for edge in tyk2s.connections]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e278a01-432f-4fb5-a004-a76eff100058", + "metadata": {}, + "outputs": [], + "source": [ + "solvated_transformations = [Transformation(stateA=solvated[edge[0]], \n", + " stateB=solvated[edge[1]], \n", + " protocol=protocol) \n", + " for edge in tyk2s.connections]" + ] + }, + { + "cell_type": "markdown", + "id": "36c79a08-d4aa-4388-a0af-be51906f7672", + "metadata": {}, + "source": [ + "## Create the `AlchemicalNetwork`" + ] + }, + { + "cell_type": "markdown", + "id": "6ebc1174-b348-4e44-97f7-444c44c8d6d0", + "metadata": {}, + "source": [ + "An `AlchemicalNetwork` is simply the combination of `ChemicalSystem`s (nodes) and `Transformation`s (directed edges) that we want to evaluate $\\Delta G$s for. This data structure functions as a declaration of what you want to compute, and is the central object on which systems like `fah-alchemy` operate. \n", + "\n", + "We'll finish here by creating an `AlchemicalNetwork` from the collection of objects we've built so far." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "b45baada-efa4-46f7-a2cb-6e24a9309f1e", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'solvent_network' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[27], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m an \u001b[38;5;241m=\u001b[39m AlchemicalNetwork(edges\u001b[38;5;241m=\u001b[39m(\u001b[43msolvent_network\u001b[49m \u001b[38;5;241m+\u001b[39m complex_network), name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtyk2_relative_benchmark\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mNameError\u001b[0m: name 'solvent_network' is not defined" + ] + } + ], + "source": [ + "network = AlchemicalNetwork(edges=(solvated_transformations + complex_transformations), \n", + " nodes=(solvated + complexed),\n", + " name=\"tyk2_relative_benchmark\")\n", + "network" + ] + }, + { + "cell_type": "markdown", + "id": "262c5908-640d-45dc-9ca4-6d912428b8ac", + "metadata": {}, + "source": [ + "That's it! We simply toss in all `Transformation`s (edges) and `ChemicalSystem`s (nodes) we want included in this `AlchemicalNetwork`, and optionally give it a name that means something to us (it need not be unique, but can be used to query for network(s) from `fah-alchemy` later)." + ] + }, + { + "cell_type": "markdown", + "id": "43849b09-5031-4eb0-989c-ceca2110f736", + "metadata": {}, + "source": [ + "We could have chosen here to leave the `nodes` argument off, since every `ChemicalSystem` we included was already represented among the `edges`, but we show it here for completeness. In this way, it's possible to include `ChemicalSystem`s in the network that aren't connected via any `Transformation`s to others, though in practice there isn't much utility in this." + ] + }, + { + "cell_type": "markdown", + "id": "4e4bfff8-0072-4ad7-9b5e-3ac3f6bdc551", + "metadata": {}, + "source": [ + "### Optional: Run a `Protocol` locally" + ] + }, + { + "cell_type": "markdown", + "id": "954ce90a-2c7f-4ffc-a218-e5c8298d9ce5", + "metadata": {}, + "source": [ + "We can run our parameterized `NonEqulibriumCyclingProtocol` locally as a way to check if things are working as we expect. We'll pick one of our `Transformation`s out from our `AlchemicalNetwork`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "751b25e3-4d1e-4d5e-a870-12cc12021f73", + "metadata": {}, + "outputs": [], + "source": [ + "transformation = list(network.edges)[0]" + ] + }, + { + "cell_type": "markdown", + "id": "7b068982-1e5d-478d-9adc-6f86059ebbbd", + "metadata": {}, + "source": [ + "We'll generate a `ProtocolDAG` that encodes the actual operations to perform in order to execute the `Protocol`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdcc8b9f-65aa-4f8b-8661-1fe4eeb997b3", + "metadata": {}, + "outputs": [], + "source": [ + "protocoldag = transformation.create()" + ] + }, + { + "cell_type": "markdown", + "id": "1fea6b7d-0ba8-4804-be0c-6e3bf935bc78", + "metadata": {}, + "source": [ + "And we'll run it locally, in-process. This will run each `ProtocolUnit` in the `ProtocolDAG` in series, in dependency order:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "271b0164-9e0e-4578-b637-81124903e455", + "metadata": {}, + "outputs": [], + "source": [ + "from gufe.protocols.protocoldag import execute_DAG" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58c4e234-a112-4ba7-9eec-e55b64e39ba1", + "metadata": {}, + "outputs": [], + "source": [ + "protocoldagresult = execute_DAG(protocoldag)" + ] + }, + { + "cell_type": "markdown", + "id": "f6e222af-ae1c-4437-aaac-0fe0f5d9fb16", + "metadata": {}, + "source": [ + "The above will raise an exception if at any point execution failed." + ] + }, + { + "cell_type": "markdown", + "id": "303bb240-a7fc-4271-9cea-76a6f565c1d0", + "metadata": {}, + "source": [ + "## Submitting to a `fah-alchemy` instance" + ] + }, + { + "cell_type": "markdown", + "id": "422ebed7-ea7b-4f40-8ec5-efa31caf40eb", + "metadata": {}, + "source": [ + "We'd like to evaluate the `Transformation`s we defined in our `AlchemicalNetwork`, but to do this at scale we'll submit this to a `fah-alchemy` API instance.\n", + "\n", + "Deploying `fah-alchemy` is outside of the scope of this tutorial, and we assume here that there is already an instance in place and network-reachable from the machine running this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "00ed9c82-84cd-4d7c-8e58-cbb56de87789", + "metadata": {}, + "outputs": [], + "source": [ + "from getpass import getpass\n", + "\n", + "from fah_alchemy import FahAlchemyClient, Scope" + ] + }, + { + "cell_type": "markdown", + "id": "398a8a77-57ef-469f-a3d1-63e3bad393d0", + "metadata": {}, + "source": [ + "Instantiate a `FahAlchemyClient`, giving the URL to the target `FahAlchemyAPI`, as well as your user identifier and key (password):" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "45f3da41-ff39-48fa-9002-1a25e48c9a56", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'FahAlchemyClient' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m faclient \u001b[38;5;241m=\u001b[39m \u001b[43mFahAlchemyClient\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://api.targetserver.org\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124musername\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m getpass())\n", + "\u001b[0;31mNameError\u001b[0m: name 'FahAlchemyClient' is not defined" + ] + } + ], + "source": [ + "faclient = FahAlchemyClient(\"https://api.targetserver.org\",\n", + " \"username\",\n", + " getpass())" + ] + }, + { + "cell_type": "markdown", + "id": "56dad5d8-78c6-4800-a5fc-acfa4ee2ac36", + "metadata": {}, + "source": [ + "We'll then use this client to submit our `AlchemicalNetwork`, indicating the `Scope` (organization, campaign, project) that this network should be submitted under:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fc39a375-dc81-4f22-8b8c-7c8a3de32137", + "metadata": {}, + "outputs": [], + "source": [ + "faclient.create_network(network, Scope('my_org', 'my_campaign', 'my_project'))" + ] + }, + { + "cell_type": "markdown", + "id": "e56caa55-ff6e-41b7-865c-ab52258e9588", + "metadata": {}, + "source": [ + "We can then action which `Transformation`s we want computed:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a9e1c2f4-d6ce-4df8-a725-b5f3374fefcf", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From dcfc1fd4ff3bc29b44abc4677bf6afc2444c4663 Mon Sep 17 00:00:00 2001 From: David Dotson Date: Thu, 16 Feb 2023 09:54:40 -0700 Subject: [PATCH 02/14] Updates to notebook and binder env to support it --- .binder/environment.yml | 15 ++ networks/Preparing AlchemicalNetworks.ipynb | 212 ++++---------------- 2 files changed, 55 insertions(+), 172 deletions(-) diff --git a/.binder/environment.yml b/.binder/environment.yml index 67009c4..00bf481 100644 --- a/.binder/environment.yml +++ b/.binder/environment.yml @@ -2,6 +2,7 @@ name: openfe-notebooks channels: - jaimergp/label/unsupported-cudatoolkit-shim - conda-forge + - openeye dependencies: - MDAnalysis - click @@ -31,3 +32,17 @@ dependencies: - typing_extensions - gufe==0.5.* - openfe==0.5.* + + ## needed for perses + - openmoltools + - cloudpathlib + - dask + - distributed + - openeye-toolkits + + - pip: + - git+https://github.com/OpenFreeEnergy/gufe + - git+https://github.com/OpenFreeEnergy/openfe + - git+https://github.com/dotsdl/openfe-benchmarks@ligandnetwork + - git+https://github.com/mikemhenry/openff-models.git@support_nested_models + - git+https://github.com/choderalab/perses@protocol-neqcyc diff --git a/networks/Preparing AlchemicalNetworks.ipynb b/networks/Preparing AlchemicalNetworks.ipynb index 7be925f..ea1de86 100644 --- a/networks/Preparing AlchemicalNetworks.ipynb +++ b/networks/Preparing AlchemicalNetworks.ipynb @@ -28,7 +28,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "70c6fe1e-3655-4189-bd97-94ba324cd159", + "id": "27b4aa61-ca82-44fa-be84-c802f1083a29", "metadata": {}, "outputs": [], "source": [ @@ -114,7 +114,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -460,7 +460,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "id": "183b23a6-1839-4586-bbbe-60b2defd2f01", "metadata": {}, "outputs": [], @@ -478,14 +478,17 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 12, "id": "d7bac455-ddc1-4735-96d7-5c6681ab3cbc", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [ { "data": { "text/plain": [ - "{'lambda_functions': {'lambda_sterics_core': 'lambda',\n", + "{'num_replicates': 1,\n", + " 'lambda_functions': {'lambda_sterics_core': 'lambda',\n", " 'lambda_electrostatics_core': 'lambda',\n", " 'lambda_sterics_insert': 'select(step(lambda - 0.5), 1.0, 2.0 * lambda)',\n", " 'lambda_sterics_delete': 'select(step(lambda - 0.5), 2.0 * (lambda - 0.5), 0.0)',\n", @@ -496,12 +499,6 @@ " 'lambda_torsions': 'lambda'},\n", " 'softcore_LJ_v2': True,\n", " 'interpolate_old_and_new_14s': False,\n", - " 'phase': 'vacuum',\n", - " 'forcefield_files': ['amber/ff14SB.xml',\n", - " 'amber/tip3p_standard.xml',\n", - " 'amber/tip3p_HFE_multivalent.xml',\n", - " 'amber/phosaa10.xml'],\n", - " 'small_molecule_forcefield': 'openff-2.0.0',\n", " 'timestep': 4.0 ,\n", " 'neq_splitting': 'V R H O R V',\n", " 'eq_steps': 1000,\n", @@ -510,7 +507,7 @@ " 'save_frequency': 100}" ] }, - "execution_count": 23, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -530,23 +527,12 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 13, "id": "998ef869-7086-4c24-9c53-2d4e9553b6b9", - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "\"NonEqCyclingSettings\" is immutable and does not support item assignment", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mprotocol_settings\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave_frequency\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m200\u001b[39m\n", - "File \u001b[0;32m~/.conda/envs/fah-alchemy-client/lib/python3.9/site-packages/pydantic/main.py:360\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__setattr__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: \"NonEqCyclingSettings\" is immutable and does not support item assignment" - ] - } - ], + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "protocol_settings.save_frequency = 200" ] @@ -561,36 +547,22 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 16, "id": "19abd504-1e44-413e-8740-42413163a25a", "metadata": {}, - "outputs": [ - { - "ename": "ValidationError", - "evalue": "3 validation errors for ForcefieldSettings\nvdW\n field required (type=value_error.missing)\nelectrostatics\n field required (type=value_error.missing)\ngbsa\n field required (type=value_error.missing)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValidationError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[24], line 12\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgufe\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msettings\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m 3\u001b[0m Settings, \n\u001b[1;32m 4\u001b[0m ThermoSettings, \n\u001b[1;32m 5\u001b[0m ForcefieldSettings,\n\u001b[1;32m 6\u001b[0m )\n\u001b[1;32m 7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mperses\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprotocols\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msettings\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m NonEqCyclingSettings\n\u001b[1;32m 9\u001b[0m settings \u001b[38;5;241m=\u001b[39m Settings(\n\u001b[1;32m 10\u001b[0m settings_version\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 11\u001b[0m forcefield_file\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfoobar.xml\u001b[39m\u001b[38;5;124m\"\u001b[39m, \n\u001b[0;32m---> 12\u001b[0m forcefield_settings\u001b[38;5;241m=\u001b[39m\u001b[43mForcefieldSettings\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 13\u001b[0m thermo_settings\u001b[38;5;241m=\u001b[39mThermoSettings(temperature\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m300\u001b[39m\u001b[38;5;241m*\u001b[39munit\u001b[38;5;241m.\u001b[39mkelvin),\n\u001b[1;32m 14\u001b[0m protocol_settings\u001b[38;5;241m=\u001b[39mprotocol_settings,\n\u001b[1;32m 15\u001b[0m )\n", - "File \u001b[0;32m~/.conda/envs/fah-alchemy-client/lib/python3.9/site-packages/pydantic/main.py:342\u001b[0m, in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mValidationError\u001b[0m: 3 validation errors for ForcefieldSettings\nvdW\n field required (type=value_error.missing)\nelectrostatics\n field required (type=value_error.missing)\ngbsa\n field required (type=value_error.missing)" - ] - } - ], + "outputs": [], "source": [ "from openff.units import unit\n", "from gufe.settings.models import (\n", " Settings, \n", " ThermoSettings, \n", - " ForcefieldSettings,\n", + " OpenMMSystemGeneratorFFSettings,\n", ")\n", "from perses.protocols.settings import NonEqCyclingSettings\n", "\n", "settings = Settings(\n", " settings_version=0,\n", - " forcefield_file=\"foobar.xml\", \n", - " forcefield_settings=ForcefieldSettings(),\n", + " forcefield_settings=OpenMMSystemGeneratorFFSettings(),\n", " thermo_settings=ThermoSettings(temperature=300*unit.kelvin),\n", " protocol_settings=protocol_settings,\n", ")" @@ -606,22 +578,10 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 17, "id": "bd1d0a0d-00c9-44a1-820c-186b4ee05334", "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'settings' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m protocol \u001b[38;5;241m=\u001b[39m NonEquilibriumCyclingProtocol(\u001b[43msettings\u001b[49m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'settings' is not defined" - ] - } - ], + "outputs": [], "source": [ "protocol = NonEquilibriumCyclingProtocol(settings)" ] @@ -644,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 18, "id": "abdcc8cb-6460-42f5-9a3b-f360041fd1e4", "metadata": {}, "outputs": [ @@ -665,7 +625,7 @@ " ('lig_ejm_45', 'lig_ejm_55')]" ] }, - "execution_count": 26, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -692,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "9c1b4bf5-8fcb-4ce8-8eba-12ad3c4c93f8", "metadata": {}, "outputs": [], @@ -700,12 +660,12 @@ "complexed_transformations = [Transformation(stateA=complexed[edge[0]], \n", " stateB=complexed[edge[1]], \n", " protocol=protocol) \n", - " for edge in tyk2s.connections]" + " for edge in tyk2_system.connections]" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "2e278a01-432f-4fb5-a004-a76eff100058", "metadata": {}, "outputs": [], @@ -713,7 +673,7 @@ "solvated_transformations = [Transformation(stateA=solvated[edge[0]], \n", " stateB=solvated[edge[1]], \n", " protocol=protocol) \n", - " for edge in tyk2s.connections]" + " for edge in tyk2_system.connections]" ] }, { @@ -736,25 +696,29 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 24, "id": "b45baada-efa4-46f7-a2cb-6e24a9309f1e", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'solvent_network' is not defined", + "ename": "TypeError", + "evalue": "unhashable type: 'Settings'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[27], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m an \u001b[38;5;241m=\u001b[39m AlchemicalNetwork(edges\u001b[38;5;241m=\u001b[39m(\u001b[43msolvent_network\u001b[49m \u001b[38;5;241m+\u001b[39m complex_network), name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtyk2_relative_benchmark\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'solvent_network' is not defined" + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[24], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m network \u001b[38;5;241m=\u001b[39m \u001b[43mAlchemicalNetwork\u001b[49m\u001b[43m(\u001b[49m\u001b[43medges\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msolvated_transformations\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcomplexed_transformations\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mnodes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msolvated\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcomplexed\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtyk2_relative_benchmark\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m network\n", + "File \u001b[0;32m~/.conda/envs/openfe-notebooks/lib/python3.9/site-packages/gufe/tokenization.py:55\u001b[0m, in \u001b[0;36m_GufeTokenizableMeta.__call__\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 55\u001b[0m instance \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# add to registry if not already present\u001b[39;00m\n\u001b[1;32m 57\u001b[0m TOKENIZABLE_REGISTRY\u001b[38;5;241m.\u001b[39msetdefault(instance\u001b[38;5;241m.\u001b[39mkey, instance)\n", + "File \u001b[0;32m~/.conda/envs/openfe-notebooks/lib/python3.9/site-packages/gufe/network.py:31\u001b[0m, in \u001b[0;36mAlchemicalNetwork.__init__\u001b[0;34m(self, edges, nodes, name)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 27\u001b[0m edges: Optional[Iterable[Transformation]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 28\u001b[0m nodes: Optional[Iterable[ChemicalSystem]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 29\u001b[0m name: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 30\u001b[0m ):\n\u001b[0;32m---> 31\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_edges: FrozenSet[Transformation] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mfrozenset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43medges\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m edges \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mfrozenset\u001b[39m()\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_nodes: FrozenSet[ChemicalSystem]\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name \u001b[38;5;241m=\u001b[39m name\n", + "File \u001b[0;32m~/.conda/envs/openfe-notebooks/lib/python3.9/site-packages/gufe/transformations/transformation.py:111\u001b[0m, in \u001b[0;36mTransformation.__hash__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__hash__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 111\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mhash\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 112\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 113\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stateA\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 114\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stateB\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 115\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 116\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 117\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_protocol\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 118\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 119\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/.conda/envs/openfe-notebooks/lib/python3.9/site-packages/gufe/protocols/protocol.py:122\u001b[0m, in \u001b[0;36mProtocol.__hash__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__hash__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mhash\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_settings\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'Settings'" ] } ], "source": [ - "network = AlchemicalNetwork(edges=(solvated_transformations + complex_transformations), \n", - " nodes=(solvated + complexed),\n", + "network = AlchemicalNetwork(edges=(solvated_transformations + complexed_transformations), \n", + " nodes=(list(solvated.values()) + list(complexed.values())),\n", " name=\"tyk2_relative_benchmark\")\n", "network" ] @@ -854,102 +818,6 @@ "source": [ "The above will raise an exception if at any point execution failed." ] - }, - { - "cell_type": "markdown", - "id": "303bb240-a7fc-4271-9cea-76a6f565c1d0", - "metadata": {}, - "source": [ - "## Submitting to a `fah-alchemy` instance" - ] - }, - { - "cell_type": "markdown", - "id": "422ebed7-ea7b-4f40-8ec5-efa31caf40eb", - "metadata": {}, - "source": [ - "We'd like to evaluate the `Transformation`s we defined in our `AlchemicalNetwork`, but to do this at scale we'll submit this to a `fah-alchemy` API instance.\n", - "\n", - "Deploying `fah-alchemy` is outside of the scope of this tutorial, and we assume here that there is already an instance in place and network-reachable from the machine running this notebook." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "00ed9c82-84cd-4d7c-8e58-cbb56de87789", - "metadata": {}, - "outputs": [], - "source": [ - "from getpass import getpass\n", - "\n", - "from fah_alchemy import FahAlchemyClient, Scope" - ] - }, - { - "cell_type": "markdown", - "id": "398a8a77-57ef-469f-a3d1-63e3bad393d0", - "metadata": {}, - "source": [ - "Instantiate a `FahAlchemyClient`, giving the URL to the target `FahAlchemyAPI`, as well as your user identifier and key (password):" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "45f3da41-ff39-48fa-9002-1a25e48c9a56", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'FahAlchemyClient' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m faclient \u001b[38;5;241m=\u001b[39m \u001b[43mFahAlchemyClient\u001b[49m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://api.targetserver.org\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 2\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124musername\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 3\u001b[0m getpass())\n", - "\u001b[0;31mNameError\u001b[0m: name 'FahAlchemyClient' is not defined" - ] - } - ], - "source": [ - "faclient = FahAlchemyClient(\"https://api.targetserver.org\",\n", - " \"username\",\n", - " getpass())" - ] - }, - { - "cell_type": "markdown", - "id": "56dad5d8-78c6-4800-a5fc-acfa4ee2ac36", - "metadata": {}, - "source": [ - "We'll then use this client to submit our `AlchemicalNetwork`, indicating the `Scope` (organization, campaign, project) that this network should be submitted under:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc39a375-dc81-4f22-8b8c-7c8a3de32137", - "metadata": {}, - "outputs": [], - "source": [ - "faclient.create_network(network, Scope('my_org', 'my_campaign', 'my_project'))" - ] - }, - { - "cell_type": "markdown", - "id": "e56caa55-ff6e-41b7-865c-ab52258e9588", - "metadata": {}, - "source": [ - "We can then action which `Transformation`s we want computed:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a9e1c2f4-d6ce-4df8-a725-b5f3374fefcf", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -968,7 +836,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.15" + "version": "3.9.16" } }, "nbformat": 4, From 1e8185ab98fd0ccdb796593912efd89aee0760a4 Mon Sep 17 00:00:00 2001 From: richard gowers Date: Mon, 10 Apr 2023 15:14:29 +0100 Subject: [PATCH 03/14] update AlchemicalNetwork tutorial --- networks/Preparing AlchemicalNetworks.ipynb | 628 +-- networks/data/tyk2_ligands.sdf | 1048 ++++ networks/data/tyk2_protein.pdb | 5265 +++++++++++++++++++ 3 files changed, 6510 insertions(+), 431 deletions(-) create mode 100644 networks/data/tyk2_ligands.sdf create mode 100644 networks/data/tyk2_protein.pdb diff --git a/networks/Preparing AlchemicalNetworks.ipynb b/networks/Preparing AlchemicalNetworks.ipynb index ea1de86..397a268 100644 --- a/networks/Preparing AlchemicalNetworks.ipynb +++ b/networks/Preparing AlchemicalNetworks.ipynb @@ -5,7 +5,7 @@ "id": "4ed54201-aa1f-4ba9-bdb7-1c7f5a2cbd05", "metadata": {}, "source": [ - "# Preparing `AlchemicalNetwork`s for use with `fah-alchemy`" + "# Preparing `AlchemicalNetwork`s" ] }, { @@ -13,16 +13,20 @@ "id": "6da80e66-6e17-407f-9ba4-a25ded4777bc", "metadata": {}, "source": [ - "`fah-alchemy` is a platform for evluating the free energy differences between chemical systems in an alchemical network.\n", - "This notebook will illustrate how to build alchemical networks suitable for submission to a deployed `fah-alchemy` instance." - ] - }, - { - "cell_type": "markdown", - "id": "9d6eaa9e-daa0-4fee-8e72-d1116a1d4954", - "metadata": {}, - "source": [ - "`fah-alchemy` works in terms of `gufe` objects; the `gufe` module defines the data model for `AlchemicalNetwork`s and all objects they are composed of. We'll import the classes of objects we'll use in this tutorial here." + "This notebook will illustrate how to build `AlchemicalNetwork` objects,\n", + "from a starting point of chemical models stored in sdf and pdb files.\n", + "\n", + "An `AlchemicalNetwork` is used to represent an entire network of calculations,\n", + "and is composed of many smaller objects:\n", + "\n", + "- An `AlchemicalNetwork` composed of \n", + " - each node a `ChemicalSystem`\n", + " - each containing many components, such as `SmallMoleculeComponent`, `ProteinComponent`\n", + " - internally each Component usually wraps an RDKit representation\n", + " - each directed edge a `Transformation`, containing\n", + " - two `ChemicalSystem`s, the 'A' and 'B' side\n", + " - zero or more `Mapping` objects relating these two sides\n", + " - a `Protocol` defining the computational method to be applied to other items" ] }, { @@ -44,127 +48,12 @@ "execution_count": 2, "id": "6bb3f4a4-2135-494a-8365-9ef229dd124d", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "LICENSE: Could not open license file \"oe_license.txt\" in local directory\n", - "LICENSE: N.B. OE_LICENSE environment variable is not set\n", - "LICENSE: N.B. OE_DIR environment variable is not set\n", - "LICENSE: No product keys!\n", - "LICENSE: No product keys!\n", - "LICENSE: No product keys!\n", - "LICENSE: No product keys!\n" - ] - } - ], - "source": [ - "from gufe import AlchemicalNetwork, Transformation, ChemicalSystem\n", - "from gufe.components import ProteinComponent, SmallMoleculeComponent, SolventComponent\n", - "\n", - "from openff.units import unit" - ] - }, - { - "cell_type": "markdown", - "id": "99190957-890d-4729-ac72-bef35fbb212f", - "metadata": {}, - "source": [ - "## Sample network from `openfe-benchmark`" - ] - }, - { - "cell_type": "markdown", - "id": "c7362410-7045-42a6-8249-93a1e832d8c9", - "metadata": {}, - "source": [ - "We'll use a sample network in `openfe-benchmark` for demonstration purposes. The sources can be found here: https://github.com/OpenFreeEnergy/openfe-benchmarks\n", - "\n", - "In particular, we'll use the `tyk2` network. We'll extract ligands manually from the ligand SDF, and the protein target from its PDB." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "08f7ddfd-4fd5-4bef-8bf9-b2a286b094c6", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" - ] - } - ], - "source": [ - "from importlib import resources\n", - "from rdkit import Chem\n", - "\n", - "from openfe_benchmarks import tyk2" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "d3d7946f-6b31-4dfd-b337-7a41d717e391", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tyk2_system = tyk2.get_system()\n", - "tyk2_system" - ] - }, - { - "cell_type": "markdown", - "id": "7aff6317-abe0-40df-99db-9d1eb121b20e", - "metadata": {}, - "source": [ - "The connections for the network are defined here; we'll use these for building up our own `AlchemicalNetwork`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ec682c29-12cf-46dc-aea7-8b7e45324529", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('lig_ejm_31', 'lig_ejm_50'),\n", - " ('lig_ejm_46', 'lig_jmc_23'),\n", - " ('lig_ejm_31', 'lig_ejm_55'),\n", - " ('lig_ejm_31', 'lig_ejm_48'),\n", - " ('lig_ejm_31', 'lig_ejm_54'),\n", - " ('lig_ejm_31', 'lig_ejm_47'),\n", - " ('lig_ejm_31', 'lig_ejm_46'),\n", - " ('lig_ejm_46', 'lig_jmc_27'),\n", - " ('lig_ejm_46', 'lig_jmc_28'),\n", - " ('lig_ejm_42', 'lig_ejm_43'),\n", - " ('lig_ejm_31', 'lig_ejm_42'),\n", - " ('lig_ejm_45', 'lig_ejm_55')]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "tyk2_system.connections" + "import openfe\n", + "from gufe import AlchemicalNetwork\n", + "from openff.units import unit\n", + "from rdkit import Chem" ] }, { @@ -172,41 +61,28 @@ "id": "5d3285c5-0b7e-461f-a761-dabd01182775", "metadata": {}, "source": [ - "## Define `ChemicalSystem`s for network nodes" - ] - }, - { - "cell_type": "markdown", - "id": "90ddf9d5-71d0-44ed-8eea-5ea4d512f615", - "metadata": {}, - "source": [ - "An `AlchemicalNetwork` features `ChemicalSystem`s as nodes and `Transformation`s as directed edges between nodes. We'll start by defining the nodes for our network.\n", + "## Define `ChemicalSystem`s for network nodes\n", "\n", + "We'll start by defining the nodes for our network.\n", "A `ChemicalSystem` is made of one or more `Component`s. These can be one of `ProteinComponent`, `SmallMoleculeComponent`, or `SolventComponent`, and potentially others as needed. This design allows for memory efficient representation of large networks with perhaps hundreds or thousands of nodes, but perhaps far fewer variants in proteins, ligands, etc." ] }, { "cell_type": "markdown", - "id": "0b64f1c8-0fa0-4b10-baba-7014f9de9440", + "id": "69822065", "metadata": {}, "source": [ - "### Define `Component`s for a given `ChemicalSystem`" - ] - }, - { - "cell_type": "markdown", - "id": "90fd672d-ce78-48de-811a-7490ada1ea3b", - "metadata": {}, - "source": [ - "Let's start by assembling the ligands. These are defined as `SmallMoleculeComponent`s, and can be initialized with RDKit molecules. \n", + "### Reading Ligands\n", + "\n", + "The ligands are concatenated in a single sdf file, we'll read these using RDKit.\n", "\n", - "We'll read a multimolecule SDF from `openfe-benchmarks` and create a `SmallMoleculeComponent` for each ligand in the file:" + "Each of the ligands have been pre-docked into the protein and aligned to their common scaffold. It is important to recognize that any processing required to prepare ligand and protein structures for alchemical free energy calculations should be done *before* the steps we are taking here." ] }, { "cell_type": "code", - "execution_count": 6, - "id": "7880e75b-d28d-411c-b5c1-3aafbed31099", + "execution_count": 3, + "id": "16527065", "metadata": {}, "outputs": [ { @@ -227,32 +103,32 @@ " SmallMoleculeComponent(name=lig_jmc_28)]" ] }, - "execution_count": 6, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "with resources.path('openfe_benchmarks.data',\n", - " 'tyk2_ligands.sdf') as fn:\n", - " ligands_sdf = Chem.SDMolSupplier(str(fn), removeHs=False)\n", - " ligands = [SmallMoleculeComponent(rdkit_ligand) for rdkit_ligand in ligands_sdf]\n", - "\n", + "ligands = [\n", + " openfe.SmallMoleculeComponent(m) for m in Chem.SDMolSupplier('data/tyk2_ligands.sdf', removeHs=False)\n", + "]\n", "ligands" ] }, { "cell_type": "markdown", - "id": "d0f8e9a5-80a1-4eef-86e9-b47a9ba1f9c5", + "id": "0bb2cb69", "metadata": {}, "source": [ - "We'll also load our protein into a `ProteinComponent`:" + "### Reading the protein\n", + "\n", + "The protein is supplied as a PDB file, readable via the `ProteinComponent.from_pdb_file` class method." ] }, { "cell_type": "code", - "execution_count": 7, - "id": "8ffcd2d6-47ec-46ee-bfe8-8182915f01bd", + "execution_count": 4, + "id": "a31c89a4", "metadata": {}, "outputs": [ { @@ -261,15 +137,13 @@ "ProteinComponent(name=tyk2)" ] }, - "execution_count": 7, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "with resources.path('openfe_benchmarks.data',\n", - " 'tyk2_protein.pdb') as fn:\n", - " protein = ProteinComponent.from_pdb_file(str(fn), name='tyk2')\n", + "protein = openfe.ProteinComponent.from_pdb_file('./data/tyk2_protein.pdb', name='tyk2')\n", "\n", "protein" ] @@ -279,12 +153,18 @@ "id": "e653c208-1f69-4f08-a38e-55b7123292d9", "metadata": {}, "source": [ - "We'll also need at least one `SolventComponent` to encode our choice of solvent and counterions, with concentration:" + "### Defining the solvent\n", + "\n", + "We'll also need at least one `SolventComponent` to encode our choice of solvent and counterions, with concentration.\n", + "The concentration is defined as having units supplied by `openff.units`, this package is used to avoid confusion.\n", + "\n", + "\n", + "The `SolventComponent` doesn't actually perform any actual solvation (packing water molecules, ions); that is performed just before simulation time during `Protocol` execution." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "id": "3c47166d-143b-41f0-9dce-7805e56d7191", "metadata": {}, "outputs": [ @@ -294,54 +174,35 @@ "SolventComponent(name=O, Na+, Cl-)" ] }, - "execution_count": 8, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "solvent = SolventComponent(positive_ion='Na', \n", - " negative_ion='Cl',\n", - " neutralize=True, \n", - " ion_concentration=0.15*unit.molar)\n", + "solvent = openfe.SolventComponent(positive_ion='Na', \n", + " negative_ion='Cl',\n", + " neutralize=True, \n", + " ion_concentration=0.15*unit.molar)\n", "solvent" ] }, - { - "cell_type": "markdown", - "id": "9306e96d-f257-4b71-83e1-9814701570cd", - "metadata": {}, - "source": [ - "The `SolventComponent` doesn't actually perform any actual solvation (packing water molecules, ions); that is performed just before simulation time during `Protocol` execution." - ] - }, - { - "cell_type": "markdown", - "id": "ea4dfd8d-4ab6-44c8-a268-abb3d1505803", - "metadata": {}, - "source": [ - "Each of the ligands have been pre-docked into the protein and aligned to their common scaffold. It is important to recognize that any processing required to prepare ligand and protein structures for alchemical free energy calculations should be done *before* the steps we are taking here." - ] - }, { "cell_type": "markdown", "id": "b0b11357-2179-496b-9bff-89303e1c9c33", "metadata": {}, "source": [ - "### Build the `ChemicalSystem`s" - ] - }, - { - "cell_type": "markdown", - "id": "c314a1a2-afcc-4390-9ed0-16fae3d48c6e", - "metadata": {}, - "source": [ - "We can now construct the `ChemicalSystem`s we want represented in our network. Since we are planning to perform relative binding free energy (RBFE) calculations, we'll define both *complex* and *solvent* variants for each ligand." + "### Build the `ChemicalSystem`s\n", + "\n", + "We can now construct the `ChemicalSystem`s we want represented in our network. Since we are planning to perform relative binding free energy (RBFE) calculations, we'll define both *complex* and *solvent* variants for each ligand.\n", + "\n", + "This produces a dictionary mapping the ligand name to the `ChemicalSystem` that contains that ligand.\n", + "There are two dictionaries, for complexed and solvated ligands respectively." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "id": "10edc8b3-5f45-4e37-b1d5-bd508601e352", "metadata": {}, "outputs": [ @@ -363,25 +224,27 @@ " 'lig_jmc_28': ChemicalSystem(name=lig_jmc_28_complex, components={'ligand': SmallMoleculeComponent(name=lig_jmc_28), 'solvent': SolventComponent(name=O, Na+, Cl-), 'protein': ProteinComponent(name=tyk2)})}" ] }, - "execution_count": 9, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "complexed = {l.name: ChemicalSystem(components={'ligand': l, \n", - " 'solvent': solvent, \n", - " 'protein': protein}, \n", - " name=f\"{l.name}_complex\") \n", + "complexed = {l.name: openfe.ChemicalSystem(components={'ligand': l,\n", + " 'solvent': solvent, \n", + " 'protein': protein}, \n", + " name=f\"{l.name}_complex\") \n", " for l in ligands}\n", "complexed" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "id": "c8a0b60d-bfae-4f99-9f4d-656c982433b9", - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { @@ -401,27 +264,19 @@ " 'lig_jmc_28': ChemicalSystem(name=lig_jmc_28_water, components={'ligand': SmallMoleculeComponent(name=lig_jmc_28), 'solvent': SolventComponent(name=O, Na+, Cl-)})}" ] }, - "execution_count": 10, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "solvated = {l.name: ChemicalSystem(components={'ligand': l, \n", - " 'solvent': solvent}, \n", - " name=f\"{l.name}_water\") \n", + "solvated = {l.name: openfe.ChemicalSystem(components={'ligand': l, \n", + " 'solvent': solvent}, \n", + " name=f\"{l.name}_water\") \n", " for l in ligands}\n", "solvated" ] }, - { - "cell_type": "markdown", - "id": "7566fda4-6c8d-4003-8601-45f05face65a", - "metadata": {}, - "source": [ - "We now have all our network nodes defined. Next, we need to define the `Transformation`s that we wish to perform between them." - ] - }, { "cell_type": "markdown", "id": "7bc3a65a-979c-4114-b948-f7315796709d", @@ -435,7 +290,7 @@ "id": "0a92b4c1-fffb-410c-9c76-177fe3ea0a87", "metadata": {}, "source": [ - "A `Transformation` is a directed edge between two `ChemicalSystem`s. It includes a `Protocol` parameterized with `Settings`, and if optionally a `ComponentMapping`. \n", + "A `Transformation` is a directed edge between two `ChemicalSystem`s. It includes a `Protocol` parameterized with `Settings`, and optionally a `ComponentMapping`. \n", "\n", "The `Protocol` defines the actual computational method used to evaluate the `Transformation` to yield estimates for the free energy difference between the `ChemicalSystem`s.\n", "\n", @@ -460,12 +315,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "id": "183b23a6-1839-4586-bbbe-60b2defd2f01", "metadata": {}, "outputs": [], "source": [ - "from perses.protocols.nonequilibrium_cycling import NonEquilibriumCyclingProtocol" + "from openfe.protocols import openmm_rfe\n" ] }, { @@ -473,47 +328,83 @@ "id": "9e686c7c-9ef7-4e97-b384-63334eeb894e", "metadata": {}, "source": [ - "Any given `Protocol` features a `default_settings` method, which can be used to get the default settings that are specific to that `Protocol`:" + "Any given `Protocol` has a `default_settings()` method, which can be used to get the default settings that are specific to that `Protocol`:" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "id": "d7bac455-ddc1-4735-96d7-5c6681ab3cbc", "metadata": { + "scrolled": true, "tags": [] }, "outputs": [ { "data": { "text/plain": [ - "{'num_replicates': 1,\n", - " 'lambda_functions': {'lambda_sterics_core': 'lambda',\n", - " 'lambda_electrostatics_core': 'lambda',\n", - " 'lambda_sterics_insert': 'select(step(lambda - 0.5), 1.0, 2.0 * lambda)',\n", - " 'lambda_sterics_delete': 'select(step(lambda - 0.5), 2.0 * (lambda - 0.5), 0.0)',\n", - " 'lambda_electrostatics_insert': 'select(step(lambda - 0.5), 2.0 * (lambda - 0.5), 0.0)',\n", - " 'lambda_electrostatics_delete': 'select(step(lambda - 0.5), 1.0, 2.0 * lambda)',\n", - " 'lambda_bonds': 'lambda',\n", - " 'lambda_angles': 'lambda',\n", - " 'lambda_torsions': 'lambda'},\n", - " 'softcore_LJ_v2': True,\n", - " 'interpolate_old_and_new_14s': False,\n", - " 'timestep': 4.0 ,\n", - " 'neq_splitting': 'V R H O R V',\n", - " 'eq_steps': 1000,\n", - " 'neq_steps': 100,\n", - " 'platform': 'CUDA',\n", - " 'save_frequency': 100}" + "{'forcefield_settings': {'constraints': 'hbonds',\n", + " 'rigid_water': True,\n", + " 'remove_com': False,\n", + " 'hydrogen_mass': 4.0,\n", + " 'forcefields': ['amber/ff14SB.xml',\n", + " 'amber/tip3p_standard.xml',\n", + " 'amber/tip3p_HFE_multivalent.xml',\n", + " 'amber/phosaa10.xml'],\n", + " 'small_molecule_forcefield': 'openff-2.0.0'},\n", + " 'thermo_settings': {'temperature': 298.15 ,\n", + " 'pressure': 0.9869232667160129 ,\n", + " 'ph': None,\n", + " 'redox_potential': None},\n", + " 'system_settings': {'nonbonded_method': 'PME',\n", + " 'nonbonded_cutoff': 1.0 },\n", + " 'solvation_settings': {'solvent_model': 'tip3p',\n", + " 'solvent_padding': 1.2 },\n", + " 'alchemical_settings': {'lambda_functions': 'default',\n", + " 'lambda_windows': 11,\n", + " 'unsampled_endstates': False,\n", + " 'use_dispersion_correction': False,\n", + " 'softcore_LJ_v2': True,\n", + " 'softcore_electrostatics': True,\n", + " 'softcore_alpha': 0.85,\n", + " 'softcore_electrostatics_alpha': 0.3,\n", + " 'softcore_sigma_Q': 1.0,\n", + " 'interpolate_old_and_new_14s': False,\n", + " 'flatten_torsions': False},\n", + " 'alchemical_sampler_settings': {'online_analysis_interval': None,\n", + " 'n_repeats': 3,\n", + " 'sampler_method': 'repex',\n", + " 'online_analysis_target_error': 0.1 ,\n", + " 'online_analysis_minimum_iterations': 500,\n", + " 'flatness_criteria': 'logZ-flatness',\n", + " 'gamma0': 1.0,\n", + " 'n_replicas': 11},\n", + " 'engine_settings': {'compute_platform': None},\n", + " 'integrator_settings': {'timestep': 4 ,\n", + " 'collision_rate': 1 ,\n", + " 'n_steps': 250 ,\n", + " 'reassign_velocities': False,\n", + " 'splitting': 'V R O R V',\n", + " 'n_restart_attempts': 20,\n", + " 'constraint_tolerance': 1e-06,\n", + " 'barostat_frequency': 25 },\n", + " 'simulation_settings': {'equilibration_length': 2.0 ,\n", + " 'production_length': 5.0 ,\n", + " 'forcefield_cache': None,\n", + " 'minimization_steps': 5000,\n", + " 'output_filename': 'simulation.nc',\n", + " 'output_indices': 'all',\n", + " 'checkpoint_interval': 250 ,\n", + " 'checkpoint_storage': 'checkpoint.nc'}}" ] }, - "execution_count": 12, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "protocol_settings = NonEquilibriumCyclingProtocol.default_settings()\n", + "protocol_settings = openmm_rfe.RelativeHybridTopologyProtocol.default_settings()\n", "protocol_settings.dict()" ] }, @@ -527,45 +418,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "998ef869-7086-4c24-9c53-2d4e9553b6b9", "metadata": { "tags": [] }, "outputs": [], "source": [ - "protocol_settings.save_frequency = 200" - ] - }, - { - "cell_type": "markdown", - "id": "8af105c1-08fe-4f49-a422-a6d6307695d5", - "metadata": {}, - "source": [ - "We'll construct our full `Settings` for our chosen `NonEquilibriumCyclingProtocol`, which will include the more general `ThermoSettings` and `ForcefieldSettings` as well:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "19abd504-1e44-413e-8740-42413163a25a", - "metadata": {}, - "outputs": [], - "source": [ - "from openff.units import unit\n", - "from gufe.settings.models import (\n", - " Settings, \n", - " ThermoSettings, \n", - " OpenMMSystemGeneratorFFSettings,\n", - ")\n", - "from perses.protocols.settings import NonEqCyclingSettings\n", - "\n", - "settings = Settings(\n", - " settings_version=0,\n", - " forcefield_settings=OpenMMSystemGeneratorFFSettings(),\n", - " thermo_settings=ThermoSettings(temperature=300*unit.kelvin),\n", - " protocol_settings=protocol_settings,\n", - ")" + "protocol_settings.thermo_settings.temperature = 299 * unit.kelvin" ] }, { @@ -573,17 +433,17 @@ "id": "70e558b5-93e4-4c91-8ff0-bc5b99646a71", "metadata": {}, "source": [ - "We can now produce a parameterized `NonEquilibriumCyclingProtocol` instance:" + "We can now produce a parameterized `RelativeHybridTopologyProtocol` instance:" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 11, "id": "bd1d0a0d-00c9-44a1-820c-186b4ee05334", "metadata": {}, "outputs": [], "source": [ - "protocol = NonEquilibriumCyclingProtocol(settings)" + "protocol = openmm_rfe.RelativeHybridTopologyProtocol(protocol_settings)" ] }, { @@ -599,47 +459,34 @@ "id": "8ccdec56-519e-4293-86c4-60b0255cfcad", "metadata": {}, "source": [ - "We can now construct the `Transformation`s we want represented in our network. We'll use the predefined connections from the `tyk2` system from above as the basis for our choices here, but you could use any network planner of your choice to generate connections and use those instead." + "We can now construct the `Transformation`s we want represented in our network.\n", + "\n", + "We'll use the predefined connections from the `tyk2` system from above as the basis for our choices here, but you could use any network planner of your choice to generate connections and use those instead." ] }, { "cell_type": "code", - "execution_count": 18, - "id": "abdcc8cb-6460-42f5-9a3b-f360041fd1e4", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[('lig_ejm_31', 'lig_ejm_50'),\n", - " ('lig_ejm_46', 'lig_jmc_23'),\n", - " ('lig_ejm_31', 'lig_ejm_55'),\n", - " ('lig_ejm_31', 'lig_ejm_48'),\n", - " ('lig_ejm_31', 'lig_ejm_54'),\n", - " ('lig_ejm_31', 'lig_ejm_47'),\n", - " ('lig_ejm_31', 'lig_ejm_46'),\n", - " ('lig_ejm_46', 'lig_jmc_27'),\n", - " ('lig_ejm_46', 'lig_jmc_28'),\n", - " ('lig_ejm_42', 'lig_ejm_43'),\n", - " ('lig_ejm_31', 'lig_ejm_42'),\n", - " ('lig_ejm_45', 'lig_ejm_55')]" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "tyk2_system.connections" - ] - }, - { - "cell_type": "markdown", - "id": "b1b65f19-15bb-49df-88e8-ddf8adccf8e1", + "execution_count": 12, + "id": "4a73c2d6", "metadata": {}, + "outputs": [], "source": [ - "**TODO: need to add mappings for each edge; these would be included in the `Transformation` creations below.**" + "from openfe.setup import LomapAtomMapper\n", + "\n", + "mapper = LomapAtomMapper(element_change=False)\n", + "\n", + "connections = [(\"lig_ejm_31\", \"lig_ejm_50\"),\n", + " (\"lig_ejm_46\", \"lig_jmc_23\"),\n", + " (\"lig_ejm_31\", \"lig_ejm_55\"),\n", + " (\"lig_ejm_31\", \"lig_ejm_48\"),\n", + " (\"lig_ejm_31\", \"lig_ejm_54\"),\n", + " (\"lig_ejm_31\", \"lig_ejm_47\"),\n", + " (\"lig_ejm_31\", \"lig_ejm_46\"),\n", + " (\"lig_ejm_46\", \"lig_jmc_27\"),\n", + " (\"lig_ejm_46\", \"lig_jmc_28\"),\n", + " (\"lig_ejm_42\", \"lig_ejm_43\"),\n", + " (\"lig_ejm_31\", \"lig_ejm_42\"),\n", + " (\"lig_ejm_45\", \"lig_ejm_55\"),]" ] }, { @@ -652,28 +499,32 @@ }, { "cell_type": "code", - "execution_count": 20, - "id": "9c1b4bf5-8fcb-4ce8-8eba-12ad3c4c93f8", - "metadata": {}, - "outputs": [], - "source": [ - "complexed_transformations = [Transformation(stateA=complexed[edge[0]], \n", - " stateB=complexed[edge[1]], \n", - " protocol=protocol) \n", - " for edge in tyk2_system.connections]" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "2e278a01-432f-4fb5-a004-a76eff100058", + "execution_count": 13, + "id": "f3379b4a", "metadata": {}, "outputs": [], "source": [ - "solvated_transformations = [Transformation(stateA=solvated[edge[0]], \n", - " stateB=solvated[edge[1]], \n", - " protocol=protocol) \n", - " for edge in tyk2_system.connections]" + "complexed_transformations = []\n", + "solvated_transformations = []\n", + "\n", + "for (ligA_name, ligB_name) in connections:\n", + " ligA = complexed[ligA_name]['ligand']\n", + " ligB = complexed[ligB_name]['ligand']\n", + " \n", + " mapping = next(mapper.suggest_mappings(ligA, ligB))\n", + " \n", + " complexed_transformations.append(\n", + " openfe.Transformation(stateA=complexed[ligA_name], \n", + " stateB=complexed[ligB_name], \n", + " mapping={'ligand': mapping},\n", + " protocol=protocol) \n", + " )\n", + " solvated_transformations.append(\n", + " openfe.Transformation(stateA=solvated[ligA_name], \n", + " stateB=solvated[ligB_name], \n", + " mapping={'ligand': mapping},\n", + " protocol=protocol) \n", + " )" ] }, { @@ -689,31 +540,26 @@ "id": "6ebc1174-b348-4e44-97f7-444c44c8d6d0", "metadata": {}, "source": [ - "An `AlchemicalNetwork` is simply the combination of `ChemicalSystem`s (nodes) and `Transformation`s (directed edges) that we want to evaluate $\\Delta G$s for. This data structure functions as a declaration of what you want to compute, and is the central object on which systems like `fah-alchemy` operate. \n", + "An `AlchemicalNetwork` is simply the combination of `ChemicalSystem`s (nodes) and `Transformation`s (directed edges) that we want to evaluate $\\Delta G$s for. This data structure functions as a declaration of what you want to compute.\n", "\n", "We'll finish here by creating an `AlchemicalNetwork` from the collection of objects we've built so far." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 14, "id": "b45baada-efa4-46f7-a2cb-6e24a9309f1e", "metadata": {}, "outputs": [ { - "ename": "TypeError", - "evalue": "unhashable type: 'Settings'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[24], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m network \u001b[38;5;241m=\u001b[39m \u001b[43mAlchemicalNetwork\u001b[49m\u001b[43m(\u001b[49m\u001b[43medges\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msolvated_transformations\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mcomplexed_transformations\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[43m \u001b[49m\u001b[43mnodes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msolvated\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mcomplexed\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtyk2_relative_benchmark\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 4\u001b[0m network\n", - "File \u001b[0;32m~/.conda/envs/openfe-notebooks/lib/python3.9/site-packages/gufe/tokenization.py:55\u001b[0m, in \u001b[0;36m_GufeTokenizableMeta.__call__\u001b[0;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[1;32m 54\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mcls\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 55\u001b[0m instance \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__call__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[38;5;66;03m# add to registry if not already present\u001b[39;00m\n\u001b[1;32m 57\u001b[0m TOKENIZABLE_REGISTRY\u001b[38;5;241m.\u001b[39msetdefault(instance\u001b[38;5;241m.\u001b[39mkey, instance)\n", - "File \u001b[0;32m~/.conda/envs/openfe-notebooks/lib/python3.9/site-packages/gufe/network.py:31\u001b[0m, in \u001b[0;36mAlchemicalNetwork.__init__\u001b[0;34m(self, edges, nodes, name)\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\n\u001b[1;32m 26\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 27\u001b[0m edges: Optional[Iterable[Transformation]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 28\u001b[0m nodes: Optional[Iterable[ChemicalSystem]] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 29\u001b[0m name: Optional[\u001b[38;5;28mstr\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m 30\u001b[0m ):\n\u001b[0;32m---> 31\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_edges: FrozenSet[Transformation] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mfrozenset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43medges\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mif\u001b[39;00m edges \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mfrozenset\u001b[39m()\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_nodes: FrozenSet[ChemicalSystem]\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_name \u001b[38;5;241m=\u001b[39m name\n", - "File \u001b[0;32m~/.conda/envs/openfe-notebooks/lib/python3.9/site-packages/gufe/transformations/transformation.py:111\u001b[0m, in \u001b[0;36mTransformation.__hash__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 110\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__hash__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 111\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mhash\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[1;32m 112\u001b[0m \u001b[43m \u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 113\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stateA\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 114\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_stateB\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 115\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mapping\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 116\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 117\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_protocol\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 118\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 119\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.conda/envs/openfe-notebooks/lib/python3.9/site-packages/gufe/protocols/protocol.py:122\u001b[0m, in \u001b[0;36mProtocol.__hash__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__hash__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mhash\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__class__\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;18;43m__name__\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_settings\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'Settings'" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -728,7 +574,7 @@ "id": "262c5908-640d-45dc-9ca4-6d912428b8ac", "metadata": {}, "source": [ - "That's it! We simply toss in all `Transformation`s (edges) and `ChemicalSystem`s (nodes) we want included in this `AlchemicalNetwork`, and optionally give it a name that means something to us (it need not be unique, but can be used to query for network(s) from `fah-alchemy` later)." + "That's it! We simply toss in all `Transformation`s (edges) and `ChemicalSystem`s (nodes) we want included in this `AlchemicalNetwork`, and optionally give it a name that means something to us (it need not be unique, but can be used to query for network(s) later)." ] }, { @@ -738,86 +584,6 @@ "source": [ "We could have chosen here to leave the `nodes` argument off, since every `ChemicalSystem` we included was already represented among the `edges`, but we show it here for completeness. In this way, it's possible to include `ChemicalSystem`s in the network that aren't connected via any `Transformation`s to others, though in practice there isn't much utility in this." ] - }, - { - "cell_type": "markdown", - "id": "4e4bfff8-0072-4ad7-9b5e-3ac3f6bdc551", - "metadata": {}, - "source": [ - "### Optional: Run a `Protocol` locally" - ] - }, - { - "cell_type": "markdown", - "id": "954ce90a-2c7f-4ffc-a218-e5c8298d9ce5", - "metadata": {}, - "source": [ - "We can run our parameterized `NonEqulibriumCyclingProtocol` locally as a way to check if things are working as we expect. We'll pick one of our `Transformation`s out from our `AlchemicalNetwork`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "751b25e3-4d1e-4d5e-a870-12cc12021f73", - "metadata": {}, - "outputs": [], - "source": [ - "transformation = list(network.edges)[0]" - ] - }, - { - "cell_type": "markdown", - "id": "7b068982-1e5d-478d-9adc-6f86059ebbbd", - "metadata": {}, - "source": [ - "We'll generate a `ProtocolDAG` that encodes the actual operations to perform in order to execute the `Protocol`:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fdcc8b9f-65aa-4f8b-8661-1fe4eeb997b3", - "metadata": {}, - "outputs": [], - "source": [ - "protocoldag = transformation.create()" - ] - }, - { - "cell_type": "markdown", - "id": "1fea6b7d-0ba8-4804-be0c-6e3bf935bc78", - "metadata": {}, - "source": [ - "And we'll run it locally, in-process. This will run each `ProtocolUnit` in the `ProtocolDAG` in series, in dependency order:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "271b0164-9e0e-4578-b637-81124903e455", - "metadata": {}, - "outputs": [], - "source": [ - "from gufe.protocols.protocoldag import execute_DAG" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "58c4e234-a112-4ba7-9eec-e55b64e39ba1", - "metadata": {}, - "outputs": [], - "source": [ - "protocoldagresult = execute_DAG(protocoldag)" - ] - }, - { - "cell_type": "markdown", - "id": "f6e222af-ae1c-4437-aaac-0fe0f5d9fb16", - "metadata": {}, - "source": [ - "The above will raise an exception if at any point execution failed." - ] } ], "metadata": { @@ -836,7 +602,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.16" + "version": "3.10.10" } }, "nbformat": 4, diff --git a/networks/data/tyk2_ligands.sdf b/networks/data/tyk2_ligands.sdf new file mode 100644 index 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0 0 0 0 0 + -9.5769 -11.9102 -18.4820 N 0 0 0 0 0 0 + -10.4125 -12.4719 -18.5849 H 0 0 0 0 0 0 + -8.6418 -12.0583 -19.4429 C 0 0 0 0 0 0 + -7.5768 -11.4445 -19.4773 O 0 0 0 0 0 0 + -9.0300 -13.0424 -20.5007 C 0 0 2 0 0 0 + -7.9790 -13.8599 -21.2474 C 0 0 1 0 0 0 + -8.6725 -12.7173 -21.9374 C 0 0 0 0 0 0 + -8.0920 -11.8168 -22.1314 H 0 0 0 0 0 0 + -9.4254 -12.9488 -22.6885 H 0 0 0 0 0 0 + -8.2615 -14.8677 -21.5420 H 0 0 0 0 0 0 + -6.2468 -13.7466 -21.0219 Cl 0 0 0 0 0 0 + -9.9956 -13.5261 -20.3580 H 0 0 0 0 0 0 + -11.0483 -11.0477 -14.3796 H 0 0 0 0 0 0 + -9.6507 -9.0421 -14.0475 H 0 0 0 0 0 0 + -4.7400 -7.5863 -15.8220 Cl 0 0 0 0 0 0 + -3.7318 -5.0262 -16.4169 H 0 0 0 0 0 0 + 1 2 1 0 0 0 + 2 7 1 0 0 0 + 2 3 2 0 0 0 + 3 4 1 0 0 0 + 3 36 1 0 0 0 + 4 5 2 0 0 0 + 4 35 1 0 0 0 + 5 6 1 0 0 0 + 5 10 1 0 0 0 + 6 7 2 0 0 0 + 6 9 1 0 0 0 + 7 8 1 0 0 0 + 10 11 2 0 0 0 + 10 12 1 0 0 0 + 12 13 1 0 0 0 + 12 14 1 0 0 0 + 14 19 1 0 0 0 + 14 15 2 0 0 0 + 15 16 1 0 0 0 + 15 34 1 0 0 0 + 16 17 2 0 0 0 + 16 33 1 0 0 0 + 17 18 1 0 0 0 + 18 19 2 0 0 0 + 18 21 1 0 0 0 + 19 20 1 0 0 0 + 21 22 1 0 0 0 + 21 23 1 0 0 0 + 23 24 2 0 0 0 + 23 25 1 0 0 0 + 25 27 1 0 0 0 + 25 26 1 0 0 0 + 25 32 1 0 0 0 + 26 27 1 0 0 0 + 26 30 1 0 0 0 + 26 31 1 0 0 0 + 27 28 1 0 0 0 + 27 29 1 0 0 0 +M END +$$$$ +lig_jmc_28 + 3D + Schrodinger Suite 2021-1. + 39 41 0 0 1 0 999 V2000 + -4.7734 -2.7882 -16.4516 H 0 0 0 0 0 0 + -5.3693 -3.6534 -16.1928 C 0 0 0 0 0 0 + -4.7771 -4.9295 -16.1748 C 0 0 0 0 0 0 + -5.5384 -6.0580 -15.8222 C 0 0 0 0 0 0 + -6.9079 -5.9170 -15.4723 C 0 0 0 0 0 0 + -7.5021 -4.6266 -15.5098 C 0 0 0 0 0 0 + -6.7258 -3.5070 -15.8679 C 0 0 0 0 0 0 + -7.1773 -2.5281 -15.8986 H 0 0 0 0 0 0 + -9.1766 -4.3876 -15.1626 Cl 0 0 0 0 0 0 + -7.7200 -7.1066 -15.0434 C 0 0 0 0 0 0 + -8.1390 -7.1894 -13.8908 O 0 0 0 0 0 0 + -7.9167 -8.0154 -16.0156 N 0 0 0 0 0 0 + -7.4475 -7.8102 -16.8862 H 0 0 0 0 0 0 + -8.7431 -9.1695 -16.0273 C 0 0 0 0 0 0 + -9.5769 -9.5853 -14.9643 C 0 0 0 0 0 0 + -10.3771 -10.7245 -15.1583 C 0 0 0 0 0 0 + -10.3740 -11.4620 -16.2885 N 0 0 0 0 0 0 + -9.5582 -11.0924 -17.3052 C 0 0 0 0 0 0 + -8.7548 -9.9313 -17.2084 C 0 0 0 0 0 0 + -8.1464 -9.5935 -18.0346 H 0 0 0 0 0 0 + -9.5735 -11.8790 -18.4906 N 0 0 0 0 0 0 + -10.4150 -12.4196 -18.6299 H 0 0 0 0 0 0 + -8.6246 -12.0098 -19.4382 C 0 0 0 0 0 0 + -7.5447 -11.4192 -19.4291 O 0 0 0 0 0 0 + -9.0144 -12.9414 -20.5374 C 0 0 2 0 0 0 + -7.9968 -13.8618 -21.2117 C 0 0 1 0 0 0 + -8.4999 -12.6404 -21.9316 C 0 0 0 0 0 0 + -7.8110 -11.8050 -22.0554 H 0 0 0 0 0 0 + -9.1848 -12.7929 -22.7629 H 0 0 0 0 0 0 + -8.4286 -14.8054 -21.5270 H 0 0 0 0 0 0 + -6.5243 -14.0038 -20.8986 C 0 0 0 0 0 0 + -6.0378 -13.0412 -20.7510 H 0 0 0 0 0 0 + -6.0098 -14.5270 -21.7113 H 0 0 0 0 0 0 + -6.4164 -14.5984 -19.9932 H 0 0 0 0 0 0 + -10.0347 -13.3181 -20.4861 H 0 0 0 0 0 0 + -11.0392 -11.0690 -14.3787 H 0 0 0 0 0 0 + -9.6406 -9.0508 -14.0279 H 0 0 0 0 0 0 + -4.7554 -7.5923 -15.8220 Cl 0 0 0 0 0 0 + -3.7300 -5.0400 -16.4263 H 0 0 0 0 0 0 + 1 2 1 0 0 0 + 2 7 1 0 0 0 + 2 3 2 0 0 0 + 3 4 1 0 0 0 + 3 39 1 0 0 0 + 4 5 2 0 0 0 + 4 38 1 0 0 0 + 5 6 1 0 0 0 + 5 10 1 0 0 0 + 6 7 2 0 0 0 + 6 9 1 0 0 0 + 7 8 1 0 0 0 + 10 11 2 0 0 0 + 10 12 1 0 0 0 + 12 13 1 0 0 0 + 12 14 1 0 0 0 + 14 19 1 0 0 0 + 14 15 2 0 0 0 + 15 16 1 0 0 0 + 15 37 1 0 0 0 + 16 17 2 0 0 0 + 16 36 1 0 0 0 + 17 18 1 0 0 0 + 18 19 2 0 0 0 + 18 21 1 0 0 0 + 19 20 1 0 0 0 + 21 22 1 0 0 0 + 21 23 1 0 0 0 + 23 24 2 0 0 0 + 23 25 1 0 0 0 + 25 27 1 0 0 0 + 25 26 1 0 0 0 + 25 35 1 0 0 0 + 26 27 1 0 0 0 + 26 30 1 0 0 0 + 26 31 1 0 0 0 + 27 28 1 0 0 0 + 27 29 1 0 0 0 + 31 32 1 0 0 0 + 31 33 1 0 0 0 + 31 34 1 0 0 0 +M END +$$$$ diff --git a/networks/data/tyk2_protein.pdb b/networks/data/tyk2_protein.pdb new file mode 100644 index 0000000..8b0ba04 --- /dev/null +++ b/networks/data/tyk2_protein.pdb @@ -0,0 +1,5265 @@ +HEADER TRANSFERASE/TRANSFERASE INHIBITOR 08-AUG-12 4GIH +TITLE TYK2 (JH1) IN COMPLEX WITH +TITLE 2 2,6-DICHLORO-N-{2-[(CYCLOPROPYLCARBONYL) +TITLE 3 AMINO]PYRIDIN-4-YL}BENZAMIDE +EXPDTA X-RAY DIFFRACTION +REMARK 2 RESOLUTION. 2.00 ANGSTROMS +REMARK 3 R VALUE : 0.188000 +REMARK 3 FREE R VALUE : 0.217000 +REMARK 4 4GIH COMPLIES WITH FORMAT V. 3.30, +REMARK 200 TEMPERATURE (KELVIN) : 173.00 +REMARK 200 PH : 6.50 +REMARK 350 BIOMOLECULE: 1 +REMARK 350 APPLY THE FOLLOWING TO CHAINS: A +REMARK 350 BIOMT1 1 1.000000 0.000000 0.000000 0.000000 +REMARK 350 BIOMT2 1 0.000000 1.000000 0.000000 0.000000 +REMARK 350 BIOMT3 1 0.000000 0.000000 1.000000 0.000000 +REMARK 888 +REMARK 888 WRITTEN BY MAESTRO (A PRODUCT OF SCHRODINGER, LLC) +SEQRES 1 A 290 ACE THR VAL PHE HIS LYS ARG TYR LEU LYS LYS ILE ARG +SEQRES 2 A 290 ASP LEU GLY GLU GLY HIS PHE GLY LYS VAL SER LEU TYR +SEQRES 3 A 290 CYS TYR ASP PRO THR ASN ASP GLY THR GLY GLU MET VAL +SEQRES 4 A 290 ALA VAL LYS ALA LEU LYS ALA ASP ALA GLY PRO GLN HIS +SEQRES 5 A 290 ARG SER GLY TRP LYS GLN GLU ILE ASP ILE LEU ARG THR +SEQRES 6 A 290 LEU TYR HIS GLU HIS ILE ILE LYS TYR LYS GLY CYS CYS +SEQRES 7 A 290 GLU ASP ALA GLY ALA ALA SER LEU GLN LEU VAL MET GLU +SEQRES 8 A 290 TYR VAL PRO LEU GLY SER LEU ARG ASP TYR LEU PRO ARG +SEQRES 9 A 290 HIS SER ILE GLY LEU ALA GLN LEU LEU LEU PHE ALA GLN +SEQRES 10 A 290 GLN ILE CYS GLU GLY MET ALA TYR LEU HIS ALA GLN HIS +SEQRES 11 A 290 TYR ILE HIS ARG ASN LEU ALA ALA ARG ASN VAL LEU LEU +SEQRES 12 A 290 ASP ASN ASP ARG LEU VAL LYS ILE GLY ASP PHE GLY LEU +SEQRES 13 A 290 ALA LYS ALA VAL PRO GLU GLY HIS GLU TYR TYR ARG VAL +SEQRES 14 A 290 ARG GLU ASP GLY ASP SER PRO VAL PHE TRP TYR ALA PRO +SEQRES 15 A 290 GLU CYS LEU LYS GLU TYR LYS PHE TYR TYR ALA SER ASP +SEQRES 16 A 290 VAL TRP SER PHE GLY VAL THR LEU TYR GLU LEU LEU THR +SEQRES 17 A 290 HIS CYS ASP SER SER GLN SER PRO PRO THR LYS PHE LEU +SEQRES 18 A 290 GLU LEU ILE GLY ILE ALA GLN GLY GLN MET THR VAL LEU +SEQRES 19 A 290 ARG LEU THR GLU LEU LEU GLU ARG GLY GLU ARG LEU PRO +SEQRES 20 A 290 ARG PRO ASP LYS CYS PRO ALA GLU VAL TYR HIS LEU MET +SEQRES 21 A 290 LYS ASN CYS TRP GLU THR GLU ALA SER PHE ARG PRO THR +SEQRES 22 A 290 PHE GLU ASN LEU ILE PRO ILE LEU LYS THR VAL HIS GLU +SEQRES 23 A 290 LYS TYR ARG NME +FORMUL 2 HOH *46(H2 0) +HELIX 1 1 HIS A 940 HIS A 940 1 +HELIX 2 2 ARG A 941 ARG A 941 1 +HELIX 3 3 SER A 942 SER A 942 1 +HELIX 4 4 GLY A 943 GLY A 943 1 +HELIX 5 5 TRP A 944 TRP A 944 1 +HELIX 6 6 LYS A 945 LYS A 945 1 +HELIX 7 7 GLN A 946 GLN A 946 1 +HELIX 8 8 GLU A 947 GLU A 947 1 +HELIX 9 9 ILE A 948 ILE A 948 1 +HELIX 10 10 ASP A 949 ASP A 949 1 +HELIX 11 11 ILE A 950 ILE A 950 1 +HELIX 12 12 LEU A 951 LEU A 951 1 +HELIX 13 13 ARG A 952 ARG A 952 1 +HELIX 14 14 THR A 953 THR A 953 1 +HELIX 15 15 LEU A 986 LEU A 986 1 +HELIX 16 16 ARG A 987 ARG A 987 1 +HELIX 17 17 ASP A 988 ASP A 988 1 +HELIX 18 18 TYR A 989 TYR A 989 1 +HELIX 19 19 LEU A 997 LEU A 997 1 +HELIX 20 20 ALA A 998 ALA A 998 1 +HELIX 21 21 GLN A 999 GLN A 999 1 +HELIX 22 22 LEU A 1000 LEU A 1000 1 +HELIX 23 23 LEU A 1001 LEU A 1001 1 +HELIX 24 24 LEU A 1002 LEU A 1002 1 +HELIX 25 25 PHE A 1003 PHE A 1003 1 +HELIX 26 26 ALA A 1004 ALA A 1004 1 +HELIX 27 27 GLN A 1005 GLN A 1005 1 +HELIX 28 28 GLN A 1006 GLN A 1006 1 +HELIX 29 29 ILE A 1007 ILE A 1007 1 +HELIX 30 30 CYS A 1008 CYS A 1008 1 +HELIX 31 31 GLU A 1009 GLU A 1009 1 +HELIX 32 32 GLY A 1010 GLY A 1010 1 +HELIX 33 33 MET A 1011 MET A 1011 1 +HELIX 34 34 ALA A 1012 ALA A 1012 1 +HELIX 35 35 TYR A 1013 TYR A 1013 1 +HELIX 36 36 LEU A 1014 LEU A 1014 1 +HELIX 37 37 HIS A 1015 HIS A 1015 1 +HELIX 38 38 ALA A 1016 ALA A 1016 1 +HELIX 39 39 TYR A 1080 TYR A 1080 1 +HELIX 40 40 ALA A 1081 ALA A 1081 1 +HELIX 41 41 SER A 1082 SER A 1082 1 +HELIX 42 42 ASP A 1083 ASP A 1083 1 +HELIX 43 43 VAL A 1084 VAL A 1084 1 +HELIX 44 44 TRP A 1085 TRP A 1085 1 +HELIX 45 45 SER A 1086 SER A 1086 1 +HELIX 46 46 PHE A 1087 PHE A 1087 1 +HELIX 47 47 GLY A 1088 GLY A 1088 1 +HELIX 48 48 VAL A 1089 VAL A 1089 1 +HELIX 49 49 THR A 1090 THR A 1090 1 +HELIX 50 50 LEU A 1091 LEU A 1091 1 +HELIX 51 51 TYR A 1092 TYR A 1092 1 +HELIX 52 52 GLU A 1093 GLU A 1093 1 +HELIX 53 53 LEU A 1094 LEU A 1094 1 +HELIX 54 54 LEU A 1095 LEU A 1095 1 +HELIX 55 55 LYS A 1107 LYS A 1107 1 +HELIX 56 56 PHE A 1108 PHE A 1108 1 +HELIX 57 57 LEU A 1109 LEU A 1109 1 +HELIX 58 58 GLU A 1110 GLU A 1110 1 +HELIX 59 59 LEU A 1111 LEU A 1111 1 +HELIX 60 60 ILE A 1112 ILE A 1112 1 +HELIX 61 61 THR A 1120 THR A 1120 1 +HELIX 62 62 VAL A 1121 VAL A 1121 1 +HELIX 63 63 LEU A 1122 LEU A 1122 1 +HELIX 64 64 ARG A 1123 ARG A 1123 1 +HELIX 65 65 LEU A 1124 LEU A 1124 1 +HELIX 66 66 THR A 1125 THR A 1125 1 +HELIX 67 67 GLU A 1126 GLU A 1126 1 +HELIX 68 68 LEU A 1127 LEU A 1127 1 +HELIX 69 69 LEU A 1128 LEU A 1128 1 +HELIX 70 70 GLU A 1129 GLU A 1129 1 +HELIX 71 71 GLU A 1143 GLU A 1143 1 +HELIX 72 72 VAL A 1144 VAL A 1144 1 +HELIX 73 73 TYR A 1145 TYR A 1145 1 +HELIX 74 74 HIS A 1146 HIS A 1146 1 +HELIX 75 75 LEU A 1147 LEU A 1147 1 +HELIX 76 76 MET A 1148 MET A 1148 1 +HELIX 77 77 LYS A 1149 LYS A 1149 1 +HELIX 78 78 ASN A 1150 ASN A 1150 1 +HELIX 79 79 CYS A 1151 CYS A 1151 1 +HELIX 80 80 PHE A 1162 PHE A 1162 1 +HELIX 81 81 GLU A 1163 GLU A 1163 1 +HELIX 82 82 ASN A 1164 ASN A 1164 1 +HELIX 83 83 LEU A 1165 LEU A 1165 1 +HELIX 84 84 ILE A 1166 ILE A 1166 1 +HELIX 85 85 PRO A 1167 PRO A 1167 1 +HELIX 86 86 ILE A 1168 ILE A 1168 1 +HELIX 87 87 LEU A 1169 LEU A 1169 1 +HELIX 88 88 LYS A 1170 LYS A 1170 1 +HELIX 89 89 THR A 1171 THR A 1171 1 +HELIX 90 90 VAL A 1172 VAL A 1172 1 +HELIX 91 91 HIS A 1173 HIS A 1173 1 +HELIX 92 92 GLU A 1174 GLU A 1174 1 +HELIX 93 93 LYS A 1175 LYS A 1175 1 +HELIX 94 94 TYR A 1176 TYR A 1176 1 +TURN 1 1 THR A 890 THR A 890 +TURN 2 2 VAL A 891 VAL A 891 +TURN 3 3 HIS A 893 HIS A 893 +TURN 4 4 LYS A 894 LYS A 894 +TURN 5 5 ARG A 895 ARG A 895 +TURN 6 6 TYR A 896 TYR A 896 +TURN 7 7 GLY A 906 GLY A 906 +TURN 8 8 HIS A 907 HIS A 907 +TURN 9 9 PHE A 908 PHE A 908 +TURN 10 10 GLY A 909 GLY A 909 +TURN 11 11 ASP A 917 ASP A 917 +TURN 12 12 PRO A 918 PRO A 918 +TURN 13 13 THR A 919 THR A 919 +TURN 14 14 ASN A 920 ASN A 920 +TURN 15 15 ASP A 921 ASP A 921 +TURN 16 16 GLY A 922 GLY A 922 +TURN 17 17 THR A 923 THR A 923 +TURN 18 18 GLY A 924 GLY A 924 +TURN 19 19 ALA A 931 ALA A 931 +TURN 20 20 LEU A 932 LEU A 932 +TURN 21 21 LYS A 933 LYS A 933 +TURN 22 22 ALA A 934 ALA A 934 +TURN 23 23 ASP A 935 ASP A 935 +TURN 24 24 ALA A 936 ALA A 936 +TURN 25 25 GLY A 937 GLY A 937 +TURN 26 26 PRO A 938 PRO A 938 +TURN 27 27 GLN A 939 GLN A 939 +TURN 28 28 LEU A 954 LEU A 954 +TURN 29 29 TYR A 955 TYR A 955 +TURN 30 30 HIS A 956 HIS A 956 +TURN 31 31 GLU A 957 GLU A 957 +TURN 32 32 HIS A 958 HIS A 958 +TURN 33 33 ILE A 959 ILE A 959 +TURN 34 34 ILE A 960 ILE A 960 +TURN 35 35 LYS A 961 LYS A 961 +TURN 36 36 ALA A 969 ALA A 969 +TURN 37 37 GLY A 970 GLY A 970 +TURN 38 38 ALA A 971 ALA A 971 +TURN 39 39 ALA A 972 ALA A 972 +TURN 40 40 TYR A 980 TYR A 980 +TURN 41 41 VAL A 981 VAL A 981 +TURN 42 42 PRO A 982 PRO A 982 +TURN 43 43 LEU A 983 LEU A 983 +TURN 44 44 LEU A 990 LEU A 990 +TURN 45 45 PRO A 991 PRO A 991 +TURN 46 46 ARG A 992 ARG A 992 +TURN 47 47 HIS A 993 HIS A 993 +TURN 48 48 SER A 994 SER A 994 +TURN 49 49 ILE A 995 ILE A 995 +TURN 50 50 GLY A 996 GLY A 996 +TURN 51 51 GLN A1017 GLN A1017 +TURN 52 52 HIS A1018 HIS A1018 +TURN 53 53 HIS A1021 HIS A1021 +TURN 54 54 ARG A1022 ARG A1022 +TURN 55 55 ASN A1023 ASN A1023 +TURN 56 56 LEU A1024 LEU A1024 +TURN 57 57 ALA A1025 ALA A1025 +TURN 58 58 ALA A1026 ALA A1026 +TURN 59 59 ARG A1027 ARG A1027 +TURN 60 60 ASN A1028 ASN A1028 +TURN 61 61 ASP A1034 ASP A1034 +TURN 62 62 ARG A1035 ARG A1035 +TURN 63 63 GLY A1040 GLY A1040 +TURN 64 64 ASP A1041 ASP A1041 +TURN 65 65 PHE A1042 PHE A1042 +TURN 66 66 GLY A1043 GLY A1043 +TURN 67 67 LEU A1044 LEU A1044 +TURN 68 68 ALA A1045 ALA A1045 +TURN 69 69 VAL A1048 VAL A1048 +TURN 70 70 PRO A1049 PRO A1049 +TURN 71 71 GLU A1050 GLU A1050 +TURN 72 72 GLY A1051 GLY A1051 +TURN 73 73 HIS A1052 HIS A1052 +TURN 74 74 GLU A1053 GLU A1053 +TURN 75 75 VAL A1057 VAL A1057 +TURN 76 76 ARG A1058 ARG A1058 +TURN 77 77 GLU A1059 GLU A1059 +TURN 78 78 ASP A1060 ASP A1060 +TURN 79 79 GLY A1061 GLY A1061 +TURN 80 80 ASP A1062 ASP A1062 +TURN 81 81 SER A1063 SER A1063 +TURN 82 82 PRO A1064 PRO A1064 +TURN 83 83 VAL A1065 VAL A1065 +TURN 84 84 PHE A1066 PHE A1066 +TURN 85 85 TRP A1067 TRP A1067 +TURN 86 86 TYR A1068 TYR A1068 +TURN 87 87 ALA A1069 ALA A1069 +TURN 88 88 PRO A1070 PRO A1070 +TURN 89 89 GLU A1071 GLU A1071 +TURN 90 90 CYS A1072 CYS A1072 +TURN 91 91 LEU A1073 LEU A1073 +TURN 92 92 LYS A1074 LYS A1074 +TURN 93 93 GLU A1075 GLU A1075 +TURN 94 94 TYR A1076 TYR A1076 +TURN 95 95 THR A1096 THR A1096 +TURN 96 96 HIS A1097 HIS A1097 +TURN 97 97 CYS A1098 CYS A1098 +TURN 98 98 ASP A1099 ASP A1099 +TURN 99 99 SER A1100 SER A1100 +TURN 100 100 SER A1101 SER A1101 +TURN 101 101 GLN A1102 GLN A1102 +TURN 102 102 SER A1103 SER A1103 +TURN 103 103 PRO A1104 PRO A1104 +TURN 104 104 PRO A1105 PRO A1105 +TURN 105 105 THR A1106 THR A1106 +TURN 106 106 GLY A1113 GLY A1113 +TURN 107 107 ILE A1114 ILE A1114 +TURN 108 108 ALA A1115 ALA A1115 +TURN 109 109 GLN A1116 GLN A1116 +TURN 110 110 GLY A1117 GLY A1117 +TURN 111 111 GLN A1118 GLN A1118 +TURN 112 112 MET A1119 MET A1119 +TURN 113 113 ARG A1130 ARG A1130 +TURN 114 114 GLY A1131 GLY A1131 +TURN 115 115 GLU A1132 GLU A1132 +TURN 116 116 ARG A1133 ARG A1133 +TURN 117 117 LEU A1134 LEU A1134 +TURN 118 118 PRO A1135 PRO A1135 +TURN 119 119 ARG A1136 ARG A1136 +TURN 120 120 PRO A1137 PRO A1137 +TURN 121 121 ASP A1138 ASP A1138 +TURN 122 122 LYS A1139 LYS A1139 +TURN 123 123 CYS A1140 CYS A1140 +TURN 124 124 PRO A1141 PRO A1141 +TURN 125 125 ALA A1142 ALA A1142 +TURN 126 126 TRP A1152 TRP A1152 +TURN 127 127 GLU A1153 GLU A1153 +TURN 128 128 THR A1154 THR A1154 +TURN 129 129 GLU A1155 GLU A1155 +TURN 130 130 ALA A1156 ALA A1156 +TURN 131 131 SER A1157 SER A1157 +TURN 132 132 PHE A1158 PHE A1158 +TURN 133 133 ARG A1159 ARG A1159 +TURN 134 134 PRO A1160 PRO A1160 +TURN 135 135 THR A1161 THR A1161 +TURN 136 136 ARG A1177 ARG A1177 +SHEET 1 1 1 PHE A 892 PHE A 892 0 +SHEET 1 2 1 LEU A 897 LEU A 897 0 +SHEET 1 3 1 LYS A 898 LYS A 898 0 +SHEET 1 4 1 LYS A 899 LYS A 899 0 +SHEET 1 5 1 ILE A 900 ILE A 900 0 +SHEET 1 6 1 ARG A 901 ARG A 901 0 +SHEET 1 7 1 ASP A 902 ASP A 902 0 +SHEET 1 8 1 LEU A 903 LEU A 903 0 +SHEET 1 9 1 GLY A 904 GLY A 904 0 +SHEET 1 10 1 GLU A 905 GLU A 905 0 +SHEET 1 11 1 LYS A 910 LYS A 910 0 +SHEET 1 12 1 VAL A 911 VAL A 911 0 +SHEET 1 13 1 SER A 912 SER A 912 0 +SHEET 1 14 1 LEU A 913 LEU A 913 0 +SHEET 1 15 1 TYR A 914 TYR A 914 0 +SHEET 1 16 1 CYS A 915 CYS A 915 0 +SHEET 1 17 1 TYR A 916 TYR A 916 0 +SHEET 1 18 1 GLU A 925 GLU A 925 0 +SHEET 1 19 1 MET A 926 MET A 926 0 +SHEET 1 20 1 VAL A 927 VAL A 927 0 +SHEET 1 21 1 ALA A 928 ALA A 928 0 +SHEET 1 22 1 VAL A 929 VAL A 929 0 +SHEET 1 23 1 LYS A 930 LYS A 930 0 +SHEET 1 24 1 TYR A 962 TYR A 962 0 +SHEET 1 25 1 LYS A 963 LYS A 963 0 +SHEET 1 26 1 GLY A 964 GLY A 964 0 +SHEET 1 27 1 CYS A 965 CYS A 965 0 +SHEET 1 28 1 CYS A 966 CYS A 966 0 +SHEET 1 29 1 GLU A 967 GLU A 967 0 +SHEET 1 30 1 ASP A 968 ASP A 968 0 +SHEET 1 31 1 SER A 973 SER A 973 0 +SHEET 1 32 1 LEU A 974 LEU A 974 0 +SHEET 1 33 1 GLN A 975 GLN A 975 0 +SHEET 1 34 1 LEU A 976 LEU A 976 0 +SHEET 1 35 1 VAL A 977 VAL A 977 0 +SHEET 1 36 1 MET A 978 MET A 978 0 +SHEET 1 37 1 GLU A 979 GLU A 979 0 +SHEET 1 38 1 GLY A 984 GLY A 984 0 +SHEET 1 39 1 SER A 985 SER A 985 0 +SHEET 1 40 1 TYR A1019 TYR A1019 0 +SHEET 1 41 1 ILE A1020 ILE A1020 0 +SHEET 1 42 1 VAL A1029 VAL A1029 0 +SHEET 1 43 1 LEU A1030 LEU A1030 0 +SHEET 1 44 1 LEU A1031 LEU A1031 0 +SHEET 1 45 1 ASP A1032 ASP A1032 0 +SHEET 1 46 1 ASN A1033 ASN A1033 0 +SHEET 1 47 1 LEU A1036 LEU A1036 0 +SHEET 1 48 1 VAL A1037 VAL A1037 0 +SHEET 1 49 1 LYS A1038 LYS A1038 0 +SHEET 1 50 1 ILE A1039 ILE A1039 0 +SHEET 1 51 1 LYS A1046 LYS A1046 0 +SHEET 1 52 1 ALA A1047 ALA A1047 0 +SHEET 1 53 1 TYR A1054 TYR A1054 0 +SHEET 1 54 1 TYR A1055 TYR A1055 0 +SHEET 1 55 1 ARG A1056 ARG A1056 0 +SHEET 1 56 1 LYS A1077 LYS A1077 0 +SHEET 1 57 1 PHE A1078 PHE A1078 0 +SHEET 1 58 1 TYR A1079 TYR A1079 0 +CRYST1 36.165 74.378 105.855 90.00 90.00 90.00 P 21 21 21 4 +HETATM 1 CH3 ACE A 889 -15.732 -12.864 4.220 1.00 0.00 C +HETATM 2 C ACE A 889 -15.275 -14.196 3.637 1.00 0.00 C +HETATM 3 O ACE A 889 -14.652 -14.991 4.340 1.00 0.00 O +HETATM 4 H1 ACE A 889 -16.002 -12.982 5.270 1.00 0.00 H +HETATM 5 H2 ACE A 889 -14.929 -12.130 4.152 1.00 0.00 H +HETATM 6 H3 ACE A 889 -16.601 -12.476 3.688 1.00 0.00 H +ATOM 7 N THR A 890 -15.598 -14.429 2.353 1.00 62.43 N +ATOM 8 CA THR A 890 -15.253 -15.636 1.596 1.00 60.76 C +ATOM 9 C THR A 890 -13.728 -15.808 1.437 1.00 61.63 C +ATOM 10 O THR A 890 -13.057 -14.865 1.010 1.00 59.62 O +ATOM 11 CB THR A 890 -15.878 -15.600 0.175 1.00 68.55 C +ATOM 12 OG1 THR A 890 -17.275 -15.404 0.279 1.00 68.98 O +ATOM 13 CG2 THR A 890 -15.641 -16.864 -0.673 1.00 67.10 C +ATOM 14 H THR A 890 -16.131 -13.741 1.843 1.00 62.43 H +ATOM 15 HA THR A 890 -15.657 -16.495 2.138 1.00 60.76 H +ATOM 16 HB THR A 890 -15.486 -14.740 -0.370 1.00 68.55 H +ATOM 17 HG1 THR A 890 -17.660 -15.485 -0.597 1.00 68.98 H +ATOM 18 HG21 THR A 890 -16.174 -16.811 -1.622 1.00 67.10 H +ATOM 19 HG22 THR A 890 -14.586 -17.003 -0.909 1.00 67.10 H +ATOM 20 HG23 THR A 890 -15.983 -17.757 -0.151 1.00 67.10 H +ATOM 21 N VAL A 891 -13.227 -17.004 1.789 1.00 57.37 N +ATOM 22 CA VAL A 891 -11.803 -17.336 1.789 1.00 55.39 C +ATOM 23 C VAL A 891 -11.483 -18.439 0.760 1.00 56.21 C +ATOM 24 O VAL A 891 -12.250 -19.389 0.602 1.00 57.24 O +ATOM 25 CB VAL A 891 -11.320 -17.758 3.209 1.00 60.85 C +ATOM 26 CG1 VAL A 891 -11.950 -19.060 3.745 1.00 62.96 C +ATOM 27 CG2 VAL A 891 -9.784 -17.834 3.316 1.00 59.88 C +ATOM 28 H VAL A 891 -13.847 -17.732 2.112 1.00 57.37 H +ATOM 29 HA VAL A 891 -11.234 -16.448 1.521 1.00 55.39 H +ATOM 30 HB VAL A 891 -11.634 -16.961 3.885 1.00 60.85 H +ATOM 31 HG11 VAL A 891 -11.669 -19.225 4.785 1.00 62.96 H +ATOM 32 HG12 VAL A 891 -13.039 -19.021 3.707 1.00 62.96 H +ATOM 33 HG13 VAL A 891 -11.625 -19.935 3.183 1.00 62.96 H +ATOM 34 HG21 VAL A 891 -9.472 -17.993 4.349 1.00 59.88 H +ATOM 35 HG22 VAL A 891 -9.371 -18.651 2.725 1.00 59.88 H +ATOM 36 HG23 VAL A 891 -9.317 -16.908 2.976 1.00 59.88 H +ATOM 37 N PHE A 892 -10.332 -18.268 0.093 1.00 49.19 N +ATOM 38 CA PHE A 892 -9.736 -19.179 -0.880 1.00 47.07 C +ATOM 39 C PHE A 892 -8.379 -19.653 -0.337 1.00 52.74 C +ATOM 40 O PHE A 892 -7.608 -18.826 0.150 1.00 53.11 O +ATOM 41 CB PHE A 892 -9.546 -18.424 -2.216 1.00 45.10 C +ATOM 42 CG PHE A 892 -10.806 -18.200 -3.035 1.00 44.18 C +ATOM 43 CD1 PHE A 892 -11.099 -19.048 -4.122 1.00 44.87 C +ATOM 44 CD2 PHE A 892 -11.756 -17.229 -2.654 1.00 45.07 C +ATOM 45 CE1 PHE A 892 -12.289 -18.908 -4.823 1.00 45.27 C +ATOM 46 CE2 PHE A 892 -12.944 -17.110 -3.362 1.00 46.87 C +ATOM 47 CZ PHE A 892 -13.211 -17.944 -4.440 1.00 44.38 C +ATOM 48 H PHE A 892 -9.787 -17.438 0.287 1.00 49.19 H +ATOM 49 HA PHE A 892 -10.371 -20.052 -1.044 1.00 47.07 H +ATOM 50 HB3 PHE A 892 -8.819 -18.942 -2.845 1.00 45.10 H +ATOM 51 HB2 PHE A 892 -9.116 -17.444 -2.011 1.00 45.10 H +ATOM 52 HD1 PHE A 892 -10.391 -19.801 -4.420 1.00 44.87 H +ATOM 53 HD2 PHE A 892 -11.569 -16.581 -1.809 1.00 45.07 H +ATOM 54 HE1 PHE A 892 -12.501 -19.556 -5.660 1.00 45.27 H +ATOM 55 HE2 PHE A 892 -13.672 -16.374 -3.064 1.00 46.87 H +ATOM 56 HZ PHE A 892 -14.142 -17.847 -4.977 1.00 44.38 H +ATOM 57 N HIS A 893 -8.096 -20.961 -0.436 1.00 50.80 N +ATOM 58 CA HIS A 893 -6.829 -21.549 0.015 1.00 51.86 C +ATOM 59 C HIS A 893 -5.854 -21.664 -1.167 1.00 53.50 C +ATOM 60 O HIS A 893 -6.229 -22.208 -2.207 1.00 52.26 O +ATOM 61 CB HIS A 893 -7.080 -22.921 0.672 1.00 55.68 C +ATOM 62 CG HIS A 893 -8.193 -22.954 1.691 1.00 61.81 C +ATOM 63 ND1 HIS A 893 -9.452 -23.496 1.413 1.00 65.50 N +ATOM 64 CD2 HIS A 893 -8.184 -22.495 2.993 1.00 65.19 C +ATOM 65 CE1 HIS A 893 -10.148 -23.333 2.531 1.00 67.02 C +ATOM 66 NE2 HIS A 893 -9.446 -22.747 3.501 1.00 67.16 N +ATOM 67 H HIS A 893 -8.769 -21.596 -0.844 1.00 50.80 H +ATOM 68 HA HIS A 893 -6.383 -20.908 0.779 1.00 51.86 H +ATOM 69 HB3 HIS A 893 -6.171 -23.242 1.179 1.00 55.68 H +ATOM 70 HB2 HIS A 893 -7.292 -23.675 -0.087 1.00 55.68 H +ATOM 71 HD2 HIS A 893 -7.410 -22.016 3.574 1.00 65.19 H +ATOM 72 HE1 HIS A 893 -11.176 -23.645 2.643 1.00 67.02 H +ATOM 73 HE2 HIS A 893 -9.776 -22.523 4.430 1.00 67.16 H +ATOM 74 N LYS A 894 -4.615 -21.166 -0.990 1.00 50.22 N +ATOM 75 CA LYS A 894 -3.603 -21.102 -2.055 1.00 49.02 C +ATOM 76 C LYS A 894 -3.093 -22.481 -2.531 1.00 53.09 C +ATOM 77 O LYS A 894 -2.649 -22.591 -3.672 1.00 51.68 O +ATOM 78 CB LYS A 894 -2.443 -20.167 -1.638 1.00 52.12 C +ATOM 79 CG LYS A 894 -1.600 -19.701 -2.841 1.00 67.25 C +ATOM 80 CD LYS A 894 -0.571 -18.609 -2.518 1.00 79.06 C +ATOM 81 CE LYS A 894 0.105 -18.102 -3.806 1.00 86.18 C +ATOM 82 NZ LYS A 894 1.179 -17.132 -3.537 1.00 95.28 N1+ +ATOM 83 H LYS A 894 -4.367 -20.734 -0.111 1.00 50.22 H +ATOM 84 HA LYS A 894 -4.096 -20.649 -2.916 1.00 49.02 H +ATOM 85 HB3 LYS A 894 -1.815 -20.639 -0.881 1.00 52.12 H +ATOM 86 HB2 LYS A 894 -2.857 -19.278 -1.165 1.00 52.12 H +ATOM 87 HG3 LYS A 894 -2.267 -19.334 -3.621 1.00 67.25 H +ATOM 88 HG2 LYS A 894 -1.071 -20.551 -3.272 1.00 67.25 H +ATOM 89 HD3 LYS A 894 0.177 -19.010 -1.831 1.00 79.06 H +ATOM 90 HD2 LYS A 894 -1.058 -17.783 -1.996 1.00 79.06 H +ATOM 91 HE3 LYS A 894 -0.633 -17.631 -4.457 1.00 86.18 H +ATOM 92 HE2 LYS A 894 0.534 -18.936 -4.361 1.00 86.18 H +ATOM 93 HZ1 LYS A 894 1.906 -17.573 -2.992 1.00 95.28 H +ATOM 94 HZ2 LYS A 894 1.567 -16.796 -4.406 1.00 95.28 H +ATOM 95 HZ3 LYS A 894 0.818 -16.346 -3.009 1.00 95.28 H +ATOM 96 N ARG A 895 -3.219 -23.513 -1.678 1.00 51.74 N +ATOM 97 CA ARG A 895 -2.936 -24.922 -1.981 1.00 52.94 C +ATOM 98 C ARG A 895 -3.820 -25.522 -3.099 1.00 55.31 C +ATOM 99 O ARG A 895 -3.417 -26.518 -3.700 1.00 55.61 O +ATOM 100 CB ARG A 895 -3.055 -25.742 -0.675 1.00 57.62 C +ATOM 101 CG ARG A 895 -4.449 -25.700 -0.018 1.00 70.70 C +ATOM 102 CD ARG A 895 -4.551 -26.532 1.269 1.00 85.20 C +ATOM 103 NE ARG A 895 -5.826 -26.282 1.965 1.00 97.62 N +ATOM 104 CZ ARG A 895 -6.474 -27.088 2.829 1.00116.87 C +ATOM 105 NH1 ARG A 895 -6.007 -28.297 3.178 1.00109.22 N +ATOM 106 NH2 ARG A 895 -7.629 -26.662 3.356 1.00103.80 N1+ +ATOM 107 H ARG A 895 -3.593 -23.332 -0.758 1.00 51.74 H +ATOM 108 HA ARG A 895 -1.902 -24.980 -2.325 1.00 52.94 H +ATOM 109 HB3 ARG A 895 -2.322 -25.366 0.041 1.00 57.62 H +ATOM 110 HB2 ARG A 895 -2.772 -26.779 -0.866 1.00 57.62 H +ATOM 111 HG3 ARG A 895 -5.142 -26.145 -0.732 1.00 70.70 H +ATOM 112 HG2 ARG A 895 -4.799 -24.681 0.135 1.00 70.70 H +ATOM 113 HD3 ARG A 895 -3.681 -26.417 1.916 1.00 85.20 H +ATOM 114 HD2 ARG A 895 -4.581 -27.578 0.965 1.00 85.20 H +ATOM 115 HE ARG A 895 -6.255 -25.393 1.754 1.00 97.62 H +ATOM 116 HH12 ARG A 895 -6.515 -28.879 3.827 1.00109.22 H +ATOM 117 HH11 ARG A 895 -5.149 -28.646 2.769 1.00109.22 H +ATOM 118 HH22 ARG A 895 -8.146 -27.249 3.995 1.00103.80 H +ATOM 119 HH21 ARG A 895 -8.011 -25.764 3.096 1.00103.80 H +ATOM 120 N TYR A 896 -4.993 -24.917 -3.353 1.00 49.98 N +ATOM 121 CA TYR A 896 -5.948 -25.341 -4.378 1.00 49.28 C +ATOM 122 C TYR A 896 -5.900 -24.464 -5.642 1.00 50.16 C +ATOM 123 O TYR A 896 -6.491 -24.862 -6.644 1.00 48.17 O +ATOM 124 CB TYR A 896 -7.364 -25.348 -3.770 1.00 51.36 C +ATOM 125 CG TYR A 896 -7.587 -26.246 -2.560 1.00 55.87 C +ATOM 126 CD1 TYR A 896 -6.988 -27.524 -2.475 1.00 59.63 C +ATOM 127 CD2 TYR A 896 -8.449 -25.818 -1.529 1.00 57.59 C +ATOM 128 CE1 TYR A 896 -7.261 -28.365 -1.380 1.00 63.33 C +ATOM 129 CE2 TYR A 896 -8.717 -26.657 -0.430 1.00 61.39 C +ATOM 130 CZ TYR A 896 -8.130 -27.935 -0.360 1.00 71.07 C +ATOM 131 OH TYR A 896 -8.409 -28.757 0.692 1.00 74.22 O +ATOM 132 H TYR A 896 -5.258 -24.104 -2.814 1.00 49.98 H +ATOM 133 HA TYR A 896 -5.716 -26.351 -4.712 1.00 49.28 H +ATOM 134 HB3 TYR A 896 -8.095 -25.639 -4.525 1.00 51.36 H +ATOM 135 HB2 TYR A 896 -7.618 -24.330 -3.482 1.00 51.36 H +ATOM 136 HD1 TYR A 896 -6.338 -27.885 -3.257 1.00 59.63 H +ATOM 137 HD2 TYR A 896 -8.922 -24.848 -1.582 1.00 57.59 H +ATOM 138 HE1 TYR A 896 -6.810 -29.346 -1.334 1.00 63.33 H +ATOM 139 HE2 TYR A 896 -9.385 -26.322 0.350 1.00 61.39 H +ATOM 140 HH TYR A 896 -7.997 -29.621 0.612 1.00 74.22 H +ATOM 141 N LEU A 897 -5.197 -23.318 -5.597 1.00 46.72 N +ATOM 142 CA LEU A 897 -5.001 -22.416 -6.732 1.00 44.74 C +ATOM 143 C LEU A 897 -3.851 -22.919 -7.625 1.00 48.16 C +ATOM 144 O LEU A 897 -2.685 -22.753 -7.264 1.00 48.59 O +ATOM 145 CB LEU A 897 -4.714 -20.987 -6.202 1.00 44.20 C +ATOM 146 CG LEU A 897 -5.924 -20.222 -5.622 1.00 48.88 C +ATOM 147 CD1 LEU A 897 -5.488 -18.979 -4.825 1.00 51.85 C +ATOM 148 CD2 LEU A 897 -6.961 -19.837 -6.697 1.00 47.58 C +ATOM 149 H LEU A 897 -4.710 -23.068 -4.748 1.00 46.72 H +ATOM 150 HA LEU A 897 -5.907 -22.403 -7.338 1.00 44.74 H +ATOM 151 HB3 LEU A 897 -4.297 -20.372 -6.995 1.00 44.20 H +ATOM 152 HB2 LEU A 897 -3.930 -21.049 -5.447 1.00 44.20 H +ATOM 153 HG LEU A 897 -6.405 -20.889 -4.908 1.00 48.88 H +ATOM 154 HD11 LEU A 897 -5.824 -19.067 -3.792 1.00 51.85 H +ATOM 155 HD12 LEU A 897 -4.406 -18.850 -4.810 1.00 51.85 H +ATOM 156 HD13 LEU A 897 -5.915 -18.056 -5.220 1.00 51.85 H +ATOM 157 HD21 LEU A 897 -7.934 -19.654 -6.242 1.00 47.58 H +ATOM 158 HD22 LEU A 897 -6.673 -18.927 -7.224 1.00 47.58 H +ATOM 159 HD23 LEU A 897 -7.099 -20.607 -7.454 1.00 47.58 H +ATOM 160 N LYS A 898 -4.206 -23.500 -8.784 1.00 44.15 N +ATOM 161 CA LYS A 898 -3.275 -23.916 -9.836 1.00 44.11 C +ATOM 162 C LYS A 898 -3.159 -22.787 -10.871 1.00 46.50 C +ATOM 163 O LYS A 898 -4.147 -22.500 -11.546 1.00 45.72 O +ATOM 164 CB LYS A 898 -3.788 -25.205 -10.521 1.00 48.28 C +ATOM 165 CG LYS A 898 -3.559 -26.517 -9.749 1.00 70.59 C +ATOM 166 CD LYS A 898 -4.421 -26.693 -8.490 1.00 82.83 C +ATOM 167 CE LYS A 898 -4.393 -28.144 -7.981 1.00 97.68 C +ATOM 168 NZ LYS A 898 -5.185 -28.320 -6.753 1.00107.26 N1+ +ATOM 169 H LYS A 898 -5.188 -23.612 -9.002 1.00 44.15 H +ATOM 170 HA LYS A 898 -2.289 -24.117 -9.411 1.00 44.11 H +ATOM 171 HB3 LYS A 898 -3.252 -25.321 -11.464 1.00 48.28 H +ATOM 172 HB2 LYS A 898 -4.839 -25.106 -10.797 1.00 48.28 H +ATOM 173 HG3 LYS A 898 -2.505 -26.604 -9.484 1.00 70.59 H +ATOM 174 HG2 LYS A 898 -3.761 -27.344 -10.431 1.00 70.59 H +ATOM 175 HD3 LYS A 898 -5.444 -26.381 -8.702 1.00 82.83 H +ATOM 176 HD2 LYS A 898 -4.049 -26.031 -7.707 1.00 82.83 H +ATOM 177 HE3 LYS A 898 -3.368 -28.453 -7.777 1.00 97.68 H +ATOM 178 HE2 LYS A 898 -4.788 -28.816 -8.743 1.00 97.68 H +ATOM 179 HZ1 LYS A 898 -4.817 -27.724 -6.025 1.00107.26 H +ATOM 180 HZ2 LYS A 898 -6.153 -28.082 -6.930 1.00107.26 H +ATOM 181 HZ3 LYS A 898 -5.133 -29.284 -6.455 1.00107.26 H +ATOM 182 N LYS A 899 -1.972 -22.166 -10.979 1.00 42.86 N +ATOM 183 CA LYS A 899 -1.712 -21.052 -11.897 1.00 42.35 C +ATOM 184 C LYS A 899 -1.708 -21.508 -13.365 1.00 45.75 C +ATOM 185 O LYS A 899 -1.008 -22.465 -13.695 1.00 46.24 O +ATOM 186 CB LYS A 899 -0.382 -20.375 -11.512 1.00 45.46 C +ATOM 187 CG LYS A 899 -0.117 -19.063 -12.271 1.00 54.73 C +ATOM 188 CD LYS A 899 1.114 -18.319 -11.740 1.00 64.90 C +ATOM 189 CE LYS A 899 1.426 -17.068 -12.571 1.00 74.92 C +ATOM 190 NZ LYS A 899 2.594 -16.337 -12.051 1.00 84.51 N1+ +ATOM 191 H LYS A 899 -1.198 -22.461 -10.402 1.00 42.86 H +ATOM 192 HA LYS A 899 -2.511 -20.319 -11.766 1.00 42.35 H +ATOM 193 HB3 LYS A 899 0.450 -21.065 -11.661 1.00 45.46 H +ATOM 194 HB2 LYS A 899 -0.393 -20.156 -10.446 1.00 45.46 H +ATOM 195 HG3 LYS A 899 -0.991 -18.414 -12.196 1.00 54.73 H +ATOM 196 HG2 LYS A 899 0.021 -19.269 -13.333 1.00 54.73 H +ATOM 197 HD3 LYS A 899 1.973 -18.993 -11.741 1.00 64.90 H +ATOM 198 HD2 LYS A 899 0.939 -18.039 -10.700 1.00 64.90 H +ATOM 199 HE3 LYS A 899 0.570 -16.395 -12.579 1.00 74.92 H +ATOM 200 HE2 LYS A 899 1.622 -17.344 -13.608 1.00 74.92 H +ATOM 201 HZ1 LYS A 899 2.755 -15.539 -12.651 1.00 84.51 H +ATOM 202 HZ2 LYS A 899 2.411 -16.027 -11.108 1.00 84.51 H +ATOM 203 HZ3 LYS A 899 3.406 -16.938 -12.065 1.00 84.51 H +ATOM 204 N ILE A 900 -2.473 -20.796 -14.207 1.00 41.32 N +ATOM 205 CA ILE A 900 -2.604 -21.076 -15.636 1.00 41.22 C +ATOM 206 C ILE A 900 -1.686 -20.142 -16.451 1.00 44.92 C +ATOM 207 O ILE A 900 -0.918 -20.640 -17.272 1.00 45.41 O +ATOM 208 CB ILE A 900 -4.078 -20.931 -16.133 1.00 44.36 C +ATOM 209 CG1 ILE A 900 -5.025 -21.868 -15.338 1.00 45.50 C +ATOM 210 CG2 ILE A 900 -4.224 -21.186 -17.654 1.00 46.17 C +ATOM 211 CD1 ILE A 900 -6.527 -21.664 -15.604 1.00 56.14 C +ATOM 212 H ILE A 900 -3.010 -20.012 -13.856 1.00 41.32 H +ATOM 213 HA ILE A 900 -2.293 -22.103 -15.839 1.00 41.22 H +ATOM 214 HB ILE A 900 -4.399 -19.906 -15.939 1.00 44.36 H +ATOM 215 HG13 ILE A 900 -4.869 -21.739 -14.267 1.00 45.50 H +ATOM 216 HG12 ILE A 900 -4.762 -22.906 -15.546 1.00 45.50 H +ATOM 217 HG21 ILE A 900 -5.257 -21.102 -17.988 1.00 46.17 H +ATOM 218 HG22 ILE A 900 -3.655 -20.476 -18.252 1.00 46.17 H +ATOM 219 HG23 ILE A 900 -3.880 -22.187 -17.914 1.00 46.17 H +ATOM 220 HD11 ILE A 900 -6.981 -22.574 -15.996 1.00 56.14 H +ATOM 221 HD12 ILE A 900 -7.057 -21.403 -14.689 1.00 56.14 H +ATOM 222 HD13 ILE A 900 -6.724 -20.869 -16.318 1.00 56.14 H +ATOM 223 N ARG A 901 -1.769 -18.822 -16.202 1.00 40.97 N +ATOM 224 CA ARG A 901 -0.997 -17.785 -16.899 1.00 41.13 C +ATOM 225 C ARG A 901 -1.153 -16.426 -16.197 1.00 44.46 C +ATOM 226 O ARG A 901 -2.028 -16.266 -15.349 1.00 43.07 O +ATOM 227 CB ARG A 901 -1.415 -17.689 -18.393 1.00 41.29 C +ATOM 228 CG ARG A 901 -2.893 -17.330 -18.655 1.00 52.38 C +ATOM 229 CD ARG A 901 -3.184 -17.151 -20.154 1.00 59.92 C +ATOM 230 NE ARG A 901 -4.626 -17.047 -20.439 1.00 68.07 N +ATOM 231 CZ ARG A 901 -5.470 -18.056 -20.731 1.00 83.41 C +ATOM 232 NH1 ARG A 901 -5.064 -19.333 -20.802 1.00 64.57 N +ATOM 233 NH2 ARG A 901 -6.760 -17.779 -20.958 1.00 77.22 N1+ +ATOM 234 H ARG A 901 -2.406 -18.484 -15.493 1.00 40.97 H +ATOM 235 HA ARG A 901 0.058 -18.059 -16.843 1.00 41.13 H +ATOM 236 HB3 ARG A 901 -1.194 -18.631 -18.895 1.00 41.29 H +ATOM 237 HB2 ARG A 901 -0.782 -16.954 -18.894 1.00 41.29 H +ATOM 238 HG3 ARG A 901 -3.069 -16.365 -18.179 1.00 52.38 H +ATOM 239 HG2 ARG A 901 -3.588 -18.026 -18.182 1.00 52.38 H +ATOM 240 HD3 ARG A 901 -2.658 -17.871 -20.782 1.00 59.92 H +ATOM 241 HD2 ARG A 901 -2.800 -16.172 -20.446 1.00 59.92 H +ATOM 242 HE ARG A 901 -5.007 -16.113 -20.381 1.00 68.07 H +ATOM 243 HH12 ARG A 901 -5.714 -20.071 -21.028 1.00 64.57 H +ATOM 244 HH11 ARG A 901 -4.093 -19.560 -20.642 1.00 64.57 H +ATOM 245 HH22 ARG A 901 -7.411 -18.517 -21.182 1.00 77.22 H +ATOM 246 HH21 ARG A 901 -7.103 -16.830 -20.897 1.00 77.22 H +ATOM 247 N ASP A 902 -0.327 -15.455 -16.617 1.00 41.92 N +ATOM 248 CA ASP A 902 -0.466 -14.033 -16.281 1.00 41.86 C +ATOM 249 C ASP A 902 -1.534 -13.379 -17.179 1.00 43.46 C +ATOM 250 O ASP A 902 -1.649 -13.756 -18.347 1.00 42.68 O +ATOM 251 CB ASP A 902 0.870 -13.255 -16.417 1.00 46.10 C +ATOM 252 CG ASP A 902 2.075 -13.816 -15.638 1.00 63.12 C +ATOM 253 OD1 ASP A 902 1.868 -14.510 -14.618 1.00 62.46 O +ATOM 254 OD2 ASP A 902 3.207 -13.412 -15.980 1.00 75.36 O1- +ATOM 255 H ASP A 902 0.372 -15.668 -17.314 1.00 41.92 H +ATOM 256 HA ASP A 902 -0.795 -13.952 -15.245 1.00 41.86 H +ATOM 257 HB3 ASP A 902 0.722 -12.227 -16.081 1.00 46.10 H +ATOM 258 HB2 ASP A 902 1.153 -13.205 -17.470 1.00 46.10 H +ATOM 259 N LEU A 903 -2.271 -12.398 -16.628 1.00 39.42 N +ATOM 260 CA LEU A 903 -3.273 -11.609 -17.359 1.00 38.97 C +ATOM 261 C LEU A 903 -2.810 -10.164 -17.625 1.00 45.51 C +ATOM 262 O LEU A 903 -3.374 -9.534 -18.520 1.00 47.25 O +ATOM 263 CB LEU A 903 -4.619 -11.607 -16.593 1.00 37.74 C +ATOM 264 CG LEU A 903 -5.296 -12.990 -16.448 1.00 40.14 C +ATOM 265 CD1 LEU A 903 -6.586 -12.886 -15.603 1.00 39.42 C +ATOM 266 CD2 LEU A 903 -5.545 -13.682 -17.807 1.00 39.39 C +ATOM 267 H LEU A 903 -2.124 -12.142 -15.660 1.00 39.42 H +ATOM 268 HA LEU A 903 -3.440 -12.040 -18.346 1.00 38.97 H +ATOM 269 HB3 LEU A 903 -5.320 -10.938 -17.097 1.00 37.74 H +ATOM 270 HB2 LEU A 903 -4.461 -11.178 -15.603 1.00 37.74 H +ATOM 271 HG LEU A 903 -4.609 -13.626 -15.891 1.00 40.14 H +ATOM 272 HD11 LEU A 903 -7.473 -13.231 -16.134 1.00 39.42 H +ATOM 273 HD12 LEU A 903 -6.505 -13.482 -14.694 1.00 39.42 H +ATOM 274 HD13 LEU A 903 -6.792 -11.860 -15.296 1.00 39.42 H +ATOM 275 HD21 LEU A 903 -6.525 -14.154 -17.871 1.00 39.39 H +ATOM 276 HD22 LEU A 903 -5.480 -12.984 -18.642 1.00 39.39 H +ATOM 277 HD23 LEU A 903 -4.806 -14.466 -17.979 1.00 39.39 H +ATOM 278 N GLY A 904 -1.802 -9.669 -16.884 1.00 41.87 N +ATOM 279 CA GLY A 904 -1.253 -8.324 -17.054 1.00 43.49 C +ATOM 280 C GLY A 904 -1.005 -7.670 -15.690 1.00 48.38 C +ATOM 281 O GLY A 904 -1.010 -8.335 -14.653 1.00 47.59 O +ATOM 282 H GLY A 904 -1.396 -10.235 -16.152 1.00 41.87 H +ATOM 283 HA3 GLY A 904 -1.915 -7.681 -17.638 1.00 43.49 H +ATOM 284 HA2 GLY A 904 -0.308 -8.393 -17.594 1.00 43.49 H +ATOM 285 N GLU A 905 -0.757 -6.351 -15.724 1.00 46.73 N +ATOM 286 CA GLU A 905 -0.417 -5.520 -14.569 1.00 47.46 C +ATOM 287 C GLU A 905 -1.527 -4.496 -14.305 1.00 51.67 C +ATOM 288 O GLU A 905 -1.856 -3.713 -15.197 1.00 51.35 O +ATOM 289 CB GLU A 905 0.926 -4.797 -14.834 1.00 51.27 C +ATOM 290 CG GLU A 905 2.146 -5.732 -15.018 1.00 66.56 C +ATOM 291 CD GLU A 905 2.576 -6.520 -13.769 1.00 94.70 C +ATOM 292 OE1 GLU A 905 2.395 -6.005 -12.643 1.00 96.75 O +ATOM 293 OE2 GLU A 905 3.103 -7.639 -13.964 1.00 86.75 O1- +ATOM 294 H GLU A 905 -0.773 -5.869 -16.611 1.00 46.73 H +ATOM 295 HA GLU A 905 -0.307 -6.133 -13.677 1.00 47.46 H +ATOM 296 HB3 GLU A 905 1.125 -4.080 -14.036 1.00 51.27 H +ATOM 297 HB2 GLU A 905 0.832 -4.191 -15.737 1.00 51.27 H +ATOM 298 HG3 GLU A 905 3.002 -5.131 -15.326 1.00 66.56 H +ATOM 299 HG2 GLU A 905 1.960 -6.422 -15.843 1.00 66.56 H +ATOM 300 N GLY A 906 -2.038 -4.492 -13.063 1.00 48.81 N +ATOM 301 CA GLY A 906 -2.898 -3.437 -12.529 1.00 50.44 C +ATOM 302 C GLY A 906 -2.033 -2.425 -11.758 1.00 59.13 C +ATOM 303 O GLY A 906 -0.802 -2.468 -11.826 1.00 59.62 O +ATOM 304 H GLY A 906 -1.693 -5.164 -12.391 1.00 48.81 H +ATOM 305 HA3 GLY A 906 -3.632 -3.885 -11.863 1.00 50.44 H +ATOM 306 HA2 GLY A 906 -3.453 -2.925 -13.317 1.00 50.44 H +ATOM 307 N HIS A 907 -2.685 -1.495 -11.037 1.00 58.87 N +ATOM 308 CA HIS A 907 -2.024 -0.392 -10.326 1.00 61.58 C +ATOM 309 C HIS A 907 -1.031 -0.830 -9.225 1.00 65.75 C +ATOM 310 O HIS A 907 0.140 -0.461 -9.313 1.00 67.01 O +ATOM 311 CB HIS A 907 -3.071 0.615 -9.806 1.00 64.07 C +ATOM 312 CG HIS A 907 -2.464 1.855 -9.191 1.00 71.10 C +ATOM 313 ND1 HIS A 907 -2.421 2.072 -7.824 1.00 74.01 N +ATOM 314 CD2 HIS A 907 -1.844 2.946 -9.759 1.00 75.68 C +ATOM 315 CE1 HIS A 907 -1.781 3.227 -7.629 1.00 76.67 C +ATOM 316 NE2 HIS A 907 -1.401 3.813 -8.758 1.00 78.22 N +ATOM 317 H HIS A 907 -3.695 -1.465 -11.060 1.00 58.87 H +ATOM 318 HA HIS A 907 -1.436 0.136 -11.080 1.00 61.58 H +ATOM 319 HB3 HIS A 907 -3.722 0.137 -9.074 1.00 64.07 H +ATOM 320 HB2 HIS A 907 -3.717 0.935 -10.625 1.00 64.07 H +ATOM 321 HD1 HIS A 907 -2.801 1.473 -7.103 1.00 74.01 H +ATOM 322 HD2 HIS A 907 -1.664 3.165 -10.801 1.00 75.68 H +ATOM 323 HE1 HIS A 907 -1.598 3.647 -6.651 1.00 76.67 H +ATOM 324 N PHE A 908 -1.500 -1.593 -8.220 1.00 61.05 N +ATOM 325 CA PHE A 908 -0.670 -2.041 -7.090 1.00 61.65 C +ATOM 326 C PHE A 908 0.158 -3.312 -7.352 1.00 63.63 C +ATOM 327 O PHE A 908 1.051 -3.603 -6.555 1.00 65.29 O +ATOM 328 CB PHE A 908 -1.529 -2.186 -5.816 1.00 63.30 C +ATOM 329 CG PHE A 908 -2.026 -0.870 -5.245 1.00 67.27 C +ATOM 330 CD1 PHE A 908 -1.113 0.118 -4.816 1.00 73.63 C +ATOM 331 CD2 PHE A 908 -3.407 -0.616 -5.143 1.00 68.80 C +ATOM 332 CE1 PHE A 908 -1.582 1.333 -4.333 1.00 76.93 C +ATOM 333 CE2 PHE A 908 -3.854 0.595 -4.639 1.00 73.69 C +ATOM 334 CZ PHE A 908 -2.947 1.573 -4.254 1.00 75.01 C +ATOM 335 H PHE A 908 -2.475 -1.851 -8.194 1.00 61.05 H +ATOM 336 HA PHE A 908 0.075 -1.268 -6.889 1.00 61.65 H +ATOM 337 HB3 PHE A 908 -0.967 -2.687 -5.026 1.00 63.30 H +ATOM 338 HB2 PHE A 908 -2.382 -2.833 -6.027 1.00 63.30 H +ATOM 339 HD1 PHE A 908 -0.050 -0.062 -4.872 1.00 73.63 H +ATOM 340 HD2 PHE A 908 -4.123 -1.361 -5.449 1.00 68.80 H +ATOM 341 HE1 PHE A 908 -0.886 2.096 -4.018 1.00 76.93 H +ATOM 342 HE2 PHE A 908 -4.914 0.778 -4.550 1.00 73.69 H +ATOM 343 HZ PHE A 908 -3.306 2.520 -3.880 1.00 75.01 H +ATOM 344 N GLY A 909 -0.118 -4.030 -8.451 1.00 56.05 N +ATOM 345 CA GLY A 909 0.641 -5.220 -8.813 1.00 53.59 C +ATOM 346 C GLY A 909 -0.135 -6.069 -9.822 1.00 52.99 C +ATOM 347 O GLY A 909 -1.181 -5.674 -10.342 1.00 51.95 O +ATOM 348 H GLY A 909 -0.847 -3.737 -9.086 1.00 56.05 H +ATOM 349 HA3 GLY A 909 0.870 -5.821 -7.931 1.00 53.59 H +ATOM 350 HA2 GLY A 909 1.590 -4.917 -9.258 1.00 53.59 H +ATOM 351 N LYS A 910 0.446 -7.242 -10.114 1.00 47.39 N +ATOM 352 CA LYS A 910 0.039 -8.174 -11.161 1.00 44.86 C +ATOM 353 C LYS A 910 -1.302 -8.879 -10.907 1.00 43.37 C +ATOM 354 O LYS A 910 -1.655 -9.140 -9.759 1.00 42.02 O +ATOM 355 CB LYS A 910 1.174 -9.198 -11.371 1.00 48.19 C +ATOM 356 CG LYS A 910 1.463 -10.135 -10.177 1.00 70.18 C +ATOM 357 CD LYS A 910 2.696 -11.044 -10.355 1.00 80.07 C +ATOM 358 CE LYS A 910 2.500 -12.254 -11.290 1.00 90.27 C +ATOM 359 NZ LYS A 910 2.509 -11.895 -12.720 1.00 99.37 N1+ +ATOM 360 H LYS A 910 1.299 -7.487 -9.634 1.00 47.39 H +ATOM 361 HA LYS A 910 -0.058 -7.598 -12.079 1.00 44.86 H +ATOM 362 HB3 LYS A 910 2.088 -8.663 -11.624 1.00 48.19 H +ATOM 363 HB2 LYS A 910 0.918 -9.787 -12.250 1.00 48.19 H +ATOM 364 HG3 LYS A 910 0.595 -10.759 -9.970 1.00 70.18 H +ATOM 365 HG2 LYS A 910 1.611 -9.535 -9.280 1.00 70.18 H +ATOM 366 HD3 LYS A 910 2.986 -11.415 -9.371 1.00 80.07 H +ATOM 367 HD2 LYS A 910 3.543 -10.445 -10.692 1.00 80.07 H +ATOM 368 HE3 LYS A 910 1.575 -12.777 -11.046 1.00 90.27 H +ATOM 369 HE2 LYS A 910 3.311 -12.965 -11.133 1.00 90.27 H +ATOM 370 HZ1 LYS A 910 1.759 -11.248 -12.914 1.00 99.37 H +ATOM 371 HZ2 LYS A 910 3.388 -11.459 -12.961 1.00 99.37 H +ATOM 372 HZ3 LYS A 910 2.383 -12.725 -13.287 1.00 99.37 H +ATOM 373 N VAL A 911 -1.971 -9.225 -12.016 1.00 37.15 N +ATOM 374 CA VAL A 911 -3.138 -10.101 -12.065 1.00 34.94 C +ATOM 375 C VAL A 911 -2.732 -11.374 -12.827 1.00 36.97 C +ATOM 376 O VAL A 911 -2.030 -11.287 -13.838 1.00 35.74 O +ATOM 377 CB VAL A 911 -4.323 -9.449 -12.832 1.00 38.64 C +ATOM 378 CG1 VAL A 911 -5.604 -10.312 -12.836 1.00 37.50 C +ATOM 379 CG2 VAL A 911 -4.665 -8.046 -12.301 1.00 39.45 C +ATOM 380 H VAL A 911 -1.594 -8.964 -12.919 1.00 37.15 H +ATOM 381 HA VAL A 911 -3.461 -10.380 -11.067 1.00 34.94 H +ATOM 382 HB VAL A 911 -4.013 -9.331 -13.869 1.00 38.64 H +ATOM 383 HG11 VAL A 911 -6.413 -9.808 -13.365 1.00 37.50 H +ATOM 384 HG12 VAL A 911 -5.459 -11.273 -13.327 1.00 37.50 H +ATOM 385 HG13 VAL A 911 -5.949 -10.509 -11.820 1.00 37.50 H +ATOM 386 HG21 VAL A 911 -5.489 -7.603 -12.859 1.00 39.45 H +ATOM 387 HG22 VAL A 911 -4.967 -8.091 -11.259 1.00 39.45 H +ATOM 388 HG23 VAL A 911 -3.822 -7.359 -12.381 1.00 39.45 H +ATOM 389 N SER A 912 -3.188 -12.530 -12.325 1.00 32.82 N +ATOM 390 CA SER A 912 -2.920 -13.839 -12.909 1.00 32.44 C +ATOM 391 C SER A 912 -4.187 -14.693 -12.861 1.00 36.01 C +ATOM 392 O SER A 912 -4.950 -14.617 -11.899 1.00 34.87 O +ATOM 393 CB SER A 912 -1.780 -14.527 -12.136 1.00 36.04 C +ATOM 394 OG SER A 912 -0.549 -13.884 -12.379 1.00 45.71 O +ATOM 395 H SER A 912 -3.765 -12.527 -11.494 1.00 32.82 H +ATOM 396 HA SER A 912 -2.642 -13.737 -13.958 1.00 32.44 H +ATOM 397 HB3 SER A 912 -1.683 -15.572 -12.436 1.00 36.04 H +ATOM 398 HB2 SER A 912 -1.975 -14.516 -11.066 1.00 36.04 H +ATOM 399 HG SER A 912 -0.594 -13.012 -11.977 1.00 45.71 H +ATOM 400 N LEU A 913 -4.360 -15.518 -13.900 1.00 32.94 N +ATOM 401 CA LEU A 913 -5.421 -16.505 -14.016 1.00 32.73 C +ATOM 402 C LEU A 913 -5.002 -17.794 -13.295 1.00 36.98 C +ATOM 403 O LEU A 913 -3.924 -18.324 -13.568 1.00 35.71 O +ATOM 404 CB LEU A 913 -5.708 -16.725 -15.517 1.00 33.31 C +ATOM 405 CG LEU A 913 -6.770 -17.793 -15.857 1.00 38.67 C +ATOM 406 CD1 LEU A 913 -8.125 -17.524 -15.168 1.00 39.26 C +ATOM 407 CD2 LEU A 913 -6.897 -17.945 -17.387 1.00 40.96 C +ATOM 408 H LEU A 913 -3.653 -15.554 -14.623 1.00 32.94 H +ATOM 409 HA LEU A 913 -6.319 -16.103 -13.545 1.00 32.73 H +ATOM 410 HB3 LEU A 913 -4.776 -16.989 -16.019 1.00 33.31 H +ATOM 411 HB2 LEU A 913 -6.024 -15.778 -15.951 1.00 33.31 H +ATOM 412 HG LEU A 913 -6.411 -18.748 -15.479 1.00 38.67 H +ATOM 413 HD11 LEU A 913 -8.969 -17.675 -15.835 1.00 39.26 H +ATOM 414 HD12 LEU A 913 -8.270 -18.198 -14.324 1.00 39.26 H +ATOM 415 HD13 LEU A 913 -8.203 -16.505 -14.788 1.00 39.26 H +ATOM 416 HD21 LEU A 913 -6.656 -18.960 -17.702 1.00 40.96 H +ATOM 417 HD22 LEU A 913 -7.896 -17.721 -17.755 1.00 40.96 H +ATOM 418 HD23 LEU A 913 -6.216 -17.279 -17.917 1.00 40.96 H +ATOM 419 N TYR A 914 -5.887 -18.270 -12.410 1.00 34.34 N +ATOM 420 CA TYR A 914 -5.761 -19.522 -11.672 1.00 35.26 C +ATOM 421 C TYR A 914 -7.035 -20.352 -11.864 1.00 40.97 C +ATOM 422 O TYR A 914 -8.122 -19.790 -11.984 1.00 41.41 O +ATOM 423 CB TYR A 914 -5.585 -19.230 -10.167 1.00 36.25 C +ATOM 424 CG TYR A 914 -4.258 -18.632 -9.738 1.00 37.55 C +ATOM 425 CD1 TYR A 914 -3.251 -19.461 -9.207 1.00 41.10 C +ATOM 426 CD2 TYR A 914 -4.046 -17.242 -9.800 1.00 37.73 C +ATOM 427 CE1 TYR A 914 -2.054 -18.909 -8.717 1.00 42.72 C +ATOM 428 CE2 TYR A 914 -2.845 -16.686 -9.315 1.00 39.24 C +ATOM 429 CZ TYR A 914 -1.847 -17.519 -8.776 1.00 49.59 C +ATOM 430 OH TYR A 914 -0.685 -16.979 -8.308 1.00 54.45 O +ATOM 431 H TYR A 914 -6.749 -17.762 -12.251 1.00 34.34 H +ATOM 432 HA TYR A 914 -4.909 -20.094 -12.040 1.00 35.26 H +ATOM 433 HB3 TYR A 914 -5.705 -20.158 -9.605 1.00 36.25 H +ATOM 434 HB2 TYR A 914 -6.386 -18.578 -9.817 1.00 36.25 H +ATOM 435 HD1 TYR A 914 -3.405 -20.526 -9.149 1.00 41.10 H +ATOM 436 HD2 TYR A 914 -4.816 -16.599 -10.198 1.00 37.73 H +ATOM 437 HE1 TYR A 914 -1.299 -19.558 -8.297 1.00 42.72 H +ATOM 438 HE2 TYR A 914 -2.701 -15.617 -9.335 1.00 39.24 H +ATOM 439 HH TYR A 914 -0.088 -17.635 -7.941 1.00 54.45 H +ATOM 440 N CYS A 915 -6.871 -21.680 -11.807 1.00 39.05 N +ATOM 441 CA CYS A 915 -7.950 -22.649 -11.663 1.00 40.20 C +ATOM 442 C CYS A 915 -7.988 -23.052 -10.181 1.00 43.77 C +ATOM 443 O CYS A 915 -7.093 -23.769 -9.728 1.00 43.29 O +ATOM 444 CB CYS A 915 -7.745 -23.856 -12.604 1.00 41.99 C +ATOM 445 SG CYS A 915 -9.062 -25.098 -12.432 1.00 48.59 S +ATOM 446 H CYS A 915 -5.935 -22.054 -11.712 1.00 39.05 H +ATOM 447 HA CYS A 915 -8.905 -22.191 -11.927 1.00 40.20 H +ATOM 448 HB3 CYS A 915 -6.786 -24.339 -12.412 1.00 41.99 H +ATOM 449 HB2 CYS A 915 -7.727 -23.525 -13.642 1.00 41.99 H +ATOM 450 HG CYS A 915 -10.049 -24.342 -12.923 1.00 48.59 H +ATOM 451 N TYR A 916 -9.007 -22.565 -9.452 1.00 39.62 N +ATOM 452 CA TYR A 916 -9.266 -22.950 -8.066 1.00 39.96 C +ATOM 453 C TYR A 916 -9.955 -24.317 -8.054 1.00 45.20 C +ATOM 454 O TYR A 916 -11.103 -24.411 -8.485 1.00 45.42 O +ATOM 455 CB TYR A 916 -10.123 -21.883 -7.362 1.00 40.18 C +ATOM 456 CG TYR A 916 -10.338 -22.159 -5.882 1.00 41.11 C +ATOM 457 CD1 TYR A 916 -9.235 -22.141 -5.005 1.00 41.72 C +ATOM 458 CD2 TYR A 916 -11.625 -22.431 -5.372 1.00 43.06 C +ATOM 459 CE1 TYR A 916 -9.421 -22.339 -3.626 1.00 42.69 C +ATOM 460 CE2 TYR A 916 -11.807 -22.654 -3.991 1.00 44.62 C +ATOM 461 CZ TYR A 916 -10.704 -22.596 -3.116 1.00 47.56 C +ATOM 462 OH TYR A 916 -10.866 -22.784 -1.774 1.00 49.11 O +ATOM 463 H TYR A 916 -9.717 -21.999 -9.899 1.00 39.62 H +ATOM 464 HA TYR A 916 -8.313 -23.020 -7.541 1.00 39.96 H +ATOM 465 HB3 TYR A 916 -11.089 -21.777 -7.861 1.00 40.18 H +ATOM 466 HB2 TYR A 916 -9.641 -20.909 -7.450 1.00 40.18 H +ATOM 467 HD1 TYR A 916 -8.244 -21.965 -5.390 1.00 41.72 H +ATOM 468 HD2 TYR A 916 -12.478 -22.459 -6.035 1.00 43.06 H +ATOM 469 HE1 TYR A 916 -8.578 -22.297 -2.958 1.00 42.69 H +ATOM 470 HE2 TYR A 916 -12.797 -22.853 -3.609 1.00 44.62 H +ATOM 471 HH TYR A 916 -11.780 -22.931 -1.519 1.00 49.11 H +ATOM 472 N ASP A 917 -9.224 -25.343 -7.600 1.00 46.60 N +ATOM 473 CA ASP A 917 -9.548 -26.742 -7.857 1.00 48.33 C +ATOM 474 C ASP A 917 -9.310 -27.589 -6.586 1.00 54.57 C +ATOM 475 O ASP A 917 -8.280 -28.261 -6.507 1.00 54.85 O +ATOM 476 CB ASP A 917 -8.720 -27.202 -9.088 1.00 50.11 C +ATOM 477 CG ASP A 917 -8.948 -28.619 -9.626 1.00 62.07 C +ATOM 478 OD1 ASP A 917 -10.074 -29.144 -9.481 1.00 63.23 O +ATOM 479 OD2 ASP A 917 -8.018 -29.099 -10.308 1.00 69.60 O1- +ATOM 480 H ASP A 917 -8.291 -25.168 -7.248 1.00 46.60 H +ATOM 481 HA ASP A 917 -10.604 -26.840 -8.110 1.00 48.33 H +ATOM 482 HB3 ASP A 917 -7.660 -27.061 -8.876 1.00 50.11 H +ATOM 483 HB2 ASP A 917 -8.925 -26.518 -9.911 1.00 50.11 H +ATOM 484 N PRO A 918 -10.233 -27.531 -5.592 1.00 52.47 N +ATOM 485 CA PRO A 918 -10.097 -28.298 -4.337 1.00 54.47 C +ATOM 486 C PRO A 918 -10.108 -29.832 -4.468 1.00 61.35 C +ATOM 487 O PRO A 918 -9.390 -30.493 -3.718 1.00 62.40 O +ATOM 488 CB PRO A 918 -11.249 -27.795 -3.449 1.00 56.45 C +ATOM 489 CG PRO A 918 -11.619 -26.442 -4.028 1.00 58.90 C +ATOM 490 CD PRO A 918 -11.398 -26.646 -5.519 1.00 53.43 C +ATOM 491 HA PRO A 918 -9.154 -28.002 -3.890 1.00 54.47 H +ATOM 492 HB3 PRO A 918 -10.970 -27.730 -2.397 1.00 56.45 H +ATOM 493 HB2 PRO A 918 -12.112 -28.460 -3.518 1.00 56.45 H +ATOM 494 HG3 PRO A 918 -10.924 -25.686 -3.658 1.00 58.90 H +ATOM 495 HG2 PRO A 918 -12.631 -26.122 -3.778 1.00 58.90 H +ATOM 496 HD2 PRO A 918 -12.253 -27.150 -5.969 1.00 53.43 H +ATOM 497 HD3 PRO A 918 -11.278 -25.684 -6.008 1.00 53.43 H +ATOM 498 N THR A 919 -10.909 -30.361 -5.407 1.00 59.02 N +ATOM 499 CA THR A 919 -11.072 -31.799 -5.649 1.00 61.06 C +ATOM 500 C THR A 919 -9.985 -32.394 -6.578 1.00 66.39 C +ATOM 501 O THR A 919 -9.864 -33.618 -6.624 1.00 67.71 O +ATOM 502 CB THR A 919 -12.460 -32.093 -6.275 1.00 69.24 C +ATOM 503 OG1 THR A 919 -12.585 -31.469 -7.533 1.00 68.64 O +ATOM 504 CG2 THR A 919 -13.638 -31.637 -5.400 1.00 68.24 C +ATOM 505 H THR A 919 -11.466 -29.756 -5.994 1.00 59.02 H +ATOM 506 HA THR A 919 -11.011 -32.326 -4.695 1.00 61.06 H +ATOM 507 HB THR A 919 -12.561 -33.168 -6.437 1.00 69.24 H +ATOM 508 HG1 THR A 919 -13.251 -31.932 -8.052 1.00 68.64 H +ATOM 509 HG21 THR A 919 -14.591 -31.908 -5.855 1.00 68.24 H +ATOM 510 HG22 THR A 919 -13.598 -32.106 -4.417 1.00 68.24 H +ATOM 511 HG23 THR A 919 -13.640 -30.556 -5.253 1.00 68.24 H +ATOM 512 N ASN A 920 -9.199 -31.539 -7.261 1.00 62.42 N +ATOM 513 CA ASN A 920 -8.037 -31.885 -8.094 1.00 62.48 C +ATOM 514 C ASN A 920 -8.379 -32.616 -9.418 1.00 68.30 C +ATOM 515 O ASN A 920 -7.483 -33.225 -10.005 1.00 68.75 O +ATOM 516 CB ASN A 920 -6.961 -32.649 -7.260 1.00 64.50 C +ATOM 517 CG ASN A 920 -5.483 -32.372 -7.591 1.00 90.28 C +ATOM 518 OD1 ASN A 920 -4.611 -32.755 -6.815 1.00 80.74 O +ATOM 519 ND2 ASN A 920 -5.167 -31.729 -8.718 1.00 86.67 N +ATOM 520 H ASN A 920 -9.390 -30.550 -7.183 1.00 62.42 H +ATOM 521 HA ASN A 920 -7.608 -30.945 -8.433 1.00 62.48 H +ATOM 522 HB3 ASN A 920 -7.149 -33.724 -7.262 1.00 64.50 H +ATOM 523 HB2 ASN A 920 -7.058 -32.342 -6.217 1.00 64.50 H +ATOM 524 HD22 ASN A 920 -4.198 -31.571 -8.954 1.00 86.67 H +ATOM 525 HD21 ASN A 920 -5.888 -31.454 -9.372 1.00 86.67 H +ATOM 526 N ASP A 921 -9.638 -32.539 -9.885 1.00 65.11 N +ATOM 527 CA ASP A 921 -10.110 -33.196 -11.116 1.00 65.45 C +ATOM 528 C ASP A 921 -9.985 -32.313 -12.380 1.00 68.10 C +ATOM 529 O ASP A 921 -10.142 -32.837 -13.482 1.00 68.54 O +ATOM 530 CB ASP A 921 -11.542 -33.785 -10.972 1.00 68.72 C +ATOM 531 CG ASP A 921 -12.662 -32.858 -10.464 1.00 75.04 C +ATOM 532 OD1 ASP A 921 -12.517 -31.619 -10.549 1.00 72.94 O +ATOM 533 OD2 ASP A 921 -13.716 -33.411 -10.080 1.00 81.22 O1- +ATOM 534 H ASP A 921 -10.326 -31.995 -9.382 1.00 65.11 H +ATOM 535 HA ASP A 921 -9.464 -34.056 -11.306 1.00 65.45 H +ATOM 536 HB3 ASP A 921 -11.484 -34.629 -10.282 1.00 68.72 H +ATOM 537 HB2 ASP A 921 -11.865 -34.214 -11.922 1.00 68.72 H +ATOM 538 N GLY A 922 -9.711 -31.008 -12.210 1.00 62.38 N +ATOM 539 CA GLY A 922 -9.551 -30.041 -13.297 1.00 60.09 C +ATOM 540 C GLY A 922 -10.863 -29.330 -13.668 1.00 63.42 C +ATOM 541 O GLY A 922 -10.834 -28.480 -14.557 1.00 62.74 O +ATOM 542 H GLY A 922 -9.599 -30.648 -11.270 1.00 62.38 H +ATOM 543 HA3 GLY A 922 -9.136 -30.518 -14.186 1.00 60.09 H +ATOM 544 HA2 GLY A 922 -8.828 -29.287 -12.986 1.00 60.09 H +ATOM 545 N THR A 923 -11.995 -29.645 -13.011 1.00 59.47 N +ATOM 546 CA THR A 923 -13.310 -29.037 -13.264 1.00 58.79 C +ATOM 547 C THR A 923 -13.573 -27.766 -12.417 1.00 58.95 C +ATOM 548 O THR A 923 -14.667 -27.206 -12.506 1.00 58.00 O +ATOM 549 CB THR A 923 -14.472 -30.055 -13.053 1.00 71.45 C +ATOM 550 OG1 THR A 923 -14.846 -30.240 -11.699 1.00 74.26 O +ATOM 551 CG2 THR A 923 -14.234 -31.420 -13.720 1.00 71.82 C +ATOM 552 H THR A 923 -11.966 -30.341 -12.275 1.00 59.47 H +ATOM 553 HA THR A 923 -13.355 -28.722 -14.308 1.00 58.79 H +ATOM 554 HB THR A 923 -15.355 -29.634 -13.535 1.00 71.45 H +ATOM 555 HG1 THR A 923 -14.145 -30.727 -11.249 1.00 74.26 H +ATOM 556 HG21 THR A 923 -15.102 -32.069 -13.603 1.00 71.82 H +ATOM 557 HG22 THR A 923 -14.051 -31.305 -14.788 1.00 71.82 H +ATOM 558 HG23 THR A 923 -13.377 -31.943 -13.294 1.00 71.82 H +ATOM 559 N GLY A 924 -12.571 -27.323 -11.637 1.00 53.95 N +ATOM 560 CA GLY A 924 -12.623 -26.151 -10.763 1.00 52.08 C +ATOM 561 C GLY A 924 -12.788 -24.830 -11.535 1.00 53.45 C +ATOM 562 O GLY A 924 -12.426 -24.728 -12.708 1.00 52.18 O +ATOM 563 H GLY A 924 -11.706 -27.844 -11.632 1.00 53.95 H +ATOM 564 HA3 GLY A 924 -11.683 -26.125 -10.215 1.00 52.08 H +ATOM 565 HA2 GLY A 924 -13.425 -26.270 -10.033 1.00 52.08 H +ATOM 566 N GLU A 925 -13.324 -23.810 -10.844 1.00 49.00 N +ATOM 567 CA GLU A 925 -13.602 -22.474 -11.376 1.00 47.60 C +ATOM 568 C GLU A 925 -12.333 -21.684 -11.730 1.00 48.02 C +ATOM 569 O GLU A 925 -11.391 -21.647 -10.938 1.00 45.98 O +ATOM 570 CB GLU A 925 -14.441 -21.680 -10.347 1.00 49.51 C +ATOM 571 CG GLU A 925 -15.856 -22.247 -10.096 1.00 66.63 C +ATOM 572 CD GLU A 925 -16.796 -22.122 -11.304 1.00 94.52 C +ATOM 573 OE1 GLU A 925 -16.972 -20.979 -11.784 1.00 89.72 O +ATOM 574 OE2 GLU A 925 -17.331 -23.170 -11.725 1.00 93.17 O1- +ATOM 575 H GLU A 925 -13.590 -23.957 -9.880 1.00 49.00 H +ATOM 576 HA GLU A 925 -14.185 -22.600 -12.290 1.00 47.60 H +ATOM 577 HB3 GLU A 925 -14.507 -20.633 -10.646 1.00 49.51 H +ATOM 578 HB2 GLU A 925 -13.910 -21.669 -9.394 1.00 49.51 H +ATOM 579 HG3 GLU A 925 -16.312 -21.709 -9.264 1.00 66.63 H +ATOM 580 HG2 GLU A 925 -15.791 -23.287 -9.773 1.00 66.63 H +ATOM 581 N MET A 926 -12.376 -21.016 -12.893 1.00 43.85 N +ATOM 582 CA MET A 926 -11.372 -20.052 -13.336 1.00 42.46 C +ATOM 583 C MET A 926 -11.611 -18.704 -12.636 1.00 43.50 C +ATOM 584 O MET A 926 -12.731 -18.192 -12.664 1.00 43.72 O +ATOM 585 CB MET A 926 -11.429 -19.907 -14.871 1.00 44.66 C +ATOM 586 CG MET A 926 -11.085 -21.206 -15.622 1.00 50.18 C +ATOM 587 SD MET A 926 -11.210 -21.107 -17.430 1.00 55.18 S +ATOM 588 CE MET A 926 -9.740 -20.126 -17.815 1.00 50.66 C +ATOM 589 H MET A 926 -13.209 -21.073 -13.464 1.00 43.85 H +ATOM 590 HA MET A 926 -10.384 -20.428 -13.070 1.00 42.46 H +ATOM 591 HB3 MET A 926 -10.737 -19.124 -15.180 1.00 44.66 H +ATOM 592 HB2 MET A 926 -12.419 -19.565 -15.178 1.00 44.66 H +ATOM 593 HG3 MET A 926 -11.755 -22.005 -15.304 1.00 50.18 H +ATOM 594 HG2 MET A 926 -10.078 -21.532 -15.362 1.00 50.18 H +ATOM 595 HE1 MET A 926 -9.634 -20.012 -18.894 1.00 50.66 H +ATOM 596 HE2 MET A 926 -9.824 -19.135 -17.376 1.00 50.66 H +ATOM 597 HE3 MET A 926 -8.844 -20.609 -17.427 1.00 50.66 H +ATOM 598 N VAL A 927 -10.551 -18.168 -12.017 1.00 36.95 N +ATOM 599 CA VAL A 927 -10.570 -16.927 -11.247 1.00 34.96 C +ATOM 600 C VAL A 927 -9.331 -16.077 -11.575 1.00 36.30 C +ATOM 601 O VAL A 927 -8.232 -16.612 -11.722 1.00 35.86 O +ATOM 602 CB VAL A 927 -10.610 -17.194 -9.711 1.00 39.31 C +ATOM 603 CG1 VAL A 927 -11.992 -17.683 -9.251 1.00 40.52 C +ATOM 604 CG2 VAL A 927 -9.507 -18.134 -9.180 1.00 39.38 C +ATOM 605 H VAL A 927 -9.662 -18.654 -12.039 1.00 36.95 H +ATOM 606 HA VAL A 927 -11.448 -16.339 -11.522 1.00 34.96 H +ATOM 607 HB VAL A 927 -10.456 -16.236 -9.212 1.00 39.31 H +ATOM 608 HG11 VAL A 927 -12.038 -17.771 -8.165 1.00 40.52 H +ATOM 609 HG12 VAL A 927 -12.769 -16.982 -9.554 1.00 40.52 H +ATOM 610 HG13 VAL A 927 -12.235 -18.658 -9.674 1.00 40.52 H +ATOM 611 HG21 VAL A 927 -9.586 -18.255 -8.099 1.00 39.38 H +ATOM 612 HG22 VAL A 927 -9.578 -19.128 -9.623 1.00 39.38 H +ATOM 613 HG23 VAL A 927 -8.510 -17.750 -9.382 1.00 39.38 H +ATOM 614 N ALA A 928 -9.546 -14.756 -11.667 1.00 33.12 N +ATOM 615 CA ALA A 928 -8.506 -13.752 -11.857 1.00 33.29 C +ATOM 616 C ALA A 928 -8.086 -13.216 -10.483 1.00 37.02 C +ATOM 617 O ALA A 928 -8.817 -12.428 -9.884 1.00 37.68 O +ATOM 618 CB ALA A 928 -9.054 -12.636 -12.757 1.00 33.26 C +ATOM 619 H ALA A 928 -10.476 -14.395 -11.503 1.00 33.12 H +ATOM 620 HA ALA A 928 -7.640 -14.190 -12.357 1.00 33.29 H +ATOM 621 HB1 ALA A 928 -8.316 -11.845 -12.901 1.00 33.26 H +ATOM 622 HB2 ALA A 928 -9.308 -13.028 -13.742 1.00 33.26 H +ATOM 623 HB3 ALA A 928 -9.953 -12.181 -12.339 1.00 33.26 H +ATOM 624 N VAL A 929 -6.929 -13.685 -9.998 1.00 32.01 N +ATOM 625 CA VAL A 929 -6.385 -13.331 -8.692 1.00 31.43 C +ATOM 626 C VAL A 929 -5.339 -12.221 -8.875 1.00 34.06 C +ATOM 627 O VAL A 929 -4.322 -12.445 -9.535 1.00 33.04 O +ATOM 628 CB VAL A 929 -5.698 -14.547 -8.012 1.00 35.67 C +ATOM 629 CG1 VAL A 929 -5.130 -14.225 -6.617 1.00 35.66 C +ATOM 630 CG2 VAL A 929 -6.643 -15.756 -7.910 1.00 35.86 C +ATOM 631 H VAL A 929 -6.365 -14.309 -10.561 1.00 32.01 H +ATOM 632 HA VAL A 929 -7.179 -12.977 -8.034 1.00 31.43 H +ATOM 633 HB VAL A 929 -4.857 -14.854 -8.629 1.00 35.67 H +ATOM 634 HG11 VAL A 929 -4.714 -15.115 -6.143 1.00 35.66 H +ATOM 635 HG12 VAL A 929 -4.328 -13.487 -6.657 1.00 35.66 H +ATOM 636 HG13 VAL A 929 -5.914 -13.838 -5.965 1.00 35.66 H +ATOM 637 HG21 VAL A 929 -6.160 -16.590 -7.401 1.00 35.86 H +ATOM 638 HG22 VAL A 929 -7.545 -15.504 -7.357 1.00 35.86 H +ATOM 639 HG23 VAL A 929 -6.942 -16.112 -8.895 1.00 35.86 H +ATOM 640 N LYS A 930 -5.610 -11.055 -8.273 1.00 30.97 N +ATOM 641 CA LYS A 930 -4.688 -9.925 -8.224 1.00 30.96 C +ATOM 642 C LYS A 930 -3.874 -9.976 -6.925 1.00 37.68 C +ATOM 643 O LYS A 930 -4.421 -10.310 -5.875 1.00 36.55 O +ATOM 644 CB LYS A 930 -5.482 -8.618 -8.397 1.00 33.16 C +ATOM 645 CG LYS A 930 -4.599 -7.367 -8.542 1.00 48.15 C +ATOM 646 CD LYS A 930 -5.416 -6.121 -8.900 1.00 51.77 C +ATOM 647 CE LYS A 930 -4.523 -4.890 -9.108 1.00 60.76 C +ATOM 648 NZ LYS A 930 -5.318 -3.714 -9.493 1.00 63.79 N1+ +ATOM 649 H LYS A 930 -6.465 -10.954 -7.740 1.00 30.97 H +ATOM 650 HA LYS A 930 -3.992 -9.993 -9.053 1.00 30.96 H +ATOM 651 HB3 LYS A 930 -6.191 -8.486 -7.580 1.00 33.16 H +ATOM 652 HB2 LYS A 930 -6.087 -8.710 -9.299 1.00 33.16 H +ATOM 653 HG3 LYS A 930 -3.847 -7.538 -9.312 1.00 48.15 H +ATOM 654 HG2 LYS A 930 -4.040 -7.183 -7.626 1.00 48.15 H +ATOM 655 HD3 LYS A 930 -6.143 -5.928 -8.109 1.00 51.77 H +ATOM 656 HD2 LYS A 930 -5.995 -6.317 -9.804 1.00 51.77 H +ATOM 657 HE3 LYS A 930 -3.789 -5.088 -9.888 1.00 60.76 H +ATOM 658 HE2 LYS A 930 -3.969 -4.658 -8.199 1.00 60.76 H +ATOM 659 HZ1 LYS A 930 -5.988 -3.511 -8.759 1.00 63.79 H +ATOM 660 HZ2 LYS A 930 -4.721 -2.911 -9.618 1.00 63.79 H +ATOM 661 HZ3 LYS A 930 -5.818 -3.896 -10.350 1.00 63.79 H +ATOM 662 N ALA A 931 -2.580 -9.642 -7.031 1.00 37.96 N +ATOM 663 CA ALA A 931 -1.623 -9.687 -5.933 1.00 40.37 C +ATOM 664 C ALA A 931 -0.830 -8.379 -5.859 1.00 49.88 C +ATOM 665 O ALA A 931 -0.449 -7.826 -6.890 1.00 48.28 O +ATOM 666 CB ALA A 931 -0.678 -10.880 -6.150 1.00 41.64 C +ATOM 667 H ALA A 931 -2.209 -9.351 -7.928 1.00 37.96 H +ATOM 668 HA ALA A 931 -2.145 -9.825 -4.984 1.00 40.37 H +ATOM 669 HB1 ALA A 931 0.015 -10.988 -5.314 1.00 41.64 H +ATOM 670 HB2 ALA A 931 -1.235 -11.814 -6.232 1.00 41.64 H +ATOM 671 HB3 ALA A 931 -0.088 -10.766 -7.060 1.00 41.64 H +ATOM 672 N LEU A 932 -0.559 -7.956 -4.618 1.00 51.32 N +ATOM 673 CA LEU A 932 0.354 -6.877 -4.255 1.00 53.72 C +ATOM 674 C LEU A 932 1.768 -7.462 -4.129 1.00 62.30 C +ATOM 675 O LEU A 932 1.938 -8.492 -3.473 1.00 63.20 O +ATOM 676 CB LEU A 932 -0.203 -6.237 -2.959 1.00 54.88 C +ATOM 677 CG LEU A 932 0.643 -5.234 -2.140 1.00 61.02 C +ATOM 678 CD1 LEU A 932 1.578 -5.910 -1.118 1.00 63.21 C +ATOM 679 CD2 LEU A 932 1.333 -4.164 -3.002 1.00 63.43 C +ATOM 680 H LEU A 932 -0.898 -8.507 -3.839 1.00 51.32 H +ATOM 681 HA LEU A 932 0.353 -6.121 -5.044 1.00 53.72 H +ATOM 682 HB3 LEU A 932 -0.550 -7.017 -2.280 1.00 54.88 H +ATOM 683 HB2 LEU A 932 -1.098 -5.708 -3.275 1.00 54.88 H +ATOM 684 HG LEU A 932 -0.082 -4.685 -1.537 1.00 61.02 H +ATOM 685 HD11 LEU A 932 2.578 -5.486 -1.128 1.00 63.21 H +ATOM 686 HD12 LEU A 932 1.192 -5.785 -0.107 1.00 63.21 H +ATOM 687 HD13 LEU A 932 1.671 -6.983 -1.281 1.00 63.21 H +ATOM 688 HD21 LEU A 932 1.351 -3.201 -2.490 1.00 63.43 H +ATOM 689 HD22 LEU A 932 2.361 -4.440 -3.233 1.00 63.43 H +ATOM 690 HD23 LEU A 932 0.815 -4.017 -3.948 1.00 63.43 H +ATOM 691 N LYS A 933 2.746 -6.805 -4.775 1.00 61.35 N +ATOM 692 CA LYS A 933 4.151 -7.223 -4.806 1.00 63.52 C +ATOM 693 C LYS A 933 4.791 -7.220 -3.401 1.00 70.14 C +ATOM 694 O LYS A 933 4.505 -6.330 -2.602 1.00 70.50 O +ATOM 695 CB LYS A 933 4.920 -6.342 -5.819 1.00 66.45 C +ATOM 696 CG LYS A 933 5.118 -4.872 -5.401 1.00 83.56 C +ATOM 697 CD LYS A 933 5.932 -4.051 -6.408 1.00 96.14 C +ATOM 698 CE LYS A 933 6.299 -2.668 -5.844 1.00107.75 C +ATOM 699 NZ LYS A 933 7.319 -1.991 -6.663 1.00116.59 N1+ +ATOM 700 H LYS A 933 2.523 -5.965 -5.289 1.00 61.35 H +ATOM 701 HA LYS A 933 4.168 -8.249 -5.180 1.00 63.52 H +ATOM 702 HB3 LYS A 933 4.426 -6.386 -6.790 1.00 66.45 H +ATOM 703 HB2 LYS A 933 5.906 -6.784 -5.971 1.00 66.45 H +ATOM 704 HG3 LYS A 933 5.647 -4.840 -4.451 1.00 83.56 H +ATOM 705 HG2 LYS A 933 4.149 -4.396 -5.240 1.00 83.56 H +ATOM 706 HD3 LYS A 933 5.354 -3.933 -7.326 1.00 96.14 H +ATOM 707 HD2 LYS A 933 6.830 -4.606 -6.684 1.00 96.14 H +ATOM 708 HE3 LYS A 933 6.712 -2.771 -4.840 1.00107.75 H +ATOM 709 HE2 LYS A 933 5.413 -2.038 -5.765 1.00107.75 H +ATOM 710 HZ1 LYS A 933 8.154 -2.563 -6.671 1.00116.59 H +ATOM 711 HZ2 LYS A 933 6.989 -1.866 -7.610 1.00116.59 H +ATOM 712 HZ3 LYS A 933 7.539 -1.092 -6.261 1.00116.59 H +ATOM 713 N ALA A 934 5.650 -8.217 -3.131 1.00 68.33 N +ATOM 714 CA ALA A 934 6.298 -8.425 -1.831 1.00 69.84 C +ATOM 715 C ALA A 934 7.265 -7.306 -1.401 1.00 75.29 C +ATOM 716 O ALA A 934 7.435 -7.096 -0.201 1.00 75.68 O +ATOM 717 CB ALA A 934 7.019 -9.781 -1.846 1.00 71.23 C +ATOM 718 H ALA A 934 5.847 -8.909 -3.840 1.00 68.33 H +ATOM 719 HA ALA A 934 5.511 -8.468 -1.076 1.00 69.84 H +ATOM 720 HB1 ALA A 934 7.460 -10.006 -0.874 1.00 71.23 H +ATOM 721 HB2 ALA A 934 6.330 -10.591 -2.085 1.00 71.23 H +ATOM 722 HB3 ALA A 934 7.820 -9.799 -2.587 1.00 71.23 H +ATOM 723 N ASP A 935 7.863 -6.609 -2.380 1.00 72.17 N +ATOM 724 CA ASP A 935 8.836 -5.527 -2.197 1.00 73.50 C +ATOM 725 C ASP A 935 8.185 -4.144 -1.962 1.00 75.83 C +ATOM 726 O ASP A 935 8.922 -3.179 -1.754 1.00 76.32 O +ATOM 727 CB ASP A 935 9.864 -5.475 -3.361 1.00 76.23 C +ATOM 728 CG ASP A 935 9.276 -5.328 -4.776 1.00 89.05 C +ATOM 729 OD1 ASP A 935 8.665 -6.306 -5.264 1.00 88.80 O +ATOM 730 OD2 ASP A 935 9.356 -4.200 -5.311 1.00 97.21 O1- +ATOM 731 H ASP A 935 7.672 -6.851 -3.344 1.00 72.17 H +ATOM 732 HA ASP A 935 9.406 -5.746 -1.292 1.00 73.50 H +ATOM 733 HB3 ASP A 935 10.432 -6.406 -3.352 1.00 76.23 H +ATOM 734 HB2 ASP A 935 10.609 -4.697 -3.179 1.00 76.23 H +ATOM 735 N ALA A 936 6.842 -4.054 -1.996 1.00 70.25 N +ATOM 736 CA ALA A 936 6.074 -2.822 -1.798 1.00 69.55 C +ATOM 737 C ALA A 936 6.250 -2.196 -0.405 1.00 73.05 C +ATOM 738 O ALA A 936 6.327 -2.920 0.589 1.00 73.05 O +ATOM 739 CB ALA A 936 4.591 -3.122 -2.034 1.00 68.72 C +ATOM 740 H ALA A 936 6.301 -4.891 -2.162 1.00 70.25 H +ATOM 741 HA ALA A 936 6.404 -2.099 -2.546 1.00 69.55 H +ATOM 742 HB1 ALA A 936 3.968 -2.252 -1.839 1.00 68.72 H +ATOM 743 HB2 ALA A 936 4.413 -3.419 -3.064 1.00 68.72 H +ATOM 744 HB3 ALA A 936 4.241 -3.921 -1.381 1.00 68.72 H +ATOM 745 N GLY A 937 6.263 -0.854 -0.374 1.00 68.94 N +ATOM 746 CA GLY A 937 6.335 -0.057 0.850 1.00 69.45 C +ATOM 747 C GLY A 937 4.933 0.143 1.460 1.00 71.44 C +ATOM 748 O GLY A 937 3.934 -0.265 0.865 1.00 69.33 O +ATOM 749 H GLY A 937 6.175 -0.344 -1.241 1.00 68.94 H +ATOM 750 HA3 GLY A 937 6.771 0.911 0.607 1.00 69.45 H +ATOM 751 HA2 GLY A 937 6.996 -0.534 1.576 1.00 69.45 H +ATOM 752 N PRO A 938 4.847 0.779 2.651 1.00 68.74 N +ATOM 753 CA PRO A 938 3.589 0.933 3.409 1.00 68.21 C +ATOM 754 C PRO A 938 2.501 1.788 2.729 1.00 71.10 C +ATOM 755 O PRO A 938 1.321 1.556 2.990 1.00 70.04 O +ATOM 756 CB PRO A 938 4.043 1.532 4.751 1.00 71.88 C +ATOM 757 CG PRO A 938 5.339 2.259 4.429 1.00 77.34 C +ATOM 758 CD PRO A 938 5.977 1.366 3.372 1.00 72.04 C +ATOM 759 HA PRO A 938 3.163 -0.053 3.593 1.00 68.21 H +ATOM 760 HB3 PRO A 938 4.244 0.723 5.455 1.00 71.88 H +ATOM 761 HB2 PRO A 938 3.305 2.188 5.216 1.00 71.88 H +ATOM 762 HG3 PRO A 938 5.975 2.421 5.300 1.00 77.34 H +ATOM 763 HG2 PRO A 938 5.111 3.233 3.994 1.00 77.34 H +ATOM 764 HD2 PRO A 938 6.648 1.939 2.732 1.00 72.04 H +ATOM 765 HD3 PRO A 938 6.549 0.565 3.842 1.00 72.04 H +ATOM 766 N GLN A 939 2.905 2.724 1.853 1.00 67.61 N +ATOM 767 CA GLN A 939 2.021 3.560 1.038 1.00 66.36 C +ATOM 768 C GLN A 939 1.200 2.757 0.008 1.00 68.88 C +ATOM 769 O GLN A 939 0.018 3.051 -0.168 1.00 67.37 O +ATOM 770 CB GLN A 939 2.817 4.723 0.389 1.00 68.30 C +ATOM 771 CG GLN A 939 3.855 4.379 -0.712 1.00 77.62 C +ATOM 772 CD GLN A 939 5.122 3.666 -0.222 1.00 79.26 C +ATOM 773 OE1 GLN A 939 5.529 3.809 0.929 1.00 70.91 O +ATOM 774 NE2 GLN A 939 5.769 2.907 -1.106 1.00 67.19 N +ATOM 775 H GLN A 939 3.896 2.872 1.712 1.00 67.61 H +ATOM 776 HA GLN A 939 1.304 4.015 1.725 1.00 66.36 H +ATOM 777 HB3 GLN A 939 3.296 5.311 1.174 1.00 68.30 H +ATOM 778 HB2 GLN A 939 2.092 5.403 -0.059 1.00 68.30 H +ATOM 779 HG3 GLN A 939 4.183 5.309 -1.175 1.00 77.62 H +ATOM 780 HG2 GLN A 939 3.393 3.803 -1.513 1.00 77.62 H +ATOM 781 HE22 GLN A 939 6.630 2.450 -0.845 1.00 67.19 H +ATOM 782 HE21 GLN A 939 5.407 2.784 -2.042 1.00 67.19 H +ATOM 783 N HIS A 940 1.825 1.743 -0.617 1.00 64.95 N +ATOM 784 CA HIS A 940 1.173 0.836 -1.562 1.00 63.06 C +ATOM 785 C HIS A 940 0.336 -0.247 -0.865 1.00 62.55 C +ATOM 786 O HIS A 940 -0.666 -0.669 -1.435 1.00 60.85 O +ATOM 787 CB HIS A 940 2.221 0.189 -2.485 1.00 64.66 C +ATOM 788 CG HIS A 940 2.915 1.133 -3.435 1.00 69.42 C +ATOM 789 ND1 HIS A 940 4.296 1.361 -3.394 1.00 71.01 N +ATOM 790 CD2 HIS A 940 2.373 1.877 -4.463 1.00 73.12 C +ATOM 791 CE1 HIS A 940 4.528 2.217 -4.381 1.00 71.34 C +ATOM 792 NE2 HIS A 940 3.425 2.560 -5.046 1.00 72.94 N +ATOM 793 H HIS A 940 2.793 1.547 -0.404 1.00 64.95 H +ATOM 794 HA HIS A 940 0.488 1.415 -2.185 1.00 63.06 H +ATOM 795 HB3 HIS A 940 1.764 -0.598 -3.086 1.00 64.66 H +ATOM 796 HB2 HIS A 940 2.982 -0.293 -1.874 1.00 64.66 H +ATOM 797 HD2 HIS A 940 1.359 1.973 -4.821 1.00 73.12 H +ATOM 798 HE1 HIS A 940 5.511 2.597 -4.622 1.00 71.34 H +ATOM 799 HE2 HIS A 940 3.370 3.192 -5.834 1.00 72.94 H +ATOM 800 N ARG A 941 0.746 -0.666 0.344 1.00 57.11 N +ATOM 801 CA ARG A 941 0.052 -1.670 1.157 1.00 55.94 C +ATOM 802 C ARG A 941 -1.244 -1.135 1.786 1.00 58.15 C +ATOM 803 O ARG A 941 -2.224 -1.878 1.834 1.00 56.90 O +ATOM 804 CB ARG A 941 1.002 -2.196 2.248 1.00 56.27 C +ATOM 805 CG ARG A 941 2.156 -3.035 1.684 1.00 60.46 C +ATOM 806 CD ARG A 941 3.217 -3.361 2.743 1.00 68.18 C +ATOM 807 NE ARG A 941 4.165 -4.372 2.252 1.00 73.45 N +ATOM 808 CZ ARG A 941 4.018 -5.707 2.308 1.00 81.37 C +ATOM 809 NH1 ARG A 941 2.961 -6.288 2.896 1.00 62.04 N +ATOM 810 NH2 ARG A 941 4.956 -6.483 1.753 1.00 66.65 N1+ +ATOM 811 H ARG A 941 1.591 -0.278 0.739 1.00 57.11 H +ATOM 812 HA ARG A 941 -0.219 -2.508 0.511 1.00 55.94 H +ATOM 813 HB3 ARG A 941 0.446 -2.825 2.943 1.00 56.27 H +ATOM 814 HB2 ARG A 941 1.388 -1.363 2.836 1.00 56.27 H +ATOM 815 HG3 ARG A 941 2.640 -2.439 0.911 1.00 60.46 H +ATOM 816 HG2 ARG A 941 1.797 -3.928 1.174 1.00 60.46 H +ATOM 817 HD3 ARG A 941 2.794 -3.586 3.722 1.00 68.18 H +ATOM 818 HD2 ARG A 941 3.836 -2.474 2.881 1.00 68.18 H +ATOM 819 HE ARG A 941 4.974 -4.003 1.765 1.00 73.45 H +ATOM 820 HH12 ARG A 941 2.887 -7.298 2.921 1.00 62.04 H +ATOM 821 HH11 ARG A 941 2.246 -5.725 3.329 1.00 62.04 H +ATOM 822 HH22 ARG A 941 4.856 -7.489 1.785 1.00 66.65 H +ATOM 823 HH21 ARG A 941 5.763 -6.083 1.293 1.00 66.65 H +ATOM 824 N SER A 942 -1.239 0.131 2.236 1.00 54.55 N +ATOM 825 CA SER A 942 -2.417 0.822 2.767 1.00 53.96 C +ATOM 826 C SER A 942 -3.462 1.145 1.681 1.00 53.99 C +ATOM 827 O SER A 942 -4.658 1.084 1.968 1.00 53.04 O +ATOM 828 CB SER A 942 -1.974 2.067 3.564 1.00 59.58 C +ATOM 829 OG SER A 942 -1.436 3.085 2.742 1.00 72.10 O +ATOM 830 H SER A 942 -0.386 0.673 2.189 1.00 54.55 H +ATOM 831 HA SER A 942 -2.895 0.143 3.476 1.00 53.96 H +ATOM 832 HB3 SER A 942 -1.237 1.795 4.320 1.00 59.58 H +ATOM 833 HB2 SER A 942 -2.827 2.484 4.101 1.00 59.58 H +ATOM 834 HG SER A 942 -0.537 2.843 2.506 1.00 72.10 H +ATOM 835 N GLY A 943 -2.996 1.435 0.454 1.00 48.43 N +ATOM 836 CA GLY A 943 -3.852 1.679 -0.702 1.00 46.49 C +ATOM 837 C GLY A 943 -4.391 0.364 -1.290 1.00 48.75 C +ATOM 838 O GLY A 943 -5.500 0.360 -1.821 1.00 47.89 O +ATOM 839 H GLY A 943 -1.998 1.478 0.302 1.00 48.43 H +ATOM 840 HA3 GLY A 943 -3.269 2.198 -1.462 1.00 46.49 H +ATOM 841 HA2 GLY A 943 -4.681 2.335 -0.430 1.00 46.49 H +ATOM 842 N TRP A 944 -3.627 -0.738 -1.184 1.00 44.46 N +ATOM 843 CA TRP A 944 -4.021 -2.081 -1.615 1.00 42.89 C +ATOM 844 C TRP A 944 -5.169 -2.670 -0.781 1.00 46.22 C +ATOM 845 O TRP A 944 -6.091 -3.245 -1.358 1.00 45.15 O +ATOM 846 CB TRP A 944 -2.793 -3.010 -1.631 1.00 42.12 C +ATOM 847 CG TRP A 944 -3.080 -4.467 -1.824 1.00 42.65 C +ATOM 848 CD1 TRP A 944 -2.885 -5.439 -0.904 1.00 46.33 C +ATOM 849 CD2 TRP A 944 -3.681 -5.119 -2.981 1.00 41.21 C +ATOM 850 NE1 TRP A 944 -3.284 -6.649 -1.429 1.00 45.34 N +ATOM 851 CE2 TRP A 944 -3.782 -6.515 -2.707 1.00 45.67 C +ATOM 852 CE3 TRP A 944 -4.149 -4.674 -4.238 1.00 41.70 C +ATOM 853 CZ2 TRP A 944 -4.298 -7.428 -3.640 1.00 44.10 C +ATOM 854 CZ3 TRP A 944 -4.676 -5.579 -5.179 1.00 42.27 C +ATOM 855 CH2 TRP A 944 -4.740 -6.953 -4.884 1.00 43.10 C +ATOM 856 H TRP A 944 -2.705 -0.662 -0.777 1.00 44.46 H +ATOM 857 HA TRP A 944 -4.393 -2.009 -2.639 1.00 42.89 H +ATOM 858 HB3 TRP A 944 -2.239 -2.906 -0.699 1.00 42.12 H +ATOM 859 HB2 TRP A 944 -2.112 -2.699 -2.425 1.00 42.12 H +ATOM 860 HD1 TRP A 944 -2.471 -5.278 0.081 1.00 46.33 H +ATOM 861 HE1 TRP A 944 -3.228 -7.524 -0.922 1.00 45.34 H +ATOM 862 HE3 TRP A 944 -4.103 -3.624 -4.481 1.00 41.70 H +ATOM 863 HZ2 TRP A 944 -4.352 -8.480 -3.407 1.00 44.10 H +ATOM 864 HZ3 TRP A 944 -5.029 -5.218 -6.131 1.00 42.27 H +ATOM 865 HH2 TRP A 944 -5.129 -7.641 -5.615 1.00 43.10 H +ATOM 866 N LYS A 945 -5.105 -2.485 0.550 1.00 43.92 N +ATOM 867 CA LYS A 945 -6.191 -2.822 1.471 1.00 44.34 C +ATOM 868 C LYS A 945 -7.476 -2.042 1.160 1.00 46.56 C +ATOM 869 O LYS A 945 -8.554 -2.623 1.243 1.00 45.36 O +ATOM 870 CB LYS A 945 -5.751 -2.571 2.926 1.00 49.05 C +ATOM 871 CG LYS A 945 -4.726 -3.589 3.445 1.00 67.10 C +ATOM 872 CD LYS A 945 -4.305 -3.287 4.894 1.00 82.17 C +ATOM 873 CE LYS A 945 -3.208 -4.218 5.438 1.00 95.75 C +ATOM 874 NZ LYS A 945 -1.895 -3.952 4.822 1.00106.63 N1+ +ATOM 875 H LYS A 945 -4.304 -2.017 0.951 1.00 43.92 H +ATOM 876 HA LYS A 945 -6.411 -3.885 1.350 1.00 44.34 H +ATOM 877 HB3 LYS A 945 -6.625 -2.624 3.579 1.00 49.05 H +ATOM 878 HB2 LYS A 945 -5.361 -1.556 3.025 1.00 49.05 H +ATOM 879 HG3 LYS A 945 -3.858 -3.605 2.789 1.00 67.10 H +ATOM 880 HG2 LYS A 945 -5.156 -4.590 3.397 1.00 67.10 H +ATOM 881 HD3 LYS A 945 -5.182 -3.377 5.537 1.00 82.17 H +ATOM 882 HD2 LYS A 945 -3.988 -2.245 4.978 1.00 82.17 H +ATOM 883 HE3 LYS A 945 -3.481 -5.262 5.281 1.00 95.75 H +ATOM 884 HE2 LYS A 945 -3.108 -4.074 6.514 1.00 95.75 H +ATOM 885 HZ1 LYS A 945 -1.958 -4.096 3.824 1.00106.63 H +ATOM 886 HZ2 LYS A 945 -1.626 -2.996 5.006 1.00106.63 H +ATOM 887 HZ3 LYS A 945 -1.206 -4.578 5.214 1.00106.63 H +ATOM 888 N GLN A 946 -7.327 -0.768 0.757 1.00 43.11 N +ATOM 889 CA GLN A 946 -8.435 0.101 0.379 1.00 42.59 C +ATOM 890 C GLN A 946 -9.104 -0.329 -0.944 1.00 44.21 C +ATOM 891 O GLN A 946 -10.323 -0.212 -1.029 1.00 44.62 O +ATOM 892 CB GLN A 946 -7.949 1.562 0.342 1.00 44.07 C +ATOM 893 CG GLN A 946 -9.112 2.570 0.435 1.00 72.22 C +ATOM 894 CD GLN A 946 -8.722 4.039 0.262 1.00 92.80 C +ATOM 895 OE1 GLN A 946 -9.605 4.886 0.154 1.00 86.97 O +ATOM 896 NE2 GLN A 946 -7.426 4.364 0.235 1.00 85.94 N +ATOM 897 H GLN A 946 -6.402 -0.366 0.700 1.00 43.11 H +ATOM 898 HA GLN A 946 -9.185 0.018 1.169 1.00 42.59 H +ATOM 899 HB3 GLN A 946 -7.370 1.733 -0.566 1.00 44.07 H +ATOM 900 HB2 GLN A 946 -7.272 1.737 1.180 1.00 44.07 H +ATOM 901 HG3 GLN A 946 -9.613 2.463 1.399 1.00 72.22 H +ATOM 902 HG2 GLN A 946 -9.861 2.347 -0.322 1.00 72.22 H +ATOM 903 HE22 GLN A 946 -7.136 5.321 0.106 1.00 85.94 H +ATOM 904 HE21 GLN A 946 -6.724 3.644 0.323 1.00 85.94 H +ATOM 905 N GLU A 947 -8.341 -0.874 -1.917 1.00 38.42 N +ATOM 906 CA GLU A 947 -8.896 -1.497 -3.128 1.00 36.42 C +ATOM 907 C GLU A 947 -9.773 -2.720 -2.812 1.00 41.24 C +ATOM 908 O GLU A 947 -10.865 -2.832 -3.369 1.00 41.44 O +ATOM 909 CB GLU A 947 -7.793 -1.835 -4.165 1.00 36.47 C +ATOM 910 CG GLU A 947 -8.321 -2.647 -5.383 1.00 37.83 C +ATOM 911 CD GLU A 947 -7.332 -2.963 -6.510 1.00 51.81 C +ATOM 912 OE1 GLU A 947 -6.118 -2.704 -6.360 1.00 50.51 O +ATOM 913 OE2 GLU A 947 -7.819 -3.483 -7.537 1.00 44.67 O1- +ATOM 914 H GLU A 947 -7.338 -0.927 -1.800 1.00 38.42 H +ATOM 915 HA GLU A 947 -9.549 -0.755 -3.593 1.00 36.42 H +ATOM 916 HB3 GLU A 947 -6.983 -2.382 -3.682 1.00 36.47 H +ATOM 917 HB2 GLU A 947 -7.355 -0.901 -4.515 1.00 36.47 H +ATOM 918 HG3 GLU A 947 -9.177 -2.127 -5.813 1.00 37.83 H +ATOM 919 HG2 GLU A 947 -8.692 -3.617 -5.051 1.00 37.83 H +ATOM 920 N ILE A 948 -9.272 -3.599 -1.926 1.00 37.07 N +ATOM 921 CA ILE A 948 -9.967 -4.801 -1.461 1.00 37.44 C +ATOM 922 C ILE A 948 -11.283 -4.464 -0.730 1.00 42.50 C +ATOM 923 O ILE A 948 -12.288 -5.117 -1.003 1.00 40.78 O +ATOM 924 CB ILE A 948 -9.054 -5.688 -0.558 1.00 40.85 C +ATOM 925 CG1 ILE A 948 -7.887 -6.291 -1.375 1.00 40.33 C +ATOM 926 CG2 ILE A 948 -9.796 -6.814 0.199 1.00 41.59 C +ATOM 927 CD1 ILE A 948 -6.671 -6.699 -0.527 1.00 45.34 C +ATOM 928 H ILE A 948 -8.358 -3.431 -1.528 1.00 37.07 H +ATOM 929 HA ILE A 948 -10.228 -5.382 -2.348 1.00 37.44 H +ATOM 930 HB ILE A 948 -8.610 -5.037 0.195 1.00 40.85 H +ATOM 931 HG13 ILE A 948 -7.538 -5.577 -2.122 1.00 40.33 H +ATOM 932 HG12 ILE A 948 -8.245 -7.151 -1.939 1.00 40.33 H +ATOM 933 HG21 ILE A 948 -9.110 -7.460 0.744 1.00 41.59 H +ATOM 934 HG22 ILE A 948 -10.494 -6.421 0.937 1.00 41.59 H +ATOM 935 HG23 ILE A 948 -10.361 -7.442 -0.491 1.00 41.59 H +ATOM 936 HD11 ILE A 948 -6.199 -7.594 -0.931 1.00 45.34 H +ATOM 937 HD12 ILE A 948 -5.924 -5.906 -0.520 1.00 45.34 H +ATOM 938 HD13 ILE A 948 -6.932 -6.902 0.510 1.00 45.34 H +ATOM 939 N ASP A 949 -11.260 -3.427 0.129 1.00 41.37 N +ATOM 940 CA ASP A 949 -12.433 -2.867 0.812 1.00 43.58 C +ATOM 941 C ASP A 949 -13.472 -2.293 -0.166 1.00 46.27 C +ATOM 942 O ASP A 949 -14.650 -2.611 -0.023 1.00 45.58 O +ATOM 943 CB ASP A 949 -12.082 -1.793 1.877 1.00 47.36 C +ATOM 944 CG ASP A 949 -11.137 -2.216 3.016 1.00 66.46 C +ATOM 945 OD1 ASP A 949 -11.033 -3.431 3.296 1.00 66.64 O +ATOM 946 OD2 ASP A 949 -10.616 -1.293 3.681 1.00 76.34 O1- +ATOM 947 H ASP A 949 -10.380 -2.965 0.319 1.00 41.37 H +ATOM 948 HA ASP A 949 -12.919 -3.699 1.326 1.00 43.58 H +ATOM 949 HB3 ASP A 949 -13.001 -1.432 2.342 1.00 47.36 H +ATOM 950 HB2 ASP A 949 -11.627 -0.936 1.377 1.00 47.36 H +ATOM 951 N ILE A 950 -13.021 -1.484 -1.143 1.00 42.07 N +ATOM 952 CA ILE A 950 -13.865 -0.856 -2.165 1.00 41.21 C +ATOM 953 C ILE A 950 -14.584 -1.879 -3.065 1.00 44.57 C +ATOM 954 O ILE A 950 -15.796 -1.776 -3.223 1.00 44.89 O +ATOM 955 CB ILE A 950 -13.074 0.179 -3.030 1.00 43.50 C +ATOM 956 CG1 ILE A 950 -12.772 1.447 -2.199 1.00 44.25 C +ATOM 957 CG2 ILE A 950 -13.743 0.588 -4.366 1.00 43.82 C +ATOM 958 CD1 ILE A 950 -11.740 2.389 -2.835 1.00 47.81 C +ATOM 959 H ILE A 950 -12.034 -1.263 -1.187 1.00 42.07 H +ATOM 960 HA ILE A 950 -14.646 -0.310 -1.630 1.00 41.21 H +ATOM 961 HB ILE A 950 -12.115 -0.277 -3.283 1.00 43.50 H +ATOM 962 HG13 ILE A 950 -12.436 1.183 -1.197 1.00 44.25 H +ATOM 963 HG12 ILE A 950 -13.700 1.993 -2.049 1.00 44.25 H +ATOM 964 HG21 ILE A 950 -13.176 1.363 -4.881 1.00 43.82 H +ATOM 965 HG22 ILE A 950 -13.808 -0.244 -5.066 1.00 43.82 H +ATOM 966 HG23 ILE A 950 -14.750 0.974 -4.203 1.00 43.82 H +ATOM 967 HD11 ILE A 950 -11.203 2.958 -2.077 1.00 47.81 H +ATOM 968 HD12 ILE A 950 -11.002 1.842 -3.421 1.00 47.81 H +ATOM 969 HD13 ILE A 950 -12.224 3.110 -3.494 1.00 47.81 H +ATOM 970 N LEU A 951 -13.844 -2.860 -3.609 1.00 41.27 N +ATOM 971 CA LEU A 951 -14.394 -3.889 -4.496 1.00 41.85 C +ATOM 972 C LEU A 951 -15.322 -4.889 -3.774 1.00 47.62 C +ATOM 973 O LEU A 951 -16.249 -5.398 -4.403 1.00 47.54 O +ATOM 974 CB LEU A 951 -13.231 -4.574 -5.249 1.00 41.42 C +ATOM 975 CG LEU A 951 -13.649 -5.508 -6.410 1.00 46.53 C +ATOM 976 CD1 LEU A 951 -14.414 -4.752 -7.521 1.00 46.18 C +ATOM 977 CD2 LEU A 951 -12.432 -6.278 -6.956 1.00 49.35 C +ATOM 978 H LEU A 951 -12.847 -2.896 -3.436 1.00 41.27 H +ATOM 979 HA LEU A 951 -15.012 -3.367 -5.227 1.00 41.85 H +ATOM 980 HB3 LEU A 951 -12.633 -5.137 -4.530 1.00 41.42 H +ATOM 981 HB2 LEU A 951 -12.564 -3.808 -5.649 1.00 41.42 H +ATOM 982 HG LEU A 951 -14.325 -6.266 -6.019 1.00 46.53 H +ATOM 983 HD11 LEU A 951 -14.019 -4.940 -8.518 1.00 46.18 H +ATOM 984 HD12 LEU A 951 -15.464 -5.047 -7.534 1.00 46.18 H +ATOM 985 HD13 LEU A 951 -14.385 -3.672 -7.381 1.00 46.18 H +ATOM 986 HD21 LEU A 951 -12.413 -6.333 -8.044 1.00 49.35 H +ATOM 987 HD22 LEU A 951 -11.496 -5.819 -6.643 1.00 49.35 H +ATOM 988 HD23 LEU A 951 -12.429 -7.303 -6.584 1.00 49.35 H +ATOM 989 N ARG A 952 -15.086 -5.118 -2.470 1.00 45.43 N +ATOM 990 CA ARG A 952 -15.912 -5.959 -1.601 1.00 47.25 C +ATOM 991 C ARG A 952 -17.298 -5.357 -1.286 1.00 52.09 C +ATOM 992 O ARG A 952 -18.247 -6.129 -1.145 1.00 53.68 O +ATOM 993 CB ARG A 952 -15.091 -6.318 -0.340 1.00 49.60 C +ATOM 994 CG ARG A 952 -15.843 -7.068 0.779 1.00 64.86 C +ATOM 995 CD ARG A 952 -14.937 -7.823 1.770 1.00 76.56 C +ATOM 996 NE ARG A 952 -13.881 -6.986 2.376 1.00 87.65 N +ATOM 997 CZ ARG A 952 -12.715 -7.407 2.908 1.00104.51 C +ATOM 998 NH1 ARG A 952 -12.352 -8.699 2.914 1.00 93.37 N +ATOM 999 NH2 ARG A 952 -11.884 -6.512 3.453 1.00 92.17 N1+ +ATOM 1000 H ARG A 952 -14.301 -4.663 -2.025 1.00 45.43 H +ATOM 1001 HA ARG A 952 -16.101 -6.892 -2.134 1.00 47.25 H +ATOM 1002 HB3 ARG A 952 -14.697 -5.398 0.092 1.00 49.60 H +ATOM 1003 HB2 ARG A 952 -14.226 -6.906 -0.649 1.00 49.60 H +ATOM 1004 HG3 ARG A 952 -16.421 -7.837 0.264 1.00 64.86 H +ATOM 1005 HG2 ARG A 952 -16.577 -6.448 1.297 1.00 64.86 H +ATOM 1006 HD3 ARG A 952 -14.592 -8.768 1.357 1.00 76.56 H +ATOM 1007 HD2 ARG A 952 -15.557 -8.089 2.626 1.00 76.56 H +ATOM 1008 HE ARG A 952 -14.078 -5.996 2.402 1.00 87.65 H +ATOM 1009 HH12 ARG A 952 -11.476 -8.988 3.325 1.00 93.37 H +ATOM 1010 HH11 ARG A 952 -12.951 -9.389 2.485 1.00 93.37 H +ATOM 1011 HH22 ARG A 952 -12.087 -5.519 3.426 1.00 92.17 H +ATOM 1012 HH21 ARG A 952 -11.009 -6.805 3.861 1.00 92.17 H +ATOM 1013 N THR A 953 -17.407 -4.018 -1.203 1.00 47.85 N +ATOM 1014 CA THR A 953 -18.679 -3.317 -0.978 1.00 48.24 C +ATOM 1015 C THR A 953 -19.466 -3.052 -2.282 1.00 50.96 C +ATOM 1016 O THR A 953 -20.681 -2.871 -2.196 1.00 52.21 O +ATOM 1017 CB THR A 953 -18.474 -1.955 -0.257 1.00 56.81 C +ATOM 1018 OG1 THR A 953 -17.729 -1.035 -1.034 1.00 60.06 O +ATOM 1019 CG2 THR A 953 -17.848 -2.087 1.139 1.00 56.45 C +ATOM 1020 H THR A 953 -16.592 -3.432 -1.323 1.00 47.85 H +ATOM 1021 HA THR A 953 -19.313 -3.933 -0.338 1.00 48.24 H +ATOM 1022 HB THR A 953 -19.453 -1.494 -0.115 1.00 56.81 H +ATOM 1023 HG1 THR A 953 -17.579 -0.237 -0.518 1.00 60.06 H +ATOM 1024 HG21 THR A 953 -17.701 -1.110 1.601 1.00 56.45 H +ATOM 1025 HG22 THR A 953 -18.495 -2.666 1.798 1.00 56.45 H +ATOM 1026 HG23 THR A 953 -16.885 -2.591 1.111 1.00 56.45 H +ATOM 1027 N LEU A 954 -18.797 -3.053 -3.450 1.00 45.03 N +ATOM 1028 CA LEU A 954 -19.441 -2.879 -4.754 1.00 43.59 C +ATOM 1029 C LEU A 954 -20.046 -4.200 -5.254 1.00 47.51 C +ATOM 1030 O LEU A 954 -19.407 -5.248 -5.151 1.00 46.90 O +ATOM 1031 CB LEU A 954 -18.416 -2.343 -5.779 1.00 41.91 C +ATOM 1032 CG LEU A 954 -17.939 -0.893 -5.544 1.00 44.98 C +ATOM 1033 CD1 LEU A 954 -16.672 -0.604 -6.360 1.00 43.77 C +ATOM 1034 CD2 LEU A 954 -19.026 0.163 -5.817 1.00 45.62 C +ATOM 1035 H LEU A 954 -17.798 -3.203 -3.455 1.00 45.03 H +ATOM 1036 HA LEU A 954 -20.251 -2.157 -4.649 1.00 43.59 H +ATOM 1037 HB3 LEU A 954 -18.832 -2.405 -6.787 1.00 41.91 H +ATOM 1038 HB2 LEU A 954 -17.551 -3.010 -5.778 1.00 41.91 H +ATOM 1039 HG LEU A 954 -17.680 -0.790 -4.492 1.00 44.98 H +ATOM 1040 HD11 LEU A 954 -16.372 0.440 -6.268 1.00 43.77 H +ATOM 1041 HD12 LEU A 954 -15.835 -1.211 -6.014 1.00 43.77 H +ATOM 1042 HD13 LEU A 954 -16.821 -0.822 -7.417 1.00 43.77 H +ATOM 1043 HD21 LEU A 954 -18.846 1.060 -5.227 1.00 45.62 H +ATOM 1044 HD22 LEU A 954 -19.047 0.454 -6.867 1.00 45.62 H +ATOM 1045 HD23 LEU A 954 -20.022 -0.184 -5.556 1.00 45.62 H +ATOM 1046 N TYR A 955 -21.260 -4.105 -5.819 1.00 44.63 N +ATOM 1047 CA TYR A 955 -21.983 -5.219 -6.422 1.00 45.07 C +ATOM 1048 C TYR A 955 -22.881 -4.667 -7.538 1.00 45.96 C +ATOM 1049 O TYR A 955 -23.879 -4.007 -7.245 1.00 46.41 O +ATOM 1050 CB TYR A 955 -22.766 -5.994 -5.338 1.00 49.65 C +ATOM 1051 CG TYR A 955 -23.413 -7.280 -5.825 1.00 54.91 C +ATOM 1052 CD1 TYR A 955 -22.690 -8.491 -5.774 1.00 57.41 C +ATOM 1053 CD2 TYR A 955 -24.729 -7.275 -6.334 1.00 57.57 C +ATOM 1054 CE1 TYR A 955 -23.279 -9.687 -6.228 1.00 59.75 C +ATOM 1055 CE2 TYR A 955 -25.314 -8.469 -6.797 1.00 60.10 C +ATOM 1056 CZ TYR A 955 -24.590 -9.676 -6.742 1.00 68.68 C +ATOM 1057 OH TYR A 955 -25.161 -10.833 -7.187 1.00 72.06 O +ATOM 1058 H TYR A 955 -21.733 -3.211 -5.844 1.00 44.63 H +ATOM 1059 HA TYR A 955 -21.264 -5.905 -6.873 1.00 45.07 H +ATOM 1060 HB3 TYR A 955 -23.529 -5.356 -4.888 1.00 49.65 H +ATOM 1061 HB2 TYR A 955 -22.091 -6.258 -4.522 1.00 49.65 H +ATOM 1062 HD1 TYR A 955 -21.681 -8.504 -5.389 1.00 57.41 H +ATOM 1063 HD2 TYR A 955 -25.291 -6.353 -6.378 1.00 57.57 H +ATOM 1064 HE1 TYR A 955 -22.717 -10.608 -6.181 1.00 59.75 H +ATOM 1065 HE2 TYR A 955 -26.320 -8.458 -7.190 1.00 60.10 H +ATOM 1066 HH TYR A 955 -24.592 -11.599 -7.087 1.00 72.06 H +ATOM 1067 N HIS A 956 -22.500 -4.944 -8.794 1.00 39.68 N +ATOM 1068 CA HIS A 956 -23.212 -4.512 -9.996 1.00 38.26 C +ATOM 1069 C HIS A 956 -22.866 -5.445 -11.162 1.00 41.07 C +ATOM 1070 O HIS A 956 -21.760 -5.986 -11.208 1.00 39.48 O +ATOM 1071 CB HIS A 956 -22.844 -3.047 -10.318 1.00 37.69 C +ATOM 1072 CG HIS A 956 -23.836 -2.324 -11.193 1.00 40.30 C +ATOM 1073 ND1 HIS A 956 -23.676 -2.210 -12.577 1.00 40.74 N +ATOM 1074 CD2 HIS A 956 -25.002 -1.686 -10.825 1.00 42.24 C +ATOM 1075 CE1 HIS A 956 -24.733 -1.519 -12.984 1.00 40.29 C +ATOM 1076 NE2 HIS A 956 -25.550 -1.179 -11.989 1.00 41.53 N +ATOM 1077 H HIS A 956 -21.665 -5.494 -8.953 1.00 39.68 H +ATOM 1078 HA HIS A 956 -24.284 -4.585 -9.796 1.00 38.26 H +ATOM 1079 HB3 HIS A 956 -21.857 -2.986 -10.778 1.00 37.69 H +ATOM 1080 HB2 HIS A 956 -22.772 -2.476 -9.395 1.00 37.69 H +ATOM 1081 HD2 HIS A 956 -25.467 -1.557 -9.859 1.00 42.24 H +ATOM 1082 HE1 HIS A 956 -24.909 -1.252 -14.014 1.00 40.29 H +ATOM 1083 HE2 HIS A 956 -26.411 -0.655 -12.072 1.00 41.53 H +ATOM 1084 N GLU A 957 -23.808 -5.580 -12.110 1.00 37.79 N +ATOM 1085 CA GLU A 957 -23.646 -6.366 -13.338 1.00 37.07 C +ATOM 1086 C GLU A 957 -22.581 -5.811 -14.309 1.00 38.36 C +ATOM 1087 O GLU A 957 -22.069 -6.584 -15.119 1.00 38.29 O +ATOM 1088 CB GLU A 957 -25.017 -6.521 -14.032 1.00 39.83 C +ATOM 1089 CG GLU A 957 -25.639 -5.202 -14.545 1.00 51.33 C +ATOM 1090 CD GLU A 957 -26.973 -5.377 -15.283 1.00 75.08 C +ATOM 1091 OE1 GLU A 957 -27.876 -4.551 -15.023 1.00 76.02 O +ATOM 1092 OE2 GLU A 957 -27.077 -6.311 -16.109 1.00 61.54 O1- +ATOM 1093 H GLU A 957 -24.697 -5.109 -11.999 1.00 37.79 H +ATOM 1094 HA GLU A 957 -23.315 -7.363 -13.038 1.00 37.07 H +ATOM 1095 HB3 GLU A 957 -25.713 -7.007 -13.347 1.00 39.83 H +ATOM 1096 HB2 GLU A 957 -24.898 -7.214 -14.866 1.00 39.83 H +ATOM 1097 HG3 GLU A 957 -24.955 -4.731 -15.244 1.00 51.33 H +ATOM 1098 HG2 GLU A 957 -25.753 -4.493 -13.725 1.00 51.33 H +ATOM 1099 N HIS A 958 -22.266 -4.507 -14.202 1.00 33.06 N +ATOM 1100 CA HIS A 958 -21.263 -3.805 -15.008 1.00 30.23 C +ATOM 1101 C HIS A 958 -20.045 -3.370 -14.175 1.00 34.52 C +ATOM 1102 O HIS A 958 -19.363 -2.415 -14.546 1.00 33.24 O +ATOM 1103 CB HIS A 958 -21.913 -2.635 -15.775 1.00 29.49 C +ATOM 1104 CG HIS A 958 -23.094 -3.030 -16.626 1.00 32.84 C +ATOM 1105 ND1 HIS A 958 -23.069 -4.116 -17.483 1.00 34.50 N +ATOM 1106 CD2 HIS A 958 -24.367 -2.515 -16.722 1.00 35.61 C +ATOM 1107 CE1 HIS A 958 -24.286 -4.227 -18.018 1.00 34.74 C +ATOM 1108 NE2 HIS A 958 -25.123 -3.285 -17.606 1.00 35.88 N +ATOM 1109 H HIS A 958 -22.751 -3.939 -13.519 1.00 33.06 H +ATOM 1110 HA HIS A 958 -20.862 -4.495 -15.749 1.00 30.23 H +ATOM 1111 HB3 HIS A 958 -21.178 -2.172 -16.435 1.00 29.49 H +ATOM 1112 HB2 HIS A 958 -22.237 -1.860 -15.079 1.00 29.49 H +ATOM 1113 HD1 HIS A 958 -22.273 -4.714 -17.661 1.00 34.50 H +ATOM 1114 HD2 HIS A 958 -24.807 -1.676 -16.210 1.00 35.61 H +ATOM 1115 HE1 HIS A 958 -24.562 -4.994 -18.726 1.00 34.74 H +ATOM 1116 N ILE A 959 -19.758 -4.122 -13.102 1.00 33.55 N +ATOM 1117 CA ILE A 959 -18.513 -4.085 -12.339 1.00 33.44 C +ATOM 1118 C ILE A 959 -18.048 -5.543 -12.152 1.00 38.25 C +ATOM 1119 O ILE A 959 -18.891 -6.426 -11.973 1.00 38.75 O +ATOM 1120 CB ILE A 959 -18.700 -3.369 -10.959 1.00 37.20 C +ATOM 1121 CG1 ILE A 959 -18.910 -1.848 -11.173 1.00 36.98 C +ATOM 1122 CG2 ILE A 959 -17.554 -3.612 -9.949 1.00 38.30 C +ATOM 1123 CD1 ILE A 959 -19.229 -1.049 -9.902 1.00 43.20 C +ATOM 1124 H ILE A 959 -20.381 -4.880 -12.858 1.00 33.55 H +ATOM 1125 HA ILE A 959 -17.747 -3.561 -12.911 1.00 33.44 H +ATOM 1126 HB ILE A 959 -19.610 -3.762 -10.502 1.00 37.20 H +ATOM 1127 HG13 ILE A 959 -19.722 -1.677 -11.879 1.00 36.98 H +ATOM 1128 HG12 ILE A 959 -18.021 -1.424 -11.638 1.00 36.98 H +ATOM 1129 HG21 ILE A 959 -17.680 -3.036 -9.034 1.00 38.30 H +ATOM 1130 HG22 ILE A 959 -17.504 -4.652 -9.633 1.00 38.30 H +ATOM 1131 HG23 ILE A 959 -16.590 -3.347 -10.379 1.00 38.30 H +ATOM 1132 HD11 ILE A 959 -19.719 -0.110 -10.158 1.00 43.20 H +ATOM 1133 HD12 ILE A 959 -19.889 -1.604 -9.235 1.00 43.20 H +ATOM 1134 HD13 ILE A 959 -18.323 -0.797 -9.352 1.00 43.20 H +ATOM 1135 N ILE A 960 -16.722 -5.778 -12.199 1.00 34.04 N +ATOM 1136 CA ILE A 960 -16.103 -7.089 -11.969 1.00 34.30 C +ATOM 1137 C ILE A 960 -16.404 -7.598 -10.542 1.00 39.83 C +ATOM 1138 O ILE A 960 -16.171 -6.877 -9.571 1.00 39.81 O +ATOM 1139 CB ILE A 960 -14.562 -7.077 -12.223 1.00 35.69 C +ATOM 1140 CG1 ILE A 960 -13.980 -8.512 -12.252 1.00 36.05 C +ATOM 1141 CG2 ILE A 960 -13.761 -6.184 -11.246 1.00 35.48 C +ATOM 1142 CD1 ILE A 960 -12.566 -8.631 -12.844 1.00 37.96 C +ATOM 1143 H ILE A 960 -16.085 -5.009 -12.369 1.00 34.04 H +ATOM 1144 HA ILE A 960 -16.557 -7.774 -12.688 1.00 34.30 H +ATOM 1145 HB ILE A 960 -14.424 -6.656 -13.220 1.00 35.69 H +ATOM 1146 HG13 ILE A 960 -14.642 -9.150 -12.839 1.00 36.05 H +ATOM 1147 HG12 ILE A 960 -13.984 -8.936 -11.247 1.00 36.05 H +ATOM 1148 HG21 ILE A 960 -12.742 -6.020 -11.594 1.00 35.48 H +ATOM 1149 HG22 ILE A 960 -14.222 -5.203 -11.131 1.00 35.48 H +ATOM 1150 HG23 ILE A 960 -13.683 -6.633 -10.257 1.00 35.48 H +ATOM 1151 HD11 ILE A 960 -11.934 -9.264 -12.221 1.00 37.96 H +ATOM 1152 HD12 ILE A 960 -12.597 -9.084 -13.834 1.00 37.96 H +ATOM 1153 HD13 ILE A 960 -12.065 -7.670 -12.948 1.00 37.96 H +ATOM 1154 N LYS A 961 -16.984 -8.804 -10.450 1.00 38.47 N +ATOM 1155 CA LYS A 961 -17.508 -9.334 -9.197 1.00 40.15 C +ATOM 1156 C LYS A 961 -16.382 -9.870 -8.302 1.00 44.21 C +ATOM 1157 O LYS A 961 -15.549 -10.660 -8.753 1.00 42.58 O +ATOM 1158 CB LYS A 961 -18.572 -10.416 -9.471 1.00 43.99 C +ATOM 1159 CG LYS A 961 -19.531 -10.611 -8.279 1.00 65.82 C +ATOM 1160 CD LYS A 961 -20.376 -11.889 -8.358 1.00 82.27 C +ATOM 1161 CE LYS A 961 -19.590 -13.141 -7.933 1.00 98.09 C +ATOM 1162 NZ LYS A 961 -20.439 -14.344 -7.951 1.00108.33 N1+ +ATOM 1163 H LYS A 961 -17.162 -9.351 -11.285 1.00 38.47 H +ATOM 1164 HA LYS A 961 -18.005 -8.516 -8.672 1.00 40.15 H +ATOM 1165 HB3 LYS A 961 -18.087 -11.352 -9.750 1.00 43.99 H +ATOM 1166 HB2 LYS A 961 -19.179 -10.126 -10.330 1.00 43.99 H +ATOM 1167 HG3 LYS A 961 -20.200 -9.751 -8.233 1.00 65.82 H +ATOM 1168 HG2 LYS A 961 -18.986 -10.605 -7.335 1.00 65.82 H +ATOM 1169 HD3 LYS A 961 -20.746 -12.012 -9.378 1.00 82.27 H +ATOM 1170 HD2 LYS A 961 -21.259 -11.767 -7.729 1.00 82.27 H +ATOM 1171 HE3 LYS A 961 -19.197 -13.012 -6.923 1.00 98.09 H +ATOM 1172 HE2 LYS A 961 -18.735 -13.304 -8.589 1.00 98.09 H +ATOM 1173 HZ1 LYS A 961 -20.777 -14.500 -8.890 1.00108.33 H +ATOM 1174 HZ2 LYS A 961 -19.902 -15.143 -7.647 1.00108.33 H +ATOM 1175 HZ3 LYS A 961 -21.224 -14.213 -7.329 1.00108.33 H +ATOM 1176 N TYR A 962 -16.430 -9.444 -7.032 1.00 42.43 N +ATOM 1177 CA TYR A 962 -15.610 -9.940 -5.935 1.00 43.53 C +ATOM 1178 C TYR A 962 -16.001 -11.390 -5.592 1.00 47.09 C +ATOM 1179 O TYR A 962 -17.139 -11.625 -5.182 1.00 48.07 O +ATOM 1180 CB TYR A 962 -15.807 -8.977 -4.743 1.00 46.00 C +ATOM 1181 CG TYR A 962 -15.052 -9.322 -3.473 1.00 48.99 C +ATOM 1182 CD1 TYR A 962 -13.693 -8.973 -3.349 1.00 50.72 C +ATOM 1183 CD2 TYR A 962 -15.708 -9.972 -2.407 1.00 51.43 C +ATOM 1184 CE1 TYR A 962 -12.986 -9.292 -2.173 1.00 52.08 C +ATOM 1185 CE2 TYR A 962 -15.003 -10.287 -1.231 1.00 53.15 C +ATOM 1186 CZ TYR A 962 -13.638 -9.957 -1.118 1.00 59.98 C +ATOM 1187 OH TYR A 962 -12.946 -10.296 0.007 1.00 63.78 O +ATOM 1188 H TYR A 962 -17.144 -8.781 -6.769 1.00 42.43 H +ATOM 1189 HA TYR A 962 -14.562 -9.907 -6.242 1.00 43.53 H +ATOM 1190 HB3 TYR A 962 -16.868 -8.902 -4.498 1.00 46.00 H +ATOM 1191 HB2 TYR A 962 -15.510 -7.972 -5.041 1.00 46.00 H +ATOM 1192 HD1 TYR A 962 -13.193 -8.463 -4.159 1.00 50.72 H +ATOM 1193 HD2 TYR A 962 -16.752 -10.238 -2.492 1.00 51.43 H +ATOM 1194 HE1 TYR A 962 -11.942 -9.032 -2.085 1.00 52.08 H +ATOM 1195 HE2 TYR A 962 -15.506 -10.796 -0.421 1.00 53.15 H +ATOM 1196 HH TYR A 962 -12.006 -10.107 -0.068 1.00 63.78 H +ATOM 1197 N LYS A 963 -15.056 -12.326 -5.771 1.00 42.37 N +ATOM 1198 CA LYS A 963 -15.203 -13.726 -5.366 1.00 42.56 C +ATOM 1199 C LYS A 963 -14.894 -13.913 -3.870 1.00 48.47 C +ATOM 1200 O LYS A 963 -15.618 -14.649 -3.202 1.00 50.00 O +ATOM 1201 CB LYS A 963 -14.273 -14.612 -6.220 1.00 43.06 C +ATOM 1202 CG LYS A 963 -14.767 -14.929 -7.644 1.00 48.30 C +ATOM 1203 CD LYS A 963 -15.641 -16.192 -7.735 1.00 61.25 C +ATOM 1204 CE LYS A 963 -15.976 -16.567 -9.192 1.00 72.82 C +ATOM 1205 NZ LYS A 963 -16.598 -17.901 -9.298 1.00 84.70 N1+ +ATOM 1206 H LYS A 963 -14.146 -12.058 -6.119 1.00 42.37 H +ATOM 1207 HA LYS A 963 -16.234 -14.043 -5.540 1.00 42.56 H +ATOM 1208 HB3 LYS A 963 -14.079 -15.557 -5.715 1.00 43.06 H +ATOM 1209 HB2 LYS A 963 -13.305 -14.125 -6.282 1.00 43.06 H +ATOM 1210 HG3 LYS A 963 -13.891 -15.083 -8.273 1.00 48.30 H +ATOM 1211 HG2 LYS A 963 -15.294 -14.072 -8.064 1.00 48.30 H +ATOM 1212 HD3 LYS A 963 -16.561 -16.038 -7.170 1.00 61.25 H +ATOM 1213 HD2 LYS A 963 -15.118 -17.020 -7.253 1.00 61.25 H +ATOM 1214 HE3 LYS A 963 -15.071 -16.580 -9.798 1.00 72.82 H +ATOM 1215 HE2 LYS A 963 -16.640 -15.822 -9.633 1.00 72.82 H +ATOM 1216 HZ1 LYS A 963 -17.463 -17.922 -8.778 1.00 84.70 H +ATOM 1217 HZ2 LYS A 963 -16.786 -18.114 -10.268 1.00 84.70 H +ATOM 1218 HZ3 LYS A 963 -15.968 -18.599 -8.928 1.00 84.70 H +ATOM 1219 N GLY A 964 -13.828 -13.257 -3.383 1.00 44.37 N +ATOM 1220 CA GLY A 964 -13.322 -13.425 -2.026 1.00 45.35 C +ATOM 1221 C GLY A 964 -11.876 -12.923 -1.976 1.00 49.58 C +ATOM 1222 O GLY A 964 -11.436 -12.157 -2.835 1.00 45.71 O +ATOM 1223 H GLY A 964 -13.284 -12.660 -3.993 1.00 44.37 H +ATOM 1224 HA3 GLY A 964 -13.348 -14.475 -1.734 1.00 45.35 H +ATOM 1225 HA2 GLY A 964 -13.937 -12.871 -1.318 1.00 45.35 H +ATOM 1226 N CYS A 965 -11.142 -13.356 -0.943 1.00 49.54 N +ATOM 1227 CA CYS A 965 -9.711 -13.105 -0.776 1.00 51.02 C +ATOM 1228 C CYS A 965 -8.996 -14.425 -0.468 1.00 57.60 C +ATOM 1229 O CYS A 965 -9.612 -15.360 0.039 1.00 57.77 O +ATOM 1230 CB CYS A 965 -9.421 -12.070 0.332 1.00 52.68 C +ATOM 1231 SG CYS A 965 -9.626 -10.388 -0.314 1.00 55.96 S +ATOM 1232 H CYS A 965 -11.563 -13.969 -0.256 1.00 49.54 H +ATOM 1233 HA CYS A 965 -9.296 -12.749 -1.718 1.00 51.02 H +ATOM 1234 HB3 CYS A 965 -8.393 -12.146 0.688 1.00 52.68 H +ATOM 1235 HB2 CYS A 965 -10.070 -12.219 1.196 1.00 52.68 H +ATOM 1236 HG CYS A 965 -8.572 -10.428 -1.135 1.00 55.96 H +ATOM 1237 N CYS A 966 -7.692 -14.458 -0.772 1.00 56.83 N +ATOM 1238 CA CYS A 966 -6.805 -15.582 -0.501 1.00 59.37 C +ATOM 1239 C CYS A 966 -5.684 -15.111 0.433 1.00 64.61 C +ATOM 1240 O CYS A 966 -5.062 -14.084 0.157 1.00 62.66 O +ATOM 1241 CB CYS A 966 -6.273 -16.194 -1.818 1.00 59.95 C +ATOM 1242 SG CYS A 966 -5.028 -17.487 -1.549 1.00 65.77 S +ATOM 1243 H CYS A 966 -7.257 -13.642 -1.183 1.00 56.83 H +ATOM 1244 HA CYS A 966 -7.354 -16.359 0.028 1.00 59.37 H +ATOM 1245 HB3 CYS A 966 -5.833 -15.427 -2.450 1.00 59.95 H +ATOM 1246 HB2 CYS A 966 -7.087 -16.624 -2.397 1.00 59.95 H +ATOM 1247 HG CYS A 966 -5.856 -18.347 -0.947 1.00 65.77 H +ATOM 1248 N GLU A 967 -5.426 -15.894 1.495 1.00 64.28 N +ATOM 1249 CA GLU A 967 -4.285 -15.722 2.393 1.00 65.86 C +ATOM 1250 C GLU A 967 -2.977 -16.001 1.633 1.00 70.57 C +ATOM 1251 O GLU A 967 -2.751 -17.140 1.218 1.00 70.73 O +ATOM 1252 CB GLU A 967 -4.432 -16.675 3.601 1.00 69.10 C +ATOM 1253 CG GLU A 967 -3.299 -16.537 4.661 1.00 81.26 C +ATOM 1254 CD GLU A 967 -2.819 -17.832 5.345 1.00 98.19 C +ATOM 1255 OE1 GLU A 967 -1.791 -17.730 6.051 1.00 78.88 O +ATOM 1256 OE2 GLU A 967 -3.452 -18.898 5.170 1.00 93.22 O1- +ATOM 1257 H GLU A 967 -5.983 -16.723 1.647 1.00 64.28 H +ATOM 1258 HA GLU A 967 -4.285 -14.693 2.759 1.00 65.86 H +ATOM 1259 HB3 GLU A 967 -4.506 -17.692 3.214 1.00 69.10 H +ATOM 1260 HB2 GLU A 967 -5.388 -16.487 4.092 1.00 69.10 H +ATOM 1261 HG3 GLU A 967 -3.640 -15.856 5.442 1.00 81.26 H +ATOM 1262 HG2 GLU A 967 -2.419 -16.055 4.236 1.00 81.26 H +ATOM 1263 N ASP A 968 -2.146 -14.959 1.486 1.00 66.99 N +ATOM 1264 CA ASP A 968 -0.819 -15.061 0.893 1.00 67.07 C +ATOM 1265 C ASP A 968 0.183 -15.084 2.058 1.00 72.70 C +ATOM 1266 O ASP A 968 0.385 -14.059 2.713 1.00 72.95 O +ATOM 1267 CB ASP A 968 -0.562 -13.898 -0.102 1.00 67.90 C +ATOM 1268 CG ASP A 968 0.531 -14.115 -1.163 1.00 83.41 C +ATOM 1269 OD1 ASP A 968 1.212 -15.166 -1.136 1.00 85.94 O +ATOM 1270 OD2 ASP A 968 0.684 -13.199 -2.001 1.00 90.83 O1- +ATOM 1271 H ASP A 968 -2.394 -14.052 1.856 1.00 66.99 H +ATOM 1272 HA ASP A 968 -0.742 -16.003 0.347 1.00 67.07 H +ATOM 1273 HB3 ASP A 968 -0.318 -12.982 0.436 1.00 67.90 H +ATOM 1274 HB2 ASP A 968 -1.498 -13.674 -0.611 1.00 67.90 H +ATOM 1275 N ALA A 969 0.769 -16.269 2.300 1.00 69.93 N +ATOM 1276 CA ALA A 969 1.803 -16.496 3.313 1.00 70.98 C +ATOM 1277 C ALA A 969 3.133 -15.800 2.972 1.00 74.28 C +ATOM 1278 O ALA A 969 3.798 -15.301 3.880 1.00 74.55 O +ATOM 1279 CB ALA A 969 1.999 -18.007 3.503 1.00 72.54 C +ATOM 1280 H ALA A 969 0.520 -17.067 1.734 1.00 69.93 H +ATOM 1281 HA ALA A 969 1.442 -16.082 4.258 1.00 70.98 H +ATOM 1282 HB1 ALA A 969 2.753 -18.215 4.264 1.00 72.54 H +ATOM 1283 HB2 ALA A 969 1.072 -18.482 3.826 1.00 72.54 H +ATOM 1284 HB3 ALA A 969 2.317 -18.490 2.579 1.00 72.54 H +ATOM 1285 N GLY A 970 3.455 -15.723 1.668 1.00 69.45 N +ATOM 1286 CA GLY A 970 4.486 -14.840 1.127 1.00 69.02 C +ATOM 1287 C GLY A 970 3.895 -13.429 0.997 1.00 72.14 C +ATOM 1288 O GLY A 970 2.682 -13.266 0.899 1.00 71.87 O +ATOM 1289 H GLY A 970 2.832 -16.144 0.993 1.00 69.45 H +ATOM 1290 HA3 GLY A 970 4.784 -15.198 0.141 1.00 69.02 H +ATOM 1291 HA2 GLY A 970 5.374 -14.835 1.761 1.00 69.02 H +ATOM 1292 N ALA A 971 4.753 -12.396 1.021 1.00 67.87 N +ATOM 1293 CA ALA A 971 4.399 -10.968 0.975 1.00 66.36 C +ATOM 1294 C ALA A 971 3.642 -10.423 2.209 1.00 68.68 C +ATOM 1295 O ALA A 971 3.602 -9.204 2.353 1.00 67.93 O +ATOM 1296 CB ALA A 971 3.635 -10.620 -0.324 1.00 65.39 C +ATOM 1297 H ALA A 971 5.740 -12.592 1.108 1.00 67.87 H +ATOM 1298 HA ALA A 971 5.351 -10.437 0.943 1.00 66.36 H +ATOM 1299 HB1 ALA A 971 3.520 -9.542 -0.439 1.00 65.39 H +ATOM 1300 HB2 ALA A 971 4.163 -10.985 -1.204 1.00 65.39 H +ATOM 1301 HB3 ALA A 971 2.631 -11.045 -0.345 1.00 65.39 H +ATOM 1302 N ALA A 972 3.071 -11.293 3.066 1.00 64.62 N +ATOM 1303 CA ALA A 972 2.294 -10.981 4.274 1.00 64.42 C +ATOM 1304 C ALA A 972 1.103 -10.047 3.984 1.00 65.37 C +ATOM 1305 O ALA A 972 1.060 -8.921 4.484 1.00 65.17 O +ATOM 1306 CB ALA A 972 3.227 -10.442 5.377 1.00 66.93 C +ATOM 1307 H ALA A 972 3.134 -12.278 2.848 1.00 64.62 H +ATOM 1308 HA ALA A 972 1.876 -11.926 4.624 1.00 64.42 H +ATOM 1309 HB1 ALA A 972 2.682 -10.275 6.307 1.00 66.93 H +ATOM 1310 HB2 ALA A 972 4.025 -11.154 5.594 1.00 66.93 H +ATOM 1311 HB3 ALA A 972 3.695 -9.499 5.094 1.00 66.93 H +ATOM 1312 N SER A 973 0.184 -10.522 3.129 1.00 59.44 N +ATOM 1313 CA SER A 973 -0.932 -9.741 2.593 1.00 57.90 C +ATOM 1314 C SER A 973 -2.075 -10.655 2.119 1.00 59.01 C +ATOM 1315 O SER A 973 -1.921 -11.876 2.086 1.00 59.49 O +ATOM 1316 CB SER A 973 -0.403 -8.798 1.481 1.00 60.76 C +ATOM 1317 OG SER A 973 0.026 -9.508 0.334 1.00 68.97 O +ATOM 1318 H SER A 973 0.267 -11.466 2.774 1.00 59.44 H +ATOM 1319 HA SER A 973 -1.339 -9.130 3.402 1.00 57.90 H +ATOM 1320 HB3 SER A 973 0.421 -8.183 1.842 1.00 60.76 H +ATOM 1321 HB2 SER A 973 -1.186 -8.105 1.171 1.00 60.76 H +ATOM 1322 HG SER A 973 0.794 -10.035 0.572 1.00 68.97 H +ATOM 1323 N LEU A 974 -3.205 -10.033 1.747 1.00 52.28 N +ATOM 1324 CA LEU A 974 -4.302 -10.697 1.044 1.00 50.34 C +ATOM 1325 C LEU A 974 -4.106 -10.533 -0.466 1.00 50.70 C +ATOM 1326 O LEU A 974 -3.776 -9.438 -0.918 1.00 49.90 O +ATOM 1327 CB LEU A 974 -5.652 -10.057 1.444 1.00 50.33 C +ATOM 1328 CG LEU A 974 -6.133 -10.385 2.872 1.00 55.89 C +ATOM 1329 CD1 LEU A 974 -7.390 -9.560 3.225 1.00 55.99 C +ATOM 1330 CD2 LEU A 974 -6.349 -11.900 3.087 1.00 57.89 C +ATOM 1331 H LEU A 974 -3.270 -9.028 1.815 1.00 52.28 H +ATOM 1332 HA LEU A 974 -4.317 -11.761 1.282 1.00 50.34 H +ATOM 1333 HB3 LEU A 974 -6.429 -10.378 0.746 1.00 50.33 H +ATOM 1334 HB2 LEU A 974 -5.580 -8.974 1.322 1.00 50.33 H +ATOM 1335 HG LEU A 974 -5.348 -10.068 3.560 1.00 55.89 H +ATOM 1336 HD11 LEU A 974 -7.239 -9.004 4.151 1.00 55.99 H +ATOM 1337 HD12 LEU A 974 -7.635 -8.832 2.451 1.00 55.99 H +ATOM 1338 HD13 LEU A 974 -8.277 -10.179 3.359 1.00 55.99 H +ATOM 1339 HD21 LEU A 974 -7.274 -12.124 3.617 1.00 57.89 H +ATOM 1340 HD22 LEU A 974 -6.384 -12.447 2.144 1.00 57.89 H +ATOM 1341 HD23 LEU A 974 -5.535 -12.324 3.676 1.00 57.89 H +ATOM 1342 N GLN A 975 -4.395 -11.603 -1.216 1.00 44.54 N +ATOM 1343 CA GLN A 975 -4.655 -11.549 -2.651 1.00 42.17 C +ATOM 1344 C GLN A 975 -6.158 -11.329 -2.878 1.00 43.56 C +ATOM 1345 O GLN A 975 -6.971 -11.919 -2.168 1.00 42.93 O +ATOM 1346 CB GLN A 975 -4.191 -12.855 -3.311 1.00 43.83 C +ATOM 1347 CG GLN A 975 -2.665 -12.973 -3.441 1.00 59.19 C +ATOM 1348 CD GLN A 975 -2.252 -14.354 -3.950 1.00 77.91 C +ATOM 1349 OE1 GLN A 975 -1.888 -14.512 -5.113 1.00 74.64 O +ATOM 1350 NE2 GLN A 975 -2.324 -15.365 -3.082 1.00 71.32 N +ATOM 1351 H GLN A 975 -4.643 -12.473 -0.760 1.00 44.54 H +ATOM 1352 HA GLN A 975 -4.101 -10.725 -3.104 1.00 42.17 H +ATOM 1353 HB3 GLN A 975 -4.625 -12.926 -4.306 1.00 43.83 H +ATOM 1354 HB2 GLN A 975 -4.582 -13.695 -2.742 1.00 43.83 H +ATOM 1355 HG3 GLN A 975 -2.186 -12.784 -2.484 1.00 59.19 H +ATOM 1356 HG2 GLN A 975 -2.291 -12.209 -4.121 1.00 59.19 H +ATOM 1357 HE22 GLN A 975 -2.096 -16.302 -3.374 1.00 71.32 H +ATOM 1358 HE21 GLN A 975 -2.627 -15.202 -2.132 1.00 71.32 H +ATOM 1359 N LEU A 976 -6.488 -10.492 -3.868 1.00 37.72 N +ATOM 1360 CA LEU A 976 -7.847 -10.131 -4.255 1.00 36.76 C +ATOM 1361 C LEU A 976 -8.327 -11.093 -5.350 1.00 38.66 C +ATOM 1362 O LEU A 976 -7.956 -10.919 -6.512 1.00 37.25 O +ATOM 1363 CB LEU A 976 -7.819 -8.656 -4.717 1.00 36.56 C +ATOM 1364 CG LEU A 976 -9.143 -8.055 -5.237 1.00 41.61 C +ATOM 1365 CD1 LEU A 976 -10.288 -8.184 -4.216 1.00 43.42 C +ATOM 1366 CD2 LEU A 976 -8.926 -6.598 -5.699 1.00 42.85 C +ATOM 1367 H LEU A 976 -5.752 -10.099 -4.441 1.00 37.72 H +ATOM 1368 HA LEU A 976 -8.510 -10.216 -3.392 1.00 36.76 H +ATOM 1369 HB3 LEU A 976 -7.085 -8.565 -5.513 1.00 36.56 H +ATOM 1370 HB2 LEU A 976 -7.445 -8.039 -3.900 1.00 36.56 H +ATOM 1371 HG LEU A 976 -9.442 -8.613 -6.124 1.00 41.61 H +ATOM 1372 HD11 LEU A 976 -10.930 -7.303 -4.187 1.00 43.42 H +ATOM 1373 HD12 LEU A 976 -10.920 -9.035 -4.469 1.00 43.42 H +ATOM 1374 HD13 LEU A 976 -9.917 -8.343 -3.204 1.00 43.42 H +ATOM 1375 HD21 LEU A 976 -9.526 -5.885 -5.134 1.00 42.85 H +ATOM 1376 HD22 LEU A 976 -7.888 -6.282 -5.594 1.00 42.85 H +ATOM 1377 HD23 LEU A 976 -9.185 -6.482 -6.752 1.00 42.85 H +ATOM 1378 N VAL A 977 -9.134 -12.091 -4.958 1.00 35.03 N +ATOM 1379 CA VAL A 977 -9.722 -13.059 -5.882 1.00 34.91 C +ATOM 1380 C VAL A 977 -10.993 -12.447 -6.502 1.00 38.85 C +ATOM 1381 O VAL A 977 -11.896 -12.025 -5.778 1.00 39.08 O +ATOM 1382 CB VAL A 977 -10.085 -14.406 -5.191 1.00 39.71 C +ATOM 1383 CG1 VAL A 977 -10.564 -15.484 -6.188 1.00 39.45 C +ATOM 1384 CG2 VAL A 977 -8.900 -14.967 -4.381 1.00 39.89 C +ATOM 1385 H VAL A 977 -9.424 -12.162 -3.993 1.00 35.03 H +ATOM 1386 HA VAL A 977 -9.005 -13.279 -6.676 1.00 34.91 H +ATOM 1387 HB VAL A 977 -10.902 -14.237 -4.489 1.00 39.71 H +ATOM 1388 HG11 VAL A 977 -10.841 -16.401 -5.668 1.00 39.45 H +ATOM 1389 HG12 VAL A 977 -11.429 -15.174 -6.770 1.00 39.45 H +ATOM 1390 HG13 VAL A 977 -9.779 -15.739 -6.898 1.00 39.45 H +ATOM 1391 HG21 VAL A 977 -9.153 -15.924 -3.927 1.00 39.89 H +ATOM 1392 HG22 VAL A 977 -8.024 -15.122 -5.011 1.00 39.89 H +ATOM 1393 HG23 VAL A 977 -8.610 -14.298 -3.573 1.00 39.89 H +ATOM 1394 N MET A 978 -11.015 -12.404 -7.839 1.00 34.03 N +ATOM 1395 CA MET A 978 -12.113 -11.896 -8.660 1.00 34.32 C +ATOM 1396 C MET A 978 -12.538 -12.993 -9.643 1.00 36.69 C +ATOM 1397 O MET A 978 -11.818 -13.978 -9.822 1.00 35.63 O +ATOM 1398 CB MET A 978 -11.644 -10.647 -9.443 1.00 36.81 C +ATOM 1399 CG MET A 978 -10.999 -9.544 -8.586 1.00 41.50 C +ATOM 1400 SD MET A 978 -10.358 -8.128 -9.522 1.00 45.78 S +ATOM 1401 CE MET A 978 -8.942 -8.902 -10.352 1.00 42.96 C +ATOM 1402 H MET A 978 -10.221 -12.753 -8.360 1.00 34.03 H +ATOM 1403 HA MET A 978 -12.972 -11.635 -8.040 1.00 34.32 H +ATOM 1404 HB3 MET A 978 -12.500 -10.218 -9.964 1.00 36.81 H +ATOM 1405 HB2 MET A 978 -10.949 -10.945 -10.226 1.00 36.81 H +ATOM 1406 HG3 MET A 978 -10.167 -9.942 -8.007 1.00 41.50 H +ATOM 1407 HG2 MET A 978 -11.729 -9.178 -7.865 1.00 41.50 H +ATOM 1408 HE1 MET A 978 -8.326 -8.141 -10.832 1.00 42.96 H +ATOM 1409 HE2 MET A 978 -8.328 -9.444 -9.634 1.00 42.96 H +ATOM 1410 HE3 MET A 978 -9.276 -9.600 -11.120 1.00 42.96 H +ATOM 1411 N GLU A 979 -13.692 -12.793 -10.297 1.00 33.63 N +ATOM 1412 CA GLU A 979 -14.132 -13.639 -11.407 1.00 33.95 C +ATOM 1413 C GLU A 979 -13.224 -13.450 -12.637 1.00 35.56 C +ATOM 1414 O GLU A 979 -12.741 -12.343 -12.884 1.00 32.73 O +ATOM 1415 CB GLU A 979 -15.611 -13.341 -11.735 1.00 36.17 C +ATOM 1416 CG GLU A 979 -15.895 -11.927 -12.301 1.00 42.68 C +ATOM 1417 CD GLU A 979 -17.362 -11.631 -12.644 1.00 58.73 C +ATOM 1418 OE1 GLU A 979 -18.206 -12.554 -12.607 1.00 57.48 O +ATOM 1419 OE2 GLU A 979 -17.624 -10.444 -12.930 1.00 43.43 O1- +ATOM 1420 H GLU A 979 -14.250 -11.971 -10.107 1.00 33.63 H +ATOM 1421 HA GLU A 979 -14.064 -14.680 -11.082 1.00 33.95 H +ATOM 1422 HB3 GLU A 979 -16.208 -13.495 -10.835 1.00 36.17 H +ATOM 1423 HB2 GLU A 979 -15.958 -14.090 -12.449 1.00 36.17 H +ATOM 1424 HG3 GLU A 979 -15.329 -11.770 -13.218 1.00 42.68 H +ATOM 1425 HG2 GLU A 979 -15.547 -11.175 -11.591 1.00 42.68 H +ATOM 1426 N TYR A 980 -13.046 -14.537 -13.401 1.00 33.04 N +ATOM 1427 CA TYR A 980 -12.435 -14.501 -14.725 1.00 34.05 C +ATOM 1428 C TYR A 980 -13.483 -14.026 -15.745 1.00 39.90 C +ATOM 1429 O TYR A 980 -14.472 -14.726 -15.958 1.00 41.17 O +ATOM 1430 CB TYR A 980 -11.868 -15.895 -15.054 1.00 36.60 C +ATOM 1431 CG TYR A 980 -11.332 -16.062 -16.464 1.00 38.89 C +ATOM 1432 CD1 TYR A 980 -10.181 -15.355 -16.870 1.00 40.20 C +ATOM 1433 CD2 TYR A 980 -11.982 -16.925 -17.372 1.00 40.57 C +ATOM 1434 CE1 TYR A 980 -9.674 -15.525 -18.172 1.00 41.79 C +ATOM 1435 CE2 TYR A 980 -11.480 -17.085 -18.677 1.00 41.35 C +ATOM 1436 CZ TYR A 980 -10.325 -16.386 -19.077 1.00 46.65 C +ATOM 1437 OH TYR A 980 -9.838 -16.543 -20.341 1.00 46.21 O +ATOM 1438 H TYR A 980 -13.471 -15.414 -13.135 1.00 33.04 H +ATOM 1439 HA TYR A 980 -11.599 -13.798 -14.714 1.00 34.05 H +ATOM 1440 HB3 TYR A 980 -12.642 -16.648 -14.893 1.00 36.60 H +ATOM 1441 HB2 TYR A 980 -11.066 -16.137 -14.355 1.00 36.60 H +ATOM 1442 HD1 TYR A 980 -9.678 -14.693 -16.181 1.00 40.20 H +ATOM 1443 HD2 TYR A 980 -12.868 -17.466 -17.073 1.00 40.57 H +ATOM 1444 HE1 TYR A 980 -8.783 -14.995 -18.472 1.00 41.79 H +ATOM 1445 HE2 TYR A 980 -11.986 -17.745 -19.366 1.00 41.35 H +ATOM 1446 HH TYR A 980 -10.419 -17.063 -20.902 1.00 46.21 H +ATOM 1447 N VAL A 981 -13.243 -12.844 -16.331 1.00 35.70 N +ATOM 1448 CA VAL A 981 -14.105 -12.203 -17.322 1.00 35.13 C +ATOM 1449 C VAL A 981 -13.580 -12.610 -18.729 1.00 39.95 C +ATOM 1450 O VAL A 981 -12.511 -12.123 -19.104 1.00 40.49 O +ATOM 1451 CB VAL A 981 -14.061 -10.658 -17.131 1.00 37.53 C +ATOM 1452 CG1 VAL A 981 -14.865 -9.900 -18.194 1.00 37.76 C +ATOM 1453 CG2 VAL A 981 -14.577 -10.251 -15.736 1.00 36.89 C +ATOM 1454 H VAL A 981 -12.403 -12.336 -16.094 1.00 35.70 H +ATOM 1455 HA VAL A 981 -15.141 -12.487 -17.157 1.00 35.13 H +ATOM 1456 HB VAL A 981 -13.027 -10.316 -17.194 1.00 37.53 H +ATOM 1457 HG11 VAL A 981 -15.030 -8.860 -17.916 1.00 37.76 H +ATOM 1458 HG12 VAL A 981 -14.337 -9.900 -19.144 1.00 37.76 H +ATOM 1459 HG13 VAL A 981 -15.848 -10.347 -18.332 1.00 37.76 H +ATOM 1460 HG21 VAL A 981 -14.545 -9.169 -15.601 1.00 36.89 H +ATOM 1461 HG22 VAL A 981 -15.608 -10.571 -15.585 1.00 36.89 H +ATOM 1462 HG23 VAL A 981 -13.974 -10.689 -14.942 1.00 36.89 H +ATOM 1463 N PRO A 982 -14.241 -13.581 -19.416 1.00 37.29 N +ATOM 1464 CA PRO A 982 -13.592 -14.428 -20.446 1.00 36.67 C +ATOM 1465 C PRO A 982 -12.871 -13.788 -21.645 1.00 38.06 C +ATOM 1466 O PRO A 982 -11.842 -14.330 -22.047 1.00 37.41 O +ATOM 1467 CB PRO A 982 -14.705 -15.369 -20.926 1.00 39.18 C +ATOM 1468 CG PRO A 982 -15.606 -15.513 -19.720 1.00 44.96 C +ATOM 1469 CD PRO A 982 -15.576 -14.120 -19.111 1.00 40.09 C +ATOM 1470 HA PRO A 982 -12.855 -15.022 -19.904 1.00 36.67 H +ATOM 1471 HB3 PRO A 982 -14.326 -16.330 -21.276 1.00 39.18 H +ATOM 1472 HB2 PRO A 982 -15.269 -14.915 -21.745 1.00 39.18 H +ATOM 1473 HG3 PRO A 982 -15.167 -16.226 -19.021 1.00 44.96 H +ATOM 1474 HG2 PRO A 982 -16.612 -15.855 -19.966 1.00 44.96 H +ATOM 1475 HD2 PRO A 982 -16.322 -13.488 -19.589 1.00 40.09 H +ATOM 1476 HD3 PRO A 982 -15.833 -14.200 -18.058 1.00 40.09 H +ATOM 1477 N LEU A 983 -13.410 -12.695 -22.212 1.00 33.36 N +ATOM 1478 CA LEU A 983 -12.867 -12.076 -23.431 1.00 31.91 C +ATOM 1479 C LEU A 983 -11.838 -10.960 -23.155 1.00 34.62 C +ATOM 1480 O LEU A 983 -11.266 -10.441 -24.114 1.00 34.03 O +ATOM 1481 CB LEU A 983 -14.023 -11.600 -24.346 1.00 31.79 C +ATOM 1482 CG LEU A 983 -14.935 -12.735 -24.868 1.00 36.75 C +ATOM 1483 CD1 LEU A 983 -16.149 -12.165 -25.617 1.00 37.07 C +ATOM 1484 CD2 LEU A 983 -14.182 -13.760 -25.739 1.00 39.07 C +ATOM 1485 H LEU A 983 -14.253 -12.283 -21.835 1.00 33.36 H +ATOM 1486 HA LEU A 983 -12.308 -12.827 -23.990 1.00 31.91 H +ATOM 1487 HB3 LEU A 983 -13.617 -11.066 -25.205 1.00 31.79 H +ATOM 1488 HB2 LEU A 983 -14.631 -10.871 -23.818 1.00 31.79 H +ATOM 1489 HG LEU A 983 -15.336 -13.264 -24.002 1.00 36.75 H +ATOM 1490 HD11 LEU A 983 -16.944 -12.908 -25.684 1.00 37.07 H +ATOM 1491 HD12 LEU A 983 -16.559 -11.295 -25.106 1.00 37.07 H +ATOM 1492 HD13 LEU A 983 -15.890 -11.865 -26.633 1.00 37.07 H +ATOM 1493 HD21 LEU A 983 -14.730 -14.004 -26.649 1.00 39.07 H +ATOM 1494 HD22 LEU A 983 -13.202 -13.396 -26.048 1.00 39.07 H +ATOM 1495 HD23 LEU A 983 -14.030 -14.692 -25.194 1.00 39.07 H +ATOM 1496 N GLY A 984 -11.577 -10.645 -21.872 1.00 30.54 N +ATOM 1497 CA GLY A 984 -10.542 -9.702 -21.438 1.00 29.44 C +ATOM 1498 C GLY A 984 -10.893 -8.247 -21.772 1.00 30.46 C +ATOM 1499 O GLY A 984 -12.035 -7.935 -22.104 1.00 29.51 O +ATOM 1500 H GLY A 984 -12.090 -11.120 -21.142 1.00 30.54 H +ATOM 1501 HA3 GLY A 984 -9.590 -9.962 -21.901 1.00 29.44 H +ATOM 1502 HA2 GLY A 984 -10.415 -9.792 -20.359 1.00 29.44 H +ATOM 1503 N SER A 985 -9.900 -7.349 -21.647 1.00 25.72 N +ATOM 1504 CA SER A 985 -10.058 -5.906 -21.855 1.00 25.55 C +ATOM 1505 C SER A 985 -10.449 -5.533 -23.295 1.00 28.85 C +ATOM 1506 O SER A 985 -10.017 -6.199 -24.235 1.00 26.31 O +ATOM 1507 CB SER A 985 -8.804 -5.158 -21.361 1.00 29.24 C +ATOM 1508 OG SER A 985 -7.724 -5.184 -22.273 1.00 35.45 O +ATOM 1509 H SER A 985 -8.976 -7.668 -21.385 1.00 25.72 H +ATOM 1510 HA SER A 985 -10.867 -5.593 -21.205 1.00 25.55 H +ATOM 1511 HB3 SER A 985 -8.479 -5.544 -20.395 1.00 29.24 H +ATOM 1512 HB2 SER A 985 -9.060 -4.111 -21.202 1.00 29.24 H +ATOM 1513 HG SER A 985 -7.369 -6.078 -22.315 1.00 35.45 H +ATOM 1514 N LEU A 986 -11.219 -4.440 -23.432 1.00 27.52 N +ATOM 1515 CA LEU A 986 -11.584 -3.835 -24.717 1.00 27.90 C +ATOM 1516 C LEU A 986 -10.369 -3.303 -25.499 1.00 31.20 C +ATOM 1517 O LEU A 986 -10.426 -3.281 -26.727 1.00 30.54 O +ATOM 1518 CB LEU A 986 -12.621 -2.712 -24.492 1.00 27.75 C +ATOM 1519 CG LEU A 986 -14.045 -3.208 -24.157 1.00 32.92 C +ATOM 1520 CD1 LEU A 986 -14.956 -2.019 -23.808 1.00 33.36 C +ATOM 1521 CD2 LEU A 986 -14.667 -4.058 -25.284 1.00 36.70 C +ATOM 1522 H LEU A 986 -11.542 -3.953 -22.605 1.00 27.52 H +ATOM 1523 HA LEU A 986 -12.030 -4.614 -25.334 1.00 27.90 H +ATOM 1524 HB3 LEU A 986 -12.688 -2.084 -25.381 1.00 27.75 H +ATOM 1525 HB2 LEU A 986 -12.263 -2.052 -23.700 1.00 27.75 H +ATOM 1526 HG LEU A 986 -13.980 -3.829 -23.262 1.00 32.92 H +ATOM 1527 HD11 LEU A 986 -15.724 -2.314 -23.100 1.00 33.36 H +ATOM 1528 HD12 LEU A 986 -14.399 -1.202 -23.350 1.00 33.36 H +ATOM 1529 HD13 LEU A 986 -15.463 -1.625 -24.690 1.00 33.36 H +ATOM 1530 HD21 LEU A 986 -15.731 -3.859 -25.406 1.00 36.70 H +ATOM 1531 HD22 LEU A 986 -14.192 -3.868 -26.245 1.00 36.70 H +ATOM 1532 HD23 LEU A 986 -14.561 -5.121 -25.075 1.00 36.70 H +ATOM 1533 N ARG A 987 -9.286 -2.929 -24.793 1.00 29.06 N +ATOM 1534 CA ARG A 987 -8.017 -2.516 -25.394 1.00 29.96 C +ATOM 1535 C ARG A 987 -7.291 -3.652 -26.137 1.00 34.81 C +ATOM 1536 O ARG A 987 -6.680 -3.387 -27.172 1.00 34.10 O +ATOM 1537 CB ARG A 987 -7.130 -1.836 -24.336 1.00 32.11 C +ATOM 1538 CG ARG A 987 -5.888 -1.139 -24.920 1.00 46.98 C +ATOM 1539 CD ARG A 987 -5.085 -0.392 -23.849 1.00 59.29 C +ATOM 1540 NE ARG A 987 -3.827 0.162 -24.375 1.00 65.62 N +ATOM 1541 CZ ARG A 987 -2.647 -0.473 -24.496 1.00 80.85 C +ATOM 1542 NH1 ARG A 987 -1.601 0.204 -24.984 1.00 71.30 N +ATOM 1543 NH2 ARG A 987 -2.483 -1.757 -24.139 1.00 64.23 N1+ +ATOM 1544 H ARG A 987 -9.312 -2.973 -23.784 1.00 29.06 H +ATOM 1545 HA ARG A 987 -8.262 -1.763 -26.133 1.00 29.96 H +ATOM 1546 HB3 ARG A 987 -6.823 -2.568 -23.588 1.00 32.11 H +ATOM 1547 HB2 ARG A 987 -7.722 -1.088 -23.808 1.00 32.11 H +ATOM 1548 HG3 ARG A 987 -6.268 -0.398 -25.626 1.00 46.98 H +ATOM 1549 HG2 ARG A 987 -5.251 -1.807 -25.500 1.00 46.98 H +ATOM 1550 HD3 ARG A 987 -4.953 -0.979 -22.940 1.00 59.29 H +ATOM 1551 HD2 ARG A 987 -5.653 0.492 -23.566 1.00 59.29 H +ATOM 1552 HE ARG A 987 -3.886 1.115 -24.706 1.00 65.62 H +ATOM 1553 HH12 ARG A 987 -0.701 -0.246 -25.082 1.00 71.30 H +ATOM 1554 HH11 ARG A 987 -1.705 1.173 -25.259 1.00 71.30 H +ATOM 1555 HH22 ARG A 987 -1.576 -2.198 -24.220 1.00 64.23 H +ATOM 1556 HH21 ARG A 987 -3.272 -2.303 -23.824 1.00 64.23 H +ATOM 1557 N ASP A 988 -7.393 -4.883 -25.612 1.00 32.33 N +ATOM 1558 CA ASP A 988 -6.818 -6.091 -26.208 1.00 32.03 C +ATOM 1559 C ASP A 988 -7.773 -6.753 -27.216 1.00 34.79 C +ATOM 1560 O ASP A 988 -7.292 -7.360 -28.172 1.00 33.45 O +ATOM 1561 CB ASP A 988 -6.383 -7.129 -25.141 1.00 34.09 C +ATOM 1562 CG ASP A 988 -5.386 -6.633 -24.076 1.00 50.64 C +ATOM 1563 OD1 ASP A 988 -4.621 -5.682 -24.352 1.00 52.94 O +ATOM 1564 OD2 ASP A 988 -5.324 -7.306 -23.023 1.00 55.56 O1- +ATOM 1565 H ASP A 988 -7.905 -5.013 -24.750 1.00 32.33 H +ATOM 1566 HA ASP A 988 -5.926 -5.807 -26.770 1.00 32.03 H +ATOM 1567 HB3 ASP A 988 -5.929 -7.992 -25.631 1.00 34.09 H +ATOM 1568 HB2 ASP A 988 -7.271 -7.493 -24.620 1.00 34.09 H +ATOM 1569 N TYR A 989 -9.092 -6.640 -26.993 1.00 30.87 N +ATOM 1570 CA TYR A 989 -10.115 -7.301 -27.799 1.00 30.93 C +ATOM 1571 C TYR A 989 -10.388 -6.577 -29.130 1.00 31.61 C +ATOM 1572 O TYR A 989 -10.312 -7.223 -30.174 1.00 30.88 O +ATOM 1573 CB TYR A 989 -11.391 -7.498 -26.954 1.00 32.58 C +ATOM 1574 CG TYR A 989 -12.492 -8.296 -27.630 1.00 35.88 C +ATOM 1575 CD1 TYR A 989 -12.518 -9.702 -27.516 1.00 38.76 C +ATOM 1576 CD2 TYR A 989 -13.493 -7.638 -28.372 1.00 37.33 C +ATOM 1577 CE1 TYR A 989 -13.542 -10.443 -28.137 1.00 41.81 C +ATOM 1578 CE2 TYR A 989 -14.514 -8.378 -28.996 1.00 39.43 C +ATOM 1579 CZ TYR A 989 -14.541 -9.782 -28.878 1.00 47.08 C +ATOM 1580 OH TYR A 989 -15.532 -10.501 -29.479 1.00 54.57 O +ATOM 1581 H TYR A 989 -9.417 -6.150 -26.170 1.00 30.87 H +ATOM 1582 HA TYR A 989 -9.748 -8.300 -28.046 1.00 30.93 H +ATOM 1583 HB3 TYR A 989 -11.794 -6.531 -26.652 1.00 32.58 H +ATOM 1584 HB2 TYR A 989 -11.134 -8.012 -26.026 1.00 32.58 H +ATOM 1585 HD1 TYR A 989 -11.755 -10.216 -26.949 1.00 38.76 H +ATOM 1586 HD2 TYR A 989 -13.476 -6.563 -28.464 1.00 37.33 H +ATOM 1587 HE1 TYR A 989 -13.559 -11.519 -28.045 1.00 41.81 H +ATOM 1588 HE2 TYR A 989 -15.272 -7.863 -29.563 1.00 39.43 H +ATOM 1589 HH TYR A 989 -16.158 -9.956 -29.961 1.00 54.57 H +ATOM 1590 N LEU A 990 -10.706 -5.269 -29.079 1.00 26.64 N +ATOM 1591 CA LEU A 990 -11.169 -4.493 -30.238 1.00 26.55 C +ATOM 1592 C LEU A 990 -10.208 -4.338 -31.443 1.00 32.26 C +ATOM 1593 O LEU A 990 -10.730 -4.254 -32.554 1.00 32.40 O +ATOM 1594 CB LEU A 990 -11.721 -3.111 -29.816 1.00 25.29 C +ATOM 1595 CG LEU A 990 -12.967 -3.137 -28.906 1.00 28.64 C +ATOM 1596 CD1 LEU A 990 -13.282 -1.724 -28.386 1.00 28.39 C +ATOM 1597 CD2 LEU A 990 -14.197 -3.752 -29.604 1.00 27.70 C +ATOM 1598 H LEU A 990 -10.708 -4.785 -28.191 1.00 26.64 H +ATOM 1599 HA LEU A 990 -12.009 -5.067 -30.634 1.00 26.55 H +ATOM 1600 HB3 LEU A 990 -11.971 -2.532 -30.706 1.00 25.29 H +ATOM 1601 HB2 LEU A 990 -10.926 -2.554 -29.318 1.00 25.29 H +ATOM 1602 HG LEU A 990 -12.740 -3.756 -28.038 1.00 28.64 H +ATOM 1603 HD11 LEU A 990 -14.289 -1.658 -27.974 1.00 28.39 H +ATOM 1604 HD12 LEU A 990 -12.591 -1.441 -27.593 1.00 28.39 H +ATOM 1605 HD13 LEU A 990 -13.197 -0.978 -29.176 1.00 28.39 H +ATOM 1606 HD21 LEU A 990 -15.056 -3.083 -29.591 1.00 27.70 H +ATOM 1607 HD22 LEU A 990 -14.006 -3.989 -30.651 1.00 27.70 H +ATOM 1608 HD23 LEU A 990 -14.506 -4.670 -29.108 1.00 27.70 H +ATOM 1609 N PRO A 991 -8.862 -4.368 -31.272 1.00 29.17 N +ATOM 1610 CA PRO A 991 -7.934 -4.390 -32.422 1.00 30.32 C +ATOM 1611 C PRO A 991 -7.982 -5.660 -33.292 1.00 37.04 C +ATOM 1612 O PRO A 991 -7.685 -5.568 -34.482 1.00 39.83 O +ATOM 1613 CB PRO A 991 -6.545 -4.175 -31.796 1.00 31.53 C +ATOM 1614 CG PRO A 991 -6.828 -3.468 -30.483 1.00 34.07 C +ATOM 1615 CD PRO A 991 -8.127 -4.120 -30.032 1.00 29.12 C +ATOM 1616 HA PRO A 991 -8.172 -3.530 -33.048 1.00 30.32 H +ATOM 1617 HB3 PRO A 991 -5.880 -3.597 -32.438 1.00 31.53 H +ATOM 1618 HB2 PRO A 991 -6.059 -5.130 -31.592 1.00 31.53 H +ATOM 1619 HG3 PRO A 991 -6.998 -2.407 -30.671 1.00 34.07 H +ATOM 1620 HG2 PRO A 991 -6.017 -3.559 -29.761 1.00 34.07 H +ATOM 1621 HD2 PRO A 991 -7.914 -5.074 -29.554 1.00 29.12 H +ATOM 1622 HD3 PRO A 991 -8.663 -3.490 -29.323 1.00 29.12 H +ATOM 1623 N ARG A 992 -8.364 -6.801 -32.693 1.00 33.12 N +ATOM 1624 CA ARG A 992 -8.473 -8.101 -33.362 1.00 35.22 C +ATOM 1625 C ARG A 992 -9.923 -8.477 -33.721 1.00 42.35 C +ATOM 1626 O ARG A 992 -10.101 -9.442 -34.464 1.00 43.35 O +ATOM 1627 CB ARG A 992 -7.847 -9.181 -32.452 1.00 36.79 C +ATOM 1628 CG ARG A 992 -6.321 -9.042 -32.266 1.00 49.00 C +ATOM 1629 CD ARG A 992 -5.895 -8.581 -30.862 1.00 65.39 C +ATOM 1630 NE ARG A 992 -4.429 -8.630 -30.709 1.00 83.52 N +ATOM 1631 CZ ARG A 992 -3.697 -8.245 -29.646 1.00101.34 C +ATOM 1632 NH1 ARG A 992 -4.252 -7.730 -28.539 1.00 87.97 N +ATOM 1633 NH2 ARG A 992 -2.366 -8.382 -29.694 1.00 90.05 N1+ +ATOM 1634 H ARG A 992 -8.610 -6.788 -31.713 1.00 33.12 H +ATOM 1635 HA ARG A 992 -7.920 -8.089 -34.303 1.00 35.22 H +ATOM 1636 HB3 ARG A 992 -8.028 -10.163 -32.891 1.00 36.79 H +ATOM 1637 HB2 ARG A 992 -8.353 -9.200 -31.486 1.00 36.79 H +ATOM 1638 HG3 ARG A 992 -5.962 -8.307 -32.988 1.00 49.00 H +ATOM 1639 HG2 ARG A 992 -5.815 -9.970 -32.534 1.00 49.00 H +ATOM 1640 HD3 ARG A 992 -6.407 -9.141 -30.079 1.00 65.39 H +ATOM 1641 HD2 ARG A 992 -6.177 -7.534 -30.743 1.00 65.39 H +ATOM 1642 HE ARG A 992 -3.934 -9.003 -31.505 1.00 83.52 H +ATOM 1643 HH12 ARG A 992 -3.684 -7.448 -27.753 1.00 87.97 H +ATOM 1644 HH11 ARG A 992 -5.259 -7.626 -28.485 1.00 87.97 H +ATOM 1645 HH22 ARG A 992 -1.795 -8.104 -28.909 1.00 90.05 H +ATOM 1646 HH21 ARG A 992 -1.920 -8.767 -30.514 1.00 90.05 H +ATOM 1647 N HIS A 993 -10.920 -7.742 -33.199 1.00 39.31 N +ATOM 1648 CA HIS A 993 -12.343 -8.029 -33.380 1.00 40.02 C +ATOM 1649 C HIS A 993 -13.064 -6.735 -33.771 1.00 44.39 C +ATOM 1650 O HIS A 993 -13.235 -5.856 -32.926 1.00 42.90 O +ATOM 1651 CB HIS A 993 -12.933 -8.636 -32.087 1.00 40.24 C +ATOM 1652 CG HIS A 993 -12.280 -9.913 -31.615 1.00 43.95 C +ATOM 1653 ND1 HIS A 993 -11.178 -9.921 -30.777 1.00 44.82 N +ATOM 1654 CD2 HIS A 993 -12.584 -11.239 -31.832 1.00 46.86 C +ATOM 1655 CE1 HIS A 993 -10.862 -11.196 -30.543 1.00 45.15 C +ATOM 1656 NE2 HIS A 993 -11.674 -12.053 -31.151 1.00 46.64 N +ATOM 1657 H HIS A 993 -10.700 -6.953 -32.607 1.00 39.31 H +ATOM 1658 HA HIS A 993 -12.488 -8.755 -34.182 1.00 40.02 H +ATOM 1659 HB3 HIS A 993 -13.998 -8.834 -32.223 1.00 40.24 H +ATOM 1660 HB2 HIS A 993 -12.864 -7.911 -31.275 1.00 40.24 H +ATOM 1661 HD1 HIS A 993 -10.706 -9.101 -30.417 1.00 44.82 H +ATOM 1662 HD2 HIS A 993 -13.381 -11.668 -32.421 1.00 46.86 H +ATOM 1663 HE1 HIS A 993 -10.035 -11.499 -29.918 1.00 45.15 H +ATOM 1664 N SER A 994 -13.470 -6.644 -35.048 1.00 43.61 N +ATOM 1665 CA SER A 994 -14.209 -5.516 -35.617 1.00 44.09 C +ATOM 1666 C SER A 994 -15.687 -5.527 -35.171 1.00 48.55 C +ATOM 1667 O SER A 994 -16.525 -6.165 -35.809 1.00 49.91 O +ATOM 1668 CB SER A 994 -14.007 -5.502 -37.150 1.00 50.40 C +ATOM 1669 OG SER A 994 -14.571 -6.626 -37.802 1.00 64.99 O +ATOM 1670 H SER A 994 -13.279 -7.408 -35.685 1.00 43.61 H +ATOM 1671 HA SER A 994 -13.757 -4.597 -35.238 1.00 44.09 H +ATOM 1672 HB3 SER A 994 -12.944 -5.455 -37.390 1.00 50.40 H +ATOM 1673 HB2 SER A 994 -14.458 -4.603 -37.573 1.00 50.40 H +ATOM 1674 HG SER A 994 -15.515 -6.632 -37.619 1.00 64.99 H +ATOM 1675 N ILE A 995 -15.965 -4.828 -34.059 1.00 42.04 N +ATOM 1676 CA ILE A 995 -17.296 -4.714 -33.464 1.00 40.91 C +ATOM 1677 C ILE A 995 -18.064 -3.520 -34.068 1.00 43.90 C +ATOM 1678 O ILE A 995 -17.480 -2.455 -34.267 1.00 43.46 O +ATOM 1679 CB ILE A 995 -17.192 -4.556 -31.916 1.00 42.36 C +ATOM 1680 CG1 ILE A 995 -16.536 -5.790 -31.248 1.00 42.65 C +ATOM 1681 CG2 ILE A 995 -18.529 -4.242 -31.218 1.00 41.71 C +ATOM 1682 CD1 ILE A 995 -17.228 -7.139 -31.513 1.00 43.19 C +ATOM 1683 H ILE A 995 -15.219 -4.349 -33.575 1.00 42.04 H +ATOM 1684 HA ILE A 995 -17.863 -5.619 -33.689 1.00 40.91 H +ATOM 1685 HB ILE A 995 -16.532 -3.711 -31.714 1.00 42.36 H +ATOM 1686 HG13 ILE A 995 -16.475 -5.629 -30.172 1.00 42.65 H +ATOM 1687 HG12 ILE A 995 -15.501 -5.868 -31.578 1.00 42.65 H +ATOM 1688 HG21 ILE A 995 -18.434 -4.276 -30.134 1.00 41.71 H +ATOM 1689 HG22 ILE A 995 -18.899 -3.250 -31.464 1.00 41.71 H +ATOM 1690 HG23 ILE A 995 -19.292 -4.958 -31.512 1.00 41.71 H +ATOM 1691 HD11 ILE A 995 -16.639 -7.748 -32.199 1.00 43.19 H +ATOM 1692 HD12 ILE A 995 -17.348 -7.704 -30.589 1.00 43.19 H +ATOM 1693 HD13 ILE A 995 -18.221 -7.025 -31.946 1.00 43.19 H +ATOM 1694 N GLY A 996 -19.363 -3.733 -34.347 1.00 39.60 N +ATOM 1695 CA GLY A 996 -20.252 -2.755 -34.979 1.00 39.64 C +ATOM 1696 C GLY A 996 -20.596 -1.588 -34.040 1.00 40.81 C +ATOM 1697 O GLY A 996 -20.520 -1.711 -32.818 1.00 40.20 O +ATOM 1698 H GLY A 996 -19.764 -4.635 -34.139 1.00 39.60 H +ATOM 1699 HA3 GLY A 996 -21.173 -3.256 -35.273 1.00 39.64 H +ATOM 1700 HA2 GLY A 996 -19.788 -2.377 -35.892 1.00 39.64 H +ATOM 1701 N LEU A 997 -20.997 -0.458 -34.647 1.00 36.29 N +ATOM 1702 CA LEU A 997 -21.277 0.836 -34.014 1.00 34.21 C +ATOM 1703 C LEU A 997 -22.273 0.785 -32.840 1.00 36.59 C +ATOM 1704 O LEU A 997 -21.980 1.361 -31.794 1.00 34.53 O +ATOM 1705 CB LEU A 997 -21.711 1.829 -35.121 1.00 34.53 C +ATOM 1706 CG LEU A 997 -22.124 3.249 -34.665 1.00 38.00 C +ATOM 1707 CD1 LEU A 997 -21.037 3.947 -33.824 1.00 36.32 C +ATOM 1708 CD2 LEU A 997 -22.558 4.106 -35.869 1.00 39.98 C +ATOM 1709 H LEU A 997 -21.051 -0.455 -35.657 1.00 36.29 H +ATOM 1710 HA LEU A 997 -20.326 1.179 -33.603 1.00 34.21 H +ATOM 1711 HB3 LEU A 997 -22.545 1.391 -35.671 1.00 34.53 H +ATOM 1712 HB2 LEU A 997 -20.897 1.921 -35.842 1.00 34.53 H +ATOM 1713 HG LEU A 997 -23.015 3.149 -34.047 1.00 38.00 H +ATOM 1714 HD11 LEU A 997 -20.949 5.005 -34.060 1.00 36.32 H +ATOM 1715 HD12 LEU A 997 -21.263 3.882 -32.760 1.00 36.32 H +ATOM 1716 HD13 LEU A 997 -20.056 3.499 -33.980 1.00 36.32 H +ATOM 1717 HD21 LEU A 997 -23.419 4.725 -35.614 1.00 39.98 H +ATOM 1718 HD22 LEU A 997 -21.763 4.769 -36.208 1.00 39.98 H +ATOM 1719 HD23 LEU A 997 -22.845 3.494 -36.724 1.00 39.98 H +ATOM 1720 N ALA A 998 -23.409 0.089 -33.016 1.00 34.25 N +ATOM 1721 CA ALA A 998 -24.444 -0.055 -31.989 1.00 34.26 C +ATOM 1722 C ALA A 998 -23.979 -0.830 -30.750 1.00 36.18 C +ATOM 1723 O ALA A 998 -24.385 -0.484 -29.643 1.00 35.22 O +ATOM 1724 CB ALA A 998 -25.666 -0.747 -32.595 1.00 36.52 C +ATOM 1725 H ALA A 998 -23.583 -0.370 -33.900 1.00 34.25 H +ATOM 1726 HA ALA A 998 -24.741 0.949 -31.677 1.00 34.26 H +ATOM 1727 HB1 ALA A 998 -26.532 -0.659 -31.938 1.00 36.52 H +ATOM 1728 HB2 ALA A 998 -25.920 -0.291 -33.546 1.00 36.52 H +ATOM 1729 HB3 ALA A 998 -25.492 -1.807 -32.778 1.00 36.52 H +ATOM 1730 N GLN A 999 -23.130 -1.847 -30.960 1.00 32.90 N +ATOM 1731 CA GLN A 999 -22.559 -2.689 -29.914 1.00 31.66 C +ATOM 1732 C GLN A 999 -21.446 -1.975 -29.116 1.00 31.83 C +ATOM 1733 O GLN A 999 -21.293 -2.274 -27.934 1.00 31.42 O +ATOM 1734 CB GLN A 999 -22.134 -4.016 -30.571 1.00 33.78 C +ATOM 1735 CG GLN A 999 -21.621 -5.106 -29.611 1.00 53.37 C +ATOM 1736 CD GLN A 999 -21.398 -6.443 -30.329 1.00 79.04 C +ATOM 1737 OE1 GLN A 999 -22.211 -6.864 -31.150 1.00 77.41 O +ATOM 1738 NE2 GLN A 999 -20.304 -7.132 -30.005 1.00 70.04 N +ATOM 1739 H GLN A 999 -22.847 -2.066 -31.906 1.00 32.90 H +ATOM 1740 HA GLN A 999 -23.358 -2.921 -29.208 1.00 31.66 H +ATOM 1741 HB3 GLN A 999 -21.389 -3.822 -31.342 1.00 33.78 H +ATOM 1742 HB2 GLN A 999 -23.006 -4.407 -31.098 1.00 33.78 H +ATOM 1743 HG3 GLN A 999 -22.345 -5.266 -28.811 1.00 53.37 H +ATOM 1744 HG2 GLN A 999 -20.695 -4.784 -29.133 1.00 53.37 H +ATOM 1745 HE22 GLN A 999 -20.123 -8.026 -30.439 1.00 70.04 H +ATOM 1746 HE21 GLN A 999 -19.667 -6.780 -29.305 1.00 70.04 H +ATOM 1747 N LEU A1000 -20.751 -0.997 -29.732 1.00 27.73 N +ATOM 1748 CA LEU A1000 -19.837 -0.069 -29.050 1.00 27.15 C +ATOM 1749 C LEU A1000 -20.595 0.953 -28.181 1.00 30.03 C +ATOM 1750 O LEU A1000 -20.124 1.276 -27.092 1.00 28.40 O +ATOM 1751 CB LEU A1000 -18.945 0.674 -30.074 1.00 27.39 C +ATOM 1752 CG LEU A1000 -17.991 -0.227 -30.886 1.00 31.72 C +ATOM 1753 CD1 LEU A1000 -17.371 0.546 -32.070 1.00 31.24 C +ATOM 1754 CD2 LEU A1000 -16.934 -0.917 -29.997 1.00 32.46 C +ATOM 1755 H LEU A1000 -20.916 -0.817 -30.713 1.00 27.73 H +ATOM 1756 HA LEU A1000 -19.200 -0.650 -28.383 1.00 27.15 H +ATOM 1757 HB3 LEU A1000 -18.351 1.434 -29.562 1.00 27.39 H +ATOM 1758 HB2 LEU A1000 -19.586 1.222 -30.765 1.00 27.39 H +ATOM 1759 HG LEU A1000 -18.585 -1.019 -31.332 1.00 31.72 H +ATOM 1760 HD11 LEU A1000 -16.292 0.426 -32.140 1.00 31.24 H +ATOM 1761 HD12 LEU A1000 -17.786 0.194 -33.015 1.00 31.24 H +ATOM 1762 HD13 LEU A1000 -17.569 1.617 -32.011 1.00 31.24 H +ATOM 1763 HD21 LEU A1000 -15.920 -0.822 -30.385 1.00 32.46 H +ATOM 1764 HD22 LEU A1000 -16.925 -0.518 -28.983 1.00 32.46 H +ATOM 1765 HD23 LEU A1000 -17.146 -1.982 -29.914 1.00 32.46 H +ATOM 1766 N LEU A1001 -21.763 1.420 -28.659 1.00 27.18 N +ATOM 1767 CA LEU A1001 -22.668 2.317 -27.931 1.00 26.38 C +ATOM 1768 C LEU A1001 -23.415 1.608 -26.786 1.00 30.80 C +ATOM 1769 O LEU A1001 -23.803 2.280 -25.831 1.00 31.19 O +ATOM 1770 CB LEU A1001 -23.664 2.964 -28.916 1.00 26.55 C +ATOM 1771 CG LEU A1001 -23.009 3.940 -29.919 1.00 30.43 C +ATOM 1772 CD1 LEU A1001 -23.960 4.224 -31.098 1.00 31.54 C +ATOM 1773 CD2 LEU A1001 -22.500 5.228 -29.235 1.00 29.53 C +ATOM 1774 H LEU A1001 -22.074 1.125 -29.576 1.00 27.18 H +ATOM 1775 HA LEU A1001 -22.066 3.104 -27.475 1.00 26.38 H +ATOM 1776 HB3 LEU A1001 -24.448 3.493 -28.371 1.00 26.55 H +ATOM 1777 HB2 LEU A1001 -24.174 2.171 -29.464 1.00 26.55 H +ATOM 1778 HG LEU A1001 -22.132 3.458 -30.347 1.00 30.43 H +ATOM 1779 HD11 LEU A1001 -23.986 5.276 -31.382 1.00 31.54 H +ATOM 1780 HD12 LEU A1001 -23.649 3.668 -31.980 1.00 31.54 H +ATOM 1781 HD13 LEU A1001 -24.984 3.918 -30.880 1.00 31.54 H +ATOM 1782 HD21 LEU A1001 -22.846 6.137 -29.725 1.00 29.53 H +ATOM 1783 HD22 LEU A1001 -22.816 5.291 -28.193 1.00 29.53 H +ATOM 1784 HD23 LEU A1001 -21.410 5.261 -29.241 1.00 29.53 H +ATOM 1785 N LEU A1002 -23.566 0.274 -26.876 1.00 28.48 N +ATOM 1786 CA LEU A1002 -24.071 -0.573 -25.798 1.00 29.15 C +ATOM 1787 C LEU A1002 -23.034 -0.720 -24.674 1.00 32.78 C +ATOM 1788 O LEU A1002 -23.419 -0.645 -23.512 1.00 33.09 O +ATOM 1789 CB LEU A1002 -24.495 -1.945 -26.361 1.00 30.13 C +ATOM 1790 CG LEU A1002 -25.243 -2.859 -25.364 1.00 35.32 C +ATOM 1791 CD1 LEU A1002 -26.559 -2.231 -24.853 1.00 35.94 C +ATOM 1792 CD2 LEU A1002 -25.461 -4.257 -25.970 1.00 37.39 C +ATOM 1793 H LEU A1002 -23.251 -0.204 -27.709 1.00 28.48 H +ATOM 1794 HA LEU A1002 -24.950 -0.077 -25.383 1.00 29.15 H +ATOM 1795 HB3 LEU A1002 -23.607 -2.464 -26.722 1.00 30.13 H +ATOM 1796 HB2 LEU A1002 -25.123 -1.797 -27.238 1.00 30.13 H +ATOM 1797 HG LEU A1002 -24.597 -3.010 -24.498 1.00 35.32 H +ATOM 1798 HD11 LEU A1002 -27.382 -2.945 -24.829 1.00 35.94 H +ATOM 1799 HD12 LEU A1002 -26.434 -1.855 -23.837 1.00 35.94 H +ATOM 1800 HD13 LEU A1002 -26.882 -1.394 -25.473 1.00 35.94 H +ATOM 1801 HD21 LEU A1002 -25.178 -5.038 -25.263 1.00 37.39 H +ATOM 1802 HD22 LEU A1002 -26.499 -4.429 -26.254 1.00 37.39 H +ATOM 1803 HD23 LEU A1002 -24.861 -4.403 -26.868 1.00 37.39 H +ATOM 1804 N PHE A1003 -21.743 -0.871 -25.023 1.00 28.23 N +ATOM 1805 CA PHE A1003 -20.641 -0.882 -24.054 1.00 28.18 C +ATOM 1806 C PHE A1003 -20.492 0.469 -23.336 1.00 29.97 C +ATOM 1807 O PHE A1003 -20.248 0.473 -22.133 1.00 28.15 O +ATOM 1808 CB PHE A1003 -19.312 -1.272 -24.735 1.00 29.26 C +ATOM 1809 CG PHE A1003 -19.273 -2.605 -25.466 1.00 30.64 C +ATOM 1810 CD1 PHE A1003 -20.024 -3.718 -25.024 1.00 35.83 C +ATOM 1811 CD2 PHE A1003 -18.349 -2.783 -26.518 1.00 32.41 C +ATOM 1812 CE1 PHE A1003 -19.919 -4.932 -25.687 1.00 37.36 C +ATOM 1813 CE2 PHE A1003 -18.254 -4.007 -27.165 1.00 35.63 C +ATOM 1814 CZ PHE A1003 -19.051 -5.071 -26.763 1.00 35.58 C +ATOM 1815 H PHE A1003 -21.488 -0.949 -25.998 1.00 28.23 H +ATOM 1816 HA PHE A1003 -20.872 -1.624 -23.287 1.00 28.18 H +ATOM 1817 HB3 PHE A1003 -18.513 -1.280 -23.993 1.00 29.26 H +ATOM 1818 HB2 PHE A1003 -19.040 -0.498 -25.453 1.00 29.26 H +ATOM 1819 HD1 PHE A1003 -20.697 -3.642 -24.184 1.00 35.83 H +ATOM 1820 HD2 PHE A1003 -17.720 -1.964 -26.835 1.00 32.41 H +ATOM 1821 HE1 PHE A1003 -20.509 -5.774 -25.354 1.00 37.36 H +ATOM 1822 HE2 PHE A1003 -17.555 -4.132 -27.978 1.00 35.63 H +ATOM 1823 HZ PHE A1003 -18.975 -6.020 -27.272 1.00 35.58 H +ATOM 1824 N ALA A1004 -20.705 1.578 -24.067 1.00 26.49 N +ATOM 1825 CA ALA A1004 -20.726 2.944 -23.542 1.00 26.36 C +ATOM 1826 C ALA A1004 -21.860 3.187 -22.532 1.00 28.13 C +ATOM 1827 O ALA A1004 -21.618 3.810 -21.500 1.00 27.39 O +ATOM 1828 CB ALA A1004 -20.837 3.926 -24.715 1.00 27.23 C +ATOM 1829 H ALA A1004 -20.890 1.484 -25.057 1.00 26.49 H +ATOM 1830 HA ALA A1004 -19.773 3.115 -23.041 1.00 26.36 H +ATOM 1831 HB1 ALA A1004 -20.637 4.947 -24.390 1.00 27.23 H +ATOM 1832 HB2 ALA A1004 -20.122 3.678 -25.500 1.00 27.23 H +ATOM 1833 HB3 ALA A1004 -21.829 3.918 -25.164 1.00 27.23 H +ATOM 1834 N GLN A1005 -23.060 2.661 -22.839 1.00 24.98 N +ATOM 1835 CA GLN A1005 -24.241 2.670 -21.976 1.00 26.13 C +ATOM 1836 C GLN A1005 -24.005 1.880 -20.676 1.00 30.14 C +ATOM 1837 O GLN A1005 -24.287 2.400 -19.601 1.00 29.23 O +ATOM 1838 CB GLN A1005 -25.433 2.097 -22.770 1.00 27.37 C +ATOM 1839 CG GLN A1005 -26.787 2.123 -22.034 1.00 34.59 C +ATOM 1840 CD GLN A1005 -27.753 1.102 -22.634 1.00 45.52 C +ATOM 1841 OE1 GLN A1005 -28.014 1.121 -23.835 1.00 37.97 O +ATOM 1842 NE2 GLN A1005 -28.296 0.216 -21.798 1.00 40.39 N +ATOM 1843 H GLN A1005 -23.170 2.180 -23.722 1.00 24.98 H +ATOM 1844 HA GLN A1005 -24.463 3.706 -21.713 1.00 26.13 H +ATOM 1845 HB3 GLN A1005 -25.200 1.069 -23.046 1.00 27.37 H +ATOM 1846 HB2 GLN A1005 -25.542 2.624 -23.717 1.00 27.37 H +ATOM 1847 HG3 GLN A1005 -27.223 3.120 -22.095 1.00 34.59 H +ATOM 1848 HG2 GLN A1005 -26.674 1.906 -20.973 1.00 34.59 H +ATOM 1849 HE22 GLN A1005 -28.941 -0.481 -22.142 1.00 40.39 H +ATOM 1850 HE21 GLN A1005 -28.027 0.204 -20.821 1.00 40.39 H +ATOM 1851 N GLN A1006 -23.477 0.652 -20.811 1.00 27.56 N +ATOM 1852 CA GLN A1006 -23.173 -0.270 -19.716 1.00 27.14 C +ATOM 1853 C GLN A1006 -22.072 0.236 -18.768 1.00 28.50 C +ATOM 1854 O GLN A1006 -22.194 0.035 -17.560 1.00 29.35 O +ATOM 1855 CB GLN A1006 -22.838 -1.652 -20.304 1.00 28.20 C +ATOM 1856 CG GLN A1006 -24.071 -2.360 -20.908 1.00 30.44 C +ATOM 1857 CD GLN A1006 -23.732 -3.689 -21.587 1.00 43.69 C +ATOM 1858 OE1 GLN A1006 -22.570 -4.021 -21.806 1.00 40.01 O +ATOM 1859 NE2 GLN A1006 -24.762 -4.459 -21.939 1.00 39.19 N +ATOM 1860 H GLN A1006 -23.289 0.300 -21.741 1.00 27.56 H +ATOM 1861 HA GLN A1006 -24.079 -0.372 -19.117 1.00 27.14 H +ATOM 1862 HB3 GLN A1006 -22.408 -2.289 -19.530 1.00 28.20 H +ATOM 1863 HB2 GLN A1006 -22.063 -1.537 -21.063 1.00 28.20 H +ATOM 1864 HG3 GLN A1006 -24.572 -1.725 -21.635 1.00 30.44 H +ATOM 1865 HG2 GLN A1006 -24.815 -2.535 -20.135 1.00 30.44 H +ATOM 1866 HE22 GLN A1006 -24.602 -5.353 -22.387 1.00 39.19 H +ATOM 1867 HE21 GLN A1006 -25.716 -4.155 -21.777 1.00 39.19 H +ATOM 1868 N ILE A1007 -21.062 0.943 -19.312 1.00 24.53 N +ATOM 1869 CA ILE A1007 -20.069 1.692 -18.536 1.00 23.52 C +ATOM 1870 C ILE A1007 -20.727 2.816 -17.713 1.00 26.94 C +ATOM 1871 O ILE A1007 -20.405 2.941 -16.536 1.00 25.88 O +ATOM 1872 CB ILE A1007 -18.931 2.286 -19.427 1.00 26.23 C +ATOM 1873 CG1 ILE A1007 -18.024 1.167 -19.988 1.00 26.44 C +ATOM 1874 CG2 ILE A1007 -18.040 3.356 -18.749 1.00 26.63 C +ATOM 1875 CD1 ILE A1007 -17.291 1.555 -21.285 1.00 29.19 C +ATOM 1876 H ILE A1007 -21.013 1.046 -20.317 1.00 24.53 H +ATOM 1877 HA ILE A1007 -19.614 0.992 -17.831 1.00 23.52 H +ATOM 1878 HB ILE A1007 -19.417 2.769 -20.276 1.00 26.23 H +ATOM 1879 HG13 ILE A1007 -18.608 0.270 -20.180 1.00 26.44 H +ATOM 1880 HG12 ILE A1007 -17.298 0.870 -19.230 1.00 26.44 H +ATOM 1881 HG21 ILE A1007 -17.216 3.653 -19.397 1.00 26.63 H +ATOM 1882 HG22 ILE A1007 -18.590 4.267 -18.511 1.00 26.63 H +ATOM 1883 HG23 ILE A1007 -17.606 2.974 -17.825 1.00 26.63 H +ATOM 1884 HD11 ILE A1007 -17.143 0.685 -21.925 1.00 29.19 H +ATOM 1885 HD12 ILE A1007 -17.847 2.290 -21.866 1.00 29.19 H +ATOM 1886 HD13 ILE A1007 -16.313 1.980 -21.071 1.00 29.19 H +ATOM 1887 N CYS A1008 -21.665 3.570 -18.316 1.00 24.96 N +ATOM 1888 CA CYS A1008 -22.405 4.643 -17.644 1.00 26.23 C +ATOM 1889 C CYS A1008 -23.328 4.144 -16.518 1.00 28.78 C +ATOM 1890 O CYS A1008 -23.468 4.853 -15.526 1.00 27.82 O +ATOM 1891 CB CYS A1008 -23.199 5.533 -18.619 1.00 27.44 C +ATOM 1892 SG CYS A1008 -22.060 6.457 -19.678 1.00 31.30 S +ATOM 1893 H CYS A1008 -21.894 3.406 -19.288 1.00 24.96 H +ATOM 1894 HA CYS A1008 -21.653 5.263 -17.158 1.00 26.23 H +ATOM 1895 HB3 CYS A1008 -23.802 6.261 -18.076 1.00 27.44 H +ATOM 1896 HB2 CYS A1008 -23.879 4.950 -19.240 1.00 27.44 H +ATOM 1897 HG CYS A1008 -21.658 5.407 -20.404 1.00 31.30 H +ATOM 1898 N GLU A1009 -23.905 2.938 -16.664 1.00 27.13 N +ATOM 1899 CA GLU A1009 -24.712 2.274 -15.636 1.00 27.79 C +ATOM 1900 C GLU A1009 -23.884 1.789 -14.430 1.00 31.65 C +ATOM 1901 O GLU A1009 -24.361 1.902 -13.301 1.00 32.67 O +ATOM 1902 CB GLU A1009 -25.530 1.130 -16.269 1.00 29.33 C +ATOM 1903 CG GLU A1009 -26.650 1.632 -17.202 1.00 35.16 C +ATOM 1904 CD GLU A1009 -27.432 0.501 -17.874 1.00 58.21 C +ATOM 1905 OE1 GLU A1009 -26.942 -0.003 -18.909 1.00 48.70 O +ATOM 1906 OE2 GLU A1009 -28.529 0.175 -17.369 1.00 60.77 O1- +ATOM 1907 H GLU A1009 -23.769 2.425 -17.524 1.00 27.13 H +ATOM 1908 HA GLU A1009 -25.420 3.006 -15.244 1.00 27.79 H +ATOM 1909 HB3 GLU A1009 -25.961 0.497 -15.492 1.00 29.33 H +ATOM 1910 HB2 GLU A1009 -24.857 0.492 -16.843 1.00 29.33 H +ATOM 1911 HG3 GLU A1009 -26.235 2.264 -17.984 1.00 35.16 H +ATOM 1912 HG2 GLU A1009 -27.341 2.262 -16.641 1.00 35.16 H +ATOM 1913 N GLY A1010 -22.657 1.295 -14.679 1.00 27.84 N +ATOM 1914 CA GLY A1010 -21.732 0.852 -13.634 1.00 27.29 C +ATOM 1915 C GLY A1010 -21.077 2.043 -12.915 1.00 30.56 C +ATOM 1916 O GLY A1010 -20.854 1.969 -11.707 1.00 30.70 O +ATOM 1917 H GLY A1010 -22.337 1.226 -15.636 1.00 27.84 H +ATOM 1918 HA3 GLY A1010 -20.956 0.239 -14.093 1.00 27.29 H +ATOM 1919 HA2 GLY A1010 -22.247 0.220 -12.909 1.00 27.29 H +ATOM 1920 N MET A1011 -20.798 3.140 -13.643 1.00 27.80 N +ATOM 1921 CA MET A1011 -20.230 4.381 -13.113 1.00 26.42 C +ATOM 1922 C MET A1011 -21.251 5.243 -12.348 1.00 31.30 C +ATOM 1923 O MET A1011 -20.854 5.901 -11.387 1.00 31.07 O +ATOM 1924 CB MET A1011 -19.591 5.196 -14.253 1.00 27.40 C +ATOM 1925 CG MET A1011 -18.266 4.634 -14.804 1.00 29.13 C +ATOM 1926 SD MET A1011 -16.888 4.453 -13.634 1.00 32.16 S +ATOM 1927 CE MET A1011 -16.919 6.084 -12.860 1.00 29.76 C +ATOM 1928 H MET A1011 -20.981 3.129 -14.638 1.00 27.80 H +ATOM 1929 HA MET A1011 -19.451 4.119 -12.397 1.00 26.42 H +ATOM 1930 HB3 MET A1011 -19.416 6.218 -13.918 1.00 27.40 H +ATOM 1931 HB2 MET A1011 -20.304 5.291 -15.073 1.00 27.40 H +ATOM 1932 HG3 MET A1011 -17.929 5.278 -15.615 1.00 29.13 H +ATOM 1933 HG2 MET A1011 -18.424 3.657 -15.250 1.00 29.13 H +ATOM 1934 HE1 MET A1011 -15.990 6.273 -12.328 1.00 29.76 H +ATOM 1935 HE2 MET A1011 -17.055 6.852 -13.618 1.00 29.76 H +ATOM 1936 HE3 MET A1011 -17.742 6.148 -12.148 1.00 29.76 H +ATOM 1937 N ALA A1012 -22.535 5.211 -12.749 1.00 27.75 N +ATOM 1938 CA ALA A1012 -23.634 5.849 -12.016 1.00 28.69 C +ATOM 1939 C ALA A1012 -23.888 5.180 -10.659 1.00 34.11 C +ATOM 1940 O ALA A1012 -24.101 5.891 -9.678 1.00 34.50 O +ATOM 1941 CB ALA A1012 -24.917 5.843 -12.856 1.00 29.26 C +ATOM 1942 H ALA A1012 -22.793 4.685 -13.573 1.00 27.75 H +ATOM 1943 HA ALA A1012 -23.360 6.890 -11.829 1.00 28.69 H +ATOM 1944 HB1 ALA A1012 -25.767 6.222 -12.287 1.00 29.26 H +ATOM 1945 HB2 ALA A1012 -24.815 6.478 -13.734 1.00 29.26 H +ATOM 1946 HB3 ALA A1012 -25.167 4.837 -13.194 1.00 29.26 H +ATOM 1947 N TYR A1013 -23.802 3.837 -10.621 1.00 31.58 N +ATOM 1948 CA TYR A1013 -23.833 3.052 -9.389 1.00 32.97 C +ATOM 1949 C TYR A1013 -22.647 3.367 -8.458 1.00 36.19 C +ATOM 1950 O TYR A1013 -22.873 3.547 -7.265 1.00 37.27 O +ATOM 1951 CB TYR A1013 -23.952 1.546 -9.717 1.00 34.46 C +ATOM 1952 CG TYR A1013 -23.724 0.624 -8.528 1.00 37.28 C +ATOM 1953 CD1 TYR A1013 -24.733 0.447 -7.560 1.00 41.29 C +ATOM 1954 CD2 TYR A1013 -22.475 -0.004 -8.357 1.00 37.10 C +ATOM 1955 CE1 TYR A1013 -24.484 -0.335 -6.415 1.00 41.87 C +ATOM 1956 CE2 TYR A1013 -22.228 -0.795 -7.220 1.00 38.57 C +ATOM 1957 CZ TYR A1013 -23.227 -0.947 -6.240 1.00 45.11 C +ATOM 1958 OH TYR A1013 -22.971 -1.682 -5.119 1.00 46.37 O +ATOM 1959 H TYR A1013 -23.632 3.324 -11.475 1.00 31.58 H +ATOM 1960 HA TYR A1013 -24.744 3.336 -8.858 1.00 32.97 H +ATOM 1961 HB3 TYR A1013 -23.229 1.281 -10.489 1.00 34.46 H +ATOM 1962 HB2 TYR A1013 -24.932 1.339 -10.149 1.00 34.46 H +ATOM 1963 HD1 TYR A1013 -25.691 0.933 -7.679 1.00 41.29 H +ATOM 1964 HD2 TYR A1013 -21.699 0.134 -9.093 1.00 37.10 H +ATOM 1965 HE1 TYR A1013 -25.254 -0.453 -5.668 1.00 41.87 H +ATOM 1966 HE2 TYR A1013 -21.271 -1.279 -7.103 1.00 38.57 H +ATOM 1967 HH TYR A1013 -23.715 -1.718 -4.512 1.00 46.37 H +ATOM 1968 N LEU A1014 -21.426 3.458 -9.016 1.00 29.93 N +ATOM 1969 CA LEU A1014 -20.195 3.785 -8.288 1.00 29.88 C +ATOM 1970 C LEU A1014 -20.270 5.149 -7.570 1.00 33.60 C +ATOM 1971 O LEU A1014 -19.846 5.249 -6.419 1.00 34.49 O +ATOM 1972 CB LEU A1014 -19.001 3.715 -9.269 1.00 29.28 C +ATOM 1973 CG LEU A1014 -17.604 3.885 -8.634 1.00 33.67 C +ATOM 1974 CD1 LEU A1014 -17.247 2.688 -7.739 1.00 34.68 C +ATOM 1975 CD2 LEU A1014 -16.526 4.121 -9.705 1.00 34.91 C +ATOM 1976 H LEU A1014 -21.321 3.285 -10.007 1.00 29.93 H +ATOM 1977 HA LEU A1014 -20.068 3.014 -7.526 1.00 29.88 H +ATOM 1978 HB3 LEU A1014 -19.131 4.492 -10.023 1.00 29.28 H +ATOM 1979 HB2 LEU A1014 -19.028 2.770 -9.814 1.00 29.28 H +ATOM 1980 HG LEU A1014 -17.600 4.778 -8.012 1.00 33.67 H +ATOM 1981 HD11 LEU A1014 -16.188 2.679 -7.476 1.00 34.68 H +ATOM 1982 HD12 LEU A1014 -17.808 2.711 -6.806 1.00 34.68 H +ATOM 1983 HD13 LEU A1014 -17.477 1.744 -8.233 1.00 34.68 H +ATOM 1984 HD21 LEU A1014 -15.644 3.497 -9.557 1.00 34.91 H +ATOM 1985 HD22 LEU A1014 -16.898 3.915 -10.707 1.00 34.91 H +ATOM 1986 HD23 LEU A1014 -16.193 5.159 -9.688 1.00 34.91 H +ATOM 1987 N HIS A1015 -20.849 6.150 -8.253 1.00 30.39 N +ATOM 1988 CA HIS A1015 -21.057 7.508 -7.748 1.00 30.40 C +ATOM 1989 C HIS A1015 -22.203 7.615 -6.730 1.00 37.10 C +ATOM 1990 O HIS A1015 -22.134 8.480 -5.858 1.00 37.94 O +ATOM 1991 CB HIS A1015 -21.294 8.459 -8.934 1.00 29.83 C +ATOM 1992 CG HIS A1015 -20.101 8.684 -9.828 1.00 31.72 C +ATOM 1993 ND1 HIS A1015 -20.082 9.715 -10.769 1.00 32.83 N +ATOM 1994 CD2 HIS A1015 -18.898 8.007 -9.891 1.00 32.40 C +ATOM 1995 CE1 HIS A1015 -18.890 9.629 -11.339 1.00 31.99 C +ATOM 1996 NE2 HIS A1015 -18.143 8.644 -10.852 1.00 31.94 N +ATOM 1997 H HIS A1015 -21.169 5.978 -9.197 1.00 30.39 H +ATOM 1998 HA HIS A1015 -20.152 7.828 -7.229 1.00 30.40 H +ATOM 1999 HB3 HIS A1015 -21.608 9.438 -8.569 1.00 29.83 H +ATOM 2000 HB2 HIS A1015 -22.112 8.089 -9.554 1.00 29.83 H +ATOM 2001 HD2 HIS A1015 -18.524 7.155 -9.345 1.00 32.40 H +ATOM 2002 HE1 HIS A1015 -18.573 10.275 -12.138 1.00 31.99 H +ATOM 2003 HE2 HIS A1015 -17.190 8.424 -11.119 1.00 31.94 H +ATOM 2004 N ALA A1016 -23.211 6.730 -6.834 1.00 34.33 N +ATOM 2005 CA ALA A1016 -24.292 6.585 -5.856 1.00 35.70 C +ATOM 2006 C ALA A1016 -23.821 5.922 -4.546 1.00 40.39 C +ATOM 2007 O ALA A1016 -24.360 6.244 -3.489 1.00 42.00 O +ATOM 2008 CB ALA A1016 -25.451 5.800 -6.489 1.00 36.70 C +ATOM 2009 H ALA A1016 -23.210 6.064 -7.594 1.00 34.33 H +ATOM 2010 HA ALA A1016 -24.662 7.581 -5.608 1.00 35.70 H +ATOM 2011 HB1 ALA A1016 -26.291 5.712 -5.799 1.00 36.70 H +ATOM 2012 HB2 ALA A1016 -25.818 6.300 -7.386 1.00 36.70 H +ATOM 2013 HB3 ALA A1016 -25.152 4.791 -6.772 1.00 36.70 H +ATOM 2014 N GLN A1017 -22.787 5.064 -4.635 1.00 37.13 N +ATOM 2015 CA GLN A1017 -22.036 4.512 -3.502 1.00 37.34 C +ATOM 2016 C GLN A1017 -20.962 5.483 -2.955 1.00 40.83 C +ATOM 2017 O GLN A1017 -20.257 5.120 -2.013 1.00 41.85 O +ATOM 2018 CB GLN A1017 -21.413 3.155 -3.915 1.00 37.57 C +ATOM 2019 CG GLN A1017 -22.410 2.027 -4.261 1.00 50.69 C +ATOM 2020 CD GLN A1017 -23.428 1.735 -3.159 1.00 63.42 C +ATOM 2021 OE1 GLN A1017 -23.055 1.428 -2.030 1.00 61.45 O +ATOM 2022 NE2 GLN A1017 -24.719 1.815 -3.487 1.00 53.50 N +ATOM 2023 H GLN A1017 -22.426 4.834 -5.551 1.00 37.13 H +ATOM 2024 HA GLN A1017 -22.727 4.347 -2.674 1.00 37.34 H +ATOM 2025 HB3 GLN A1017 -20.770 2.791 -3.113 1.00 37.57 H +ATOM 2026 HB2 GLN A1017 -20.758 3.304 -4.773 1.00 37.57 H +ATOM 2027 HG3 GLN A1017 -21.859 1.109 -4.466 1.00 50.69 H +ATOM 2028 HG2 GLN A1017 -22.944 2.264 -5.177 1.00 50.69 H +ATOM 2029 HE22 GLN A1017 -25.427 1.631 -2.791 1.00 53.50 H +ATOM 2030 HE21 GLN A1017 -24.995 2.075 -4.422 1.00 53.50 H +ATOM 2031 N HIS A1018 -20.868 6.694 -3.535 1.00 43.65 N +ATOM 2032 CA HIS A1018 -19.987 7.804 -3.159 1.00 42.72 C +ATOM 2033 C HIS A1018 -18.485 7.544 -3.385 1.00 42.39 C +ATOM 2034 O HIS A1018 -17.658 8.063 -2.636 1.00 42.28 O +ATOM 2035 CB HIS A1018 -20.325 8.353 -1.751 1.00 46.64 C +ATOM 2036 CG HIS A1018 -21.690 8.984 -1.654 1.00 52.37 C +ATOM 2037 ND1 HIS A1018 -21.945 10.278 -2.076 1.00 54.35 N +ATOM 2038 CD2 HIS A1018 -22.888 8.509 -1.166 1.00 56.51 C +ATOM 2039 CE1 HIS A1018 -23.236 10.521 -1.839 1.00 55.92 C +ATOM 2040 NE2 HIS A1018 -23.871 9.493 -1.289 1.00 57.64 N +ATOM 2041 H HIS A1018 -21.499 6.901 -4.297 1.00 43.65 H +ATOM 2042 HA HIS A1018 -20.228 8.589 -3.873 1.00 42.72 H +ATOM 2043 HB3 HIS A1018 -19.613 9.122 -1.457 1.00 46.64 H +ATOM 2044 HB2 HIS A1018 -20.245 7.572 -0.994 1.00 46.64 H +ATOM 2045 HD1 HIS A1018 -21.281 10.930 -2.475 1.00 54.35 H +ATOM 2046 HD2 HIS A1018 -23.116 7.542 -0.744 1.00 56.51 H +ATOM 2047 HE1 HIS A1018 -23.715 11.462 -2.067 1.00 55.92 H +ATOM 2048 N TYR A1019 -18.162 6.786 -4.443 1.00 35.47 N +ATOM 2049 CA TYR A1019 -16.796 6.540 -4.899 1.00 33.30 C +ATOM 2050 C TYR A1019 -16.560 7.203 -6.265 1.00 34.14 C +ATOM 2051 O TYR A1019 -17.489 7.334 -7.060 1.00 33.14 O +ATOM 2052 CB TYR A1019 -16.557 5.022 -5.022 1.00 34.28 C +ATOM 2053 CG TYR A1019 -16.661 4.215 -3.739 1.00 38.78 C +ATOM 2054 CD1 TYR A1019 -15.668 4.330 -2.746 1.00 41.85 C +ATOM 2055 CD2 TYR A1019 -17.732 3.318 -3.549 1.00 40.74 C +ATOM 2056 CE1 TYR A1019 -15.753 3.568 -1.566 1.00 44.99 C +ATOM 2057 CE2 TYR A1019 -17.808 2.539 -2.378 1.00 43.80 C +ATOM 2058 CZ TYR A1019 -16.817 2.663 -1.385 1.00 51.67 C +ATOM 2059 OH TYR A1019 -16.885 1.906 -0.251 1.00 54.79 O +ATOM 2060 H TYR A1019 -18.894 6.392 -5.020 1.00 35.47 H +ATOM 2061 HA TYR A1019 -16.081 6.951 -4.188 1.00 33.30 H +ATOM 2062 HB3 TYR A1019 -15.565 4.835 -5.438 1.00 34.28 H +ATOM 2063 HB2 TYR A1019 -17.263 4.613 -5.740 1.00 34.28 H +ATOM 2064 HD1 TYR A1019 -14.835 4.996 -2.893 1.00 41.85 H +ATOM 2065 HD2 TYR A1019 -18.492 3.220 -4.308 1.00 40.74 H +ATOM 2066 HE1 TYR A1019 -14.987 3.663 -0.812 1.00 44.99 H +ATOM 2067 HE2 TYR A1019 -18.625 1.845 -2.245 1.00 43.80 H +ATOM 2068 HH TYR A1019 -16.158 2.067 0.354 1.00 54.79 H +ATOM 2069 N ILE A1020 -15.289 7.536 -6.521 1.00 29.77 N +ATOM 2070 CA ILE A1020 -14.732 7.899 -7.826 1.00 28.07 C +ATOM 2071 C ILE A1020 -13.745 6.798 -8.262 1.00 32.77 C +ATOM 2072 O ILE A1020 -13.145 6.156 -7.399 1.00 34.18 O +ATOM 2073 CB ILE A1020 -13.975 9.261 -7.762 1.00 30.74 C +ATOM 2074 CG1 ILE A1020 -12.981 9.361 -6.573 1.00 31.83 C +ATOM 2075 CG2 ILE A1020 -14.990 10.419 -7.776 1.00 31.74 C +ATOM 2076 CD1 ILE A1020 -12.103 10.614 -6.545 1.00 34.37 C +ATOM 2077 H ILE A1020 -14.600 7.396 -5.793 1.00 29.77 H +ATOM 2078 HA ILE A1020 -15.529 7.954 -8.569 1.00 28.07 H +ATOM 2079 HB ILE A1020 -13.387 9.362 -8.676 1.00 30.74 H +ATOM 2080 HG13 ILE A1020 -12.317 8.498 -6.563 1.00 31.83 H +ATOM 2081 HG12 ILE A1020 -13.528 9.323 -5.635 1.00 31.83 H +ATOM 2082 HG21 ILE A1020 -14.504 11.393 -7.804 1.00 31.74 H +ATOM 2083 HG22 ILE A1020 -15.631 10.362 -8.656 1.00 31.74 H +ATOM 2084 HG23 ILE A1020 -15.638 10.391 -6.901 1.00 31.74 H +ATOM 2085 HD11 ILE A1020 -11.396 10.561 -5.718 1.00 34.37 H +ATOM 2086 HD12 ILE A1020 -11.523 10.701 -7.459 1.00 34.37 H +ATOM 2087 HD13 ILE A1020 -12.688 11.525 -6.418 1.00 34.37 H +ATOM 2088 N HIS A1021 -13.614 6.578 -9.582 1.00 29.57 N +ATOM 2089 CA HIS A1021 -12.786 5.511 -10.155 1.00 28.01 C +ATOM 2090 C HIS A1021 -11.322 5.937 -10.347 1.00 30.31 C +ATOM 2091 O HIS A1021 -10.436 5.175 -9.969 1.00 28.41 O +ATOM 2092 CB HIS A1021 -13.403 5.013 -11.479 1.00 27.19 C +ATOM 2093 CG HIS A1021 -12.853 3.693 -11.959 1.00 29.30 C +ATOM 2094 ND1 HIS A1021 -11.667 3.594 -12.689 1.00 30.35 N +ATOM 2095 CD2 HIS A1021 -13.363 2.425 -11.774 1.00 30.30 C +ATOM 2096 CE1 HIS A1021 -11.504 2.297 -12.912 1.00 29.49 C +ATOM 2097 NE2 HIS A1021 -12.474 1.556 -12.382 1.00 29.77 N +ATOM 2098 H HIS A1021 -14.123 7.159 -10.238 1.00 29.57 H +ATOM 2099 HA HIS A1021 -12.801 4.668 -9.461 1.00 28.01 H +ATOM 2100 HB3 HIS A1021 -13.282 5.753 -12.271 1.00 27.19 H +ATOM 2101 HB2 HIS A1021 -14.474 4.880 -11.356 1.00 27.19 H +ATOM 2102 HD2 HIS A1021 -14.255 2.085 -11.273 1.00 30.30 H +ATOM 2103 HE1 HIS A1021 -10.667 1.883 -13.456 1.00 29.49 H +ATOM 2104 HE2 HIS A1021 -12.538 0.547 -12.425 1.00 29.77 H +ATOM 2105 N ARG A1022 -11.096 7.121 -10.943 1.00 26.88 N +ATOM 2106 CA ARG A1022 -9.784 7.722 -11.235 1.00 26.62 C +ATOM 2107 C ARG A1022 -8.908 6.986 -12.278 1.00 29.84 C +ATOM 2108 O ARG A1022 -7.774 7.416 -12.489 1.00 30.79 O +ATOM 2109 CB ARG A1022 -8.980 7.994 -9.941 1.00 26.30 C +ATOM 2110 CG ARG A1022 -9.678 8.917 -8.919 1.00 34.16 C +ATOM 2111 CD ARG A1022 -8.867 9.205 -7.638 1.00 40.37 C +ATOM 2112 NE ARG A1022 -7.494 9.662 -7.927 1.00 53.37 N +ATOM 2113 CZ ARG A1022 -6.339 9.249 -7.370 1.00 66.01 C +ATOM 2114 NH1 ARG A1022 -6.301 8.376 -6.352 1.00 50.08 N +ATOM 2115 NH2 ARG A1022 -5.186 9.718 -7.861 1.00 58.24 N1+ +ATOM 2116 H ARG A1022 -11.894 7.664 -11.248 1.00 26.88 H +ATOM 2117 HA ARG A1022 -10.003 8.685 -11.691 1.00 26.62 H +ATOM 2118 HB3 ARG A1022 -8.038 8.461 -10.224 1.00 26.30 H +ATOM 2119 HB2 ARG A1022 -8.703 7.055 -9.468 1.00 26.30 H +ATOM 2120 HG3 ARG A1022 -10.571 8.380 -8.599 1.00 34.16 H +ATOM 2121 HG2 ARG A1022 -10.035 9.838 -9.374 1.00 34.16 H +ATOM 2122 HD3 ARG A1022 -8.962 8.416 -6.896 1.00 40.37 H +ATOM 2123 HD2 ARG A1022 -9.304 10.086 -7.170 1.00 40.37 H +ATOM 2124 HE ARG A1022 -7.428 10.323 -8.688 1.00 53.37 H +ATOM 2125 HH12 ARG A1022 -5.420 8.065 -5.971 1.00 50.08 H +ATOM 2126 HH11 ARG A1022 -7.160 7.948 -6.033 1.00 50.08 H +ATOM 2127 HH22 ARG A1022 -4.301 9.402 -7.489 1.00 58.24 H +ATOM 2128 HH21 ARG A1022 -5.185 10.345 -8.654 1.00 58.24 H +ATOM 2129 N ASN A1023 -9.418 5.931 -12.935 1.00 25.28 N +ATOM 2130 CA ASN A1023 -8.672 5.162 -13.934 1.00 24.95 C +ATOM 2131 C ASN A1023 -9.615 4.581 -15.005 1.00 27.49 C +ATOM 2132 O ASN A1023 -9.413 3.447 -15.436 1.00 27.32 O +ATOM 2133 CB ASN A1023 -7.816 4.068 -13.232 1.00 27.86 C +ATOM 2134 CG ASN A1023 -6.588 3.573 -14.014 1.00 46.94 C +ATOM 2135 OD1 ASN A1023 -6.243 4.089 -15.076 1.00 37.71 O +ATOM 2136 ND2 ASN A1023 -5.925 2.544 -13.483 1.00 47.99 N +ATOM 2137 H ASN A1023 -10.358 5.614 -12.735 1.00 25.28 H +ATOM 2138 HA ASN A1023 -8.030 5.821 -14.520 1.00 24.95 H +ATOM 2139 HB3 ASN A1023 -8.437 3.219 -12.941 1.00 27.86 H +ATOM 2140 HB2 ASN A1023 -7.417 4.470 -12.303 1.00 27.86 H +ATOM 2141 HD22 ASN A1023 -5.119 2.161 -13.957 1.00 47.99 H +ATOM 2142 HD21 ASN A1023 -6.237 2.118 -12.622 1.00 47.99 H +ATOM 2143 N LEU A1024 -10.648 5.336 -15.411 1.00 23.70 N +ATOM 2144 CA LEU A1024 -11.619 4.879 -16.405 1.00 23.85 C +ATOM 2145 C LEU A1024 -11.026 4.992 -17.823 1.00 29.77 C +ATOM 2146 O LEU A1024 -10.781 6.099 -18.301 1.00 30.30 O +ATOM 2147 CB LEU A1024 -12.947 5.643 -16.212 1.00 23.60 C +ATOM 2148 CG LEU A1024 -14.133 5.123 -17.053 1.00 28.86 C +ATOM 2149 CD1 LEU A1024 -14.514 3.664 -16.722 1.00 29.18 C +ATOM 2150 CD2 LEU A1024 -15.338 6.057 -16.884 1.00 32.57 C +ATOM 2151 H LEU A1024 -10.782 6.267 -15.035 1.00 23.70 H +ATOM 2152 HA LEU A1024 -11.821 3.825 -16.203 1.00 23.85 H +ATOM 2153 HB3 LEU A1024 -12.786 6.698 -16.431 1.00 23.60 H +ATOM 2154 HB2 LEU A1024 -13.227 5.603 -15.157 1.00 23.60 H +ATOM 2155 HG LEU A1024 -13.855 5.162 -18.107 1.00 28.86 H +ATOM 2156 HD11 LEU A1024 -15.581 3.551 -16.533 1.00 29.18 H +ATOM 2157 HD12 LEU A1024 -14.259 2.998 -17.547 1.00 29.18 H +ATOM 2158 HD13 LEU A1024 -14.004 3.295 -15.832 1.00 29.18 H +ATOM 2159 HD21 LEU A1024 -16.218 5.670 -17.397 1.00 32.57 H +ATOM 2160 HD22 LEU A1024 -15.592 6.194 -15.833 1.00 32.57 H +ATOM 2161 HD23 LEU A1024 -15.118 7.041 -17.292 1.00 32.57 H +ATOM 2162 N ALA A1025 -10.781 3.825 -18.437 1.00 25.59 N +ATOM 2163 CA ALA A1025 -10.140 3.647 -19.741 1.00 24.45 C +ATOM 2164 C ALA A1025 -10.518 2.265 -20.300 1.00 24.35 C +ATOM 2165 O ALA A1025 -10.969 1.410 -19.538 1.00 23.60 O +ATOM 2166 CB ALA A1025 -8.617 3.790 -19.578 1.00 25.60 C +ATOM 2167 H ALA A1025 -11.033 2.965 -17.971 1.00 25.59 H +ATOM 2168 HA ALA A1025 -10.493 4.413 -20.432 1.00 24.45 H +ATOM 2169 HB1 ALA A1025 -8.133 3.871 -20.549 1.00 25.60 H +ATOM 2170 HB2 ALA A1025 -8.354 4.689 -19.019 1.00 25.60 H +ATOM 2171 HB3 ALA A1025 -8.180 2.936 -19.064 1.00 25.60 H +ATOM 2172 N ALA A1026 -10.313 2.052 -21.613 1.00 21.93 N +ATOM 2173 CA ALA A1026 -10.616 0.790 -22.307 1.00 22.59 C +ATOM 2174 C ALA A1026 -9.798 -0.431 -21.830 1.00 27.08 C +ATOM 2175 O ALA A1026 -10.290 -1.554 -21.927 1.00 26.36 O +ATOM 2176 CB ALA A1026 -10.453 0.991 -23.819 1.00 24.44 C +ATOM 2177 H ALA A1026 -9.928 2.793 -22.186 1.00 21.93 H +ATOM 2178 HA ALA A1026 -11.668 0.563 -22.117 1.00 22.59 H +ATOM 2179 HB1 ALA A1026 -10.733 0.090 -24.365 1.00 24.44 H +ATOM 2180 HB2 ALA A1026 -11.089 1.800 -24.179 1.00 24.44 H +ATOM 2181 HB3 ALA A1026 -9.423 1.233 -24.083 1.00 24.44 H +ATOM 2182 N ARG A1027 -8.591 -0.185 -21.289 1.00 25.25 N +ATOM 2183 CA ARG A1027 -7.742 -1.139 -20.565 1.00 25.45 C +ATOM 2184 C ARG A1027 -8.425 -1.710 -19.307 1.00 28.29 C +ATOM 2185 O ARG A1027 -8.223 -2.878 -18.976 1.00 27.42 O +ATOM 2186 CB ARG A1027 -6.432 -0.403 -20.184 1.00 27.95 C +ATOM 2187 CG ARG A1027 -5.435 -1.193 -19.307 1.00 42.70 C +ATOM 2188 CD ARG A1027 -4.162 -0.408 -18.947 1.00 56.67 C +ATOM 2189 NE ARG A1027 -3.234 -0.266 -20.085 1.00 69.04 N +ATOM 2190 CZ ARG A1027 -2.191 0.580 -20.180 1.00 82.45 C +ATOM 2191 NH1 ARG A1027 -1.417 0.527 -21.271 1.00 58.12 N +ATOM 2192 NH2 ARG A1027 -1.893 1.468 -19.218 1.00 78.84 N1+ +ATOM 2193 H ARG A1027 -8.243 0.766 -21.316 1.00 25.25 H +ATOM 2194 HA ARG A1027 -7.506 -1.970 -21.231 1.00 25.45 H +ATOM 2195 HB3 ARG A1027 -6.693 0.509 -19.645 1.00 27.95 H +ATOM 2196 HB2 ARG A1027 -5.932 -0.070 -21.091 1.00 27.95 H +ATOM 2197 HG3 ARG A1027 -5.149 -2.068 -19.890 1.00 42.70 H +ATOM 2198 HG2 ARG A1027 -5.880 -1.580 -18.390 1.00 42.70 H +ATOM 2199 HD3 ARG A1027 -3.675 -0.813 -18.060 1.00 56.67 H +ATOM 2200 HD2 ARG A1027 -4.467 0.608 -18.695 1.00 56.67 H +ATOM 2201 HE ARG A1027 -3.396 -0.904 -20.850 1.00 69.04 H +ATOM 2202 HH12 ARG A1027 -0.649 1.172 -21.381 1.00 58.12 H +ATOM 2203 HH11 ARG A1027 -1.615 -0.134 -22.007 1.00 58.12 H +ATOM 2204 HH22 ARG A1027 -1.107 2.094 -19.316 1.00 78.84 H +ATOM 2205 HH21 ARG A1027 -2.462 1.513 -18.385 1.00 78.84 H +ATOM 2206 N ASN A1028 -9.194 -0.846 -18.632 1.00 25.48 N +ATOM 2207 CA ASN A1028 -9.823 -1.077 -17.334 1.00 25.69 C +ATOM 2208 C ASN A1028 -11.317 -1.429 -17.473 1.00 31.11 C +ATOM 2209 O ASN A1028 -11.999 -1.498 -16.454 1.00 31.65 O +ATOM 2210 CB ASN A1028 -9.602 0.176 -16.449 1.00 25.12 C +ATOM 2211 CG ASN A1028 -8.115 0.495 -16.245 1.00 40.47 C +ATOM 2212 OD1 ASN A1028 -7.402 -0.253 -15.588 1.00 35.79 O +ATOM 2213 ND2 ASN A1028 -7.627 1.597 -16.814 1.00 30.20 N +ATOM 2214 H ASN A1028 -9.333 0.079 -19.016 1.00 25.48 H +ATOM 2215 HA ASN A1028 -9.355 -1.930 -16.841 1.00 25.69 H +ATOM 2216 HB3 ASN A1028 -10.038 0.026 -15.461 1.00 25.12 H +ATOM 2217 HB2 ASN A1028 -10.111 1.042 -16.878 1.00 25.12 H +ATOM 2218 HD22 ASN A1028 -6.658 1.847 -16.685 1.00 30.20 H +ATOM 2219 HD21 ASN A1028 -8.239 2.229 -17.310 1.00 30.20 H +ATOM 2220 N VAL A1029 -11.798 -1.668 -18.706 1.00 26.95 N +ATOM 2221 CA VAL A1029 -13.139 -2.175 -18.986 1.00 26.70 C +ATOM 2222 C VAL A1029 -12.988 -3.520 -19.714 1.00 31.33 C +ATOM 2223 O VAL A1029 -12.373 -3.583 -20.778 1.00 31.48 O +ATOM 2224 CB VAL A1029 -13.976 -1.193 -19.852 1.00 30.65 C +ATOM 2225 CG1 VAL A1029 -15.338 -1.776 -20.277 1.00 31.45 C +ATOM 2226 CG2 VAL A1029 -14.220 0.131 -19.105 1.00 29.49 C +ATOM 2227 H VAL A1029 -11.183 -1.583 -19.503 1.00 26.95 H +ATOM 2228 HA VAL A1029 -13.684 -2.346 -18.056 1.00 26.70 H +ATOM 2229 HB VAL A1029 -13.412 -0.960 -20.757 1.00 30.65 H +ATOM 2230 HG11 VAL A1029 -15.924 -1.049 -20.837 1.00 31.45 H +ATOM 2231 HG12 VAL A1029 -15.230 -2.654 -20.910 1.00 31.45 H +ATOM 2232 HG13 VAL A1029 -15.921 -2.070 -19.403 1.00 31.45 H +ATOM 2233 HG21 VAL A1029 -14.744 0.849 -19.734 1.00 29.49 H +ATOM 2234 HG22 VAL A1029 -14.822 -0.027 -18.209 1.00 29.49 H +ATOM 2235 HG23 VAL A1029 -13.291 0.602 -18.790 1.00 29.49 H +ATOM 2236 N LEU A1030 -13.548 -4.562 -19.088 1.00 29.26 N +ATOM 2237 CA LEU A1030 -13.434 -5.968 -19.450 1.00 29.87 C +ATOM 2238 C LEU A1030 -14.733 -6.447 -20.113 1.00 34.28 C +ATOM 2239 O LEU A1030 -15.816 -6.037 -19.705 1.00 33.78 O +ATOM 2240 CB LEU A1030 -13.138 -6.783 -18.169 1.00 30.51 C +ATOM 2241 CG LEU A1030 -11.936 -6.290 -17.328 1.00 34.35 C +ATOM 2242 CD1 LEU A1030 -11.856 -7.064 -16.001 1.00 34.54 C +ATOM 2243 CD2 LEU A1030 -10.610 -6.344 -18.112 1.00 35.67 C +ATOM 2244 H LEU A1030 -14.085 -4.388 -18.246 1.00 29.26 H +ATOM 2245 HA LEU A1030 -12.621 -6.109 -20.154 1.00 29.87 H +ATOM 2246 HB3 LEU A1030 -12.970 -7.825 -18.443 1.00 30.51 H +ATOM 2247 HB2 LEU A1030 -14.023 -6.771 -17.529 1.00 30.51 H +ATOM 2248 HG LEU A1030 -12.099 -5.248 -17.048 1.00 34.35 H +ATOM 2249 HD11 LEU A1030 -10.853 -7.056 -15.576 1.00 34.54 H +ATOM 2250 HD12 LEU A1030 -12.517 -6.617 -15.257 1.00 34.54 H +ATOM 2251 HD13 LEU A1030 -12.155 -8.106 -16.124 1.00 34.54 H +ATOM 2252 HD21 LEU A1030 -9.796 -6.774 -17.533 1.00 35.67 H +ATOM 2253 HD22 LEU A1030 -10.700 -6.943 -19.016 1.00 35.67 H +ATOM 2254 HD23 LEU A1030 -10.295 -5.343 -18.409 1.00 35.67 H +ATOM 2255 N LEU A1031 -14.593 -7.319 -21.118 1.00 30.49 N +ATOM 2256 CA LEU A1031 -15.677 -7.865 -21.925 1.00 32.51 C +ATOM 2257 C LEU A1031 -15.990 -9.291 -21.448 1.00 35.33 C +ATOM 2258 O LEU A1031 -15.113 -10.153 -21.506 1.00 35.33 O +ATOM 2259 CB LEU A1031 -15.209 -7.845 -23.396 1.00 33.91 C +ATOM 2260 CG LEU A1031 -16.302 -8.174 -24.433 1.00 41.72 C +ATOM 2261 CD1 LEU A1031 -17.357 -7.065 -24.522 1.00 42.04 C +ATOM 2262 CD2 LEU A1031 -15.696 -8.439 -25.818 1.00 45.52 C +ATOM 2263 H LEU A1031 -13.662 -7.617 -21.389 1.00 30.49 H +ATOM 2264 HA LEU A1031 -16.567 -7.241 -21.825 1.00 32.51 H +ATOM 2265 HB3 LEU A1031 -14.375 -8.541 -23.507 1.00 33.91 H +ATOM 2266 HB2 LEU A1031 -14.790 -6.866 -23.627 1.00 33.91 H +ATOM 2267 HG LEU A1031 -16.801 -9.093 -24.128 1.00 41.72 H +ATOM 2268 HD11 LEU A1031 -17.957 -7.181 -25.423 1.00 42.04 H +ATOM 2269 HD12 LEU A1031 -18.043 -7.102 -23.679 1.00 42.04 H +ATOM 2270 HD13 LEU A1031 -16.903 -6.074 -24.546 1.00 42.04 H +ATOM 2271 HD21 LEU A1031 -16.156 -9.306 -26.290 1.00 45.52 H +ATOM 2272 HD22 LEU A1031 -15.839 -7.592 -26.491 1.00 45.52 H +ATOM 2273 HD23 LEU A1031 -14.625 -8.629 -25.764 1.00 45.52 H +ATOM 2274 N ASP A1032 -17.230 -9.515 -20.985 1.00 33.70 N +ATOM 2275 CA ASP A1032 -17.712 -10.827 -20.543 1.00 35.49 C +ATOM 2276 C ASP A1032 -18.166 -11.661 -21.755 1.00 42.98 C +ATOM 2277 O ASP A1032 -17.727 -12.798 -21.921 1.00 43.01 O +ATOM 2278 CB ASP A1032 -18.822 -10.678 -19.476 1.00 38.24 C +ATOM 2279 CG ASP A1032 -19.218 -11.987 -18.779 1.00 57.45 C +ATOM 2280 OD1 ASP A1032 -20.169 -12.639 -19.268 1.00 62.50 O +ATOM 2281 OD2 ASP A1032 -18.465 -12.387 -17.864 1.00 62.05 O1- +ATOM 2282 H ASP A1032 -17.907 -8.764 -20.978 1.00 33.70 H +ATOM 2283 HA ASP A1032 -16.879 -11.358 -20.082 1.00 35.49 H +ATOM 2284 HB3 ASP A1032 -19.703 -10.217 -19.924 1.00 38.24 H +ATOM 2285 HB2 ASP A1032 -18.498 -9.962 -18.720 1.00 38.24 H +ATOM 2286 N ASN A1033 -19.010 -11.038 -22.589 1.00 43.24 N +ATOM 2287 CA ASN A1033 -19.496 -11.544 -23.869 1.00 46.44 C +ATOM 2288 C ASN A1033 -19.762 -10.349 -24.801 1.00 51.69 C +ATOM 2289 O ASN A1033 -19.561 -9.203 -24.398 1.00 49.09 O +ATOM 2290 CB ASN A1033 -20.703 -12.502 -23.675 1.00 48.31 C +ATOM 2291 CG ASN A1033 -21.947 -11.875 -23.033 1.00 66.28 C +ATOM 2292 OD1 ASN A1033 -22.671 -11.117 -23.674 1.00 59.86 O +ATOM 2293 ND2 ASN A1033 -22.211 -12.205 -21.767 1.00 59.92 N +ATOM 2294 H ASN A1033 -19.289 -10.090 -22.377 1.00 43.24 H +ATOM 2295 HA ASN A1033 -18.694 -12.117 -24.331 1.00 46.44 H +ATOM 2296 HB3 ASN A1033 -20.389 -13.358 -23.076 1.00 48.31 H +ATOM 2297 HB2 ASN A1033 -21.002 -12.918 -24.638 1.00 48.31 H +ATOM 2298 HD22 ASN A1033 -23.026 -11.828 -21.307 1.00 59.92 H +ATOM 2299 HD21 ASN A1033 -21.579 -12.797 -21.242 1.00 59.92 H +ATOM 2300 N ASP A1034 -20.218 -10.631 -26.032 1.00 52.34 N +ATOM 2301 CA ASP A1034 -20.535 -9.640 -27.073 1.00 53.71 C +ATOM 2302 C ASP A1034 -21.507 -8.516 -26.646 1.00 56.50 C +ATOM 2303 O ASP A1034 -21.409 -7.420 -27.190 1.00 55.06 O +ATOM 2304 CB ASP A1034 -20.990 -10.266 -28.427 1.00 59.25 C +ATOM 2305 CG ASP A1034 -21.924 -11.494 -28.402 1.00 77.62 C +ATOM 2306 OD1 ASP A1034 -22.639 -11.706 -27.397 1.00 79.51 O +ATOM 2307 OD2 ASP A1034 -22.007 -12.140 -29.469 1.00 88.40 O1- +ATOM 2308 H ASP A1034 -20.398 -11.592 -26.284 1.00 52.34 H +ATOM 2309 HA ASP A1034 -19.585 -9.140 -27.273 1.00 53.71 H +ATOM 2310 HB3 ASP A1034 -20.095 -10.532 -28.993 1.00 59.25 H +ATOM 2311 HB2 ASP A1034 -21.495 -9.512 -29.032 1.00 59.25 H +ATOM 2312 N ARG A1035 -22.406 -8.796 -25.687 1.00 52.42 N +ATOM 2313 CA ARG A1035 -23.448 -7.882 -25.216 1.00 52.12 C +ATOM 2314 C ARG A1035 -23.343 -7.580 -23.704 1.00 53.91 C +ATOM 2315 O ARG A1035 -24.336 -7.124 -23.136 1.00 54.42 O +ATOM 2316 CB ARG A1035 -24.835 -8.467 -25.595 1.00 56.06 C +ATOM 2317 CG ARG A1035 -25.086 -8.703 -27.101 1.00 68.03 C +ATOM 2318 CD ARG A1035 -24.897 -7.444 -27.966 1.00 74.26 C +ATOM 2319 NE ARG A1035 -25.309 -7.644 -29.365 1.00 83.22 N +ATOM 2320 CZ ARG A1035 -26.494 -7.332 -29.925 1.00103.41 C +ATOM 2321 NH1 ARG A1035 -27.506 -6.799 -29.222 1.00 95.23 N +ATOM 2322 NH2 ARG A1035 -26.668 -7.561 -31.233 1.00 92.56 N1+ +ATOM 2323 H ARG A1035 -22.419 -9.724 -25.282 1.00 52.42 H +ATOM 2324 HA ARG A1035 -23.344 -6.910 -25.700 1.00 52.12 H +ATOM 2325 HB3 ARG A1035 -25.620 -7.801 -25.237 1.00 56.06 H +ATOM 2326 HB2 ARG A1035 -24.983 -9.410 -25.066 1.00 56.06 H +ATOM 2327 HG3 ARG A1035 -26.057 -9.166 -27.280 1.00 68.03 H +ATOM 2328 HG2 ARG A1035 -24.352 -9.442 -27.423 1.00 68.03 H +ATOM 2329 HD3 ARG A1035 -23.832 -7.242 -28.056 1.00 74.26 H +ATOM 2330 HD2 ARG A1035 -25.331 -6.559 -27.506 1.00 74.26 H +ATOM 2331 HE ARG A1035 -24.607 -8.071 -29.954 1.00 83.22 H +ATOM 2332 HH12 ARG A1035 -28.382 -6.569 -29.667 1.00 95.23 H +ATOM 2333 HH11 ARG A1035 -27.412 -6.662 -28.223 1.00 95.23 H +ATOM 2334 HH22 ARG A1035 -27.544 -7.335 -31.681 1.00 92.56 H +ATOM 2335 HH21 ARG A1035 -25.921 -7.962 -31.782 1.00 92.56 H +ATOM 2336 N LEU A1036 -22.174 -7.803 -23.070 1.00 47.46 N +ATOM 2337 CA LEU A1036 -21.967 -7.538 -21.640 1.00 44.95 C +ATOM 2338 C LEU A1036 -20.533 -7.065 -21.368 1.00 44.87 C +ATOM 2339 O LEU A1036 -19.582 -7.752 -21.739 1.00 44.01 O +ATOM 2340 CB LEU A1036 -22.340 -8.791 -20.812 1.00 46.48 C +ATOM 2341 CG LEU A1036 -22.180 -8.679 -19.274 1.00 50.35 C +ATOM 2342 CD1 LEU A1036 -22.973 -7.502 -18.671 1.00 50.69 C +ATOM 2343 CD2 LEU A1036 -22.529 -10.018 -18.591 1.00 54.50 C +ATOM 2344 H LEU A1036 -21.382 -8.171 -23.579 1.00 47.46 H +ATOM 2345 HA LEU A1036 -22.630 -6.724 -21.343 1.00 44.95 H +ATOM 2346 HB3 LEU A1036 -21.719 -9.617 -21.159 1.00 46.48 H +ATOM 2347 HB2 LEU A1036 -23.369 -9.073 -21.040 1.00 46.48 H +ATOM 2348 HG LEU A1036 -21.127 -8.494 -19.056 1.00 50.35 H +ATOM 2349 HD11 LEU A1036 -23.473 -7.760 -17.737 1.00 50.69 H +ATOM 2350 HD12 LEU A1036 -22.306 -6.670 -18.448 1.00 50.69 H +ATOM 2351 HD13 LEU A1036 -23.741 -7.137 -19.353 1.00 50.69 H +ATOM 2352 HD21 LEU A1036 -21.721 -10.346 -17.938 1.00 54.50 H +ATOM 2353 HD22 LEU A1036 -23.433 -9.959 -17.986 1.00 54.50 H +ATOM 2354 HD23 LEU A1036 -22.691 -10.816 -19.315 1.00 54.50 H +ATOM 2355 N VAL A1037 -20.428 -5.918 -20.680 1.00 38.25 N +ATOM 2356 CA VAL A1037 -19.183 -5.244 -20.316 1.00 36.27 C +ATOM 2357 C VAL A1037 -19.170 -4.944 -18.803 1.00 39.75 C +ATOM 2358 O VAL A1037 -20.229 -4.712 -18.218 1.00 42.27 O +ATOM 2359 CB VAL A1037 -19.016 -3.946 -21.177 1.00 39.78 C +ATOM 2360 CG1 VAL A1037 -19.009 -2.583 -20.456 1.00 38.54 C +ATOM 2361 CG2 VAL A1037 -17.802 -4.070 -22.103 1.00 38.84 C +ATOM 2362 H VAL A1037 -21.272 -5.428 -20.415 1.00 38.25 H +ATOM 2363 HA VAL A1037 -18.349 -5.920 -20.508 1.00 36.27 H +ATOM 2364 HB VAL A1037 -19.873 -3.887 -21.850 1.00 39.78 H +ATOM 2365 HG11 VAL A1037 -18.916 -1.774 -21.181 1.00 38.54 H +ATOM 2366 HG12 VAL A1037 -19.935 -2.413 -19.907 1.00 38.54 H +ATOM 2367 HG13 VAL A1037 -18.179 -2.490 -19.757 1.00 38.54 H +ATOM 2368 HG21 VAL A1037 -17.730 -3.211 -22.767 1.00 38.84 H +ATOM 2369 HG22 VAL A1037 -16.873 -4.154 -21.540 1.00 38.84 H +ATOM 2370 HG23 VAL A1037 -17.890 -4.953 -22.732 1.00 38.84 H +ATOM 2371 N LYS A1038 -17.968 -4.982 -18.204 1.00 33.36 N +ATOM 2372 CA LYS A1038 -17.714 -4.878 -16.766 1.00 33.06 C +ATOM 2373 C LYS A1038 -16.485 -4.002 -16.491 1.00 35.92 C +ATOM 2374 O LYS A1038 -15.436 -4.235 -17.082 1.00 35.20 O +ATOM 2375 CB LYS A1038 -17.479 -6.290 -16.190 1.00 36.83 C +ATOM 2376 CG LYS A1038 -18.749 -7.145 -16.080 1.00 43.54 C +ATOM 2377 CD LYS A1038 -18.421 -8.608 -15.749 1.00 43.25 C +ATOM 2378 CE LYS A1038 -19.663 -9.482 -15.509 1.00 48.33 C +ATOM 2379 NZ LYS A1038 -20.306 -9.193 -14.214 1.00 58.37 N1+ +ATOM 2380 H LYS A1038 -17.149 -5.184 -18.767 1.00 33.36 H +ATOM 2381 HA LYS A1038 -18.574 -4.425 -16.277 1.00 33.06 H +ATOM 2382 HB3 LYS A1038 -17.052 -6.212 -15.188 1.00 36.83 H +ATOM 2383 HB2 LYS A1038 -16.730 -6.807 -16.793 1.00 36.83 H +ATOM 2384 HG3 LYS A1038 -19.317 -7.118 -17.009 1.00 43.54 H +ATOM 2385 HG2 LYS A1038 -19.391 -6.713 -15.312 1.00 43.54 H +ATOM 2386 HD3 LYS A1038 -17.751 -8.642 -14.891 1.00 43.25 H +ATOM 2387 HD2 LYS A1038 -17.852 -9.036 -16.575 1.00 43.25 H +ATOM 2388 HE3 LYS A1038 -19.380 -10.535 -15.514 1.00 48.33 H +ATOM 2389 HE2 LYS A1038 -20.386 -9.341 -16.312 1.00 48.33 H +ATOM 2390 HZ1 LYS A1038 -20.568 -8.218 -14.175 1.00 58.37 H +ATOM 2391 HZ2 LYS A1038 -21.132 -9.766 -14.115 1.00 58.37 H +ATOM 2392 HZ3 LYS A1038 -19.651 -9.405 -13.472 1.00 58.37 H +ATOM 2393 N ILE A1039 -16.614 -3.050 -15.556 1.00 32.33 N +ATOM 2394 CA ILE A1039 -15.529 -2.171 -15.108 1.00 30.26 C +ATOM 2395 C ILE A1039 -14.627 -2.911 -14.094 1.00 36.96 C +ATOM 2396 O ILE A1039 -15.145 -3.519 -13.160 1.00 36.41 O +ATOM 2397 CB ILE A1039 -16.108 -0.894 -14.429 1.00 32.08 C +ATOM 2398 CG1 ILE A1039 -17.001 -0.080 -15.398 1.00 32.40 C +ATOM 2399 CG2 ILE A1039 -15.044 0.034 -13.803 1.00 30.46 C +ATOM 2400 CD1 ILE A1039 -18.035 0.790 -14.673 1.00 32.90 C +ATOM 2401 H ILE A1039 -17.509 -2.910 -15.106 1.00 32.33 H +ATOM 2402 HA ILE A1039 -14.934 -1.867 -15.971 1.00 30.26 H +ATOM 2403 HB ILE A1039 -16.746 -1.235 -13.613 1.00 32.08 H +ATOM 2404 HG13 ILE A1039 -17.551 -0.733 -16.075 1.00 32.40 H +ATOM 2405 HG12 ILE A1039 -16.380 0.544 -16.043 1.00 32.40 H +ATOM 2406 HG21 ILE A1039 -15.498 0.926 -13.372 1.00 30.46 H +ATOM 2407 HG22 ILE A1039 -14.493 -0.448 -12.997 1.00 30.46 H +ATOM 2408 HG23 ILE A1039 -14.320 0.364 -14.550 1.00 30.46 H +ATOM 2409 HD11 ILE A1039 -18.793 1.149 -15.368 1.00 32.90 H +ATOM 2410 HD12 ILE A1039 -18.548 0.231 -13.890 1.00 32.90 H +ATOM 2411 HD13 ILE A1039 -17.570 1.656 -14.208 1.00 32.90 H +ATOM 2412 N GLY A1040 -13.302 -2.826 -14.292 1.00 34.99 N +ATOM 2413 CA GLY A1040 -12.289 -3.434 -13.430 1.00 35.46 C +ATOM 2414 C GLY A1040 -11.231 -2.398 -13.023 1.00 40.57 C +ATOM 2415 O GLY A1040 -11.286 -1.237 -13.431 1.00 40.24 O +ATOM 2416 H GLY A1040 -12.951 -2.287 -15.075 1.00 34.99 H +ATOM 2417 HA3 GLY A1040 -11.814 -4.254 -13.968 1.00 35.46 H +ATOM 2418 HA2 GLY A1040 -12.724 -3.850 -12.522 1.00 35.46 H +ATOM 2419 N ASP A1041 -10.271 -2.859 -12.199 1.00 37.60 N +ATOM 2420 CA ASP A1041 -9.139 -2.132 -11.605 1.00 37.19 C +ATOM 2421 C ASP A1041 -9.620 -0.924 -10.778 1.00 39.90 C +ATOM 2422 O ASP A1041 -9.731 0.180 -11.309 1.00 40.83 O +ATOM 2423 CB ASP A1041 -8.025 -1.778 -12.635 1.00 38.16 C +ATOM 2424 CG ASP A1041 -6.667 -1.263 -12.095 1.00 45.68 C +ATOM 2425 OD1 ASP A1041 -5.762 -1.083 -12.938 1.00 47.53 O +ATOM 2426 OD2 ASP A1041 -6.511 -1.058 -10.870 1.00 46.86 O1- +ATOM 2427 H ASP A1041 -10.325 -3.828 -11.918 1.00 37.60 H +ATOM 2428 HA ASP A1041 -8.695 -2.841 -10.905 1.00 37.19 H +ATOM 2429 HB3 ASP A1041 -8.418 -1.044 -13.339 1.00 38.16 H +ATOM 2430 HB2 ASP A1041 -7.825 -2.662 -13.241 1.00 38.16 H +ATOM 2431 N PHE A1042 -9.874 -1.177 -9.486 1.00 32.99 N +ATOM 2432 CA PHE A1042 -10.252 -0.180 -8.482 1.00 32.48 C +ATOM 2433 C PHE A1042 -9.048 0.246 -7.614 1.00 35.62 C +ATOM 2434 O PHE A1042 -9.241 0.681 -6.480 1.00 35.44 O +ATOM 2435 CB PHE A1042 -11.446 -0.723 -7.659 1.00 35.36 C +ATOM 2436 CG PHE A1042 -12.746 -0.802 -8.444 1.00 36.03 C +ATOM 2437 CD1 PHE A1042 -13.628 0.299 -8.473 1.00 39.47 C +ATOM 2438 CD2 PHE A1042 -12.997 -1.898 -9.298 1.00 37.32 C +ATOM 2439 CE1 PHE A1042 -14.757 0.263 -9.281 1.00 39.56 C +ATOM 2440 CE2 PHE A1042 -14.124 -1.908 -10.107 1.00 40.21 C +ATOM 2441 CZ PHE A1042 -15.004 -0.835 -10.094 1.00 38.02 C +ATOM 2442 H PHE A1042 -9.761 -2.121 -9.143 1.00 32.99 H +ATOM 2443 HA PHE A1042 -10.592 0.732 -8.974 1.00 32.48 H +ATOM 2444 HB3 PHE A1042 -11.630 -0.106 -6.779 1.00 35.36 H +ATOM 2445 HB2 PHE A1042 -11.210 -1.720 -7.281 1.00 35.36 H +ATOM 2446 HD1 PHE A1042 -13.434 1.171 -7.865 1.00 39.47 H +ATOM 2447 HD2 PHE A1042 -12.311 -2.731 -9.331 1.00 37.32 H +ATOM 2448 HE1 PHE A1042 -15.438 1.099 -9.293 1.00 39.56 H +ATOM 2449 HE2 PHE A1042 -14.309 -2.752 -10.753 1.00 40.21 H +ATOM 2450 HZ PHE A1042 -15.878 -0.849 -10.728 1.00 38.02 H +ATOM 2451 N GLY A1043 -7.822 0.119 -8.156 1.00 33.72 N +ATOM 2452 CA GLY A1043 -6.562 0.403 -7.464 1.00 35.61 C +ATOM 2453 C GLY A1043 -6.236 1.898 -7.376 1.00 40.83 C +ATOM 2454 O GLY A1043 -5.336 2.268 -6.627 1.00 42.94 O +ATOM 2455 H GLY A1043 -7.738 -0.245 -9.097 1.00 33.72 H +ATOM 2456 HA3 GLY A1043 -5.756 -0.110 -7.987 1.00 35.61 H +ATOM 2457 HA2 GLY A1043 -6.588 -0.000 -6.454 1.00 35.61 H +ATOM 2458 N LEU A1044 -6.964 2.757 -8.101 1.00 35.95 N +ATOM 2459 CA LEU A1044 -6.868 4.212 -8.002 1.00 35.55 C +ATOM 2460 C LEU A1044 -8.174 4.815 -7.450 1.00 38.20 C +ATOM 2461 O LEU A1044 -8.207 6.019 -7.209 1.00 37.43 O +ATOM 2462 CB LEU A1044 -6.463 4.769 -9.392 1.00 34.71 C +ATOM 2463 CG LEU A1044 -5.546 6.012 -9.370 1.00 41.15 C +ATOM 2464 CD1 LEU A1044 -4.267 5.793 -8.534 1.00 45.80 C +ATOM 2465 CD2 LEU A1044 -5.202 6.468 -10.803 1.00 40.55 C +ATOM 2466 H LEU A1044 -7.669 2.407 -8.738 1.00 35.95 H +ATOM 2467 HA LEU A1044 -6.111 4.481 -7.266 1.00 35.55 H +ATOM 2468 HB3 LEU A1044 -7.359 4.978 -9.980 1.00 34.71 H +ATOM 2469 HB2 LEU A1044 -5.931 3.993 -9.948 1.00 34.71 H +ATOM 2470 HG LEU A1044 -6.111 6.821 -8.906 1.00 41.15 H +ATOM 2471 HD11 LEU A1044 -3.368 6.181 -9.013 1.00 45.80 H +ATOM 2472 HD12 LEU A1044 -4.346 6.292 -7.569 1.00 45.80 H +ATOM 2473 HD13 LEU A1044 -4.091 4.735 -8.341 1.00 45.80 H +ATOM 2474 HD21 LEU A1044 -5.457 7.516 -10.941 1.00 40.55 H +ATOM 2475 HD22 LEU A1044 -4.144 6.362 -11.044 1.00 40.55 H +ATOM 2476 HD23 LEU A1044 -5.746 5.901 -11.558 1.00 40.55 H +ATOM 2477 N ALA A1045 -9.209 3.982 -7.223 1.00 34.45 N +ATOM 2478 CA ALA A1045 -10.511 4.387 -6.698 1.00 35.33 C +ATOM 2479 C ALA A1045 -10.439 4.900 -5.252 1.00 41.48 C +ATOM 2480 O ALA A1045 -9.613 4.431 -4.467 1.00 41.92 O +ATOM 2481 CB ALA A1045 -11.497 3.216 -6.820 1.00 35.84 C +ATOM 2482 H ALA A1045 -9.099 2.998 -7.419 1.00 34.45 H +ATOM 2483 HA ALA A1045 -10.854 5.202 -7.333 1.00 35.33 H +ATOM 2484 HB1 ALA A1045 -12.507 3.520 -6.545 1.00 35.84 H +ATOM 2485 HB2 ALA A1045 -11.536 2.843 -7.844 1.00 35.84 H +ATOM 2486 HB3 ALA A1045 -11.218 2.387 -6.170 1.00 35.84 H +ATOM 2487 N LYS A1046 -11.313 5.867 -4.943 1.00 39.67 N +ATOM 2488 CA LYS A1046 -11.335 6.594 -3.679 1.00 41.51 C +ATOM 2489 C LYS A1046 -12.769 6.933 -3.274 1.00 46.70 C +ATOM 2490 O LYS A1046 -13.584 7.254 -4.135 1.00 45.21 O +ATOM 2491 CB LYS A1046 -10.496 7.886 -3.832 1.00 43.95 C +ATOM 2492 CG LYS A1046 -9.006 7.725 -3.484 1.00 52.94 C +ATOM 2493 CD LYS A1046 -8.701 7.356 -2.018 1.00 61.30 C +ATOM 2494 CE LYS A1046 -9.532 8.134 -0.976 1.00 72.46 C +ATOM 2495 NZ LYS A1046 -9.060 7.912 0.401 1.00 80.50 N1+ +ATOM 2496 H LYS A1046 -11.985 6.167 -5.638 1.00 39.67 H +ATOM 2497 HA LYS A1046 -10.921 5.960 -2.893 1.00 41.51 H +ATOM 2498 HB3 LYS A1046 -10.898 8.679 -3.200 1.00 43.95 H +ATOM 2499 HB2 LYS A1046 -10.597 8.276 -4.845 1.00 43.95 H +ATOM 2500 HG3 LYS A1046 -8.499 8.654 -3.726 1.00 52.94 H +ATOM 2501 HG2 LYS A1046 -8.557 6.980 -4.141 1.00 52.94 H +ATOM 2502 HD3 LYS A1046 -7.640 7.539 -1.844 1.00 61.30 H +ATOM 2503 HD2 LYS A1046 -8.836 6.282 -1.891 1.00 61.30 H +ATOM 2504 HE3 LYS A1046 -10.578 7.837 -1.019 1.00 72.46 H +ATOM 2505 HE2 LYS A1046 -9.499 9.201 -1.183 1.00 72.46 H +ATOM 2506 HZ1 LYS A1046 -8.099 8.211 0.485 1.00 80.50 H +ATOM 2507 HZ2 LYS A1046 -9.635 8.436 1.044 1.00 80.50 H +ATOM 2508 HZ3 LYS A1046 -9.132 6.925 0.616 1.00 80.50 H +ATOM 2509 N ALA A1047 -13.023 6.914 -1.956 1.00 45.97 N +ATOM 2510 CA ALA A1047 -14.239 7.449 -1.350 1.00 47.89 C +ATOM 2511 C ALA A1047 -14.187 8.983 -1.308 1.00 53.43 C +ATOM 2512 O ALA A1047 -13.174 9.547 -0.891 1.00 53.75 O +ATOM 2513 CB ALA A1047 -14.388 6.876 0.067 1.00 51.21 C +ATOM 2514 H ALA A1047 -12.304 6.619 -1.313 1.00 45.97 H +ATOM 2515 HA ALA A1047 -15.102 7.133 -1.938 1.00 47.89 H +ATOM 2516 HB1 ALA A1047 -15.310 7.226 0.534 1.00 51.21 H +ATOM 2517 HB2 ALA A1047 -14.428 5.787 0.048 1.00 51.21 H +ATOM 2518 HB3 ALA A1047 -13.557 7.165 0.711 1.00 51.21 H +ATOM 2519 N VAL A1048 -15.298 9.611 -1.716 1.00 51.14 N +ATOM 2520 CA VAL A1048 -15.535 11.047 -1.614 1.00 52.52 C +ATOM 2521 C VAL A1048 -16.597 11.229 -0.503 1.00 61.61 C +ATOM 2522 O VAL A1048 -17.722 10.765 -0.700 1.00 62.08 O +ATOM 2523 CB VAL A1048 -16.114 11.607 -2.948 1.00 54.59 C +ATOM 2524 CG1 VAL A1048 -16.530 13.092 -2.876 1.00 55.28 C +ATOM 2525 CG2 VAL A1048 -15.135 11.410 -4.114 1.00 51.78 C +ATOM 2526 H VAL A1048 -16.088 9.063 -2.035 1.00 51.14 H +ATOM 2527 HA VAL A1048 -14.610 11.585 -1.415 1.00 52.52 H +ATOM 2528 HB VAL A1048 -17.003 11.032 -3.204 1.00 54.59 H +ATOM 2529 HG11 VAL A1048 -16.861 13.457 -3.849 1.00 55.28 H +ATOM 2530 HG12 VAL A1048 -17.357 13.256 -2.187 1.00 55.28 H +ATOM 2531 HG13 VAL A1048 -15.699 13.722 -2.555 1.00 55.28 H +ATOM 2532 HG21 VAL A1048 -15.521 11.853 -5.033 1.00 51.78 H +ATOM 2533 HG22 VAL A1048 -14.167 11.868 -3.908 1.00 51.78 H +ATOM 2534 HG23 VAL A1048 -14.975 10.351 -4.307 1.00 51.78 H +ATOM 2535 N PRO A1049 -16.236 11.864 0.641 1.00 62.00 N +ATOM 2536 CA PRO A1049 -17.184 12.176 1.737 1.00 66.09 C +ATOM 2537 C PRO A1049 -18.459 12.905 1.278 1.00 73.83 C +ATOM 2538 O PRO A1049 -18.379 13.730 0.371 1.00 71.64 O +ATOM 2539 CB PRO A1049 -16.360 13.056 2.691 1.00 69.51 C +ATOM 2540 CG PRO A1049 -14.922 12.638 2.458 1.00 71.48 C +ATOM 2541 CD PRO A1049 -14.889 12.341 0.967 1.00 63.48 C +ATOM 2542 HA PRO A1049 -17.442 11.236 2.228 1.00 66.09 H +ATOM 2543 HB3 PRO A1049 -16.661 12.943 3.733 1.00 69.51 H +ATOM 2544 HB2 PRO A1049 -16.469 14.109 2.431 1.00 69.51 H +ATOM 2545 HG3 PRO A1049 -14.718 11.723 3.016 1.00 71.48 H +ATOM 2546 HG2 PRO A1049 -14.195 13.390 2.766 1.00 71.48 H +ATOM 2547 HD2 PRO A1049 -14.690 13.250 0.396 1.00 63.48 H +ATOM 2548 HD3 PRO A1049 -14.110 11.611 0.746 1.00 63.48 H +ATOM 2549 N GLU A1050 -19.602 12.569 1.895 1.00 75.69 N +ATOM 2550 CA GLU A1050 -20.946 13.047 1.546 1.00 77.51 C +ATOM 2551 C GLU A1050 -21.137 14.581 1.530 1.00 84.15 C +ATOM 2552 O GLU A1050 -21.986 15.059 0.779 1.00 83.15 O +ATOM 2553 CB GLU A1050 -22.008 12.341 2.426 1.00 82.22 C +ATOM 2554 CG GLU A1050 -21.885 12.532 3.963 1.00 97.93 C +ATOM 2555 CD GLU A1050 -20.916 11.603 4.721 1.00120.70 C +ATOM 2556 OE1 GLU A1050 -20.359 10.662 4.111 1.00108.26 O +ATOM 2557 OE2 GLU A1050 -20.744 11.858 5.932 1.00119.67 O1- +ATOM 2558 H GLU A1050 -19.579 11.872 2.631 1.00 75.69 H +ATOM 2559 HA GLU A1050 -21.125 12.712 0.524 1.00 77.51 H +ATOM 2560 HB3 GLU A1050 -22.050 11.282 2.167 1.00 82.22 H +ATOM 2561 HB2 GLU A1050 -22.989 12.719 2.132 1.00 82.22 H +ATOM 2562 HG3 GLU A1050 -22.872 12.366 4.398 1.00 97.93 H +ATOM 2563 HG2 GLU A1050 -21.638 13.568 4.194 1.00 97.93 H +ATOM 2564 N GLY A1051 -20.338 15.319 2.321 1.00 83.36 N +ATOM 2565 CA GLY A1051 -20.349 16.784 2.369 1.00 84.58 C +ATOM 2566 C GLY A1051 -19.399 17.422 1.337 1.00 86.36 C +ATOM 2567 O GLY A1051 -19.389 18.646 1.220 1.00 86.86 O +ATOM 2568 H GLY A1051 -19.670 14.844 2.911 1.00 83.36 H +ATOM 2569 HA3 GLY A1051 -20.033 17.094 3.366 1.00 84.58 H +ATOM 2570 HA2 GLY A1051 -21.358 17.172 2.226 1.00 84.58 H +ATOM 2571 N HIS A1052 -18.603 16.617 0.612 1.00 79.97 N +ATOM 2572 CA HIS A1052 -17.629 17.044 -0.396 1.00 76.91 C +ATOM 2573 C HIS A1052 -18.094 16.595 -1.790 1.00 76.64 C +ATOM 2574 O HIS A1052 -18.787 15.585 -1.914 1.00 75.70 O +ATOM 2575 CB HIS A1052 -16.251 16.409 -0.088 1.00 77.26 C +ATOM 2576 CG HIS A1052 -15.567 16.850 1.187 1.00 83.91 C +ATOM 2577 ND1 HIS A1052 -16.152 16.768 2.440 1.00 88.78 N +ATOM 2578 CD2 HIS A1052 -14.313 17.378 1.406 1.00 86.52 C +ATOM 2579 CE1 HIS A1052 -15.265 17.223 3.327 1.00 90.57 C +ATOM 2580 NE2 HIS A1052 -14.124 17.610 2.771 1.00 89.68 N +ATOM 2581 H HIS A1052 -18.692 15.614 0.718 1.00 79.97 H +ATOM 2582 HA HIS A1052 -17.523 18.131 -0.401 1.00 76.91 H +ATOM 2583 HB3 HIS A1052 -15.563 16.629 -0.906 1.00 77.26 H +ATOM 2584 HB2 HIS A1052 -16.332 15.322 -0.062 1.00 77.26 H +ATOM 2585 HD1 HIS A1052 -17.082 16.431 2.642 1.00 88.78 H +ATOM 2586 HD2 HIS A1052 -13.535 17.600 0.692 1.00 86.52 H +ATOM 2587 HE1 HIS A1052 -15.456 17.273 4.389 1.00 90.57 H +ATOM 2588 N GLU A1053 -17.659 17.337 -2.818 1.00 70.38 N +ATOM 2589 CA GLU A1053 -17.883 17.022 -4.230 1.00 67.41 C +ATOM 2590 C GLU A1053 -16.584 16.601 -4.949 1.00 65.39 C +ATOM 2591 O GLU A1053 -16.664 16.257 -6.128 1.00 62.86 O +ATOM 2592 CB GLU A1053 -18.593 18.212 -4.927 1.00 69.51 C +ATOM 2593 CG GLU A1053 -17.779 19.512 -5.135 1.00 81.78 C +ATOM 2594 CD GLU A1053 -17.774 20.453 -3.921 1.00109.48 C +ATOM 2595 OE1 GLU A1053 -16.893 20.272 -3.050 1.00107.26 O +ATOM 2596 OE2 GLU A1053 -18.640 21.354 -3.893 1.00107.68 O1- +ATOM 2597 H GLU A1053 -17.139 18.188 -2.639 1.00 70.38 H +ATOM 2598 HA GLU A1053 -18.554 16.164 -4.313 1.00 67.41 H +ATOM 2599 HB3 GLU A1053 -19.513 18.442 -4.385 1.00 69.51 H +ATOM 2600 HB2 GLU A1053 -18.934 17.872 -5.906 1.00 69.51 H +ATOM 2601 HG3 GLU A1053 -18.205 20.053 -5.981 1.00 81.78 H +ATOM 2602 HG2 GLU A1053 -16.758 19.288 -5.435 1.00 81.78 H +ATOM 2603 N TYR A1054 -15.432 16.620 -4.250 1.00 59.91 N +ATOM 2604 CA TYR A1054 -14.128 16.231 -4.792 1.00 57.06 C +ATOM 2605 C TYR A1054 -13.225 15.603 -3.718 1.00 59.88 C +ATOM 2606 O TYR A1054 -13.480 15.733 -2.520 1.00 61.73 O +ATOM 2607 CB TYR A1054 -13.443 17.442 -5.481 1.00 57.75 C +ATOM 2608 CG TYR A1054 -12.910 18.540 -4.568 1.00 61.72 C +ATOM 2609 CD1 TYR A1054 -13.741 19.618 -4.206 1.00 65.51 C +ATOM 2610 CD2 TYR A1054 -11.587 18.494 -4.077 1.00 63.03 C +ATOM 2611 CE1 TYR A1054 -13.273 20.625 -3.342 1.00 68.28 C +ATOM 2612 CE2 TYR A1054 -11.118 19.497 -3.207 1.00 66.37 C +ATOM 2613 CZ TYR A1054 -11.960 20.564 -2.838 1.00 76.35 C +ATOM 2614 OH TYR A1054 -11.500 21.539 -2.001 1.00 81.80 O +ATOM 2615 H TYR A1054 -15.438 16.902 -3.281 1.00 59.91 H +ATOM 2616 HA TYR A1054 -14.295 15.452 -5.536 1.00 57.06 H +ATOM 2617 HB3 TYR A1054 -14.127 17.883 -6.202 1.00 57.75 H +ATOM 2618 HB2 TYR A1054 -12.603 17.084 -6.077 1.00 57.75 H +ATOM 2619 HD1 TYR A1054 -14.747 19.671 -4.589 1.00 65.51 H +ATOM 2620 HD2 TYR A1054 -10.931 17.684 -4.358 1.00 63.03 H +ATOM 2621 HE1 TYR A1054 -13.928 21.440 -3.069 1.00 68.28 H +ATOM 2622 HE2 TYR A1054 -10.112 19.450 -2.823 1.00 66.37 H +ATOM 2623 HH TYR A1054 -12.176 22.187 -1.785 1.00 81.80 H +ATOM 2624 N TYR A1055 -12.149 14.975 -4.212 1.00 53.09 N +ATOM 2625 CA TYR A1055 -11.007 14.471 -3.457 1.00 53.10 C +ATOM 2626 C TYR A1055 -9.726 15.055 -4.077 1.00 56.32 C +ATOM 2627 O TYR A1055 -9.573 14.988 -5.298 1.00 53.18 O +ATOM 2628 CB TYR A1055 -11.033 12.925 -3.512 1.00 53.04 C +ATOM 2629 CG TYR A1055 -9.744 12.229 -3.105 1.00 55.26 C +ATOM 2630 CD1 TYR A1055 -9.403 12.116 -1.742 1.00 59.33 C +ATOM 2631 CD2 TYR A1055 -8.864 11.731 -4.090 1.00 54.82 C +ATOM 2632 CE1 TYR A1055 -8.189 11.509 -1.366 1.00 60.69 C +ATOM 2633 CE2 TYR A1055 -7.648 11.129 -3.714 1.00 56.92 C +ATOM 2634 CZ TYR A1055 -7.317 11.005 -2.350 1.00 68.28 C +ATOM 2635 OH TYR A1055 -6.152 10.396 -1.981 1.00 72.13 O +ATOM 2636 H TYR A1055 -12.056 14.902 -5.217 1.00 53.09 H +ATOM 2637 HA TYR A1055 -11.071 14.792 -2.416 1.00 53.10 H +ATOM 2638 HB3 TYR A1055 -11.259 12.613 -4.532 1.00 53.04 H +ATOM 2639 HB2 TYR A1055 -11.853 12.545 -2.902 1.00 53.04 H +ATOM 2640 HD1 TYR A1055 -10.066 12.505 -0.983 1.00 59.33 H +ATOM 2641 HD2 TYR A1055 -9.109 11.827 -5.138 1.00 54.82 H +ATOM 2642 HE1 TYR A1055 -7.931 11.427 -0.320 1.00 60.69 H +ATOM 2643 HE2 TYR A1055 -6.979 10.757 -4.475 1.00 56.92 H +ATOM 2644 HH TYR A1055 -5.609 10.143 -2.731 1.00 72.13 H +ATOM 2645 N ARG A1056 -8.817 15.579 -3.235 1.00 56.46 N +ATOM 2646 CA ARG A1056 -7.489 16.027 -3.666 1.00 57.19 C +ATOM 2647 C ARG A1056 -6.566 14.824 -3.911 1.00 61.89 C +ATOM 2648 O ARG A1056 -6.398 13.993 -3.019 1.00 62.29 O +ATOM 2649 CB ARG A1056 -6.850 16.976 -2.627 1.00 60.29 C +ATOM 2650 CG ARG A1056 -6.976 18.462 -2.999 1.00 71.82 C +ATOM 2651 CD ARG A1056 -6.155 19.357 -2.056 1.00 85.08 C +ATOM 2652 NE ARG A1056 -6.336 20.793 -2.326 1.00 93.43 N +ATOM 2653 CZ ARG A1056 -7.282 21.607 -1.821 1.00108.81 C +ATOM 2654 NH1 ARG A1056 -8.250 21.157 -1.008 1.00 96.57 N +ATOM 2655 NH2 ARG A1056 -7.252 22.907 -2.136 1.00 96.30 N1+ +ATOM 2656 H ARG A1056 -8.994 15.588 -2.241 1.00 56.46 H +ATOM 2657 HA ARG A1056 -7.612 16.564 -4.604 1.00 57.19 H +ATOM 2658 HB3 ARG A1056 -5.781 16.771 -2.542 1.00 60.29 H +ATOM 2659 HB2 ARG A1056 -7.256 16.789 -1.632 1.00 60.29 H +ATOM 2660 HG3 ARG A1056 -8.009 18.791 -3.096 1.00 71.82 H +ATOM 2661 HG2 ARG A1056 -6.539 18.558 -3.995 1.00 71.82 H +ATOM 2662 HD3 ARG A1056 -5.101 19.189 -2.278 1.00 85.08 H +ATOM 2663 HD2 ARG A1056 -6.280 19.090 -1.006 1.00 85.08 H +ATOM 2664 HE ARG A1056 -5.661 21.191 -2.965 1.00 93.43 H +ATOM 2665 HH12 ARG A1056 -8.966 21.778 -0.657 1.00 96.57 H +ATOM 2666 HH11 ARG A1056 -8.277 20.181 -0.750 1.00 96.57 H +ATOM 2667 HH22 ARG A1056 -7.936 23.542 -1.749 1.00 96.30 H +ATOM 2668 HH21 ARG A1056 -6.504 23.276 -2.706 1.00 96.30 H +ATOM 2669 N VAL A1057 -5.959 14.797 -5.106 1.00 58.85 N +ATOM 2670 CA VAL A1057 -5.003 13.780 -5.542 1.00 59.44 C +ATOM 2671 C VAL A1057 -3.557 14.281 -5.359 1.00 68.20 C +ATOM 2672 O VAL A1057 -3.329 15.483 -5.206 1.00 69.34 O +ATOM 2673 CB VAL A1057 -5.225 13.404 -7.038 1.00 60.80 C +ATOM 2674 CG1 VAL A1057 -6.682 12.977 -7.283 1.00 59.13 C +ATOM 2675 CG2 VAL A1057 -4.790 14.446 -8.089 1.00 60.31 C +ATOM 2676 H VAL A1057 -6.124 15.552 -5.759 1.00 58.85 H +ATOM 2677 HA VAL A1057 -5.125 12.877 -4.941 1.00 59.44 H +ATOM 2678 HB VAL A1057 -4.617 12.519 -7.229 1.00 60.80 H +ATOM 2679 HG11 VAL A1057 -6.821 12.608 -8.299 1.00 59.13 H +ATOM 2680 HG12 VAL A1057 -6.979 12.188 -6.594 1.00 59.13 H +ATOM 2681 HG13 VAL A1057 -7.368 13.812 -7.139 1.00 59.13 H +ATOM 2682 HG21 VAL A1057 -4.998 14.090 -9.099 1.00 60.31 H +ATOM 2683 HG22 VAL A1057 -5.330 15.380 -7.961 1.00 60.31 H +ATOM 2684 HG23 VAL A1057 -3.723 14.663 -8.049 1.00 60.31 H +ATOM 2685 N ARG A1058 -2.607 13.334 -5.385 1.00 67.56 N +ATOM 2686 CA ARG A1058 -1.172 13.599 -5.282 1.00 70.37 C +ATOM 2687 C ARG A1058 -0.536 13.789 -6.667 1.00 74.80 C +ATOM 2688 O ARG A1058 -1.065 13.300 -7.667 1.00 72.29 O +ATOM 2689 CB ARG A1058 -0.482 12.425 -4.555 1.00 73.21 C +ATOM 2690 CG ARG A1058 -1.096 12.069 -3.190 1.00 89.24 C +ATOM 2691 CD ARG A1058 -0.267 11.012 -2.441 1.00105.70 C +ATOM 2692 NE ARG A1058 -0.997 10.452 -1.291 1.00119.40 N +ATOM 2693 CZ ARG A1058 -1.790 9.362 -1.293 1.00134.49 C +ATOM 2694 NH1 ARG A1058 -1.990 8.620 -2.394 1.00120.12 N +ATOM 2695 NH2 ARG A1058 -2.400 9.001 -0.157 1.00122.85 N1+ +ATOM 2696 H ARG A1058 -2.868 12.371 -5.537 1.00 67.56 H +ATOM 2697 HA ARG A1058 -1.012 14.506 -4.696 1.00 70.37 H +ATOM 2698 HB3 ARG A1058 0.576 12.656 -4.421 1.00 73.21 H +ATOM 2699 HB2 ARG A1058 -0.516 11.536 -5.188 1.00 73.21 H +ATOM 2700 HG3 ARG A1058 -2.147 11.782 -3.259 1.00 89.24 H +ATOM 2701 HG2 ARG A1058 -1.072 12.989 -2.605 1.00 89.24 H +ATOM 2702 HD3 ARG A1058 0.578 11.523 -1.980 1.00105.70 H +ATOM 2703 HD2 ARG A1058 0.168 10.261 -3.102 1.00105.70 H +ATOM 2704 HE ARG A1058 -0.910 10.974 -0.431 1.00119.40 H +ATOM 2705 HH12 ARG A1058 -2.574 7.793 -2.360 1.00120.12 H +ATOM 2706 HH11 ARG A1058 -1.525 8.863 -3.256 1.00120.12 H +ATOM 2707 HH22 ARG A1058 -2.991 8.183 -0.135 1.00122.85 H +ATOM 2708 HH21 ARG A1058 -2.266 9.536 0.689 1.00122.85 H +ATOM 2709 N GLU A1059 0.632 14.450 -6.673 1.00 74.50 N +ATOM 2710 CA GLU A1059 1.553 14.525 -7.807 1.00 74.23 C +ATOM 2711 C GLU A1059 2.254 13.163 -7.955 1.00 77.46 C +ATOM 2712 O GLU A1059 3.042 12.795 -7.083 1.00 79.77 O +ATOM 2713 CB GLU A1059 2.560 15.668 -7.559 1.00 78.25 C +ATOM 2714 CG GLU A1059 1.890 17.055 -7.402 1.00 89.76 C +ATOM 2715 CD GLU A1059 2.828 18.212 -7.020 1.00113.50 C +ATOM 2716 OE1 GLU A1059 4.065 18.018 -7.008 1.00107.60 O +ATOM 2717 OE2 GLU A1059 2.274 19.295 -6.731 1.00107.56 O1- +ATOM 2718 H GLU A1059 0.989 14.826 -5.807 1.00 74.50 H +ATOM 2719 HA GLU A1059 0.987 14.746 -8.714 1.00 74.23 H +ATOM 2720 HB3 GLU A1059 3.270 15.702 -8.387 1.00 78.25 H +ATOM 2721 HB2 GLU A1059 3.147 15.447 -6.666 1.00 78.25 H +ATOM 2722 HG3 GLU A1059 1.105 17.011 -6.647 1.00 89.76 H +ATOM 2723 HG2 GLU A1059 1.396 17.315 -8.337 1.00 89.76 H +ATOM 2724 N ASP A1060 1.895 12.418 -9.011 1.00 70.23 N +ATOM 2725 CA ASP A1060 2.226 11.001 -9.159 1.00 68.85 C +ATOM 2726 C ASP A1060 2.470 10.671 -10.644 1.00 68.50 C +ATOM 2727 O ASP A1060 1.778 11.208 -11.510 1.00 66.16 O +ATOM 2728 CB ASP A1060 1.134 10.133 -8.477 1.00 69.42 C +ATOM 2729 CG ASP A1060 1.308 8.615 -8.603 1.00 81.45 C +ATOM 2730 OD1 ASP A1060 0.565 8.022 -9.413 1.00 81.04 O +ATOM 2731 OD2 ASP A1060 2.259 8.097 -7.975 1.00 89.83 O1- +ATOM 2732 H ASP A1060 1.248 12.789 -9.693 1.00 70.23 H +ATOM 2733 HA ASP A1060 3.171 10.811 -8.645 1.00 68.85 H +ATOM 2734 HB3 ASP A1060 0.147 10.430 -8.837 1.00 69.42 H +ATOM 2735 HB2 ASP A1060 1.112 10.376 -7.413 1.00 69.42 H +ATOM 2736 N GLY A1061 3.463 9.803 -10.905 1.00 63.47 N +ATOM 2737 CA GLY A1061 3.919 9.458 -12.253 1.00 61.92 C +ATOM 2738 C GLY A1061 3.025 8.411 -12.936 1.00 61.80 C +ATOM 2739 O GLY A1061 2.926 8.424 -14.163 1.00 61.49 O +ATOM 2740 H GLY A1061 3.960 9.380 -10.133 1.00 63.47 H +ATOM 2741 HA3 GLY A1061 4.930 9.056 -12.184 1.00 61.92 H +ATOM 2742 HA2 GLY A1061 3.980 10.354 -12.873 1.00 61.92 H +ATOM 2743 N ASP A1062 2.365 7.532 -12.159 1.00 55.28 N +ATOM 2744 CA ASP A1062 1.462 6.477 -12.645 1.00 52.23 C +ATOM 2745 C ASP A1062 0.018 6.962 -12.892 1.00 50.35 C +ATOM 2746 O ASP A1062 -0.809 6.161 -13.330 1.00 49.00 O +ATOM 2747 CB ASP A1062 1.471 5.215 -11.744 1.00 54.90 C +ATOM 2748 CG ASP A1062 2.871 4.616 -11.542 1.00 73.36 C +ATOM 2749 OD1 ASP A1062 3.348 3.959 -12.494 1.00 75.14 O +ATOM 2750 OD2 ASP A1062 3.499 4.932 -10.507 1.00 82.10 O1- +ATOM 2751 H ASP A1062 2.477 7.575 -11.155 1.00 55.28 H +ATOM 2752 HA ASP A1062 1.824 6.157 -13.624 1.00 52.23 H +ATOM 2753 HB3 ASP A1062 0.835 4.437 -12.171 1.00 54.90 H +ATOM 2754 HB2 ASP A1062 1.050 5.454 -10.766 1.00 54.90 H +ATOM 2755 N SER A1063 -0.262 8.254 -12.645 1.00 43.08 N +ATOM 2756 CA SER A1063 -1.541 8.912 -12.926 1.00 39.71 C +ATOM 2757 C SER A1063 -1.834 8.917 -14.445 1.00 36.74 C +ATOM 2758 O SER A1063 -0.950 9.325 -15.200 1.00 35.65 O +ATOM 2759 CB SER A1063 -1.451 10.363 -12.416 1.00 44.05 C +ATOM 2760 OG SER A1063 -1.561 10.394 -11.008 1.00 56.19 O +ATOM 2761 H SER A1063 0.470 8.845 -12.278 1.00 43.08 H +ATOM 2762 HA SER A1063 -2.308 8.385 -12.360 1.00 39.71 H +ATOM 2763 HB3 SER A1063 -2.274 10.959 -12.814 1.00 44.05 H +ATOM 2764 HB2 SER A1063 -0.529 10.848 -12.736 1.00 44.05 H +ATOM 2765 HG SER A1063 -0.820 9.909 -10.633 1.00 56.19 H +ATOM 2766 N PRO A1064 -3.031 8.450 -14.878 1.00 30.99 N +ATOM 2767 CA PRO A1064 -3.409 8.434 -16.303 1.00 29.59 C +ATOM 2768 C PRO A1064 -3.804 9.836 -16.823 1.00 32.01 C +ATOM 2769 O PRO A1064 -4.987 10.123 -17.008 1.00 29.67 O +ATOM 2770 CB PRO A1064 -4.545 7.398 -16.344 1.00 29.56 C +ATOM 2771 CG PRO A1064 -5.231 7.543 -14.998 1.00 33.51 C +ATOM 2772 CD PRO A1064 -4.076 7.842 -14.049 1.00 30.17 C +ATOM 2773 HA PRO A1064 -2.580 8.066 -16.908 1.00 29.59 H +ATOM 2774 HB3 PRO A1064 -4.115 6.399 -16.418 1.00 29.56 H +ATOM 2775 HB2 PRO A1064 -5.230 7.518 -17.184 1.00 29.56 H +ATOM 2776 HG3 PRO A1064 -5.811 6.671 -14.703 1.00 33.51 H +ATOM 2777 HG2 PRO A1064 -5.910 8.392 -15.038 1.00 33.51 H +ATOM 2778 HD2 PRO A1064 -4.396 8.489 -13.233 1.00 30.17 H +ATOM 2779 HD3 PRO A1064 -3.692 6.913 -13.624 1.00 30.17 H +ATOM 2780 N VAL A1065 -2.786 10.690 -17.026 1.00 29.66 N +ATOM 2781 CA VAL A1065 -2.902 12.106 -17.382 1.00 29.04 C +ATOM 2782 C VAL A1065 -3.565 12.389 -18.746 1.00 30.30 C +ATOM 2783 O VAL A1065 -4.209 13.429 -18.873 1.00 30.19 O +ATOM 2784 CB VAL A1065 -1.522 12.819 -17.348 1.00 34.62 C +ATOM 2785 CG1 VAL A1065 -0.968 12.886 -15.914 1.00 34.92 C +ATOM 2786 CG2 VAL A1065 -0.478 12.246 -18.328 1.00 35.44 C +ATOM 2787 H VAL A1065 -1.845 10.366 -16.844 1.00 29.66 H +ATOM 2788 HA VAL A1065 -3.532 12.562 -16.618 1.00 29.04 H +ATOM 2789 HB VAL A1065 -1.673 13.855 -17.651 1.00 34.62 H +ATOM 2790 HG11 VAL A1065 -0.022 13.424 -15.880 1.00 34.92 H +ATOM 2791 HG12 VAL A1065 -1.661 13.400 -15.247 1.00 34.92 H +ATOM 2792 HG13 VAL A1065 -0.786 11.895 -15.503 1.00 34.92 H +ATOM 2793 HG21 VAL A1065 0.477 12.764 -18.227 1.00 35.44 H +ATOM 2794 HG22 VAL A1065 -0.296 11.188 -18.145 1.00 35.44 H +ATOM 2795 HG23 VAL A1065 -0.788 12.357 -19.367 1.00 35.44 H +ATOM 2796 N PHE A1066 -3.447 11.462 -19.715 1.00 25.62 N +ATOM 2797 CA PHE A1066 -4.084 11.560 -21.034 1.00 24.82 C +ATOM 2798 C PHE A1066 -5.578 11.163 -21.042 1.00 26.45 C +ATOM 2799 O PHE A1066 -6.220 11.285 -22.086 1.00 25.78 O +ATOM 2800 CB PHE A1066 -3.251 10.779 -22.074 1.00 26.47 C +ATOM 2801 CG PHE A1066 -1.844 11.322 -22.286 1.00 27.98 C +ATOM 2802 CD1 PHE A1066 -1.649 12.632 -22.772 1.00 31.46 C +ATOM 2803 CD2 PHE A1066 -0.713 10.538 -21.974 1.00 29.97 C +ATOM 2804 CE1 PHE A1066 -0.365 13.135 -22.936 1.00 33.77 C +ATOM 2805 CE2 PHE A1066 0.563 11.055 -22.150 1.00 33.43 C +ATOM 2806 CZ PHE A1066 0.737 12.349 -22.625 1.00 32.31 C +ATOM 2807 H PHE A1066 -2.904 10.626 -19.541 1.00 25.62 H +ATOM 2808 HA PHE A1066 -4.075 12.609 -21.337 1.00 24.82 H +ATOM 2809 HB3 PHE A1066 -3.756 10.782 -23.040 1.00 26.47 H +ATOM 2810 HB2 PHE A1066 -3.184 9.732 -21.779 1.00 26.47 H +ATOM 2811 HD1 PHE A1066 -2.496 13.256 -23.012 1.00 31.46 H +ATOM 2812 HD2 PHE A1066 -0.829 9.530 -21.608 1.00 29.97 H +ATOM 2813 HE1 PHE A1066 -0.222 14.140 -23.303 1.00 33.77 H +ATOM 2814 HE2 PHE A1066 1.426 10.449 -21.914 1.00 33.43 H +ATOM 2815 HZ PHE A1066 1.733 12.745 -22.753 1.00 32.31 H +ATOM 2816 N TRP A1067 -6.110 10.745 -19.880 1.00 22.72 N +ATOM 2817 CA TRP A1067 -7.531 10.523 -19.603 1.00 23.65 C +ATOM 2818 C TRP A1067 -8.099 11.559 -18.615 1.00 26.48 C +ATOM 2819 O TRP A1067 -9.285 11.467 -18.304 1.00 25.01 O +ATOM 2820 CB TRP A1067 -7.743 9.090 -19.059 1.00 21.85 C +ATOM 2821 CG TRP A1067 -7.607 7.990 -20.066 1.00 23.25 C +ATOM 2822 CD1 TRP A1067 -8.642 7.341 -20.645 1.00 25.76 C +ATOM 2823 CD2 TRP A1067 -6.398 7.444 -20.678 1.00 23.75 C +ATOM 2824 NE1 TRP A1067 -8.162 6.446 -21.576 1.00 25.21 N +ATOM 2825 CE2 TRP A1067 -6.788 6.472 -21.648 1.00 27.61 C +ATOM 2826 CE3 TRP A1067 -5.010 7.675 -20.529 1.00 25.53 C +ATOM 2827 CZ2 TRP A1067 -5.855 5.779 -22.436 1.00 27.61 C +ATOM 2828 CZ3 TRP A1067 -4.067 6.991 -21.323 1.00 27.51 C +ATOM 2829 CH2 TRP A1067 -4.488 6.046 -22.275 1.00 28.26 C +ATOM 2830 H TRP A1067 -5.501 10.650 -19.078 1.00 22.72 H +ATOM 2831 HA TRP A1067 -8.122 10.631 -20.512 1.00 23.65 H +ATOM 2832 HB3 TRP A1067 -8.742 8.988 -18.631 1.00 21.85 H +ATOM 2833 HB2 TRP A1067 -7.054 8.892 -18.237 1.00 21.85 H +ATOM 2834 HD1 TRP A1067 -9.684 7.521 -20.419 1.00 25.76 H +ATOM 2835 HE1 TRP A1067 -8.763 5.856 -22.143 1.00 25.21 H +ATOM 2836 HE3 TRP A1067 -4.665 8.393 -19.801 1.00 25.53 H +ATOM 2837 HZ2 TRP A1067 -6.179 5.054 -23.162 1.00 27.61 H +ATOM 2838 HZ3 TRP A1067 -3.015 7.191 -21.200 1.00 27.51 H +ATOM 2839 HH2 TRP A1067 -3.763 5.530 -22.885 1.00 28.26 H +ATOM 2840 N TYR A1068 -7.272 12.497 -18.115 1.00 25.06 N +ATOM 2841 CA TYR A1068 -7.642 13.429 -17.048 1.00 24.40 C +ATOM 2842 C TYR A1068 -8.088 14.804 -17.559 1.00 29.09 C +ATOM 2843 O TYR A1068 -7.539 15.325 -18.530 1.00 29.03 O +ATOM 2844 CB TYR A1068 -6.472 13.562 -16.048 1.00 25.40 C +ATOM 2845 CG TYR A1068 -6.380 12.528 -14.932 1.00 26.29 C +ATOM 2846 CD1 TYR A1068 -7.163 11.349 -14.925 1.00 27.65 C +ATOM 2847 CD2 TYR A1068 -5.512 12.778 -13.847 1.00 27.90 C +ATOM 2848 CE1 TYR A1068 -7.106 10.464 -13.835 1.00 28.32 C +ATOM 2849 CE2 TYR A1068 -5.448 11.886 -12.759 1.00 27.93 C +ATOM 2850 CZ TYR A1068 -6.256 10.733 -12.747 1.00 32.15 C +ATOM 2851 OH TYR A1068 -6.220 9.881 -11.685 1.00 34.10 O +ATOM 2852 H TYR A1068 -6.314 12.541 -18.432 1.00 25.06 H +ATOM 2853 HA TYR A1068 -8.495 13.017 -16.518 1.00 24.40 H +ATOM 2854 HB3 TYR A1068 -6.558 14.518 -15.532 1.00 25.40 H +ATOM 2855 HB2 TYR A1068 -5.525 13.618 -16.580 1.00 25.40 H +ATOM 2856 HD1 TYR A1068 -7.828 11.106 -15.737 1.00 27.65 H +ATOM 2857 HD2 TYR A1068 -4.905 13.672 -13.836 1.00 27.90 H +ATOM 2858 HE1 TYR A1068 -7.730 9.583 -13.840 1.00 28.32 H +ATOM 2859 HE2 TYR A1068 -4.789 12.095 -11.929 1.00 27.93 H +ATOM 2860 HH TYR A1068 -6.736 9.083 -11.841 1.00 34.10 H +ATOM 2861 N ALA A1069 -9.063 15.368 -16.828 1.00 26.27 N +ATOM 2862 CA ALA A1069 -9.621 16.707 -17.003 1.00 27.33 C +ATOM 2863 C ALA A1069 -8.659 17.817 -16.518 1.00 31.87 C +ATOM 2864 O ALA A1069 -7.763 17.513 -15.730 1.00 30.37 O +ATOM 2865 CB ALA A1069 -10.947 16.754 -16.231 1.00 27.85 C +ATOM 2866 H ALA A1069 -9.448 14.841 -16.054 1.00 26.27 H +ATOM 2867 HA ALA A1069 -9.810 16.823 -18.066 1.00 27.33 H +ATOM 2868 HB1 ALA A1069 -11.438 17.722 -16.320 1.00 27.85 H +ATOM 2869 HB2 ALA A1069 -11.646 16.004 -16.594 1.00 27.85 H +ATOM 2870 HB3 ALA A1069 -10.785 16.554 -15.172 1.00 27.85 H +ATOM 2871 N PRO A1070 -8.853 19.082 -16.970 1.00 28.98 N +ATOM 2872 CA PRO A1070 -7.923 20.191 -16.665 1.00 30.25 C +ATOM 2873 C PRO A1070 -7.700 20.518 -15.175 1.00 34.14 C +ATOM 2874 O PRO A1070 -6.573 20.852 -14.818 1.00 33.30 O +ATOM 2875 CB PRO A1070 -8.493 21.396 -17.433 1.00 33.24 C +ATOM 2876 CG PRO A1070 -9.331 20.784 -18.540 1.00 37.12 C +ATOM 2877 CD PRO A1070 -9.901 19.534 -17.888 1.00 31.16 C +ATOM 2878 HA PRO A1070 -6.962 19.920 -17.107 1.00 30.25 H +ATOM 2879 HB3 PRO A1070 -7.714 22.051 -17.825 1.00 33.24 H +ATOM 2880 HB2 PRO A1070 -9.134 22.001 -16.791 1.00 33.24 H +ATOM 2881 HG3 PRO A1070 -8.680 20.494 -19.364 1.00 37.12 H +ATOM 2882 HG2 PRO A1070 -10.095 21.455 -18.934 1.00 37.12 H +ATOM 2883 HD2 PRO A1070 -10.795 19.780 -17.314 1.00 31.16 H +ATOM 2884 HD3 PRO A1070 -10.174 18.813 -18.654 1.00 31.16 H +ATOM 2885 N GLU A1071 -8.744 20.397 -14.334 1.00 30.48 N +ATOM 2886 CA GLU A1071 -8.670 20.632 -12.886 1.00 31.43 C +ATOM 2887 C GLU A1071 -8.015 19.474 -12.104 1.00 36.54 C +ATOM 2888 O GLU A1071 -7.610 19.696 -10.965 1.00 37.63 O +ATOM 2889 CB GLU A1071 -10.061 21.021 -12.323 1.00 32.56 C +ATOM 2890 CG GLU A1071 -11.111 19.899 -12.136 1.00 35.76 C +ATOM 2891 CD GLU A1071 -11.663 19.243 -13.409 1.00 37.74 C +ATOM 2892 OE1 GLU A1071 -11.773 19.921 -14.455 1.00 31.58 O +ATOM 2893 OE2 GLU A1071 -12.001 18.047 -13.308 1.00 25.56 O1- +ATOM 2894 H GLU A1071 -9.652 20.128 -14.691 1.00 30.48 H +ATOM 2895 HA GLU A1071 -8.027 21.502 -12.737 1.00 31.43 H +ATOM 2896 HB3 GLU A1071 -10.485 21.824 -12.925 1.00 32.56 H +ATOM 2897 HB2 GLU A1071 -9.904 21.480 -11.346 1.00 32.56 H +ATOM 2898 HG3 GLU A1071 -11.971 20.327 -11.620 1.00 35.76 H +ATOM 2899 HG2 GLU A1071 -10.723 19.135 -11.462 1.00 35.76 H +ATOM 2900 N CYS A1072 -7.886 18.284 -12.717 1.00 32.49 N +ATOM 2901 CA CYS A1072 -7.132 17.152 -12.167 1.00 32.58 C +ATOM 2902 C CYS A1072 -5.622 17.310 -12.419 1.00 37.39 C +ATOM 2903 O CYS A1072 -4.826 16.835 -11.611 1.00 38.08 O +ATOM 2904 CB CYS A1072 -7.587 15.806 -12.759 1.00 31.25 C +ATOM 2905 SG CYS A1072 -9.352 15.554 -12.470 1.00 34.42 S +ATOM 2906 H CYS A1072 -8.257 18.161 -13.649 1.00 32.49 H +ATOM 2907 HA CYS A1072 -7.287 17.115 -11.087 1.00 32.58 H +ATOM 2908 HB3 CYS A1072 -7.039 14.977 -12.309 1.00 31.25 H +ATOM 2909 HB2 CYS A1072 -7.406 15.766 -13.832 1.00 31.25 H +ATOM 2910 HG CYS A1072 -9.290 15.627 -11.137 1.00 34.42 H +ATOM 2911 N LEU A1073 -5.266 17.967 -13.536 1.00 33.94 N +ATOM 2912 CA LEU A1073 -3.891 18.219 -13.963 1.00 35.43 C +ATOM 2913 C LEU A1073 -3.294 19.478 -13.307 1.00 43.48 C +ATOM 2914 O LEU A1073 -2.085 19.509 -13.081 1.00 44.33 O +ATOM 2915 CB LEU A1073 -3.869 18.390 -15.499 1.00 35.06 C +ATOM 2916 CG LEU A1073 -4.341 17.170 -16.321 1.00 37.87 C +ATOM 2917 CD1 LEU A1073 -4.517 17.552 -17.804 1.00 38.52 C +ATOM 2918 CD2 LEU A1073 -3.436 15.937 -16.139 1.00 39.19 C +ATOM 2919 H LEU A1073 -5.988 18.313 -14.153 1.00 33.94 H +ATOM 2920 HA LEU A1073 -3.265 17.368 -13.686 1.00 35.43 H +ATOM 2921 HB3 LEU A1073 -2.858 18.647 -15.817 1.00 35.06 H +ATOM 2922 HB2 LEU A1073 -4.489 19.252 -15.755 1.00 35.06 H +ATOM 2923 HG LEU A1073 -5.329 16.889 -15.963 1.00 37.87 H +ATOM 2924 HD11 LEU A1073 -5.382 17.049 -18.237 1.00 38.52 H +ATOM 2925 HD12 LEU A1073 -4.671 18.624 -17.940 1.00 38.52 H +ATOM 2926 HD13 LEU A1073 -3.644 17.286 -18.396 1.00 38.52 H +ATOM 2927 HD21 LEU A1073 -3.149 15.499 -17.094 1.00 39.19 H +ATOM 2928 HD22 LEU A1073 -2.519 16.169 -15.600 1.00 39.19 H +ATOM 2929 HD23 LEU A1073 -3.949 15.160 -15.574 1.00 39.19 H +ATOM 2930 N LYS A1074 -4.137 20.497 -13.064 1.00 42.49 N +ATOM 2931 CA LYS A1074 -3.729 21.836 -12.643 1.00 45.58 C +ATOM 2932 C LYS A1074 -3.934 22.044 -11.135 1.00 51.72 C +ATOM 2933 O LYS A1074 -2.973 22.372 -10.442 1.00 52.85 O +ATOM 2934 CB LYS A1074 -4.514 22.869 -13.483 1.00 49.18 C +ATOM 2935 CG LYS A1074 -4.055 24.328 -13.301 1.00 71.38 C +ATOM 2936 CD LYS A1074 -4.725 25.346 -14.249 1.00 85.44 C +ATOM 2937 CE LYS A1074 -6.183 25.718 -13.902 1.00 97.19 C +ATOM 2938 NZ LYS A1074 -7.173 24.740 -14.390 1.00104.18 N1+ +ATOM 2939 H LYS A1074 -5.112 20.390 -13.308 1.00 42.49 H +ATOM 2940 HA LYS A1074 -2.667 21.980 -12.852 1.00 45.58 H +ATOM 2941 HB3 LYS A1074 -5.576 22.781 -13.261 1.00 49.18 H +ATOM 2942 HB2 LYS A1074 -4.402 22.609 -14.535 1.00 49.18 H +ATOM 2943 HG3 LYS A1074 -2.977 24.371 -13.462 1.00 71.38 H +ATOM 2944 HG2 LYS A1074 -4.211 24.646 -12.269 1.00 71.38 H +ATOM 2945 HD3 LYS A1074 -4.664 24.985 -15.278 1.00 85.44 H +ATOM 2946 HD2 LYS A1074 -4.129 26.259 -14.237 1.00 85.44 H +ATOM 2947 HE3 LYS A1074 -6.429 26.674 -14.365 1.00 97.19 H +ATOM 2948 HE2 LYS A1074 -6.297 25.853 -12.826 1.00 97.19 H +ATOM 2949 HZ1 LYS A1074 -7.139 24.699 -15.403 1.00104.18 H +ATOM 2950 HZ2 LYS A1074 -6.976 23.827 -14.010 1.00104.18 H +ATOM 2951 HZ3 LYS A1074 -8.101 25.030 -14.113 1.00104.18 H +ATOM 2952 N GLU A1075 -5.180 21.863 -10.665 1.00 48.00 N +ATOM 2953 CA GLU A1075 -5.585 22.077 -9.272 1.00 49.76 C +ATOM 2954 C GLU A1075 -5.469 20.825 -8.390 1.00 53.28 C +ATOM 2955 O GLU A1075 -5.564 20.955 -7.169 1.00 53.10 O +ATOM 2956 CB GLU A1075 -7.027 22.637 -9.249 1.00 51.48 C +ATOM 2957 CG GLU A1075 -7.124 24.120 -9.674 1.00 66.34 C +ATOM 2958 CD GLU A1075 -6.547 25.137 -8.670 1.00 98.88 C +ATOM 2959 OE1 GLU A1075 -6.396 24.794 -7.475 1.00 95.63 O +ATOM 2960 OE2 GLU A1075 -6.278 26.270 -9.122 1.00 98.68 O1- +ATOM 2961 H GLU A1075 -5.913 21.578 -11.297 1.00 48.00 H +ATOM 2962 HA GLU A1075 -4.918 22.813 -8.819 1.00 49.76 H +ATOM 2963 HB3 GLU A1075 -7.494 22.492 -8.274 1.00 51.48 H +ATOM 2964 HB2 GLU A1075 -7.647 22.060 -9.934 1.00 51.48 H +ATOM 2965 HG3 GLU A1075 -8.175 24.373 -9.823 1.00 66.34 H +ATOM 2966 HG2 GLU A1075 -6.644 24.255 -10.644 1.00 66.34 H +ATOM 2967 N TYR A1076 -5.279 19.648 -9.014 1.00 50.13 N +ATOM 2968 CA TYR A1076 -5.184 18.331 -8.378 1.00 50.19 C +ATOM 2969 C TYR A1076 -6.446 17.957 -7.577 1.00 52.11 C +ATOM 2970 O TYR A1076 -6.327 17.366 -6.507 1.00 53.52 O +ATOM 2971 CB TYR A1076 -3.875 18.202 -7.561 1.00 54.13 C +ATOM 2972 CG TYR A1076 -2.614 18.454 -8.368 1.00 57.78 C +ATOM 2973 CD1 TYR A1076 -1.910 19.668 -8.226 1.00 62.12 C +ATOM 2974 CD2 TYR A1076 -2.156 17.481 -9.280 1.00 57.80 C +ATOM 2975 CE1 TYR A1076 -0.757 19.910 -8.998 1.00 64.82 C +ATOM 2976 CE2 TYR A1076 -1.005 17.725 -10.054 1.00 59.85 C +ATOM 2977 CZ TYR A1076 -0.305 18.940 -9.913 1.00 71.28 C +ATOM 2978 OH TYR A1076 0.811 19.178 -10.660 1.00 75.29 O +ATOM 2979 H TYR A1076 -5.206 19.646 -10.021 1.00 50.13 H +ATOM 2980 HA TYR A1076 -5.136 17.606 -9.189 1.00 50.19 H +ATOM 2981 HB3 TYR A1076 -3.793 17.199 -7.144 1.00 54.13 H +ATOM 2982 HB2 TYR A1076 -3.889 18.877 -6.704 1.00 54.13 H +ATOM 2983 HD1 TYR A1076 -2.257 20.423 -7.536 1.00 62.12 H +ATOM 2984 HD2 TYR A1076 -2.693 16.552 -9.399 1.00 57.80 H +ATOM 2985 HE1 TYR A1076 -0.223 20.842 -8.889 1.00 64.82 H +ATOM 2986 HE2 TYR A1076 -0.665 16.978 -10.756 1.00 59.85 H +ATOM 2987 HH TYR A1076 1.035 18.453 -11.248 1.00 75.29 H +ATOM 2988 N LYS A1077 -7.625 18.303 -8.117 1.00 44.81 N +ATOM 2989 CA LYS A1077 -8.935 17.968 -7.561 1.00 43.60 C +ATOM 2990 C LYS A1077 -9.643 17.020 -8.530 1.00 41.76 C +ATOM 2991 O LYS A1077 -9.712 17.322 -9.722 1.00 38.21 O +ATOM 2992 CB LYS A1077 -9.773 19.248 -7.392 1.00 46.62 C +ATOM 2993 CG LYS A1077 -9.225 20.227 -6.347 1.00 58.33 C +ATOM 2994 CD LYS A1077 -10.181 21.413 -6.133 1.00 67.59 C +ATOM 2995 CE LYS A1077 -9.692 22.437 -5.097 1.00 79.50 C +ATOM 2996 NZ LYS A1077 -8.547 23.225 -5.587 1.00 87.69 N1+ +ATOM 2997 H LYS A1077 -7.639 18.785 -9.007 1.00 44.81 H +ATOM 2998 HA LYS A1077 -8.838 17.480 -6.589 1.00 43.60 H +ATOM 2999 HB3 LYS A1077 -10.784 18.965 -7.093 1.00 46.62 H +ATOM 3000 HB2 LYS A1077 -9.869 19.760 -8.351 1.00 46.62 H +ATOM 3001 HG3 LYS A1077 -8.247 20.587 -6.666 1.00 58.33 H +ATOM 3002 HG2 LYS A1077 -9.067 19.705 -5.403 1.00 58.33 H +ATOM 3003 HD3 LYS A1077 -11.151 21.030 -5.811 1.00 67.59 H +ATOM 3004 HD2 LYS A1077 -10.370 21.908 -7.087 1.00 67.59 H +ATOM 3005 HE3 LYS A1077 -9.420 21.938 -4.166 1.00 79.50 H +ATOM 3006 HE2 LYS A1077 -10.500 23.130 -4.860 1.00 79.50 H +ATOM 3007 HZ1 LYS A1077 -8.803 23.715 -6.432 1.00 87.69 H +ATOM 3008 HZ2 LYS A1077 -8.267 23.900 -4.892 1.00 87.69 H +ATOM 3009 HZ3 LYS A1077 -7.769 22.614 -5.792 1.00 87.69 H +ATOM 3010 N PHE A1078 -10.177 15.913 -7.995 1.00 36.93 N +ATOM 3011 CA PHE A1078 -10.895 14.905 -8.764 1.00 34.49 C +ATOM 3012 C PHE A1078 -12.338 14.858 -8.243 1.00 39.60 C +ATOM 3013 O PHE A1078 -12.585 14.362 -7.144 1.00 40.48 O +ATOM 3014 CB PHE A1078 -10.163 13.553 -8.631 1.00 35.26 C +ATOM 3015 CG PHE A1078 -10.493 12.591 -9.753 1.00 34.98 C +ATOM 3016 CD1 PHE A1078 -9.616 12.456 -10.849 1.00 36.30 C +ATOM 3017 CD2 PHE A1078 -11.752 11.962 -9.802 1.00 35.94 C +ATOM 3018 CE1 PHE A1078 -9.990 11.692 -11.946 1.00 35.94 C +ATOM 3019 CE2 PHE A1078 -12.103 11.200 -10.902 1.00 37.30 C +ATOM 3020 CZ PHE A1078 -11.232 11.074 -11.975 1.00 34.53 C +ATOM 3021 H PHE A1078 -10.074 15.726 -7.005 1.00 36.93 H +ATOM 3022 HA PHE A1078 -10.911 15.172 -9.822 1.00 34.49 H +ATOM 3023 HB3 PHE A1078 -10.381 13.079 -7.673 1.00 35.26 H +ATOM 3024 HB2 PHE A1078 -9.086 13.717 -8.642 1.00 35.26 H +ATOM 3025 HD1 PHE A1078 -8.660 12.960 -10.852 1.00 36.30 H +ATOM 3026 HD2 PHE A1078 -12.462 12.090 -8.999 1.00 35.94 H +ATOM 3027 HE1 PHE A1078 -9.324 11.597 -12.788 1.00 35.94 H +ATOM 3028 HE2 PHE A1078 -13.078 10.743 -10.924 1.00 37.30 H +ATOM 3029 HZ PHE A1078 -11.523 10.502 -12.843 1.00 34.53 H +ATOM 3030 N TYR A1079 -13.251 15.383 -9.069 1.00 35.49 N +ATOM 3031 CA TYR A1079 -14.699 15.432 -8.867 1.00 35.71 C +ATOM 3032 C TYR A1079 -15.372 14.145 -9.384 1.00 36.13 C +ATOM 3033 O TYR A1079 -14.705 13.288 -9.964 1.00 33.76 O +ATOM 3034 CB TYR A1079 -15.243 16.673 -9.619 1.00 37.14 C +ATOM 3035 CG TYR A1079 -14.753 18.019 -9.104 1.00 41.82 C +ATOM 3036 CD1 TYR A1079 -15.584 18.803 -8.276 1.00 45.55 C +ATOM 3037 CD2 TYR A1079 -13.470 18.498 -9.452 1.00 42.95 C +ATOM 3038 CE1 TYR A1079 -15.127 20.039 -7.777 1.00 48.19 C +ATOM 3039 CE2 TYR A1079 -13.011 19.728 -8.945 1.00 44.97 C +ATOM 3040 CZ TYR A1079 -13.835 20.496 -8.101 1.00 54.63 C +ATOM 3041 OH TYR A1079 -13.381 21.681 -7.599 1.00 58.40 O +ATOM 3042 H TYR A1079 -12.940 15.728 -9.968 1.00 35.49 H +ATOM 3043 HA TYR A1079 -14.910 15.528 -7.804 1.00 35.71 H +ATOM 3044 HB3 TYR A1079 -16.331 16.687 -9.569 1.00 37.14 H +ATOM 3045 HB2 TYR A1079 -15.004 16.605 -10.682 1.00 37.14 H +ATOM 3046 HD1 TYR A1079 -16.571 18.454 -8.007 1.00 45.55 H +ATOM 3047 HD2 TYR A1079 -12.828 17.919 -10.099 1.00 42.95 H +ATOM 3048 HE1 TYR A1079 -15.767 20.629 -7.137 1.00 48.19 H +ATOM 3049 HE2 TYR A1079 -12.025 20.081 -9.205 1.00 44.97 H +ATOM 3050 HH TYR A1079 -14.028 22.123 -7.043 1.00 58.40 H +ATOM 3051 N TYR A1080 -16.702 14.042 -9.223 1.00 32.40 N +ATOM 3052 CA TYR A1080 -17.512 13.046 -9.939 1.00 30.87 C +ATOM 3053 C TYR A1080 -17.542 13.317 -11.455 1.00 32.51 C +ATOM 3054 O TYR A1080 -17.430 12.377 -12.238 1.00 30.72 O +ATOM 3055 CB TYR A1080 -18.938 12.999 -9.362 1.00 32.98 C +ATOM 3056 CG TYR A1080 -19.032 12.486 -7.936 1.00 35.85 C +ATOM 3057 CD1 TYR A1080 -18.695 11.147 -7.644 1.00 37.45 C +ATOM 3058 CD2 TYR A1080 -19.477 13.336 -6.902 1.00 38.07 C +ATOM 3059 CE1 TYR A1080 -18.818 10.655 -6.331 1.00 40.49 C +ATOM 3060 CE2 TYR A1080 -19.600 12.844 -5.588 1.00 40.41 C +ATOM 3061 CZ TYR A1080 -19.277 11.503 -5.305 1.00 44.30 C +ATOM 3062 OH TYR A1080 -19.410 11.026 -4.036 1.00 44.69 O +ATOM 3063 H TYR A1080 -17.208 14.750 -8.710 1.00 32.40 H +ATOM 3064 HA TYR A1080 -17.052 12.066 -9.794 1.00 30.87 H +ATOM 3065 HB3 TYR A1080 -19.560 12.345 -9.974 1.00 32.98 H +ATOM 3066 HB2 TYR A1080 -19.400 13.985 -9.425 1.00 32.98 H +ATOM 3067 HD1 TYR A1080 -18.346 10.490 -8.426 1.00 37.45 H +ATOM 3068 HD2 TYR A1080 -19.735 14.363 -7.111 1.00 38.07 H +ATOM 3069 HE1 TYR A1080 -18.566 9.627 -6.116 1.00 40.49 H +ATOM 3070 HE2 TYR A1080 -19.946 13.496 -4.799 1.00 40.41 H +ATOM 3071 HH TYR A1080 -19.090 10.127 -3.938 1.00 44.69 H +ATOM 3072 N ALA A1081 -17.605 14.604 -11.842 1.00 29.56 N +ATOM 3073 CA ALA A1081 -17.512 15.064 -13.228 1.00 27.95 C +ATOM 3074 C ALA A1081 -16.170 14.748 -13.915 1.00 27.99 C +ATOM 3075 O ALA A1081 -16.124 14.729 -15.142 1.00 26.14 O +ATOM 3076 CB ALA A1081 -17.798 16.573 -13.264 1.00 28.95 C +ATOM 3077 H ALA A1081 -17.713 15.325 -11.141 1.00 29.56 H +ATOM 3078 HA ALA A1081 -18.290 14.551 -13.796 1.00 27.95 H +ATOM 3079 HB1 ALA A1081 -17.793 16.956 -14.285 1.00 28.95 H +ATOM 3080 HB2 ALA A1081 -18.781 16.794 -12.847 1.00 28.95 H +ATOM 3081 HB3 ALA A1081 -17.061 17.135 -12.690 1.00 28.95 H +ATOM 3082 N SER A1082 -15.102 14.505 -13.138 1.00 25.39 N +ATOM 3083 CA SER A1082 -13.776 14.151 -13.646 1.00 24.95 C +ATOM 3084 C SER A1082 -13.745 12.745 -14.282 1.00 27.35 C +ATOM 3085 O SER A1082 -13.055 12.563 -15.284 1.00 26.99 O +ATOM 3086 CB SER A1082 -12.763 14.260 -12.500 1.00 28.68 C +ATOM 3087 OG SER A1082 -12.732 15.566 -11.967 1.00 33.92 O +ATOM 3088 H SER A1082 -15.195 14.533 -12.132 1.00 25.39 H +ATOM 3089 HA SER A1082 -13.498 14.869 -14.420 1.00 24.95 H +ATOM 3090 HB3 SER A1082 -11.764 14.000 -12.839 1.00 28.68 H +ATOM 3091 HB2 SER A1082 -13.008 13.569 -11.703 1.00 28.68 H +ATOM 3092 HG SER A1082 -12.333 16.169 -12.606 1.00 33.92 H +ATOM 3093 N ASP A1083 -14.537 11.799 -13.743 1.00 23.13 N +ATOM 3094 CA ASP A1083 -14.788 10.488 -14.354 1.00 22.18 C +ATOM 3095 C ASP A1083 -15.611 10.564 -15.651 1.00 25.02 C +ATOM 3096 O ASP A1083 -15.428 9.691 -16.496 1.00 23.63 O +ATOM 3097 CB ASP A1083 -15.474 9.498 -13.386 1.00 24.19 C +ATOM 3098 CG ASP A1083 -14.601 8.937 -12.259 1.00 28.00 C +ATOM 3099 OD1 ASP A1083 -13.433 8.581 -12.529 1.00 27.87 O +ATOM 3100 OD2 ASP A1083 -15.162 8.716 -11.167 1.00 31.18 O1- +ATOM 3101 H ASP A1083 -15.083 12.016 -12.921 1.00 23.13 H +ATOM 3102 HA ASP A1083 -13.818 10.082 -14.642 1.00 22.18 H +ATOM 3103 HB3 ASP A1083 -15.865 8.648 -13.943 1.00 24.19 H +ATOM 3104 HB2 ASP A1083 -16.333 9.997 -12.937 1.00 24.19 H +ATOM 3105 N VAL A1084 -16.478 11.581 -15.808 1.00 23.20 N +ATOM 3106 CA VAL A1084 -17.237 11.816 -17.043 1.00 22.46 C +ATOM 3107 C VAL A1084 -16.326 12.236 -18.215 1.00 25.25 C +ATOM 3108 O VAL A1084 -16.560 11.779 -19.334 1.00 23.49 O +ATOM 3109 CB VAL A1084 -18.378 12.855 -16.849 1.00 25.40 C +ATOM 3110 CG1 VAL A1084 -19.004 13.409 -18.149 1.00 24.52 C +ATOM 3111 CG2 VAL A1084 -19.467 12.272 -15.928 1.00 25.38 C +ATOM 3112 H VAL A1084 -16.590 12.265 -15.073 1.00 23.20 H +ATOM 3113 HA VAL A1084 -17.704 10.870 -17.322 1.00 22.46 H +ATOM 3114 HB VAL A1084 -17.970 13.719 -16.335 1.00 25.40 H +ATOM 3115 HG11 VAL A1084 -19.744 14.170 -17.923 1.00 24.52 H +ATOM 3116 HG12 VAL A1084 -18.280 13.911 -18.790 1.00 24.52 H +ATOM 3117 HG13 VAL A1084 -19.487 12.624 -18.732 1.00 24.52 H +ATOM 3118 HG21 VAL A1084 -20.235 13.007 -15.704 1.00 25.38 H +ATOM 3119 HG22 VAL A1084 -19.957 11.412 -16.385 1.00 25.38 H +ATOM 3120 HG23 VAL A1084 -19.053 11.952 -14.972 1.00 25.38 H +ATOM 3121 N TRP A1085 -15.279 13.034 -17.932 1.00 23.54 N +ATOM 3122 CA TRP A1085 -14.206 13.342 -18.882 1.00 23.20 C +ATOM 3123 C TRP A1085 -13.454 12.077 -19.319 1.00 25.99 C +ATOM 3124 O TRP A1085 -13.253 11.889 -20.518 1.00 26.38 O +ATOM 3125 CB TRP A1085 -13.269 14.426 -18.303 1.00 22.62 C +ATOM 3126 CG TRP A1085 -12.128 14.881 -19.175 1.00 22.84 C +ATOM 3127 CD1 TRP A1085 -11.055 14.132 -19.515 1.00 25.30 C +ATOM 3128 CD2 TRP A1085 -11.921 16.174 -19.828 1.00 22.79 C +ATOM 3129 NE1 TRP A1085 -10.233 14.839 -20.359 1.00 24.54 N +ATOM 3130 CE2 TRP A1085 -10.718 16.104 -20.594 1.00 26.50 C +ATOM 3131 CE3 TRP A1085 -12.618 17.404 -19.853 1.00 24.28 C +ATOM 3132 CZ2 TRP A1085 -10.252 17.180 -21.365 1.00 26.19 C +ATOM 3133 CZ3 TRP A1085 -12.152 18.496 -20.615 1.00 25.94 C +ATOM 3134 CH2 TRP A1085 -10.975 18.384 -21.378 1.00 26.22 C +ATOM 3135 H TRP A1085 -15.153 13.373 -16.988 1.00 23.54 H +ATOM 3136 HA TRP A1085 -14.660 13.744 -19.784 1.00 23.20 H +ATOM 3137 HB3 TRP A1085 -12.840 14.080 -17.363 1.00 22.62 H +ATOM 3138 HB2 TRP A1085 -13.860 15.308 -18.052 1.00 22.62 H +ATOM 3139 HD1 TRP A1085 -10.890 13.120 -19.184 1.00 25.30 H +ATOM 3140 HE1 TRP A1085 -9.371 14.455 -20.731 1.00 24.54 H +ATOM 3141 HE3 TRP A1085 -13.527 17.507 -19.285 1.00 24.28 H +ATOM 3142 HZ2 TRP A1085 -9.346 17.075 -21.938 1.00 26.19 H +ATOM 3143 HZ3 TRP A1085 -12.701 19.424 -20.620 1.00 25.94 H +ATOM 3144 HH2 TRP A1085 -10.626 19.223 -21.962 1.00 26.22 H +ATOM 3145 N SER A1086 -13.092 11.228 -18.343 1.00 23.27 N +ATOM 3146 CA SER A1086 -12.412 9.953 -18.565 1.00 22.29 C +ATOM 3147 C SER A1086 -13.274 8.947 -19.353 1.00 25.76 C +ATOM 3148 O SER A1086 -12.707 8.181 -20.130 1.00 25.35 O +ATOM 3149 CB SER A1086 -11.978 9.362 -17.215 1.00 24.31 C +ATOM 3150 OG SER A1086 -11.144 10.256 -16.504 1.00 27.11 O +ATOM 3151 H SER A1086 -13.281 11.465 -17.378 1.00 23.27 H +ATOM 3152 HA SER A1086 -11.518 10.152 -19.159 1.00 22.29 H +ATOM 3153 HB3 SER A1086 -11.420 8.439 -17.370 1.00 24.31 H +ATOM 3154 HB2 SER A1086 -12.840 9.115 -16.597 1.00 24.31 H +ATOM 3155 HG SER A1086 -10.415 10.519 -17.077 1.00 27.11 H +ATOM 3156 N PHE A1087 -14.612 9.007 -19.195 1.00 22.49 N +ATOM 3157 CA PHE A1087 -15.589 8.295 -20.022 1.00 21.87 C +ATOM 3158 C PHE A1087 -15.618 8.765 -21.483 1.00 26.76 C +ATOM 3159 O PHE A1087 -15.773 7.924 -22.363 1.00 27.22 O +ATOM 3160 CB PHE A1087 -16.998 8.282 -19.373 1.00 24.21 C +ATOM 3161 CG PHE A1087 -18.139 7.909 -20.310 1.00 25.86 C +ATOM 3162 CD1 PHE A1087 -18.280 6.574 -20.745 1.00 28.01 C +ATOM 3163 CD2 PHE A1087 -18.909 8.917 -20.932 1.00 26.70 C +ATOM 3164 CE1 PHE A1087 -19.174 6.264 -21.760 1.00 28.28 C +ATOM 3165 CE2 PHE A1087 -19.803 8.585 -21.941 1.00 28.37 C +ATOM 3166 CZ PHE A1087 -19.928 7.266 -22.358 1.00 26.26 C +ATOM 3167 H PHE A1087 -14.996 9.642 -18.508 1.00 22.49 H +ATOM 3168 HA PHE A1087 -15.264 7.254 -20.052 1.00 21.87 H +ATOM 3169 HB3 PHE A1087 -17.220 9.244 -18.916 1.00 24.21 H +ATOM 3170 HB2 PHE A1087 -17.001 7.559 -18.562 1.00 24.21 H +ATOM 3171 HD1 PHE A1087 -17.662 5.798 -20.320 1.00 28.01 H +ATOM 3172 HD2 PHE A1087 -18.781 9.951 -20.647 1.00 26.70 H +ATOM 3173 HE1 PHE A1087 -19.270 5.244 -22.097 1.00 28.28 H +ATOM 3174 HE2 PHE A1087 -20.388 9.359 -22.416 1.00 28.37 H +ATOM 3175 HZ PHE A1087 -20.613 7.022 -23.156 1.00 26.26 H +ATOM 3176 N GLY A1088 -15.463 10.078 -21.725 1.00 25.20 N +ATOM 3177 CA GLY A1088 -15.397 10.647 -23.072 1.00 24.53 C +ATOM 3178 C GLY A1088 -14.144 10.153 -23.812 1.00 25.56 C +ATOM 3179 O GLY A1088 -14.215 9.886 -25.010 1.00 24.64 O +ATOM 3180 H GLY A1088 -15.360 10.718 -20.948 1.00 25.20 H +ATOM 3181 HA3 GLY A1088 -15.369 11.734 -23.000 1.00 24.53 H +ATOM 3182 HA2 GLY A1088 -16.295 10.385 -23.634 1.00 24.53 H +ATOM 3183 N VAL A1089 -13.018 9.990 -23.092 1.00 22.27 N +ATOM 3184 CA VAL A1089 -11.773 9.430 -23.621 1.00 21.54 C +ATOM 3185 C VAL A1089 -11.845 7.892 -23.772 1.00 25.32 C +ATOM 3186 O VAL A1089 -11.253 7.367 -24.713 1.00 25.91 O +ATOM 3187 CB VAL A1089 -10.538 9.818 -22.757 1.00 24.92 C +ATOM 3188 CG1 VAL A1089 -9.205 9.300 -23.330 1.00 24.42 C +ATOM 3189 CG2 VAL A1089 -10.419 11.342 -22.593 1.00 24.24 C +ATOM 3190 H VAL A1089 -13.022 10.219 -22.107 1.00 22.27 H +ATOM 3191 HA VAL A1089 -11.612 9.844 -24.616 1.00 21.54 H +ATOM 3192 HB VAL A1089 -10.659 9.389 -21.761 1.00 24.92 H +ATOM 3193 HG11 VAL A1089 -8.358 9.619 -22.723 1.00 24.42 H +ATOM 3194 HG12 VAL A1089 -9.165 8.214 -23.374 1.00 24.42 H +ATOM 3195 HG13 VAL A1089 -9.045 9.679 -24.340 1.00 24.42 H +ATOM 3196 HG21 VAL A1089 -9.521 11.613 -22.042 1.00 24.24 H +ATOM 3197 HG22 VAL A1089 -10.373 11.837 -23.563 1.00 24.24 H +ATOM 3198 HG23 VAL A1089 -11.258 11.760 -22.043 1.00 24.24 H +ATOM 3199 N THR A1090 -12.615 7.201 -22.911 1.00 22.27 N +ATOM 3200 CA THR A1090 -12.918 5.771 -23.052 1.00 21.73 C +ATOM 3201 C THR A1090 -13.803 5.497 -24.282 1.00 24.89 C +ATOM 3202 O THR A1090 -13.496 4.581 -25.038 1.00 24.37 O +ATOM 3203 CB THR A1090 -13.638 5.177 -21.811 1.00 26.43 C +ATOM 3204 OG1 THR A1090 -12.855 5.394 -20.661 1.00 23.21 O +ATOM 3205 CG2 THR A1090 -13.917 3.664 -21.880 1.00 26.50 C +ATOM 3206 H THR A1090 -13.071 7.685 -22.149 1.00 22.27 H +ATOM 3207 HA THR A1090 -11.974 5.239 -23.187 1.00 21.73 H +ATOM 3208 HB THR A1090 -14.587 5.688 -21.652 1.00 26.43 H +ATOM 3209 HG1 THR A1090 -12.746 6.342 -20.526 1.00 23.21 H +ATOM 3210 HG21 THR A1090 -14.289 3.293 -20.925 1.00 26.50 H +ATOM 3211 HG22 THR A1090 -14.666 3.418 -22.633 1.00 26.50 H +ATOM 3212 HG23 THR A1090 -13.011 3.107 -22.118 1.00 26.50 H +ATOM 3213 N LEU A1091 -14.844 6.326 -24.480 1.00 21.38 N +ATOM 3214 CA LEU A1091 -15.754 6.315 -25.626 1.00 21.26 C +ATOM 3215 C LEU A1091 -15.027 6.594 -26.953 1.00 25.55 C +ATOM 3216 O LEU A1091 -15.338 5.943 -27.948 1.00 24.92 O +ATOM 3217 CB LEU A1091 -16.902 7.321 -25.358 1.00 21.18 C +ATOM 3218 CG LEU A1091 -18.043 7.343 -26.400 1.00 26.34 C +ATOM 3219 CD1 LEU A1091 -18.736 5.972 -26.517 1.00 27.66 C +ATOM 3220 CD2 LEU A1091 -19.046 8.478 -26.096 1.00 28.91 C +ATOM 3221 H LEU A1091 -15.031 7.043 -23.789 1.00 21.38 H +ATOM 3222 HA LEU A1091 -16.172 5.309 -25.684 1.00 21.26 H +ATOM 3223 HB3 LEU A1091 -16.476 8.322 -25.277 1.00 21.18 H +ATOM 3224 HB2 LEU A1091 -17.339 7.107 -24.381 1.00 21.18 H +ATOM 3225 HG LEU A1091 -17.612 7.573 -27.373 1.00 26.34 H +ATOM 3226 HD11 LEU A1091 -19.820 6.049 -26.604 1.00 27.66 H +ATOM 3227 HD12 LEU A1091 -18.386 5.428 -27.392 1.00 27.66 H +ATOM 3228 HD13 LEU A1091 -18.522 5.346 -25.651 1.00 27.66 H +ATOM 3229 HD21 LEU A1091 -19.158 9.136 -26.959 1.00 28.91 H +ATOM 3230 HD22 LEU A1091 -20.039 8.108 -25.839 1.00 28.91 H +ATOM 3231 HD23 LEU A1091 -18.721 9.099 -25.260 1.00 28.91 H +ATOM 3232 N TYR A1092 -14.031 7.499 -26.923 1.00 23.80 N +ATOM 3233 CA TYR A1092 -13.099 7.753 -28.021 1.00 23.59 C +ATOM 3234 C TYR A1092 -12.315 6.491 -28.425 1.00 28.07 C +ATOM 3235 O TYR A1092 -12.272 6.181 -29.614 1.00 28.26 O +ATOM 3236 CB TYR A1092 -12.173 8.936 -27.658 1.00 24.03 C +ATOM 3237 CG TYR A1092 -11.033 9.197 -28.626 1.00 25.49 C +ATOM 3238 CD1 TYR A1092 -11.301 9.688 -29.920 1.00 27.64 C +ATOM 3239 CD2 TYR A1092 -9.705 8.903 -28.251 1.00 25.28 C +ATOM 3240 CE1 TYR A1092 -10.251 9.863 -30.838 1.00 27.61 C +ATOM 3241 CE2 TYR A1092 -8.652 9.096 -29.166 1.00 25.84 C +ATOM 3242 CZ TYR A1092 -8.929 9.573 -30.462 1.00 31.24 C +ATOM 3243 OH TYR A1092 -7.926 9.744 -31.367 1.00 30.99 O +ATOM 3244 H TYR A1092 -13.854 8.006 -26.066 1.00 23.80 H +ATOM 3245 HA TYR A1092 -13.698 8.053 -28.883 1.00 23.59 H +ATOM 3246 HB3 TYR A1092 -11.731 8.761 -26.683 1.00 24.03 H +ATOM 3247 HB2 TYR A1092 -12.756 9.847 -27.538 1.00 24.03 H +ATOM 3248 HD1 TYR A1092 -12.315 9.900 -30.224 1.00 27.64 H +ATOM 3249 HD2 TYR A1092 -9.491 8.520 -27.264 1.00 25.28 H +ATOM 3250 HE1 TYR A1092 -10.457 10.196 -31.843 1.00 27.61 H +ATOM 3251 HE2 TYR A1092 -7.639 8.871 -28.867 1.00 25.84 H +ATOM 3252 HH TYR A1092 -7.106 9.333 -31.072 1.00 30.99 H +ATOM 3253 N GLU A1093 -11.768 5.761 -27.435 1.00 24.82 N +ATOM 3254 CA GLU A1093 -11.086 4.484 -27.646 1.00 23.87 C +ATOM 3255 C GLU A1093 -11.975 3.389 -28.258 1.00 29.02 C +ATOM 3256 O GLU A1093 -11.477 2.664 -29.115 1.00 29.63 O +ATOM 3257 CB GLU A1093 -10.427 3.973 -26.356 1.00 24.44 C +ATOM 3258 CG GLU A1093 -9.237 4.821 -25.884 1.00 27.04 C +ATOM 3259 CD GLU A1093 -8.637 4.226 -24.611 1.00 39.48 C +ATOM 3260 OE1 GLU A1093 -9.243 4.419 -23.533 1.00 26.77 O +ATOM 3261 OE2 GLU A1093 -7.591 3.554 -24.738 1.00 32.38 O1- +ATOM 3262 H GLU A1093 -11.845 6.074 -26.476 1.00 24.82 H +ATOM 3263 HA GLU A1093 -10.281 4.673 -28.357 1.00 23.87 H +ATOM 3264 HB3 GLU A1093 -10.087 2.947 -26.501 1.00 24.44 H +ATOM 3265 HB2 GLU A1093 -11.167 3.920 -25.560 1.00 24.44 H +ATOM 3266 HG3 GLU A1093 -9.531 5.852 -25.702 1.00 27.04 H +ATOM 3267 HG2 GLU A1093 -8.481 4.862 -26.670 1.00 27.04 H +ATOM 3268 N LEU A1094 -13.259 3.299 -27.860 1.00 26.56 N +ATOM 3269 CA LEU A1094 -14.192 2.306 -28.409 1.00 27.12 C +ATOM 3270 C LEU A1094 -14.478 2.531 -29.905 1.00 29.23 C +ATOM 3271 O LEU A1094 -14.481 1.561 -30.660 1.00 30.58 O +ATOM 3272 CB LEU A1094 -15.520 2.246 -27.615 1.00 27.75 C +ATOM 3273 CG LEU A1094 -15.421 2.018 -26.089 1.00 33.45 C +ATOM 3274 CD1 LEU A1094 -16.789 1.644 -25.482 1.00 34.25 C +ATOM 3275 CD2 LEU A1094 -14.323 1.027 -25.666 1.00 37.20 C +ATOM 3276 H LEU A1094 -13.614 3.919 -27.144 1.00 26.56 H +ATOM 3277 HA LEU A1094 -13.708 1.330 -28.327 1.00 27.12 H +ATOM 3278 HB3 LEU A1094 -16.113 1.436 -28.041 1.00 27.75 H +ATOM 3279 HB2 LEU A1094 -16.100 3.154 -27.790 1.00 27.75 H +ATOM 3280 HG LEU A1094 -15.166 2.977 -25.652 1.00 33.45 H +ATOM 3281 HD11 LEU A1094 -17.053 2.322 -24.669 1.00 34.25 H +ATOM 3282 HD12 LEU A1094 -17.592 1.700 -26.216 1.00 34.25 H +ATOM 3283 HD13 LEU A1094 -16.794 0.632 -25.076 1.00 34.25 H +ATOM 3284 HD21 LEU A1094 -14.466 0.703 -24.636 1.00 37.20 H +ATOM 3285 HD22 LEU A1094 -14.313 0.140 -26.299 1.00 37.20 H +ATOM 3286 HD23 LEU A1094 -13.335 1.487 -25.711 1.00 37.20 H +ATOM 3287 N LEU A1095 -14.672 3.802 -30.304 1.00 26.35 N +ATOM 3288 CA LEU A1095 -14.961 4.209 -31.682 1.00 28.85 C +ATOM 3289 C LEU A1095 -13.734 4.120 -32.619 1.00 32.34 C +ATOM 3290 O LEU A1095 -13.931 3.898 -33.813 1.00 33.48 O +ATOM 3291 CB LEU A1095 -15.577 5.632 -31.691 1.00 29.95 C +ATOM 3292 CG LEU A1095 -17.110 5.748 -31.482 1.00 37.01 C +ATOM 3293 CD1 LEU A1095 -17.928 4.993 -32.548 1.00 41.99 C +ATOM 3294 CD2 LEU A1095 -17.598 5.424 -30.060 1.00 37.43 C +ATOM 3295 H LEU A1095 -14.644 4.545 -29.618 1.00 26.35 H +ATOM 3296 HA LEU A1095 -15.694 3.506 -32.081 1.00 28.85 H +ATOM 3297 HB3 LEU A1095 -15.375 6.094 -32.658 1.00 29.95 H +ATOM 3298 HB2 LEU A1095 -15.062 6.260 -30.963 1.00 29.95 H +ATOM 3299 HG LEU A1095 -17.340 6.802 -31.630 1.00 37.01 H +ATOM 3300 HD11 LEU A1095 -18.648 5.658 -33.026 1.00 41.99 H +ATOM 3301 HD12 LEU A1095 -17.300 4.585 -33.335 1.00 41.99 H +ATOM 3302 HD13 LEU A1095 -18.483 4.155 -32.126 1.00 41.99 H +ATOM 3303 HD21 LEU A1095 -18.614 5.029 -30.052 1.00 37.43 H +ATOM 3304 HD22 LEU A1095 -16.973 4.692 -29.555 1.00 37.43 H +ATOM 3305 HD23 LEU A1095 -17.608 6.330 -29.455 1.00 37.43 H +ATOM 3306 N THR A1096 -12.506 4.251 -32.084 1.00 28.49 N +ATOM 3307 CA THR A1096 -11.253 3.995 -32.816 1.00 28.46 C +ATOM 3308 C THR A1096 -10.852 2.497 -32.815 1.00 34.09 C +ATOM 3309 O THR A1096 -9.856 2.153 -33.452 1.00 34.31 O +ATOM 3310 CB THR A1096 -10.059 4.808 -32.236 1.00 31.93 C +ATOM 3311 OG1 THR A1096 -9.741 4.419 -30.916 1.00 28.23 O +ATOM 3312 CG2 THR A1096 -10.258 6.329 -32.269 1.00 33.74 C +ATOM 3313 H THR A1096 -12.415 4.453 -31.098 1.00 28.49 H +ATOM 3314 HA THR A1096 -11.377 4.297 -33.856 1.00 28.46 H +ATOM 3315 HB THR A1096 -9.172 4.597 -32.833 1.00 31.93 H +ATOM 3316 HG1 THR A1096 -10.439 4.736 -30.336 1.00 28.23 H +ATOM 3317 HG21 THR A1096 -9.410 6.848 -31.821 1.00 33.74 H +ATOM 3318 HG22 THR A1096 -10.359 6.687 -33.292 1.00 33.74 H +ATOM 3319 HG23 THR A1096 -11.150 6.635 -31.735 1.00 33.74 H +ATOM 3320 N HIS A1097 -11.615 1.642 -32.103 1.00 31.69 N +ATOM 3321 CA HIS A1097 -11.381 0.204 -31.900 1.00 31.97 C +ATOM 3322 C HIS A1097 -10.062 -0.104 -31.165 1.00 33.69 C +ATOM 3323 O HIS A1097 -9.451 -1.146 -31.396 1.00 33.12 O +ATOM 3324 CB HIS A1097 -11.554 -0.597 -33.212 1.00 35.14 C +ATOM 3325 CG HIS A1097 -12.938 -0.518 -33.800 1.00 40.55 C +ATOM 3326 ND1 HIS A1097 -13.357 0.538 -34.617 1.00 43.74 N +ATOM 3327 CD2 HIS A1097 -13.982 -1.406 -33.645 1.00 43.94 C +ATOM 3328 CE1 HIS A1097 -14.618 0.257 -34.914 1.00 44.01 C +ATOM 3329 NE2 HIS A1097 -15.038 -0.885 -34.369 1.00 44.57 N +ATOM 3330 H HIS A1097 -12.425 2.012 -31.624 1.00 31.69 H +ATOM 3331 HA HIS A1097 -12.165 -0.123 -31.216 1.00 31.97 H +ATOM 3332 HB3 HIS A1097 -11.341 -1.653 -33.046 1.00 35.14 H +ATOM 3333 HB2 HIS A1097 -10.844 -0.264 -33.967 1.00 35.14 H +ATOM 3334 HD2 HIS A1097 -14.056 -2.331 -33.092 1.00 43.94 H +ATOM 3335 HE1 HIS A1097 -15.240 0.888 -35.531 1.00 44.01 H +ATOM 3336 HE2 HIS A1097 -15.963 -1.292 -34.456 1.00 44.57 H +ATOM 3337 N CYS A1098 -9.658 0.828 -30.285 1.00 28.70 N +ATOM 3338 CA CYS A1098 -8.478 0.796 -29.422 1.00 27.70 C +ATOM 3339 C CYS A1098 -7.155 0.700 -30.208 1.00 30.22 C +ATOM 3340 O CYS A1098 -6.198 0.093 -29.727 1.00 27.75 O +ATOM 3341 CB CYS A1098 -8.592 -0.268 -28.315 1.00 27.86 C +ATOM 3342 SG CYS A1098 -10.039 0.093 -27.277 1.00 31.27 S +ATOM 3343 H CYS A1098 -10.254 1.635 -30.150 1.00 28.70 H +ATOM 3344 HA CYS A1098 -8.453 1.767 -28.928 1.00 27.70 H +ATOM 3345 HB3 CYS A1098 -7.706 -0.255 -27.682 1.00 27.86 H +ATOM 3346 HB2 CYS A1098 -8.685 -1.270 -28.729 1.00 27.86 H +ATOM 3347 HG CYS A1098 -10.014 -1.059 -26.600 1.00 31.27 H +ATOM 3348 N ASP A1099 -7.130 1.319 -31.404 1.00 28.20 N +ATOM 3349 CA ASP A1099 -5.967 1.423 -32.283 1.00 29.09 C +ATOM 3350 C ASP A1099 -4.871 2.253 -31.590 1.00 32.04 C +ATOM 3351 O ASP A1099 -5.137 3.386 -31.191 1.00 29.80 O +ATOM 3352 CB ASP A1099 -6.383 2.011 -33.658 1.00 31.01 C +ATOM 3353 CG ASP A1099 -5.334 2.058 -34.786 1.00 42.86 C +ATOM 3354 OD1 ASP A1099 -4.205 1.546 -34.615 1.00 44.82 O +ATOM 3355 OD2 ASP A1099 -5.706 2.585 -35.856 1.00 55.40 O1- +ATOM 3356 H ASP A1099 -7.968 1.778 -31.732 1.00 28.20 H +ATOM 3357 HA ASP A1099 -5.599 0.408 -32.440 1.00 29.09 H +ATOM 3358 HB3 ASP A1099 -6.782 3.015 -33.511 1.00 31.01 H +ATOM 3359 HB2 ASP A1099 -7.232 1.442 -34.033 1.00 31.01 H +ATOM 3360 N SER A1100 -3.673 1.660 -31.457 1.00 30.26 N +ATOM 3361 CA SER A1100 -2.501 2.255 -30.813 1.00 30.30 C +ATOM 3362 C SER A1100 -2.007 3.557 -31.475 1.00 34.31 C +ATOM 3363 O SER A1100 -1.544 4.442 -30.757 1.00 34.07 O +ATOM 3364 CB SER A1100 -1.395 1.186 -30.688 1.00 35.14 C +ATOM 3365 OG SER A1100 -0.812 0.863 -31.936 1.00 49.21 O +ATOM 3366 H SER A1100 -3.544 0.722 -31.816 1.00 30.26 H +ATOM 3367 HA SER A1100 -2.810 2.508 -29.798 1.00 30.30 H +ATOM 3368 HB3 SER A1100 -1.789 0.278 -30.231 1.00 35.14 H +ATOM 3369 HB2 SER A1100 -0.604 1.548 -30.030 1.00 35.14 H +ATOM 3370 HG SER A1100 -0.171 0.161 -31.804 1.00 49.21 H +ATOM 3371 N SER A1101 -2.165 3.675 -32.806 1.00 31.40 N +ATOM 3372 CA SER A1101 -1.859 4.886 -33.572 1.00 31.93 C +ATOM 3373 C SER A1101 -2.867 6.034 -33.343 1.00 35.91 C +ATOM 3374 O SER A1101 -2.506 7.191 -33.557 1.00 36.89 O +ATOM 3375 CB SER A1101 -1.697 4.523 -35.064 1.00 36.50 C +ATOM 3376 OG SER A1101 -2.929 4.318 -35.729 1.00 42.36 O +ATOM 3377 H SER A1101 -2.558 2.902 -33.327 1.00 31.40 H +ATOM 3378 HA SER A1101 -0.886 5.241 -33.226 1.00 31.93 H +ATOM 3379 HB3 SER A1101 -1.073 3.636 -35.181 1.00 36.50 H +ATOM 3380 HB2 SER A1101 -1.179 5.333 -35.580 1.00 36.50 H +ATOM 3381 HG SER A1101 -3.291 3.462 -35.475 1.00 42.36 H +ATOM 3382 N GLN A1102 -4.091 5.700 -32.898 1.00 29.85 N +ATOM 3383 CA GLN A1102 -5.158 6.649 -32.581 1.00 28.09 C +ATOM 3384 C GLN A1102 -5.318 6.881 -31.067 1.00 29.55 C +ATOM 3385 O GLN A1102 -6.205 7.643 -30.690 1.00 27.73 O +ATOM 3386 CB GLN A1102 -6.487 6.161 -33.198 1.00 29.07 C +ATOM 3387 CG GLN A1102 -6.456 6.050 -34.735 1.00 37.94 C +ATOM 3388 CD GLN A1102 -7.845 5.775 -35.316 1.00 47.98 C +ATOM 3389 OE1 GLN A1102 -8.717 6.640 -35.277 1.00 45.96 O +ATOM 3390 NE2 GLN A1102 -8.059 4.581 -35.868 1.00 43.70 N +ATOM 3391 H GLN A1102 -4.308 4.725 -32.740 1.00 29.85 H +ATOM 3392 HA GLN A1102 -4.936 7.620 -33.021 1.00 28.09 H +ATOM 3393 HB3 GLN A1102 -7.282 6.854 -32.919 1.00 29.07 H +ATOM 3394 HB2 GLN A1102 -6.763 5.199 -32.764 1.00 29.07 H +ATOM 3395 HG3 GLN A1102 -5.755 5.277 -35.050 1.00 37.94 H +ATOM 3396 HG2 GLN A1102 -6.095 6.985 -35.164 1.00 37.94 H +ATOM 3397 HE22 GLN A1102 -8.960 4.358 -36.266 1.00 43.70 H +ATOM 3398 HE21 GLN A1102 -7.316 3.889 -35.901 1.00 43.70 H +ATOM 3399 N SER A1103 -4.496 6.236 -30.218 1.00 26.76 N +ATOM 3400 CA SER A1103 -4.636 6.266 -28.758 1.00 25.27 C +ATOM 3401 C SER A1103 -4.416 7.666 -28.134 1.00 28.60 C +ATOM 3402 O SER A1103 -3.673 8.456 -28.717 1.00 28.62 O +ATOM 3403 CB SER A1103 -3.733 5.180 -28.139 1.00 27.32 C +ATOM 3404 OG SER A1103 -2.362 5.531 -28.107 1.00 35.29 O +ATOM 3405 H SER A1103 -3.775 5.633 -30.588 1.00 26.76 H +ATOM 3406 HA SER A1103 -5.664 5.963 -28.587 1.00 25.27 H +ATOM 3407 HB3 SER A1103 -3.855 4.235 -28.667 1.00 27.32 H +ATOM 3408 HB2 SER A1103 -4.052 4.997 -27.114 1.00 27.32 H +ATOM 3409 HG SER A1103 -1.996 5.451 -28.995 1.00 35.29 H +ATOM 3410 N PRO A1104 -5.039 7.954 -26.962 1.00 25.17 N +ATOM 3411 CA PRO A1104 -4.893 9.264 -26.293 1.00 24.67 C +ATOM 3412 C PRO A1104 -3.456 9.762 -26.008 1.00 26.87 C +ATOM 3413 O PRO A1104 -3.206 10.927 -26.311 1.00 25.99 O +ATOM 3414 CB PRO A1104 -5.768 9.172 -25.030 1.00 26.01 C +ATOM 3415 CG PRO A1104 -6.761 8.063 -25.336 1.00 29.46 C +ATOM 3416 CD PRO A1104 -5.996 7.109 -26.241 1.00 25.85 C +ATOM 3417 HA PRO A1104 -5.359 9.984 -26.965 1.00 24.67 H +ATOM 3418 HB3 PRO A1104 -6.271 10.113 -24.806 1.00 26.01 H +ATOM 3419 HB2 PRO A1104 -5.186 8.913 -24.146 1.00 26.01 H +ATOM 3420 HG3 PRO A1104 -7.598 8.483 -25.894 1.00 29.46 H +ATOM 3421 HG2 PRO A1104 -7.161 7.577 -24.447 1.00 29.46 H +ATOM 3422 HD2 PRO A1104 -5.453 6.369 -25.652 1.00 25.85 H +ATOM 3423 HD3 PRO A1104 -6.704 6.583 -26.879 1.00 25.85 H +ATOM 3424 N PRO A1105 -2.512 8.903 -25.540 1.00 24.58 N +ATOM 3425 CA PRO A1105 -1.095 9.290 -25.409 1.00 24.76 C +ATOM 3426 C PRO A1105 -0.414 9.681 -26.728 1.00 29.78 C +ATOM 3427 O PRO A1105 0.275 10.696 -26.749 1.00 30.24 O +ATOM 3428 CB PRO A1105 -0.416 8.074 -24.751 1.00 25.88 C +ATOM 3429 CG PRO A1105 -1.547 7.335 -24.062 1.00 29.87 C +ATOM 3430 CD PRO A1105 -2.716 7.555 -25.010 1.00 25.57 C +ATOM 3431 HA PRO A1105 -1.051 10.139 -24.727 1.00 24.76 H +ATOM 3432 HB3 PRO A1105 0.372 8.361 -24.055 1.00 25.88 H +ATOM 3433 HB2 PRO A1105 0.031 7.420 -25.501 1.00 25.88 H +ATOM 3434 HG3 PRO A1105 -1.760 7.813 -23.106 1.00 29.87 H +ATOM 3435 HG2 PRO A1105 -1.332 6.283 -23.876 1.00 29.87 H +ATOM 3436 HD2 PRO A1105 -2.673 6.841 -25.829 1.00 25.57 H +ATOM 3437 HD3 PRO A1105 -3.660 7.421 -24.492 1.00 25.57 H +ATOM 3438 N THR A1106 -0.644 8.900 -27.798 1.00 26.99 N +ATOM 3439 CA THR A1106 -0.097 9.143 -29.134 1.00 27.47 C +ATOM 3440 C THR A1106 -0.563 10.483 -29.734 1.00 31.84 C +ATOM 3441 O THR A1106 0.284 11.263 -30.166 1.00 32.09 O +ATOM 3442 CB THR A1106 -0.470 7.995 -30.114 1.00 33.90 C +ATOM 3443 OG1 THR A1106 0.169 6.812 -29.680 1.00 35.61 O +ATOM 3444 CG2 THR A1106 -0.091 8.228 -31.588 1.00 32.59 C +ATOM 3445 H THR A1106 -1.225 8.079 -27.705 1.00 26.99 H +ATOM 3446 HA THR A1106 0.990 9.178 -29.039 1.00 27.47 H +ATOM 3447 HB THR A1106 -1.542 7.801 -30.070 1.00 33.90 H +ATOM 3448 HG1 THR A1106 1.077 6.817 -30.001 1.00 35.61 H +ATOM 3449 HG21 THR A1106 -0.167 7.307 -32.166 1.00 32.59 H +ATOM 3450 HG22 THR A1106 -0.747 8.958 -32.064 1.00 32.59 H +ATOM 3451 HG23 THR A1106 0.931 8.592 -31.679 1.00 32.59 H +ATOM 3452 N LYS A1107 -1.881 10.734 -29.718 1.00 27.48 N +ATOM 3453 CA LYS A1107 -2.498 11.914 -30.325 1.00 28.03 C +ATOM 3454 C LYS A1107 -2.239 13.220 -29.553 1.00 32.41 C +ATOM 3455 O LYS A1107 -2.116 14.260 -30.199 1.00 32.90 O +ATOM 3456 CB LYS A1107 -4.004 11.655 -30.524 1.00 29.00 C +ATOM 3457 CG LYS A1107 -4.332 10.463 -31.452 1.00 40.91 C +ATOM 3458 CD LYS A1107 -3.799 10.551 -32.893 1.00 47.46 C +ATOM 3459 CE LYS A1107 -4.424 11.691 -33.711 1.00 56.00 C +ATOM 3460 NZ LYS A1107 -3.900 11.720 -35.087 1.00 65.65 N1+ +ATOM 3461 H LYS A1107 -2.514 10.051 -29.322 1.00 27.48 H +ATOM 3462 HA LYS A1107 -2.042 12.056 -31.305 1.00 28.03 H +ATOM 3463 HB3 LYS A1107 -4.486 12.553 -30.910 1.00 29.00 H +ATOM 3464 HB2 LYS A1107 -4.459 11.467 -29.551 1.00 29.00 H +ATOM 3465 HG3 LYS A1107 -5.412 10.339 -31.485 1.00 40.91 H +ATOM 3466 HG2 LYS A1107 -3.949 9.546 -31.011 1.00 40.91 H +ATOM 3467 HD3 LYS A1107 -3.999 9.605 -33.395 1.00 47.46 H +ATOM 3468 HD2 LYS A1107 -2.713 10.638 -32.883 1.00 47.46 H +ATOM 3469 HE3 LYS A1107 -4.217 12.652 -33.244 1.00 56.00 H +ATOM 3470 HE2 LYS A1107 -5.507 11.575 -33.750 1.00 56.00 H +ATOM 3471 HZ1 LYS A1107 -2.898 11.867 -35.052 1.00 65.65 H +ATOM 3472 HZ2 LYS A1107 -4.094 10.843 -35.549 1.00 65.65 H +ATOM 3473 HZ3 LYS A1107 -4.332 12.478 -35.596 1.00 65.65 H +ATOM 3474 N PHE A1108 -2.117 13.159 -28.214 1.00 27.91 N +ATOM 3475 CA PHE A1108 -1.728 14.312 -27.396 1.00 26.74 C +ATOM 3476 C PHE A1108 -0.225 14.638 -27.463 1.00 30.76 C +ATOM 3477 O PHE A1108 0.112 15.819 -27.437 1.00 31.53 O +ATOM 3478 CB PHE A1108 -2.223 14.161 -25.939 1.00 27.18 C +ATOM 3479 CG PHE A1108 -3.708 14.423 -25.709 1.00 27.38 C +ATOM 3480 CD1 PHE A1108 -4.294 15.643 -26.113 1.00 29.59 C +ATOM 3481 CD2 PHE A1108 -4.499 13.500 -24.989 1.00 28.32 C +ATOM 3482 CE1 PHE A1108 -5.631 15.902 -25.840 1.00 30.31 C +ATOM 3483 CE2 PHE A1108 -5.834 13.775 -24.728 1.00 29.39 C +ATOM 3484 CZ PHE A1108 -6.397 14.972 -25.151 1.00 28.52 C +ATOM 3485 H PHE A1108 -2.248 12.280 -27.730 1.00 27.91 H +ATOM 3486 HA PHE A1108 -2.218 15.191 -27.816 1.00 26.74 H +ATOM 3487 HB3 PHE A1108 -1.680 14.842 -25.282 1.00 27.18 H +ATOM 3488 HB2 PHE A1108 -1.976 13.159 -25.590 1.00 27.18 H +ATOM 3489 HD1 PHE A1108 -3.706 16.390 -26.627 1.00 29.59 H +ATOM 3490 HD2 PHE A1108 -4.075 12.576 -24.629 1.00 28.32 H +ATOM 3491 HE1 PHE A1108 -6.075 16.835 -26.155 1.00 30.31 H +ATOM 3492 HE2 PHE A1108 -6.435 13.059 -24.188 1.00 29.39 H +ATOM 3493 HZ PHE A1108 -7.436 15.180 -24.942 1.00 28.52 H +ATOM 3494 N LEU A1109 0.650 13.622 -27.593 1.00 27.77 N +ATOM 3495 CA LEU A1109 2.100 13.811 -27.752 1.00 28.51 C +ATOM 3496 C LEU A1109 2.516 14.255 -29.168 1.00 34.25 C +ATOM 3497 O LEU A1109 3.626 14.765 -29.315 1.00 34.88 O +ATOM 3498 CB LEU A1109 2.860 12.542 -27.309 1.00 28.11 C +ATOM 3499 CG LEU A1109 2.842 12.310 -25.779 1.00 32.10 C +ATOM 3500 CD1 LEU A1109 3.338 10.893 -25.419 1.00 30.45 C +ATOM 3501 CD2 LEU A1109 3.605 13.410 -25.010 1.00 36.17 C +ATOM 3502 H LEU A1109 0.319 12.666 -27.597 1.00 27.77 H +ATOM 3503 HA LEU A1109 2.404 14.626 -27.095 1.00 28.51 H +ATOM 3504 HB3 LEU A1109 3.901 12.592 -27.634 1.00 28.11 H +ATOM 3505 HB2 LEU A1109 2.437 11.682 -27.830 1.00 28.11 H +ATOM 3506 HG LEU A1109 1.804 12.362 -25.449 1.00 32.10 H +ATOM 3507 HD11 LEU A1109 2.593 10.366 -24.822 1.00 30.45 H +ATOM 3508 HD12 LEU A1109 3.521 10.287 -26.306 1.00 30.45 H +ATOM 3509 HD13 LEU A1109 4.266 10.903 -24.848 1.00 30.45 H +ATOM 3510 HD21 LEU A1109 4.230 13.014 -24.210 1.00 36.17 H +ATOM 3511 HD22 LEU A1109 4.255 13.987 -25.667 1.00 36.17 H +ATOM 3512 HD23 LEU A1109 2.908 14.111 -24.549 1.00 36.17 H +ATOM 3513 N GLU A1110 1.626 14.116 -30.166 1.00 32.96 N +ATOM 3514 CA GLU A1110 1.757 14.754 -31.480 1.00 35.44 C +ATOM 3515 C GLU A1110 1.538 16.278 -31.416 1.00 38.81 C +ATOM 3516 O GLU A1110 2.214 17.004 -32.143 1.00 38.69 O +ATOM 3517 CB GLU A1110 0.776 14.106 -32.477 1.00 36.99 C +ATOM 3518 CG GLU A1110 1.234 12.724 -32.992 1.00 47.30 C +ATOM 3519 CD GLU A1110 0.184 11.941 -33.801 1.00 70.21 C +ATOM 3520 OE1 GLU A1110 -0.902 12.492 -34.093 1.00 64.58 O +ATOM 3521 OE2 GLU A1110 0.490 10.773 -34.124 1.00 65.32 O1- +ATOM 3522 H GLU A1110 0.748 13.648 -29.986 1.00 32.96 H +ATOM 3523 HA GLU A1110 2.773 14.593 -31.847 1.00 35.44 H +ATOM 3524 HB3 GLU A1110 0.605 14.769 -33.327 1.00 36.99 H +ATOM 3525 HB2 GLU A1110 -0.188 13.999 -31.980 1.00 36.99 H +ATOM 3526 HG3 GLU A1110 1.562 12.107 -32.159 1.00 47.30 H +ATOM 3527 HG2 GLU A1110 2.115 12.857 -33.622 1.00 47.30 H +ATOM 3528 N LEU A1111 0.621 16.731 -30.540 1.00 35.92 N +ATOM 3529 CA LEU A1111 0.318 18.147 -30.309 1.00 37.14 C +ATOM 3530 C LEU A1111 1.372 18.829 -29.418 1.00 40.62 C +ATOM 3531 O LEU A1111 1.740 19.971 -29.693 1.00 41.55 O +ATOM 3532 CB LEU A1111 -1.087 18.286 -29.673 1.00 36.92 C +ATOM 3533 CG LEU A1111 -2.260 17.801 -30.555 1.00 41.87 C +ATOM 3534 CD1 LEU A1111 -3.572 17.728 -29.743 1.00 41.05 C +ATOM 3535 CD2 LEU A1111 -2.410 18.636 -31.845 1.00 44.81 C +ATOM 3536 H LEU A1111 0.107 16.071 -29.975 1.00 35.92 H +ATOM 3537 HA LEU A1111 0.322 18.664 -31.270 1.00 37.14 H +ATOM 3538 HB3 LEU A1111 -1.265 19.329 -29.406 1.00 36.92 H +ATOM 3539 HB2 LEU A1111 -1.100 17.738 -28.731 1.00 36.92 H +ATOM 3540 HG LEU A1111 -2.038 16.781 -30.864 1.00 41.87 H +ATOM 3541 HD11 LEU A1111 -4.009 16.731 -29.803 1.00 41.05 H +ATOM 3542 HD12 LEU A1111 -3.410 17.942 -28.686 1.00 41.05 H +ATOM 3543 HD13 LEU A1111 -4.325 18.435 -30.089 1.00 41.05 H +ATOM 3544 HD21 LEU A1111 -3.440 18.931 -32.041 1.00 44.81 H +ATOM 3545 HD22 LEU A1111 -1.820 19.552 -31.810 1.00 44.81 H +ATOM 3546 HD23 LEU A1111 -2.074 18.066 -32.712 1.00 44.81 H +ATOM 3547 N ILE A1112 1.827 18.124 -28.368 1.00 34.80 N +ATOM 3548 CA ILE A1112 2.759 18.628 -27.356 1.00 34.93 C +ATOM 3549 C ILE A1112 4.230 18.572 -27.815 1.00 40.11 C +ATOM 3550 O ILE A1112 4.994 19.473 -27.471 1.00 41.74 O +ATOM 3551 CB ILE A1112 2.614 17.819 -26.027 1.00 36.99 C +ATOM 3552 CG1 ILE A1112 1.226 18.047 -25.391 1.00 35.84 C +ATOM 3553 CG2 ILE A1112 3.707 18.065 -24.963 1.00 37.00 C +ATOM 3554 CD1 ILE A1112 0.810 16.938 -24.417 1.00 35.00 C +ATOM 3555 H ILE A1112 1.452 17.199 -28.201 1.00 34.80 H +ATOM 3556 HA ILE A1112 2.514 19.672 -27.148 1.00 34.93 H +ATOM 3557 HB ILE A1112 2.677 16.764 -26.294 1.00 36.99 H +ATOM 3558 HG13 ILE A1112 0.460 18.112 -26.162 1.00 35.84 H +ATOM 3559 HG12 ILE A1112 1.208 19.011 -24.881 1.00 35.84 H +ATOM 3560 HG21 ILE A1112 3.496 17.533 -24.036 1.00 37.00 H +ATOM 3561 HG22 ILE A1112 4.688 17.726 -25.289 1.00 37.00 H +ATOM 3562 HG23 ILE A1112 3.786 19.123 -24.726 1.00 37.00 H +ATOM 3563 HD11 ILE A1112 -0.272 16.926 -24.293 1.00 35.00 H +ATOM 3564 HD12 ILE A1112 1.106 15.951 -24.774 1.00 35.00 H +ATOM 3565 HD13 ILE A1112 1.261 17.086 -23.438 1.00 35.00 H +ATOM 3566 N GLY A1113 4.598 17.509 -28.549 1.00 36.74 N +ATOM 3567 CA GLY A1113 5.986 17.125 -28.807 1.00 38.59 C +ATOM 3568 C GLY A1113 6.458 16.152 -27.712 1.00 41.94 C +ATOM 3569 O GLY A1113 5.723 15.847 -26.772 1.00 39.30 O +ATOM 3570 H GLY A1113 3.896 16.830 -28.812 1.00 36.74 H +ATOM 3571 HA3 GLY A1113 6.645 17.994 -28.846 1.00 38.59 H +ATOM 3572 HA2 GLY A1113 6.038 16.630 -29.777 1.00 38.59 H +ATOM 3573 N ILE A1114 7.697 15.653 -27.849 1.00 41.30 N +ATOM 3574 CA ILE A1114 8.305 14.668 -26.942 1.00 41.02 C +ATOM 3575 C ILE A1114 9.607 15.170 -26.275 1.00 47.14 C +ATOM 3576 O ILE A1114 10.221 14.411 -25.524 1.00 48.03 O +ATOM 3577 CB ILE A1114 8.578 13.319 -27.673 1.00 43.24 C +ATOM 3578 CG1 ILE A1114 9.541 13.438 -28.881 1.00 44.89 C +ATOM 3579 CG2 ILE A1114 7.256 12.629 -28.061 1.00 42.60 C +ATOM 3580 CD1 ILE A1114 10.047 12.085 -29.402 1.00 50.07 C +ATOM 3581 H ILE A1114 8.254 15.920 -28.647 1.00 41.30 H +ATOM 3582 HA ILE A1114 7.632 14.455 -26.109 1.00 41.02 H +ATOM 3583 HB ILE A1114 9.054 12.649 -26.955 1.00 43.24 H +ATOM 3584 HG13 ILE A1114 10.411 14.042 -28.624 1.00 44.89 H +ATOM 3585 HG12 ILE A1114 9.044 13.969 -29.694 1.00 44.89 H +ATOM 3586 HG21 ILE A1114 7.425 11.627 -28.454 1.00 42.60 H +ATOM 3587 HG22 ILE A1114 6.597 12.528 -27.198 1.00 42.60 H +ATOM 3588 HG23 ILE A1114 6.716 13.194 -28.821 1.00 42.60 H +ATOM 3589 HD11 ILE A1114 10.937 12.217 -30.019 1.00 50.07 H +ATOM 3590 HD12 ILE A1114 10.311 11.414 -28.585 1.00 50.07 H +ATOM 3591 HD13 ILE A1114 9.294 11.590 -30.015 1.00 50.07 H +ATOM 3592 N ALA A1115 9.991 16.433 -26.530 1.00 42.26 N +ATOM 3593 CA ALA A1115 11.194 17.067 -25.991 1.00 43.31 C +ATOM 3594 C ALA A1115 10.820 18.401 -25.327 1.00 48.36 C +ATOM 3595 O ALA A1115 11.240 19.462 -25.791 1.00 49.43 O +ATOM 3596 CB ALA A1115 12.223 17.220 -27.127 1.00 45.24 C +ATOM 3597 H ALA A1115 9.424 17.010 -27.133 1.00 42.26 H +ATOM 3598 HA ALA A1115 11.645 16.449 -25.212 1.00 43.31 H +ATOM 3599 HB1 ALA A1115 13.140 17.690 -26.770 1.00 45.24 H +ATOM 3600 HB2 ALA A1115 12.495 16.247 -27.537 1.00 45.24 H +ATOM 3601 HB3 ALA A1115 11.832 17.826 -27.946 1.00 45.24 H +ATOM 3602 N GLN A1116 10.012 18.309 -24.258 1.00 44.35 N +ATOM 3603 CA GLN A1116 9.464 19.450 -23.516 1.00 44.30 C +ATOM 3604 C GLN A1116 9.925 19.478 -22.046 1.00 49.79 C +ATOM 3605 O GLN A1116 9.447 20.327 -21.292 1.00 49.85 O +ATOM 3606 CB GLN A1116 7.918 19.420 -23.615 1.00 44.27 C +ATOM 3607 CG GLN A1116 7.347 19.441 -25.051 1.00 53.29 C +ATOM 3608 CD GLN A1116 7.835 20.599 -25.930 1.00 72.09 C +ATOM 3609 OE1 GLN A1116 8.158 20.396 -27.098 1.00 65.48 O +ATOM 3610 NE2 GLN A1116 7.883 21.818 -25.389 1.00 65.05 N +ATOM 3611 H GLN A1116 9.716 17.395 -23.938 1.00 44.35 H +ATOM 3612 HA GLN A1116 9.827 20.386 -23.941 1.00 44.30 H +ATOM 3613 HB3 GLN A1116 7.499 20.263 -23.064 1.00 44.27 H +ATOM 3614 HB2 GLN A1116 7.541 18.532 -23.106 1.00 44.27 H +ATOM 3615 HG3 GLN A1116 6.262 19.478 -25.007 1.00 53.29 H +ATOM 3616 HG2 GLN A1116 7.588 18.503 -25.555 1.00 53.29 H +ATOM 3617 HE22 GLN A1116 8.200 22.601 -25.942 1.00 65.05 H +ATOM 3618 HE21 GLN A1116 7.607 21.967 -24.430 1.00 65.05 H +ATOM 3619 N GLY A1117 10.844 18.574 -21.657 1.00 46.41 N +ATOM 3620 CA GLY A1117 11.368 18.465 -20.296 1.00 44.66 C +ATOM 3621 C GLY A1117 10.279 17.930 -19.358 1.00 42.63 C +ATOM 3622 O GLY A1117 9.610 16.945 -19.673 1.00 37.23 O +ATOM 3623 H GLY A1117 11.190 17.910 -22.334 1.00 46.41 H +ATOM 3624 HA3 GLY A1117 11.738 19.434 -19.957 1.00 44.66 H +ATOM 3625 HA2 GLY A1117 12.213 17.776 -20.293 1.00 44.66 H +ATOM 3626 N GLN A1118 10.122 18.593 -18.202 1.00 39.82 N +ATOM 3627 CA GLN A1118 9.103 18.311 -17.187 1.00 38.37 C +ATOM 3628 C GLN A1118 7.702 18.861 -17.532 1.00 40.30 C +ATOM 3629 O GLN A1118 6.772 18.611 -16.767 1.00 41.07 O +ATOM 3630 CB GLN A1118 9.579 18.887 -15.835 1.00 41.27 C +ATOM 3631 CG GLN A1118 10.836 18.198 -15.270 1.00 52.25 C +ATOM 3632 CD GLN A1118 11.326 18.877 -13.990 1.00 66.80 C +ATOM 3633 OE1 GLN A1118 11.669 20.058 -14.005 1.00 64.29 O +ATOM 3634 NE2 GLN A1118 11.383 18.133 -12.884 1.00 50.92 N +ATOM 3635 H GLN A1118 10.710 19.396 -18.014 1.00 39.82 H +ATOM 3636 HA GLN A1118 9.006 17.229 -17.079 1.00 38.37 H +ATOM 3637 HB3 GLN A1118 8.784 18.797 -15.092 1.00 41.27 H +ATOM 3638 HB2 GLN A1118 9.757 19.958 -15.946 1.00 41.27 H +ATOM 3639 HG3 GLN A1118 11.650 18.234 -15.995 1.00 52.25 H +ATOM 3640 HG2 GLN A1118 10.632 17.142 -15.085 1.00 52.25 H +ATOM 3641 HE22 GLN A1118 11.710 18.541 -12.020 1.00 50.92 H +ATOM 3642 HE21 GLN A1118 11.099 17.164 -12.899 1.00 50.92 H +ATOM 3643 N MET A1119 7.569 19.615 -18.638 1.00 34.93 N +ATOM 3644 CA MET A1119 6.375 20.393 -18.980 1.00 34.58 C +ATOM 3645 C MET A1119 5.309 19.636 -19.797 1.00 34.58 C +ATOM 3646 O MET A1119 4.432 20.303 -20.340 1.00 34.71 O +ATOM 3647 CB MET A1119 6.788 21.702 -19.694 1.00 38.63 C +ATOM 3648 CG MET A1119 7.827 22.543 -18.940 1.00 44.95 C +ATOM 3649 SD MET A1119 8.154 24.140 -19.731 1.00 52.97 S +ATOM 3650 CE MET A1119 9.330 24.826 -18.536 1.00 51.70 C +ATOM 3651 H MET A1119 8.371 19.756 -19.237 1.00 34.93 H +ATOM 3652 HA MET A1119 5.885 20.689 -18.052 1.00 34.58 H +ATOM 3653 HB3 MET A1119 5.908 22.328 -19.853 1.00 38.63 H +ATOM 3654 HB2 MET A1119 7.169 21.473 -20.690 1.00 38.63 H +ATOM 3655 HG3 MET A1119 8.773 22.006 -18.862 1.00 44.95 H +ATOM 3656 HG2 MET A1119 7.483 22.729 -17.922 1.00 44.95 H +ATOM 3657 HE1 MET A1119 9.653 25.818 -18.853 1.00 51.70 H +ATOM 3658 HE2 MET A1119 8.868 24.912 -17.553 1.00 51.70 H +ATOM 3659 HE3 MET A1119 10.208 24.186 -18.455 1.00 51.70 H +ATOM 3660 N THR A1120 5.357 18.294 -19.887 1.00 29.62 N +ATOM 3661 CA THR A1120 4.382 17.500 -20.655 1.00 28.01 C +ATOM 3662 C THR A1120 2.922 17.666 -20.173 1.00 30.48 C +ATOM 3663 O THR A1120 2.044 17.875 -21.008 1.00 31.09 O +ATOM 3664 CB THR A1120 4.714 15.984 -20.657 1.00 32.05 C +ATOM 3665 OG1 THR A1120 6.047 15.796 -21.089 1.00 33.94 O +ATOM 3666 CG2 THR A1120 3.806 15.122 -21.558 1.00 34.10 C +ATOM 3667 H THR A1120 6.099 17.780 -19.434 1.00 29.62 H +ATOM 3668 HA THR A1120 4.429 17.856 -21.687 1.00 28.01 H +ATOM 3669 HB THR A1120 4.654 15.596 -19.640 1.00 32.05 H +ATOM 3670 HG1 THR A1120 6.086 15.941 -22.042 1.00 33.94 H +ATOM 3671 HG21 THR A1120 4.157 14.090 -21.595 1.00 34.10 H +ATOM 3672 HG22 THR A1120 2.778 15.093 -21.197 1.00 34.10 H +ATOM 3673 HG23 THR A1120 3.789 15.501 -22.580 1.00 34.10 H +ATOM 3674 N VAL A1121 2.703 17.617 -18.846 1.00 28.98 N +ATOM 3675 CA VAL A1121 1.392 17.805 -18.215 1.00 29.28 C +ATOM 3676 C VAL A1121 0.911 19.271 -18.238 1.00 34.54 C +ATOM 3677 O VAL A1121 -0.286 19.494 -18.411 1.00 33.68 O +ATOM 3678 CB VAL A1121 1.377 17.249 -16.760 1.00 33.58 C +ATOM 3679 CG1 VAL A1121 0.177 17.681 -15.888 1.00 33.26 C +ATOM 3680 CG2 VAL A1121 1.457 15.714 -16.784 1.00 32.88 C +ATOM 3681 H VAL A1121 3.476 17.441 -18.218 1.00 28.98 H +ATOM 3682 HA VAL A1121 0.669 17.224 -18.791 1.00 29.28 H +ATOM 3683 HB VAL A1121 2.275 17.603 -16.253 1.00 33.58 H +ATOM 3684 HG11 VAL A1121 0.163 17.145 -14.938 1.00 33.26 H +ATOM 3685 HG12 VAL A1121 0.205 18.744 -15.644 1.00 33.26 H +ATOM 3686 HG13 VAL A1121 -0.765 17.478 -16.396 1.00 33.26 H +ATOM 3687 HG21 VAL A1121 1.485 15.304 -15.775 1.00 32.88 H +ATOM 3688 HG22 VAL A1121 0.594 15.285 -17.294 1.00 32.88 H +ATOM 3689 HG23 VAL A1121 2.350 15.362 -17.300 1.00 32.88 H +ATOM 3690 N LEU A1122 1.842 20.235 -18.115 1.00 32.43 N +ATOM 3691 CA LEU A1122 1.585 21.672 -18.261 1.00 33.01 C +ATOM 3692 C LEU A1122 1.097 22.028 -19.675 1.00 36.61 C +ATOM 3693 O LEU A1122 0.147 22.792 -19.813 1.00 35.87 O +ATOM 3694 CB LEU A1122 2.865 22.455 -17.871 1.00 35.17 C +ATOM 3695 CG LEU A1122 2.831 23.990 -18.076 1.00 42.10 C +ATOM 3696 CD1 LEU A1122 1.697 24.666 -17.275 1.00 43.87 C +ATOM 3697 CD2 LEU A1122 4.213 24.615 -17.788 1.00 45.28 C +ATOM 3698 H LEU A1122 2.802 19.973 -17.936 1.00 32.43 H +ATOM 3699 HA LEU A1122 0.790 21.933 -17.561 1.00 33.01 H +ATOM 3700 HB3 LEU A1122 3.699 22.056 -18.448 1.00 35.17 H +ATOM 3701 HB2 LEU A1122 3.102 22.243 -16.827 1.00 35.17 H +ATOM 3702 HG LEU A1122 2.640 24.189 -19.131 1.00 42.10 H +ATOM 3703 HD11 LEU A1122 2.023 25.573 -16.765 1.00 43.87 H +ATOM 3704 HD12 LEU A1122 0.879 24.954 -17.937 1.00 43.87 H +ATOM 3705 HD13 LEU A1122 1.280 24.004 -16.515 1.00 43.87 H +ATOM 3706 HD21 LEU A1122 4.553 25.207 -18.639 1.00 45.28 H +ATOM 3707 HD22 LEU A1122 4.206 25.272 -16.919 1.00 45.28 H +ATOM 3708 HD23 LEU A1122 4.975 23.858 -17.601 1.00 45.28 H +ATOM 3709 N ARG A1123 1.746 21.445 -20.692 1.00 32.17 N +ATOM 3710 CA ARG A1123 1.467 21.679 -22.105 1.00 32.60 C +ATOM 3711 C ARG A1123 0.200 20.951 -22.588 1.00 33.95 C +ATOM 3712 O ARG A1123 -0.493 21.472 -23.462 1.00 32.13 O +ATOM 3713 CB ARG A1123 2.736 21.303 -22.888 1.00 37.04 C +ATOM 3714 CG ARG A1123 2.892 21.997 -24.254 1.00 59.77 C +ATOM 3715 CD ARG A1123 4.363 22.141 -24.691 1.00 74.58 C +ATOM 3716 NE ARG A1123 5.119 23.067 -23.819 1.00 91.08 N +ATOM 3717 CZ ARG A1123 5.295 24.392 -23.966 1.00107.84 C +ATOM 3718 NH1 ARG A1123 4.844 25.057 -25.040 1.00 95.16 N +ATOM 3719 NH2 ARG A1123 5.939 25.067 -23.005 1.00 96.44 N1+ +ATOM 3720 H ARG A1123 2.526 20.832 -20.491 1.00 32.17 H +ATOM 3721 HA ARG A1123 1.305 22.752 -22.216 1.00 32.60 H +ATOM 3722 HB3 ARG A1123 2.821 20.221 -22.983 1.00 37.04 H +ATOM 3723 HB2 ARG A1123 3.583 21.603 -22.275 1.00 37.04 H +ATOM 3724 HG3 ARG A1123 2.490 23.004 -24.140 1.00 59.77 H +ATOM 3725 HG2 ARG A1123 2.293 21.525 -25.033 1.00 59.77 H +ATOM 3726 HD3 ARG A1123 4.461 22.348 -25.756 1.00 74.58 H +ATOM 3727 HD2 ARG A1123 4.854 21.184 -24.530 1.00 74.58 H +ATOM 3728 HE ARG A1123 5.455 22.651 -22.962 1.00 91.08 H +ATOM 3729 HH12 ARG A1123 4.979 26.054 -25.126 1.00 95.16 H +ATOM 3730 HH11 ARG A1123 4.356 24.557 -25.770 1.00 95.16 H +ATOM 3731 HH22 ARG A1123 6.066 26.066 -23.080 1.00 96.44 H +ATOM 3732 HH21 ARG A1123 6.276 24.592 -22.179 1.00 96.44 H +ATOM 3733 N LEU A1124 -0.112 19.805 -21.954 1.00 29.63 N +ATOM 3734 CA LEU A1124 -1.388 19.096 -22.055 1.00 27.63 C +ATOM 3735 C LEU A1124 -2.543 19.916 -21.454 1.00 32.17 C +ATOM 3736 O LEU A1124 -3.597 19.988 -22.075 1.00 30.80 O +ATOM 3737 CB LEU A1124 -1.248 17.704 -21.396 1.00 26.83 C +ATOM 3738 CG LEU A1124 -2.516 16.823 -21.323 1.00 30.02 C +ATOM 3739 CD1 LEU A1124 -3.141 16.553 -22.711 1.00 28.94 C +ATOM 3740 CD2 LEU A1124 -2.214 15.525 -20.543 1.00 30.63 C +ATOM 3741 H LEU A1124 0.538 19.434 -21.274 1.00 29.63 H +ATOM 3742 HA LEU A1124 -1.597 18.955 -23.116 1.00 27.63 H +ATOM 3743 HB3 LEU A1124 -0.887 17.844 -20.379 1.00 26.83 H +ATOM 3744 HB2 LEU A1124 -0.465 17.145 -21.905 1.00 26.83 H +ATOM 3745 HG LEU A1124 -3.267 17.355 -20.740 1.00 30.02 H +ATOM 3746 HD11 LEU A1124 -3.376 15.502 -22.874 1.00 28.94 H +ATOM 3747 HD12 LEU A1124 -4.075 17.103 -22.827 1.00 28.94 H +ATOM 3748 HD13 LEU A1124 -2.488 16.857 -23.527 1.00 28.94 H +ATOM 3749 HD21 LEU A1124 -2.585 14.631 -21.039 1.00 30.63 H +ATOM 3750 HD22 LEU A1124 -1.145 15.379 -20.382 1.00 30.63 H +ATOM 3751 HD23 LEU A1124 -2.678 15.549 -19.558 1.00 30.63 H +ATOM 3752 N THR A1125 -2.322 20.555 -20.291 1.00 30.97 N +ATOM 3753 CA THR A1125 -3.292 21.455 -19.661 1.00 31.58 C +ATOM 3754 C THR A1125 -3.564 22.701 -20.526 1.00 35.29 C +ATOM 3755 O THR A1125 -4.728 23.042 -20.712 1.00 34.46 O +ATOM 3756 CB THR A1125 -2.835 21.924 -18.252 1.00 40.93 C +ATOM 3757 OG1 THR A1125 -2.671 20.794 -17.421 1.00 39.64 O +ATOM 3758 CG2 THR A1125 -3.789 22.892 -17.526 1.00 41.19 C +ATOM 3759 H THR A1125 -1.430 20.452 -19.824 1.00 30.97 H +ATOM 3760 HA THR A1125 -4.231 20.908 -19.552 1.00 31.58 H +ATOM 3761 HB THR A1125 -1.860 22.404 -18.322 1.00 40.93 H +ATOM 3762 HG1 THR A1125 -1.976 20.241 -17.794 1.00 39.64 H +ATOM 3763 HG21 THR A1125 -3.407 23.132 -16.535 1.00 41.19 H +ATOM 3764 HG22 THR A1125 -3.900 23.840 -18.054 1.00 41.19 H +ATOM 3765 HG23 THR A1125 -4.782 22.457 -17.407 1.00 41.19 H +ATOM 3766 N GLU A1126 -2.501 23.309 -21.082 1.00 33.17 N +ATOM 3767 CA GLU A1126 -2.564 24.482 -21.955 1.00 34.02 C +ATOM 3768 C GLU A1126 -3.392 24.279 -23.235 1.00 37.28 C +ATOM 3769 O GLU A1126 -4.216 25.145 -23.522 1.00 35.08 O +ATOM 3770 CB GLU A1126 -1.143 24.993 -22.262 1.00 36.83 C +ATOM 3771 CG GLU A1126 -0.506 25.747 -21.070 1.00 44.51 C +ATOM 3772 CD GLU A1126 0.990 26.084 -21.204 1.00 58.80 C +ATOM 3773 OE1 GLU A1126 1.481 26.792 -20.298 1.00 49.54 O +ATOM 3774 OE2 GLU A1126 1.634 25.646 -22.185 1.00 45.75 O1- +ATOM 3775 H GLU A1126 -1.572 22.967 -20.873 1.00 33.17 H +ATOM 3776 HA GLU A1126 -3.075 25.262 -21.386 1.00 34.02 H +ATOM 3777 HB3 GLU A1126 -1.153 25.649 -23.133 1.00 36.83 H +ATOM 3778 HB2 GLU A1126 -0.524 24.139 -22.539 1.00 36.83 H +ATOM 3779 HG3 GLU A1126 -0.641 25.179 -20.151 1.00 44.51 H +ATOM 3780 HG2 GLU A1126 -1.051 26.680 -20.919 1.00 44.51 H +ATOM 3781 N LEU A1127 -3.213 23.152 -23.954 1.00 34.11 N +ATOM 3782 CA LEU A1127 -4.014 22.845 -25.150 1.00 33.79 C +ATOM 3783 C LEU A1127 -5.491 22.548 -24.835 1.00 35.23 C +ATOM 3784 O LEU A1127 -6.346 22.943 -25.626 1.00 34.81 O +ATOM 3785 CB LEU A1127 -3.321 21.794 -26.055 1.00 33.69 C +ATOM 3786 CG LEU A1127 -3.213 20.336 -25.549 1.00 37.11 C +ATOM 3787 CD1 LEU A1127 -4.490 19.501 -25.786 1.00 35.27 C +ATOM 3788 CD2 LEU A1127 -1.978 19.633 -26.146 1.00 40.08 C +ATOM 3789 H LEU A1127 -2.527 22.467 -23.671 1.00 34.11 H +ATOM 3790 HA LEU A1127 -4.026 23.762 -25.744 1.00 33.79 H +ATOM 3791 HB3 LEU A1127 -2.314 22.167 -26.235 1.00 33.69 H +ATOM 3792 HB2 LEU A1127 -3.792 21.791 -27.039 1.00 33.69 H +ATOM 3793 HG LEU A1127 -3.048 20.386 -24.476 1.00 37.11 H +ATOM 3794 HD11 LEU A1127 -4.753 18.940 -24.890 1.00 35.27 H +ATOM 3795 HD12 LEU A1127 -5.351 20.108 -26.056 1.00 35.27 H +ATOM 3796 HD13 LEU A1127 -4.367 18.778 -26.592 1.00 35.27 H +ATOM 3797 HD21 LEU A1127 -2.160 18.585 -26.384 1.00 40.08 H +ATOM 3798 HD22 LEU A1127 -1.629 20.117 -27.058 1.00 40.08 H +ATOM 3799 HD23 LEU A1127 -1.156 19.657 -25.433 1.00 40.08 H +ATOM 3800 N LEU A1128 -5.779 21.925 -23.675 1.00 30.68 N +ATOM 3801 CA LEU A1128 -7.149 21.698 -23.203 1.00 29.82 C +ATOM 3802 C LEU A1128 -7.868 23.003 -22.796 1.00 37.95 C +ATOM 3803 O LEU A1128 -9.078 23.099 -23.001 1.00 37.53 O +ATOM 3804 CB LEU A1128 -7.182 20.679 -22.042 1.00 28.63 C +ATOM 3805 CG LEU A1128 -6.730 19.245 -22.401 1.00 31.65 C +ATOM 3806 CD1 LEU A1128 -6.586 18.387 -21.126 1.00 31.42 C +ATOM 3807 CD2 LEU A1128 -7.611 18.567 -23.467 1.00 31.56 C +ATOM 3808 H LEU A1128 -5.032 21.625 -23.062 1.00 30.68 H +ATOM 3809 HA LEU A1128 -7.696 21.275 -24.042 1.00 29.82 H +ATOM 3810 HB3 LEU A1128 -8.195 20.625 -21.642 1.00 28.63 H +ATOM 3811 HB2 LEU A1128 -6.560 21.058 -21.229 1.00 28.63 H +ATOM 3812 HG LEU A1128 -5.745 19.305 -22.850 1.00 31.65 H +ATOM 3813 HD11 LEU A1128 -5.551 18.077 -20.987 1.00 31.42 H +ATOM 3814 HD12 LEU A1128 -6.877 18.930 -20.226 1.00 31.42 H +ATOM 3815 HD13 LEU A1128 -7.192 17.482 -21.154 1.00 31.42 H +ATOM 3816 HD21 LEU A1128 -7.634 17.483 -23.356 1.00 31.56 H +ATOM 3817 HD22 LEU A1128 -8.638 18.927 -23.434 1.00 31.56 H +ATOM 3818 HD23 LEU A1128 -7.222 18.763 -24.465 1.00 31.56 H +ATOM 3819 N GLU A1129 -7.122 23.993 -22.277 1.00 37.30 N +ATOM 3820 CA GLU A1129 -7.628 25.331 -21.956 1.00 38.66 C +ATOM 3821 C GLU A1129 -7.904 26.197 -23.204 1.00 42.10 C +ATOM 3822 O GLU A1129 -8.801 27.037 -23.141 1.00 42.78 O +ATOM 3823 CB GLU A1129 -6.670 26.032 -20.967 1.00 41.41 C +ATOM 3824 CG GLU A1129 -6.622 25.388 -19.556 1.00 54.54 C +ATOM 3825 CD GLU A1129 -7.801 25.679 -18.612 1.00 90.90 C +ATOM 3826 OE1 GLU A1129 -8.677 26.501 -18.958 1.00 94.26 O +ATOM 3827 OE2 GLU A1129 -7.797 25.060 -17.524 1.00 91.13 O1- +ATOM 3828 H GLU A1129 -6.137 23.838 -22.107 1.00 37.30 H +ATOM 3829 HA GLU A1129 -8.591 25.207 -21.456 1.00 38.66 H +ATOM 3830 HB3 GLU A1129 -6.907 27.094 -20.890 1.00 41.41 H +ATOM 3831 HB2 GLU A1129 -5.662 26.002 -21.380 1.00 41.41 H +ATOM 3832 HG3 GLU A1129 -5.711 25.719 -19.055 1.00 54.54 H +ATOM 3833 HG2 GLU A1129 -6.543 24.307 -19.642 1.00 54.54 H +ATOM 3834 N ARG A1130 -7.195 25.946 -24.324 1.00 37.87 N +ATOM 3835 CA ARG A1130 -7.499 26.535 -25.639 1.00 37.84 C +ATOM 3836 C ARG A1130 -8.697 25.873 -26.351 1.00 41.63 C +ATOM 3837 O ARG A1130 -9.138 26.406 -27.369 1.00 42.60 O +ATOM 3838 CB ARG A1130 -6.265 26.498 -26.566 1.00 37.09 C +ATOM 3839 CG ARG A1130 -5.073 27.342 -26.080 1.00 43.14 C +ATOM 3840 CD ARG A1130 -4.008 27.588 -27.166 1.00 47.74 C +ATOM 3841 NE ARG A1130 -3.331 26.356 -27.610 1.00 51.85 N +ATOM 3842 CZ ARG A1130 -2.239 25.772 -27.088 1.00 60.61 C +ATOM 3843 NH1 ARG A1130 -1.609 26.260 -26.010 1.00 54.41 N +ATOM 3844 NH2 ARG A1130 -1.770 24.664 -27.673 1.00 41.74 N1+ +ATOM 3845 H ARG A1130 -6.458 25.254 -24.302 1.00 37.87 H +ATOM 3846 HA ARG A1130 -7.769 27.582 -25.491 1.00 37.84 H +ATOM 3847 HB3 ARG A1130 -6.554 26.873 -27.549 1.00 37.09 H +ATOM 3848 HB2 ARG A1130 -5.950 25.465 -26.724 1.00 37.09 H +ATOM 3849 HG3 ARG A1130 -4.609 26.808 -25.257 1.00 43.14 H +ATOM 3850 HG2 ARG A1130 -5.394 28.295 -25.660 1.00 43.14 H +ATOM 3851 HD3 ARG A1130 -3.308 28.371 -26.873 1.00 47.74 H +ATOM 3852 HD2 ARG A1130 -4.508 27.966 -28.058 1.00 47.74 H +ATOM 3853 HE ARG A1130 -3.771 25.882 -28.391 1.00 51.85 H +ATOM 3854 HH12 ARG A1130 -0.771 25.815 -25.659 1.00 54.41 H +ATOM 3855 HH11 ARG A1130 -1.965 27.084 -25.548 1.00 54.41 H +ATOM 3856 HH22 ARG A1130 -0.975 24.169 -27.288 1.00 41.74 H +ATOM 3857 HH21 ARG A1130 -2.235 24.287 -28.490 1.00 41.74 H +ATOM 3858 N GLY A1131 -9.207 24.747 -25.824 1.00 36.22 N +ATOM 3859 CA GLY A1131 -10.366 24.048 -26.376 1.00 35.40 C +ATOM 3860 C GLY A1131 -9.974 22.960 -27.390 1.00 37.10 C +ATOM 3861 O GLY A1131 -10.871 22.416 -28.034 1.00 36.00 O +ATOM 3862 H GLY A1131 -8.801 24.371 -24.979 1.00 36.22 H +ATOM 3863 HA3 GLY A1131 -11.060 24.747 -26.845 1.00 35.40 H +ATOM 3864 HA2 GLY A1131 -10.909 23.585 -25.554 1.00 35.40 H +ATOM 3865 N GLU A1132 -8.676 22.623 -27.532 1.00 32.13 N +ATOM 3866 CA GLU A1132 -8.205 21.537 -28.397 1.00 31.71 C +ATOM 3867 C GLU A1132 -8.525 20.162 -27.793 1.00 33.67 C +ATOM 3868 O GLU A1132 -8.257 19.935 -26.613 1.00 31.64 O +ATOM 3869 CB GLU A1132 -6.694 21.640 -28.670 1.00 33.82 C +ATOM 3870 CG GLU A1132 -6.247 22.982 -29.287 1.00 48.90 C +ATOM 3871 CD GLU A1132 -4.778 23.022 -29.744 1.00 65.96 C +ATOM 3872 OE1 GLU A1132 -4.195 21.948 -30.019 1.00 51.67 O +ATOM 3873 OE2 GLU A1132 -4.249 24.151 -29.826 1.00 68.15 O1- +ATOM 3874 H GLU A1132 -7.970 23.086 -26.976 1.00 32.13 H +ATOM 3875 HA GLU A1132 -8.720 21.634 -29.353 1.00 31.71 H +ATOM 3876 HB3 GLU A1132 -6.426 20.815 -29.330 1.00 33.82 H +ATOM 3877 HB2 GLU A1132 -6.135 21.468 -27.752 1.00 33.82 H +ATOM 3878 HG3 GLU A1132 -6.417 23.786 -28.569 1.00 48.90 H +ATOM 3879 HG2 GLU A1132 -6.869 23.208 -30.154 1.00 48.90 H +ATOM 3880 N ARG A1133 -9.080 19.277 -28.631 1.00 30.19 N +ATOM 3881 CA ARG A1133 -9.515 17.933 -28.263 1.00 28.71 C +ATOM 3882 C ARG A1133 -8.945 16.892 -29.230 1.00 31.32 C +ATOM 3883 O ARG A1133 -8.505 17.230 -30.331 1.00 31.52 O +ATOM 3884 CB ARG A1133 -11.063 17.876 -28.305 1.00 28.69 C +ATOM 3885 CG ARG A1133 -11.789 18.793 -27.309 1.00 29.69 C +ATOM 3886 CD ARG A1133 -11.505 18.482 -25.831 1.00 29.80 C +ATOM 3887 NE ARG A1133 -12.141 19.468 -24.947 1.00 30.88 N +ATOM 3888 CZ ARG A1133 -11.592 20.560 -24.393 1.00 38.38 C +ATOM 3889 NH1 ARG A1133 -10.317 20.890 -24.594 1.00 29.25 N +ATOM 3890 NH2 ARG A1133 -12.340 21.342 -23.612 1.00 30.33 N1+ +ATOM 3891 H ARG A1133 -9.223 19.537 -29.599 1.00 30.19 H +ATOM 3892 HA ARG A1133 -9.150 17.666 -27.270 1.00 28.71 H +ATOM 3893 HB3 ARG A1133 -11.401 16.856 -28.123 1.00 28.69 H +ATOM 3894 HB2 ARG A1133 -11.407 18.118 -29.312 1.00 28.69 H +ATOM 3895 HG3 ARG A1133 -12.848 18.588 -27.471 1.00 29.69 H +ATOM 3896 HG2 ARG A1133 -11.659 19.851 -27.538 1.00 29.69 H +ATOM 3897 HD3 ARG A1133 -10.455 18.302 -25.603 1.00 29.80 H +ATOM 3898 HD2 ARG A1133 -12.018 17.554 -25.586 1.00 29.80 H +ATOM 3899 HE ARG A1133 -13.129 19.321 -24.780 1.00 30.88 H +ATOM 3900 HH12 ARG A1133 -9.923 21.702 -24.135 1.00 29.25 H +ATOM 3901 HH11 ARG A1133 -9.733 20.336 -25.209 1.00 29.25 H +ATOM 3902 HH22 ARG A1133 -11.950 22.154 -23.148 1.00 30.33 H +ATOM 3903 HH21 ARG A1133 -13.297 21.079 -23.419 1.00 30.33 H +ATOM 3904 N LEU A1134 -9.055 15.621 -28.811 1.00 26.90 N +ATOM 3905 CA LEU A1134 -8.918 14.425 -29.644 1.00 26.48 C +ATOM 3906 C LEU A1134 -9.928 14.480 -30.815 1.00 31.07 C +ATOM 3907 O LEU A1134 -11.084 14.845 -30.581 1.00 29.22 O +ATOM 3908 CB LEU A1134 -9.227 13.190 -28.766 1.00 25.80 C +ATOM 3909 CG LEU A1134 -8.165 12.863 -27.694 1.00 28.43 C +ATOM 3910 CD1 LEU A1134 -8.717 11.905 -26.614 1.00 27.29 C +ATOM 3911 CD2 LEU A1134 -6.854 12.349 -28.325 1.00 31.24 C +ATOM 3912 H LEU A1134 -9.416 15.453 -27.879 1.00 26.90 H +ATOM 3913 HA LEU A1134 -7.887 14.406 -29.992 1.00 26.48 H +ATOM 3914 HB3 LEU A1134 -9.362 12.309 -29.394 1.00 25.80 H +ATOM 3915 HB2 LEU A1134 -10.189 13.350 -28.280 1.00 25.80 H +ATOM 3916 HG LEU A1134 -7.928 13.796 -27.183 1.00 28.43 H +ATOM 3917 HD11 LEU A1134 -8.755 12.402 -25.644 1.00 27.29 H +ATOM 3918 HD12 LEU A1134 -9.729 11.570 -26.840 1.00 27.29 H +ATOM 3919 HD13 LEU A1134 -8.107 11.011 -26.487 1.00 27.29 H +ATOM 3920 HD21 LEU A1134 -5.992 12.885 -27.926 1.00 31.24 H +ATOM 3921 HD22 LEU A1134 -6.697 11.289 -28.144 1.00 31.24 H +ATOM 3922 HD23 LEU A1134 -6.840 12.471 -29.408 1.00 31.24 H +ATOM 3923 N PRO A1135 -9.478 14.161 -32.051 1.00 29.27 N +ATOM 3924 CA PRO A1135 -10.320 14.269 -33.257 1.00 29.71 C +ATOM 3925 C PRO A1135 -11.484 13.263 -33.276 1.00 33.63 C +ATOM 3926 O PRO A1135 -11.495 12.320 -32.486 1.00 33.51 O +ATOM 3927 CB PRO A1135 -9.324 14.011 -34.400 1.00 32.38 C +ATOM 3928 CG PRO A1135 -8.283 13.086 -33.794 1.00 35.83 C +ATOM 3929 CD PRO A1135 -8.159 13.605 -32.368 1.00 30.45 C +ATOM 3930 HA PRO A1135 -10.734 15.274 -33.346 1.00 29.71 H +ATOM 3931 HB3 PRO A1135 -8.853 14.953 -34.686 1.00 32.38 H +ATOM 3932 HB2 PRO A1135 -9.779 13.590 -35.298 1.00 32.38 H +ATOM 3933 HG3 PRO A1135 -7.340 13.077 -34.339 1.00 35.83 H +ATOM 3934 HG2 PRO A1135 -8.673 12.068 -33.776 1.00 35.83 H +ATOM 3935 HD2 PRO A1135 -7.859 12.805 -31.691 1.00 30.45 H +ATOM 3936 HD3 PRO A1135 -7.419 14.404 -32.314 1.00 30.45 H +ATOM 3937 N ARG A1136 -12.446 13.482 -34.185 1.00 30.63 N +ATOM 3938 CA ARG A1136 -13.533 12.542 -34.459 1.00 30.68 C +ATOM 3939 C ARG A1136 -12.962 11.261 -35.110 1.00 36.51 C +ATOM 3940 O ARG A1136 -12.376 11.380 -36.188 1.00 36.37 O +ATOM 3941 CB ARG A1136 -14.560 13.241 -35.376 1.00 28.69 C +ATOM 3942 CG ARG A1136 -15.806 12.379 -35.679 1.00 33.51 C +ATOM 3943 CD ARG A1136 -16.847 13.056 -36.590 1.00 37.56 C +ATOM 3944 NE ARG A1136 -16.358 13.227 -37.972 1.00 52.22 N +ATOM 3945 CZ ARG A1136 -16.281 12.291 -38.939 1.00 71.16 C +ATOM 3946 NH1 ARG A1136 -15.733 12.618 -40.115 1.00 60.58 N +ATOM 3947 NH2 ARG A1136 -16.728 11.038 -38.774 1.00 53.25 N1+ +ATOM 3948 H ARG A1136 -12.389 14.291 -34.792 1.00 30.63 H +ATOM 3949 HA ARG A1136 -14.039 12.330 -33.523 1.00 30.68 H +ATOM 3950 HB3 ARG A1136 -14.076 13.535 -36.309 1.00 28.69 H +ATOM 3951 HB2 ARG A1136 -14.883 14.169 -34.905 1.00 28.69 H +ATOM 3952 HG3 ARG A1136 -16.278 12.004 -34.771 1.00 33.51 H +ATOM 3953 HG2 ARG A1136 -15.454 11.489 -36.199 1.00 33.51 H +ATOM 3954 HD3 ARG A1136 -16.998 14.074 -36.231 1.00 37.56 H +ATOM 3955 HD2 ARG A1136 -17.825 12.577 -36.532 1.00 37.56 H +ATOM 3956 HE ARG A1136 -15.982 14.141 -38.177 1.00 52.22 H +ATOM 3957 HH12 ARG A1136 -15.652 11.923 -40.844 1.00 60.58 H +ATOM 3958 HH11 ARG A1136 -15.382 13.551 -40.280 1.00 60.58 H +ATOM 3959 HH22 ARG A1136 -16.634 10.363 -39.522 1.00 53.25 H +ATOM 3960 HH21 ARG A1136 -17.185 10.766 -37.912 1.00 53.25 H +ATOM 3961 N PRO A1137 -13.133 10.074 -34.473 1.00 34.19 N +ATOM 3962 CA PRO A1137 -12.794 8.777 -35.100 1.00 34.79 C +ATOM 3963 C PRO A1137 -13.518 8.551 -36.438 1.00 39.69 C +ATOM 3964 O PRO A1137 -14.654 9.000 -36.591 1.00 37.63 O +ATOM 3965 CB PRO A1137 -13.235 7.730 -34.059 1.00 35.61 C +ATOM 3966 CG PRO A1137 -13.270 8.479 -32.741 1.00 38.50 C +ATOM 3967 CD PRO A1137 -13.716 9.872 -33.145 1.00 34.62 C +ATOM 3968 HA PRO A1137 -11.711 8.740 -35.234 1.00 34.79 H +ATOM 3969 HB3 PRO A1137 -12.572 6.866 -34.029 1.00 35.61 H +ATOM 3970 HB2 PRO A1137 -14.230 7.351 -34.286 1.00 35.61 H +ATOM 3971 HG3 PRO A1137 -12.259 8.534 -32.343 1.00 38.50 H +ATOM 3972 HG2 PRO A1137 -13.904 8.021 -31.984 1.00 38.50 H +ATOM 3973 HD2 PRO A1137 -14.802 9.926 -33.221 1.00 34.62 H +ATOM 3974 HD3 PRO A1137 -13.386 10.595 -32.401 1.00 34.62 H +ATOM 3975 N ASP A1138 -12.853 7.868 -37.383 1.00 39.28 N +ATOM 3976 CA ASP A1138 -13.426 7.546 -38.693 1.00 41.69 C +ATOM 3977 C ASP A1138 -14.685 6.666 -38.532 1.00 47.14 C +ATOM 3978 O ASP A1138 -14.612 5.633 -37.867 1.00 46.83 O +ATOM 3979 CB ASP A1138 -12.387 6.871 -39.624 1.00 45.59 C +ATOM 3980 CG ASP A1138 -12.841 6.652 -41.080 1.00 68.02 C +ATOM 3981 OD1 ASP A1138 -13.557 7.527 -41.620 1.00 70.17 O +ATOM 3982 OD2 ASP A1138 -12.365 5.662 -41.676 1.00 78.75 O1- +ATOM 3983 H ASP A1138 -11.925 7.516 -37.195 1.00 39.28 H +ATOM 3984 HA ASP A1138 -13.719 8.501 -39.135 1.00 41.69 H +ATOM 3985 HB3 ASP A1138 -12.074 5.921 -39.186 1.00 45.59 H +ATOM 3986 HB2 ASP A1138 -11.481 7.478 -39.648 1.00 45.59 H +ATOM 3987 N LYS A1139 -15.800 7.116 -39.131 1.00 45.46 N +ATOM 3988 CA LYS A1139 -17.138 6.507 -39.107 1.00 46.10 C +ATOM 3989 C LYS A1139 -17.911 6.699 -37.781 1.00 48.03 C +ATOM 3990 O LYS A1139 -19.010 6.158 -37.663 1.00 49.27 O +ATOM 3991 CB LYS A1139 -17.123 5.027 -39.585 1.00 50.14 C +ATOM 3992 CG LYS A1139 -16.482 4.798 -40.972 1.00 69.71 C +ATOM 3993 CD LYS A1139 -17.332 5.278 -42.167 1.00 83.93 C +ATOM 3994 CE LYS A1139 -18.382 4.259 -42.647 1.00 98.25 C +ATOM 3995 NZ LYS A1139 -17.770 3.130 -43.374 1.00107.70 N1+ +ATOM 3996 H LYS A1139 -15.734 7.966 -39.674 1.00 45.46 H +ATOM 3997 HA LYS A1139 -17.712 7.081 -39.834 1.00 46.10 H +ATOM 3998 HB3 LYS A1139 -18.137 4.626 -39.598 1.00 50.14 H +ATOM 3999 HB2 LYS A1139 -16.590 4.415 -38.858 1.00 50.14 H +ATOM 4000 HG3 LYS A1139 -16.252 3.738 -41.079 1.00 69.71 H +ATOM 4001 HG2 LYS A1139 -15.512 5.290 -41.017 1.00 69.71 H +ATOM 4002 HD3 LYS A1139 -16.672 5.557 -42.990 1.00 83.93 H +ATOM 4003 HD2 LYS A1139 -17.850 6.199 -41.901 1.00 83.93 H +ATOM 4004 HE3 LYS A1139 -19.082 4.749 -43.326 1.00 98.25 H +ATOM 4005 HE2 LYS A1139 -18.968 3.879 -41.809 1.00 98.25 H +ATOM 4006 HZ1 LYS A1139 -17.132 2.641 -42.762 1.00107.70 H +ATOM 4007 HZ2 LYS A1139 -18.490 2.494 -43.686 1.00107.70 H +ATOM 4008 HZ3 LYS A1139 -17.261 3.480 -44.174 1.00107.70 H +ATOM 4009 N CYS A1140 -17.379 7.501 -36.836 1.00 40.64 N +ATOM 4010 CA CYS A1140 -18.096 7.964 -35.642 1.00 37.44 C +ATOM 4011 C CYS A1140 -19.135 9.023 -36.075 1.00 40.28 C +ATOM 4012 O CYS A1140 -18.716 10.015 -36.674 1.00 40.75 O +ATOM 4013 CB CYS A1140 -17.119 8.571 -34.608 1.00 35.57 C +ATOM 4014 SG CYS A1140 -17.956 9.233 -33.132 1.00 37.88 S +ATOM 4015 H CYS A1140 -16.464 7.904 -36.986 1.00 40.64 H +ATOM 4016 HA CYS A1140 -18.547 7.094 -35.169 1.00 37.44 H +ATOM 4017 HB3 CYS A1140 -16.551 9.391 -35.044 1.00 35.57 H +ATOM 4018 HB2 CYS A1140 -16.407 7.814 -34.285 1.00 35.57 H +ATOM 4019 HG CYS A1140 -18.601 10.222 -33.759 1.00 37.88 H +ATOM 4020 N PRO A1141 -20.442 8.825 -35.776 1.00 34.69 N +ATOM 4021 CA PRO A1141 -21.483 9.846 -36.018 1.00 34.83 C +ATOM 4022 C PRO A1141 -21.161 11.214 -35.391 1.00 36.98 C +ATOM 4023 O PRO A1141 -20.604 11.262 -34.294 1.00 34.16 O +ATOM 4024 CB PRO A1141 -22.754 9.238 -35.404 1.00 36.57 C +ATOM 4025 CG PRO A1141 -22.512 7.741 -35.389 1.00 40.74 C +ATOM 4026 CD PRO A1141 -21.014 7.635 -35.148 1.00 36.09 C +ATOM 4027 HA PRO A1141 -21.603 9.941 -37.099 1.00 34.83 H +ATOM 4028 HB3 PRO A1141 -23.645 9.500 -35.973 1.00 36.57 H +ATOM 4029 HB2 PRO A1141 -22.903 9.592 -34.384 1.00 36.57 H +ATOM 4030 HG3 PRO A1141 -22.751 7.330 -36.371 1.00 40.74 H +ATOM 4031 HG2 PRO A1141 -23.108 7.209 -34.648 1.00 40.74 H +ATOM 4032 HD2 PRO A1141 -20.788 7.650 -34.081 1.00 36.09 H +ATOM 4033 HD3 PRO A1141 -20.634 6.709 -35.576 1.00 36.09 H +ATOM 4034 N ALA A1142 -21.496 12.292 -36.118 1.00 34.46 N +ATOM 4035 CA ALA A1142 -21.237 13.677 -35.716 1.00 34.01 C +ATOM 4036 C ALA A1142 -21.858 14.063 -34.363 1.00 35.94 C +ATOM 4037 O ALA A1142 -21.196 14.741 -33.582 1.00 33.77 O +ATOM 4038 CB ALA A1142 -21.717 14.626 -36.823 1.00 35.94 C +ATOM 4039 H ALA A1142 -21.956 12.168 -37.008 1.00 34.46 H +ATOM 4040 HA ALA A1142 -20.155 13.788 -35.623 1.00 34.01 H +ATOM 4041 HB1 ALA A1142 -21.528 15.667 -36.559 1.00 35.94 H +ATOM 4042 HB2 ALA A1142 -21.199 14.427 -37.761 1.00 35.94 H +ATOM 4043 HB3 ALA A1142 -22.788 14.519 -37.005 1.00 35.94 H +ATOM 4044 N GLU A1143 -23.085 13.585 -34.095 1.00 34.04 N +ATOM 4045 CA GLU A1143 -23.816 13.835 -32.853 1.00 34.34 C +ATOM 4046 C GLU A1143 -23.305 12.992 -31.664 1.00 35.13 C +ATOM 4047 O GLU A1143 -23.390 13.458 -30.528 1.00 31.94 O +ATOM 4048 CB GLU A1143 -25.318 13.650 -33.135 1.00 37.10 C +ATOM 4049 CG GLU A1143 -26.246 14.080 -31.978 1.00 51.53 C +ATOM 4050 CD GLU A1143 -27.740 14.125 -32.340 1.00 70.35 C +ATOM 4051 OE1 GLU A1143 -28.153 13.422 -33.291 1.00 77.38 O +ATOM 4052 OE2 GLU A1143 -28.460 14.869 -31.638 1.00 51.45 O1- +ATOM 4053 H GLU A1143 -23.564 13.026 -34.790 1.00 34.04 H +ATOM 4054 HA GLU A1143 -23.664 14.883 -32.589 1.00 34.34 H +ATOM 4055 HB3 GLU A1143 -25.505 12.606 -33.385 1.00 37.10 H +ATOM 4056 HB2 GLU A1143 -25.577 14.225 -34.027 1.00 37.10 H +ATOM 4057 HG3 GLU A1143 -25.936 15.065 -31.626 1.00 51.53 H +ATOM 4058 HG2 GLU A1143 -26.129 13.400 -31.134 1.00 51.53 H +ATOM 4059 N VAL A1144 -22.731 11.805 -31.940 1.00 32.23 N +ATOM 4060 CA VAL A1144 -22.009 10.987 -30.957 1.00 30.46 C +ATOM 4061 C VAL A1144 -20.648 11.617 -30.584 1.00 31.37 C +ATOM 4062 O VAL A1144 -20.236 11.503 -29.430 1.00 28.93 O +ATOM 4063 CB VAL A1144 -21.807 9.523 -31.459 1.00 34.90 C +ATOM 4064 CG1 VAL A1144 -20.858 8.650 -30.606 1.00 34.06 C +ATOM 4065 CG2 VAL A1144 -23.162 8.803 -31.593 1.00 35.48 C +ATOM 4066 H VAL A1144 -22.686 11.488 -32.898 1.00 32.23 H +ATOM 4067 HA VAL A1144 -22.610 10.949 -30.046 1.00 30.46 H +ATOM 4068 HB VAL A1144 -21.371 9.566 -32.457 1.00 34.90 H +ATOM 4069 HG11 VAL A1144 -20.841 7.622 -30.969 1.00 34.06 H +ATOM 4070 HG12 VAL A1144 -19.827 9.004 -30.638 1.00 34.06 H +ATOM 4071 HG13 VAL A1144 -21.173 8.625 -29.562 1.00 34.06 H +ATOM 4072 HG21 VAL A1144 -23.040 7.798 -31.997 1.00 35.48 H +ATOM 4073 HG22 VAL A1144 -23.654 8.713 -30.625 1.00 35.48 H +ATOM 4074 HG23 VAL A1144 -23.843 9.336 -32.256 1.00 35.48 H +ATOM 4075 N TYR A1145 -20.010 12.320 -31.540 1.00 28.72 N +ATOM 4076 CA TYR A1145 -18.815 13.130 -31.305 1.00 28.17 C +ATOM 4077 C TYR A1145 -19.111 14.402 -30.483 1.00 32.85 C +ATOM 4078 O TYR A1145 -18.270 14.789 -29.676 1.00 32.29 O +ATOM 4079 CB TYR A1145 -18.111 13.449 -32.643 1.00 29.44 C +ATOM 4080 CG TYR A1145 -16.805 14.215 -32.495 1.00 31.74 C +ATOM 4081 CD1 TYR A1145 -15.727 13.605 -31.826 1.00 33.16 C +ATOM 4082 CD2 TYR A1145 -16.667 15.532 -32.980 1.00 33.69 C +ATOM 4083 CE1 TYR A1145 -14.533 14.312 -31.597 1.00 33.08 C +ATOM 4084 CE2 TYR A1145 -15.457 16.230 -32.785 1.00 34.55 C +ATOM 4085 CZ TYR A1145 -14.395 15.627 -32.079 1.00 36.66 C +ATOM 4086 OH TYR A1145 -13.233 16.310 -31.864 1.00 35.20 O +ATOM 4087 H TYR A1145 -20.399 12.353 -32.473 1.00 28.72 H +ATOM 4088 HA TYR A1145 -18.129 12.519 -30.716 1.00 28.17 H +ATOM 4089 HB3 TYR A1145 -18.775 14.007 -33.302 1.00 29.44 H +ATOM 4090 HB2 TYR A1145 -17.894 12.516 -33.163 1.00 29.44 H +ATOM 4091 HD1 TYR A1145 -15.827 12.586 -31.495 1.00 33.16 H +ATOM 4092 HD2 TYR A1145 -17.486 16.012 -33.496 1.00 33.69 H +ATOM 4093 HE1 TYR A1145 -13.720 13.833 -31.071 1.00 33.08 H +ATOM 4094 HE2 TYR A1145 -15.352 17.237 -33.161 1.00 34.55 H +ATOM 4095 HH TYR A1145 -12.591 15.787 -31.372 1.00 35.20 H +ATOM 4096 N HIS A1146 -20.303 15.000 -30.665 1.00 31.34 N +ATOM 4097 CA HIS A1146 -20.779 16.145 -29.881 1.00 31.80 C +ATOM 4098 C HIS A1146 -21.146 15.778 -28.433 1.00 32.90 C +ATOM 4099 O HIS A1146 -20.947 16.611 -27.550 1.00 31.97 O +ATOM 4100 CB HIS A1146 -21.955 16.843 -30.592 1.00 33.81 C +ATOM 4101 CG HIS A1146 -21.650 17.391 -31.965 1.00 37.86 C +ATOM 4102 ND1 HIS A1146 -22.623 17.473 -32.968 1.00 40.76 N +ATOM 4103 CD2 HIS A1146 -20.458 17.881 -32.464 1.00 39.65 C +ATOM 4104 CE1 HIS A1146 -21.991 17.984 -34.016 1.00 40.91 C +ATOM 4105 NE2 HIS A1146 -20.706 18.244 -33.776 1.00 40.63 N +ATOM 4106 H HIS A1146 -20.940 14.639 -31.363 1.00 31.34 H +ATOM 4107 HA HIS A1146 -19.959 16.864 -29.820 1.00 31.80 H +ATOM 4108 HB3 HIS A1146 -22.316 17.675 -29.985 1.00 33.81 H +ATOM 4109 HB2 HIS A1146 -22.794 16.152 -30.679 1.00 33.81 H +ATOM 4110 HD2 HIS A1146 -19.485 17.986 -32.008 1.00 39.65 H +ATOM 4111 HE1 HIS A1146 -22.472 18.171 -34.965 1.00 40.91 H +ATOM 4112 HE2 HIS A1146 -20.043 18.638 -34.428 1.00 40.63 H +ATOM 4113 N LEU A1147 -21.611 14.536 -28.202 1.00 29.07 N +ATOM 4114 CA LEU A1147 -21.778 13.940 -26.872 1.00 28.56 C +ATOM 4115 C LEU A1147 -20.424 13.756 -26.160 1.00 31.25 C +ATOM 4116 O LEU A1147 -20.338 13.997 -24.958 1.00 29.28 O +ATOM 4117 CB LEU A1147 -22.590 12.626 -27.011 1.00 29.23 C +ATOM 4118 CG LEU A1147 -22.817 11.793 -25.720 1.00 34.50 C +ATOM 4119 CD1 LEU A1147 -24.077 10.917 -25.842 1.00 34.68 C +ATOM 4120 CD2 LEU A1147 -21.606 10.922 -25.312 1.00 36.84 C +ATOM 4121 H LEU A1147 -21.788 13.924 -28.986 1.00 29.07 H +ATOM 4122 HA LEU A1147 -22.369 14.632 -26.270 1.00 28.56 H +ATOM 4123 HB3 LEU A1147 -22.149 11.983 -27.771 1.00 29.23 H +ATOM 4124 HB2 LEU A1147 -23.558 12.917 -27.419 1.00 29.23 H +ATOM 4125 HG LEU A1147 -23.013 12.499 -24.911 1.00 34.50 H +ATOM 4126 HD11 LEU A1147 -24.525 10.753 -24.862 1.00 34.68 H +ATOM 4127 HD12 LEU A1147 -24.837 11.371 -26.478 1.00 34.68 H +ATOM 4128 HD13 LEU A1147 -23.850 9.940 -26.269 1.00 34.68 H +ATOM 4129 HD21 LEU A1147 -21.884 9.888 -25.107 1.00 36.84 H +ATOM 4130 HD22 LEU A1147 -20.841 10.896 -26.088 1.00 36.84 H +ATOM 4131 HD23 LEU A1147 -21.140 11.306 -24.405 1.00 36.84 H +ATOM 4132 N MET A1148 -19.396 13.361 -26.929 1.00 29.35 N +ATOM 4133 CA MET A1148 -18.014 13.182 -26.484 1.00 30.67 C +ATOM 4134 C MET A1148 -17.339 14.513 -26.107 1.00 31.09 C +ATOM 4135 O MET A1148 -16.638 14.554 -25.099 1.00 28.70 O +ATOM 4136 CB MET A1148 -17.240 12.431 -27.592 1.00 34.60 C +ATOM 4137 CG MET A1148 -16.460 11.203 -27.102 1.00 40.60 C +ATOM 4138 SD MET A1148 -16.144 9.982 -28.405 1.00 46.94 S +ATOM 4139 CE MET A1148 -14.969 10.895 -29.430 1.00 42.17 C +ATOM 4140 H MET A1148 -19.567 13.172 -27.907 1.00 29.35 H +ATOM 4141 HA MET A1148 -18.055 12.563 -25.586 1.00 30.67 H +ATOM 4142 HB3 MET A1148 -16.552 13.112 -28.095 1.00 34.60 H +ATOM 4143 HB2 MET A1148 -17.922 12.103 -28.375 1.00 34.60 H +ATOM 4144 HG3 MET A1148 -17.018 10.688 -26.320 1.00 40.60 H +ATOM 4145 HG2 MET A1148 -15.515 11.514 -26.657 1.00 40.60 H +ATOM 4146 HE1 MET A1148 -14.862 10.415 -30.402 1.00 42.17 H +ATOM 4147 HE2 MET A1148 -15.302 11.918 -29.569 1.00 42.17 H +ATOM 4148 HE3 MET A1148 -13.997 10.931 -28.953 1.00 42.17 H +ATOM 4149 N LYS A1149 -17.598 15.580 -26.886 1.00 27.88 N +ATOM 4150 CA LYS A1149 -17.154 16.946 -26.595 1.00 27.39 C +ATOM 4151 C LYS A1149 -17.917 17.609 -25.435 1.00 30.73 C +ATOM 4152 O LYS A1149 -17.329 18.451 -24.759 1.00 30.19 O +ATOM 4153 CB LYS A1149 -17.207 17.818 -27.865 1.00 30.13 C +ATOM 4154 CG LYS A1149 -16.108 17.472 -28.889 1.00 40.57 C +ATOM 4155 CD LYS A1149 -15.915 18.539 -29.980 1.00 49.92 C +ATOM 4156 CE LYS A1149 -15.173 19.793 -29.484 1.00 55.31 C +ATOM 4157 NZ LYS A1149 -14.911 20.746 -30.577 1.00 61.18 N1+ +ATOM 4158 H LYS A1149 -18.158 15.460 -27.720 1.00 27.88 H +ATOM 4159 HA LYS A1149 -16.112 16.888 -26.279 1.00 27.39 H +ATOM 4160 HB3 LYS A1149 -17.079 18.859 -27.571 1.00 30.13 H +ATOM 4161 HB2 LYS A1149 -18.193 17.764 -28.329 1.00 30.13 H +ATOM 4162 HG3 LYS A1149 -16.348 16.522 -29.364 1.00 40.57 H +ATOM 4163 HG2 LYS A1149 -15.157 17.313 -28.382 1.00 40.57 H +ATOM 4164 HD3 LYS A1149 -16.890 18.818 -30.383 1.00 49.92 H +ATOM 4165 HD2 LYS A1149 -15.361 18.095 -30.805 1.00 49.92 H +ATOM 4166 HE3 LYS A1149 -14.215 19.514 -29.049 1.00 55.31 H +ATOM 4167 HE2 LYS A1149 -15.743 20.306 -28.710 1.00 55.31 H +ATOM 4168 HZ1 LYS A1149 -14.403 21.541 -30.215 1.00 61.18 H +ATOM 4169 HZ2 LYS A1149 -14.362 20.296 -31.294 1.00 61.18 H +ATOM 4170 HZ3 LYS A1149 -15.788 21.058 -30.970 1.00 61.18 H +ATOM 4171 N ASN A1150 -19.178 17.207 -25.195 1.00 27.52 N +ATOM 4172 CA ASN A1150 -19.961 17.623 -24.027 1.00 27.53 C +ATOM 4173 C ASN A1150 -19.491 16.903 -22.745 1.00 29.61 C +ATOM 4174 O ASN A1150 -19.527 17.504 -21.674 1.00 29.79 O +ATOM 4175 CB ASN A1150 -21.466 17.425 -24.321 1.00 28.97 C +ATOM 4176 CG ASN A1150 -22.410 17.975 -23.243 1.00 41.34 C +ATOM 4177 OD1 ASN A1150 -23.302 17.265 -22.787 1.00 34.75 O +ATOM 4178 ND2 ASN A1150 -22.230 19.234 -22.833 1.00 35.14 N +ATOM 4179 H ASN A1150 -19.616 16.537 -25.812 1.00 27.52 H +ATOM 4180 HA ASN A1150 -19.788 18.690 -23.883 1.00 27.53 H +ATOM 4181 HB3 ASN A1150 -21.683 16.367 -24.477 1.00 28.97 H +ATOM 4182 HB2 ASN A1150 -21.722 17.929 -25.254 1.00 28.97 H +ATOM 4183 HD22 ASN A1150 -22.824 19.619 -22.112 1.00 35.14 H +ATOM 4184 HD21 ASN A1150 -21.495 19.813 -23.220 1.00 35.14 H +ATOM 4185 N CYS A1151 -18.978 15.668 -22.883 1.00 24.02 N +ATOM 4186 CA CYS A1151 -18.226 14.967 -21.839 1.00 24.87 C +ATOM 4187 C CYS A1151 -16.843 15.601 -21.590 1.00 28.35 C +ATOM 4188 O CYS A1151 -16.282 15.363 -20.527 1.00 26.56 O +ATOM 4189 CB CYS A1151 -18.067 13.466 -22.147 1.00 25.12 C +ATOM 4190 SG CYS A1151 -19.666 12.631 -21.997 1.00 29.56 S +ATOM 4191 H CYS A1151 -19.008 15.218 -23.787 1.00 24.02 H +ATOM 4192 HA CYS A1151 -18.778 15.060 -20.903 1.00 24.87 H +ATOM 4193 HB3 CYS A1151 -17.376 12.992 -21.451 1.00 25.12 H +ATOM 4194 HB2 CYS A1151 -17.671 13.297 -23.146 1.00 25.12 H +ATOM 4195 HG CYS A1151 -20.221 13.209 -23.068 1.00 29.56 H +ATOM 4196 N TRP A1152 -16.334 16.407 -22.539 1.00 24.56 N +ATOM 4197 CA TRP A1152 -15.064 17.120 -22.440 1.00 24.31 C +ATOM 4198 C TRP A1152 -15.252 18.648 -22.357 1.00 28.86 C +ATOM 4199 O TRP A1152 -14.462 19.368 -22.962 1.00 29.11 O +ATOM 4200 CB TRP A1152 -14.150 16.769 -23.635 1.00 23.52 C +ATOM 4201 CG TRP A1152 -13.769 15.351 -23.905 1.00 23.91 C +ATOM 4202 CD1 TRP A1152 -13.464 14.411 -22.983 1.00 26.64 C +ATOM 4203 CD2 TRP A1152 -13.493 14.748 -25.205 1.00 24.07 C +ATOM 4204 NE1 TRP A1152 -13.028 13.271 -23.624 1.00 25.72 N +ATOM 4205 CE2 TRP A1152 -13.021 13.419 -24.996 1.00 26.84 C +ATOM 4206 CE3 TRP A1152 -13.581 15.195 -26.545 1.00 25.78 C +ATOM 4207 CZ2 TRP A1152 -12.651 12.581 -26.063 1.00 25.92 C +ATOM 4208 CZ3 TRP A1152 -13.229 14.360 -27.624 1.00 27.26 C +ATOM 4209 CH2 TRP A1152 -12.760 13.055 -27.383 1.00 27.17 C +ATOM 4210 H TRP A1152 -16.861 16.563 -23.386 1.00 24.56 H +ATOM 4211 HA TRP A1152 -14.549 16.820 -21.531 1.00 24.31 H +ATOM 4212 HB3 TRP A1152 -13.195 17.278 -23.504 1.00 23.52 H +ATOM 4213 HB2 TRP A1152 -14.587 17.165 -24.551 1.00 23.52 H +ATOM 4214 HD1 TRP A1152 -13.530 14.552 -21.914 1.00 26.64 H +ATOM 4215 HE1 TRP A1152 -12.743 12.443 -23.118 1.00 25.72 H +ATOM 4216 HE3 TRP A1152 -13.922 16.199 -26.746 1.00 25.78 H +ATOM 4217 HZ2 TRP A1152 -12.280 11.586 -25.875 1.00 25.92 H +ATOM 4218 HZ3 TRP A1152 -13.304 14.727 -28.637 1.00 27.26 H +ATOM 4219 HH2 TRP A1152 -12.468 12.425 -28.209 1.00 27.17 H +ATOM 4220 N GLU A1153 -16.243 19.166 -21.612 1.00 25.75 N +ATOM 4221 CA GLU A1153 -16.288 20.607 -21.306 1.00 26.82 C +ATOM 4222 C GLU A1153 -15.165 20.957 -20.317 1.00 30.62 C +ATOM 4223 O GLU A1153 -14.982 20.213 -19.355 1.00 26.79 O +ATOM 4224 CB GLU A1153 -17.661 21.015 -20.731 1.00 28.38 C +ATOM 4225 CG GLU A1153 -18.853 20.780 -21.684 1.00 35.13 C +ATOM 4226 CD GLU A1153 -19.078 21.826 -22.787 1.00 54.88 C +ATOM 4227 OE1 GLU A1153 -18.283 22.786 -22.903 1.00 51.30 O +ATOM 4228 OE2 GLU A1153 -20.072 21.633 -23.523 1.00 50.04 O1- +ATOM 4229 H GLU A1153 -16.909 18.570 -21.143 1.00 25.75 H +ATOM 4230 HA GLU A1153 -16.126 21.170 -22.227 1.00 26.82 H +ATOM 4231 HB3 GLU A1153 -17.632 22.059 -20.419 1.00 28.38 H +ATOM 4232 HB2 GLU A1153 -17.847 20.473 -19.807 1.00 28.38 H +ATOM 4233 HG3 GLU A1153 -19.767 20.707 -21.096 1.00 35.13 H +ATOM 4234 HG2 GLU A1153 -18.736 19.816 -22.172 1.00 35.13 H +ATOM 4235 N THR A1154 -14.438 22.064 -20.561 1.00 30.88 N +ATOM 4236 CA THR A1154 -13.383 22.571 -19.668 1.00 31.88 C +ATOM 4237 C THR A1154 -13.939 22.913 -18.271 1.00 35.93 C +ATOM 4238 O THR A1154 -13.325 22.547 -17.269 1.00 35.75 O +ATOM 4239 CB THR A1154 -12.699 23.849 -20.233 1.00 43.66 C +ATOM 4240 OG1 THR A1154 -12.085 23.555 -21.471 1.00 44.97 O +ATOM 4241 CG2 THR A1154 -11.623 24.496 -19.338 1.00 43.44 C +ATOM 4242 H THR A1154 -14.652 22.640 -21.363 1.00 30.88 H +ATOM 4243 HA THR A1154 -12.631 21.787 -19.556 1.00 31.88 H +ATOM 4244 HB THR A1154 -13.464 24.600 -20.437 1.00 43.66 H +ATOM 4245 HG1 THR A1154 -11.510 24.290 -21.702 1.00 44.97 H +ATOM 4246 HG21 THR A1154 -11.136 25.325 -19.853 1.00 43.44 H +ATOM 4247 HG22 THR A1154 -12.041 24.904 -18.417 1.00 43.44 H +ATOM 4248 HG23 THR A1154 -10.847 23.781 -19.063 1.00 43.44 H +ATOM 4249 N GLU A1155 -15.129 23.536 -18.254 1.00 34.13 N +ATOM 4250 CA GLU A1155 -15.937 23.784 -17.069 1.00 35.75 C +ATOM 4251 C GLU A1155 -16.604 22.467 -16.627 1.00 37.89 C +ATOM 4252 O GLU A1155 -17.335 21.863 -17.414 1.00 35.58 O +ATOM 4253 CB GLU A1155 -16.967 24.873 -17.437 1.00 38.82 C +ATOM 4254 CG GLU A1155 -17.778 25.452 -16.258 1.00 56.26 C +ATOM 4255 CD GLU A1155 -17.038 26.450 -15.348 1.00 89.36 C +ATOM 4256 OE1 GLU A1155 -15.839 26.728 -15.577 1.00 94.02 O +ATOM 4257 OE2 GLU A1155 -17.704 26.925 -14.403 1.00 89.74 O1- +ATOM 4258 H GLU A1155 -15.563 23.781 -19.134 1.00 34.13 H +ATOM 4259 HA GLU A1155 -15.283 24.149 -16.275 1.00 35.75 H +ATOM 4260 HB3 GLU A1155 -17.672 24.451 -18.153 1.00 38.82 H +ATOM 4261 HB2 GLU A1155 -16.479 25.683 -17.982 1.00 38.82 H +ATOM 4262 HG3 GLU A1155 -18.176 24.641 -15.648 1.00 56.26 H +ATOM 4263 HG2 GLU A1155 -18.645 25.972 -16.663 1.00 56.26 H +ATOM 4264 N ALA A1156 -16.319 22.041 -15.385 1.00 35.19 N +ATOM 4265 CA ALA A1156 -16.766 20.766 -14.818 1.00 35.22 C +ATOM 4266 C ALA A1156 -18.288 20.639 -14.646 1.00 38.77 C +ATOM 4267 O ALA A1156 -18.817 19.540 -14.816 1.00 37.90 O +ATOM 4268 CB ALA A1156 -16.044 20.526 -13.484 1.00 36.33 C +ATOM 4269 H ALA A1156 -15.724 22.604 -14.793 1.00 35.19 H +ATOM 4270 HA ALA A1156 -16.466 19.984 -15.513 1.00 35.22 H +ATOM 4271 HB1 ALA A1156 -16.329 19.570 -13.043 1.00 36.33 H +ATOM 4272 HB2 ALA A1156 -14.962 20.512 -13.622 1.00 36.33 H +ATOM 4273 HB3 ALA A1156 -16.273 21.306 -12.757 1.00 36.33 H +ATOM 4274 N SER A1157 -18.958 21.763 -14.341 1.00 35.61 N +ATOM 4275 CA SER A1157 -20.409 21.866 -14.176 1.00 35.08 C +ATOM 4276 C SER A1157 -21.211 21.665 -15.479 1.00 37.46 C +ATOM 4277 O SER A1157 -22.359 21.228 -15.400 1.00 37.36 O +ATOM 4278 CB SER A1157 -20.732 23.206 -13.483 1.00 40.33 C +ATOM 4279 OG SER A1157 -20.526 24.317 -14.335 1.00 48.92 O +ATOM 4280 H SER A1157 -18.442 22.623 -14.209 1.00 35.61 H +ATOM 4281 HA SER A1157 -20.705 21.071 -13.490 1.00 35.08 H +ATOM 4282 HB3 SER A1157 -20.130 23.330 -12.582 1.00 40.33 H +ATOM 4283 HB2 SER A1157 -21.774 23.216 -13.163 1.00 40.33 H +ATOM 4284 HG SER A1157 -20.612 25.124 -13.820 1.00 48.92 H +ATOM 4285 N PHE A1158 -20.600 21.963 -16.641 1.00 32.83 N +ATOM 4286 CA PHE A1158 -21.225 21.832 -17.962 1.00 32.10 C +ATOM 4287 C PHE A1158 -21.262 20.383 -18.487 1.00 33.92 C +ATOM 4288 O PHE A1158 -22.027 20.112 -19.414 1.00 31.85 O +ATOM 4289 CB PHE A1158 -20.509 22.753 -18.976 1.00 33.90 C +ATOM 4290 CG PHE A1158 -20.583 24.263 -18.780 1.00 37.34 C +ATOM 4291 CD1 PHE A1158 -21.365 24.870 -17.770 1.00 41.32 C +ATOM 4292 CD2 PHE A1158 -19.866 25.090 -19.671 1.00 39.43 C +ATOM 4293 CE1 PHE A1158 -21.392 26.253 -17.648 1.00 43.51 C +ATOM 4294 CE2 PHE A1158 -19.906 26.471 -19.532 1.00 43.26 C +ATOM 4295 CZ PHE A1158 -20.663 27.049 -18.523 1.00 42.52 C +ATOM 4296 H PHE A1158 -19.654 22.318 -16.628 1.00 32.83 H +ATOM 4297 HA PHE A1158 -22.266 22.152 -17.891 1.00 32.10 H +ATOM 4298 HB3 PHE A1158 -20.891 22.555 -19.979 1.00 33.90 H +ATOM 4299 HB2 PHE A1158 -19.452 22.489 -19.006 1.00 33.90 H +ATOM 4300 HD1 PHE A1158 -21.944 24.280 -17.075 1.00 41.32 H +ATOM 4301 HD2 PHE A1158 -19.276 24.647 -20.460 1.00 39.43 H +ATOM 4302 HE1 PHE A1158 -21.982 26.712 -16.868 1.00 43.51 H +ATOM 4303 HE2 PHE A1158 -19.346 27.095 -20.212 1.00 43.26 H +ATOM 4304 HZ PHE A1158 -20.689 28.124 -18.419 1.00 42.52 H +ATOM 4305 N ARG A1159 -20.451 19.483 -17.901 1.00 27.94 N +ATOM 4306 CA ARG A1159 -20.401 18.062 -18.254 1.00 27.11 C +ATOM 4307 C ARG A1159 -21.635 17.307 -17.701 1.00 29.97 C +ATOM 4308 O ARG A1159 -22.017 17.562 -16.558 1.00 28.45 O +ATOM 4309 CB ARG A1159 -19.085 17.447 -17.725 1.00 27.28 C +ATOM 4310 CG ARG A1159 -17.826 18.123 -18.307 1.00 37.58 C +ATOM 4311 CD ARG A1159 -16.511 17.352 -18.102 1.00 43.51 C +ATOM 4312 NE ARG A1159 -16.026 17.327 -16.716 1.00 43.50 N +ATOM 4313 CZ ARG A1159 -14.971 17.962 -16.172 1.00 45.54 C +ATOM 4314 NH1 ARG A1159 -14.222 18.856 -16.830 1.00 36.88 N +ATOM 4315 NH2 ARG A1159 -14.648 17.676 -14.910 1.00 30.01 N1+ +ATOM 4316 H ARG A1159 -19.858 19.776 -17.138 1.00 27.94 H +ATOM 4317 HA ARG A1159 -20.378 18.025 -19.340 1.00 27.11 H +ATOM 4318 HB3 ARG A1159 -19.064 16.393 -18.001 1.00 27.28 H +ATOM 4319 HB2 ARG A1159 -19.056 17.476 -16.635 1.00 27.28 H +ATOM 4320 HG3 ARG A1159 -17.736 19.090 -17.808 1.00 37.58 H +ATOM 4321 HG2 ARG A1159 -17.947 18.333 -19.368 1.00 37.58 H +ATOM 4322 HD3 ARG A1159 -15.755 17.616 -18.840 1.00 43.51 H +ATOM 4323 HD2 ARG A1159 -16.727 16.298 -18.268 1.00 43.51 H +ATOM 4324 HE ARG A1159 -16.515 16.670 -16.123 1.00 43.50 H +ATOM 4325 HH12 ARG A1159 -13.443 19.305 -16.367 1.00 36.88 H +ATOM 4326 HH11 ARG A1159 -14.479 19.148 -17.766 1.00 36.88 H +ATOM 4327 HH22 ARG A1159 -13.828 18.092 -14.480 1.00 30.01 H +ATOM 4328 HH21 ARG A1159 -15.177 16.993 -14.389 1.00 30.01 H +ATOM 4329 N PRO A1160 -22.245 16.402 -18.505 1.00 26.80 N +ATOM 4330 CA PRO A1160 -23.417 15.614 -18.080 1.00 26.98 C +ATOM 4331 C PRO A1160 -23.023 14.501 -17.094 1.00 32.02 C +ATOM 4332 O PRO A1160 -22.041 13.808 -17.342 1.00 31.35 O +ATOM 4333 CB PRO A1160 -23.937 15.030 -19.406 1.00 28.34 C +ATOM 4334 CG PRO A1160 -22.695 14.855 -20.268 1.00 31.76 C +ATOM 4335 CD PRO A1160 -21.826 16.039 -19.861 1.00 27.47 C +ATOM 4336 HA PRO A1160 -24.178 16.252 -17.628 1.00 26.98 H +ATOM 4337 HB3 PRO A1160 -24.608 15.750 -19.876 1.00 28.34 H +ATOM 4338 HB2 PRO A1160 -24.491 14.098 -19.289 1.00 28.34 H +ATOM 4339 HG3 PRO A1160 -22.907 14.822 -21.337 1.00 31.76 H +ATOM 4340 HG2 PRO A1160 -22.192 13.923 -20.004 1.00 31.76 H +ATOM 4341 HD2 PRO A1160 -20.769 15.781 -19.915 1.00 27.47 H +ATOM 4342 HD3 PRO A1160 -22.012 16.890 -20.516 1.00 27.47 H +ATOM 4343 N THR A1161 -23.789 14.324 -16.007 1.00 29.10 N +ATOM 4344 CA THR A1161 -23.603 13.217 -15.056 1.00 28.91 C +ATOM 4345 C THR A1161 -23.827 11.838 -15.722 1.00 29.63 C +ATOM 4346 O THR A1161 -24.454 11.766 -16.780 1.00 26.66 O +ATOM 4347 CB THR A1161 -24.557 13.351 -13.832 1.00 34.86 C +ATOM 4348 OG1 THR A1161 -25.865 12.856 -14.057 1.00 34.53 O +ATOM 4349 CG2 THR A1161 -24.617 14.763 -13.229 1.00 35.90 C +ATOM 4350 H THR A1161 -24.607 14.901 -15.863 1.00 29.10 H +ATOM 4351 HA THR A1161 -22.573 13.254 -14.697 1.00 28.91 H +ATOM 4352 HB THR A1161 -24.153 12.711 -13.047 1.00 34.86 H +ATOM 4353 HG1 THR A1161 -26.454 13.221 -13.388 1.00 34.53 H +ATOM 4354 HG21 THR A1161 -25.175 14.766 -12.292 1.00 35.90 H +ATOM 4355 HG22 THR A1161 -23.616 15.139 -13.017 1.00 35.90 H +ATOM 4356 HG23 THR A1161 -25.103 15.469 -13.903 1.00 35.90 H +ATOM 4357 N PHE A1162 -23.334 10.762 -15.088 1.00 26.26 N +ATOM 4358 CA PHE A1162 -23.561 9.392 -15.560 1.00 25.89 C +ATOM 4359 C PHE A1162 -25.040 8.966 -15.610 1.00 30.07 C +ATOM 4360 O PHE A1162 -25.403 8.218 -16.515 1.00 29.53 O +ATOM 4361 CB PHE A1162 -22.699 8.388 -14.777 1.00 27.11 C +ATOM 4362 CG PHE A1162 -21.246 8.362 -15.201 1.00 25.98 C +ATOM 4363 CD1 PHE A1162 -20.898 7.838 -16.459 1.00 26.41 C +ATOM 4364 CD2 PHE A1162 -20.245 8.943 -14.402 1.00 27.86 C +ATOM 4365 CE1 PHE A1162 -19.573 7.805 -16.862 1.00 26.21 C +ATOM 4366 CE2 PHE A1162 -18.922 8.912 -14.824 1.00 29.34 C +ATOM 4367 CZ PHE A1162 -18.586 8.329 -16.039 1.00 25.75 C +ATOM 4368 H PHE A1162 -22.822 10.869 -14.222 1.00 26.26 H +ATOM 4369 HA PHE A1162 -23.226 9.366 -16.598 1.00 25.89 H +ATOM 4370 HB3 PHE A1162 -23.079 7.379 -14.930 1.00 27.11 H +ATOM 4371 HB2 PHE A1162 -22.764 8.571 -13.705 1.00 27.11 H +ATOM 4372 HD1 PHE A1162 -21.672 7.455 -17.102 1.00 26.41 H +ATOM 4373 HD2 PHE A1162 -20.507 9.395 -13.461 1.00 27.86 H +ATOM 4374 HE1 PHE A1162 -19.316 7.369 -17.815 1.00 26.21 H +ATOM 4375 HE2 PHE A1162 -18.153 9.342 -14.206 1.00 29.34 H +ATOM 4376 HZ PHE A1162 -17.554 8.293 -16.350 1.00 25.75 H +ATOM 4377 N GLU A1163 -25.873 9.500 -14.699 1.00 27.87 N +ATOM 4378 CA GLU A1163 -27.330 9.355 -14.724 1.00 28.69 C +ATOM 4379 C GLU A1163 -28.021 10.100 -15.882 1.00 33.26 C +ATOM 4380 O GLU A1163 -29.087 9.652 -16.303 1.00 32.51 O +ATOM 4381 CB GLU A1163 -27.927 9.769 -13.366 1.00 31.37 C +ATOM 4382 CG GLU A1163 -27.608 8.760 -12.239 1.00 44.54 C +ATOM 4383 CD GLU A1163 -28.145 9.129 -10.846 1.00 68.84 C +ATOM 4384 OE1 GLU A1163 -28.711 10.233 -10.679 1.00 67.02 O +ATOM 4385 OE2 GLU A1163 -27.958 8.284 -9.944 1.00 62.94 O1- +ATOM 4386 H GLU A1163 -25.498 10.099 -13.976 1.00 27.87 H +ATOM 4387 HA GLU A1163 -27.551 8.297 -14.871 1.00 28.69 H +ATOM 4388 HB3 GLU A1163 -29.009 9.882 -13.452 1.00 31.37 H +ATOM 4389 HB2 GLU A1163 -27.540 10.753 -13.095 1.00 31.37 H +ATOM 4390 HG3 GLU A1163 -26.530 8.625 -12.158 1.00 44.54 H +ATOM 4391 HG2 GLU A1163 -28.013 7.785 -12.512 1.00 44.54 H +ATOM 4392 N ASN A1164 -27.417 11.190 -16.392 1.00 30.86 N +ATOM 4393 CA ASN A1164 -27.882 11.893 -17.596 1.00 30.74 C +ATOM 4394 C ASN A1164 -27.503 11.131 -18.879 1.00 32.00 C +ATOM 4395 O ASN A1164 -28.280 11.151 -19.832 1.00 31.36 O +ATOM 4396 CB ASN A1164 -27.317 13.336 -17.689 1.00 30.05 C +ATOM 4397 CG ASN A1164 -27.571 14.258 -16.490 1.00 41.44 C +ATOM 4398 OD1 ASN A1164 -26.746 15.121 -16.200 1.00 30.45 O +ATOM 4399 ND2 ASN A1164 -28.701 14.110 -15.795 1.00 30.02 N +ATOM 4400 H ASN A1164 -26.552 11.521 -15.984 1.00 30.86 H +ATOM 4401 HA ASN A1164 -28.972 11.954 -17.582 1.00 30.74 H +ATOM 4402 HB3 ASN A1164 -27.734 13.831 -18.567 1.00 30.05 H +ATOM 4403 HB2 ASN A1164 -26.240 13.302 -17.853 1.00 30.05 H +ATOM 4404 HD22 ASN A1164 -28.879 14.703 -14.997 1.00 30.02 H +ATOM 4405 HD21 ASN A1164 -29.374 13.396 -16.037 1.00 30.02 H +ATOM 4406 N LEU A1165 -26.326 10.480 -18.882 1.00 27.37 N +ATOM 4407 CA LEU A1165 -25.766 9.760 -20.028 1.00 26.17 C +ATOM 4408 C LEU A1165 -26.478 8.435 -20.352 1.00 30.32 C +ATOM 4409 O LEU A1165 -26.504 8.068 -21.525 1.00 29.57 O +ATOM 4410 CB LEU A1165 -24.259 9.516 -19.791 1.00 24.98 C +ATOM 4411 CG LEU A1165 -23.381 10.779 -19.932 1.00 27.47 C +ATOM 4412 CD1 LEU A1165 -21.985 10.567 -19.304 1.00 26.17 C +ATOM 4413 CD2 LEU A1165 -23.312 11.261 -21.400 1.00 28.90 C +ATOM 4414 H LEU A1165 -25.739 10.526 -18.060 1.00 27.37 H +ATOM 4415 HA LEU A1165 -25.886 10.396 -20.907 1.00 26.17 H +ATOM 4416 HB3 LEU A1165 -23.883 8.764 -20.486 1.00 24.98 H +ATOM 4417 HB2 LEU A1165 -24.131 9.083 -18.798 1.00 24.98 H +ATOM 4418 HG LEU A1165 -23.853 11.578 -19.359 1.00 27.47 H +ATOM 4419 HD11 LEU A1165 -21.169 10.782 -19.992 1.00 26.17 H +ATOM 4420 HD12 LEU A1165 -21.850 11.218 -18.439 1.00 26.17 H +ATOM 4421 HD13 LEU A1165 -21.842 9.544 -18.956 1.00 26.17 H +ATOM 4422 HD21 LEU A1165 -22.294 11.315 -21.782 1.00 28.90 H +ATOM 4423 HD22 LEU A1165 -23.857 10.602 -22.076 1.00 28.90 H +ATOM 4424 HD23 LEU A1165 -23.747 12.255 -21.501 1.00 28.90 H +ATOM 4425 N ILE A1166 -27.040 7.749 -19.341 1.00 27.97 N +ATOM 4426 CA ILE A1166 -27.745 6.467 -19.487 1.00 28.85 C +ATOM 4427 C ILE A1166 -28.953 6.492 -20.460 1.00 33.75 C +ATOM 4428 O ILE A1166 -28.925 5.705 -21.406 1.00 32.47 O +ATOM 4429 CB ILE A1166 -28.146 5.868 -18.102 1.00 32.86 C +ATOM 4430 CG1 ILE A1166 -26.891 5.392 -17.344 1.00 32.40 C +ATOM 4431 CG2 ILE A1166 -29.182 4.719 -18.139 1.00 34.79 C +ATOM 4432 CD1 ILE A1166 -27.093 5.273 -15.829 1.00 41.27 C +ATOM 4433 H ILE A1166 -26.962 8.109 -18.400 1.00 27.97 H +ATOM 4434 HA ILE A1166 -27.016 5.782 -19.927 1.00 28.85 H +ATOM 4435 HB ILE A1166 -28.578 6.676 -17.513 1.00 32.86 H +ATOM 4436 HG13 ILE A1166 -26.060 6.066 -17.526 1.00 32.40 H +ATOM 4437 HG12 ILE A1166 -26.559 4.439 -17.750 1.00 32.40 H +ATOM 4438 HG21 ILE A1166 -29.368 4.322 -17.141 1.00 34.79 H +ATOM 4439 HG22 ILE A1166 -30.151 5.039 -18.522 1.00 34.79 H +ATOM 4440 HG23 ILE A1166 -28.826 3.895 -18.757 1.00 34.79 H +ATOM 4441 HD11 ILE A1166 -26.139 5.370 -15.317 1.00 41.27 H +ATOM 4442 HD12 ILE A1166 -27.751 6.049 -15.441 1.00 41.27 H +ATOM 4443 HD13 ILE A1166 -27.519 4.310 -15.552 1.00 41.27 H +ATOM 4444 N PRO A1167 -29.961 7.381 -20.270 1.00 32.13 N +ATOM 4445 CA PRO A1167 -31.104 7.471 -21.200 1.00 32.58 C +ATOM 4446 C PRO A1167 -30.756 7.999 -22.606 1.00 34.66 C +ATOM 4447 O PRO A1167 -31.441 7.626 -23.559 1.00 34.80 O +ATOM 4448 CB PRO A1167 -32.110 8.379 -20.476 1.00 35.43 C +ATOM 4449 CG PRO A1167 -31.254 9.243 -19.567 1.00 39.58 C +ATOM 4450 CD PRO A1167 -30.159 8.280 -19.129 1.00 34.49 C +ATOM 4451 HA PRO A1167 -31.550 6.482 -21.322 1.00 32.58 H +ATOM 4452 HB3 PRO A1167 -32.779 7.765 -19.871 1.00 35.43 H +ATOM 4453 HB2 PRO A1167 -32.734 8.973 -21.146 1.00 35.43 H +ATOM 4454 HG3 PRO A1167 -31.803 9.681 -18.734 1.00 39.58 H +ATOM 4455 HG2 PRO A1167 -30.817 10.057 -20.147 1.00 39.58 H +ATOM 4456 HD2 PRO A1167 -29.269 8.825 -18.831 1.00 34.49 H +ATOM 4457 HD3 PRO A1167 -30.503 7.693 -18.277 1.00 34.49 H +ATOM 4458 N ILE A1168 -29.692 8.816 -22.719 1.00 31.51 N +ATOM 4459 CA ILE A1168 -29.174 9.311 -23.996 1.00 32.13 C +ATOM 4460 C ILE A1168 -28.515 8.176 -24.809 1.00 34.20 C +ATOM 4461 O ILE A1168 -28.819 8.042 -25.992 1.00 33.26 O +ATOM 4462 CB ILE A1168 -28.157 10.480 -23.813 1.00 35.61 C +ATOM 4463 CG1 ILE A1168 -28.818 11.695 -23.122 1.00 36.57 C +ATOM 4464 CG2 ILE A1168 -27.491 10.936 -25.133 1.00 37.22 C +ATOM 4465 CD1 ILE A1168 -27.816 12.756 -22.635 1.00 41.21 C +ATOM 4466 H ILE A1168 -29.178 9.087 -21.893 1.00 31.51 H +ATOM 4467 HA ILE A1168 -30.019 9.689 -24.577 1.00 32.13 H +ATOM 4468 HB ILE A1168 -27.363 10.125 -23.154 1.00 35.61 H +ATOM 4469 HG13 ILE A1168 -29.409 11.372 -22.266 1.00 36.57 H +ATOM 4470 HG12 ILE A1168 -29.529 12.160 -23.806 1.00 36.57 H +ATOM 4471 HG21 ILE A1168 -26.825 11.783 -24.973 1.00 37.22 H +ATOM 4472 HG22 ILE A1168 -26.886 10.153 -25.588 1.00 37.22 H +ATOM 4473 HG23 ILE A1168 -28.240 11.243 -25.864 1.00 37.22 H +ATOM 4474 HD11 ILE A1168 -28.223 13.321 -21.796 1.00 41.21 H +ATOM 4475 HD12 ILE A1168 -26.881 12.305 -22.300 1.00 41.21 H +ATOM 4476 HD13 ILE A1168 -27.581 13.469 -23.424 1.00 41.21 H +ATOM 4477 N LEU A1169 -27.664 7.367 -24.155 1.00 29.74 N +ATOM 4478 CA LEU A1169 -26.967 6.225 -24.756 1.00 29.80 C +ATOM 4479 C LEU A1169 -27.885 5.031 -25.075 1.00 35.11 C +ATOM 4480 O LEU A1169 -27.557 4.280 -25.991 1.00 36.14 O +ATOM 4481 CB LEU A1169 -25.777 5.814 -23.865 1.00 29.09 C +ATOM 4482 CG LEU A1169 -24.581 6.789 -23.960 1.00 32.75 C +ATOM 4483 CD1 LEU A1169 -23.587 6.570 -22.808 1.00 32.66 C +ATOM 4484 CD2 LEU A1169 -23.883 6.727 -25.337 1.00 36.03 C +ATOM 4485 H LEU A1169 -27.456 7.545 -23.180 1.00 29.74 H +ATOM 4486 HA LEU A1169 -26.565 6.559 -25.711 1.00 29.80 H +ATOM 4487 HB3 LEU A1169 -25.421 4.823 -24.144 1.00 29.09 H +ATOM 4488 HB2 LEU A1169 -26.125 5.721 -22.835 1.00 29.09 H +ATOM 4489 HG LEU A1169 -24.969 7.801 -23.834 1.00 32.75 H +ATOM 4490 HD11 LEU A1169 -23.208 7.523 -22.440 1.00 32.66 H +ATOM 4491 HD12 LEU A1169 -24.047 6.060 -21.962 1.00 32.66 H +ATOM 4492 HD13 LEU A1169 -22.731 5.971 -23.114 1.00 32.66 H +ATOM 4493 HD21 LEU A1169 -22.797 6.684 -25.255 1.00 36.03 H +ATOM 4494 HD22 LEU A1169 -24.195 5.856 -25.915 1.00 36.03 H +ATOM 4495 HD23 LEU A1169 -24.124 7.609 -25.930 1.00 36.03 H +ATOM 4496 N LYS A1170 -29.028 4.905 -24.376 1.00 32.29 N +ATOM 4497 CA LYS A1170 -30.116 3.981 -24.717 1.00 33.11 C +ATOM 4498 C LYS A1170 -30.825 4.360 -26.028 1.00 37.39 C +ATOM 4499 O LYS A1170 -31.078 3.477 -26.846 1.00 37.67 O +ATOM 4500 CB LYS A1170 -31.124 3.899 -23.551 1.00 37.22 C +ATOM 4501 CG LYS A1170 -30.641 3.002 -22.404 1.00 43.25 C +ATOM 4502 CD LYS A1170 -31.551 3.030 -21.170 1.00 49.41 C +ATOM 4503 CE LYS A1170 -31.093 2.016 -20.108 1.00 57.38 C +ATOM 4504 NZ LYS A1170 -31.915 2.081 -18.888 1.00 64.71 N1+ +ATOM 4505 H LYS A1170 -29.207 5.536 -23.606 1.00 32.29 H +ATOM 4506 HA LYS A1170 -29.678 2.992 -24.863 1.00 33.11 H +ATOM 4507 HB3 LYS A1170 -32.069 3.483 -23.907 1.00 37.22 H +ATOM 4508 HB2 LYS A1170 -31.357 4.899 -23.187 1.00 37.22 H +ATOM 4509 HG3 LYS A1170 -29.633 3.290 -22.111 1.00 43.25 H +ATOM 4510 HG2 LYS A1170 -30.573 1.978 -22.773 1.00 43.25 H +ATOM 4511 HD3 LYS A1170 -32.578 2.820 -21.472 1.00 49.41 H +ATOM 4512 HD2 LYS A1170 -31.550 4.038 -20.751 1.00 49.41 H +ATOM 4513 HE3 LYS A1170 -30.052 2.195 -19.835 1.00 57.38 H +ATOM 4514 HE2 LYS A1170 -31.143 1.003 -20.508 1.00 57.38 H +ATOM 4515 HZ1 LYS A1170 -32.877 1.875 -19.115 1.00 64.71 H +ATOM 4516 HZ2 LYS A1170 -31.574 1.402 -18.221 1.00 64.71 H +ATOM 4517 HZ3 LYS A1170 -31.852 3.005 -18.484 1.00 64.71 H +ATOM 4518 N THR A1171 -31.102 5.663 -26.212 1.00 34.80 N +ATOM 4519 CA THR A1171 -31.738 6.219 -27.411 1.00 35.33 C +ATOM 4520 C THR A1171 -30.800 6.208 -28.638 1.00 39.05 C +ATOM 4521 O THR A1171 -31.260 5.941 -29.748 1.00 39.67 O +ATOM 4522 CB THR A1171 -32.220 7.675 -27.167 1.00 44.81 C +ATOM 4523 OG1 THR A1171 -33.137 7.683 -26.089 1.00 44.59 O +ATOM 4524 CG2 THR A1171 -32.908 8.359 -28.363 1.00 42.98 C +ATOM 4525 H THR A1171 -30.845 6.329 -25.497 1.00 34.80 H +ATOM 4526 HA THR A1171 -32.610 5.604 -27.647 1.00 35.33 H +ATOM 4527 HB THR A1171 -31.373 8.291 -26.863 1.00 44.81 H +ATOM 4528 HG1 THR A1171 -32.663 7.467 -25.282 1.00 44.59 H +ATOM 4529 HG21 THR A1171 -33.310 9.332 -28.077 1.00 42.98 H +ATOM 4530 HG22 THR A1171 -32.218 8.533 -29.189 1.00 42.98 H +ATOM 4531 HG23 THR A1171 -33.736 7.757 -28.739 1.00 42.98 H +ATOM 4532 N VAL A1172 -29.501 6.461 -28.407 1.00 34.64 N +ATOM 4533 CA VAL A1172 -28.437 6.393 -29.409 1.00 35.04 C +ATOM 4534 C VAL A1172 -28.142 4.943 -29.854 1.00 37.21 C +ATOM 4535 O VAL A1172 -27.962 4.722 -31.050 1.00 35.63 O +ATOM 4536 CB VAL A1172 -27.153 7.107 -28.887 1.00 39.11 C +ATOM 4537 CG1 VAL A1172 -25.823 6.718 -29.557 1.00 39.01 C +ATOM 4538 CG2 VAL A1172 -27.323 8.636 -28.939 1.00 39.06 C +ATOM 4539 H VAL A1172 -29.207 6.702 -27.470 1.00 34.64 H +ATOM 4540 HA VAL A1172 -28.785 6.931 -30.293 1.00 35.04 H +ATOM 4541 HB VAL A1172 -27.044 6.843 -27.837 1.00 39.11 H +ATOM 4542 HG11 VAL A1172 -25.005 7.334 -29.184 1.00 39.01 H +ATOM 4543 HG12 VAL A1172 -25.555 5.686 -29.335 1.00 39.01 H +ATOM 4544 HG13 VAL A1172 -25.867 6.841 -30.639 1.00 39.01 H +ATOM 4545 HG21 VAL A1172 -26.478 9.144 -28.472 1.00 39.06 H +ATOM 4546 HG22 VAL A1172 -27.395 8.990 -29.967 1.00 39.06 H +ATOM 4547 HG23 VAL A1172 -28.223 8.960 -28.417 1.00 39.06 H +ATOM 4548 N HIS A1173 -28.170 3.976 -28.917 1.00 33.86 N +ATOM 4549 CA HIS A1173 -28.096 2.540 -29.214 1.00 34.98 C +ATOM 4550 C HIS A1173 -29.277 2.060 -30.084 1.00 39.49 C +ATOM 4551 O HIS A1173 -29.043 1.349 -31.059 1.00 38.50 O +ATOM 4552 CB HIS A1173 -27.984 1.732 -27.901 1.00 35.81 C +ATOM 4553 CG HIS A1173 -28.090 0.234 -28.062 1.00 39.76 C +ATOM 4554 ND1 HIS A1173 -27.102 -0.535 -28.649 1.00 41.19 N +ATOM 4555 CD2 HIS A1173 -29.100 -0.647 -27.743 1.00 42.70 C +ATOM 4556 CE1 HIS A1173 -27.538 -1.797 -28.663 1.00 41.61 C +ATOM 4557 NE2 HIS A1173 -28.744 -1.941 -28.129 1.00 43.01 N +ATOM 4558 H HIS A1173 -28.305 4.222 -27.946 1.00 33.86 H +ATOM 4559 HA HIS A1173 -27.183 2.367 -29.788 1.00 34.98 H +ATOM 4560 HB3 HIS A1173 -28.760 2.045 -27.201 1.00 35.81 H +ATOM 4561 HB2 HIS A1173 -27.032 1.952 -27.414 1.00 35.81 H +ATOM 4562 HD1 HIS A1173 -26.205 -0.215 -28.995 1.00 41.19 H +ATOM 4563 HD2 HIS A1173 -30.059 -0.445 -27.288 1.00 42.70 H +ATOM 4564 HE1 HIS A1173 -26.961 -2.619 -29.062 1.00 41.61 H +ATOM 4565 N GLU A1174 -30.502 2.495 -29.734 1.00 37.68 N +ATOM 4566 CA GLU A1174 -31.744 2.256 -30.476 1.00 39.70 C +ATOM 4567 C GLU A1174 -31.727 2.760 -31.930 1.00 42.94 C +ATOM 4568 O GLU A1174 -32.312 2.102 -32.790 1.00 44.72 O +ATOM 4569 CB GLU A1174 -32.935 2.873 -29.702 1.00 42.32 C +ATOM 4570 CG GLU A1174 -33.555 1.928 -28.651 1.00 60.01 C +ATOM 4571 CD GLU A1174 -34.443 0.805 -29.219 1.00 92.87 C +ATOM 4572 OE1 GLU A1174 -34.832 0.871 -30.407 1.00 94.43 O +ATOM 4573 OE2 GLU A1174 -34.715 -0.138 -28.446 1.00 90.73 O1- +ATOM 4574 H GLU A1174 -30.599 3.061 -28.901 1.00 37.68 H +ATOM 4575 HA GLU A1174 -31.882 1.176 -30.525 1.00 39.70 H +ATOM 4576 HB3 GLU A1174 -33.706 3.247 -30.379 1.00 42.32 H +ATOM 4577 HB2 GLU A1174 -32.594 3.762 -29.175 1.00 42.32 H +ATOM 4578 HG3 GLU A1174 -34.171 2.518 -27.971 1.00 60.01 H +ATOM 4579 HG2 GLU A1174 -32.763 1.493 -28.038 1.00 60.01 H +ATOM 4580 N LYS A1175 -31.050 3.893 -32.178 1.00 37.93 N +ATOM 4581 CA LYS A1175 -30.917 4.504 -33.500 1.00 37.53 C +ATOM 4582 C LYS A1175 -30.053 3.674 -34.468 1.00 41.18 C +ATOM 4583 O LYS A1175 -30.453 3.507 -35.620 1.00 39.68 O +ATOM 4584 CB LYS A1175 -30.402 5.952 -33.337 1.00 38.66 C +ATOM 4585 CG LYS A1175 -30.187 6.714 -34.659 1.00 48.52 C +ATOM 4586 CD LYS A1175 -29.780 8.180 -34.438 1.00 57.93 C +ATOM 4587 CE LYS A1175 -29.465 8.944 -35.737 1.00 71.38 C +ATOM 4588 NZ LYS A1175 -30.655 9.138 -36.585 1.00 86.87 N1+ +ATOM 4589 H LYS A1175 -30.605 4.381 -31.413 1.00 37.93 H +ATOM 4590 HA LYS A1175 -31.920 4.562 -33.929 1.00 37.53 H +ATOM 4591 HB3 LYS A1175 -29.462 5.948 -32.786 1.00 38.66 H +ATOM 4592 HB2 LYS A1175 -31.109 6.507 -32.720 1.00 38.66 H +ATOM 4593 HG3 LYS A1175 -31.100 6.660 -35.253 1.00 48.52 H +ATOM 4594 HG2 LYS A1175 -29.406 6.228 -35.246 1.00 48.52 H +ATOM 4595 HD3 LYS A1175 -28.898 8.205 -33.797 1.00 57.93 H +ATOM 4596 HD2 LYS A1175 -30.561 8.700 -33.881 1.00 57.93 H +ATOM 4597 HE3 LYS A1175 -28.705 8.416 -36.313 1.00 71.38 H +ATOM 4598 HE2 LYS A1175 -29.056 9.926 -35.497 1.00 71.38 H +ATOM 4599 HZ1 LYS A1175 -31.353 9.660 -36.073 1.00 86.87 H +ATOM 4600 HZ2 LYS A1175 -30.398 9.649 -37.418 1.00 86.87 H +ATOM 4601 HZ3 LYS A1175 -31.032 8.238 -36.845 1.00 86.87 H +ATOM 4602 N TYR A1176 -28.889 3.197 -33.995 1.00 37.88 N +ATOM 4603 CA TYR A1176 -27.868 2.567 -34.838 1.00 38.84 C +ATOM 4604 C TYR A1176 -27.921 1.028 -34.865 1.00 43.27 C +ATOM 4605 O TYR A1176 -27.210 0.447 -35.688 1.00 45.60 O +ATOM 4606 CB TYR A1176 -26.473 3.100 -34.436 1.00 39.66 C +ATOM 4607 CG TYR A1176 -26.302 4.594 -34.682 1.00 42.14 C +ATOM 4608 CD1 TYR A1176 -26.315 5.101 -35.999 1.00 44.87 C +ATOM 4609 CD2 TYR A1176 -26.162 5.487 -33.601 1.00 42.46 C +ATOM 4610 CE1 TYR A1176 -26.229 6.489 -36.227 1.00 44.19 C +ATOM 4611 CE2 TYR A1176 -26.089 6.875 -33.825 1.00 43.50 C +ATOM 4612 CZ TYR A1176 -26.135 7.378 -35.138 1.00 49.64 C +ATOM 4613 OH TYR A1176 -26.106 8.725 -35.354 1.00 54.89 O +ATOM 4614 H TYR A1176 -28.635 3.369 -33.031 1.00 37.88 H +ATOM 4615 HA TYR A1176 -28.033 2.870 -35.874 1.00 38.84 H +ATOM 4616 HB3 TYR A1176 -25.694 2.591 -35.006 1.00 39.66 H +ATOM 4617 HB2 TYR A1176 -26.271 2.873 -33.387 1.00 39.66 H +ATOM 4618 HD1 TYR A1176 -26.412 4.428 -36.839 1.00 44.87 H +ATOM 4619 HD2 TYR A1176 -26.126 5.113 -32.593 1.00 42.46 H +ATOM 4620 HE1 TYR A1176 -26.257 6.870 -37.237 1.00 44.19 H +ATOM 4621 HE2 TYR A1176 -26.007 7.551 -32.986 1.00 43.50 H +ATOM 4622 HH TYR A1176 -26.186 9.238 -34.543 1.00 54.89 H +ATOM 4623 N ARG A1177 -28.744 0.383 -34.014 1.00 37.76 N +ATOM 4624 CA ARG A1177 -28.935 -1.075 -34.023 1.00 53.07 C +ATOM 4625 C ARG A1177 -29.798 -1.553 -35.209 1.00 74.57 C +ATOM 4626 O ARG A1177 -30.502 -0.752 -35.827 1.00 46.90 O +ATOM 4627 CB ARG A1177 -29.496 -1.560 -32.664 1.00 52.87 C +ATOM 4628 CG ARG A1177 -30.982 -1.254 -32.399 1.00 64.39 C +ATOM 4629 CD ARG A1177 -31.514 -1.954 -31.138 1.00 74.69 C +ATOM 4630 NE ARG A1177 -32.978 -1.851 -31.034 1.00 82.78 N +ATOM 4631 CZ ARG A1177 -33.907 -2.569 -31.694 1.00 98.29 C +ATOM 4632 NH1 ARG A1177 -33.589 -3.553 -32.550 1.00 84.15 N +ATOM 4633 NH2 ARG A1177 -35.199 -2.283 -31.493 1.00 87.75 N1+ +ATOM 4634 H ARG A1177 -29.299 0.909 -33.353 1.00 37.76 H +ATOM 4635 HA ARG A1177 -27.950 -1.532 -34.140 1.00 53.07 H +ATOM 4636 HB3 ARG A1177 -28.888 -1.173 -31.846 1.00 52.87 H +ATOM 4637 HB2 ARG A1177 -29.369 -2.643 -32.620 1.00 52.87 H +ATOM 4638 HG3 ARG A1177 -31.630 -1.450 -33.253 1.00 64.39 H +ATOM 4639 HG2 ARG A1177 -31.028 -0.179 -32.238 1.00 64.39 H +ATOM 4640 HD3 ARG A1177 -31.155 -1.396 -30.273 1.00 74.69 H +ATOM 4641 HD2 ARG A1177 -31.139 -2.971 -31.017 1.00 74.69 H +ATOM 4642 HE ARG A1177 -33.319 -1.100 -30.445 1.00 82.78 H +ATOM 4643 HH12 ARG A1177 -34.318 -4.055 -33.040 1.00 84.15 H +ATOM 4644 HH11 ARG A1177 -32.621 -3.779 -32.721 1.00 84.15 H +ATOM 4645 HH22 ARG A1177 -35.921 -2.787 -31.991 1.00 87.75 H +ATOM 4646 HH21 ARG A1177 -35.454 -1.517 -30.880 1.00 87.75 H +HETATM 4647 N NME A1178 -29.749 -2.869 -35.465 1.00 0.00 N +HETATM 4648 C NME A1178 -30.535 -3.522 -36.502 1.00 0.00 C +HETATM 4649 H NME A1178 -29.154 -3.459 -34.899 1.00 0.00 H +HETATM 4650 H1 NME A1178 -30.743 -2.862 -37.345 1.00 0.00 H +HETATM 4651 H2 NME A1178 -29.994 -4.388 -36.884 1.00 0.00 H +HETATM 4652 H3 NME A1178 -31.485 -3.868 -36.092 1.00 0.00 H +TER 4653 NME A1178 +HETATM 4654 O HOH A1301 -7.805 13.717 -21.733 1.00 23.87 O +HETATM 4655 H1 HOH A1301 -7.442 12.805 -21.938 1.00 23.87 H +HETATM 4656 H2 HOH A1301 -7.037 14.314 -21.478 1.00 23.87 H +HETATM 4657 O HOH A1302 -7.300 4.134 -29.382 1.00 26.50 O +HETATM 4658 H1 HOH A1302 -6.564 3.946 -30.038 1.00 26.50 H +HETATM 4659 H2 HOH A1302 -8.155 4.275 -29.890 1.00 26.50 H +HETATM 4660 O HOH A1303 -21.765 11.544 -12.438 1.00 28.48 O +HETATM 4661 H1 HOH A1303 -21.483 12.499 -12.298 1.00 28.48 H +HETATM 4662 H2 HOH A1303 -21.181 10.951 -11.877 1.00 28.48 H +HETATM 4663 O HOH A1304 -9.035 2.704 -10.281 1.00 29.39 O +HETATM 4664 H1 HOH A1304 -9.409 1.932 -10.803 1.00 29.39 H +HETATM 4665 H2 HOH A1304 -9.652 3.492 -10.355 1.00 29.39 H +HETATM 4666 O HOH A1305 -10.327 13.333 -14.896 1.00 24.85 O +HETATM 4667 H1 HOH A1305 -11.176 12.866 -15.157 1.00 24.85 H +HETATM 4668 H2 HOH A1305 -10.176 13.221 -13.913 1.00 24.85 H +HETATM 4669 O HOH A1306 -11.177 8.182 -14.397 1.00 26.19 O +HETATM 4670 H1 HOH A1306 -11.137 8.958 -15.033 1.00 26.19 H +HETATM 4671 H2 HOH A1306 -11.960 8.307 -13.778 1.00 26.19 H +HETATM 4672 O HOH A1307 -5.575 15.209 -20.665 1.00 23.28 O +HETATM 4673 H1 HOH A1307 -6.230 15.538 -19.979 1.00 23.28 H +HETATM 4674 H2 HOH A1307 -4.863 14.683 -20.193 1.00 23.28 H +HETATM 4675 O HOH A1310 -7.035 2.198 -22.168 1.00 28.39 O +HETATM 4676 H1 HOH A1310 -7.249 2.734 -22.991 1.00 28.39 H +HETATM 4677 H2 HOH A1310 -6.202 2.562 -21.741 1.00 28.39 H +HETATM 4678 O HOH A1319 -24.975 8.619 -9.410 1.00 28.29 O +HETATM 4679 H1 HOH A1319 -25.977 8.575 -9.470 1.00 28.29 H +HETATM 4680 H2 HOH A1319 -24.615 7.682 -9.428 1.00 28.29 H +HETATM 4681 O HOH A1322 9.260 15.797 -22.620 1.00 33.09 O +HETATM 4682 H1 HOH A1322 9.238 15.995 -21.638 1.00 33.09 H +HETATM 4683 H2 HOH A1322 9.718 14.919 -22.770 1.00 33.09 H +HETATM 4684 O HOH A1323 -15.175 20.060 -25.940 1.00 37.91 O +HETATM 4685 H1 HOH A1323 -15.823 19.549 -25.369 1.00 37.91 H +HETATM 4686 H2 HOH A1323 -15.622 20.901 -26.256 1.00 37.91 H +HETATM 4687 O HOH A1325 -7.181 -8.809 -21.143 1.00 38.47 O +HETATM 4688 H1 HOH A1325 -6.991 -9.743 -20.831 1.00 38.47 H +HETATM 4689 H2 HOH A1325 -6.455 -8.515 -21.774 1.00 38.47 H +HETATM 4690 O HOH A1326 7.389 15.381 -18.526 1.00 32.26 O +HETATM 4691 H1 HOH A1326 8.315 15.733 -18.681 1.00 32.26 H +HETATM 4692 H2 HOH A1326 6.915 15.335 -19.410 1.00 32.26 H +HETATM 4693 O HOH A1329 -19.714 -6.468 -9.012 1.00 36.73 O +HETATM 4694 H1 HOH A1329 -19.884 -6.507 -10.000 1.00 36.73 H +HETATM 4695 H2 HOH A1329 -18.743 -6.288 -8.854 1.00 36.73 H +HETATM 4696 O HOH A1330 4.141 19.654 -16.124 1.00 36.06 O +HETATM 4697 H1 HOH A1330 5.109 19.392 -16.168 1.00 36.06 H +HETATM 4698 H2 HOH A1330 3.830 19.606 -15.174 1.00 36.06 H +HETATM 4699 O HOH A1333 4.982 16.327 -16.962 1.00 30.05 O +HETATM 4700 H1 HOH A1333 5.483 17.156 -16.704 1.00 30.05 H +HETATM 4701 H2 HOH A1333 5.635 15.674 -17.353 1.00 30.05 H +HETATM 4702 O HOH A1339 -8.401 9.401 -34.311 1.00 35.17 O +HETATM 4703 H1 HOH A1339 -8.558 8.457 -34.614 1.00 35.17 H +HETATM 4704 H2 HOH A1339 -8.352 9.426 -33.310 1.00 35.17 H +HETATM 4705 O HOH A1344 -1.246 -9.880 -2.385 1.00 46.63 O +HETATM 4706 H1 HOH A1344 -2.115 -9.784 -1.894 1.00 46.63 H +HETATM 4707 H2 HOH A1344 -0.512 -10.016 -1.717 1.00 46.63 H +HETATM 4708 O HOH A1346 -2.546 14.870 -33.057 1.00 40.94 O +HETATM 4709 H1 HOH A1346 -1.902 14.222 -33.475 1.00 40.94 H +HETATM 4710 H2 HOH A1346 -2.400 14.877 -32.065 1.00 40.94 H +HETATM 4711 O HOH A1349 -15.113 -20.319 -14.132 1.00 44.85 O +HETATM 4712 H1 HOH A1349 -15.777 -20.591 -13.428 1.00 44.85 H +HETATM 4713 H2 HOH A1349 -15.496 -19.565 -14.665 1.00 44.85 H +HETATM 4714 O HOH A1350 -21.241 18.275 -13.756 1.00 31.90 O +HETATM 4715 H1 HOH A1350 -21.949 18.493 -14.431 1.00 31.90 H +HETATM 4716 H2 HOH A1350 -20.384 18.717 -14.034 1.00 31.90 H +HETATM 4717 O HOH A1352 -12.770 -28.593 -8.238 1.00 61.97 O +HETATM 4718 H1 HOH A1352 -11.940 -28.665 -8.798 1.00 61.97 H +HETATM 4719 H2 HOH A1352 -13.089 -29.522 -8.039 1.00 61.97 H +HETATM 4720 O HOH A1353 0.753 -1.961 -23.720 1.00 67.38 O +HETATM 4721 H1 HOH A1353 1.280 -1.741 -22.898 1.00 67.38 H +HETATM 4722 H2 HOH A1353 1.234 -2.673 -24.233 1.00 67.38 H +HETATM 4723 O HOH A1356 -12.183 -25.973 -15.343 1.00 59.40 O +HETATM 4724 H1 HOH A1356 -12.353 -25.498 -14.476 1.00 59.40 H +HETATM 4725 H2 HOH A1356 -11.675 -26.817 -15.157 1.00 59.40 H +HETATM 4726 O HOH A1360 0.186 23.081 -25.812 1.00 35.26 O +HETATM 4727 H1 HOH A1360 0.760 23.841 -25.494 1.00 35.26 H +HETATM 4728 H2 HOH A1360 -0.066 22.506 -25.030 1.00 35.26 H +HETATM 4729 O HOH A1361 6.492 16.072 -23.950 1.00 37.72 O +HETATM 4730 H1 HOH A1361 7.475 15.916 -23.823 1.00 37.72 H +HETATM 4731 H2 HOH A1361 6.255 15.954 -24.918 1.00 37.72 H +HETATM 4732 O HOH A1362 -12.299 4.648 -36.322 1.00 42.24 O +HETATM 4733 H1 HOH A1362 -13.007 4.928 -36.975 1.00 42.24 H +HETATM 4734 H2 HOH A1362 -12.726 4.127 -35.581 1.00 42.24 H +HETATM 4735 O HOH A1364 -16.446 24.494 -21.075 1.00 34.81 O +HETATM 4736 H1 HOH A1364 -17.058 23.972 -21.679 1.00 34.81 H +HETATM 4737 H2 HOH A1364 -16.735 25.452 -21.068 1.00 34.81 H +HETATM 4738 O HOH A1366 -15.941 9.353 -41.405 1.00 52.63 O +HETATM 4739 H1 HOH A1366 -15.091 8.827 -41.524 1.00 52.63 H +HETATM 4740 H2 HOH A1366 -16.453 9.327 -42.264 1.00 52.63 H +HETATM 4741 O HOH A1367 -16.053 4.806 -35.554 1.00 51.77 O +HETATM 4742 H1 HOH A1367 -15.366 4.407 -34.941 1.00 51.77 H +HETATM 4743 H2 HOH A1367 -15.611 5.066 -36.416 1.00 51.77 H +HETATM 4744 O HOH A1374 -11.258 22.843 -15.256 1.00 45.38 O +HETATM 4745 H1 HOH A1374 -11.414 21.917 -14.904 1.00 45.38 H +HETATM 4746 H2 HOH A1374 -11.922 23.020 -15.986 1.00 45.38 H +HETATM 4747 O HOH A1379 1.460 25.653 -25.134 1.00 53.63 O +HETATM 4748 H1 HOH A1379 1.558 25.650 -24.132 1.00 53.63 H +HETATM 4749 H2 HOH A1379 1.812 26.519 -25.489 1.00 53.63 H +HETATM 4750 O HOH A1381 -10.785 -4.363 -35.468 1.00 52.69 O +HETATM 4751 H1 HOH A1381 -10.882 -4.287 -34.472 1.00 52.69 H +HETATM 4752 H2 HOH A1381 -9.819 -4.506 -35.690 1.00 52.69 H +HETATM 4753 O HOH A1382 11.586 21.266 -17.552 1.00 44.12 O +HETATM 4754 H1 HOH A1382 12.072 22.022 -17.992 1.00 44.12 H +HETATM 4755 H2 HOH A1382 11.771 21.284 -16.568 1.00 44.12 H +HETATM 4756 O HOH A1388 -20.268 13.044 -1.959 1.00 51.68 O +HETATM 4757 H1 HOH A1388 -19.953 13.996 -1.925 1.00 51.68 H +HETATM 4758 H2 HOH A1388 -19.556 12.459 -1.568 1.00 51.68 H +HETATM 4759 O HOH A1394 -8.203 1.938 -4.105 1.00 43.33 O +HETATM 4760 H1 HOH A1394 -8.463 1.333 -4.861 1.00 43.33 H +HETATM 4761 H2 HOH A1394 -8.767 2.767 -4.151 1.00 43.33 H +HETATM 4762 O HOH A1396 -27.765 -2.906 -18.989 1.00 50.68 O +HETATM 4763 H1 HOH A1396 -27.731 -1.905 -18.908 1.00 50.68 H +HETATM 4764 H2 HOH A1396 -27.014 -3.284 -18.440 1.00 50.68 H +HETATM 4765 O HOH A1397 -30.874 11.872 -15.139 1.00 52.33 O +HETATM 4766 H1 HOH A1397 -30.767 11.983 -14.146 1.00 52.33 H +HETATM 4767 H2 HOH A1397 -30.470 10.998 -15.416 1.00 52.33 H +HETATM 4768 O HOH A1400 -36.880 -3.993 -33.716 1.00 50.72 O +HETATM 4769 H1 HOH A1400 -37.147 -3.437 -34.504 1.00 50.72 H +HETATM 4770 H2 HOH A1400 -37.524 -4.752 -33.618 1.00 50.72 H +HETATM 4771 O HOH A1401 -24.206 -1.279 -35.770 1.00 53.88 O +HETATM 4772 H1 HOH A1401 -23.632 -1.063 -36.562 1.00 53.88 H +HETATM 4773 H2 HOH A1401 -25.118 -0.889 -35.914 1.00 53.88 H +HETATM 4774 O HOH A1406 -1.292 8.841 -35.635 1.00 44.73 O +HETATM 4775 H1 HOH A1406 -1.662 8.240 -34.923 1.00 44.73 H +HETATM 4776 H2 HOH A1406 -0.648 9.487 -35.214 1.00 44.73 H +HETATM 4777 O HOH A1412 -0.946 21.652 -15.116 1.00 53.19 O +HETATM 4778 H1 HOH A1412 -1.600 21.432 -15.844 1.00 53.19 H +HETATM 4779 H2 HOH A1412 -1.078 21.016 -14.353 1.00 53.19 H +HETATM 4780 O HOH A1415 -23.399 -9.578 -15.048 1.00 55.01 O +HETATM 4781 H1 HOH A1415 -23.421 -8.698 -15.526 1.00 55.01 H +HETATM 4782 H2 HOH A1415 -24.267 -10.053 -15.194 1.00 55.01 H +HETATM 4783 O HOH A1416 5.715 22.275 -28.185 1.00110.14 O +HETATM 4784 H1 HOH A1416 6.698 22.109 -28.267 1.00110.14 H +HETATM 4785 H2 HOH A1416 5.253 21.393 -28.063 1.00110.14 H +HETATM 4786 O HOH A1417 -27.286 10.455 -33.143 1.00 53.90 O +HETATM 4787 H1 HOH A1417 -27.557 11.412 -33.288 1.00 53.90 H +HETATM 4788 H2 HOH A1417 -27.516 10.197 -32.205 1.00 53.90 H +HETATM 4789 O HOH A1419 -33.125 0.444 -35.100 1.00 58.72 O +HETATM 4790 H1 HOH A1419 -32.946 1.047 -34.319 1.00 58.72 H +HETATM 4791 H2 HOH A1419 -32.253 0.120 -35.472 1.00 58.72 H +CONECT 1 4 5 6 +CONECT 4 1 +CONECT 5 1 +CONECT 6 1 +CONECT 4654 4655 4656 +CONECT 4655 4654 +CONECT 4656 4654 +CONECT 4657 4658 4659 +CONECT 4658 4657 +CONECT 4659 4657 +CONECT 4660 4661 4662 +CONECT 4661 4660 +CONECT 4662 4660 +CONECT 4663 4664 4665 +CONECT 4664 4663 +CONECT 4665 4663 +CONECT 4666 4667 4668 +CONECT 4667 4666 +CONECT 4668 4666 +CONECT 4669 4670 4671 +CONECT 4670 4669 +CONECT 4671 4669 +CONECT 4672 4673 4674 +CONECT 4673 4672 +CONECT 4674 4672 +CONECT 4675 4676 4677 +CONECT 4676 4675 +CONECT 4677 4675 +CONECT 4678 4679 4680 +CONECT 4679 4678 +CONECT 4680 4678 +CONECT 4681 4682 4683 +CONECT 4682 4681 +CONECT 4683 4681 +CONECT 4684 4685 4686 +CONECT 4685 4684 +CONECT 4686 4684 +CONECT 4687 4688 4689 +CONECT 4688 4687 +CONECT 4689 4687 +CONECT 4690 4691 4692 +CONECT 4691 4690 +CONECT 4692 4690 +CONECT 4693 4694 4695 +CONECT 4694 4693 +CONECT 4695 4693 +CONECT 4696 4697 4698 +CONECT 4697 4696 +CONECT 4698 4696 +CONECT 4699 4700 4701 +CONECT 4700 4699 +CONECT 4701 4699 +CONECT 4702 4703 4704 +CONECT 4703 4702 +CONECT 4704 4702 +CONECT 4705 4706 4707 +CONECT 4706 4705 +CONECT 4707 4705 +CONECT 4708 4709 4710 +CONECT 4709 4708 +CONECT 4710 4708 +CONECT 4711 4712 4713 +CONECT 4712 4711 +CONECT 4713 4711 +CONECT 4714 4715 4716 +CONECT 4715 4714 +CONECT 4716 4714 +CONECT 4717 4718 4719 +CONECT 4718 4717 +CONECT 4719 4717 +CONECT 4720 4721 4722 +CONECT 4721 4720 +CONECT 4722 4720 +CONECT 4723 4724 4725 +CONECT 4724 4723 +CONECT 4725 4723 +CONECT 4726 4727 4728 +CONECT 4727 4726 +CONECT 4728 4726 +CONECT 4729 4730 4731 +CONECT 4730 4729 +CONECT 4731 4729 +CONECT 4732 4733 4734 +CONECT 4733 4732 +CONECT 4734 4732 +CONECT 4735 4736 4737 +CONECT 4736 4735 +CONECT 4737 4735 +CONECT 4738 4739 4740 +CONECT 4739 4738 +CONECT 4740 4738 +CONECT 4741 4742 4743 +CONECT 4742 4741 +CONECT 4743 4741 +CONECT 4744 4745 4746 +CONECT 4745 4744 +CONECT 4746 4744 +CONECT 4747 4748 4749 +CONECT 4748 4747 +CONECT 4749 4747 +CONECT 4750 4751 4752 +CONECT 4751 4750 +CONECT 4752 4750 +CONECT 4753 4754 4755 +CONECT 4754 4753 +CONECT 4755 4753 +CONECT 4756 4757 4758 +CONECT 4757 4756 +CONECT 4758 4756 +CONECT 4759 4760 4761 +CONECT 4760 4759 +CONECT 4761 4759 +CONECT 4762 4763 4764 +CONECT 4763 4762 +CONECT 4764 4762 +CONECT 4765 4766 4767 +CONECT 4766 4765 +CONECT 4767 4765 +CONECT 4768 4769 4770 +CONECT 4769 4768 +CONECT 4770 4768 +CONECT 4771 4772 4773 +CONECT 4772 4771 +CONECT 4773 4771 +CONECT 4774 4775 4776 +CONECT 4775 4774 +CONECT 4776 4774 +CONECT 4777 4778 4779 +CONECT 4778 4777 +CONECT 4779 4777 +CONECT 4780 4781 4782 +CONECT 4781 4780 +CONECT 4782 4780 +CONECT 4783 4784 4785 +CONECT 4784 4783 +CONECT 4785 4783 +CONECT 4786 4787 4788 +CONECT 4787 4786 +CONECT 4788 4786 +CONECT 4789 4790 4791 +CONECT 4790 4789 +CONECT 4791 4789 +END From 8a04b328c9082967c6a1f721e2719245bc494fa7 Mon Sep 17 00:00:00 2001 From: richard gowers Date: Mon, 10 Apr 2023 15:15:32 +0100 Subject: [PATCH 04/14] update env file for v0.7 --- .binder/environment.yml | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/.binder/environment.yml b/.binder/environment.yml index 00bf481..bceb41b 100644 --- a/.binder/environment.yml +++ b/.binder/environment.yml @@ -19,7 +19,6 @@ dependencies: - openff-forcefields - openmm - openmmtools - - openmmforcefields==0.11.2 - pip - plugcli - pymbar @@ -30,8 +29,8 @@ dependencies: - python==3.9.* - rdkit - typing_extensions - - gufe==0.5.* - - openfe==0.5.* + - gufe==0.7.* + - openfe==0.7.* ## needed for perses - openmoltools @@ -39,10 +38,3 @@ dependencies: - dask - distributed - openeye-toolkits - - - pip: - - git+https://github.com/OpenFreeEnergy/gufe - - git+https://github.com/OpenFreeEnergy/openfe - - git+https://github.com/dotsdl/openfe-benchmarks@ligandnetwork - - git+https://github.com/mikemhenry/openff-models.git@support_nested_models - - git+https://github.com/choderalab/perses@protocol-neqcyc From 4f9d7a70b881144e3a47d1276cdb667a9d0e8f76 Mon Sep 17 00:00:00 2001 From: richard gowers Date: Mon, 10 Apr 2023 15:24:47 +0100 Subject: [PATCH 05/14] update ligand_networks_for_devs for 0.7 --- networks/ligand_networks_for_developers.ipynb | 66 +++++++++++-------- 1 file changed, 37 insertions(+), 29 deletions(-) diff --git a/networks/ligand_networks_for_developers.ipynb b/networks/ligand_networks_for_developers.ipynb index dcb76d5..0ed2789 100644 --- a/networks/ligand_networks_for_developers.ipynb +++ b/networks/ligand_networks_for_developers.ipynb @@ -28,7 +28,7 @@ "metadata": {}, "outputs": [], "source": [ - "from openfe.setup import SmallMoleculeComponent" + "from openfe import SmallMoleculeComponent" ] }, { @@ -39,7 +39,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -194,7 +194,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -225,7 +225,7 @@ "outputs": [ { "data": { - "image/png": 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zn4q/+Q7ChYItsFqtCxYsOHDgQK9evf70pz8NHDhQ7kRdpfk5QkEQCgsLExMTExMT5csF5wSHRtuwd+/eadOmzZs37/3339+4cePLL78sS4zaev63/5f3ROWAKpXByepEavqOLDJsPastUSc/mH/e2Ff3unyCLDmnTJliiI19Z/jAe/T0h5OWMp+/lZ3de74r/+P9oqs+YvGaEN2uisenWZ55zF9RJtXXN7TgWZIk1juFGmvVgrllD00UHTY5YnYDN998s0ajefHFF3Nycn73u9+53W65E3WVJkW4b9++nJyc22+//fzzz3/mmWdkjQad11VFWFRUdOrUKa/XG/JVm8126tSpwEGGKLdixYrJkyePHj160KBBzz333BtvvBH5DJV27yVP79h5rKZeavsAnUtkNx+ounzeToe7tRLqIqtfX/5w/t74X/b/Nl6brVX/7PK1srPk97v37CqZdKssBSM660on3+Ha9U2lo67E568XQl8+5xalEp+/wu7w/LSvZOItQnVVhHN2Cxs2bHj++ecvuuiip59+2ul0Hj58WO5EXaXJodFZs2ZNmzbt4MGD+/fvX7p06YEDB2RNB53UVUU4ePDgPn367N69O+Srr7zySp8+fR566KEu+tPD6OjRo0OHDg08vvjii0+ePMnzfCQDeHhx1PN5pTVer19q55e4feKxivqb/rlHENv7JWEh+f21s6cK5SWSz8tLUgXvz9S0cRhZ8nn95SVlj9wj+SNb26JQ9mguX1woeb3TT1pG/lzy35rQA9NvHO6RP5dMOFoh8T6/tbL0oYmSL/THOyVrGCHV1dW5XK60tDR583Sd4BFhVVXVt99+O336dCLKysq69dZb165dK3O+Mzy8aHO1+E61Zs2at956i4hWrFixffv2COaKUjhHGJpgq+FLi4jIWVurcdcHHqtsNaIo2k8cM8THRyzJnPXlBRYX38J4pSVeXtx/yv7C6h//NMrURcGas/3nDV/BCYnniei1cvsgveaCGE2bXyXxvK/ghHXhs8b7IvfByPHxu75jhztaaZLf768orX7l/1Ke/GsXBevuXnjhhVtvvTUjI6PNPYuLi/Py8ppvP3LkSBfkCg9BEKqrqxumdpSUlBgMBqPRGHi1d+/eRUVynvaudvpW7yj94PuKn4rr3LygYhm/X8xI0v1uSEru1b2uHpjUsGdJSUlFRcWTTz5ZWFjYra+ADRcUYWg1y14pWrOCiBIKLIf/Mb9o5UtEdNzD61nGPukWe6RiFGpMK/v8xcO0XSfN1fvE5/9XPHpxbpLgDHuw1r1nrfu0tv69/u0dFkhej2PNasea1V2aKiwkj8ex4SPDxPvV5/WWO0vUWb58+fr167dt29aenZctW7Zs2bLW9/F4PPv27UtLS0tNTY2JiQlHxnNSVVUlimKyiiu+brgqxVzNk99Vb134VzbBoDKlegpOMM56vrRIZU5n1OpIBqv3CvPWHFu2tZAhpuH6AEGQiKi42vPO9pKP88qzknT/nnrRiAGJRDRr1qxIxot+HStCj8dTW1ublpamnPvsXRGv22Jz5ZoTGKLPbfVXxrd2GWTYLUu+3n8OH1ZEhlmVOHKWNXIThnhJ+r+S2oMu73v908zqHjrlgPfXvrnU/Ow/m2wWBMFisaSkpKgj+yYYDXw+3+OPP75nz56tW7e2c2rB8OHDL7rooubby8rKNm/eHHh8/PjxK664IvBYp9MFrszMyMhIT09v/iAtLa2r54YHThAmqznRYfc57Il+0eXjC95dmaTiiOhwQVVfnbpozDVExGi0KlMql2JmExICD7gEA2dKVaWY2fiEQHGGK9UvJc7rF+yucfJuX+jjRqJETo9wuKz++hd2/2F09gt3D2AV8wbeTh14k3322WeXLl2amppqs9lWrVp17bXXdl2s6HFnctx71roH8ivT1NznNteqfpE7+SEQ+7/4S/xM53+3vYz6Y+NvIlmEufmVe53eMUmxi8ptRDTKoB9lkP+DfHhJgr9+66c07x/BkwvXrFkze/bsPn36nDx58qmnngqcN1IIn8937bXX7t+//6GHHvrXv/5FRHfdddfw4cNb/6qxY8c+9dRTzbdv3bq1oQhFUfz1r39dXl5eVVXldrvLy8vLy8sPHToU8htqNBqTyWQ2m9PS0sxmc+BBYEt6errJZDKZTOc49ylwgjBFdfpX0qhiRyToV1Y6nshMPOnhtzncMzNOHyaVfF6+tChwSiUkRqNlEwxcgoEzmVUpZjbecKY4DSqTmUsxq5JT2jN7dW+B/drn85weQZIaXxDAu0jwkiae2LP/yy6fsPSLwsIq9/uPDkUXBmvvj0VFRUVxcXFxcbFWq12+fPmcOXP27t3bpcmiRBzH/ndgxnaH2y2Kj2UkRnKUc1SbydK5Xu1iZ2Mq1cZUPkKXZeaaEu5JOZs5S9tDx0Ys6z12WDtwSMOGRx999J133hk9evSRI0cuvPDC3NzcaDiUFzGTJ0+ePHlyw9P4MJ1Ev/jii7///vvAY7fbXVtbW15eXlZWFvJBZWVlaWlpaWlpK99Qp9O1NKBMTEzs1atX64srnS7CoDeBuVlJj56sWlfj9IrS7Axj73b/wEs+r2C1CFYLnTze0j5sguFMR5rPdGTD4NKsSs0oq/P/7h+7nR7/2RIs/5GOb6LKg8SfmcFizKZeV9DA20kTR0T1XuHT/ZanPji2YOKAdkZVgvYWYVpa2sqVKwOPWZbV6XTt+ap9+/b5Q10NeOrUqYbHPM+3NMtCFoGLQnlJcgXdjvaqhNP/v64I3qP2Z1WK6PcS23IXin4iIpEnf4vTtlSC+yfOHO+t6YKAIVyZ0PQHI/A3xksSNftblZ2WYbgzn4sLvPz3dZ7m+xx3h5gBIkmS72R+cBEyDJOfnz969Gi3263X65Vz7oCINBpNBK4A1+v1er0+IyPj0ksvDbmD1+utrq5upSyrqqo8Hs/JkydPnjzZ0p8SOADbUlnm5+cTUbLqbBH20ao3DcqoE0Q9y6jC/Y8eOADbyg4Tes9x6rKlwMX/kkB5i+jkViIitZ5Mg4nTUL2FbIVkK6Tj/6OR8yl5ABHVe4XXPj815lLzlf1xB4DTOnag4IMPPli3bt2+ffvWrVvXnv1nzpzZ5j7Lli1rz26RxDLM30tq/l4SofJoWRFRW3/PDEsH/kMH/tPS606ih8OcqpNYhpZV2JdVROxKo7a92sd0S2Js4PHbFsfbFkd7v5Lnhdrq4A0rVqwYM2bMJ598UlZWtmHDhpC3k4YupdVqMzIyMjIyLrjggpb2CRxfbWlMWVJS4nA4AgdgW/mDPq52futwp6pVJjVnULFmNWdWcwaOTeDYVLUqQ8NxEfkYtC12yBFNhr9hCtzeN+jkVmLVdMkUOv8GYs+MTW0FlLeYqo/SV3PppsUUm0pEbl548I2DhxZeE4Gc3ULHinDYsGF6vb62tnbp0qVLlixpc/8777wz5IXUeXl5DVMMNRpNbGxsh2J0Ka/X6/f71QyjkvszvZ/heKbVfyDRT6KfWFXwaYAmGCK15OckeW4004CXJL9EKobU0TRUCv4I/+t43QBdiKtzi3z81/YQA+7gMZ8gCC+++OKcOXMuueSSpUuXPvbYY9u3bw/X4UEII71en5OTk5OT09IOdru9oqKiqqrKYrEEzk02PKiqqiooKBD8fqcg5gtivqfFWXpaljnTjlzgQQLHpqo5k5ozqLh0NRfHheGinn+Zbqlntaef1BynY5uIiK6YQ+eNaLSfsQ/99v/o89lkK6S9/6ar5xKRJFFJjeebwzXXDEoi6GgRDhgwYMCAARdeeGH//v1ffvllrVbb+v4zZ8686qqrmm+fP39+QxE+/PDDDz8cJYMWIqJHHnlk+fLlz/RKmmSS+Y1sY8LwuWkTnWzLR6H3raTDn9DF99GgO1vaJU5wLy194zeuo10Ssd0WldsWldumpxn/mG6UN0lLbjLGhvwX32xzNS9CRqPhklIanu7atWv//v1bt25lWXbcuHGDBw9ev379Pffc07WJoQsYDAaDwTBgQOjzZzfddNNnn332Yu+UC2K0Ft5v4QW7X6zihUpecAhiYEu1X/CKUrHXX+xt8R4RWpYxcIGhpCqoI09vMXCsSc21/oGxQm08rg26cO/Yp0QSpV7UtAUDVDoaNpW2zaXSPKqvolgTEdV7xRXbilCEAe0twsrKypUrVz755JMcxx04cMBoNCrwGvEIG+AtbXR60PIzaeLJmN2hb+JjVf28ZWHNBSRJkub8s++VZrPZ4/GUlpZmZWU5nU673d6tVyeAlgSmT+To1P106n66Ft8A7YJo4QW7XwjqSMHCC3ZBsPBChU/wipJFFCy8QBT6HoQahjGqTh9rbXIA1qxWmdXcdwn9VZLobWjLyv1EFLoFA9KGkTaBvA6qPEg5vyUiUZI+/6m6xf0Vpr1FGBcXd/DgwaysrNTU1PLy8rfeegvLOXa1ft4KNnA1WNkeOvAfqq+k3iNp+CMd+iYmvyNFwHorYcawnCanX8PT/v37/+1vf7v88ssHDBiQn58/YcKE6667TsZ4Ue7jjz/2+XwtnRC55pprrFZrdL69nJk+0cal4waONXAsUYtNafOL1X6hxi9YecHqF2r8YhUvWHmhxi9Y/UIVL7hFKdCdLR2AZZgSSfsF6Y10w2LiXVRfRUSU2LfFTAxDiTlUsZ/spxq21db76r1CrLaHzvftiPYWYWxs7AcffGC32202W2ZmplxLESkKQ9JYR957xqv4GDNdM59Ofk6ejs2C0Em+ibYdXRRPsRi1Ov7mO6jxO/WcOXNmzpxZVlaWnJwcFxcnV7ZuofWzp2q1OjrXppckqaqqihpfNdo5RhVrVLF9W25KryjZBdF+5nBr8wOwVX6JPLUkCcQw5DvzSVeb0NqfqjUQEXnPXhGmVXMVNm/fVAXN82lJx/oscAC9i6JAcw/WfPGh8Qq+g4dDG3CS+Pvab8MbCRiOM06e1ny7Wq3Ozu7kvxREP5vN5vV64zlWy3b5BV9aljGznFnNtXQAdmrmQ1+rsoivJ6Kzy4e1fiVa4NYcQWuNMQxF+L78UQsDu9BSHvtLnylT5E5BfYie2FT08hel9S3cPKkVsVrun2MHX3T1rq4IFpLtg3ds7yyXPB1di05idDHGyQ8b757c9r5h4lj/Uc3rr0gdXzaP1esNk6aoUtO7IhVEs8AJwtTzzst8c41QWyNUVwk1VqG2Wqi2CjVWf021UGMVW532F0bJoov0SaRPIqLATHkiIl+rS3v6nI12JuL9kjEWl3oQoQhbwuj0bEJUjH3nTRiy9Zjzx1P2Dt10QK/hRl2Q/MjNAyM5WyFp6gzPvjzPgR8lb4iZ6S1htDrdhUOTps6gCJ4WMt4z1fPDLtcPuyRPR6JqNOq+AxIfiq5prxAZgROEaZm9dBeHntEfIDrs/qpKsc7ht1qEqkrBYResFn9VpehwCFaLv7IsLCuODfSWaiXey6iJiLTxpIknXx05iinp/Ba/xl5ERJTQq2EDw5A5oTM39O95uqoIq6urJUlqaX7F3Llzn3rqKY7DSdq2qThm85OXXf3c94cOsu1cCDFGy/4qx/DRHy+J9Jw9lk17+Y2yhyf58o+2c1zI6PSavgPSXlkRyRYkImKY1IXLyqff5zl88L3+aaJELd0WZLQx5peh2SxDjFarzuqdsWQVw+HjoxI1WZu+JWyCQdPqZ2jJ6/FbLUKVJagjAw8sYp3db6kQnW1f3Xap+6RKEk4XITFkGkyleVS2l3q3cAtoeyHVVxIRmQY3bBueExWf9aNBV/1Ktz7FUKVS4XKb9mP87qVjtLnfuk7W1Ao1+WQ4j7jQn+MYknSs9MDV2a/cO1jVxkykLsHqYzJXfGR9Ya5j41oS2vrky6nir7815c/PMRoZPpYyGm368tXWhc86P10rur1EIlGIvzGWSMsyrF4fM2KU+dmFjA63jFGoJmvTdxqj1akzz1NnntfSDpLXIzRUY0NZ1tnP1md11YXuIo3kP3skNOc6Ks/Ub+wAABNLSURBVM2joh005O7gMd9ZP39IRJTcn4x9AhvidNz9V4faU5HQRt3AiRMn5s/9S7aO9FSR/+Pr0uUz/bFpTW46o5F4jqi/p/Sfw/2jc2+RKyoRMWq1ae4Lot3u3La59T3jrv6tad6CyKQKiVGpTE/9Pf7msdZ/PssX5EuCIPGN53WpVKxay6WlpcyZH3N5iFtDgHIERoQRmCHKaHUqk05lStUOCr2D5PMKtTWz1ucv+K7OFfi02evXZLqAqn6h7c/RyL9SXNBce0min9+nwm+IYWjo/Q2bWYYZfzlOdZ+GIuwGhg4d+sUXXxCRZf6cuk1rj1St+sJz0S79wEOi10lkEFwX1B+90nXkuroDOb5K48gQFzRGHmdqeL+Qmo20Tm/hUqJi1rnuokt6vbvBd/K486vNnt3f8SVFktfLaDSq9EzdZVfEXTs6+ObaoFinL5aJgvXcGY1WlZr++GTzor1fuZw8ERHD0pVP0JdPk6OEPn2EzhtByf2J01K9hYp3kr2QiKFhUyj19AKQMZJv3q3nYwZhAxRh9zPQWzrQW/ooffaCrXYF0cPVWx5UfS93qFY0P94YRbcbbaDJ6ZeU04+mPip3EIhS7TxHGDGxWu4/04fe9eqPp5ekjzHR6Jfo4H/oxBYq+IoKvjq7qzGbhj5AGadXiFRJYrbXcr/LTtRfjuDRCEUIANC2cJ0jDKMbLzb9eUzOgo0n670CEZE2ni6bTsPuJ8svVF9JAk/aBEruRwlZDV/CkZgoOFeWLK37T0ziuEkMrtUgIhQhAEB7RNuIMGDuHf3UHPvcuvzT40IiUukbBn+NSJJO8pv8jtXFr5j9dn+lvf6LTXE33h7JtFErGm/oBwAQbaKzCInoz2P6bnpieLpRG6dr8Zwfe+pLZtNDI4r++79Tz2fwtYGNtW8vJwl3liFCEQIAtMntdjudTp1Ol5DQ6v08ZXLt4OTCRaNevfeCwZlxWjVr1HMxolcn8XGiO070xIi+8y15Ul2Z6tBHevHsnTl8+UddO7+RMXb0wKFRAIA2RGzuRKepOWbKyF5TRvaqdvp+LHAcev0N5+FfDIKrj8/Sz1teq+dHssznNtcJD9836P6ltneWx1w5Ur7U0QIjQgCANkTtcdHmkuM0112Y8vD0W+62fXdj3b6B3lKOxBQ1d3tSnEi00uII3tm9N89z8Ee5okYPFCEAQBu6UREGaAdfqL/siuAt01INHEP/rXZaeCF4u23VvyMbLRqhCAEA2hCFcyfaZMx9OPhpllZ1nSHWJ0lvNx4U1n/9ue/k8chGizooQgCANnS7ESERxVx+VZObIv0h3cAQvWetcwhBy7pJkn31ykiHizIoQgCANnTHESERGe97KPjpIL3mN/E6pyC+b220wEXdprX+yvLIRosuKEIAgDZ0xxEhEcVdd5M6Kzt4y0OpBiJ6y+LwBq1NL/n99g/ejnC2qIIiBABoQ/RPnwiN5QyTpgRvGJGgHxKjsfLCf2ucwdsda1aLdQ5SKhQhAEAbomfpiY5KuG08l2wK3vJgqoGIXq+0C0F3lRFd9Y61qyOcLXqgCAEA2tBND40SEaPRGibcF7zlBmNstlZd5PV/Ya8P3m5b/abk9UQ2XbRAEQIAtMbv99fW1nIcl5SUJHeWzjCMu5eNiW14yjH0gDmBiF6vaHQsVKix1m36JNLhogOKEACgNVVVVaIopqSkcFy3XMmWTTAkjJ0YvOWu5DiTmvvJ5d1V12gIaHvndRIFUh4UIQBAa7rvcdEGhklTGPXZW4xqWeZeUwIRvV5pD96NLy1yfrUl0uGiAIoQAKA1PaAIVea0uBtuC95yryk+nmN3ONy/uHzB221vLlHg2kwoQgCA1nTT2fRNJOY+TOzZN/x4jh2fEkdEbzQeFHqPHnLv2RnpcHJDEQIAtKYHjAiJSN27b+xVo4K3PGA2qBnmM1t9oZcP3l779vLIRpMfihAAoDU9Y0RIRMYH/hD8NFXN3ZYUK0j0ZpO1mfJ2eA//FNloMkMRAgC0pmeMCIlIN2Sobujw4C0PpRpYojXVziplr82EIgQAaE13vb9aKIm5jwQ/zdGpRxlivKL0blWj23A7t37GFxdGNpqcUIQAAK3pvvdXay5mxLWavv2DtzySZiCid6sc9cFrM4mC7d0VEc4mIxQhAEBresyhUSIihjHeNy14w8Wx2svidHZB/LC60W246zZ8LFRXRTacbFCEAAAtkiSpqqqKiEwmU5s7dwtxN4xRpWcGbwmszbSy0s4HzSCUfF77h6siHU4mKEIAgBbV1tbyPG80GrVardxZwoNRqYy/fyB4y0iDfpBeU8kLG2oa3Ybb/uE7orOOFABFCADQoh51XPSM+Dvu5oyJDU8ZoimpCUT070p70HlCEp11jv9+GPF0MkARAgC0qEcWIauPSRh3b/CWWxJjMzWqEx5+m90VvN2+eqXE89TToQgBAFrUY2bTN2GYmMvqYxqeqhgmsDbT0opGd1zzWyqcn/030uEiDkUIANCiHjkiJCLOkBg/5q7gLeNT4hNV3IF67w/ORmsz1b69nESRejQUIQBAi3rqiJCIjPc8yAStsKhnmXtN8UT078pGd1zjC0/Wb/8y0uEiC0UIANCinjoiJCJVRq/Y624J3nKfKSGGZbfZXcfcjdZmqn1zSWSjRRqKELqEv7wkLPsAyCsmJiYtLS0tLU3uIF0iMfdhYpiGp0YVe1dyXIZGZWl861HvLwc8+/ZEPF3kMJLy1mBsHc/zPM9rNBqVSiV3lqYs8+fUbVrb8NQvSbxEGoa44B/lydOS//hnOdKdJnnclrmznd9sJcHfsDEQVc2QKigqcaq4a35r+tvLwSftASCSyh/Nde38puGpUxD1LBP8lhIQM2JU+r9WRjZa5ETde33k1dTUiKJoMBjUajURqdXqwIMAt9tdX1+v0WgSEhLky3iazeNx+IU4jlUzDBGpGEYV9OPqFSWXKLJuT7JsAUl01pU+cBdfXOjw+QRJiuVYTaioPkmqF0ROEJkdX/sm35H55ho2Ll620ABn7N69m+f5IUOGGAyG5q+WlpaeOnUqMTFx8ODBkc/WRYy5DzcUoUSU5/QQ0eVxuliu0fFC13fbvEcP5fulEydOZGVlDRs2TIasXQaHRmnQoEEmk+nLL0OfDX7ttddMJtOdd94Z4VQhjX7j3csOFn9pd4d8dU2187KDxeOXvxXhVGeJQtmMyXzxKcnnnXis4rKDxZsa36iiwae19ZcdLJ54rELyefniU2UzJktBw0cAudx0000jRozYvXt3yFdXrVo1YsSI2bNnRzhVl9JfernuoksCj0VJmnbCMu2EpcTX7PdRkuyrV7zxxhu33Xbbq6++GumUXQxFCGFTs+I13/Ejks/X9q5BJJ/Pd/xI7coefjYeIGoZ732wPbs5t2wU651t79cNoQghPASrxf7OcskTerTaOsnjtr+zXLBawp4KANoUO3K0ps/5be4m+f3eo79EIE/koQghPGyrV0pC52fdSqKoqPXPAKIIyxrumdqeHX35x7o6iyxQhBAedRs+lviOHRQNJvl8dRvXhDEPALRf/E13qEztmCvZQ8/lowghDPjiQtHjaXu/VokeD19cGJY8ANAhjEZjmHh/m7uxQWtW9CSYPnHasWPHkpKSmm8vLi6OfJjWFXr5A/Xe5ttLm1/oFSm+E8dYlUpotr3E5w8ZtdgbIiqrUvlOHFVnZXdBQIAO+PLLLysqKppv37dvX+TDREzCXZOsZ+4gs76m3qRu9NFWnZGl/9UV+Qd/oWM98NMqivC0mTNnyh2hvRaW1sodoSnBViP5Q3TbonLbonJbO7+J5PcLtTVhzQXQGQsWLJA7ggzY2Lj4sRNp+09E9O9Ke9OXS2po9wEZYkUEivC066+/PjMzs/n2n376ac+eaLm3EJeYTLX2UaNG9e7du/mrR44c2blzpyotI+K5WnRZnK63NsTP2Cmvf4/zXA+lAnSRqVOn9uvXr/n2bdu2bd68OfJ5IsY48X6a9Rci+mO6MU2n0V1wsf6KkSrT2RuOf/jhh1u3bpUvYFdBEZ42a9asG264ofn2hQsXRk8RBkyfPj3kBP+lS5fu3Lkz8nmIiDMmMaHuSDcuOW5sclzz7etqnM2LkFGpuEQZ74oDcNr48eOvu+665tsFQejZRcglpQQe3DVx4m/+NE+V2vQOqz///HOPLEJcLANhoOnbXwx1aLRDRL9f07d/WPIAwLkw5j7SvAV7MBQhhIE6K5vV6c7xm7A6Ha6UAYDIQxFCeMSPGceoNZ3+ckajib/1rrb3AwAINxQhhIdx0hSG6/yPE8Oyxvbd2wIAILxQhBAeXIrZkPsIo9d34msZnd4w+WEuxdz2rgAA4YarRiFsEh+Y7vruG9/Rnzu0AAWj0Wj6DUyaOqPrggG0U3JyMhFpNKEP8sfExCQnJ4dcqrBnYBgmMG9Eq9WG3MFsNvfr1y8traddR4MV6psuzNtEVC3MW1tbKwhCQkJCyF9Uj8fjdDrVarWMv6iis650yji+uNDucgUvzNvE6YV5Gcag16uzsrEwLwDICEUIYSZ53Ja5s+t3fSO521iSidHrY39zjfm5lxldZw6oAgCEBYoQuoTru23WhX8Vqq2i10NC47uQchyr1XHJKSlz5sWMGCVTQACA01CE0IU8B/Y6v/rMs+d7vrJM8noYrU6dmqG77PK4UTfqLr5U7nQAAEQoQgAAUDhMnwAAgA74/e9/n5aW1rdv3759+y5atEjuOGGA6RPdG8/zgiDozvn2ZpHkcDii4RJcgDZ98803KpXqyiuvlDtIdCkrK1u1atXo0aPlDhI2GBE2MmvWLJPJFPikM3/+fLnjtKaqqur22283mUz9+/e/99575Y7TlCiKn3zyyVVXXTVgwICGjevXr09PT+/fv39OTs6OHTtkjAfQpo8//nj8+PGTJ0+WO0jUKS8vT09PlztFOKEIGykrK3vllVdOnDhx4sSJv/71r3LHac2kSZOSk5OtVmtRUdHrr78ud5ymfvzxxx07dkycONF9ZhKF0+nMzc196623Kioqnn766cmTJ4uiKG9IgJbY7fY5c+b8/e9/lztINLJarQ888ECfPn2GDx++f/9+ueOEAYqwkfLy8oyMKFrYtiXFxcVff/31Sy+9pFKpiCgmJkbuRE0NHz785ZdfvuKKKxq2fPbZZ+edd15g0cfc3Fyn07l79275AgK0ZtasWZMmTbr44ovlDhKNjh8/vnPnzoKCgvHjx999991yxwkDFGEjVqt19uzZ/fr1u/DCC7dv3y53nBadOHEiJSXlmWee6d2795AhQzZu3Ch3orYVFhY2rPrNcVyfPn1OnTolayKA0LZt2/b1118//fTTcgeJUklJSYH7cI0fP/7o0aNer1fuROcKRdjIrl278vLyjh8/Pnv27HHjxvE8L3ei0Gw2m9vtvvnmm48fP75gwYK77767urpa7lBtEEWRCbrdGsuyODQKUcjlcj344IOLFy+OjY2VO0s0stvty5cvD/zybtiwYfDgwS3dmLQbQRE2YjQaA/+oEyZMsFgsFRUVcicKLS0tTafT3XjjjWq1+uabb9bpdEePHpU7VBuysrIKCwsDjyVJKiwszMrKkjcSQHMbN26sra2dOXNm3759x44dW1RUFDieDwEul2vdunUZGRmDBg1avnz5e++9J3eicJDgDLfbvXjxYq/XK0nSe++9l56ezvO83KFCc7vdZrP5yy+/lCTp22+/1Wg0VVVVcocKYd++fVlZWYHHNTU1sbGxe/bskSRpw4YNmZmZPp9P1nQAbcjLy+vbt6/cKaKR2+0uKSmRO0XYYB7hWW63e9u2bX/729/MZrPb7f7oo48Cl6JEIZ1Ot2jRogkTJmRlZZ06dWr58uUpKSlyh2rq6quvtlqtlZWVw4cPnzFjRm5u7quvvjp69Oh+/foVFBS8+eabIZf7AIgesbGxQ4YMkTtFNNLpdJmZmXKnCBvcYq0pnucrKyszMzOZUOsHRRWe5wsKCtLT0+Pjo3ENI5vN1vDTpdfrA7P+HQ5HSUlJ7969o/BKVwBQJhQhAAAoGi6WAQAARUMRAgCAoqEIAQBA0VCEAACgaChCAABQNBQhAAAoGooQAAAUDUUIAACKhiIEAABFQxECAICioQgBAEDRUIQAAKBoKEIAAFA0FCEAACgaihAAABQNRQgAAIqGIgQAAEVDEQIAgKKhCAEAQNFQhAAAoGgoQgAAUDQUIQAAKBqKEAAAFA1FCAAAioYiBAAARUMRAgCAoqEIAQBA0VCEAACgaChCAABQNBQhAAAoGooQAAAUDUUIAACKhiIEAABFQxECAICioQgBAEDRUIQAAKBoKEIAAFA0FCEAACgaihAAABQNRQgAAIqGIgQAAEVDEQIAgKKhCAEAQNFQhAAAoGgoQgAAUDQUIQAAKBqKEAAAFA1FCAAAioYiBAAARUMRAgCAoqEIAQBA0VCEAACgaChCAABQNBQhAAAoGooQAAAUDUUIAACKhiIEAABFQxECAICioQgBAEDRUIQAAKBoKEIAAFA0FCEAACgaihAAABQNRQgAAIqGIgQAAEVDEQIAgKKhCAEAQNFQhAAAoGj/D+Yy7TZNRoG4AAAAnnpUWHRyZGtpdFBLTCByZGtpdCAyMDIyLjA5LjEAAHice79v7T0GIOBlgABGIOYGYi4gbmBkU0gA0izMUJoJxmdk0AArJpfmBtrDyMTAwAw0jIGBlYGRjYGRnYGJg4GJk4GJi0GEQbwP6hYwADrowP4evV2LYQII9gF7BNvhwM9mG1Wo+H6QHBJ7PwMcwNgNqgg1Dg7IZiLptYepFwMAoUMjS/nWyHAAAAD0elRYdE1PTCByZGtpdCAyMDIyLjA5LjEAAHicjZJJDoMwDEX3OcW/ACghTFkyqVQVILW0d+i+91dtEDgIKcJhYTsvlu2PAtuzfXx/2C1plQJ04HPO4WO11moAO6i7231EM1f1lmmm9zi/YAyMxnKObDVPw5YxaBCZONNsiHSsV0+cjUyI3LMh0BJ4qWKKHpE935/AjMG95O6cwfwARgGyIFKmCYClDwY4R5y9sh7SpMeVYUg/Dwz12I3tQdRV5noaW5GZTyJaUgArivFtKrpwmMn2Ocxlx/y2kEVyWMq6DJVzshUOjT/8kjB+636jHG//MvnqD1TClKxqWvGtAAAAuXpUWHRTTUlMRVMgcmRraXQgMjAyMi4wOS4xAAB4nG2PMQ+CMBCF/4ojJKXptbTQY3TBxbgTBmMcSCQl2JEfL62mZ43D3b2X773hhn48FkM/lnH9l/sctqJSTLCuAq7TDfNWcXUiEJFMPJ98LKdOqpTs6t18Wd2Cgk/P07w8ptvkOew2kLPzd64QyAiUeVASq1HlTBHTWJMB1HlQEzNocmaINdiQkdjmwZZYizZnlphF+PkVvp4Fsb0Aoyh327K7y1kAAACbelRYdHJka2l0UEtMMSByZGtpdCAyMDIyLjA5LjEAAHice79v7T0GIOBlgABGIGYDYlYgbmBkU0gA0izMHGCaiZGRQQOsCBfNDdLPxMDADNTEwMjKIMIgHgQ1FAzY/sad3X/384pdIE7r44d79dZx7AOxH/q7HYjjYrMDsUuK9+zvENtvD2K3ed3bz5S9fz+I/WQrq/15HQhbDAAfQCCXSbz7NwAAAPJ6VFh0TU9MMSByZGtpdCAyMDIyLjA5LjEAAHicfZFLbsMwDET3PsVcwAJF6mMuYztIiyI20Lq9Q/a9PyrGcJQAQkkthtIjJVIdzD7nj9svHsZz1wH0z1JV/AgRdVeYwHi+vC+YttN47Ezr97J9ISGWjOKv5Glbr8eOx4Teu0QhJI+eHJHnQWHCrOYyVjv3nljzXYmEnBuk4A29ONIhcXlAAWnQVslgoHchM2dBEcpDjg0w7mCWlILeUzSGKA0yFZJcZBbKOygxtkqel/llEPtoxnWZ62jMufZvodQmLQy1FQtjfXDJRHq+7Lm0xcd/Fd39AeIKYuYPAdafAAAAwHpUWHRTTUlMRVMxIHJka2l0IDIwMjIuMDkuMQAAeJxVjT0PgjAURf+KIyaleZ/9cnTBRd0JgzEOJBKIMvLjLTg0DG16z7nvtW2627lqm+64XfkclgqsEjF4U6OVyKrmVINFBIqZ5RezeJ8hWgcibmUASCECZsoWYnBs1iKEGLeieCJv1n0UwkY8O6f/L1TUHM1jHof7Z5wS2P57GaZ3/+xnizmu5jrOL6sJS8BEJUDi/RQXR0n2TorjpHunxcnyA7mPTt8W3OPNAAAAAElFTkSuQmCC\n", + "image/png": 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", "text/plain": [ "" ] @@ -330,7 +330,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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"text/plain": [ "" ] @@ -351,7 +351,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -434,7 +434,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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"text/plain": [ "" ] @@ -455,7 +455,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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CjYJeVFRULF269OTJk126dPnTn/7Uu3dvqRO1l6bXCJ1OZ15enl6v1+v10uWCa4JToy04duzYU089tXjx4k8++WT79u3Lly+XJEZ1LX/P3zJfKM0oV0aaWa1AjX8iCwxby2oKVDFPXLpuwrvHLJxTkpyPP/54ZGjoh4N6P6yjP2SXFXGOZg62Hv1v8R8fFSy1AYvXiGC1lDz/VNnLzzlKisTa2voWvEoUhVqzs6qifOmioienCSaDFDE7gNGjR6vV6rfeeis9Pf13v/ud1WqVOlF7aVSEx48fT09PHz9+fI8ePV5++WVJo0HbtVcRXr58OTc31263e3zXYDDk5ua6TjIEufXr18+YMWP48OF9+vR57bXX3nvvvcBnKDXab3rp4KELVbViyyfoLAK762T5bYsPmazNlVA7+eif656+dCz81xP3hGvSNKrTFq6Zg0WHw3r0cMH0sZIUjGCuKZzxgOXwvlJTTQHnqHV6vn3OKogFnKPEaLL9crxg2hhnZXmAc3YI27Zte/311/v37//SSy+ZzeazZ89Knai9NDo1Om/evKeeeurUqVMnTpxYs2bNyZMnJU0HbdReRdi3b99u3bodOXLE47vvvPNOt27dnnzyyXb66n50/vz5AQMGuF7feOON2dnZPM8HMoCNF4a9nllYZbc7xFZ+iJUTLpTUjvr7UafQ2g/xC9HhqJ4/y1lcIHJ2XhRLeEeKuoXTyCJndxQXFD3zsOgIbG0LzqJnZ/L5eaLdPju7bOjpgv+r8jwx3WeyDj1dMOV8ichzjorSwieniZznX+/krH6GVFNTY7FYEhMTpc3TftxnhOXl5QcOHJg9ezYRpaamjh07dvPmzRLnu8LGCwaL159UmzZt2rBhAxGtX79+//79AcwVpHCN0LOKGi67zEJEVYaaGp51va6uJUEQzuRVhkdEBCzJq19eyi6z8l7mK97YeeFErvHNj37+07C4dgrWlOHf73E5WSLPE9GqYmMfnfr6EHWLHyXyPJeTVbHslajfB+4XI9MX/+EunPW10kSHw1FSWPnO32IX/qWdgnV0b7755tixY5OTk1s8Mj8/PzMzs+n4uXPn2iGXfzidzsrKyvqlHQUFBZGRkVFRUa53u3btevmylJe9K83cRwcLP/2x5Jf8GivvVLKMwyEkR2t/1y925pAuQ3pH1x9ZUFBQUlKycOHCvLy8Dn0HrL+gCD1bvOnC4pM/EBGVK+b+879z98URERnzSKkdsORnSaO1Vi0nvP5V/vCVM6Od5gB/6Y8ranZW137cq7XTAtFuM236yLTpo3ZN5ReizWba9nnktEdV13WVOkvQWbdu3datW/fu3duag9euXbt27drmj7HZbMePH09MTExISAgJCfFHxmtSXl4uCAJpo+Jn702K0qjNeWYrN3fjGX2oKlmvOVdostQ6s8ssXaK1amVAb7+otTsXb7qwdk8eQ0z9/QFOp0hE+ZW2D/cXfJFZnBqt/d9Z/Qdn6Ilo3rx5gYwX/HwrQpvNVl1dnZiYKKPn7CUMoPxD1Pt+IobyD1PiAKkD+UBgmI36ofMqArdgiBfFvxVUn7LYP+6VGK/qpEsOeEf1B2viX/l7o2Gn01lWVhYbG6tSqSTJJSGO455//vmjR4/u2bOnlUsLBg0a1L9//6bjRUVFu3btcr2+ePHiHXfc4Xqt1Wpdd2YmJycnJSU1fZGYmNjea8PrbmvQRlbX8tW1PHFq4qwrtp0kTSQR0X+PU2Tqjud+ICKtik3Wa5OiNPpQldsLTVKUVh+q0ocqk/Vaf6X6tcA8YumRKjNv5TyfNxJEMtucZ4tqR7x55A/D096cmsHK5wd46/hQhK+88sqaNWsSEhIMBsPGjRvvvvvu9osVRNJ/Rxe/or2LKSSW8g/RsL9JHcgHdkb1RdTtgSzCmZdKj5nt46JDVxQbiGhYpG5YpPS/yPuX6HTU7tlJi99wX1y4adOm+fPnd+vWLTs7+8UXX3RdN5IJjuPuvvvuEydOPPnkk//4xz+I6MEHHxw0aFDzHzVhwoQXX3yx6fiePXvqi1AQhFtvvbW4uLi8vNxqtRYXFxcXF585c8bjJ1Sr1XFxcfHx8YmJifHx8a4XrpGkpKS4uLi4uLhrXPvkukBI2ivLJNThlHgTnf2SBjxKpgIqOkr9H3a9Y+OF7DKL65KKR1oVqw9VeazJZL0mKUqTEKlRsC3X1bEc492vZ5ptTlFseEMAbyGnndThxF79V7ZwzjXf5uWVWz95dgC60F1r/1qUlJTk5+fn5+drNJp169YtWLDg2LFj7ZosWKhCaOQ/qPgYOWzU/xHSRbf8IcHEyIaUqqIS+ADdljkzLuLh2KvfkKmaTjo3Yln7hbOa3v3qB5599tkPP/xw+PDh586du+GGG2bOnBkMp/ICZsaMGTNmzKj/Y3h4uF8+7Y033vjjjz+6Xlut1urq6uLi4qKiIo8vSktLCwsLCwsLm/mEWq3W24RSr9d36dKl+c2VrhRh1NWhQU/RgTco53tycnTj7ym85YujLjZeKDbYiw32M4Ver1zoQ1Xe5pTJek1qjK7MZP/dG0fMNsfVEiz+mS7uoNJTxF9ZwRKVRl3uoN7jSR1GRLV2584TZS9+emHptIxWRpWD1hZhYmLi+++/73rNsqxW26p5/fHjxx2e7gbMzc2tf83zvLdVFpKouylU4MnhthYqaWDdC0cwLZASHERNojakdFp/UcSH26sCk+i3EY3/YlgEgYh4UXT9ryWYHvKrYRjFld+Lc+z8jzW2psdctHpYASKKIpd9yb0IGYa5dOnS8OHDrVarTqeT0bUDIrVaHYA7wHU6nU6nS05Ovvnmmz0eYLfbKysrmynL8vJym82WnZ2dnZ3t7au4TsB6K8tLly4REWndyjI8hUatIr6WFBr3uZdf1J2A9Y5lSCSqa0HRSZkrKHsPEZFKR3F9SaGm2jIy5JEhjy5+RUOXUEwGEdXanau+yR13c/xve+EJAHV8+y/36aefbtmy5fjx41u2bGnN8XPnzm3xmLVr17bmsEBiWFY89h4dk2DJoM8Ylk7+m07+29v7ZqKnA5nHO5ahtSXGtSVGqYNc9W63uDH6UNfrf5WZ/lVmau1H8ryzutJ9YP369ePGjfvyyy+Lioq2bdvm8XHS0K40Gk1ycnJycvL111/v7RjX+VVvc8qCggKTyeQ6AdvcV8r6hoqPky6adNGkDiNdDOn0pA6rex0a53rkUwA0WB517D3K3kOsim56nHqMJPbKyRhDDmWupMrz9P0iGrWSQhOIyMo7n3jv1JlldwUmZ/DzrQgHDhyo0+mqq6vXrFmzevXqFo+fOHGixxupMzMz65cYqtXq0NBQn2K0K7vd7nA4iFV1gMdLCg4SHMQqm/lVlCFSiQ6FKM2DZurxougQScmQKpimSkq3MLeGazO0HhZ7XOb4H4weJtzucz6n0/nWW28tWLDgpptuWrNmzXPPPbd//35/nR4EP9LpdOnp6enp6d4OMBqNJSUl5eXlZWVlrmuT9S/Ky8tzcnIcDofIW8l4mYzeV0oo1G412aQvQ+JI5e/T5lUX6cIOIqI7FtB1gxu8FdWN7vkbfTOfDHl07H9pyCIiEkUqqLLtO1t1V58Odq2nnfhWhBkZGRkZGTfccEOvXr2WL1+u0WiaP37u3Ll33nln0/ElS5bUF+HTTz/99NNBMmkhInrmmWfWrVtHNz9BPUdLnaUlx9+ns1/Sjb+nPhO9HRLmtK4pfO92y/lA5mpqRbFhRbFhdmLUH5OiWj5aCqOiQqfHeaiuXQZL0yJk1GpFdGz9Hw8fPnzixIk9e/awLDtp0qS+fftu3br14Ycfbt/E0A4iIyMjIyMzMjxfPxs1atTXX39Ntz9P0d3JWk3WSuLMZK0ia9XVFzYjOTkyl5C5xOuXUagbdmSjsgwnnZ6aPEaxORd2EomU0L9xC7ootTRwFu1dRIWZVFtOoXFEVGsX1u+9jCJ0aW0RlpaWvv/++wsXLlQoFCdPnoyKipLhPeISKztN6nCKSvPpgzhW2dNe1E6JZEsURXWPqz8r4+PjbTZbYWFhamqq2Ww2Go0dencC8KZu+UREF4pMo0jv34mc+UpHeipLSwU5uboRb1gVacI9Tyh1MaSLJm1kgxOwpSeIyHMLuiQOJE0E2U1UeorS7yEiQRS/+aXS6/Ey09oiDAsLO3XqVGpqakJCQnFx8YYNG7CdY+AUHaWT/6baUuo6lAY949OHxjlMsU7st+JnDKtQp/es/2OvXr1effXV2267LSMj49KlS1OmTLn33nsljBfkvvjiC47jvF0QueuuuyoqKoLzx0vj5RPeuK4XNoOrIZuBbEayGchWTTYj2aobjDhsdU3p7QQsw5ImknRRNHIl8RaqLSci0nf3+hUZhvTpVHKCjLn1Y9W1XK3dGaoJ+mtA7a+1RRgaGvrpp58ajUaDwZCSkiLVVkQyFRJPdy2h7G/I5tsqCK3ITTMcbKdQssWoVOGjH6CGP6kXLFgwd+7coqKimJiYsLBmfwjKXvNXT1UqVXDuTS+KYnl5OVHDu0bbRh1O6nCKSPV6gJMjzkxcjfcTsAayVZPoJIYh7spvuppmH/3oWvVvv3pHmEalKDHYuyfIaJ2PN771mesEejtFAa98PB1aTyEKD1Uf8G8WYBSKqBlPNR1XqVRpaW38LwXBz2Aw2O12UoWSouUn6F6r+tttvJ2AFRxkNxFfS0RXtw9r/k40hm1wMBHDUICfyx+0MLHz7K3pfR6bFRRnt97clrXqm7w27C8YqlH8fULf/kMOt0cqjwyffmj4cJ1o83WppchoQ6JmPB01dUbLx/qJaevnVf98R/R92zxWp4uc/rgyIak9UkEwc10g7Jqa9NGS28truBKDvdRoL6/hSo1cqdFeZuJKjfbml/35E6usa0qiq6dhuWa39uTMDQ4m4h1iVChu9SBCEXoTolbog+OvyOuTMw6cr/451+jTQwd0asWw62OeGd07kKsVomfNsR3PtJ38WbR7WJnuDaPRam8YED1rDgXwslDUw7NsPx22/HRYtPkSVa1Wdc/QPxlcy14hMFwXCLskJ97R7Dr06lq+qNpeXcsXG2xXXtiLqm2uF/mVVt7pl0mYePW2Uk04qcOJqyFTPkX38PoRrsuNEV3qBxiG4iPaf3bbEbRXEVZWVoqi6G19xaJFi1588UWFAhdpW6ZUMLsW3jLktR/PnGJb+dtmiIb9TXrk53+8KdBr9lg2cfl7RU9P5y6db+W8kNHq1N0zEt9ZH8gWJCJimIRla4tn/9529tTHvRIFscGaQnfDo0J+HZDGMsRoNKrUrsmrNzIK/PooR432pvfG9QTRZg6wcs5ig91bWRZW24yW1uzNyTR4HdeXCjOp6Bh19fIIaGMe1ZYSEcX1rR8blI7rXHXa61u6+SWGSqUSt9u0HuOwrhmnmXnAcqnKKFZdosjrmrlKoWLZx4akvvNIX6VCgqXrrC4kZf3nFW8uMm3fTM6Wvp8VyvARY2P//BqjluDXUkatSVr3UcWyV8w7NwtWO5HgceUWS6RhGVanCxk8LP6VZYwWj4yRqUZ707eZTq1Ijw9Jj/d6i4qVc16pRs9lWWK0N3rCNqXfS4WZdPkg9ZvqPue76vRnREQxvSiqm2sgTKt4dIinI2UJbdQBZGVlLVn0P2laqlLZK09soFvnUqjX78aptyetnOn1EVMBwKhUcYveFIxG895dzR8ZNuSeuMVLA5PKI0apjHvxr+GjJ1T8/RU+55LodIp8wyeLKpWsSqNITIxdsCTkNg+PhgD5cM0IA7BCVKdW6NSKZL325m6eD7DxQrmJe2PbxQ9+KLQ7BCKiLrdS3PVU/ivtf42G/oXC3LYCFUU6/Qnl7SOGoQGP1g+zDDP5NlzqroMi7AAGDBjw7bffEtHMdac+PFDQ/MFJ+hYe9xMYirj6nxdik5lW3YgiNihWnWv739TlP9u47Ivm73fZjvyXL7gs2u2MWq1MStHeckfY3cPdH64NsuWaEQbDfu5aFZsaowsY7k8AABF1SURBVF32UN/PfyyxmwUiIoal375A371EpgLa+QxdN5hiepFCQ7VllH+IjHlEDA18nBLqNoBkiP48rgdWENZDEUJ7a3q+MYgeN1pPnd4zOr0nzXpW6iAQpFp5jTBgQjWKf88e8OC7P9fdUh4SR8PfplP/pqzdlPM95Xx/9dCoNBrwGCVf3SFSJApRB+O3oVRQhAAALfPXNUI/uu/GuD+PS1+6PbvW7iQi0oTTLbNp4KNU9ivVlpKTJ00ExfT0uHL/7a9yZt/bVSXFnQRBCEUIANCyYJsRuix6oKdKwb625dLVpcZKnfvk7ypRdF9xn19p+/zH4um/be1Owp1bMD7QDwAg2ARnERLRn8d13/HCoKQoTZjW+zW/3L2040mqvOA+tnR7VuO7T+UKRQgA0AKr1Wo2m7VabUREs8/zlMjdfWPyVgx795Hr+6aEaVRshK7JqT5DHtUU0ZlN7mO/5NfsOlUeuJRBDEUIANCCgK2daDOVgnl8aJdf/z6kcNWwTXNvGtSt4WL53uNJoaaCQ2TKdx9euj0roCmDFYoQAKAFQXtetKmYMPW9N8Sufazhmh9tFHUbRqJIZ7e4D+87W3X4YnVA8wUlFCEAQAs6UBG6DEqPHHZ9w92s+k4ihqWc7xttCLxsR05AkwUlFCEAQAuCcO1EixaObbhPb1gipd5BAk/nt7oP/9+xkjOF5oAmCz4oQgCAFnS4GSERDb8h9qauDa8UXj+FiKGLX7lv2CSKtPwruU8KUYQAAC3oiDNCInphTMPHlerTKfFG4i106Wv34Y0HCvIrfdiPrPNBEQIAtKAjzgiJaNKtST0SGm5z0fdBIqJzW8h59fnyvFNc+U1uQJMFGRQhAEALgn/5hEcKlnnuvoaTwsSBFN2DbIYGDyMlWrfnssHSyg1POyEUIQBAC4Jn6wlfPTY0NTGq4Y40fSYSEZ3ZRKJQP1Zjc6zbczmw0YIIihAAoAUd9NQoEWlV7Jx70xoMXTeYwpPJXEz5h9yH392Va61/YKnMoAgBAJrjcDiqq6sVCkV0dLTUWdpi9r1p4Vq3h64xLPUeT0SNnrhWarRvPFAY2GjBAkUIANCc8vJyQRBiY2MVig65k60+VPXEsIY7MaXfS1o9VV2k0pPuw3/fke0U5PgcbhQhAEBzOu550XrzR3VTK91+2ivUlDGWqPGkMLvM8uXRksBGCwooQgCA5nSCIkzRax+6o+HWg73GkCqUin+mqgbP3f7bVjnuzYQiBABoTgddTd/IwrHprNvGvKQKpR4jiIjONpgUnsgzfX+mMrDRpIciBABoTieYERJR7+SwMQMbdnnGeGKVdPkg1RS5D8twbyYUIQBAczrHjJCI/uf+ho/hDomhrneTKNC5/3Mf/vaXimM5xoAmkxqKEACgOZ1jRkhEt/aIGpyhbzDU90FiGMr+lmwNdiVctiM7oMmkhiIEAGhOB32+mkeN92aK6EIpt5KTows73Ic3HSm5VGoJaDJJoQgBAJrTcZ+v1tToAfH9uoQ3GLp+ChHRhR3EW+vHnIK4/CsZTQpRhAAAzek0p0aJiGHohTHpDYZielH89cSZKWuX+/CGfQUlBntAw0kHRQgA4JUoiuXl5UQUFxcndRb/mHZHclqsrsFQ30lEROe2kOCoH7Pxwqpv8wIbTTIoQgAAr6qrq3mej4qK0mg0LR/dEagUzLz7ujYYSh5E+nSyVFLuD+7Dq77JNVocJAMoQgAArzrTedF6T9x9XWy42m2Aod4PEBGd+YLcnitjtDje/yE/0OGkgCIEAPCqUxZhqEYxu9HeTGlDKDSeTAVUeMR9ePlXOZxDoM4ORQgA4FWnWU3fyB9HpIVq3DbTYJV1k8JfP3M/rLDa9tF/i6izQxECAHjVKWeERBQTpn70ri4NhrqPIE0EVZ6n8l/dh5duzxI6+3O4UYQAAF511hkhET0/Kl2pcHsMt1JDvcYQNd6b6Xxx7fafywIbLdBQhAAAXnXWGSERdY3TTb41qcFQxjhS6qjwKBkaLJz429ZO/hhuFCG0C0dxgV+OAZBWSEhIYmJiYmKi1EHaxcKx3d23ZiJ1OKX/jkLjyFblftiRLMOB81XUeTFiZz/56yue53meV6vVSqVS6iyNzVx36sMDbuUhOEhwkkJJzNWL3n8ak750Wm8Jwl0h2qxli+ab9+0h59UVSA5R5EVSMaR0/7ZTKMPuuifu1eWsLkSCoABAdN/fj+46WX71z7yFlBr3HykuowfG71gwKKDJAijoftYHXlVVlSAIkZGRKpWKiFQqleuFi9Vqra2tVavVERER0mWsw1lMZDeRKoRYJRERq6x74eLkyGGzW2qkikdEgrmm8LEH+fw8E8c5RTFUwaoZhoiUDKN0a0BOFGudgsIpMAd/4GY8kPLBJjYs3OsnBQiUI0eO8Dzfr1+/yMjIpu8WFhbm5ubq9fq+ffsGPls7WTg23a0IRSr9hYgooT+pGjx95qsTZSfyTCpzflZWVmpq6sCBAwMdtD3h1Cj16dMnLi7uu+++8/juqlWr4uLiJk6cGOBUHu14YxptnkaFmZ7fzv6WNk/74s0nAhvKjeAsmjODz88VOfu0CyW3nMrfUVXr8cCd1bW3nMqfdqFE5Ox8fm7RnBmiUxYPsIAgN2rUqMGDBx85csTjuxs3bhw8ePD8+fMDnKpdDe0Tc3vPK3sziQLtf5X2v0q1pY0OE0Va/lXOe++9d//997/77ruBTtnOUITgN1XrV3EXz4kc59NHiRzHXTxX/f7qdkoFAM1bMLpbaw779HBRjbVz/sKKIgT/cFaUGT9cJ9qsLR/ahGizGj9c56zo5LdoAwSn8YMS+qSEtXgY7xR/zjMFIE/goQjBPwwfvS862/4oJlEQDP9Z78c8ANBKLMPMv69Vk8LT+VLegtB+UITgHzXbvhB5306KuhM5rmb7ppaPA4B28MjglGS9tsXDHM7OucoARQh+wOfnCTbbNX4SwWbj8+Wy/xlAUNGo2Lkju7Z4WGy4qsVjOiIsn6hz4cKF6OjopuP5+cG3C0lNMVVe8DBeK9k1Ni7rAqtUOpuMF3COk7UeNrnOt3u45M4qlVzWeVVqWtO3AALpu+++KykpaTp+/PjxwIcJmKfvue6NLRcMrj/k7iWt3v3dbvEh91wfe6GgbL8U2dobirDO3LlzpY7Qaic2SJ2gMaehSnR46LYVxYYVxYZWfhLR4XBWd+anV0BHsXTpUqkjSCBCp3xi2HXLPiCixo8bJaIcovW7Ah8qQFCEdUaMGJGSktJ0/Jdffjl69Gjg83gUF6GuqaBhw4Z17dq16bvnzp07dOjQdbG6pm9J5ZYwbVeNh79juXbHUfO1nkoFaCezZs3q2bNn0/G9e/fu2tV524Bo7si0Za5XN0xXhsXekh45ckB8cpSm/oDPPvtsz549EqVrRyjCOvPmzRs5cmTT8WXLlgVPEbrMnj3b4wL/NWvWHDp0KPB5iEgRFc14eiLdpJiwCTEebsveUmVuWoSMUqnQx7RLPgBfTJ48+d5772067nQ6O3cRJkTWdd70qQ+++fTILtGNb585ffp0pyxC3CwDfqDu3kvwdGrUJ4LDoe7eyy95AOBaLBzbvWkLdmIoQvADVWoaq73WbxtWq8WdMgAQeChC8I/wcZMYlbrNH86o1eFjH/RjHgCAVkIRgn9ETX+cUbT9rxPDslEPz/JjHgCAVkIRgn8oYuMjZz7D6Npyzyqj1UXOeFoRG+/3VAAALcJdo+A3+sdmW/67jzt/2qcNKBi1Wt2zd/SsOe0XDKCVYmJiiEit9nySPyQkJCYmxuNWhZ0DwzCudSMajcbjAfHx8T179kxMTAxsrnaHHeobb8zbSFBtzFtdXe10OiMiIjx+o9psNrPZrFKpJPxGFcw1hY9P4vPzjBaL+8a8jdRtzMswkTqdKjUNG/MCgIRQhOBnos1atmh+7eF9orWFLZkYnS709rviX1vOaIPoIQAAIDcoQmgXlv/urVj2F2dlhWC3kbPhU0gVClajVcTExi5YHDJ4mEQBAQDqoAihHdlOHjN//7Xt6I98aZFotzEarSohWXvLbWHD7tPeeLPU6QAAiFCEAAAgc1g+AQAAPnjooYcSExO7d+/evXv3FStWSB3HD7B8omPjed7pdGqv+fFmgWQymYLhFlyAFu3bt0+pVP72t7+VOkhwKSoq2rhx4/Dhw6UO4jeYETYwb968uLg41286S5YskTpOc8rLy8ePHx8XF9erV69HHnlE6jiNCYLw5Zdf3nnnnRkZGfWDW7duTUpK6tWrV3p6+sGDByWMB9CiL774YvLkyTNmzJA6SNApLi5OSkqSOoU/oQgbKCoqeuedd7KysrKysv7yl79IHac506dPj4mJqaiouHz58j//+U+p4zT2888/Hzx4cNq0adYriyjMZvPMmTM3bNhQUlLy0ksvzZgxQxAEaUMCeGM0GhcsWPDXv/5V6iDBqKKi4rHHHuvWrdugQYNOnDghdRw/QBE2UFxcnJycLHWKluXn5//www9vv/22UqkkopCQEKkTNTZo0KDly5ffcccd9SNff/31dddd59r0cebMmWaz+ciRI9IFBGjOvHnzpk+ffuONN0odJBhdvHjx0KFDOTk5kydPnjp1qtRx/ABF2EBFRcX8+fN79ux5ww037N+/X+o4XmVlZcXGxr788stdu3bt16/f9u3bpU7Usry8vPpdvxUKRbdu3XJzcyVNBODZ3r17f/jhh5deeknqIEEqOjra9RyuyZMnnz9/3m63S53oWqEIGzh8+HBmZubFixfnz58/adIknuelTuSZwWCwWq2jR4++ePHi0qVLp06dWllZKXWoFgiCwLg9bo1lWZwahSBksVieeOKJlStXhoaGSp0lGBmNxnXr1rm+ebdt29a3b19vDybtQFCEDURFRbn+o06ZMqWsrKykpETqRJ4lJiZqtdr77rtPpVKNHj1aq9WeP39e6lAtSE1NzcvLc70WRTEvLy81NVXaSABNbd++vbq6eu7cud27d58wYcLly5dd5/PBxWKxbNmyJTk5uU+fPuvWrfv444+lTuQPIlxhtVpXrlxpt9tFUfz444+TkpJ4npc6lGdWqzU+Pv67774TRfHAgQNqtbq8vFzqUB4cP348NTXV9bqqqio0NPTo0aOiKG7bti0lJYXjOEnTAbQgMzOze/fuUqcIRlartaCgQOoUfoN1hFdZrda9e/e++uqr8fHxVqv1888/d92KEoS0Wu2KFSumTJmSmpqam5u7bt262NhYqUM1NmTIkIqKitLS0kGDBs2ZM2fmzJnvvvvu8OHDe/bsmZOT88EHH3jc7gMgeISGhvbr10/qFMFIq9WmpKRIncJv8Ii1xnieLy0tTUlJYTztHxRUeJ7PyclJSkoKDw/GPYwMBkP93y6dTuda9W8ymQoKCrp27RqEd7oCgDyhCAEAQNZwswwAAMgaihAAAGQNRQgAALKGIgQAAFlDEQIAgKyhCAEAQNZQhAAAIGsoQgAAkDUUIQAAyBqKEAAAZA1FCAAAsoYiBAAAWUMRAgCArKEIAQBA1lCEAAAgayhCAACQNRQhAADIGooQAABkDUUIAACyhiIEAABZQxECAICsoQgBAEDWUIQAACBrKEIAAJA1FCEAAMgaihAAAGQNRQgAALKGIgQAAFlDEQIAgKyhCAEAQNZQhAAAIGsoQgAAkDUUIQAAyBqKEAAAZA1FCAAAsoYiBAAAWUMRAgCArKEIAQBA1lCEAAAgayhCAACQNRQhAADIGooQAABkDUUIAACyhiIEAABZQxECAICsoQgBAEDWUIQAACBrKEIAAJA1FCEAAMgaihAAAGQNRQgAALKGIgQAAFlDEQIAgKyhCAEAQNZQhAAAIGsoQgAAkDUUIQAAyBqKEAAAZA1FCAAAsoYiBAAAWUMRAgCArKEIAQBA1lCEAAAgayhCAACQNRQhAADIGooQAABkDUUIAACyhiIEAABZ+383Y7weWBd97gAAAJ56VFh0cmRraXRQS0wgcmRraXQgMjAyMi4wOS4xAAB4nHu/b+09BiDgZYAARiDmBmIuIG5gZFNIANIszFCaCcZnZNAAKyaX5gbaw8jEwMAMNIyBgZWBkY2BkZ2BiYOBiZOBiYtBhEG8D+oWMAA66MD+Hr1di2ECCPYBewTb4cDPZhtVqPh+kBwSez8DHMDYDaoINQ4OyGYi6bWHqRcDAKFDI0v51shwAAAA9HpUWHRNT0wgcmRraXQgMjAyMi4wOS4xAAB4nI2SSQ6DMAxF9znFvwAoIUxZMqlUFSC1tHfovvdXbRA4CCnCYWE7L5btjwLbs318f9gtaZUCdOBzzuFjtdZqADuou9t9RDNX9ZZppvc4v2AMjMZyjmw1T8OWMWgQmTjTbIh0rFdPnI1MiNyzIdASeKliih6RPd+fwIzBveTunMH8AEYBsiBSpgmApQ8GOEecvbIe0qTHlWFIPw8M9diN7UHUVeZ6GluRmU8iWlIAK4rxbSq6cJjJ9jnMZcf8tpBFcljKugyVc7IVDo0//JIwfut+oxxv/zL56g9UwpSsalrxrQAAALl6VFh0U01JTEVTIHJka2l0IDIwMjIuMDkuMQAAeJxtjzEPgjAQhf+KIySl6bW00GN0wcW4EwZjHEgkJdiRHy+tpmeNw929l++94YZ+PBZDP5Zx/Zf7HLaiUkywrgKu0w3zVnF1IhCRTDyffCynTqqU7OrdfFndgoJPz9O8PKbb5DnsNpCz83euEMgIlHlQEqtR5UwR01iTAdR5UBMzaHJmiDXYkJHY5sGWWIs2Z5aYRfj5Fb6eBbG9AKMod9uyu8tZAAAAm3pUWHRyZGtpdFBLTDEgcmRraXQgMjAyMi4wOS4xAAB4nHu/b+09BiDgZYAARiBmA2JWIG5gZFNIANIszBxgmomRkUEDrAgXzQ3Sz8TAwAzUxMDIyiDCIB4ENRQM2P7Gnd1/9/OKXSBO6+OHe/XWcewDsR/6ux2I42KzA7FLivfs7xDbbw9it3nd28+UvX8/iP1kK6v9eR0IWwwAH0Agl0m8+zcAAADyelRYdE1PTDEgcmRraXQgMjAyMi4wOS4xAAB4nH2RS27DMAxE9z7FXMACRepjLmM7SIsiNtC6vUP2vT8qxnCUAEJJLYbSIyVSHcw+54/bLx7Gc9cB9M9SVfwIEXVXmMB4vrwvmLbTeOxM6/eyfSEhlozir+RpW6/HjseE3rtEISSPnhyR50FhwqzmMlY7955Y812JhJwbpOANvTjSIXF5QAFp0FbJYKB3ITNnQRHKQ44NMO5glpSC3lM0higNMhWSXGQWyjsoMbZKnpf5ZRD7aMZ1metozLn2b6HUJi0MtRULY31wyUR6vuy5tMXHfxXd/QHiCmLmDwHWnwAAAMB6VFh0U01JTEVTMSByZGtpdCAyMDIyLjA5LjEAAHicVY09D4IwFEX/iiMmpXmf/XJ0wUXdCYMxDiQSiDLy4y04NAxtes+577Vtutu5apvuuF35HJYKrBIxeFOjlciq5lSDRQSKmeUXs3ifIVoHIm5lAEghAmbKFmJwbNYihBi3ongib9Z9FMJGPDun/y9U1BzNYx6H+2ecEtj+exmmd//sZ4s5ruY6zi+rCUvARCVA4v0UF0dJ9k6K46R7p8XJ8gO5j07fFtzjzQAAAABJRU5ErkJggg==", "text/plain": [ "" ] @@ -498,7 +498,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -531,7 +531,7 @@ "outputs": [], "source": [ "import itertools\n", - "from openfe.setup import Network\n", + "from openfe import LigandNetwork\n", "\n", "def simple_network_planner(three_heavies, two_heavies, mapper):\n", " mappings = []\n", @@ -541,7 +541,7 @@ " # use all suggested mappings\n", " mappings.append(mapping)\n", " \n", - " return Network(mappings)" + " return LigandNetwork(mappings)" ] }, { @@ -566,7 +566,7 @@ "outputs": [ { "data": { - "image/png": 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IRURE0rnExESmTZtG+fLl2bx5M1myZGHChAmEhIQ8URn86aefqFq1KocOHaJAgQJs3ryZvn37qgymI5oQioiIpGMRERGYzWZCQkIAqF+/Pr6+vpQqVeqx5xqGwfjx4xkyZAgWi4Xq1asTGBhI0aJFkzu2pDBNCEVERNKhuLg4vv76aypVqkRISAhOTk7MnTuXrVu3PlEZvHXrFu3atWPw4MFYLBbMZjM7duxQGUynNCEUERFJZ/bv34+7uzvHjh0D4J133mH27NlPXOZOnjyJq6srERERZMyYkRkzZtC1a1ddIk7HNCEUERFJJ+7cucOAAQOoWbMmx44dI3/+/AQEBLBmzZonLoNr166lWrVqREREUKRIEXbs2IGXl5fKYDqnQigiIpIObNu2jQoVKjBp0iQsFgudOnXixIkTdOjQ4YnKnMViYfjw4bRs2ZLo6GjeeOMNwsPDqVWrVgqkF1tTIRQREUnDoqKi8PLyolGjRvz6668ULVqUdevW8d1335E/f/4nWuPmzZu89957jBgxAoBevXqxZcsWChUqlJzRJRXRPYQiIiJp1Jo1a+jevTuXL18GoHv37owdO5acOXM+8RrHjx/H1dWVU6dOkSVLFubMmUOXLl2SK7KkUiqEIiIiaczvv/9O7969Wbp0KQCvvvoqvr6+1KtX76nWWb58OR9//DExMTEUL16clStXUrly5eSILKmcLhmLiIikEYZh8N1331G2bFmWLl2Ko6MjgwYN4vDhw09VBhMTExk0aBDt2rUjJiaGxo0bEx4erjJoxzQhFBERSQMuXLhAt27d+PHHHwGoWLEifn5+VKlS5anWuX79Oh06dLj/4+sGDBjAmDFjyJBBlcCeaUIoIiKSilksFmbNmkW5cuX48ccfyZQpE6NHj2b//v1PXQYPHjxIlSpV2Lx5M9myZeP7779nwoQJKoOiCaGIiEhq9csvv+Dp6UlwcDAAtWvXxs/PjzJlyjz1WosWLaJr167cvXuXkiVLsmrVKsqXL5/UkSWN0oRQREQklYmPj2fs2LFUrFiR4OBgsmfPzowZMwgODn7qMhgfH0+fPn3o3Lkzd+/e5e2332b//v0qg/IQTQhFRERSkYMHD2I2mzl48CAAb731FnPnzuWll1566rUiIyNp164dO3fuBODLL79k+PDhODhoHiQPUyEUERFJBe7evcvXX3/N+PHjSUxMJG/evEyZMoUPP/zwmX5sXEhICK1bt+by5cs4OTmxaNEi3nvvvWRILumBCqGIiIiN7dq1C7PZzMmTJwFo164d06dP54UXXnim9Xx8fOjVqxdxcXGULVuWlStXUrp06aSMLOmMZsYiIiI2cuvWLXr16kXdunU5efIkhQsXZuXKlSxduvSZyuC9e/fo2rUrXbt2JS4uDjc3N0JDQ1UG5bE0IRQREbGBH3/8ES8vLy5evAiA2Wxm4sSJ5M6d+5nW++2332jTpg2hoaGYTCZGjx7N4MGDn+lys9gfFUIREZEUdP36dT799FMWLVoEwMsvv4yPjw+NGzd+5jV37txJ27Zt+f3338mTJw8BAQG89dZbSRVZ7IAuGYuIiKQAwzBYtmwZZcuWZdGiRTg4ONCvXz+OHj36zGXQMAymT59O48aN+f3336lYsSJhYWEqg/LUNCEUERFJZpcvX6ZHjx6sXr0agHLlyuHn50eNGjWeec07d+7QrVu3+5PGjh074uPjQ7Zs2ZIks9gXTQhFRESSiWEY+Pr64uzszOrVq8mYMSPDhw/nwIEDz1UGz507R506dVi0aBGOjo5MmTKF7777TmVQnpkmhCIiIsng119/xdPTk23btgFQvXp1/Pz8nvsnhGzatIkOHTpw48YNChQowLJly2jQoEESJBZ7pgmhiIhIEkpMTGTSpEm4uLiwbds2smbNyuTJk9mzZ89zlUHDMBg/fjzNmjXjxo0bVK1alfDwcJVBSRKaEIqIiCSRo0ePYjab2b9/PwCNGjXCx8eHV1555bnWvX37Nu7u7ixfvhwAd3d3Zs6cSZYsWZ47swhoQigiIvLc7t27x1dffUXlypXZv38/uXLlwtfXl82bNz93GTx16hQ1atRg+fLlZMyYkdmzZ+Pr66syKElKE0IREZHnEBISgtlsJiIiAoD33nuPWbNmUaRIkedee926dXTq1Ino6GgKFy5MYGAgtWrVeu51Rf6XJoQiIiLPICYmhn79+lG7dm0iIiIoWLAgy5YtY+XKlc9dBi0WCyNGjODdd98lOjqaOnXqEB4erjIoyUYTQhERkae0efNmunbtytmzZwHo3LkzkydPJl++fM+9dlRUFB9++CFr164FoGfPnkyePJlMmTI999oij6JCKCIi8oT+/PNPBgwYgL+/PwDFixdn7ty5NGvWLEnWP378OK6urpw6dYrMmTMzd+5cunTpkiRri/wXXTIWERF5AitXrsTZ2Rl/f39MJhO9evXi2LFjSVYGV6xYQY0aNTh16hTFixdn9+7dKoOSYlQIRURE/sPVq1dp27Ytbm5uXL16ldKlSxMcHMyMGTNwcnJ67vUTExMZPHgwbdu2JSYmhkaNGhEWFkaVKlWSIL3Ik1EhFBER+ReGYbBgwQKcnZ1ZsWIFjo6ODB06lEOHDlGnTp0k2eP69es0b96ccePGATBgwAA2btxIgQIFkmR9kSelewhFRET+x7lz5/Dy8uKnn34CoHLlyvj5+VGpUqUk2+PgwYO4ublx7tw5smXLhr+/P+3bt0+y9UWehiaEIiIi/89isTBjxgzKly/PTz/9RObMmRk7diyhoaFJWga/++47ateuzblz5yhZsiQhISEqg2JTmhCKiIgAJ06cwMPDgz179gBQt25dfH19ee2115Jsj/j4eAYOHMi0adMAaN68OYsXLyZPnjxJtofIs9CEUERE7Fp8fDyjR4+mUqVK7NmzBycnJ2bNmsX27duTtAxGRkbSpEmT+2Xwiy++YO3atSqDkipoQigiInYrPDwcd3d3jhw5AsDbb7/NnDlzKFasWJLuExoaSuvWrbl06RJOTk4sXLiQVq1aJekeIs9DE0IREbE7sbGxDBo0iOrVq3PkyBHy5cvH4sWLWbduXZKXQV9fX+rVq8elS5coU6YM+/btUxmUVEcTQhERsSs7duzAw8OD06dPA/D+++8zbdq0JP+ql3v37tG7d2+8vb0BcHV1Zf78+eTMmTNJ9xFJCpoQioiIXYiKiqJbt240aNCA06dP8+KLL7JmzRqWLFmS5GXw0qVLNGjQAG9vb0wmE6NHj2bFihUqg5JqaUIoIiLp3vr16/Hy8uLSpUsAeHl5MW7cOHLlypXkewUHB9O2bVsiIyPJkycPS5YsSbIfbyeSXDQhFBGRdOvatWt06tSJd955h0uXLlGyZEm2bdvGnDlzkrwMGobBjBkzaNSoEZGRkVSoUIGwsDCVQUkTVAhFRCTdMQyDgIAAnJ2dWbJkCQ4ODgwcOJAjR47QoEGDJN8vNjaWLl260Lt3bxISEnj//ffZs2cPr7zySpLvJZIcdMlYRETSld9++43u3buzbt06AFxcXPD396dq1arJst+5c+dwc3Pj4MGDODo6MmHCBPr27YvJZEqW/USSgyaEIiKSLlgsFubMmYOzszPr1q0jU6ZMjBw5krCwsGQrg5s3b6Zq1aocPHiQAgUKsGnTJj799FOVQUlzNCEUEZE079SpU3h4eLBz504AatWqha+vL87Ozsmyn2EYTJgwgc8//xyLxULVqlUJCgpK8u8wFEkpmhCKiEialZCQwPjx46lQoQI7d+4kW7ZsTJs2jeDg4GQrg7dv36Z9+/YMGjQIi8XCxx9/THBwsMqgpGmaEIqISJp0+PBh3N3dOXDgAABNmzbF29ubEiVKJNuep06dwtXVlePHj5MxY0amTZtGt27ddIlY0jxNCEVEJE25e/cuX3zxBVWrVuXAgQPkyZOH+fPns3HjxmQtg+vXr6datWocP36cwoULs337drp3764yKOmCJoQiIpJm7NmzB7PZzM8//wxA69at+fbbbylUqFCy7WmxWBg1ahTDhw/HMAxq167NihUrKFy4cLLtKZLSNCEUEZFU7/bt2/Tu3Zs33niDn3/+mRdeeIEVK1awYsWKZC2DUVFRuLq68tVXX2EYBj169GDbtm0qg5LuaEIoIiKp2saNG/Hy8uL8+fMAfPzxx0yaNIk8efIk674RERG4urpy8uRJMmfOzJw5c/joo4+SdU8RW1EhFBGRVOnGjRv069ePBQsWAFCiRAm8vb1p2rRpsu8dGBjIRx99xO3btylWrBhBQUHJ9l2GIqmBLhmLiEiqYhgGK1asoGzZsixYsACTyUTfvn05evRospfBxMREPv/8c9q0acPt27dp2LAh4eHhKoOS7mlCKCIiqcaVK1fo2bMnK1euBMDZ2RlfX19q1aqV7Htfv36djh078tNPPwHQv39/xo4dS4YM+lelpH/6X7mIiNicYRjMmzePfv36ERUVRYYMGRgyZAhDhgwhc+bMyb7/oUOHcHV15dy5c2TLlg0/Pz86dOiQ7PuKpBYqhCIiYlNnzpyha9eubNmyBYCqVavi5+dHhQoVUmT/xYsX4+npSWxsLK+88gorV65Msb1FUgvdQygiIjaRmJjI1KlTcXFxYcuWLWTJkoWJEyeyd+/eFClk8fHx9O3blw8++IDY2FiaN29OWFiYyqDYJU0IRUQkxR0/fhyz2UxoaCgADRo0wMfHh1KlSqXI/pGRkbRv354dO3YAMHToUEaMGIGjo2OK7C+S2qgQiohIiomLi2Ps2LGMGjWK+Ph4cubMycSJEzGbzTg4pMxFq3379uHm5salS5dwcnJi4cKFtGrVKkX2FkmtVAhFRCRF7Nu3D7PZzLFjxwB49913mT17Ni+++GKKZfDz86NHjx7ExcVRunRpVq1aRZkyZVJsf5HUSvcQiohIsrpz5w79+/enVq1aHDt2jAIFCvD999+zevXqFCuD9+7do1u3bnh4eBAXF0erVq3Yt2+fyqDI/9OEUEREks3WrVvx9PTkzJkzAHzwwQdMmTKF/Pnzp1iGS5cu0aZNG0JCQjCZTIwcOZLPP/88xS5Ri6QFKoQiIpLkbt68ycCBA/H19QWgWLFizJkzh7fffjtFc+zatYs2bdoQGRlJ7ty5WbJkCc2bN0/RDCJpgf7zSEREktTq1avv/4QRgB49enDs2LEULYOGYfDtt9/SsGFDIiMjcXFxISwsTGVQ5BE0IRQRkSTx+++/07t3b5YuXQrAq6++ip+fH3Xr1k3RHLGxsXTr1o2FCxcC0KFDB3x9fcmePXuK5hBJSzQhFBGR52IYBosWLaJs2bIsXboUR0dHBg8ezOHDh1O8DJ4/f5433niDhQsX4ujoyKRJk1iyZInKoMhjaEIoIiLP7MKFC3h5ebFhwwYAKlWqhJ+fH5UrV07xLFu2bKF9+/Zcv36d/Pnzs2zZMho2bJjiOUTSIk0IRUTkqVksFmbOnEm5cuXYsGEDmTNn5ptvvmHfvn0pXgYNw2DChAm8+eabXL9+nSpVqhAeHq4yKPIUNCEUEZGn8ssvv+Dh4cGuXbsAqFOnDr6+vjb5Tr/bt29jNptZtmwZAB999BGzZs0ia9asKZ5FJC3ThFBERJ5IfHw8Y8aMoWLFiuzatYscOXIwc+ZMdu7caZMyePr0aWrVqsWyZcvImDEjs2bNwt/fX2VQ5BloQigiIo918OBB3N3dOXToEADNmjVj7ty5FC9e3CZ51q9fT6dOnYiKiqJQoUIEBgZSu3Ztm2QRSQ80IRQRkUeKjY3l888/p1q1ahw6dIi8efOyaNEifvjhB5uUQYvFwsiRI3n33XeJioqidu3aHDhwQGVQ5DlpQigiIv8qODgYDw8PTp48CUC7du2YMWMGBQsWtEmeqKgoOnfuzJo1awDo3r07U6dOJVOmTDbJI5KeqBCKiMhDoqOj+fzzz5k1axYAhQsXZtasWbRq1cpmmSIiInB1deXkyZNkzpyZ2bNn8/HHH9ssj0h6o0IoIiL3/fDDD3Tr1o2LFy8C4Onpyfjx48mdO7fNMgUFBdGlSxdu375NsWLFCAoKomrVqjbLI5Ie6R5CERHhjz/+4MMPP6RFixZcvHiRV155hS1btuDt7W2zMpiYmMiQIUNo3bo1t2/fpkGDBoSHh6sMiiQDFUIRETtmGAZLly7F2dmZ7777DgcHB/r378/Ro0dp1KiRzXLduHGDFi1aMGbMGAD69evHpk2bKFCggM0yiaRnumQsImKnLl26RI8ePe4/pFG+fHn8/PyoXr26TXMdPnwYV1dXzp49S9asWfHz8+P999+3aSaR9E4TQhERO2MYBj4+Pjg7O7NmzRoyZszIiBEjCA8Pt3kZXLJkCbVq1eLs2bO88sorhISEqAyKpABNCEVE7Mjp06fp2rUr27ZtA6BGjRr4+flRrlw5m+aKj4/ns88+Y+rUqQC89dZbLFmyhLx589o0l4i90IRQRMQOJCQkMGnSJCpUqMC2bdvIli0bU6ZMYffu3TYvg7///jtNmza9XwaHDh3K+vXrVQZFUpAmhCIi6dzRo0cxm83s378fgMaNG+Pt7c0rr7xi42Swf/9+3Nzc+O2333BycmLBggW4urraOpaI3dGEUEQknbp37x7Dhg2jcuXK7N+/n1y5cuHn58emTZtSRRn09/enbt26/Pbbb5QuXZrQ0FCVQREb0YRQRCQd2rt3L2azmRMnTgDQqlUrZs6cSZEiRWyczFpU+/Tpw9y5cwF47733WLhwITlz5rRxMhH7pQmhiEg6cvv2bfr27UudOnU4ceIEBQsWZPny5QQFBaWKMnj58mUaNGjA3LlzMZlMjBw5kqCgIJVBERvThFBEJJ3YtGkTXbt25dy5cwB06dKFyZMnp5qHM3bt2kWbNm2IjIwkd+7cLFmyhObNm9s6loigCaGISJr3559/4u7uzptvvsm5c+d46aWX2LBhA/Pnz08VZdAwDGbOnEnDhg2JjIzExcWFsLAwlUGRVESFUEQkDQsKCsLZ2Zl58+ZhMpno3bs3x44d46233rJ1NABiY2P5+OOP6dWrFwkJCbRv3569e/dSsmRJW0cTkb/RJWMRkTTo6tWr9OrVi8DAQADKlCmDr68vderUsXGyB86fP4+bmxsHDhzAwcGB8ePH069fP0wmk62jicj/UCEUEUlDDMNgwYIF9OvXjz///JMMGTIwaNAgvvjiC7JkyWLrePdt3bqVdu3acf36dfLnz8/SpUtp1KiRrWOJyCOoEIqIpBHnzp2ja9eubNq0CYDKlSvj5+dHpUqVbBvsbwzDYNKkSQwaNAiLxUKVKlUICgqiePHito4mIv9B9xCKiKRyiYmJTJ8+nfLly7Np0yayZMnC+PHjCQ0NTVVlMCYmhvfff5+BAwdisVj46KOPCA4OVhkUSQM0IRQRScUiIiLw8PBg7969ANSrVw9fX19effVVGyd72OnTp3F1deXYsWNkyJCBadOm0b17d90vKJJGaEIoIpIKxcXFMWrUKF5//XX27t2Lk5MTc+bMYdu2bamuDP7www9Uq1aNY8eOUahQIbZv306PHj1UBkXSEE0IRURSmbCwMMxmM0eOHAGgRYsWzJkzh6JFi9o42cMsFgujR4/mq6++wjAMatWqxYoVK1LFT0QRkaejCaGISCpx584dPvvsM2rUqMGRI0fInz8/S5YsYe3atamuDEZFReHm5sawYcMwDINu3bqxfft2lUGRNEoTQhGRVGD79u14enpy+vRpADp27MjUqVMpUKCAjZP904kTJ2jVqhUnT54kc+bMzJo1C3d3d1vHEpHnoEIoImJDUVFRDBo0iLlz5wLw4osvMmfOHN555x0bJ/t3QUFBdOnShdu3b1O0aFGCgoKoVq2arWOJyHPSJWMRERtZu3Yt5cqVu18Gu3XrxvHjx1NlGUxMTGTo0KG0bt2a27dv06BBA8LDw1UGRdIJTQhFRFLYtWvX6NOnDwEBAQCUKlUKX19f6tevb+Nk/+7GjRt07NiRjRs3AvDpp58yfvx4MmTQv0JE0gv9v1lEJIUYhsGSJUvo06cP169fx8HBgQEDBjB8+HCyZs1q63j/6vDhw7i6unL27FmyZs2Kr68vHTt2tHUsEUliKoQiIing4sWLdO/enfXr1wNQoUIF/Pz8qFq1qo2TPVpAQABms5nY2FhefvllVq5cScWKFW0dS0SSge4hFBFJRhaLhTlz5lCuXDnWr19PpkyZGDVqFGFhYam2DCYkJNCvXz86duxIbGwsb731FmFhYSqDIumYJoQiIsnk5MmTeHp6snPnTgBq166Nr68vZcuWtXGyR/v9999p374927dvB2DIkCF8/fXXODo62jaYiCQrTQhFRJJYQkIC48ePp2LFiuzcuZPs2bMzffp0goODU3UZ3L9/P1WqVGH79u3kyJGDoKAgRo8erTIoYgc0IRQRSUKHDh3CbDZz4MABAN58803mzp1LiRIlbBvsMfz9/enRowf37t2jdOnSrFy5MlWXVxFJWpoQiogkgbt37zJ06FCqVq3KgQMHyJMnD/Pnz2fDhg2pugzGxcXRvXt3zGYz9+7do2XLloSGhqoMitgZTQhFRJ7T7t27MZvN/PLLLwC0adOGGTNmUKhQIRsn+2+XL1+mTZs27N27F5PJxNdff82QIUNwcNCsQMTeqBCKiDyjW7duMWTIEGbOnIlhGBQqVIhZs2bh6upq62iPtXv3btq0acPVq1fJnTs3ixcv5u2337Z1LBGxERVCEZFnsGfPHkaPHs2VK1eoVKkSrVq1om/fvuTMmdPW0f6TYRjMnj2bPn36kJCQQPny5Vm5ciWlSpWydTQRsSGTYRjGs5wYHR1Nrly5iIqKSvX/ABQREYiNjaVHjx7Mnz8fgHbt2uHn50eOHDlsG0xEks2T9jVNCEVE7MD58+dp3bo14eHhODg4MG7cOPr374/JZLJ1NBFJBVQIRUTSua1bt9K+fXv++OMP8uXLx9KlS2ncuLGtY4lIKqJHyURE0inDMJg0aRJNmzbljz/+oHLlyoSHh6sMisg/qBCKiKRDMTExdOzYkQEDBmCxWOjSpQu7du3ipZdesnU0EUmFdMlYRCSd+fXXX3F1deXo0aNkyJCBqVOn0qNHD90vKCKPpEIoIpKO/Pjjj3Ts2JGbN2/ywgsvsGLFCt544w1bxxKRVE6XjEVE0gGLxcKoUaNo0aIFN2/epGbNmoSHh6sMisgT0YRQRNK9uDjIlMnWKZJPdHQ0nTt3ZvXq1QB069aNqVOnkjlzZhsnE5G0QhNCEUlzGjSAXr2sf+XODfnywRdfwF9fs1+iBIwaBR99BLlygaen9f3AQChXDjJnth4zadLD65YoASNHQseOkCMHFCkCM2Y8fMzkyeDiAtmzQ7Fi0KMH3L794PP5862ZNm6EsmWt6zRrBleuPLzOvHnWz7NkgTJlYNasZ/uz+Pnnn6levTqrV68mU6ZM+Pn5MXv2bJVBEXkqKoQikiYtWAAZMkBoKEyfDlOmgK/vg88nTIDy5SE8HL780vpru3bQoQMcPQrDh1vf//8f2vHQeRUqwIED8Pnn8OmnsGnTg88dHKz7HTtmzbB1K3z22cNr3LkDEyfCokWwcydcuAADBjz43McHhg6F0aPhxAn45htrlgULnu7PYNWqVVSvXp1ffvmFokWLEhwcjLu7+9MtIiICYDyjqKgoAzCioqKedQkRkWdSv75hlC1rGBbLg/cGDbK+ZxiG8dJLhtGq1cPndOxoGE2bPvzewIGG4ez84PVLLxlGs2YPH9O+vWE0b/7oLMuWGUa+fA9ez5tnGGAYp08/eG/mTMN44YUHr4sVM4wlSx5eZ+RIw6hV69H7/F1CQoIxZMgQAzAAo379+kZkZOSTnSwiduVJ+5omhCKSJtWsCX//FpVateDUKUhMtL6uWvXh40+cgDp1Hn6vTp2Hz/lrnb+rVct67l+2bYOmTeHFF8HJCTp3huvXISbmwTHZskHJkg9eFy4Mv/9u/ftr1+DiRTCbrZeT//pr1Cj49dfH/76joqJ45513+OabbwDo27cvmzZtomDBgo8/WUTkEfRQiYikS9mzP/zaMB4ukH+99yT+Ou/8eXj7bejWzXqvYd68sGuXtdzFxz84PmPGf57/114Wi/VXHx+oUePh4xwdH5/lww8/ZMOGDWTNmhVfX186duz4ZL8JEZH/oEIoImlSSMg/X7/66qNLlbOztbz93Z498NprD5/zb+uWKWP9+7AwSEiwPozi8P/XV5Yte7rcL7xgnS6eOQOdOj3duQC//fYbL7/8MitXrqRixYpPv4CIyL9QIRSRNOniRejXD7y8rA+AzJjxz6eG/65/f6hWzTrZa98e9u6Fb7/959O9u3fD+PHQqpX1YZLly2H9eutnJUtaC+GMGfDuu9Zj58x5+uzDh0Pv3pAzJzRvDvfuWcvmn39af0//pVatWowcOZK8efM+/cYiIo+gQigiaVLnzhAbC9WrWyd8n3wCXbs++vjKla3TvGHDrKWwcGH4+mvrV9P8Xf/+1ieSR4yw3iM4aRK89Zb1s0qVrF87M26c9QnkevVgzBhrlqfh4WG9z3DCBOsTytmzW7/Kpm/fx587ffp0HJ/k2rKIyFMwGcaT3kXzsOjoaHLlykVUVBQ5c+ZM6lwiIo/UoIG1nE2dmrTrlihhLWVPUsxERNKCJ+1respYRERExM6pEIqIiIjYOd1DKCJpzvbtybPuuXPJs66ISGqnCaGIiIiInVMhFBEREbFzKoQiIiIidk6FUERERMTOqRCKiIiI2DkVQhERERE7p0IoIqlSQkICX3/9NZUrV6Zy5cp88sknREdH2zqWiEi6pO8hFJFU59q1a7Rv355t27YBMHToUEaMGKGf4SsikkxUCEUkVQkPD8fV1ZWLFy+SI0cOFixYgJubm61jiYika7pkLCKpxsKFC6lTpw4XL17ktddeIzQ0VGVQRCQFqBCKiM3Fx8fTu3dvunTpwr1793jnnXfYt28fzs7Oto4mImIXVAhFxKYiIyNp0qQJM2bMAOCrr75i9erV5MqVy8bJRETshwqhiNjMvn37qFKlCjt37sTJyYnVq1czfPhwHBxS/z+aGjSAvn1tnQLOnQOTCQ4dsnUSEUnLUv8/dUUkXfL396du3bpcunSJMmXKsG/fPlq2bGnrWP+wfbu1cN28aeskIiLJR4VQRFJUXFwcPXr0wGw2ExcXR6tWrQgNDaVMmTK2jiYiYrdUCEUkxVy5coVGjRoxe/ZsTCYTI0eOJDAwkJw5c9o0l2HA+PHwyiuQNStUrAgrVlgvxzZsaD0mTx7rpPCjjx6cZ7HAZ59B3rxQqBAMH/7wupMng4sLZM8OxYpBjx5w+/aDz+fPh9y5YeNGKFsWcuSAZs3gypWH9/j6ayhaFDJnhkqVYMOG5PhTEBF7pkIoIili7969VKlShd27d5MrVy7Wrl3LF198kSruF/ziC5g3D2bPhuPH4dNP4YMP4Px5CAy0HvPLL9aiNm3ag/MWLLCWvdBQa6H8+mvYtOnB5w4OMH06HDtmPXbrVmuB/Ls7d2DiRFi0CHbuhAsXYMCAB59PmwaTJlmPOXIE3noLWraEU6eS789DROyPyTAM41lOjI6OJleuXERFRdn8v+5FJHXz9vamV69exMfH4+zszKpVq3j11VdtHQuAmBjIn99a1mrVevC+h4e1rHXtap0S/vmndZr3lwYNIDERgoMfvFe9OjRqBGPH/vtey5dD9+7wxx/W1/Pnw8cfw+nTULKk9b1Zs6zF8upV6+sXX4SePWHIkIf3qVYNZs60TjFffhkOHrROD0VE/u5J+5p+UomIJJt79+7xySef4OPjA0Dr1q2ZN28eTk5ONk72QEQE3L0LTZs+/H5cHLz++n+fW6HCw68LF4bff3/wets2+OYb6x7R0ZCQYN0rJsY6WQTIlu1BGfzfNaKj4fJlqFPn4X3q1IHDh5/89ygi8jgqhCKSLC5dukSbNm0ICQnBZDLxzTffMGjQIEwmk62jPcRisf66fr11Gvd3mTPDr78++tyMGR9+bTI9WO/8eXj7bejWDUaOtN5nuGsXmM0QH//fa/zvdZv//SMzjH++JyLyPFQIRSTJ7dq1izZt2hAZGUnu3LkJCAigWbNmto71r5ydrcXvwgWoX/+fn1+8aP01MfHp1g0Ls04EJ02y3ksIsGzZ062RMycUKWItkvXqPXh/zx7rZWMRkaSiQigiScYwDGbPnk2fPn1ISEjAxcWFlStXUvLv10RTGScn60Mcn35qne698Yb1Uu2ePdanfps0sU7j1q2zTvyyZrW+/zglS1oL4YwZ8O67sHs3zJnz9PkGDoSvvrKuV6mS9eGXQ4dg8eKnX0tE5FFs/3ifiKQLd+/exWw207NnTxISEmjfvj179+5N1WXwLyNHwrBhMGaM9etf3noL1q61Pqzx4oswYgQMHgwvvAC9ej3ZmpUqWb92Ztw4KF/eWuDGjHn6bL17Q//+1r9cXKxfObNmDaSSZ3JEJJ3QU8Yi8twuXrxI69at2b9/Pw4ODowbN47+/funuvsFRUTsjZ4yFpEUsWPHDtq2bcu1a9fImzcvS5cupUmTJraOJSIiT0GXjEXkmRiGwfTp02ncuDHXrl2jUqVKhIWFqQyKiKRBKoQi8tRiY2Pp0qULffr0ITExkU6dOrF7925efvllW0cTEZFnoEvGIvJUzp8/j5ubGwcOHMDR0ZGJEyfSp08f3S8oIpKGqRCKyBPbunUr7dq14/r16+TPn59ly5bRsGFDW8cSEZHnpEvGIvJYhmEwefJkmjZtyvXr16lSpQrh4eFppgwmPu23SouI2BkVQhH5T3fu3KFTp070798fi8VC586dCQ4Opnjx4raO9kSOHz+Ou7u7rWOIiKRqKoQi8khnz56ldu3aBAQEkCFDBmbMmMH8+fPJmjWrraM9VlxcHCNHjuT1119n27Zt3L1719aRRERSLd1DKCL/atOmTXTo0IEbN25QsGBBli9fTr2//0DdVCwsLAyz2cyRI0cAqFixIn/++SeFCxe2cTIRkdRJE0IReYhhGIwfP55mzZpx48YNqlevTnh4eJoog7GxsQwaNIgaNWpw5MgR8ufPz5IlS1izZo3KoIjIf9CEUETui4mJwd3dnWXLlgHg7u7OzJkzyZIli42TPV5wcDBms5lTp04B0KFDB6ZPn06BAgVsnExEJPVTIRQRAE6fPo2rqyvHjh0jY8aMTJ8+HS8vr1T//YK3bt1i8ODBzJo1C4AiRYowe/ZsWrZsaeNkIiJphwqhiPDjjz/SsWNHbt68SaFChVixYgV16tSxdazH2rBhA127duXixYsAeHh4MGHCBHLnzm3bYCIiaYzuIRSxY4Zh8M0339CiRQtu3rxJrVq1CA8PT/Vl8MaNG3Tp0oXmzZtz8eJFXn75ZTZv3oyPj4/KoIjIM1AhFLFTt27dok2bNgwdOhTDMPDy8mLbtm0UKVLE1tH+04oVKyhbtiwLFy7EZDLRt29fjh49SuPGjW0dTUQkzdIlYxE7dPLkSVq1asWJEyfIlCkT3377LZ6enraO9Z+uXr1Kz549CQoKAqBs2bL4+/tTs2ZNGycTEUn7NCEUsTPr1q2jWrVqnDhxgiJFirBjx45UXQYNw2DBggU4OzsTFBREhgwZ+OKLLzh48KDKoIhIEtGEUMROWCwWRo0axVdffQXAG2+8wfLlyylUqJCNkz3a+fPn8fLyYuPGjQBUrlwZf39/KlasaONkIiLpiyaEInYgKioKV1fX+2WwZ8+ebNmyJdWWQYvFwsyZMylfvjwbN24kc+bMjB07ltDQUJVBEZFkoAmhSDr3888/06pVK3755RcyZ87M7Nmz+fjjj20d65F++eUXPDw82LVrF2CdZPr6+lK6dGkbJxMRSb80IRRJx1atWkX16tX55ZdfKFq0KMHBwam2DCYkJDBu3DgqVqzIrl27yJ49O99++y07duxQGRQRSWaaEIqkQxaLheHDhzNy5EgA6tevz7JlyyhYsKCNk/27w4cP4+7uzoEDBwB488038fb25qWXXrJxMhER+6AJoUg6c/PmTVq2bHm/DPbp04dNmzalyjJ47949vvzyS6pWrcqBAwfIkycP8+fPZ8OGDSqDIiIpSBNCkXTk+PHjtGrVitOnT5MlSxa8vb358MMPbR3rX4WEhODu7s6JEycAcHNzY+bMman2QRcRkfRME0KRdCIwMJAaNWpw+vRpihcvzu7du1NlGYyJieHTTz+ldu3anDhxghdeeIEVK1YQGBioMigiYiMqhCJpXGJiIkOGDKFNmzbExMTQqFEjwsLCqFy5sq2j/cPWrVupUKECU6dOxTAMOnfuTEREBK1bt7Z1NBERu6ZCKJKG3bhxgxYtWjBmzBgA+vfvz8aNGylQoICNkz0sKioKT09PGjduzJkzZyhWrBg//PADCxYsIG/evLaOJyJi93QPoUgadeTIEVxdXTlz5gxZs2bFz8+P999/39ax/mHt2rV069aNy5cvA9CjRw/Gjh2Lk5OTjZOJiMhfVAhF0qClS5fi7u7OnTt3KFGiBKtWrUp1P8Hj2rVr9O7dm++//x6AV199FV9fX+rVq2fjZCIi8r90yVgkDUlISOCzzz6jQ4cO3Llzh6ZNmxIWFpaqyqBhGAQEBODs7Mz333+Pg4MDn332GYcPH1YZFBFJpTQhFEkjrl+/TocOHdi8eTMAgwYNYvTo0Tg6Oto42QOXLl2iW7durFu3DgAXFxf8/f2pWrWqjZOJiMh/USEUSQMOHTqEq6sr586dI1u2bMybN4927drZOtZ9hmHg6+vLgAEDiI6OJmPGjHz55ZcMGjSITJky2TqeiIg8hgqhSCq3ZMkSPDw8iI2NpWTJkqxcuRIXFxdbx7rv119/xdPTk23btgFQo0YN/Pz8KFeunI2TiYjIk9I9hCKpVEJCAv369aNTp07ExsbSvHlz9u/fn2rKYGJiIlOmTMHFxYVt27aRNWtWJk+ezO7du1UGRUTSGE0IRVKha9eu0b59+/tTt6FDhzJixIhUc79gREQEZrOZkJAQABo2bIiPjw8lS5a0cTIREXkWKoQiqUx4eDiurq5cvHiRHDlysGDBAtzc3GwdC4C4uDjGjRvHqFGjiIuLI2fOnEycOBEPDw9MJpOt44mIyDNSIRRJRRYuXEjXrl25d+8er776KqtWrcLZ2dnWsQAICwvDbDZz5MgRAN555x1mz55N0aJFbZxMRESel+4hFEkF4uPj6d27N126dOHevXu888477Nu3L1WUwdjYWAYNGkSNGjU4cuQI+fLlY/HixaxZs0ZlUEQkndCEUMTGIiMjadeuHTt37gTgq6++YtiwYTg42P6/14KDgzGbzZw6dQqADh06MH369FT3s5JFROT5qBCK2NC+fftwc3Pj0qVLODk58d1339GyZUtbx+LWrVsMHjyYWbNmAVCkSBFmz56dKrKJiEjSs/0IQsRO+fv7U7duXS5dukSZMmXYt29fqihcGzdupHz58vfLoIeHB8ePH08V2UREJHmoEIqksLi4OHr06IHZbCYuLo5WrVoRGhpKmTJlbJrrxo0bfPTRRzRr1owLFy7w8ssvs3nzZnx8fMidO7dNs4mISPJSIRRJQVeuXKFRo0bMnj0bk8nEyJEjCQwMJGfOnDbNFRgYiLOzMwsWLMBkMtG3b1+OHj1K48aNbZpLRERShu4hFEkhe/fupXXr1ly5coVcuXKxePFiWrRoYdNMV69epVevXgQGBgJQtmxZ/Pz8qFWrlk1ziYhIytKEUCQFeHt7U79+fa5cuYKzszP79++3aRk0DIMFCxbg7OxMYGAgGTJk4IsvvuDgwYMqgyIidkgTQpFkdO/ePT755BN8fHwAaN26NfPmzcPJyclmmc6fP4+XlxcbN24EoHLlyvj5+VGpUiWbZRIREdvShFAkmVy6dIkGDRrg4+ODyWRizJgxLF++3GZl0GKxMHPmTMqXL8/GjRvJnDkzY8eOJTQ0VGVQRMTOaUIokgx27dpFmzZtiIyMJHfu3AQEBNCsWTOb5Tl58iRms5ldu3YB8MYbb+Dr60vp0qVtlklERFIPTQhFkpBhGMyaNYuGDRsSGRmJi4sLYWFhNiuDCQkJjBs3jgoVKrBr1y6yZ8/Ot99+y44dO1QGRUTkPk0IRZLI3bt36dGjB/PmzQOgffv2+Pn5kT17dpvkOXz4MGazmfDwcADefPNNvL29eemll2ySR0REUi9NCEWSwMWLF6lXrx7z5s3DwcGBCRMmEBAQYJMyeO/ePb788kuqVq1KeHg4uXPnZt68eWzYsEFlUERE/pUmhCLPaceOHbRt25Zr166RN29eli5dSpMmTWySJSQkBLPZTEREBABubm7MnDmTQoUK2SSPiIikDZoQijwjwzCYPn06jRs35tq1a1SqVImwsDCblMGYmBg+/fRTateuTUREBAULFmT58uUEBgaqDIqIyGNpQijyDGJjY/Hy8mLRokUAdOrUCW9vb7Jly5biWbZu3YqnpydnzpwBoHPnzkyePJl8+fKleBYREUmbVAhFntL58+dxc3PjwIEDODo6MnHiRPr06YPJZErRHFFRUQwYMABfX18AihUrxty5c2nevHmK5hARkbRPhVDkKWzdupV27dpx/fp18ufPz7Jly2jYsGGK51i7di3dunXj8uXLAPTo0YOxY8fa9CegiIhI2qV7CEWegGEYTJ48maZNm3L9+nUqV65MeHh4ipfBa9eu0bFjR1q2bMnly5d59dVX2bFjBzNnzlQZFBGRZ6ZCKPIYd+7coVOnTvTv3x+LxULnzp3ZtWsXxYsXT7EMhmEQEBCAs7MzAQEBODg48Nlnn3H48GHq1auXYjlERCR90iVjkf9w9uxZXF1dOXz4MBkyZGDKlCn07NkzRe8XvHTpEt27d2ft2rUAuLi44O/vT9WqVVMsg4iIpG+aEIo8wqZNm6hatSqHDx+mYMGCbNmyhV69eqVYGTQMAx8fH5ydnVm7di0ZM2ZkxIgRhIWFqQyKiEiS0oRQ5H8YhsGECRP4/PPPsVgsVKtWjaCgIIoWLZpiGX799Vc8PT3Ztm0bANWrV8ff359y5cqlWAYREbEfmhCK/E1MTAwdOnRg0KBBWCwW3N3d2blzZ4qVwcTERKZMmYKLiwvbtm0ja9asTJo0iT179qgMiohIstGEUOT/nT59GldXV44dO0bGjBmZPn06Xl5eKXaJOCIiArPZTEhICAANGzbEx8eHkiVLpsj+IiJivzQhFAF+/PFHqlWrxrFjxyhUqBDbtm2jW7duKVIG4+PjGTlyJK+//johISHkzJkTb29vtmzZojIoIiIpQhNCsWuGYTBmzBi++OILDMOgZs2aBAYGUqRIkRTZPzw8HHd3d44cOQLAO++8w+zZs1P0fkURERFNCMVu3bp1izZt2jB06FAMw8DLy4vt27enSBmMjY1l8ODB1KhRgyNHjpAvXz4WL17MmjVrVAZFRCTFaUIodunkyZO4uroSERFBpkyZ+Pbbb/H09EyRvYODg/Hw8ODkyZMAdOjQgWnTplGwYMEU2V9EROR/qRCK3Vm3bh2dOnUiOjqaIkWKEBgYSM2aNZN931u3bvH5558zc+ZMAAoXLsycOXNo2bJlsu8tIiLyX3TJWOyGxWLh66+/5t133yU6Opo6deoQHh6eImVw48aNlC9f/n4ZNJvNREREqAyKiEiqoAmh2IWoqCg6d+7MmjVrAOjZsyeTJ08mU6ZMybrvjRs36NevHwsWLADg5ZdfxsfHh8aNGyfrviIiIk9DhVDSvZ9//plWrVrxyy+/kDlzZmbPns3HH3+c7PsGBgbSs2dPIiMjMZlM9OnTh1GjRpE9e/Zk31tERORpqBBKurZq1So6d+7MrVu3KFq0KEFBQVSrVi1Z97x69Sq9evUiMDAQgLJly+Ln50etWrWSdV8REZFnpXsIJV2yWCwMGzYMV1dXbt26Rb169QgPD0/WMmgYBgsWLMDZ2ZnAwEAyZMjAF198wcGDB1UGRUQkVdOEUNKdmzdv8sEHH7B+/XoA+vTpw4QJE8iYMWOy7XnhwgW8vLzYsGEDAJUrV8bPz49KlSol254iIiJJRRNCSVeOHz9OtWrVWL9+PVmyZGHhwoVMnTo12cqgxWJh1qxZlCtXjg0bNpA5c2bGjBlDaGioyqCIiKQZmhBKuhEYGEiXLl2IiYmhePHirFy5ksqVKyfbfidPnsTDw4Pg4GAA6tSpg5+fH6VLl062PUVERJKDJoSS5iUmJjJkyBDatGlDTEwMjRo1IiwsLNnKYEJCAuPGjaNChQoEBweTPXt2ZsyYwc6dO1UGRUQkTdKEUNK0Gzdu0LFjRzZu3AhA//79GTt2LBkyJM//tA8fPozZbCY8PByApk2b4u3tTYkSJZJlPxERkZSgQihp1pEjR3B1deXMmTNkzZoVPz8/3n///WTZ6969e4waNYqxY8eSkJBA7ty5mTJlCl26dMFkMiXLniIiIilFhVDSpKVLl+Lu7s6dO3coUaIEq1atomLFismyV0hIyP0fNQfg5ubGzJkzKVSoULLsJyIiktJ0D6GkKQkJCXz22Wd06NCBO3fu0LRpU8LCwpKlDMbExNCvXz9q165NREQEBQsWZPny5QQGBqoMiohIuqIJoaQZ169fp0OHDmzevBmAQYMGMXr0aBwdHZN8r61bt+Lp6cmZM2cA6Ny5M5MnTyZfvnxJvpeIiIitqRBKmnDo0CFcXV05d+4c2bJlY968ebRr1y7J94mKimLgwIH4+PgAUKxYMebOnUvz5s2TfC8REZHUQpeMJdVbsmQJtWvX5ty5c5QsWZKQkJBkKYNr167F2dn5fhns3r07x44dUxkUEZF0T4VQUq2EhAT69etHp06diI2NpVmzZuzfvx8XF5ck3efatWt07NiRli1bcvnyZUqVKsWOHTuYNWsWOXPmTNK9REREUiMVQkmVrl27xptvvsmUKVMAGDJkCOvWrSNPnjxJtodhGAQEBODs7ExAQAAODg4MHDiQI0eOUK9evSTbR0REJLXTPYSS6oSHh+Pq6srFixfJkSMHCxYswM3NLUn3uHTpEt27d2ft2rUAuLi44O/vT9WqVZN0HxERkbRAE0JJVRYuXEidOnW4ePEir776KqGhoUlaBg3DwMfHB2dnZ9auXUvGjBkZMWIEYWFhKoMiImK3NCGUVCE+Pp7+/fszY8YMAN555x0WLVpE7ty5k2yPM2fO4OnpydatWwGoXr06/v7+lCtXLsn2EBERSYs0IRSbi4yMpEmTJvfL4LBhw1i9enWSlcHExESmTp2Ki4sLW7duJWvWrEyaNIk9e/aoDIqIiKAJodjYvn37cHNz49KlSzg5OfHdd9/RsmXLJFs/IiICs9lMSEgIAA0aNMDHx4dSpUol2R4iIiJpnSaEYjP+/v7UrVuXS5cuUaZMGfbt25dkZTA+Pp5Ro0bx+uuvExISgpOTE3PnzmXLli0qgyIiIv9DE0JJcXFxcfTt25fZs2cD8N5777Fw4cIk+86/8PBw3N3dOXLkCAAtWrRgzpw5FC1aNEnWFxERSW80IZQUdeXKFRo1asTs2bMxmUx8/fXXBAUFJUkZjI2NZfDgwdSoUYMjR46QL18+Fi9ezNq1a1UGRURE/oMmhJJi9u7dS+vWrbly5Qq5cuVi8eLFtGjRIknWDg4OxsPDg5MnTwLQoUMHpk2bRsGCBZNkfRERkfRME0JJEd7e3tSvX58rV67g7OzM/v37k6QM3rp1i169elGvXj1OnjxJ4cKFWb16NQEBASqDIiIiT0iFUJLVvXv36Nq1K15eXsTHx9O6dWtCQkJ49dVXn3vtjRs3Ur58eWbOnAmA2WwmIiIiSZ9SFhERsQe6ZCzJ5tKlS7Rp04aQkBBMJhOjR49m8ODBmEym51r3xo0b9OvXjwULFgDw8ssv4+PjQ+PGjZMitoiIiN1RIZRksWvXLtq0aUNkZCS5c+cmICCAZs2aPfe6QUFB9OjRg8jISEwmE71792b06NFkz549CVKLiIjYJxVCSVKGYTB79mz69OlDQkICLi4urFy5kpIlSz7XulevXqVXr14EBgYCUKZMGfz8/Khdu3ZSxBYREbFruodQkszdu3cxm8307NmThIQE2rVrx969e5+rDBqGwYIFC3B2diYwMBBHR0eGDh3KwYMHVQZFRESSiCaEkiQuXrxI69at2b9/Pw4ODowdO5YBAwY81/2CFy5cwMvLiw0bNgDw+uuv4+/vT6VKlZIotYiIiIAmhJIEduzYQZUqVdi/fz958+Zl48aNDBw48JnLoMViYdasWZQrV44NGzaQOXNmxowZQ2hoqMqgiIhIMtCEUJ6ZYRjMmDGDfv36kZiYSKVKlQgKCuLll19+5jVPnjyJh4cHwcHBANSpUwc/Pz9Kly6dVLFFRETkf2hCKM8kNjaWLl260KdPHxITE+nYsSO7d+9+5jKYkJDA+PHjqVixIsHBwWTPnp0ZM2awc+dOlUEREZFkpgmhPLXz58/j5ubGgQMHcHR0ZMKECfTt2/eZLxEfOXIEd3d3wsPDAWjatCne3t6UKFEiCVOLiIjIo2hCKE9l69atVKlShQMHDpA/f342bdrEp59++kxl8N69ewwbNowqVaoQHh5O7ty5mTdvHhs3blQZFBERSUGaEMoTMQyDKVOmMHDgQCwWC5UrV2blypUUL178mdYLCQm5/6PmAFxdXZk5cyaFCxdOytgiIiLyBDQhlMe6c+cOnTp1on///lgsFjp37syuXbueqQzGxMTQr18/ateuTUREBAULFmTZsmUEBgaqDIqIiNiIJoTyn86ePYurqyuHDx8mQ4YMTJ48mV69ej3TJeKtW7fi6enJmTNnAPjwww+ZMmUK+fLlS+rYIiIi8hRUCOWRNm3aRIcOHbhx4wYFCxZk+fLl1KtX76nXiYqKYuDAgfj4+ABQrFgx5s6dS/PmzZM6soiIiDwDXTKWfzAMg/Hjx9OsWTNu3LhBtWrVCA8Pf6YyuG7dOsqVK3e/DHbv3p1jx46pDIqIiKQimhDKQ2JiYnB3d2fZsmUAfPzxx8yaNYssWbI81TrXrl2jT58+BAQEAFCqVCn8/PyeqVSKiIhI8tKEUO47ffo0NWvWZNmyZWTMmJFZs2bh5+f3VGXQMAy+//57nJ2dCQgIwMHBgYEDB3LkyBGVQRERkVRKE0IB4Mcff6Rjx47cvHmTQoUKsWLFCurUqfNUa1y6dIkePXqwZs0aAFxcXPDz86NatWrJEVlERESSiCaEds4wDL755htatGjBzZs3qVmzJuHh4U9VBg3DwNfXl3LlyrFmzRoyZszIiBEjCAsLUxkUERFJAzQhtGO3bt3io48+IigoCICuXbsyffp0MmfO/MRrnDlzBk9PT7Zu3QpA9erV8fPzo3z58smSWURERJKeJoR26uTJk9SsWZOgoCAyZcqEt7c3c+fOfeIymJiYyNSpU3FxcWHr1q1kzZqViRMnsmfPHpVBERGRNEYTQju0bt06OnXqRHR0NEWKFCEwMJCaNWs+8fkRERGYzWZCQkIAaNCgAT4+PpQqVSq5IouIiEgy0oTQjlgsFr7++mveffddoqOjqVOnDuHh4U9cBuPj4xk1ahSvv/46ISEhODk5MXfuXLZs2aIyKCIikoZpQmgnoqOj6dy5M6tXrwagR48eTJkyhUyZMj3R+QcOHMDd3Z3Dhw8D0KJFC+bMmUPRokWTLbOIiIikDE0I7cDPP/9M9erVWb16NZkzZ8bf35+ZM2c+URmMjY1l8ODBVK9encOHD5MvXz4WL17M2rVrVQZFRETSCU0I07lVq1bRuXNnbt26RdGiRQkKCnrir4LZtWsXZrOZkydPAtC+fXumT59OwYIFkzOyiIiIpDBNCNMpi8XCsGHDcHV15datW9SrV4/w8PAnKoO3bt2iV69e1K1bl5MnT1K4cGFWrVrF999/rzIoIiKSDmlCmA7dvHmTDz74gPXr1wPQu3dvJk6cSMaMGR977saNG+natSsXLlwAwGw2M3HiRHLnzp2ckUVERMSGVAjTmePHj+Pq6sqpU6fIkiUL3t7efPjhh48978aNG/Tr148FCxYAUKJECXx8fGjSpElyRxYREREb0yXjdCQwMJAaNWpw6tQpihcvzu7du5+oDAYFBeHs7MyCBQswmUz06dOHo0ePqgyKiIjYCRXCdCAxMZEhQ4bQpk0bYmJiaNSoEWFhYVSuXPk/z7t69Spt2rShdevWREZGUqZMGXbt2sXUqVPJkSNHCqUXERERW1MhTONu3LhBixYtGDNmDAD9+vVj48aNFChQ4JHnGIbBwoULcXZ2JjAwEEdHR4YOHcrBgwepXbt2SkUXERGRVEL3EKZhR44cwdXVlTNnzpA1a1Z8fX3p2LHjf55z4cIFvLy82LBhAwCvv/46/v7+VKpUKQUSi4iISGqkCWEatXTpUmrVqsWZM2coUaIEe/fu/c8yaLFYmDVrFuXKlWPDhg1kzpyZMWPGEBoaqjIoIiJi5zQhTGMSEhIYMmQIEyZMAKBp06YEBASQL1++R55z8uRJPDw8CA4OBqBOnTr4+vpSpkyZFMksIiIiqZsmhGnI9evXad68+f0y+Nlnn/HDDz88sgwmJCQwfvx4KlasSHBwMNmzZ2f69Ons3LlTZVBERETu04QwjTh06BCurq6cO3eObNmyMW/ePNq1a/fI448cOYK7uzvh4eGAdZLo7e1NiRIlUiixiIiIpBWaEKYBS5YsoXbt2pw7d46SJUsSEhLyyDJ47949hg0bRpUqVQgPDyd37tzMmzePjRs3qgyKiIjIv9KEMBVLSEjgs88+Y8qUKQA0a9aMJUuWkCdPnn89PjQ0FHd3dyIiIgBwdXVl5syZFC5cOMUyi4iISNqjCWEqde3aNd588837ZXDIkCGsW7fuX8vgnTt36NevH7Vq1SIiIoKCBQuybNkyAgMDVQZFRETksTQhTIXCw8NxdXXl4sWLZM+enQULFtC6det/PXbbtm14eHhw5swZAD788EOmTJnyn08di4iIiPydJoSpzMKFC6lTpw4XL17k1VdfJTQ09F/LYFRUFF5eXjRq1IgzZ85QtGhR1q9fz8KFC1UGRURE5KmoEKYS8fHx9O7dmy5dunDv3j3eeecd9u3bR7ly5f5x7Lp16yhXrhze3t4AdO/enePHj/P222+ndGwRERFJB3TJOBWIjIykXbt27Ny5E4Bhw4bx1Vdf4eDwcF+/du0affr0ISAgAIBSpUrh6+tL/fr1UzyziIiIpB8qhDa2b98+3NzcuHTpEk5OTixatIj33nvvoWMMw2Dp0qV88skn/PHHHzg4ONCvXz9GjBhBtmzZbJRcRERE0gsVQhvy9/ene/fuxMXFUbp0aVatWvWPnyBy6dIlevTowZo1awBwcXHBz8+PatWq2SKyiIiIpEO6h9AG4uLi6NGjB2azmbi4ON577z327dv3UBk0DANfX1/KlSvHmjVryJgxIyNGjCAsLExlUERERJKUJoQp7MqVK7Rt25bdu3djMpkYMWIEQ4cOfeh+wTNnztC1a1e2bNkCQPXq1fHz86N8+fK2ii0iIiLpmCaEKWjv3r1UqVKF3bt3kzNnTtasWcOXX355vwwmJiYydepUXFxc2LJlC1mzZmXixIns2bNHZVBERESSjSaEKcTb25tevXoRHx+Ps7MzK1eu5LXXXrv/+YkTJzCbzezduxeABg0a4OPjQ6lSpWwVWUREROyEJoTJ7N69e3Tt2hUvLy/i4+Np3bo1ISEh98tgfHw8o0ePplKlSuzduxcnJyfmzJnDli1bVAZFREQkRWhCmIwuXbpEmzZtCAkJwWQyMXr0aAYPHozJZALgwIEDuLu7c/jwYQDefvtt5syZQ7FixWwZW0REROyMCmEy2bVrF23atCEyMpLcuXMTEBBAs2bNAIiNjWXEiBFMnDiRxMRE8uXLx7Rp0+jYseP9sigiIiKSUlQIk5hhGMyePZs+ffqQkJCAi4sLK1eupGTJkoC1KJrNZk6ePAlA+/btmT59OgULFrRlbBEREbFjuocwCd29exez2UzPnj1JSEigXbt27N27l5IlS3Lr1i0++eQT6tWrx8mTJylcuDCrVq3i+++/VxkUERERm9KEMIlcvHiR1q1bs3//fhwcHBg7diwDBgzAZDLx008/4enpyYULFwAwm81MnDiR3Llz2za0iIiICCqESWLHjh20bduWa9eukTdvXr7//nuaNm3Kn3/+Sb9+/Zg/fz4AJUqUwMfHhyZNmtg2sIiIiMjf6JLxczAMg+nTp9O4cWOuXbtGxYoVCQsLo2nTpgQFBeHs7Mz8+fMxmUz06dOHo0ePqgyKiIhIqqMJ4TOKjY3Fy8uLRYsWAdCxY0d8fHyIjo6mbdu2rFixAoAyZcrg5+dH7dq1bRlXRERE5JE0IXwG58+f54033mDRokU4OjoyefJkFi1axIoVK3B2dmbFihU4OjoyZMgQDh48qDIoIiIiqZomhE9p69attGvXjuvXr5M/f36WLl1KqVKlaNGiBRs2bACgUqVK+Pv78/rrr9s4rYiIiMjjaUL4hAzDYPLkyTRt2pTr169TuXJl9u3bx88//0y5cuXYsGEDmTNn5ptvvmHfvn0qgyIiIpJmaEL4BO7cuYOHhwcBAQEAdO7cmQEDBvDRRx+xc+dOAOrUqYOvry9lypSxZVQRERGRp6ZC+Bhnz57F1dWVw4cP4+joyMSJE4mLi6N69ercvXuX7NmzM2bMGHr27ImDgwauIiIikvaoEP6HTZs20aFDB27cuEGBAgUYO3Yss2fPJiwsDICmTZvi7e1NiRIlbBtURERE5DmYDMMwnuXE6OhocuXKRVRUFDlz5kzqXDZlGAYLFy5kxowZWCwWnJ2dqVSpEsuWLSMhIQEnJyf69+/Pu+++i8lksnVcERERkX/1pH1NhVBEREQknXrSvqab3kRERETsnAqhiIiIiJ1TIRQRERGxcyqEIiIiInbObgphgwbQt6+tU4iIiIikPumuEG7fDiYT3Lxp6yQiIiIiaUO6K4QiIiIi8nTSZCE0DBg/Hl55BbJmhYoVYcUKOHcOGja0HpMnj3VS+NFHD86zWOCzzyBvXihUCIYPf3jdyZPBxQWyZ4dixaBHD7h9+8Hn8+dD7tywcSOULQs5ckCzZnDlysPrzJtn/TxLFihTBmbNSvI/AhEREZEkkyYL4RdfWEvX7Nlw/Dh8+il88AGcPw+BgdZjfvnFWtSmTXtw3oIF1rIXGmotlF9/DZs2PfjcwQGmT4djx6zHbt1qLZB/d+cOTJwIixbBzp1w4QIMGPDgcx8fGDoURo+GEyfgm2/gyy+t64mIiIikRmnuJ5XExED+/NayVqvWg/c9PKxlrWtX65Twzz+t07y/NGgAiYkQHPzgverVoVEjGDv23/davhy6d4c//rC+nj8fPv4YTp+GkiWt782aZS2WV69aXxcvDuPGwfvvP1hn1Cj44QfYs+c5f/MiIiIiT+FJ+1qGFMyUJCIi4O5daNr04ffj4uD11//73AoVHn5duDD8/vuD19u2WSd6EREQHQ0JCda9YmKsk0WAbNkelMH/XePaNbh4Ecxm8PR8cExCAuTK9XS/TxEREZGUkuYKocVi/XX9enjxxYc/y5wZfv310edmzPjwa5PpwXrnz8Pbb0O3bjBypPU+w127rOUuPv6/1/hrxvrXWj4+UKPGw8c5Oj7+9yYiIiJiC2muEDo7W4vfhQtQv/4/P7940fprYuLTrRsWZp3kTZpkvZcQYNmyp1vjhResJfXMGejU6enOFREREbGVNFcInZysD3F8+ql1IvfGG9bLu3v2WJ/6bdLEOrVbt8468cua1fr+45QsaS2EM2bAu+/C7t0wZ87T5xs+HHr3hpw5oXlzuHfPWjb//BP69Xv69URERESSW5p8ynjkSBg2DMaMsX69y1tvwdq18PLL1gndiBEweLB1Yter15OtWamS9Wtnxo2D8uVh8WLr+k/LwwN8fa0PoLi4WKeY8+dbs4mIiIikRmnuKWMREREReTJP2tfS5IRQRERERJKOCqGIiIiInVMhFBEREbFzKoQiIiIidk6FUERERMTOqRCKiIiI2DkVQhERERE7p0IoIiIiYudUCEVERETsnAqhiIiIiJ1TIRQRERGxcyqEIiIiInZOhVBERETEzqkQioiIiNg5FUIRERERO6dCKCIiImLnVAhFRERE7JwKoYiIiIidUyEUERERsXMZnvVEwzAAiI6OTrIwIiIiIpJ0/uppf/W2R3nmQnjr1i0AihUr9qxLiIiIiEgKuHXrFrly5Xrk5ybjcZXxESwWC5cvX8bJyQmTyfTMAUVEREQkeRiGwa1btyhSpAgODo++U/CZC6GIiIiIpA96qERERETEzqkQioiIiNg5FUIRERERO6dCKCIiImLnVAhFRERE7JwKoYiIiIidUyEUERERsXMqhCIiIiJ2ToVQRERExM6pEIqIiIjYORVCERERETunQigiIiJi5/4Pm6njZB4fypUAAAAASUVORK5CYII=\n", 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", 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\n", 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", "text/plain": [ "
" ] @@ -610,7 +610,7 @@ "outputs": [], "source": [ "import itertools\n", - "from openfe.setup import Network\n", + "from openfe import LigandNetwork\n", "\n", "def scored_network_planner(three_heavies, two_heavies, mappers, scorer):\n", " mappings = []\n", @@ -630,7 +630,7 @@ " \n", " mappings.append(best_mapping)\n", " \n", - " return Network(mappings)" + " return LigandNetwork(mappings)" ] }, { @@ -678,7 +678,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -800,10 +800,10 @@ { "data": { "text/plain": [ - "array([[ 0., 26., 26., 0.],\n", + "array([[ 0., 0., 0., 0.],\n", " [ 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0.],\n", - " [ 0., 26., 26., 0.]])" + " [26., 26., 0., 0.],\n", + " [26., 26., 0., 0.]])" ] }, "execution_count": 28, @@ -833,10 +833,10 @@ { "data": { "text/plain": [ - "array([[ 0., 26., 26., 0.],\n", - " [26., 0., 0., 26.],\n", - " [26., 0., 0., 26.],\n", - " [ 0., 26., 26., 0.]])" + "array([[ 0., 0., 26., 26.],\n", + " [ 0., 0., 26., 26.],\n", + " [26., 26., 0., 0.],\n", + " [26., 26., 0., 0.]])" ] }, "execution_count": 29, @@ -866,7 +866,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -877,11 +877,11 @@ } ], "source": [ - "from openfe.setup import LigandAtomMapping\n", + "from openfe import LigandAtomMapping\n", "\n", "propane_to_ethane_dict = {0: 0, 1: 1, 3: 2, 4: 3, 5: 4, 6: 5, 7: 6}\n", "propane_to_methanol_dict = {0: 0, 1: 1, 3: 2, 4: 3, 5: 4, 6: 5}\n", - "custom_network = Network([\n", + "custom_network = LigandNetwork([\n", " LigandAtomMapping(propane, ethane, propane_to_ethane_dict),\n", " LigandAtomMapping(propane, methanol, propane_to_methanol_dict)\n", "])\n", @@ -907,7 +907,7 @@ "outputs": [ { "data": { - "image/png": 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EFDCQmJZGNe6dycWfNIVUfp0M1XXDxyqt8feLRbPHdhX0J+lgEIQAIC1qtXrw4MHx8fEJCQmirybkqip1F5NrzsbrUs5rzyZwlc22vOEYJl/hddtLlX83iPHqbtGHeQWTppB4nqryyLtn3cvnbki96RqCEAAkJzo6Oj4+Pi4uToQgbGnY15wKmYucOCPJb72kr6p9oHSz6KNV7g3fSEREhRVSv0aIIARLcbXnZDre0iuABqKjoz/66KPmLhOeO3cuNTU1MDBw5MiRcrm8yX1axfJhnxl6RiGj2yOT1dc+kCstOoRcVfvAeFvyGViLktiBIQihWSzJTrj1/cVt8GmX0Fwnb8PNf1PVpce6LffyDQ7V5txdnXxP5V9BhjKxywRotbqJow2W1ev1+kceeeTAgQPBwcFZWVl9+vT55ZdfOnfu3OoPaOuwzwwlZ2QbTG9UqGsfGLUWHcLwdx8Z5W3zS5UKqc+aRBDayJ49e77++uvi4uKBAweuXLmyR48eYldkjpZjtvrGfOrzD5aRVctUfL2xIE9UKnc97dr7nEvwWr8pw2rSVpb+FCJuuQCtFBwc3Llz55ycnAbL6tevX3/o0KHff/99woQJN27cGDVq1KJFiw4cOGDJMQUZ9jWniuUuVhYbz39LpelUkkZTPieZ4tapTm25RUfR/b2b0r3+ywEelg0oHReC0BY+/PDDZcuWzZkzZ9y4cTt37oyOjv7rr7/a8memTRy9VPzo6R6Vvt1qZOb+eWgZJ2LolGvodHXIk9svfPBkX7VSgJNIALYxYsSI77//PjY2tn4Q7ty58/777zddOOzWrdvy5ctXrFhRVFTk5+fXxCGsMOyrw/KUqtUnVeuSqnVnq3WZWgNPRLS7dnP5TfLuQV7BtU/LMi06aGkGEZFcSe63ffmEd/cUpugOC0FodRqN5pVXXlm4cOGmTZuIaObMmb169Xr//fc3bNggdmlNeOeH9LcOpGn0cpJZlGocyWpkyi9PZB+9VHzkpRFdfZytXSGAIKKjo7///vu4uLgZM2aYXmFZNi0tbcmSJXX7jBkzxvRiXRBae9h3TqNLrNJd0OgTq7Tl7K1WagqG6aN20gUMTus0mg8YSK4BRETeISRXEqungvPE6lu4UliSSroKIiKf3rVdZoiIyJXTjlEXC/hTdES2CMJjx44VFxcT0dSpU83slpKScunSJSIaNWpUYGCgDQqzjbi4uIqKiqeeesr01NfXd8qUKf/73//ErapJK75J2fzbjdsaOFmmRs9mFmoiXjoR/9ad3XzVLb9BJLm5uadOnSKiAQMGhIWFNbcbz/P79+8nIj8/v7Fjx9quPrChxsvq8/PzWZb19/eveyUgIICIrifG9y/KsdKwL0NnuKDRJVbpEqq06bXDvr8/3Uk+wEUV4aaKcFUNclUpGea4q//SLndWyf7+c1PmRHeMpGvHSF9N1/+gkBhzH3b1x9oHwbf9ShtIEf75v3XDt6n6DhTq5+pwbBGEL774oum3jWVZmazZq7Lffffda6+9RkQ//fTTvffea4PCbOPmzZtEFBoaWvdKr169TN+zduWTX69v/u1GwzucWczI8sVVhjFvxJ1/b7S7s52eaYiPj3/44YeJ6K233nrppZea281gMJh2GzVq1J9//mm7+sCGTMvqz58/X1VV5ebmRkRKpZKIDAZD3bAvK/YkEVVseKvA00WozzUz7FPLmH4uygEuqkhX1TA3Zz+nhmdl7qy+LOdv/xcadj9d+4OIp7M7qVMEqX2a/tS8s5T5OxGRyoN63FoxwhBF1GR4VebnPjOry/a9Tt0lernfTr+wHElpaSnDMKZ/aSZeXl6VlZUGg8HJyUnEwupLvlH5wjeX2zAWrI/l+Pxy3Zytyd8tHSpUYQBWUresPj4+fvzYMfrMdOWl80qF/PL7b2Z+8Ipp2HejSktEQU7t+p5scdgX4eYc4aoa4KI0DfvMHEpO3IKSXz/yvffW9XvfMAq9j67+SNoy+m0l3bmKfHo2fNuNPynug9qB7LDFt+aaEqk5/bNFPxIRW1qSs/CJLp/vUwR2as8P20EhCK1Cl3I+64n7TY/l17J5nr/06H1eytrYyzx/1VulzJ9t7kSxLfHEPMJPq6Fm/pZsDa2B++l01ndPbB1JOe0/muCKs/JMDyr2fpWVcrrudeeBQ/xWvi5SUSAa07L6X1Y+G6xkuRoNEfVTKX7PvDG7d+09HP6oqFHLmBDnVv/B2p5hn3lPlhz7zPuu2yayhT9FlTmU+xdV5tAvz1KnCAoKJ7UPcSxV51NWXO1NmohowKPUbVTd+xQ8N6QmI7Im3fTUmJ+bu3R2l217ZB6SmzuDILQKTlOtSzlveuxfpSWiS+fORbjVrma9erOwk5zqdhDdEbeBNzu78AItJdKQ4mXtsJ8z3xLmcIIylNeuozIW5utSbi29knt6NfMOcGRRUVEfffRR/NX0uT0DTK88FeCxNLNwS175ND+3v6p0OwsqHvN3d5a13ERCwGGfec68YX3u5wu6LLiVhQoVjX2Vzn1BV/5LnJFyEignoeHbVJ40ZHaDi4gq3vBe3pf1X9GnXcl9ZlanLV/L1IKdCu4QWheEOp3uypUrHMeFhYWp1fY7J8KuhLuqfBXyb4oqItz8iSjPYDxSXjMn0I5a/X7iO7FaJuRszywnn3PO3Qdrrwt4TDt38+bNgoKCuqeenp69evUSsR6whGm+zF/VWv7vhkn3ertm642f5JWvzSlVMMyDPq7Pd/Zu7u3WG/aZN0qXPk92fpssXMP9fViZgobMod6T6NpRyk2kqjzSVRAjI5UnefegzpEUPJ6cbss2NW/4KGd744YY2gtn8/41v9OHOxi7uXBjA60Iwr179z7zzDNlZWVE5OLismXLlunTp1utMMfhxDAvdvV5/lphJcuHqp1+LKkOcJLPDrCXICyUe1x27iLsMXWMcp9nlKSC8JVXXtm5c2fd0yeeeOLLL79sfnewC3XL6m/oDN1Vtd/7cwM9ZwV4FBhYH4VcfftY0PJh32BXlVM7hn2Nyf0CVH0HqMOHOYdHqvoN/ECp0my/8M2p7Gptvev6boE04FEa8GiLR1Nz+lfz94ytutjk1prTJwpeWxH45npqfm6jg7E0CK9fv/7YY4+9++67S5cuJaL58+fPnj179OjRnTpJ8cpqa03xce2slH9fUn1Na3jI122Gv4eH3F5+w0659lHwrI4R8q8/lmGOug+g/D0CHtPO5ebmLl68eOPGjWIXAq1jWlafVK2rC0IicmKYLsra78ZKlku2YNg33N3ZVyHksI9RKJS9+zoPjlD1HageOlzRueFtkrbOGdDFW7XmYEar5rjJiHPmDOtzd95dmcwTna3WDXFVNd6t6vAPcg9P6Vw4tzQIu3fvnpKSUne259///vfnn39+5syZKVOmWK02hzLMzXmYmz0uNk9S99DImviX0E6Fcs8amVLN6Vve1SHk5OSMHj1a7Cqg1UzL6pOqdQ/41M7rtpth3yBG2ULns1cf6j2ur+/jm86WVxuqLFj45MLpeunzPsj+vJuhkIhW3yjeW1S5rof/ZO8mbm1f/t0ueUCg9+xFbftZOpZWnBptfM3D1bWJ/3xmLFpk7r9pYmJi/adGo7G6urpVx7cHpppZnirr/eVoz67IvPi6VrzmmRYwsTVkaPn/i4rTXZV5hRjy2ledYFxkjLzed9bv5ZpcvbHuqUJ32mXBgrqnHNfq/3d5eXkuLi6HDx/W6/VRUVGmhdhg/0zdtxOqdCcra0zDvoQqbYXdDPtaNLavT8aGcduP3Xz7h/QyjVFv5PTGhr+9zryBiEJ0+cuKfpxQdWuOXpjaiSNaca3IQy4b69HEnI+ST9bKvXw8Hmz5XGtHx/Bt6pLw3nvvvf3229evX/fyanm63ciRI5u73UmTTAvq9+/fb74Tjd2Sy2Rs679JOwZGVntH7I7m054Bd3m6/F6umZ9e0PLef7NwQT3HcT4+Pi4uLiEhIdevXy8sLNy0aVNdLyGwQ0aj8fz587GxsSdPnty9ezfd/j0YrHIKd1UNcVUNcVWFqZ3kog77LJeQUX74XOEfl0vS8zVVOiMR+bg59QlyHZ55bGTCnjsMRY3f8l526bb8crWM2dkrqG5a+21k8sB3PnK723E6nDSpLcsnLl++/NZbb73yyiuWpGB9MTHmOgBlZGSkp6fXPXVycvLwsJcZJZYzGo0ajUbOkLqDXGfWMEqOsaxUo5Z4jhTOZMH+DPHOnEFO9pKaitu/y7qrnO5Q3frll3l6qfoMqHvK8/xvv/1m+cFlMllOTo5arWYYhmXZZcuWLViwYOLEiXbbV12aysvL4+PjT5w4kZiYeOLECdO8vzp9XZTjPNRDXFXhriof6w37IkYoOgk8N61OZIhnZIjn6kav88bwvGXnNbHHG7/lhS7eZUZ2b3HV3PT8r0OD+qobpTLHFqxeJnNzd4ly5DP/rR4RXr169a677ho/fvzOnTvN9Eurr25EaL7F2uuvv+4ALdaOHj06YcKEEe7OX/+9JtfOzez2zEmXPhbt+usLVHiR7l5DAf1b3NeV0x249m4PfSuGXzZQNyJ8rrP3oqBbq4ZdokZ3+mRX3VO9Xq9SqaitLdauXr0aFha2b9++hx56SIiqoY1Ylr18+XJiYuLJkydPnDiRkpJS/+uuU6dOo0aNuvPOO//88899+/b9q7P3wiDBFpJbb9jXNry2JmfRk9pziY03sTw9m1lwuEwT6CTfE9apq7KJ0ZHMxbXz1t0O3Iy0dSPCgwcPzpgxY+rUqVu2bLEwBcHOhWlzYl3COKHvO69jFF0MJcIe057Vv7+raajREc9nOAAzwz5XV9fw8PCIiIhRo0aNHTu27jpuQEDAvn37kqp1zRzSIjYb9rUN46wOWr8t5+np+sy0BpvkDK0N9i9Oy4+v0s5Mzd8TGtR41SOnqc5dMrPL9r1OwY36tzkES4OQ5/k1a9a88sorb7zxxsqVK61aE9jSUG3Gd+zIKvnfM1qr8in1R1L7Up8H2nPYLoZSJW9seT+HcOTIkSVLlqxbt2706NFZWVnPPvtsz549x4wZI3ZdkmDhsC8iImL48OHKpkZmpvkySdW6umX1FrK3YZ95ci/vTp98mT3nYWNudoNNzjJmW8+Ax1PzLmr0c9MLvuod6NpofRdbVpqz6ElHbUZqaRB+++23q1atIqJVq1aZHhDRv/71r7Vr11qrNLCJ6OoretOtB6vy6ewOuhlLciV5BbcnCBU8G1N1VrAS7d6QIUMiIyMffPBBnU5HRGPGjDl8+HCT37kgiDYM+8zo0aNHUFBQXl5e/WX1TbLzYV+LFIFBnTd9lT3nYbak4Q0I3eSyz3sFPno177xGNz+jYEevwMZ94Iz5ubnPzOry2R6Zh6O1JLQ0CCdPnlx/JouJp6fkerM6Hk9WE6nJiHUN42UKChxMEfPpwre1d7JuKwVxU8tPt7yfo/D29t61a9emTZtu3Ljh5+fnGGsndu/efeHCBSJauXKlmdO8iYmJpnuKPfTQQxEREVYqpv3DPvOioqIOHDjQYFm9Scca9rXIqVtwpw+25yx4jNM0XDTlo5Bv7xn4yNXcuErts5mFG3sEyBsNkPXpV3Ofme14zUgtDUI3N7f6NxICR7Kk+NBZdbDGxZd6CzBHieH5/tqbvXX2ePcJq3Jzc+vXr5/YVQhm3759+/btI6KFCxeaCcKkpKS3336biHr06CFsEAo77DMvOjraFIQP+Lh19GFfi1T9Bwet25r77FO8vmG/iztUis97BT52Ne/XMs1rN4vf7Obb+O3aC2fz/jWv04efO1IzUtx9wirk3r4daOXNBKJBBVXxBiXLCzBlxlnGvRuc7SZEpgrOOe0apf9MRMqeoW7Rt761lb37ilcU1LL2sM8MU/ftZFfvrl/9Vxnal5E7+BejevidAa+tzV+9jBqteA5TK7f3CnwyNW93UaWvk3xZpybOgtacPulgzUgd/P+3WJQhvQPf+0TsKlrhm0LNwFV/3tbAt9Qls44AABRASURBVE1cVPKnxnS/e9ZkQaoSnNd//0v7fyYit39ODmz+DvVgM7Yc9pkRGRmpVCov3cw23NFD5egpaOL2z8lcZXnhOy833hTuqvqwh//CjIKNuWWeclmTdwhwsGakkvhfDi3q4e/yxfzBM7ac01jQsbA5SoUsrJPruicwuoJmiTjsM0OtVg8aNCghISE+Pn78+PG2+VDReTz8hLGosHTbR403TfB0WdPd7/lrRW9nlXjIZVN9m7guVv7dLrl/gPdTi61fqdXZIgh37NhRWVlJROaXHs6dO9e0jj40NNQGVUEDU4cH5ZXrXth92bLGow2pnGTdfNW//XuEUmG/Z0vGjBlz5swZIura1VxTRycnJ9Nu7u7uNqrMoVk47Bs3bpy/v79YRUZHRyckJMTFxUknCInIZ8FyrqqyfPfnjTdN8XErZ7k3bpa8dKPYz0nedDPSTevk3r4O0IzUFkHYp49FjUs6d+6MllTiWhzT3cdVMSOWMbbyWqGrSj4k2PPH5yM9Xez6HIOXl9ewYcNa3I1hGEt2g+bY57DPvKioqI8//rhVXZEdg99zq9niwqr//dh40wx/jzw9uzW/fElGwee9AiMb3z+H5wvfXi1z9+hAUyKaZNdfW2Bj69evLygo6K24marJZ5I+M7gGUegk829x4lkFsasfGPjC5BCZoL2JoSOKjY1dtWpVQkKCpt7sfDc3t+HDh0dHR0dFRUVFRfn5+YlYYXNM82ViY2PrNwmSBJks4M0NXFWl5tQfjTeu6OJdxnLfFVXOTS/4xnGbkSII4ZasrKysrKwBfUNDVcyNG2cy+XAdp2cZmZ5p+HvC8OTKa1lG9kD56WfKf4++/4woBYO9USqVx48fJ3sd9plRt6w+PT298S3nHBujUAS9v7nJZqQM0Zt3+FYY2cNlmjlp+d811YyUNxjyVyzs0M1IEYRwy/r1600PSrd+WPLpB0QXU6+/f8RtwAmXPpnKgALiOCJXrqarLmdwTea46oujq1PUnJ5RCn9fX7AfkyZNcmp+xVhR0W039xk8ePDBgwftdthnnmlZfWxsrNSCkEzNSDdsy5nTdDPSDT38q9ILTlTUOGozUgQhmNNbl9NblzO/+H9E9GhNXgLRZ1mbh5U1ulQA9q26ujozM9PX17dTp1Y3ijx37pzlOysUikmTWjidbrdMy+pjY2OffPJJsWsRgdzTu9OmL7OfaqIZqRPDbOzhb2pGOjst/+vQII/mmpHu+I8iqONN9UAQAjgylmVXr1790Ucf6fV6o9EYHR29e/fu7t27W36ExYsXm5k9e+7cuUOHDglRqfhMlwnj4uLELkQ0ioCWm5Gm1OgXZRRs7xmokjXVjHTp7I7YjBRBCODINm3atHXr1gMHDsTExKSnp8fExCxYsKBV0bVq1Sozq00+++wzhwnCyMhIJyen5OTkqqoqyXaUdOoW3OmDHTkL/l+TzUh39AqcfqW2GeknIf7yRrOKapuRbv5a5tKRmpHa75IvAGi/+fPnnzp1KiYmhoh69uw5d+7cY8eOsWx7Wwg5JNOyepZlExISxK5FTKr+g4LWb2uyvXhXpWJnr0BPuey3cs2rN5u+4aj2wtm85+fxBoOVyxQSghDAkSmVyrCwsLqncrlcqVRKa3lAa9QtohC7EJGph40MfPsjkjWcFENEoWrl9l6BahnzbVHlhpyyxjvQ381IGzcytVsIQgAJ+eGHH8aMGWO+x5OUIQjruI7/p//K15rcZGpGKmfok7yyHQUVTe5TdfiHojVNv90O4d8DgFR8+umnZ86ceeedd8QuxH7VX1Yvdi3i83j4Ce95zza5aYKny5ru/jKid7JK9hVXNblP+d4vS7dvtGaBgkEQAkjCnj17li5dumPHjgEDBohdi/0yLasvKipqfB9yafKZv8zzsaea3DTFx/XlO3x4opduFB8rr2lyn5JN68q//cKaBQoDQQjg4DiOe/XVV2fNmrV161ZprpBrlaioKMLZ0Xr8lr/k9o+m14Y+6e8xP9DTyPNLMgsSqrRN7lO07o2q3362ZoECQBACOLLi4uL77rtv27ZtR44cmTlzptjldACmIJTyasKGZLKANze4jBzb5Mbnu3hP93PXcvzc9IKUmoa3vCci4riC1cs0scetW2T7IAgBHNmiRYsOHz6cm5s7cuRI5m+nTp0Suy77hfkyjZmakToPjmhiE9Gbd/jGeLlUsty89IIarolrq7zBkP/CIt2l89avtI0YXBMW1tGjRydMmDBu3LijR4+KXUvb/d1r9DaPXs1LqNLuDg0advvdWBilKiT2sg2rg1ZIT0+vf/8/k7CwsBYXjPM8b/pyaHGKKcdxluzWUdTU1Hh6enIcV1ZWJtll9U1iy0tznn5En5HaeJOB5xdlFEzxcZvk7drc2+Ve3nbbjBSdZQAcWc+ebfzeMY0dLdnTYSLQxLSsPjExMSEhYdy4cWKXY0fknt6dPtnVXDPSrT0DTb8uZUZue0E5EfV3UU70upWLbFlpzqInuuzYV9eMdN26dSUlJa6uri+++KJtfoTmONRvMABA++HsaHNMzUjlPr6NN9X90VTKspvzyjfnlf9a1nAqqTE/L3fpbK6i9hTFJ5988vbbb2/YsMGKFVsGQQgAcBsEoRmmZqRtbiWqT7+as2RW40am4kIQAgDcBsvqzTPTjNQSuovn8p6fx+ubmmIqEgQhAMBt6pbVZ2RkiF2LnTLTjNQSNadPFry+QtiS2gNBCADQ0IgRIwhnR81yHf9PvxdebfPbqw7/l6uxlxOkCEIAgIZwmdASntOe9Jm/rG3v9Zq1QKa2l3sWIggBABpCEFrIe96zzTUjbRbD+C5/yfeZldapqC0QhAAADQ0bNqzubvVi12Lv/J5b7T7pIQt3ZuTygFfXeD3xtFVLai0sqAcAaAjL6luBYfxffo8tLdGcPFb/5T8qNA9ezr1tTxnj1LW77I336I33iCgnJ8eGVZqDIAQAaEJ0dHRiYmJsbCyCsEWMQhG0ZlPOohnacwl1L5YZuTKjruGul6/YtDLLIAgBAJoQFRW1ceNG3IbCQoyzOmjD1pynH6GUS6ZXejs73eVVOx2GUbu4/WOS3Pu2ljRbtmxp3AhXFAhCAIAmmObLnDp1iud5C9uuSpypGWn6I/cRZRNRPxfV8529icipa/dOn3zh1LV7g/337NljJ0GIyTIAAE0ICQnBsvrWUgQEBby6tv4rqr4Du+zc3zgF7QqCEACgaVhW3waKzl3qHqsjozt/+o3c20fEeiyBIAQAaBpWE7aHU+eunT7eKXPtAPd0RBACADQNQdgezuGRbW7MbWMIQgCApmFZvUQgCAEAmmZaVs+ybEJCQst7Q4eFIAQAaFZUVBQRYTWhY0MQAgA0C5cJpQAL6gEAmmUKQowILRcaGsqyLBG12IUgPT3dTpoVYEQIANAs07L6goKC9PR0sWvpMGQymUwmazHhGIaxZDcbwIgQAMCcESNG/PDDD7GxsT179hS7Fkm4cuXK+vXr67+yadMmuVxuvU/EiBAAwBxcJrSx5OTknTt31j1Vq9VWTUHCiBAAwDxMHLWx7Ozsrl27fvrppzb7RIwIAQDMGT58OJbV21Jubm7nzp1t+YkIQgAAc9Rq9cCBA41GY2Jioti1SEJubm55efnTTz99zz33LFu27Nq1a9b+RAQhAEALcJnQlsaPHz98+PDhw4ffc889R48ejYyMvH79ulU/EUEIANACBKEtzZ49+7PPPps3b97SpUuPHTtmNBo3bdpk1U9EEAIAtMAUhOg4anve3t4hISE3btyw6qcgCAEAWhASEnLs2LErV66IXYgknDp1Sq/Xmx7n5eWlpqb279/fqp+I5RMC8/T0jI6OHjBggNiFtAPHapOTGr8cpnZied5NXv+PJ56I4Q16Q9Z1p67dbVYggO2NHTtW7BIkoaysbMqUKV26dJk6dapard66dWtgYODChQut+qEMz/NW/QDoWNiigrxlc3SpV3ijwfJ3yZyd/Va94T55mvUKA7CxgoKCHTt2ENHQoUP/8Y9/mNnz448/rq6u9vLyWrBgga2qc2RZWVkbN240LdwcM2bM8uXLvb29rfqJCEK4xXA9M3vOw1xFJc+2IgVNGLXac/pM36UrrVEYgO0lJSUNHTqUiBYsWLB582YzewYGBhYUFAQHB2dmZtqqOhASrhG23YkTJ/r379+/f/8WZzSZdnvsscdsU1jbGPPzsp+aypaV8azh2czCiZey70vJMf+WXYUVEy9lT7yUnVSt42tqyr/7omTrB7apFgBAKLhG2HaVlZWXLl0iooKCAjO78Txv2s3d3d1GlbUebzDkLnmSraokniOiGzpjmtbQ4l9JRQY2TWsgomqWIyK+pqbsi63O/Qa5jJpg9YoBAASCESEQEZVu/9iQm01GYzuPw2trClY/x1VVClIVAIANIAiBjIX55bs+42tqBDkap9eWfLpBkEMBANgAghCobNdWnmOFOhqv01Xs+xaDQgDoKBCEksexlT98xxv0Ah6SkTFVvxwU8IAAANaDIJQ6bXISESPsMbkaTeXPB4Q9JgCAlWDWqNRpk87weq3gh9WlnCeeJ0bgiAWwvYMHD5pvrlZaWmqzYsAaEIQCqKyszM7OFruKNtJeSuYNzS6fzzeYu3ZYzTXfjYFhjAX5isCg9tQGYA+ys7M77j9wsASCUADr169fv3692FW0kTEvt7lNHNGd52+27bCMwoktLkQQggMYMGCA+UajO3bsqBFo0jWIAkEoeYJOk6mPN+isdGQAWxo1atTGjRvN7LB3714EYYeGIBTA008//fTTT5vZISoqymbFtBajcm5uk4xob1gnM+/dXVT5n+KqZjbyZo4MAGA/EIQC6NKly4gRI5rbaudtzeWd76ALZ5vbOthVZea9v5drmtvEG/SKAJwXBYAOAMsnpE49YDCjNJd2bcMoFHIfP8EPCwAgOASh1DlHjCCF8CcGVIMiBD8mAIA1IAilThXWn1EJPCKUubp5THpI2GMCAFgJglDyGMbzkZmCT2xxHf9PYQ8IAGAlCEIgr0dnMQq5UEdj1C5eMxdgyigAdBQIQiCZu4fPkpUytQtRuye4MiR3d/d60txiEgAAu4LlE0BE5DntierfDmnPJ/L6dq2vZ1TqoLWfWmMaKoCNDR48uKysjIhULV1ET0tL4zhOJsO4oqNCELZdYGDg5MmTiSgsLMzMbgzDWLKbyBgmaP2nNx+fxObl8kbDMDeVv5Nc3lLH7J7OygmeLkTk6yQnIsZZ7f/iW6r+g21QL4C1yWQyT09PS/Z0d3e3djFgVYydL/cGW2JLirPnPmLMzeZ1rb4fBeOs9lvxqscDj1ijMAAA60EQwm24Gk3h6yuqTxzlLe6dyChVMrVz4Ptb1BH220kOAKA5CEJogubk0cK3X2YrynhNtZndZEoVT4z7pId8l66UuXvYrDwAAAEhCKEZHFd94mj5ni+0f51mnJREVHvbQrmcUSh4nVbu6+8+aarH1McU/oEilwoA0A4IQmgBzxr1qZcN1zPZ0hJer5N7eskDO6lC+6KVKAA4BgQhAABIGha+AAC0y/vvvx8TE7N48WKxC4E2wogQAKDtEhMTo6OjBw8eTETx8fFilwNtgSAUHs/za9asKS0tveeee8aOHSt2OcJgWfaHH36Ii4tjWXbo0KFTp051dkY3UZA6o9EYFRU1ZMgQV1fXkydPIgg7KJwaFd6uXbvefffdzZs3x8XFiV2LMMrKykaNGjVr1qzU1NSysrL3338/NzdX7KIAxLd27drr16+/8847YhcC7YIWawIrKipasWLFO++88+abb4pdi2BWrlyZmpoaHx9v113iAGwrNTX1jTfe2Lp1q58fZlB3bBgRCmzx4sU9evSYN2+e2IUIRqPRfPXVV8888wxSEKAOz/MLFy4cPnz4448/LnYt0F4YEQrp559/3r9/f3x8vCP1ob948aJGo4mMjNy5c2dSUpK3t/esWbOCg4PFrgtATNu2bTtx4sTZs2cZpqXm9GD3EISCqaioWLBgwXPPPRceHi52LUIqKCggonnz5vXp06dv37579+7dsGFDbGxsv379xC4NQDTr1q3T6XR9+/at/yLDMFqttsXbNoG9QRAKZuXKlQzDvPzyy2IXIjDT7NANGzZMnz6diLRabVhY2Jo1a3bu3ClyZQDi+fXXX41GY93TN998MzEx8cCBA0qlUsSqoG0QhMJITU3dunWrv7//uHHjTK8UFhZ+/PHHeXl5GzZsELW09urSpQsR+fj4mJ46OzsPGjQoPT1d1KIARNatW7f6Tz09PVUqVUhIiFj1QHsgCIXh5+f37bff1n9l/vz548aNe/jhh8UqSSghISGenp6HDh26++67iUiv1//1118xMTFi1wVgR+66665evXqJXQW0EYJQGN7e3tOmTav/yrJlywYOHHjnnXeKVZJQlErlihUrXnvtNbVaHR4evmPHjpKSkuXLl4tdF4AdmTx5stglQNshCK1l9OjRDjO18sUXX3R3d//yyy+//fbbfv36HT9+3NRQCgDAAaDFGgAASJrjLHcDAABoAwQhAABIGoIQAAAkDUEIAACShiAEAABJQxACAICkIQgBAEDSEIQAACBpCEIAAJA0BCEAAEgaghAAACQNQQgAAJKGIAQAAElDEAIAgKQhCAEAQNIQhAAAIGkIQgAAkDQEIQAASBqCEAAAJA1BCAAAkoYgBAAASUMQAgCApCEIAQBA0hCEAAAgaQhCAACQNAQhAABIGoIQAAAkDUEIAACShiAEAABJQxACAICkIQgBAEDSEIQAACBpCEIAAJA0BCEAAEgaghAAACQNQQgAAJKGIAQAAElDEAIAgKQhCAEAQNIQhAAAIGkIQgAAkDQEIQAASBqCEAAAJA1BCAAAkoYgBAAASUMQAgCApCEIAQBA0v4/CvxIppQmhYEAAACHelRYdHJka2l0UEtMIHJka2l0IDIwMjIuMDkuMQAAeJx7v2/tPQYg4GWAAEYg5gBidiBuYGRTSADSLMwwmpFBA6yIWJobZB4TAwMzUDMDIysDIxsDIzuDCIN4FtQqMADa57AfSC+BcB3sEWyBA6dOGqtCxYFqDtgjsfdD1Tig6oWJg9lg9WIACl8VOKxfkxcAAADZelRYdE1PTCByZGtpdCAyMDIyLjA5LjEAAHicjZJBDoMgEEX3nOJfQIMooksV0zaNmLS2d+i+909n2uBomxoHFvPhzQA/KHBc/PnxxBzGKwXojVnXNe651loN4ARtfzgFdFPTxpVuvIXpigqOKmisyWYah7iSoUOiU2epn+ZMvwOSRNIQuQvMcURiUmO/9n/AgsG5ZZbaf6BdgckGWRI5H711SUfgro7VEtzg+uBXxn6sbsfgxWoeRvxkmYtrLAvxhqUVB6gSpTyTpZPHsKyWV1kezDr+DsrVC3Nxdbu3ZWe8AAAAoXpUWHRTTUlMRVMgcmRraXQgMjAyMi4wOS4xAAB4nF3KMQvCMBAF4L/i2EJyJJcmgXN0qYu4lw4iDgFLimbsj7cthOs53PEe3xv68dIM/dju7xDXOy2NRkCvjDprA/EQLPga9Z4r1/0/balVj5Kn+yfPZCB9r9P8Ts9UwK51k1suL0CyXAyhHCKbIyfNsXXUcbHk5dCzeQrSAlugKC2yxeUHqS9cAuvsr4sAAACaelRYdHJka2l0UEtMMSByZGtpdCAyMDIyLjA5LjEAAHice79v7T0GIOBlgABGIGYDYlYgbmBkU0gA0izMHGCaiZGRQQOsCBfNDdLPxMDADNTEwMjKIMIgHgQ1FAzYSndr7lc3YLUDcX4p+dq7iLwEsyvVOA5YnXDfB2L3/qzZbyN6xh7E3vlrzT7jk6v2g9hLQl7YG9SogdliAAwpHN5AJM2YAAAA9HpUWHRNT0wxIHJka2l0IDIwMjIuMDkuMQAAeJx9kUFuxDAIRfc5xb9ALDDGsZeTZNRW1SRSm/YOs+/9Vego9YwaFbwA9Pg2uIPb2/x6/cKvxbnrAPrn1FrxKUTUXeABxvPTy4JpO417ZVo/lu0dGWod5o/kaVsve4UxoaeQsxAPoMAmnLMF9GOtNWK1aiEWNtGQVKPIASd4Rh8Dx0FScWmuyg78IZOTFGopXAQctCap5QDUGygyFCX0bFGKWQ/IbCSHwkqm6GMpicQD8LzMD2u4LWZcl7ktxj228T2VNqWnqY3iqbYHWyfy/WX30p7vv2Vx9w0CTmJnXljSIgAAAL56VFh0U01JTEVTMSByZGtpdCAyMDIyLjA5LjEAAHicVYuxDoMwDER/pSNIqeWL4xDC2IUubXfEUFUdkIpALSMf38ASMfjku3fXtf39UnRtX+6S7rQWoADlYM5MXlnEmoYpMARqmJyqFTHNRr0wqpRBmL1PmSXYSnSbolbsrToEBDEgrZ3bE5EqpA7S42xlSvNcpvHxnebINPyu4/wZXsNCSHYjt2l5k0Zkg2iz4SjHlWRmozsyl5lEPTLNzK1/il1NzVmCUdEAAAAASUVORK5CYII=\n", + "image/png": 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", "text/plain": [ "" ] @@ -934,7 +934,7 @@ "outputs": [ { "data": { - "image/png": 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Xd+r+vDLE69WrZx9Pl5WV5eJuRETkbAsWLOCjjz4iODiY1atXExYWBkBqaipbtmyhdu3a9tfN5OK8MsQtFosuqYuIuKHNmzczevRoAObMmUP79u0B2wlXYmIiADNmzKBBgwauatGjeGWIg+6Li4i4m7y8PGJiYigpKeGPf/wjcXFx9m3Dhg3j1KlT3HzzzTz33HMu7NKzeG2IR0dHAwpxERF3YBgGsbGx7N69m6ZNm7JkyRL7u99r1qzh7bffxt/fn4yMDPz8vDaaTOe1f1I6ExcRcR+vvvoqK1euxN/fn5UrV1KnTh0ACgsL7QNOhgwZQrt27VzZpsdRiIuIiEP98MMPDBw4EICkpCRuvfVW+7bk5GT27t1LdHS0/Z1xqTiFuIiIOExhYSExMTEUFhZy7733MmbMGPu2HTt2MHPmTAAWLlxIrVq1XNWmx1KIi4iIwwwbNoxt27bRoEEDli9fbr/fXT7gpKSkhIcffphu3bq5tlEP5fUhfvz4cQoKClzcjYiI73n77bd58cUXsVgsrFixgkaNGtm3rVixgnXr1lGjRg0WLVqkASdV5LUhHhERYV9AQGfjIiLOtXfvXvurYmPHjqVLly72bcePH2fEiBEATJgwgWbNmrmiRa/gtSGuBV9ERFyjuLiYJ598kry8PG655RYmT558zvb4+HiOHDlCmzZtGD58uIu69A5eG+Kg++IiIq6QmJjIxo0bqV27NitXriQwMNC+bcOGDbz00ksApKenExQU5Ko2vYJCXERETLNmzRpmzZoFwJIlS7j88svt20pLS+nfvz8AzzzzDH/4wx9c0qM3UYiLiIgpsrOz7SNFBwwYwJ/+9Kdzti9cuJCtW7dSt25d+6tlUj0KcRERqbaysjJ69erF0aNHadeu3e+mkB04cIAJEyYAMHPmTCIjI13RptdRiIuISLVNmzaNzz//nJo1a7J69Wr7OOhyQ4YMIT8/n9tuu41nnnnGRV16H4W4iIhUy5dffmlfMjUtLY1WrVqds/3DDz/kvffeIyAggPT0dA04MZFX/0mWh/jJkyfJzc11cTciIt7n2LFj9OjRA6vVSu/eve33xMvl5+czaNAgAIYPH07btm1d0abX8uoQr1mzpn1Sjs7GRUTMZRgGTz/9NJmZmbRs2ZK0tLTf7TN16lT2799P06ZN7ffExTxeHeKgS+oiIo6ycOFCPvroI4KCgli9erV9lcxy27dvtz/gtmjRImrWrOmKNr2aQlxERCpt8+bNjBo1CoA5c+bQvn37c7ZbrVZiY2MpLS3l0Ucf5ZFHHnFBl95PIS4iIpWSl5dHTEwMJSUldOvWjQEDBvxun2XLlrF+/XpCQ0NZuHChC7r0DQpxERGpMMMw6N+/P7t376Zp06YsWbLkdxPIjh07Zj9Lnzx5Mk2bNnVFqz5BIS4iIhX26quv8uabb+Lv78/KlSupW7fu7/YZM2YMx44do23btgwZMsQFXfoOhbiIiFTIjh07GDhwIABJSUnceuutv9tn/fr1LFmyBICMjIxzhp+I+XwqxA3DcHE3IiKeqbCwkCeeeILCwkLuvfdexowZ87t9SkpKiI2NBeC55547b8iLubw+xKOjowE4ffo0x44dc3E3IiKeafjw4Wzbto0GDRqwfPny8666Nm/ePLZv305kZCQpKSku6NL3eH2IBwcH06BBA0CX1EVEquLtt98mIyMDgOXLl9OoUaPf7bN//34mT54MwOzZs6lXr55Te/RVXh/ioPviIiJVtXfvXp577jkAxo4dy3333Xfe/QYPHkxBQQGdO3f+3dKr4jgKcREROa+SkhKefPJJ8vLyuOWWW5gyZcp593v//ff54IMPCAwMJD09/XevnInjKMRFROS8EhIS2LhxI7Vr12blypXnfdL81KlT9gEnI0eO5Oqrr3Z2mz5NIS4iIr/zj3/8g1mzZgGwZMkSLr/88vPuN3nyZA4cOECzZs1ITEx0ZouCQlxERH4jOzvbfl87Li6OP/3pT+fdb+vWrcybNw+A1NRUQkNDndaj2CjERUTErqysjF69enHkyBGuu+465syZc979ygeclJWV8dhjj/HQQw85uVMBHwvxrKwsrFari7sREXFf06dP5/PPPyc0NJTVq1cTEhJy3v2WLl3K119/TVhYGPPnz3duk2LnEyEeFRWFn58fJSUlHD582NXtiIi4pS+//JKJEycCkJaWRuvWrc+735EjRxg9ejQAU6ZMsS+qJc7nEyEeEBBA48aNAV1SFxE5n2PHjtGjRw+sViu9e/fmqaeeuuC+o0aN4sSJE7Rr187+ZLq4hk+EOOi+uIjIhRiGwbPPPktmZiYtWrQgNTX1gvuuW7eOZcuWYbFYePHFFwkICHBip/JbCnERER+3cOFCPvjgA4KCgli9ejW1atU6737FxcXExcUB8MILL9CpUydntinnoRAXEfFhmzdvZtSoUYBtzfMOHTpccN85c+awY8cOGjRowLRp05zVolyEQlxExEedPHmSJ598kpKSErp162afFX4+e/bssS+7OnfuXOrUqeOsNuUiFOIiIj7IMAxiY2PZtWsXl112GUuWLLngmueGYTBw4EBOnz7N3XffTY8ePZzcrVyIQlxExAe99tprvPnmm/j7+7Ny5Urq1q17wX3fffdd1qxZQ1BQEGlpaRpw4kZ8LsSzs7MpLS11cTciIq6zY8cO+6XzKVOmcNttt11w35MnTzJkyBAAxowZQ6tWrZzSo1SMz4R4w4YNCQgIwGq1kpOT4+p2RERcorCwkJiYGAoKCrjnnnsYM2bMRfefOHEiWVlZNG/enPj4eCd1KRXlMyHu5+dHkyZNAF1SFxHfNXz4cL7//nsaNGjAihUr8Pf3v+C+W7ZsYcGCBYBtwEmNGjWc1aZUkM+EOOi+uIj4tnfeeYeMjAwAli9fTqNGjS64b1lZGbGxsVitVp544gnuv/9+Z7UplaAQFxHxAXv37uW5554DbPe277vvvovu//LLL7Nx40Zq1aplHzcq7kchLiLi5UpKSujevTu5ubncfPPNJCUlXXT/Q4cOMXbsWACSk5OJiopyRptSBQpxEREvl5iYyLfffkvt2rVZuXIlgYGBF91/5MiR5Obm0rFjR/syq+KeFOIiIl7sH//4BzNnzgRgyZIlNGvW7KL7f/bZZ6xYsQKLxUJGRsZFH3wT11OIi4h4qZycHPr06QNAXFwcf/rTny66f1FREf3797fvf8MNNzi8R6kenwzxQ4cOUVRU5OJuREQcp6ysjF69enHkyBGuu+465syZc8ljZs6cyc6dO2nUqBHJyclO6FKqy6dCPDIykpCQEACysrJc3I2IiONMnz6dzz77jNDQUFavXm3/u+9Cdu3aZQ/uefPmERER4Yw2pZp8KsQtFgvR0dGALqmLiPf68ssvmThxImBbpKV169YX3d8wDAYMGEBRURFdunQhJibGGW2KCXwqxEH3xUXEux07dowePXpgtVrp1asXTz311CWPefvtt1m7di3BwcGkpqZqwIkHUYiLiHgJwzB49tlnyczMpEWLFhWaOJabm8vQoUMBiI+Pp0WLFk7oVMyiEBcR8RKLFi3igw8+ICgoiNWrV1OrVq1LHjN+/HhycnJo0aLFJYehiPtRiIuIeIH//Oc/jBo1CoDZs2fToUOHSx6zadMmUlNTAUhLS7vkw2/ifhTiIiIe7uTJk8TExFBcXMyjjz5qnxV+MWcPOOnRowf33nuvEzoVsynERUQ8mGEY9O/fn127dnHZZZexdOnSCj2YlpGRwebNm4mIiKjQO+Tinnw2xI8fP05BQYGLuxERqZ5ly5bxxhtv4O/vz8qVK6lbt+4lj8nJyWHcuHGA7X3yi40kFffmcyEeERFBWFgYoLNxEfFsO3bsYMCAAQBMnjyZ2267rULHDR8+nLy8PG688Ub69evnyBbFwXwuxC0Wiy6pi4jHKywsJCYmhoKCAu655x776NBLWbt2LatWrcLPz08DTryAz4U46L64iHi+ESNG8P3331O/fn2WL19eoTA+ffq0/cx90KBBXH/99Y5uUxxMIS4i4mH+8pe/kJ6eDsDy5ctp3LhxhY5LSUlh165dREVFMWXKFEe2KE6iEBcR8SD79u2jb9++AIwZM4b777+/Qsft3LmT6dOnA7BgwQLCw8Md1qM4j0JcRMRDlJSU0L17d3Jzc7n55ptJSkqq0HGGYRAXF0dxcTEPPPAAjz32mIM7FWdRiIuIeIjExES++eYbIiIiWLlyJYGBgRU6buXKlXz66aeEhISwePFiDTjxIgpxEREP8M9//pOZM2cCsGTJEpo1a1ah43799VeGDRsG2H4IaN68uaNaFBfw6RA/efIkubm5Lu5GROTicnJy6N27NwD9+/ev1OXwhIQEDh8+TOvWrRk5cqSjWhQX8ckQr1mzJnXq1AF0Ni4i7q2srIxevXpx5MgRrrvuOubOnVvhYzdu3Gh/ij09PZ3g4GBHtSku4pMhDrqkLiKeISUlhc8++4zQ0FBWr15d4UljpaWlxMbGYhgGvXv35s4773Rso+ISCnGFuIi4qfXr1zNhwgQAUlNTad26dYWPTU1NZcuWLdSuXZvZs2c7qkVxMYW4QlxE3NDx48fp3r07VquVnj178tRTT1X42KysLBITEwGYMWMGDRo0cFSb4mIKcYW4iLgZwzB45plnyMzM5KqrriI9Pb1Sr4UNHTqUU6dOcfPNN/Pcc885sFNxNZ8P8czMTBd3IiJyrsWLF/PBBx8QFBTE6tWrqVWrVoWPXbNmDe+88w7+/v5kZGTg5+ezf837BJ/9X1dn4iLijv7zn//YXwWbNWtWpYaUFBYW2gecDB06lHbt2jmkR3EfCvEDBzAMw8XdiIjY1q548sknKS4u5pFHHmHQoEGVOj45OZm9e/cSHR3NpEmTHNOkuBWfDfHo6GjA9pPr8ePHXdyNiPi68vXNf/75Z6Kjo1m6dGml7oPv2LHDvqLbwoULCQsLc1Sr4kZ8NsSDg4PtT2zqkrqIuNqyZctYsWIFfn5+rFy5knr16lX42PIfAEpKSnj44Yfp1q2b4xoVt+KzIQ66Ly4i7uHHH3+038uePHkyt99+e6WOX7FiBevWraNGjRosWrRIA058iEIchbiIuE5hYSExMTEUFBRw9913Ex8fX6njjx8/zogRIwCYOHFihQejiHdQiKMQFxHXGTlyJFu3bqV+/fqsWLECf3//Sh0fHx/PkSNHaNOmjX1amfgOhTgKcRFxjb/85S+kpaUB8Prrr9O4ceNKHb9hwwZeeuklwDbgJCgoyPQexb0pxFGIi4jz7du3j759+wIwevRoHnjggUodX1paSv/+/QF45pln+MMf/mB6j+L+fDrEy18zU4iLiDOVlJTQvXt3cnNz6dSpE1OnTq10jYULF7J161bq1q1rf7VMfI9Ph/jZS69arVYXdyMivmL8+PF88803REREsGrVKgIDAyt1/IEDB+zTzWbNmkVkZKQj2hQP4NMhHhUVhcVioaSkhMOHD7u6HRHxAWvXrmXGjBkAvPLKK1V6mnzIkCHk5+dz22238fTTT5vboHgUnw7xwMBA+4MkuqQuIo528OBBevfuDUBsbCyPP/54pWt8+OGHvPfeewQEBJCenq4BJz7O5//X18NtIuIMZWVl9OrVi8OHD9O2bVvmzp1b6Rr5+fn29dSHDx9O27ZtzW5TPIxCXCEuIk4wY8YMPv30U0JDQ1m9ejU1atSodI2pU6eyf/9+mjZtar8nLr5NIa4QFxEH++qrr+yhu3jxYq6++upK19i+fTuzZ8+216hZs6apPYpnUogrxEXEgY4fP0737t0pKyujZ8+eVXoQzWq1EhsbS2lpKY8++ihdu3Y1v1HxSApxhbiIOIhhGPTt25cDBw5w1VVXkZ6eXqXhJMuWLWP9+vWEhoaycOFCB3QqnkohrhAXEQdZvHgxf/3rXwkMDGT16tXUqlWr0jWOHj3KqFGjANuEs6ZNm5rdpngwhfj/Qjw7O5vS0lIXdyMi3mLLli2MHDkSsC3Icv3111epzpgxYzh27Bht27ZlyJAhZrYoXsDnQ7xhw4YEBARgtVrJyclxdTsi4gVOnjxJTEwMxcXFPPLIIwwePLhKddavX8/SpUsByMjIqPTKbuL9fD7E/f39adKkCaBL6iJijgEDBvDzzz8THR3N0qVLq3QfvKSkhNjYWACef/55br31VrPbFC/g8yEOui8uIuZZtmwZy5cvx8/PjzfffJN69epVqc68efPYvn07kZGRpKSkmNyleAuFOApxETHHjz/+SFxcHGB7CO2OO+6oUp19+/YxadIkAGbPnk3dunXNalG8jEIchbiIVN/p06eJiYmhoKCAu+++m/j4+CrXGjx4MIWFhXTu3Jk+ffqY2KV4G4U4CnERqb4RI0awdetW6tevz4oVK/D3969Snffff58PP/yQwMDAKr9XLr5DIY5CXESq59133yUtLQ2A119/3T4dsbJOnTplH3AyatSoKi3PKr5FIY5CXESqbt++ffTt2xewBe8DDzxQ5VqTJ0/mwIEDNGvWjISEBLNaFC+mEOdMiB86dIiioiIXdyMinqKkpITu3bvz66+/0qlTJ5KTk6tca+vWrcybNw+A1NRUQkNDzWpTvJhCHIiMjCQkJASArKwsF3cjIp5iwoQJfPPNN0RERLBy5coqL8ZSPuCkrKyMxx57jIceesjkTsVbKcQBi8VCdHQ0oEvqIlIxa9eutb+//corr3DFFVdUudbSpUv5+uuvCQsLY/78+SZ1KL5AIf4/ui8uIhV18OBBevfuDUBsbCyPP/54lWsdOXKE0aNHA5CUlGQ/oRCpCIX4/yjERaQirFYrvXv35vDhw7Rt25a5c+dWq96oUaM4ceIE7du3Z+DAgSZ1Kb5CIf4/CnERqYiUlBQ++eQTQkNDWb16NTVq1KhyrXXr1rFs2TIsFgsZGRkEBASY2Kn4AoX4/yjEReRSvvrqKyZMmADAokWLqvUed3FxsX2J1hdeeIFOnTqZ0qP4FoX4/yjEReRijh8/Tvfu3SkrK6NHjx4888wz1ao3Z84cduzYQYMGDZg2bZpJXYqvUYj/j0JcRC7EMAz69u3LgQMHaN68ebWXQ92zZw9TpkwBYO7cudSpU8esVsXHKMT/pzzEjx8/TkFBgYu7ERF3kpqayl//+lcCAwNZvXo14eHhVa5lGAYDBw7k9OnT3H333fTo0cPETsXXKMT/JyIigrCwMEBn4yJyxpYtWxgxYgQAs2bNomPHjtWq9+6777JmzRqCgoJIS0vTgBOpFoX4/1gsFl1SF5FznDx5kpiYGIqLi+natSuDBw+udr3yGmPGjKFVq1ZmtCk+TCF+lvIQz8zMdHEnIuIOBgwYwM8//0yTJk149dVXq33WPGHCBLKzs2nevHm15o2LlFOIn0Vn4iJS7vXXX2f58uX4+fmxcuVK6tWrV616W7ZsYeHChYDtHnt13i8XKacQP4tCXEQAfvrpJ/s73JMmTeKOO+6oVr2ysjJiY2OxWq3ExMRw//33m9GmiEL8bApxETl9+jRPPPEE+fn53HXXXYwbN67aNV9++WU2btxIrVq1qr1Mq8jZFOJnUYiLyMiRI9m6dSv169dnxYoV+Pv7V6veoUOHGDt2LADJyclERUWZ0aYIoBA/h0JcxLe9++67pKamArBs2TJTAnfEiBHk5ubSsWNH+yV6EbMoxM9SHuJ5eXnk5eW5uBsRcab9+/fTt29fwHY2/uCDD1a75meffcYbb7xhH3BS3bN6kd9SiJ+lZs2a9uUPdTYu4jtKSkro3r07v/76KzfddBPJycnVrllUVET//v0B26tqN9xwQ7VrivyWQvw3dEldxPdMmDCBr7/+mvDwcFatWkVQUFC1a86cOZOdO3fSqFEjpk6dakKXIr+nEP8NhbiIb1m7di0pKSkAvPLKK1xxxRXVrrlr1y772fy8efOIiIiodk2R81GI/4ZCXMR3HDx4kN69ewO2md5//vOfq13TMAwGDBhAUVERXbp0ISYmpto1RS5EIf4b0dHRgEJcxNtZrVZ69+7N4cOHufbaa5k3b54pdd9++23Wrl1LcHAwqampGnAiDqUQ/w2diYv4hhkzZvDJJ59Qo0YNVq9ebcoyqLm5uQwdOhSAcePG0aJFi2rXFLkYhfhvKMRFvN9XX33F+PHjAVi8eDFt2rQxpe748ePJycmhRYsWjBkzxpSaIhejEP+Ns0PcMAwXdyMiZjt+/Dg9evSgrKyM7t2788wzz5hSd9OmTfaFYtLS0ggODjalrsjFKMR/o/yeeGFhIcePH3dxNyJiJsMw6Nu3L7/88gvNmzcnIyPDlHvWZw846dGjB/fee68J3YpcmkL8N0JCQqhfvz6gS+oi3iYtLY2//vWvBAYGsmrVKsLDw02pm56ezubNm4mIiGDOnDmm1BSpCIX4eei+uIj3+e677xg+fDhgW4jFrBXUcnJySEhIAGD69Ok0atTIlLoiFaEQPw+FuIh3OXXqFDExMRQXF/Pwww8zZMgQ02oPHz6cvLw8brrpJvr162daXZGKUIifh0JcxLsMGDCAnTt30qRJE1599VXT3t1eu3Ytq1atws/PTwNOxCUU4uehEBfxHq+//jqvv/46fn5+vPnmm0RGRppSt7Cw0D5adNCgQXTo0MGUuiKVoRA/D4W4iHf46aef7EE7ceJE/vCHP5hWOyUlhd27dxMVFcWUKVNMqytSGQrx81CIi3i+06dPExMTQ35+PnfddZf94TMz7Ny50z40ZcGCBaY95S5SWQrx8ygP8czMTKxWq4u7EZGqGDlyJP/973+JjIxkxYoVpt2vNgyDuLg4iouLefDBB3nsscdMqStSFQrx84iKisJisVBSUsLhw4dd3Y6IVNJ7771nXz3t9ddfJyoqyrTaK1eu5NNPPyUkJITFixdrwIm4lEL8PAIDA2ncuDGgS+oinmb//v08++yzgO1s/MEHHzSt9okTJxg2bBgAiYmJXHnllabVFqkKhfgF6L64iOcpKSmhe/fu/Prrr9x0000kJyebWj8hIYHDhw/TunVrRo4caWptkapQiF+AQlzE80ycOJGvv/6a8PBwVq5cSVBQkGm1N27cSEZGBmBbZlUDTsQdKMQvQCEu4lk+/vhj+xPjr7zyiqmXuktLS4mNjcUwDPr06cOdd95pWm2R6lCIX4BCXMRzHDx4kF69emEYBv369ePPf/6zqfVTU1PZsmULderUYdasWabWFqkOhfgFKMRFPIPVaqVPnz4cPnyYa6+9lvnz55taPysri8TERMC2wEuDBg1MrS9SHQrxC1CIi3iGmTNn8vHHH1OjRg1Wr15NjRo1TK0/dOhQTp06xc0338xzzz1nam2R6lKIX0B5iGdnZ1NaWuribkTkfDZs2GA/S160aBFt2rQxtf6aNWt455138Pf3JyMjAz8//ZUp7iXA1Q24q4YNGxIQEEBpaSk5OTn2UBcR95Cbm0tycjLXXXcd999/v/3dcLOcPn2atLQ0OnToQO/evWnXrp2p9UXMoBC/AH9/f5o0acL+/fs5cOCAQlzEzURERPC3v/3NYfVDQkL48MMPHVZfxAy6NnQRui8uIiLuTCF+EQpxERFxZwrxi1CIi4iIO1OIX4RCXERE3JlC/CIU4iIi4s4U4hehEBcREXemEL+I8hA/dOgQRUVFLu5GRETkXArxi4iMjCQkJASwrdwmIiLiThTiF2GxWIiOjgZ0SV3E0YqLXd2BiOdRiF+C7ouLVM2dd8LAgbZftWtDvXqQmAiGYdverBlMnQpPPw0REfD887bv/+UvcM01EBxs22fOnHPrNmsGSUnQoweEhUFUFCxadO4+c+dC27ZQsyZcdhnExcGpU2e2v/aarad//hOuvtpW54EHICfn3DqvvmrbHhICrVtDWpo5fzYiZlGIX4JCXKTqli2DgAD49ltYuBDmzYNXXjmzfdYsuPZa2LwZxo+3/fOJJ+DJJ+H772HSJNv3X3vt3LqzZsF118F//gPx8TBsGHz88Zntfn62z9u2zdbDZ5/B6NHn1igogNmzYfly+Ne/4JdfYOTIM9tffhkSEiA5GXbsgGnTbL0sW2b2n5JINRhVlJubawBGbm5uVUt4hISEBAMw4uLiXN2KiEfp3Nkwrr7aMKzWM98bM8b2PcMwjMsvN4xu3c49pkcPw+jS5dzvjRplGG3anPn68ssN44EHzt0nJsYwHnzwwr289ZZh1Kt35utXXzUMMIxdu858LzXVMBo2PPP1ZZcZxptvnlsnKckwbrnlwp8jYpaKZqzOxC9BZ+IiVXfzzWCxnPn6llvg55+hrMz29Q03nLv/jh1w223nfu+22849przO2W65xXZsuc8/hy5doEkTqFUL+vSBY8cgP//MPqGh0Lz5ma8bN4bDh23/fuQIHDgAffvaLrWX/5o6FXbvrtyfgYgjaYrZJSjERRynZs1zvzaMc0O//HsVUX7c/v3w0EMQG2u7d163LqxfbwvkkpIz+wcG/v748s+yWm3/fPll6NTp3P38/SvWj4gzKMQvQSEuUnXffPP7r1u0uHAQtmljC9yzbdgALVuee8z56rZubfv3TZugtNT2QJzf/641vvVW5fpu2NB2Fr9nD/TsWbljRZxJIX4J5SF+7NgxCgoKCA0NdXFHIp7jwAEYPhxeeMH2ENqiRb9/2vxsI0bAjTfazqBjYuDrr2Hx4t8/Ff7VVzBzJnTrZnug7e23oXy0ePPmthBftAi6drXtm5FR+d4nTYLBgyE8HB58EIqKbD8gnDhh+z2JuAPdE7+EiIgIwsLCAMjMzHRxNyKepU8fKCyEm26CAQNg0CDo1+/C+19/ve2sedUq21PrEybAlCm219DONmKE7Un2Dh1sgT9nDtx/v21b+/a2V8xmzLDVeOMNmD698r0/95ztSfrXXrO9rta5s+3fr7ii8rVEHMViGBW943SuvLw8IiIiyM3NJTw83Oy+3EqbNm3YsWMHn3zyCffcc4+r2xHxCHfeaQvU+fPNrdusGQwdavsl4q0qmrE6E68ArdomIiLuSCFeAXq4TURE3JEebKsAhbhI5a1b55i6+/Y5pq6IJ9KZeAUoxEVExB0pxCtAIS4iIu5IIV4BCnEREXFHCvEKKA/xvLw88vLyXNyNiIiIjUK8AsLCwqhduzags3ERM6xdu5brr7+e22+/nePHjzvkM+bPn8/1119P165dKSwsdMhniLiaQryCdEldxBwlJSXExcWxZcsWunTpQt26dU3/jK1btzJy5Ei2bNlC//79qVGjhumfIeIOFOIVpBAXMcdLL73E7t27adCgAcMdsAi51WolNjaWsrIyHnvsMR566CHTP0PEXSjEK0ghLlJ9J0+eZMqUKQBMnDiRWrVqmf4ZS5Ys4euvvyYsLIz5Zq/5KuJmFOIVpBAXqb65c+dy+PBhrrrqKp5//nnT6x85coQxY8YAkJSUZF8yWcRbKcQrSCEuUj2HDh1i1qxZACQnJxMYGGj6Z4waNYoTJ07Qvn17Bg4caHp9EXejEK8ghbhI9SQlJZGfn88NN9zA448/bnr9devWsWzZMiwWCxkZGQQEaFVp8X4K8Qo6O8SrOL1VxGft2rWLF198EYCZM2fi52fuXz3FxcX0798fgBdeeIFOnTqZWl/EXSnEK6j83lphYaHD3msV8VaJiYmUlpbywAMPcNddd5lef/bs2fz44480aNCAadOmmV5fxF0pxCsoJCSE+vXrA7qkLlIZ//73v1m9ejUWi4WUlBTT6+/Zs4ekpCTA9uBcnTp1TP8MEXelEK8E3RcXqRzDMOxPi/fq1Yt27dqZXn/gwIGcPn2au+++mx49ephaX8TdKcQrQSEuUjlr167l888/JygoyP5+uJneffdd1qxZQ1BQEGlpaVgsFtM/Q8SdKcQrQSEuUnFWq9V+Fj5gwACaNWtmav2TJ08yePBgAMaMGUOrVq1MrS/iCRTilaAQF6m4N998k//+97+Eh4czbtw40+tPmDCB7OxsmjdvTnx8vOn1RTyBQrwSFOIiFVNUVERiYiIAY8eOJTIy0tT6W7ZsYeHChQCkpqZqwIn4LIV4JSjERSomPT2d/fv307hxY4YMGWJq7bKyMmJjY7FarcTExHD//febWl/EkyjEK6E8xDMzM7FarS7uRsQ95ebmMnXqVAAmT55MaGioqfVffvllNm7cSHh4OHPnzjW1toinUYhXQlRUFBaLhZKSEg4fPuzqdkTc0syZMzl27BitW7fmmWeeMbX2oUOHGDt2LABTp04lKirK1PoinkYhXgmBgYE0btwY0CV1kfPJzs5m3rx5AEyfPt309ctHjBhBbm4uHTt2JC4uztTaIp5IIV5Jui8ucmGTJk2isLCQW265hUcffdTU2p9++ilvvPGGfcCJv7+/qfVFPJFCvJIU4iLn9+OPP7JkyRLAdkndzIVXioqK7GfeAwYM4IYbbjCttognU4hXkkJc5PzGjRuH1WrlkUce4fbbbze19syZM9m5cyeNGjWyPzQnIgrxSlOIi/zehg0beO+99/Dz8zN9itiuXbtITk4GYN68eURERJhaX8STKcQr6ezXzETk3CEnTz/9NNdcc42ptQcMGEBRURFdunQhJibGtNoi3kAhXkk6Exc510cffcT69esJCQlh8uTJptZ+6623WLt2LcHBwaSmpmrAichvKMQrqTzEs7OzKSsrc3E3Iq5VVlZmf297yJAhREdHm1Y7NzeXYcOGAbb77S1atDCttoi3UIhXUsOGDQkICKCsrIycnBxXtyPiUsuWLeOHH36gTp069kvqZhk/fjw5OTm0bNnS9Noi3kIhXkn+/v40adIE0CV18W2FhYVMmDABsJ0p16lTx7TamzZtIjU1FYC0tDSCg4NNqy3iTRTiVaD74iKwaNEisrKyuOyyyxg4cKBpdc8ecNKjRw/uuece02qLeBuFeBUoxMXXHT9+nOnTpwOQlJRESEiIabXT09PZvHkzERERzJkzx7S6It5IIV4FCnHxddOnT+fXX3+lbdu29OrVy7S6OTk5JCQk2D+jUaNGptUW8UYK8SpQiIsv++WXX1i0aBEAKSkppq5hPnz4cPLy8rjpppvo16+faXVFvJVCvAoU4uLLJk6cSFFREZ07d+bBBx80re7atWtZtWoVfn5+GnAiUkEK8SpQiIuv+v7771m2bBkAM2bMMG3xlcLCQvuAk0GDBtGhQwdT6op4O4V4FZSH+KFDhyguLnZxNyLOEx8fj2EYPPbYY3Tq1Mm0uikpKezevZuoqCimTJliWl0Rb6cQr4LIyEiCg4MxDIOsrCxXtyPiFF988QV/+9vf8Pf3N3XIyc6dO0lJSQFgwYIFhIeHm1ZbxNspxKvAYrHYl5fUJXXxBWcPOXn++edp2bKlaXXj4uIoLi7mwQcf5LHHHjOlroivUIhXke6Liy959913+fbbbwkNDbWv0maGlStX8umnnxISEsLixYs14ESkkhTiVaQQF19RUlLCuHHjABgxYgSNGzc2pe6JEyfsA04SExO58sorTakr4ksU4lWkEBdfsXTpUnbu3ElkZCQjR440rW5CQgKHDx+mdevWptYV8SUK8SpSiIsvyM/PZ9KkSYBtqphZD51t3LiRjIwMwLbMqgaciFSNQryKFOLiC+bNm8fBgwe54ooreOGFF0ypWVpaSmxsLIZh0KdPH+68805T6or4IoV4FSnExdsdOXKEmTNnApCcnGza2XJqaipbtmyhTp06zJo1y5SaIr5KIV5F5SF+7NgxCgoKXNyNiPmmTp3KyZMn6dChAzExMabUzMrKIjExEbAt8NKgQQNT6or4KoV4FdWuXZuaNWsCkJmZ6eJuRMy1Z88e0tPTAdvyqn5+5vxVMXToUE6dOsXNN9/Mc889Z0pNEV+mEK8ii8WiS+ritcaPH09JSQldunShS5cuptRcs2YN77zzDv7+/mRkZJj2g4GIL9N/RdWgEBdvtGXLFt58800A+3Ko1VVYWMiAAQMA29l4u3btTKkr4usU4tWgEBdvVL68avfu3bn++utNqZmcnMzevXuJjo62v7ImItWnEK8Ghbh4m48//piPP/6YwMBApk6dakrNHTt22J9yX7RoEWFhYabUFRGFeLUoxMWbWK1Wxo4dC0D//v1NWQbVMAz69+9PSUkJDz/8MI8++mi1a4rIGQrxalCIizdZvXo1//nPf6hVq5b9NbDqWr58OV988QU1atRg0aJFGnAiYjKFeDUoxMVbFBcXk5CQAMCoUaOoX79+tWseP37cvib6xIkTadasWbVrisi5FOLVUB7ieXl55OXlubgbkap78cUX2bt3Lw0bNmT48OGm1IyPj+fIkSNcc801ptUUkXMpxKshLCyM2rVrAzobF8+Vl5fHlClTAJg0aZJ9EaPq2LBhAy+99BJgG3ASGBhY7Zoi8nsK8WrSJXXxdLNnz+bo0aO0bNmSvn37VrteSUkJsbGxADzzzDPccccd1a4pIuenEK8mhbh4soMHDzJnzhwApk2bZsoZ88KFC/n++++pW7eu/dUyEXEMhXg1KcTFk02ZMoWCggJuuukm/vSnP1W73oEDB5g4cSIAs2bNIjIysto1ReTCFOLVpBAXT7Vz5077feuZM2ea8vrXkCFDyM/P5/bbb+fpp5+udj0RuTiFeDUpxMVTJSQkUFZWxkMPPUTnzp2rXe/DDz/kvffeIyAggPT0dA04EXEC/VdWTQpx8UTffvst77zzDhaLxZQhJ/n5+QwaNAiA4cOHc+2111a7pohcmkK8ms4OccMwXNyNyKUZhmEfctKnTx/atm1b7ZpJSUns37+fpk2bMmHChGrXE5GKUYhXU3R0NGAbtXj8+HEXdyNyaWvWrOGLL74gODjY/n54dWzbts3+hPvixYtNec9cRCpGIV5NISEh9iUqdUld3F1ZWZl9yMmgQYNo2rRptepZrVb69+9PaWkp3bp1o2vXrma0KSIVpBA3ge6Li6d44403+P7774mIiCA+Pr7a9ZYtW8b69eupWbMmCxYsMKFDEakMhbgJykM8MzPTxZ2IXNjp06cZP348YFvXvG7dutWqd/ToUUaNGgXYlmut7lm9iFSeQtwEOhMXT5Camsovv/xCkyZNGDx4cLXrjRkzhmPHjtG2bVuGDBliQociUlkKcRMoxMXd/frrryQnJwO2Vdpq1KhRrXrr169n6dKlAGRkZGjAiYiLKMRNoBAXdzdjxgxOnDhBmzZt6NOnT7VqnT3g5Pnnn+fWW281o0URqQKFuAkU4uLOMjMzmT9/PgDTp08nICCgWvXmzZvH9u3biYyMNGWhGBGpOoW4Cc5+sM1qtbq4G5FzTZo0idOnT3P77bdX+xWwffv2MWnSJADmzJlT7YfjRKR6FOImiIqKwmKxUFxczJEjR1zdjojdDz/8wKuvvgrYLqlXZ8iJYRgMGjSIwsJCOnfuTO/evc1qU0SqSCFugsDAQBo3bgzokrq4l/j4eKxWK926dav2vev333+fjz76iMDAQNLT002ZeiYi1aMQN4nui4u7Wb9+PR988AF+fn5MmzatWrVOnTplfy1t1KhRXH311Wa0KCLVpBA3iUJc3MnZQ0769u1b7dCdPHkyBw4c4IorriAhIcGMFkXEBApxkyjExZ188MEHbNiwgRo1ajBx4sRq1dq6dSvz5s0DbANOQkNDzWhRREygEDeJQlzcRWlpqX3IydChQ2nSpEmVa1mtVmJjYykrK+Oxxx7joYceMqtNETGBQtwk5SNJFeLiaq+99ho//vgjdevWtV9Sr6olS5bw9ddfExYWZn/XXETch0LcJDoTF3dQUFBgv3yemJhIRERElWsdOXLE/kNAUlKS/QdVEXEfCnGTlId4dnY2ZWVlLu5GfNWCBQvIzs7m8ssvJy4urlq1Ro0axYkTJ2jfvj0DBw40qUMRMZNC3CSNGjUiICCAsrIycnJyXN2O+KBjx47Zl0FNSkoiODi4yrXWrVvHsmXLsFgsZGRkVHupVhFxDIW4Sfz9/YmKigJ0SV1cY9q0aeTl5dGuXTt69uxZ5TrFxcX0798fgBdeeIFOnTqZ1aKImEwhbiLdFxdX2b9/P4sXLwYgJSUFP7+q/6c9e/ZsfvzxRxo0aFDtRWJExLEU4iZSiIurjB8/nuLiYu666y7uv//+KtfZs2cPSUlJAMydO5c6deqY1aKIOIBC3EQKcXGF//73v6xYsQKo3pATwzAYOHAgp0+f5u6776ZHjx5mtikiDqAQN5FCXFwhPj4ewzB44oknuPHGG6tc591332XNmjUEBQWRlpamASciHkAhbiKFuDjb559/zpo1awgICGDq1KlVrnPy5En7gJOxY8fSqlUrs1oUEQdSiJtIIS7OZBgGo0ePBqBfv360aNGiyrUmTJhAdnY2zZs3Jz4+3qwWRcTBFOImKg/xQ4cOUVxc7OJuxNu98847bNq0iZo1azJhwoQq19myZQsLFy4EIDU1lZCQELNaFBEHU4ibqH79+gQHB2MYBllZWa5uR7xYSUkJ48aNA2DkyJE0bNiwSnXKysqIjY3FarUSExNTrSfbRcT5FOImslgsGoQiTvHyyy+za9cuGjRowIgRI6pVZ+PGjYSHhzN37lwTOxQRZ1CIm0z3xcXRTp06xeTJkwHbvexatWpVqc6hQ4fsI0uTk5PtKw6KiOdQiJtMIS6ONnfuXA4fPkzz5s15/vnnq1xnxIgR5Obm0rFjR/syqyLiWRTiJlOIiyMdPnyYWbNmAbaz56CgoCrV+fTTT3njjTfsA078/f3NbFNEnEQhbjKFuDhSUlISp06domPHjvz5z3+uUo2ioiL7mNIBAwZwww03mNmiiDiRQtxkCnFxlN27d5ORkQHAzJkzqzzkZObMmezcuZNGjRpVa4EYEXE9hbjJFOLiKAkJCZSWlnL//fdz9913V6nGrl27SE5OBmD+/PlERESY2aKIOJlC3GTlIX7s2DEKCgpc3I14i02bNrF69WosFgspKSlVqmEYBgMGDKCoqIguXbrwxBNPmNyliDibQtxktWvXpmbNmgBkZma6uBvxBoZhMGbMGAB69uxJ+/btq1TnrbfeYu3atQQHB5OamqoBJyJeQCFuMovFokvqYqqPP/6Yzz77jKCgIPus78rKzc1l2LBhAIwbN65a66yLiPtQiDuAQlzMYrVa7WfhcXFxNGvWrEp1xo8fT05ODi1btrTXExHPpxB3AIW4mGXlypV89913hIeHk5CQUKUamzZtIjU1FYC0tDSCg4PNbFFEXEgh7gAKcTFDUVERiYmJAIwZM4bIyMhK1zh7wEmPHj245557zG5TRFxIIe4ACnExQ3p6Ovv27aNx48YMGTKkyjU2b95MREQEc+bMMblDEXE1hbgDKMSlunJzc+0LsUyaNMn+xkNl5OTk2C/BT58+nUaNGpnao4i4nkLcARTiUl2zZs3i2LFjtGrVimeffbZKNYYPH05eXh433XQT/fr1M7lDEXEHCnEHKA/xvLw88vLyXNyNeJqcnBz7bO/p06cTEBBQ6Rpr165l1apV+Pn5acCJiBdTiDtAWFgYtWvXBrTgi1TepEmTKCws5Oabb6Zbt26VPr6wsNA+4GTw4MF06NDB5A5FxF0oxB1El9SlKn788UeWLFkC2AaVVGVVtZSUFHbv3k1UVBRTpkwxu0URcSMKcQdRiEtVJCQkUFZWRteuXbnjjjsqffzOnTvta6svWLCAWrVqmd2iiLgRhbiDKMSlsr7++mveffdd/Pz8mDZtWqWPNwyDuLg4iouLefDBB3nssccc0KWIuBOFuIMoxKUyzh5y8tRTT3HttddWusbKlSv59NNPCQkJYfHixRpwIuIDFOIOohCXyvjb3/7Gl19+SUhICJMnT6708SdOnLAPOBk/fjxXXnml2S2KiBtSiDuIQlwqqqysjLFjxwK2p8nL/79TGQkJCRw+fJjWrVszcuRIs1sUETelEHeQs0PcMAwXdyPu7PXXX2f79u3Url3bHuaVsXHjRjIyMgDbMqtBQUFmtygibkoh7iDR0dEAFBQUcOLECRd3I+6qsLCQCRMmALY533Xq1KnU8aWlpcTGxmIYBn369OHOO+90QJci4q4U4g4SEhJC/fr1AV1SlwtbvHgxmZmZXHbZZQwaNKjSx6emprJlyxbq1KnDrFmzHNChiLgzhbgD6b64XMzx48ftr5JNmTKFkJCQSh2flZVlH1U6Y8YMGjRoYHqPIuLeFOIOpBCXi0lJSeHXX3/l2muvpXfv3pU+fujQoZw6dYpbbrmFvn37OqBDEXF3CnEHKr8vrhCX3zpw4AALFy4EbGFe2QEla9as4Z133sHf35/09HT8/PSfsogv0n/5DqQzcbmQiRMnUlRUxB/+8AceeuihSh1bWFjIgAEDANvZeLt27RzRooh4AIW4AynE5Xy2bdvGsmXLANu97MqurJacnMzevXuJjo5m0qRJDuhQRDyFQtyBFOJyPvHx8VitVh577DFuvvnmSh27Y8cOZs6cCcCiRYsICwtzRIsi4iEU4g5UHuKZmZlYrVYXdyPu4F//+hcfffQR/v7+JCcnV+pYwzDo378/JSUlPPzwwzz66KMO6lJEPIVC3IGaNGmCxWKhuLiYI0eOuLodcbGzh5w899xztGrVqlLHL1++nC+++IIaNWqwaNEiDTgREYW4IwUGBtKoUSNAl9QF3nvvPb755htCQ0OZOHFipY49fvw4I0aMAGwPxTVr1swBHYqIp1GIO5juiwvYlkeNj48HYPjw4TRu3LhSx8fHx3P06FGuueYahg8f7ogWRcQDKcQdTCEuAEuXLmXnzp3Uq1ePUaNGVerYDRs28NJLLwG2ASeBgYGOaFFEPJBC3MEU4pKfn2+/fD5+/HjCw8MrfGxJSQmxsbEAPPvss9xxxx0O6VFEPJNC3MEU4jJ//nwOHjzIFVdcYQ/kilq4cCHff/89devWZcaMGQ7qUEQ8lULcwRTivu3o0aP28J06dSrBwcEVPvbAgQP2M/hZs2YRGRnpkB5FxHMpxB1MIe7bpk6dysmTJ2nfvj1PPvlkpY4dMmQI+fn53H777Tz99NOOaVBEPJpC3MHKQzw7O5uysjIXdyPOtHfvXtLS0gDb8qqVGVLy4Ycf8t577xEQEKABJyJyQfqbwcEaNWpEQEAAZWVl5OTkuLodcaLx48dTUlLCvffey3333Vfh4/Lz8xk0aBAAI0aM4Nprr3VUiyLi4RTiDubv709UVBSgS+q+ZMuWLbzxxhuAbdRoZSQlJbF//34uv/xyxo8f74j2RMRLKMSdQPfFfc/YsWMBePLJJ+nYsWOFj9u2bRtz5swBbANOatas6ZD+RMQ7KMSdQCHuWz755BPWrl1LYGBgpYacWK1W+vfvT2lpKd26daNr164O7FJEvIFC3AkU4r7DarXaz8JjY2O58sorK3zssmXLWL9+PTVr1mTBggWOalFEvIhC3AkU4r7jrbfeYvPmzYSFhZGYmFjh444ePWpfjnXy5Mk0bdrUUS2KiBdRiDuBQtw3FBcXk5CQAMCoUaNo0KBBhY8dM2YMx44do23btgwePNhRLYqIl1GIO4FC3De89NJL7Nmzh4YNG1Zq0tj69etZunQpABkZGRpwIiIVphB3gvIQP3ToEMXFxS7uRhzh5MmTTJkyBbDN+w4LC6vQcWcPOHn++ee59dZbHdajiHgfhbgT1K9fn+DgYAzDICsry9XtiAPMnj2bI0eO0KJFC5577rkKHzdv3jy2b99OZGRkpd8nFxFRiDuBxWIhOjoa0CV1b3Tw4EH7u93Tpk2r8OXwffv2MWnSJADmzJlD3bp1HdWiiHgphbiT6L6490pKSiI/P5+bbrqJxx57rELHGIbBoEGDKCwspHPnzvTu3dvBXYqIN1KIO4lC3Dv9/PPPvPTSS4BtyInFYqnQce+//z4fffQRgYGBpKenV/g4EZGzKcSdRCHunRISEigtLeXBBx/kzjvvrNAxp06dsg84GTVqFFdffbUDOxQRb6YQdxKFuPfZuHEjb7/9NhaLpVIPpU2ePJnMzEyuuOIK+3vlIiJVoRB3kvIQz8zMdHEnYgbDMBgzZgwAvXv35rrrrqvQcVu3bmXevHkALF68mNDQUIf1KCLeTyHuJDoT9y7/+Mc/WLduHUFBQfb3wy/FarUSGxtLWVkZjz/+OA899JCDuxQRb6cQd5LyED969CiFhYUu7kaqo6yszH4WPmjQIC6//PIKHbdkyRK+/vprwsLCmD9/vgM7FBFfoRB3ktq1a9tnQ+uSumd78803+f7774mIiCA+Pr5Cxxw+fNge/ElJSTRp0sSRLYqIj1CIO4nFYtEldS9w+vRp+3SysWPHUq9evQodN3r0aE6cOEH79u0ZOHCgI1sUER+iEHcihbjnS0tL45dffqFJkyYVnja2bt06li1bhsViISMjg4CAAAd3KSK+QiHuRApxz/brr7+SnJwM2F4Tq8iT5cXFxfTv3x+A2NhYOnXq5NAeRcS3KMSdSCHu2WbMmMHx48e5+uqreeqppyp0zOzZs/nxxx9p0KAB06ZNc3CHIuJrFOJOpBD3XFlZWfYnyqdPn16hS+J79uwhKSkJgLlz51K7dm0Hdigivkgh7kQKcc81adIkTp8+zW233cYjjzxyyf0Nw2DgwIGcPn2au+++mx49ejihSxHxNQpxJ1KIe6YdO3awdOlSoOJDTt59913WrFlDUFAQaWlpGnAiIg6hEHei8hDPzc3l5MmTLu5GKio+Ph6r1cqjjz7Kbbfddsn9T548aX9yfezYsbRq1crRLYqIj1KIO1FYWJj9vqjOxj3DV199xfvvv4+fn1+FH0ybMGEC2dnZNG/evMKLwYiIVIVC3Mmio6MBhbgnOHvIybPPPkubNm0uecyWLVtYuHAhAKmpqYSEhDi0RxHxbQpxJ9N9cc/xwQcf8NVXXxESEsKkSZMuuX9ZWRmxsbFYrVZiYmK4//77Hd+kiPg0hbiTKcQ9Q2lpqf1S+NChQyu01vnLL7/Mxo0bCQ8PZ+7cuY5uUUREIe5sCnHPsGzZMnbs2EHdunXtl9Qv5tChQ4wdOxaA5ORkoqKiHN2iiIhC3NkU4u6voKCAiRMnApCQkFChRVpGjBhBbm4uHTt2tC+zKiLiaApxJ1OIu7+FCxeSlZVF06ZNiYuLu+T+n376KW+88YZ9wIm/v78TuhQRUYg73dkhbhiGi7uR3zp27BgpKSmAbe73pZ4uLyoqsgf9gAEDuOGGGxzeo4hIOYW4k5W/YlZQUMCJEydc3I381vTp08nNzeW6666jZ8+el9x/5syZ7Ny5k0aNGjF16lQndCgicoZC3Mlq1KhBZGQkoEvq7mb//v0sWrQIgJSUlEteFt+1a5d9NOn8+fOJiIhweI8iImdTiLuA7ou7pwkTJlBcXMydd97JAw88cNF9DcNgwIABFBUVcd999/HEE084qUsRkTMU4i6gEHc/W7duZfny5YDtEvmlBpa89dZbrF27luDgYFJTUzXgRERcQiHuAgpx9xMfH49hGPz5z3/mxhtvvOi+ubm5DB06FIBx48Zx1VVXOaFDEZHfU4i7gELcvaxbt46///3v+Pv72+9xX8z48eM5ePAgLVu2rNBCMCIijqIQdwGFuPswDIPRo0cD0K9fP1q0aHHR/Tdt2kRqaioAaWlpBAcHO7xHEZELUYi7gELcffzlL3/h3//+NzVr1mTChAkX3ffsASc9e/bknnvucVKXIiLnpxB3gfIQz8zMxGq1urgb31VSUsK4ceMA27KpjRo1uuj+6enpbN68mYiICObMmeOMFkVELkoh7gJNmjTBYrFQXFzMkSNHXN2Oz3rllVf4+eefqV+/PiNHjrzovjk5OSQkJAC2BWEaNmzojBZFRC5KIe4CgYGB9rM+XVJ3jVOnTjF58mTA9n54rVq1Lrr/8OHDycvL46abbqJfv37OaFFE5JIU4i6i++KuNW/ePA4dOsSVV155yVBeu3Ytq1atws/PTwNORMStKMRdRCHuOocPH2bmzJmAbfZ3UFDQBfctLCy0DzgZPHgwHTp0cEqPIiIVoRB3EYW460ydOpVTp07RsWPHSy6XmpKSwu7du2nSpAlTpkxxUociIhWjEHcRhbhr7N69m4yMDABmzJiBn9+F/xPYuXOnfSzp/PnzL3nfXETE2RTiLqIQd43ExERKSkq47777Lvqet2EYxMXFUVxczIMPPshjjz3mxC5FRCpGIe4iCnHn27x5M6tWrQKwn2FfyMqVK/n0008JCQlh8eLFGnAiIm5JIe4i5SGenZ1NWVmZi7vxDWPHjgWgZ8+eF31A7cSJEwwbNgywrZN+5ZVXOqU/EZHKUoi7SKNGjQgICKCsrIycnBxXt+P1Pv74Yz755BMCAwNJSkq66L4JCQkcPnyYq6+++pKLwIiIuJJC3EX8/f2JiooCdEnd0axWq33aWFxcHFdcccUF9/3222/tD76lpaVd9PUzERFXU4i7kO6LO8eqVavYsmULtWrVIjEx8YL7lZaWEhsbi2EY9OnThzvvvNN5TYqIVIFC3IXOHoQijlFUVGRf83zMmDFERkZecN/U1FS+++476tSpw6xZs5zVoohIlSnEXUhn4o6XkZHBvn37aNSoEUOHDr3gfllZWfaz9BkzZtCgQQMndSgiUnUKcRdSiDtWbm6u/SG2SZMmUbNmzQvuO3ToUE6dOsUtt9xC3759ndWiiEi1KMRdSCHuWLNnz+bYsWO0bNnyosH897//nXfeeQd/f38yMjIuuoqbiIg70d9WLqQQd5ycnBzmzp0L2OZ/BwQEnHe/goICBg4cCNjOxq+77jqn9SgiUl0KcRcqD/GDBw9SXFzs4m68y+TJkykoKODmm2/mj3/84wX3mzZtGnv37iU6OppJkyY5r0ERERMoxF2ofv36BAcHYxgG2dnZrm7Ha/z000+88sorgO0htQstmbpjxw77SNJFixYRFhbmtB5FRMygEHchi8VCdHQ0oEvqZkpISKCsrIyHH36YP/zhD+fdxzAM+vfvT0lJCV27duXRRx91cpciItWnEHcx3Rc31zfffMNf/vIXLBYL06dPv+B+y5cv54svvqBGjRosXLhQA05ExCMpxF1MIW4ewzAYPXo0AE899RTXXnvtefc7fvw4I0aMAGDixIk0a9bMWS2KiJhKIe5iCnHz/P3vf+fLL78kODiYKVOmXHC/+Ph4jh49yjXXXMPw4cOd2KGIiLkU4i6mEDdHWVmZfdTo4MGD7X+uv7VhwwZeeuklANLT0wkMDHRajyIiZlOIu5hC3BzLly9n27Zt1K5d2x7mv1VSUkJsbCwAzz77LHfccYczWxQRMZ1C3MUU4tVXWFjI+PHjARg3bhx169Y9734LFy7k+++/p169esyYMcOZLYqIOIRC3MXKXzE7evQohYWFLu7GM6WmppKZmUl0dLR99bXfOnDgABMnTgRg5syZF51mJiLiKRTiLlanTh1CQ0MBjSStihMnTjBt2jQApkyZQo0aNc6735AhQ8jPz+f222/n6aefdmKHIiKOoxB3MYvFokvq1ZCSksKJEye45ppr6NOnz3n3+fDDD3nvvfcICAggPT1dA05ExGvobzM3oBCvmgMHDrBgwQLAFub+/v6/2yc/P59BgwYBMGLEiAu+Oy4i4okU4m5AIV41kyZNoqioiDvuuIP/+7//O+8+SUlJ7N+/n8svv9z+8JuIiLdQiLsBhXjlbd++nddeew248JCTbdu2MWfOHMA24KRmzZrObFFExOEU4m5AIV558fHxWK1W/vSnP3HLLbf8brvVaqV///6UlpbSrVs3unbt6oIuRUQcSyHuBhTilfPll1/y4Ycf4u/vb38y/beWLVvG+vXrqVmzpv2+uYiIt1GIuwGFeMWdPeSkb9++tGrV6nf7HD16lFGjRgEwefJkmjZt6tQeRUScRSHuBspDPDc3l5MnT7q4G/f217/+lW+++YYaNWrYF2/5rTFjxnDs2DGuu+46Bg8e7OQORUScRyHuBmrVqkVERASgs/GLKS0tJT4+HoDhw4cTFRX1u32+/PJLli5dCmjAiYh4P4W4m9Al9Ut79dVX+emnn6hXr579cvnZSkpK6N+/PwDPP/88t956q7NbFBFxKoW4m1CIX1x+fr798nliYqL9ysXZ5s2bx/bt24mMjCQlJcXZLYqIOJ1C3E0oxC9uwYIF5OTk0KxZM/vZ9tn27dvHpEmTAJgzZ84FJ5mJiHgThbibUIhf2NGjR+2jQ6dOnUpwcPA52w3DYNCgQRQWFtK5c2d69+7tijZFRJxOIe4mFOIXlpycTF5eHu3ataN79+6/2/7+++/z0UcfERgYSHp6+nlXbxMR8UYKcTehED+/vXv3kpqaCtiWV/3tBLJTp07ZB5yMGjWKq6++2uk9ioi4ikLcTZwd4oZhuLgb9zFhwgRKSkq45557uO+++363ffLkyWRmZnLFFVeQkJDggg5FRFxHIe4moqOjASgoKODEiRMu7sY9fPfdd7zxxhuAbdToby+Tb926lXnz5gGwePFiQkNDnd6jiIgrKcTdRI0aNYiMjAR0Sb3c2LFjMQyDmJgYbrjhhnO2Wa1WYmNjKSsr4/HHH+ehhx5yUZciIq6jEHcjui9+xqeffso///lPAgICSE5O/t32JUuW8PXXXxMWFsb8+fOd36CIiBtQiLsRhbiN1WplzJgxAMTGxtK8efNzth8+fNi+PSkpiSZNmji9RxERd6AQdyMKcZu3336bzZs3ExYWxvjx43+3ffTo0Zw4cYL27dszcOBAF3QoIuIeFOJuRCEOxcXF9qfMR44cSYMGDc7Zvm7dOpYtW4bFYiEjI4OAgABXtCki4hYU4m5EIQ4vv/wyu3fvpkGDBowYMeKcbcXFxfYlV2NjY+nUqZMrWhQRcRsKcTfi6yF+8uRJJk+eDMDEiRMJCws7Z/vs2bP58ccfadCgAdOmTXNFiyIibkUh7kbKQzwzMxOr1eribpxvzpw5HDlyhKuuuornn3/+nG179uwhKSkJgLlz51K7dm0XdCgi4l4U4m6kSZMmWCwWiouLOXLkiKvbcapDhw4xe/ZsAKZNm0ZgYKB9m2EYDBw4kNOnT3P33XfTo0cPV7UpIuJWFOJuJDAwkEaNGgG2s3FfkpSURH5+PjfeeCOPP/74Odveffdd1qxZQ1BQEGlpaRpwIiLyPwpxN+OL98V//vlnXnzxRcA25OTskD558iSDBw8GbCu4tWrVyiU9ioi4I4W4m/HFEE9MTKS0tJQHHniAu+6665xtEyZMIDs7m+bNmxMfH++iDkVE3JNC3M34Woj/+9//5q233sJisZCSknLOti1btrBw4UIA0tLSCAkJcUWLIiJuSyHuZnwpxA3DsC+f2qtXL9q1a2ffVlZWRmxsLFarlZiYmPOOIRUR8XUKcTfjSyH+z3/+k88//5ygoCCmTJlyzraXX36ZjRs3Eh4ezty5c13UoYiIe1OIuxlfCfGzh5wMHDiQZs2a2bcdOnSIsWPHApCcnExUVJQrWhQRcXsKcTdTHuJZWVmUlZW5uBvHefPNN9m6dSvh4eGMGzfunG0jRowgNzeXjh072pdZFRGR31OIu5lGjRoREBBAWVkZBw8edHU7DnH69GkSExMB22tj9erVs2/79NNPeeONN/Dz8+PFF1/E39/fVW2KiLg9hbib8ff3t18+9tZL6unp6ezfv5+oqCiGDBli/35RURFxcXEAxMXF0bFjR1e1KCLiERTibsib74vn5uYydepUACZPnkxoaKh928yZM9m5cyeNGjWy7yMiIhemEHdD3hziM2bM4Pjx47Ru3Zqnn37a/v1du3aRnJwMwPz584mIiHBRhyIinkMh7oa8NcSzsrKYP38+ANOnTycgIACwvS8+YMAAioqKuO+++3jiiSdc2KWIiOdQiLuh6OhowPtCfPLkyRQWFnLrrbfy6KOP2r//1ltvsXbtWoKDg0lNTdWAExGRClKIuyFvPBP/8ccfWbJkCXDukJPc3FyGDh0KwLhx47jqqqtc1aKIiMdRiLshbwzx+Ph4rFYrjzzyCLfffrv9++PHj+fgwYO0bNnSvviLiIhUjELcDZWH+MGDBykuLnZxN9W3YcMG/vrXv+Ln58f06dPt39+0aROpqamAbcBJcHCwq1oUEfFICnE3VL9+fYKCgjAMg+zsbFe3Uy1nDzl55plnaNOmDXDugJOePXtyzz33uLJNERGPpBB3Q35+fl7zcNuHH37I+vXrCQkJYdKkSfbvp6ens3nzZiIiIpgzZ47rGhQR8WAKcTflDffFS0tLiY+PB2DIkCH2H0yys7Pt66VPnz6dhg0buqxHERFPphB3U94Q4q+//jo//PADderUsU8lAxg+fDgnT57kpptuol+/fi7sUETEsynE3ZSnh3hhYSETJkwAICEhgdq1awOwdu1aVq9ejZ+fHxkZGRpwIiJSDQpxN+XpIb5w4UKysrJo2rQpAwYMAGzBXj7gZPDgwXTo0MGVLYqIeDyFuJvy5BA/fvy4/VWypKQkQkJCAEhJSWH37t00adKEKVOmuLJFERGvoBB3U54c4tOnTyc3N5e2bdvSs2dPAH766SdSUlIAWLBgAbVq1XJliyIiXkEh7qbKQ/zo0aMUFha6uJuK++WXX1i0aBFgO/P29/fHMAzi4uIoLi7mwQcf5E9/+pOLuxQR8Q4KcTdVp04d+6ztzMxMF3dTcRMmTKCoqIjOnTvz4IMPArBy5Uo+++wzQkJCWLx4sQaciIiYRCHupiwWi8ddUv/+++95/fXXAZg5cyYWi4UTJ04wbNgwwLZO+pVXXunKFkVEvIpC3I15WojHx8djGAaPP/44N910E2B7vezw4cNcffXVjBw50sUdioh4F4W4G/OkEP/iiy/429/+hr+/P8nJyQB8++23ZGRkALZlVoOCglzZooiI11GIuzFPCXHDMBg9ejQA/fr1o2XLlpSWlhIbG4thGPTp04fOnTu7uEsREe+jEHdjnhLi7777Lhs3biQ0NNS+SltqairfffcdderUYdasWS7uUETEOynE3ZgnhHhJSYl9yMmIESNo1KgRWVlZJCYmAjBjxgwaNGjgyhZFRLyWQtyNeUKIL1myhJ9//pnIyEj7g2tDhw7l1KlT3HLLLfTt29fFHYqIeC+FuBsrD/Hc3FxOnjzp4m5+79SpU/YZ4RMmTCA8PJy///3vvPPOO/j7+5ORkYGfn/4vJiLiKPob1o3VqlWLiIgIwD3PxufPn8+hQ4e44ooreOGFFygoKGDgwIGA7Wz8uuuuc3GHIiLeTSHu5tz1kvqRI0eYOXMmAMnJyQQFBTFt2jT27t1LdHS0/QxdREQcRyHu5tw1xKdOncrJkye5/vrriYmJYceOHfZQX7RoEWFhYS7uUETE+ynE3Zw7hviePXtIT08HbE+fWywW+vfvT0lJCV27duXRRx91cYciIr5BIe7m3DHEExMTKSkpoUuXLtx7770sX76cL774gtDQUBYtWqQBJyIiTqIQd3PuFuL/+c9/WLlyJWAbNXr8+HFGjBgB2J5Qv/zyy13ZnoiITwlwdQNyceUh7i7jSMeOHQtAjx49uP766+nXrx9Hjx7lmmuuYfjw4S7uTkTEt+hM3M2dfSZuGIZLe/n444/5+OOPCQwMJCkpiQ0bNvDyyy8DtgEngYGBLu1PRMTX6EzczUVHRwOQn5/Pr7/+Sp06dVzSh9VqZcyYMQD079+fyy67jG7dugHw7LPPcscdd7ikLxERX6YzcTdXo0YNIiMjAdfeF1+9ejVbtmyhVq1aJCYmsnDhQr7//nvq1avHjBkzXNaXiIgvU4h7AFc/3FZcXExCQgIAo0ePprCwkIkTJwIwa9Ys+w8ZIiLiXApxD+DqEM/IyGDv3r00bNiQYcOGMWTIEPLz87n99tt56qmnXNKTiIgoxD2CK0M8Ly+PpKQkACZNmsRnn33GX//6VwICAkhPT9eAExERF9KDbR7AlSE+e/Zsjh49SsuWLXnyySdp3749YJsdfu211zq9HxEROUOnUR7AVSF+8OBB5syZA8C0adNISUlh//79XH755YwfP96pvYiIyO/pTNwDuCrEJ0+eTEFBAZ06dbKfiQMsXryYmjVrOrUXERH5PZ2Je4CzV21z1oIvO3futC/kMn36dOLi4igtLaVbt248/PDDTulBREQuTiHuAZo0aYLFYqGoqIgjR4445TMTEhIoKyvj//7v/9i3bx/r16+nZs2aLFiwwCmfLyIil6YQ9wCBgYE0atQIcM4l9W+//ZZ33nkHi8XC6NGjGTVqFGC7vN60aVOHf76IiFSMQtxDOOu+uGEYjB49GoA+ffqwbNkyjh07xnXXXcfgwYMd+tkiIlI5CnEPUb6GuqNDfM2aNfzrX/8iODiYhx9+mKVLlwK2BV804ERExL0oxD2EM87Ey8rK7ENO4uLimDRpEgDPP/88t9xyi8M+V0REqkYh7iGcEeIrVqxg27Zt1K5dm/DwcLZv305kZCQpKSkO+0wREak6hbiHcHSInz592r6AywsvvMDMmTMBmDNnDnXr1nXIZ4qISPUoxD2Eo0M8NTWVAwcOEBUVxdatWyksLKRz58707t3bIZ8nIiLVpxD3EOUhnpWVRVlZmam1T5w4QXJyMgB//OMfWbNmDYGBgaSnp2OxWEz9LBERMY9C3EM0btwYf39/ysrKOHjwoKm1Z8yYwYkTJ2jdujXvv/8+YJsbfvXVV5v6OSIiYi6FuIfw9/cnKioKMPeSemZmpn0VttatW5OZmckVV1xBQkKCaZ8hIiKOoRD3II64Lz5x4kROnz5Nhw4d+PDDDwHbgJMaNWqY9hkiIuIYCnEPYnaIb9++nddeew2AkpISysrKePzxx3nooYdMqS8iIo6lEPcgZof4uHHjsFqttG/fnm3bthEWFsb8+fNNqS0iIo6nEPcgZob4+vXr+eCDD7BYLOzduxeAqVOn0qRJk2rXFhER51CIexCzQvzsISdXXXUVubm5tG/fngEDBlS7RxERcR6FuAcxK8Tff/99vv76a4KCgvj555+xWCxkZGQQEBBgRpsiIuIkCnEPUh7iBw8epLi4uEo1SktLiY+PB6BWrVoAxMbG0qlTJ3OaFBERp1GIe5D69esTFBSEYRhkZ2dXqcarr77Kjz/+SI0aNTh27BgNGjRg2rRpJncqIiLOoBD3IH5+ftWaK15QUMDEiRMB2ytlAPPmzaN27dqm9SgiIs6jEPcw1bkvvmDBAnJycggJCaG0tJR77rmH7t27m92iiIg4iULcw1Q1xI8dO2afC3769GmCgoJITU3VgBMREQ+mEPcwVQ3x5ORk8vLy7E+gjx07llatWpnen4iIOI9C3MNUJcT37dtHamoqYHs6vXnz5vYn1EVExHMpxD1MVUJ8woQJ57ySlpaWRkhIiOm9iYiIcynEPUxlQ/y///0vy5cvt38dExPDfffd55DeRETEuRTiHqY8xI8ePUphYeEl9x87dqz938PDw5k7d67DehMREedSiHuYOnXqEBoaCkBmZuZF9/3ss8/4xz/+Yf86OTmZqKgoh/YnIiLOoxD3MBaLpUKX1A3DYMyYMfavO3bsSP/+/R3en4iIOI8mXnigu+66i9DQUPLz8y+4z8cff0xZWRkdOnTAz8+P119/HX9/fyd2KSIijmYxDMOoyoF5eXlERESQm5tLeHi42X2JiIj4rIpmrC6ni4iIeCiFuIiIiIdSiIuIiHgohbiIiIiHUoiLiIh4KIW4l1u3DiwW+PVXV3cCTz8N3bq5ugsREe+hEPcid94JQ4e6ugsREXEWhbiIiIiHUoi7yJ13wqBBtjPnOnWgYUN46SXIz4dnnoFataB5c1iz5swxP/wADz0EYWG2/Xv3hqNHbduefhq++AIWLLBdPrdYYN++M8du3gw33AChoXDrrfDTT2e27d4Njz5qqxkWBjfeCJ98cm6/zZrBtGnw7LO23po2tfV7tu+/h7vvhho1oF496NcPTp0y7Y9MRER+QyHuQsuWQWQkbNxoC/T+/eHPf7aF7H/+A/ffbwvqggLIyYHOnaF9e9i0Cf7xDzh0CJ54wlZrwQK45RZ4/nnbvjk58L8l1gFISIA5c2zHBgTYwrjcqVO2Hw4++QS2bLF9bteu8Msv5/Y7Z47tB4EtWyAuztbvjz/athUUwAMP2H4g+fe/4e23bfUGDnToH6GIiG8zqig3N9cAjNzc3KqW8GmdOxvG7bef+bq01DBq1jSM3r3PfC8nxzDAML7+2jDGjzeM++47t8aBA7btP/10puaQIefu8/nntn0++eTM9/72N9v3Cgsv3F+bNoaxaNGZry+/3DB69TrztdVqGA0aGEZ6uu3rl14yjDp1DOPUqXM/x8/PMA4etH391FOG8eijF/5MERGxqWjG6kzcha677sy/+/vbLkG3bXvmew0b2v55+LDtcvjnn9sud5f/at3atn337sp9VuPGZ+qC7RL+6NHQpg3Urm2r/eOPvz8TP7uGxQKNGp2psWMHtGsHNWue2ee228BqPffSvYiImEdTzFwoMPDcry2Wc79nsdj+abXafnXtCjNm/L5OeShX9LPOrgswahT8858wezZcdZXtnvbjj0Nx8aX7La9hGGfq/taFvi8iItWjEPcQ118Pf/mL7QGzgAv8rxYUBGVlla/95Ze2B+P++Efb16dOnftQXEW0aWO7x5+ff+Zs/KuvwM8PWrasfE8iInJpupzuIQYMgOPHoXt324Nwe/bA2rW2B9TKg7tZM/j2W1sAHz165iz5Uq66Ct59F777Dv77X+jRo+LHluvZE0JC4KmnYNs226X/QYNsD+aV3xYQERFzKcQ9RFSU7cy2rMz29Pi118KQIRARYTvbBRg50nZvvU0bqF//9/e0L2TePNtT5bfeartkf//9tjP/yggNtV2SP37c9ora44/DPffA4sWVqyMiIhVnMQzDqMqBFR1YLiIiIpVT0YzVmbiIiIiHUoiLiIh4KIW4iIiIh1KIi4iIeCiFuIiIiIdSiIuIiHgohbiIiIiHUoiLiIh4KIW4iIiIh1KIi4iIeCiFuIiIiIdSiIuIiHgohbiIiIiHUoiLiIh4KIW4iIiIh1KIi4iIeCiFuIiIiIdSiIuIiHgohbiIiIiHUoiLiIh4KIW4iIiIh1KIi4iIeCiFuIiIiIdSiIuIiHgohbiIiIiHUoiLiIh4KIW4iIiIhwqo6oGGYQCQl5dnWjMiIiJyJlvLs/ZCqhziJ0+eBOCyyy6ragkRERG5iJMnTxIREXHB7RbjUjF/AVarlezsbGrVqoXFYqlygyIiInIuwzA4efIkUVFR+Pld+M53lUNcREREXEsPtomIiHgohbiIiIiHUoiLiIh4KIW4iIiIh1KIi4iIeCiFuIiIiIdSiIuIiHgohbiIiIiHUoiLiIh4KIW4iIiIh1KIi4iIeCiFuIiIiIf6f6gIz3y7UZ38AAAAAElFTkSuQmCC\n", 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", 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" ] @@ -949,6 +949,14 @@ "\n", "plot_atommapping_network(new_network)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a0c364c1", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -967,7 +975,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.10.10" } }, "nbformat": 4, From 0fae0c7f4430fe80b0df57c46e4d02083e0ab70a Mon Sep 17 00:00:00 2001 From: richard gowers Date: Tue, 11 Apr 2023 18:40:07 +0100 Subject: [PATCH 06/14] update ApplyingProtocol for 0.7 --- ...pplyingProtocolToNetworkQuickrunDemo.ipynb | 89 +++++++++---------- 1 file changed, 41 insertions(+), 48 deletions(-) diff --git a/openmm-rbfe/ApplyingProtocolToNetworkQuickrunDemo.ipynb b/openmm-rbfe/ApplyingProtocolToNetworkQuickrunDemo.ipynb index ffb9439..84acb4d 100644 --- a/openmm-rbfe/ApplyingProtocolToNetworkQuickrunDemo.ipynb +++ b/openmm-rbfe/ApplyingProtocolToNetworkQuickrunDemo.ipynb @@ -26,32 +26,14 @@ "execution_count": 1, "id": "09980f3d", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "gufe v0.4\n", - "openfe v0.3.post295+gbd8a2fe\n" - ] - } - ], + "outputs": [], "source": [ "import gufe\n", "import openfe\n", "from rdkit import Chem\n", "from openff.units import unit\n", "\n", - "# notebook written for gufe v0.4 and openfe v0.4\n", - "print(f\"gufe {gufe.__version__}\")\n", - "print(f\"openfe {openfe.__version__}\")" + "# notebook written for gufe v0.7 and openfe v0.7\n" ] }, { @@ -71,12 +53,12 @@ "metadata": {}, "outputs": [], "source": [ - "def load_ligands(sdf_file) -> list[openfe.setup.SmallMoleculeComponent]:\n", + "def load_ligands(sdf_file) -> list[openfe.SmallMoleculeComponent]:\n", " # load the ligands from multi molecule SDF file\n", " # we just need to load using RDKit\n", " sdf_supp = Chem.SDMolSupplier(sdf_file, removeHs=False)\n", " # and pass the rdkit Mols into openfe\n", - " return [openfe.setup.SmallMoleculeComponent(m) for m in sdf_supp]\n", + " return [openfe.SmallMoleculeComponent(m) for m in sdf_supp]\n", "\n", "ligands = load_ligands('inputs/ligands.sdf')" ] @@ -101,7 +83,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "We have created \"\" with 7 nodes and 6 edges\n" + "We have created \"\" with 7 nodes and 6 edges\n" ] } ], @@ -161,7 +143,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -204,7 +186,7 @@ "metadata": {}, "outputs": [], "source": [ - "from openfe.protocols.openmm_rbfe import RelativeLigandTransform" + "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol" ] }, { @@ -223,7 +205,7 @@ "metadata": {}, "outputs": [], "source": [ - "settings = RelativeLigandTransform.default_settings()" + "settings = RelativeHybridTopologyProtocol.default_settings()" ] }, { @@ -243,7 +225,7 @@ "outputs": [], "source": [ "# create the Protocol we'll apply to our network\n", - "prot = RelativeLigandTransform(settings)" + "prot = RelativeHybridTopologyProtocol(settings)" ] }, { @@ -265,10 +247,10 @@ "outputs": [], "source": [ "# load our protein model\n", - "protein = openfe.setup.ProteinComponent.from_pdb_file('./inputs/181L_mod_capped_protonated.pdb')\n", + "protein = openfe.ProteinComponent.from_pdb_file('./inputs/181L_mod_capped_protonated.pdb')\n", "\n", "# define the solvent state\n", - "solvent = openfe.setup.SolventComponent(\n", + "solvent = openfe.SolventComponent(\n", " positive_ion='Na', negative_ion='Cl',\n", " neutralize=True, ion_concentration=0.15 * unit.molar\n", ")" @@ -285,6 +267,16 @@ { "cell_type": "code", "execution_count": 10, + "id": "46a87590", + "metadata": {}, + "outputs": [], + "source": [ + "openfe.ChemicalSystem?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "id": "d97e6000", "metadata": {}, "outputs": [ @@ -306,32 +298,32 @@ " # define the complete ChemicalSystem for the A and B states\n", " # these are identical except for the ligand component,\n", " # representing the alchemical shift we wish to simulate\n", - " stateA_complex = openfe.setup.ChemicalSystem(\n", + " stateA_complex = openfe.ChemicalSystem(\n", " components={'solvent': solvent,\n", " 'ligand': ligA,\n", " 'protein': protein},\n", " )\n", - " stateB_complex = openfe.setup.ChemicalSystem(\n", + " stateB_complex = openfe.ChemicalSystem(\n", " components={'solvent': solvent,\n", " 'ligand': ligB,\n", " 'protein': protein},\n", " \n", " )\n", " # similarly define the solvent leg\n", - " stateA_solvent = openfe.setup.ChemicalSystem(\n", - " components={'solvent': solvent, 'ligand': ligA}\n", + " stateA_solvent = openfe.ChemicalSystem(\n", + " components={'solvent': solvent, 'ligand': ligA},\n", " )\n", - " stateB_solvent = openfe.setup.ChemicalSystem(\n", - " components={'solvent': solvent, 'ligand': ligB}\n", + " stateB_solvent = openfe.ChemicalSystem(\n", + " components={'solvent': solvent, 'ligand': ligB},\n", " )\n", " \n", - " complex_trans = openfe.setup.Transformation(\n", + " complex_trans = openfe.Transformation(\n", " stateA=stateA_complex,\n", " stateB=stateB_complex,\n", " mapping={'ligand': edge},\n", " protocol=prot,\n", " )\n", - " solvent_trans = openfe.setup.Transformation(\n", + " solvent_trans = openfe.Transformation(\n", " stateA=stateA_solvent,\n", " stateB=stateB_solvent,\n", " mapping={'ligand': edge},\n", @@ -369,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "f328af99", "metadata": {}, "outputs": [], @@ -399,7 +391,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "id": "6fc00526", "metadata": {}, "outputs": [ @@ -407,7 +399,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "-rw-rw-r-- 1 richard richard 839K Nov 1 11:20 out.json\r\n" + "-rw-r--r-- 1 richard richard 841K Apr 11 17:26 out.json\r\n" ] } ], @@ -426,7 +418,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "id": "c81c569d", "metadata": {}, "outputs": [ @@ -436,13 +428,13 @@ "True" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "t2 = openfe.setup.Transformation.load('out.json')\n", + "t2 = openfe.Transformation.load('out.json')\n", "\n", "trans == t2" ] @@ -467,7 +459,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "id": "9ef9fd11", "metadata": { "scrolled": true @@ -485,9 +477,10 @@ " with transformation.dump(filename).\r\n", "\r\n", "Options:\r\n", - " -d DIRECTORY directory to store files in (defaults to temporary directory)\r\n", - " -o FILE output file (JSON format) for the final results\r\n", - " -h, --help Show this message and exit.\r\n" + " -d, --work-dir DIRECTORY directory to store files in (defaults to current\r\n", + " directory)\r\n", + " -o FILE output file (JSON format) for the final results\r\n", + " -h, --help Show this message and exit.\r\n" ] } ], @@ -512,7 +505,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.10" } }, "nbformat": 4, From 19935383b454b61aac490b691ea7d4fa5d2464f5 Mon Sep 17 00:00:00 2001 From: richard gowers Date: Tue, 11 Apr 2023 22:05:48 +0100 Subject: [PATCH 07/14] updates for v0.7.1 --- networks/Preparing AlchemicalNetworks.ipynb | 4 ++-- networks/ligand_networks_for_developers.ipynb | 10 +++++----- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/networks/Preparing AlchemicalNetworks.ipynb b/networks/Preparing AlchemicalNetworks.ipynb index 397a268..06c0087 100644 --- a/networks/Preparing AlchemicalNetworks.ipynb +++ b/networks/Preparing AlchemicalNetworks.ipynb @@ -381,7 +381,7 @@ " 'n_replicas': 11},\n", " 'engine_settings': {'compute_platform': None},\n", " 'integrator_settings': {'timestep': 4 ,\n", - " 'collision_rate': 1 ,\n", + " 'collision_rate': 1.0 ,\n", " 'n_steps': 250 ,\n", " 'reassign_velocities': False,\n", " 'splitting': 'V R O R V',\n", @@ -554,7 +554,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 14, diff --git a/networks/ligand_networks_for_developers.ipynb b/networks/ligand_networks_for_developers.ipynb index 0ed2789..0bac65b 100644 --- a/networks/ligand_networks_for_developers.ipynb +++ b/networks/ligand_networks_for_developers.ipynb @@ -566,7 +566,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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", + "image/png": 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uXr2KFStWQFVVHD16VGvv16+fVhX08vKy4QwbR3l5OVJSUrR9CBMSEpCfn6/r4+PjowuEffr0YSAkInJwDIDUpAkhsHPnTpjNZmzatElXFZw2bRoURUFoaKiNZ9l4ysvLcfDgQS0Q7tq1q0Yg9PX1rREIDQaDjWZMRES2wABIzcaVK1cQGxsLs9mMY8eOae0PP/wwTCYTJk+e7BBVwerKyspqBMKCggJdn1atWukCYUhICAMhEVEzxwBIzY4QAvHx8VBVFZs3b0ZpaSkAuXp2+vTpUBQFffr0sfEsbaOsrAwWi0Xbh3DXrl0oLCzU9WnVqhUiIyO1jamDg4MZCImImhkGQGrWLl++rFUFjx8/rrUPGDAAiqLg2Wefhaenpw1naFtlZWVITk7WBcKioiJdn9atW+sCYa9evRgIiYiaOAZAcggVFRWIi4uDqqr45z//ibKyMgByxWxlVTAkJMTGs7S90tJSJCUlaauMd+/eXSMQtmnTRhcIg4KCGAiJiJoYBkByOJcuXcLy5cthNptx8uRJrX3gwIFQFAWTJk2Ch4eHDWdoP0pLS5GYmKgLhNevX9f1ue+++xAZGaltTB0UFNQs3raPiKg5YwAkh1VRUYHvv/8eqqpiy5YtWlXQx8cHM2bMgKIoCA4OtvEs7cvNmzdrBMIbN27o+rRt2xaRkZF45plnMGnSpHp8bcDVtd6GIyJyaNbkNV7joWbFYDDgsccew4YNG3Du3Dm888476NKlC/Ly8vDZZ5+hd+/eiIiIQGxsbI2Q46hcXV0xaNAgvP7669i+fTtyc3Oxe/duvP3223jsscfg7u6OK1euYOPGjfjrX/96x7GiooAXXpAPX1+gTRvgjTeAyj8zAwOBt98GZs8GfHyA+fNl+6ZNQK9egJub7LNokX7cwEBg4UJg6lTAywvw8wM+/VTf58MPgd69AU9PICAAeP55oPpamOXL5Zy+/Rbo2VOOM2oUcOGCfpxly+TxFi2Ahx4CPv/cii8mEVFTIeogLy9PABB5eXl16U5kV8rLy8W3334rJkyYIIxGowAgAAhfX1/x61//WqSmptp6inatuLhY7Nq1SyxcuFAoinLHvpGRQnh5CfHSS0JkZAixapUQHh5CmM3yeOfOQrRsKcTf/ibEsWPykZgohMEgxFtvCZGZKcSyZUK4u8t/K3XuLIS3txDvviv7/P3vQhiNQmzfXtXno4+E+P57IU6eFOK774To0UOI556rOr5smRAuLkI89pgQBw4IkZQkRM+eQkydWtXHbBaiQwchNm2S42zaJETr1kIsX34PX0AiokZiTV7jJWByKBcuXMCyZcsQExOD06dPa+0RERFQFAUTJ06Eu7u77SbYxEVFAZcvA2lpQOUtg7//PbB1K3DkiKzkhYYC//xn1TnTpgFXrgDbt1e1vfYa8O9/y3EAeV7PnsDXX1f1mTwZyM8Htm2rfS4bNgDPPQdcvSqfL18OzJkDHD8OdO0q2z7/HHjrLeDiRfm8UyfgvfeAKVOqxnn7bfkae/f+vK8JEVFj4SVgotvo0KEDXn/9dZw4cQJff/01xo8fD6PRiD179mDmzJnw9/fHyy+/jPT0dFtPtckaOLAq/AFAeDhw7BhQXi6f9++v75+eDkRE6NsiIvTnVI5TXXi4PLdSXBwwYgTg7w94ewMzZwLXrgHVFzx7eFSFPwDo0EEGVkCG0HPngOhoeXm48vH228CJE9Z9DYiI7B0DIDkkg8GAUaNGYfPmzTh79iwWLlyITp06IScnB5988gmCgoIwdOhQrFq1CsXFxbaebrNy6xaNQugDY2VbXVSed+YM8PjjQHCwvJ8wKQlYvFge+++e4QAAF5ea51e+VkWF/DcmBjh4sOqRmgr88EPd5kNE1FQwAJLD8/PzwxtvvIGTJ09i27ZtGDduHIxGI3bt2oUZM2bA398fr7zyCjIyMmw91Sbh1rD0ww/Agw8CRmPt/YOCgN279W179wLdu+vPqW3chx6SHycmAmVlcvHIwIHy3Kws6+bdrp2sHp48CXTrpn906WLdWERE9o4BkOi/jEYjRo8ejS1btuDMmTN46623EBAQgOzsbHz00Ufo2bMnIiMj8eWXX6KkpMTW07Vb584Br7wCZGYCa9bI1bovvXT7/r/9LfDdd3KV79GjQGws8NlnwIIF+n579gDvvy/7LF4s7/GrHLdrVxkAP/1UBriVK4F//MP6uf/5z8C77wKffCJf5/BhuSr4ww+tH4uIyJ4xABLVwt/fH//zP/+DU6dO4auvvsLYsWNhMBiQkJCAadOmwd/fHwsWLEBmZqatp2p3Zs4EbtwABgwAfvUr4MUXAZPp9v3DwoD164G1a+Ul3D/9SS7MmD1b3++3v5WXdkNDZVhctAgYOVIe69tXhrT33pNjrF4tg5y15s0DliyRC0Z69wYiI+XHrAASUXPDVcBEdXT+/HksXboUS5Yswfnz57X2qKgoKIqC8ePHw83NzYYztL2oKBnGPv64fscNDAReflk+iIiodlwFTNQAOnbsiDfffBOnTp3C1q1bMWbMGBgMBsTHx2PKlCno2LEjXn31VRw7dszWUyUiIrojBkAiKzk7O2Ps2LH417/+hVOnTuFPf/oT/P39cfXqVXzwwQfo3r07hg8fjnXr1uHmzZu2ni4REVENvARMVA/Kysqwbds2qKqKr7/+GpXfVm3btsWcOXMwf/58dOvWzcazJCKi5syavMYASFTPzpw5g6VLl2Lp0qXIqrYXyfDhw6EoCsaNGwdXV1cbzpCIiJojBkAiO1BWVoavvvoKZrMZ33zzjVYVvP/++7WqYNfqb0tBRER0DxgAiezM6dOnsWTJEixduhQXK994FsCIESNgMpkwbtw4uNz6NhXUpMTExMDPzw9hYWHo0KGDradDRA6IAZDITpWWluKrr76CqqrYvn27VhVs164d5s6di/nz56MLN51rksLCwmCxWAAA7du3R1hYmO7RqVMnON36nndERPWIAZCoCTh16pRWFbx06RIAwMnJCSNGjICiKBg7diyrgk1EWVkZFixYgP/85z/IyMhAReUbC1fTunXrGqGwa9euMBi4GQMR1Q8GQKImpLS0FFu3boXZbMb27du19vbt2yM6Ohrz5s1DYGCg7SZIVikqKsKhQ4eQnJysPVJTU1FWVlajr7e3N0JDQ3WhsEePHnB2drbBzImoqWMAJGqiTp48iZiYGHzxxRe4fPkyAFkVHDlyJBRFwZgxYxgOmqCSkhKkpqbqQmFKSkqt7ynt7u6OkJAQXSjs1auXw7/LDBHdHQMgURN38+ZNbN26FaqqYseOHVp7hw4dtKpg586dbThDulelpaXIyMjQAqHFYoHFYkFhYWGNvi4uLggODtaFwpCQEHh4eNhg5kRkrxgAiZqR48ePY8mSJfjiiy9w5coVALIqOHr0aJhMJjzxxBOsCjYTFRUVOH78uK5SmJycjJycnBp9DQYDevbsqQuFffv25c9oIgfGAEjUDN28eRNbtmyB2WzGd999p7X7+/sjOjoa0dHR6NSpkw1nSA1BCIEzZ87oAmFSUpJ2i8CtHnzwQV0oDA0NRZs2bRp51kRkCwyARM3csWPHEBMTg2XLluHq1asAZEVo9OjRUBQFo0ePZlWwGRNC4MKFC7pQaLFYcPbs2Vr7d+rUqcYKZO5VSNT8MAASOYiSkhJs2bIFqqoiLi5Oa+/YsaN2r2DHjh1tOENqTFevXoXFYtEFw+PHj9fal3sVEjU/DIBEDujo0aNaVfDatWsAZFXwiSeegKIoGDVqFIxGo41nSY0tLy8PBw8e1IVC7lVI1DwxABI5sJKSEmzevBmqqmLnzp1ae0BAAObNm4fo6Gj4+/vbcIZka9yrkKh5YgAkIgBAZmYmzGYzli9fjuzsbACyKjhmzBgoioKRI0eyKkgAat+r8NChQyguLq7Rl3sVEtknBkAi0ikuLsamTZtgNpuRkJCgtXfq1Anz58/H3Llz4efnZ8MZkj0qKyvT7VVYudiEexUS2ScGQCK6rfT0dJjNZsTGxmr7yxmNRowdOxaKomDEiBGsCtJtca9CIvvFAEhEd1VcXIyNGzdCVVXs3r1ba+/cubNWFeRWIVQX3KuQyD4wABKRVdLS0hATE4PY2Fjk5uYCAJydnfHkk0/CZDJhxIgRXAlKVrF2r8LOnTvXCIX8A4TIOgyARPSz3LhxAxs2bIDZbMaePXu09sDAQK0q2L59exvOkJo67lVI1HAYAInonqWmpsJsNmPFihXIy8sDIKuC48aNg6IoGD58OKuCVC+4VyFR/WAAJKJ6c/36dWzYsAGqqmLfvn1a+wMPPID58+djzpw5aNeunQ1nSM0R9yoksh4DIBE1iMOHD0NVVaxcuRL5+fkAZFXwqaeegqIoePTRR1mJoQZj7V6Fffr0qbFXoaurqw1mTtQ4GACJqEEVFRVh/fr1UFUV+/fv19q7du0Kk8mE2bNn4/7777fhDMlRcK9CoioMgETUaFJSUmA2m7Fq1SqtKuji4oLx48dDURRERUWxKkiNinsVkqNiACSiRldUVIS1a9fCbDbjxx9/1Nq7deumVQXbtm1rwxmSI6ttr8Lk5GRcunSp1v7cq5CaIgZAIrKpgwcPQlVVrF69GgUFBQAAV1dXTJgwASaTCVFRUdzKg+zCrXsVJicnc69CarIYAInILhQWFmLt2rVQVRWJiYlae/fu3WEymTBr1izcd999NpwhUU3cq5CaKgZAIrI7ycnJMJvNWL16tXaDvqurK55++mkoioKhQ4fylybZLe5VSE0BAyAR2a2CggKsWbMGqqoiOTlZa+/Ro4dWFeS9VtQUcK9CsjcMgETUJCQlJUFVVXz55ZcoKioCALi5uWHixIlQFAWDBw9mVZCalJKSEqSlpelCYUpKCvcqpEbBAEhETUp+fr5WFbRYLFp7z549YTKZMHPmTLRu3dqGMyT6+bhXITUWBkAiapKEEEhMTISqqlizZg2uX78OQFYFn3nmGSiKgoiICFYFqcnjXoXUEBgAiajJy8/Px+rVq6GqKlJSUrT2oKAgKIqCGTNmoFWrVjacIVH94l6FdK8YAImo2RBC4MCBA1BVFWvXrtWqgi1atMCkSZOgKArCw8NZFaRmi3sVUl0xABJRs5SXl4dVq1ZBVVUcPnxYaw8ODobJZMKMGTPg6+truwkSNRLuVUi1YQAkomZNCIH9+/dDVVWsW7cON27cACBXVT777LMwmUwYOHAgf8GRQ+FehcQASEQOIzc3V6sKpqamau29e/eGoiiYPn06fHx8bDhDItvhXoWOpbCwEN7e3gyAROQ4hBDYt28fzGYz1q1bp+275u7ujsmTJ0NRFAwYMIBVQXJ43KuweXNycmIAJCLHlJOTg5UrV0JVVRw5ckRr79OnD0wmE6ZNm8aqIFE13Kuw+WAAJCKHJ4TA3r17oaoq1q9fj5KSEgCAh4cHpkyZAkVR0L9/f1YFiWrBvQqbJgZAIqJqsrOztapgenq61t63b1/88Y9/xMSJE204O6KmoXKvwuorkJOSkrhXoR1hACQiqoUQArt374aqqti4cSNKSkoQGhqK5OTken+tqCigb1/g44/rfWirnD4NdOkCWCxyPkT1jXsV2g8GQCKiu7h27RpWrFiBXbt2YfPmzT97nPh4YNgwICcHqL4NIQMgOTLuVWgbDIBERHUkhLinXzQMgER101z3KrSX73Wg7gHQ/r6KRESNrC7hTwjg/feBBx4A3N2BPn2AjRtluBo2TPZp1QpwcgJmz646r6ICeO01oHVroH174M9/1o/74YdA796ApycQEAA8/zxQfeHl8uUyVH77LdCzJ+DlBYwaBVy4oH+Nt94COnYE3NzkL6JvvvlZXwqiBuXj44PIyEj85je/wcqVK5GWlob8/Hzs3bsXixcvRnR0NEJDQ+Hi4oLs7Gzs2LED77//PiZPnozu3bvD19e3xvm17WnYUOLj5fd4bm6jvWSD4e6ORER18MYbwObNwP/9H/Dgg0BCAjB9ugxmmzYBTz8NZGYCLVvKgFgpNhZ45RVg/35g3z4ZDiMigBEj5HGDAfj734HAQODUKRkAX3sN+PzzqjGuXwc++ABYuVL2nz4dWLAAWL1aHv/kE2DRIkBVgdBQ4IsvgCefBNLS5FyJ7JmnpyfCw8MRHh6utd1ur8KCggIkJCQgISFB68u9Cn8mUQd5eXkCgMjLy6tLdyKiZqWwUIgWLYTYu1ffHh0txJQpQsTFCQEIkZOjPx4ZKcTgwfq2hx8W4ne/u/1rrV8vRJs2Vc+XLZNjHz9e1bZ4sRDt2lU99/MT4n//t+brPP+8/PjUKTmGxXL71yWyd6WlpeLw4cMiNjZWvPTSS2LIkCHCy8tLAKjxcHFxEaGhoSI6OlosXrxY7Nu3TxQVFdXpdSoqhHjvPSG6dJHf9yEhQmzYUPV9VP0xa5Y8JzJSiBdfFOLVV4Vo1Up+f775pn7cRYuECA4WwsNDiI4dhXjuOSEKCqqOL1smhI+PEN98I8RDDwnh6SnEyJFCZGXpx/niC3nczU2IHj3kz4Pq6prXWAEkIrqLI0eA4uKqql2lmzdlxe1OQkL0zzt0AC5frnoeFwe88458jfx8oKxMvlZRkbwsDAAeHkDXrrWPkZ8PZGXJqmJ1ERFASkrdP0cie+fs7Izg4GAEBwdj5syZAO68V6HFYoHFYsHSpUsBVO1VOH78eCxcuPC2r2PP1f6YGODNN4HPPpM/eywWYP58+bNi1iwrv57WdScicjyV96f/+9+Av7/+mJsbcOLE7c91cdE/d3KqGu/MGeDxx4Ff/hJYuFDeJ7h7NxAdDZSW3nmMW5fv3XoboxA12+7mo48+wsWLF2EwGGA0GmE0GnUfW/vc1udyBWnzZzAY0L17d3Tv3h2TJ08GcOe9CtPS0uDq6nrbAFhUJO/L/f57oPKK9AMPyO9LVQVMJtl2//36BV+A/GPvzTflxw8+KEPad99VBcCXX67q26WL/J5/7jl9ACwtBf7xj6o/+F54Qd7fW2nhQnm7x4QJVeMcOSLnxgBIRFTPgoJk0Dt7FoiMrHn83Dn5b3m5deMmJsqK36JF8q99AFi/3roxWrYE/PzkL6ihQ6va9+4FBgywbqyVK1fCYrFYd5Idc3JyatDgaeuA29Dn/pzXMRgMNg/eTk5OCAwMRGBgIMaPH6+1V+5VmJWVddtz7bnaf+WK/FkTHS2rfpXKyoCf886WDIBERHfh7S0vw/zmN7J6N3iw/AG+d69clfvYY7La9tVXsqLn7i7b76ZrV/nD+9NPgbFjgT175F//1nr1VVl56NpVrgBetgw4eLDqslFdzZo1C48++ijKy8u1R0VFRa0fW/u8Ic69GyFEo64QJakyCNprwO3cufNt527P1f7KsWJigEce0fczGm8/r9thACQiqoOFC+Vln3ffBU6elJd/wsKA11+Xvyj+8hfg978H5swBZs6U27fcTd++8nLTe+8Bf/iDrOC9+6483xq//rUMpL/9rawWBAUBW7davwL4pZdesu4EG6sMgvYcUpvLudW/1nX571JRUWG34Ts0NBQLFiyo9Zg9V/vbtZM/a06eBKZNs+7c2nAjaCIiIqoTIYQW8OwtpNb13Hbt2uGFF1647ef4xhuyEr9oUe3V/oAAWWWvXu2vbSPop56SfyguXy4r8qGh8nhltf8PfwB++qlqA/nly+V9gtX3GNyyBRg/vqoKuGSJ/IPv3XeB0aOBkhIZLnNy5AIUoO4bQbMCSERERHVSeV+l0WiEy63XK5sJe672z5sn7xP829/kCmJPT7mRfPUFJnXFCiARERFRM8G3giMiIiKiWjEAEhERETkYBkAiIiIiB8MASERERORgGACJiIiIHEydtoGpXCicn5/foJMhIrIVJycnm7+FlS0JIVCHTSGImjyDwQCvurxVTxNWl+/lOgXAgoICAEBAQMC9zYiIiIiIGlRBQQF87vIGwXXaB7CiogJZWVnw9vZ26L+QiYiIiOyVEAIFBQXw8/ODwXDnu/zqFACJiIiIqPngIhAiIiIiB8MASERERORgGACJiIiIHAwDIBEREZGDYQAkIiIicjAMgEREREQOhgGQiIiIyMH8P2b0ilRbT6mCAAAAAElFTkSuQmCC", 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" ] @@ -866,7 +866,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -934,7 +934,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] From 48070ef9983a9423c206a760e8149f1272d4d842 Mon Sep 17 00:00:00 2001 From: richard gowers Date: Tue, 11 Apr 2023 22:06:00 +0100 Subject: [PATCH 08/14] updates for v0.7.1 --- ...pplyingProtocolToNetworkQuickrunDemo.ipynb | 6 +- ...OpenFE_showcase_1_RBFE_of_T4lysozyme.ipynb | 5879 +---------------- 2 files changed, 129 insertions(+), 5756 deletions(-) diff --git a/openmm-rbfe/ApplyingProtocolToNetworkQuickrunDemo.ipynb b/openmm-rbfe/ApplyingProtocolToNetworkQuickrunDemo.ipynb index 84acb4d..547e892 100644 --- a/openmm-rbfe/ApplyingProtocolToNetworkQuickrunDemo.ipynb +++ b/openmm-rbfe/ApplyingProtocolToNetworkQuickrunDemo.ipynb @@ -83,7 +83,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "We have created \"\" with 7 nodes and 6 edges\n" + "We have created \"\" with 7 nodes and 6 edges\n" ] } ], @@ -143,7 +143,7 @@ "outputs": [ { "data": { - "image/png": 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id68E1y5qPj0oKOjBgwcrVqw4ePDg9u3by//3ip4/Sv2tR87KhZq93ApxfEsGt3tc+jWO0JZCmeHpdDXA61fWvzc76thYzfB06u9sr/Vlm9pZz/B06upki1FpVCcnr60HMKpp3Gn69OnT0qVLk5P/c3+Uz+cvWLDg0KFDZKUil6UXQqfhYzFqZf8RCKLYl1wEQUqCMWzYU2arb/ndlVmTQf8ukR3NEahvz9+6Bi8sPjNh8ODBtra2AECn0zUHJGslS/6WNW9q+uQRmovI4wAX8gs6fkjbksGVE0Rftv3dQO8Znk5WanOC6zDoMzyd+rFLK4TdPNg0bx/vI5eoLm7liUS6sLCw8ePH792798uXL+rb58+f/+DBg3PnzpEVjFyWXghp7p4Og0dR1O5elB+BE7iAX/ZxCIIAAACze19G4xaqhzQMW+DNBoDNGdwcWdEVS3l2JvfQzpJeZMOGDcOHDy/9L8JFhfm7N6UM6VZw+6rm3ucF4j7x6XO+5ebKFI3trM/U81zv5+KiMT4Oo1th1gyKjfbRcBQra4q1jcPAkb6nrtPcPUrPYzzWrFlz//79mjVrqm988uTJo0ePQkNDyUpFOksvhADAnjKL5lsdo9Er/EwCz/2zYvcqEMSiYZjLnCWg1n6lg6NNewebAgW+JYOrfiD38B6tq36uXr06MTExPDxcc9e/cFxw+Xxy7/acvzZr9hDOlMlnf8sdnpgZL5J60Gnr/FzO1vNsbKcxwgXD7Dt3rxZ1r8b91y7zIxmNm2N0KwrDhmJnj9nYApVGr+bnOHpytUv3nWcuNK0BMnR68Q86iUQyfvz4HTt2aO6yHKZxUVuvMDrda9fxtNH95NmZLxpVIwActF0sdaZTYxtXKzbNXvTisfDeDbsO3QwT1STk5uYSBOGkrcWGu7t7fn4+hjrSWTDr+g2Z3fsKLp9XbVnow34Un34qVzDEhdnA9t9rnoRUkr9ltfuaHarDpFJpaGjoly9frl69amen/SxN8uFN7rpI8btXmrtEOLEni/dXFk+MEzYU7HdXh6mejrbaOqJZBzR0mb2U0bi58iGz5wBmzwGA4/LcbLxAQGHYUF3csPJdmzUJy5cvb9OmTVBQ0IULF8jOQhqM0DZAywLhwoLshTMKY58RouI3J9TFFUozZfKOjraqLXTvar5nb5rWt0L9uXz5sre3d9OmTckOghgvRX5uct8O6v1cVqbmH8jmt7BnnKjrof4tyeuvEzbNWwKAXC5v06ZNYmLi2LFjqVQqAMyfP1/9y5Y8OzN/21rB1SjNEacEwHWO8I80TppUjgH8wrKb783yttJyDkBzdWdNCHHoOwTMvWVo27Ztly9f/vPPP3/69CkwMDA4OJjFYiUmJr5792716tUDBw4kO6ChoTPCf1Hs7D027y98eDd30yp5RjrgOCEt3jLmXaFkwMcMJxr1diBDrTVUsoFbQxktLpc7ZsyYvLy8Z8+etWjRouwnIBaJynZxGj0pf3vRxPnpnk4X84UxBeIbXOEvTkVne7lrlvqcuIJRaRiGzZo1S/1FVONlCLGId/IQZ99WzfE1APC+ULo8NS+2QAIADWytwn3YLbS1jMHodIcBw9lTZ1MsrD+Gg4PDsWPHlH9WKBTJycl+fn6kJiIHOiPUQvo5sfDxA8nbV9JvSdIvn9R3DUvMfF4gHuPmoN4aimJr63v+LppQGBYWtnHjxqCgoOjoaHT9EykFIZOlDOoiS/mu2nI8V7AkOc/TinYzwNtGrdOv68KVDgOGlfQ6wn/u5K6LkKenau7Klim2ZHBP5wpwADc6dYan0yBnJlXbb6Vt244uc5ZaSMfg8+fP79y5MyYmplatWu3bt//zz6JV5C5cuHDgwIFLly6RGI8sqBCWIWvelILbRa2h4kXSvh/TMcCu1vdStYYCAGbPAfpuDWXkkpKSAgMD5XL58+fPmzdvTnYcxNgJ71zLnFt0HQUH6P8x/X2hdKaX01SPomueGMOGPX4G3be6lX+geq2SfHyfu36Z+NULzVeWE8TRHMGmDG6BAqdh2HBX5kxPJ3ttN/6tatR2nrXYtlU7nf5kRo3H4+Xl5Sn/zGAwvLyKZnYKhUKBQODhYTIjYHUIFcIyyLMykvt3Ul+3Ojw572SuoI2DzcHaaqeAGOZz6IJ1YGMSIhqH7t27X7t2beLEibt27SI7C2Ia0qeMFD17qHoYUyD+LTGTQcFuBHh7qd3Dw6hUjGELuBxjOjoOHW3ftRfv6B7e6SOAF+8RAwB3eYUrUvOTJXIA6OhoG+7Drmat5QYQxcGJPWGG45DfgVJ81gRigVAhLFv+jj85+7YVPZQrOn9I4yvwfbXd2zsUTUBkNGzqfeCcZS7Se/Xq1R49ejg4OCQkJFjmN0qkEqSfE1N/667e82zal+zr3MK+bPv1fi5an4LR6YRcrrUH22exbFVqvrJhWy0GfaEPW/3tWfQKNBqz9yD21DlUJ5aOfg7E5Jn54CidqGJrKLMnk8nCwsIAICIiAlVBpPysatVl9vtNfctCH7YNBbuYXxBTwlJohEymWQW5cnx5an73+LQHfJEjlRLuw75a30trFbT5Kcjn+BXXRatQFUTUoUJYtiq2hjJ7W7duTUhIqF279pQpaOgsUjHOU2dRHYtqkpcVLdjNgQBYkZpfntUfFARxMlfQNS7tUDYfAIa6MG8Feo92c6BqXJihV/NzX7Pda8dRq1p1dfkDGJ+cnJwffvihTZs2WvfGxcX98MMP/fr1M3AqI4cKYbkwu/dlNKlqayizlJOTo1wKYPPmzdbWaDIlUjEUByfWuGnqWyZ7OHlZ0d4XSi/ml7Fw/GOBuPfHjPDkvHy5ohWTccnfa0U1Zzat+D0/iq0ta0KI76kb9p0tYtVcqVQaExMTExOjdW9BQUFMTMzbt28NnMrIoUJYPhjmMru8raG0DuY2V+Hh4Vwut0uXLt27W8SnDKJzth27qd9Zt6FgYV5OALAmjVOg0H5a+F0im/415/dPmQkiaXVr+tYarkfqeNSz0ej2QqEwe/SrdvEBe2KoOfWCQXQOFcLyUraGUt+y0IdNw7BTuYL3hUUtDQmpJG/zaoOnI8ebN2/27dtHo9E2btxIdhbEVPGO7iu2gFEftn1ze+tcmWJ3Fq/YwSKc2JLB/TUu/RpHaEPBZng6XVNbOEmdTfOffI7+7bZsA5WtfdwNgqigQlgBziELKHZFa7LUYtBHujJxgBWp+eq37wtuXxXFPjV8PMMLDQ1VKBTTp08PDAwkOwtimgii4PI5Qi5T34YBLPZxpgDszeJ/k/y7i/h34aTULRlc2b8LJ/kUWzhJiebu4Rb5p9fuE9b1Agz0UyAmDhXCClC2hlLfMt3TiU2jKltDqW/PXb9M6yQnc3LmzJn79++z2ezSlgJAkFJJvyZpLhkPAA1srfqw7WUEsS6NAwBvhJJBCRlzvuXmqC2c5Kq5cBLDhjUhpNqFe8ye/S1zIhNSOajXaMU4jZwg+PusLPmb8qEDlRLq5bQkOW9lKqe9g62qNZQ0MZ4fddqh/28lvpCJE4vFc+fOBYBVq1ax2ewyj0cQraRfPgGm/ev4PG/WLV7hDW7h75+ynghEBIA7nTrbm9WXba+lxGGYfadfnUMX0jy99RzZNOA4fvWqlrUYExOLr1GMACqEFYXR6c7T52XOmazaMsSZeSJHEC+S7s/mqbeGyt+x3r5LDwrTgYyYerdu3bpv374FBgaOHTuW7CyICZO8f4OLhFp32VEpTe2sHwlEjwUiBgUbVfLCSVa167kuWsVo1EzPYU2JTCbr0aMH2SlMBuosUxnlbA3lNGKc88xFZATUr7S0tHr16gmFwnv37v38889kx0FMUukLJ13hCNekcTKkcuWWWV5Okz20rHAJAJiVtc/RS2Y/O7D80tLSfHx8aDTaunXrNPcmJydv3LixZs2anz9/Nnw2o4UKYWWUszUURqX6nLhqfm/RkSNHHj16dODAgWfOnCE7C2J6CImYd+IgZ982vFDLueCHQumK1PwXBWIACLS16upkuzGd60Sj3A7wcaJpOR3EaHS/O7EUe6bec5sIZSG0trYWi7V053n+/PlPP/2ECmExaLBMZZSzNRShUOT+udzg6fTr6dOnx44dYzAYa9euJTsLYnqE/9xJGdQ1b+sazSqo7JTWPyH9RYHYiUYJ92Gfr+c11cOpjYMNV45vy+RqfUGKgyOqgkgVoUJYSeVsDSV69rDw0T3Dx9MTgiBCQkIIgpg9e3aNGjXIjoOYEknCh/TxQzJnjpOlpRTbJSeIg9n8Dh9SD2XzMcBGuTncC/QZ7eagXD5wkQ+bisGRHH6iSFrsiRiNavdzZ8PkR8wYKoSVRHFwYo2frr6lpNZQuesiCZkMzMLhw4efP3/u7e09b948srMgJkPB4+Sui0wd0Uf08rnm3kcCUc/49BWp+QIFHsS0uVzfa7EPm6m2fGAdBn2oC1NBwIrU/OJPptAcB4/Sa3jEEqBCWHkOg0eq3/8rqTWULOU77/RhEvLpWkFBwcKFCwHgjz/+sLe3L/N4BCHkct6JA8m92/NOHtScWftFLBv3OWvUp6wksawmg763lvuhOu611da7VpnpyXKiUR4LxHd5/2lqT7GyMr978IjhoUJYeRiV5jJrifqWklpDcXZvUuTlGDad7q1atSo9Pb1ly5bDhw8nOwtiAkTPHqUO65G7fhleICi2i6fA16VxesSn3+eJHKmUOd6sK/W9fnbUsnCSkhONopybtCI1X6o2vk9RIBBcvaCn/IjloEZERJCdwYTRfapJ4t/Jkr8qH2IA9W2sT+cKXgulPdl2Tv9vhE/IpHih0K5tJ/KSVtWXL19GjhypUCjOnj3r4+NDdhzEqEm/JmUvCcvftVHBySu2CweIyi+Y8Dn7oUAEAH3Y9ntqu7V1sNFcOKmYRrZWN3miL2IZk0ppZs9QbZd8eOPQfxhG13IeaZkwDLOysvr555/btWun9QAmk9mhQ4effvrJwMGMGZo+UVWy1O8pg7oS0qLb+HO+5V7IL+jmZLu9plvRcRSKz+Eo6/oNSYioC/37979w4cKoUaMOHjxIdhbEeOECPufgTt6xfVrviz8ViFek5n8USQGgJZMR7sP211wyomSPBKJRn7LsqJRbAd5uav3VWMFT2NPmVD28eeDz+Q4O5tnHQ3/QpdGqovtUL3a7fp43y55KucEtfMgXFW3F8dx1yzTnDpuEe/fuXbhwwd7eftWqVWRnQYwVjgsun0/u35F7cJdmFUyWyKd/zRnxKfOjSOppRVvn53K0joeWKohhVKYDZmOrte9aENOmo6OtUIFvKrb22dE9qq6HFk4ul7du3bp///65ublkZzElqBDqAGtiCNXZVfXQhU6d6O4IACtT8xVqlU/8JqbgznUS8lWNQqEIDQ0FgIULF3p5eZEdBzFGotinKcN7Zi+dpcgvfi3034WT4tNUCyfdDvDux9Yy2Mo6sLH3gXN+9994/vmXfaduVAdHjG5VbIWmcB+2FYadzRW8K5SoNhIyWd7WNfr4uUzOzp07P3z48ObNGyYTza2sAHRpVDf4F07mrFigeigliF/j0r9LZJG+zsNdi34jae6e1c7fwRglDgowsPj4+EuXLin/3LRp065du2oes2PHjqlTp9aoUSMuLo7BYGgegFgyeVZG/vZ1gitaRqwQAFH5BWvTODkyBQbwC8tuoTfL00pLf2Oamwd76hxmj37FlozA+Txpyrf0ScOIwqLBoqvT8vdl8ZvZWZ+q56l+tOf2w7Yt2+rq5zJFHA6nTp06eXl5UVFRffr0ITuOKUFnhLrh0GewdUDR/T8rDJvjzQKAjRkcrrxoKoU8K4N7dB8J+Upw//79e/fusVgsFovl5KSllyOHw1m6dCkArF+/HlVBRB0hFuXv3pTct4PWKvi2UDL4/wsnNbK1Pl3Pc2sNV80qiFkznEZP8j13R+vCSRQHR0ZgY/bYaeobZ3g4udKpL4WSq5z/9KbJ+3M5oZDr4iczVYsXL87Ly+vYsZoiwc4AACAASURBVCOqghWFzgh1RvwmNm3sIPW7gKOTsh7yRaPdHMJ9ihYqwhg21c7dpnkYxTXG8PBwJyen2bNnl3RASEjIli1bOnTocPfuXUMGQ4waQRTcuZa3caU8M11zZ5ZMsS6NczG/gABwo1PnlLRwEoBt246ucyNpXmUMQiZkspTBXdXvAp7KFSxKzvOg024FeqvWPgMA1wXLHQaOqNSPZPLi4uIaN25MEMSrV68aNjTVQXlkQWeEOsNo3Ny+c3f1LVpbQxFiUf52LV3hSZGZmZmQkBAeHr5t2zaRSFRsb3x8/M6dO6lU6qZNm0iJhxghSdy7tLGDsuZN1ayCYpzYncXrGpcWlV9gTcEmujveCvDup60KWvs38N572nPTvjKrICjXPgtZoL5lkAuzoa11pky+978TdvO2/6ngcSrxQ5mBsLAwuVw+efJkVAUrAZ0R6pI8KyO5fydCXFRRliTnHc8VBDFtDtVxLzoOw7z3nmY0aUFCxP+6cePG169f/fz89u7dK5FI/v77b/W9v/766/Xr1ydPnrxjxw6yEiKGJPv+VZLwQZ6dCQoF1dnFqlZd63oBQPl3ooI8J4vz12Z+1CnAcc3n3uUVLkvJT5XKAaCjo+0SX7aPttuBVEcWa/x0xyG/q162nDKmjSp88o/q4UuhZEhChjUFuxHg7a32Fzn+Fuwye4m2FzBnUVFR/fr1Y7FYiYmJLi4uZT8B+S9UCHUsf9dGzp4tqodcOd45LpUrx/+q5dbR0Va13bp+Q5/DUaBtlVFS8Hg8JycngUCg6p32999/9+7dG721LAFeKOSdOMg7dQgXFmA0KiGVARAYjQYYBXDc/te+TqMmFkbfyd/xp9aFk+IKpcv/v3BSgK3VYh/2D/ZabidjNJrDwBHsyWGVWyxC+uVT6tBf1dc+m/E15ypH2Jttt8GvaMy2ua59VgqpVNqgQYNPnz5t3bp12rRpZT8B0YAKoY4REnHygM7yjDTVlgPZ/JWp+dWsadcDvK3UhgO4Ra5n9hxARsYi79+/DwwMxDDszZs37du3z8vLo1KpACCVShs2bJiYmLhp06aQkBByQyJ6Jbx3MydyLiGT4NqWrwMAjEojcIXWKbBcOb41k3s0h68gwIlGmebhNNL13yUjirH5Kchl9lKrmnWqEjV3zVL1tr0ZUnnXuDQxThyv66Feem1+DPLaebQqf5Fp+eOPPxYsWBAQEPD69Ws66rBTKagQ6l7B9YtZi0JVDxUE0etjRqJIOt+bNc7dUbWdynapFnWPYkda92ocx7t3756bm9ugQYO7d++uXLly5MiRyl3r1q2bO3du/fr137x5g95aZix/x5+84/twjdvDZZITxNEcweYMrkCB0zBsuCsz1NNJfckIFXr1mi6zwm2DOlQ9Lc7nJfftoH4XcHMGd2sGN8DWKsrfS/3v9ty0z7Ztx6r/jcYvKyurbt26fD7/+vXr3bp1IzuOqUKFUA8IYnrTgM/JKRv9XGgYBmqtoW4HeLsaWWuoL1++fPv2rWHDhq6u/15fys7Orlu3Lo/Hu3bt2i+//EJuPER/OAd2cPZtI0SiyV+yM6WKdX4uWld+GJyQISNgf2031v975z4SiFak5H8SywAgiGkT7suuo+2JFAdH1qhJjsPH6rARKO/kodx1EaqHYpzoFpeWJpWvru4yyLnoOyXdp7rvmZuYVQX6t5mo4ODggwcP9u3b98IF1Hy88ozlHpU5OXvuXLwt6yZXqBpREMS06eBoU6w1FEalErLiC40aXs2aNTt27KiqggCwaNEiHo/Xs2dPVAXNmPh1DHfvNkIkAoCPIum7QolQoWUIDAC8K5S+K5TICACArxLZ+M9Zoz5lfRLLaljT/6rldqiOu5YqSKEwe/Srdv6u0+hJum2H7Th4hFXteqqHDAo2y4sFAOvTOAL1tc9Sv/NOHdLh32ucXr58efjwYSsrq7Vr15KdxbShQqhjPB5v3rx52w8cKDY7WNka6sz/W0PZtGjlc+yy88xFJMUs0atXrw4cOGBlZbV+/XqysyB6g+NZS8JwccWuiF7mCH+NS7/HEzlQKYt82NcCvNTHf6nY/Bjke/Ka27INVBZbc29VUajFBoX2Ytv9YM/Ikyt2Zf537bO/Nitys3UfwGgQBBEaGorj+MyZM+vUqdLNV0TL+GakKsLCwoKDg+vVqwcYhWJrD6J/R9lVt6aPdGPuy+KP+pxTzdub8vQDDBxCblStkpOTlc1F69WrV/bRiGkSRt9VcDRWey9LS3uGLQXr6Gi3wIflTNMy+YHm7smeMpvZs78uMpbI5ofWdu07Cx/cVj7EAMJ92P0+pu/P5g90sa9h/e8JKF4ozN+5wXXxH3oNQ6Ljx49HR0e7u7svWLCg7KORUqFCqEv3799/9OiRatad0+iJBTs3qPbWc7Cn5RTwZfL3376TFLBcaDRagwYNyE6B6BH/7FFC20SI0rnQqfcb+DhoGxFDsbF1HDmeFTwZs7LWRcAyOIeFFz75R7X2WaCtVX9n+7N5BWtSObtqFa19xr90xqH/MOvARgaIZGAikWjhwoUAsHr1akdHxzKPR0qHCqEuTZkyxcXFZcaMGQCA4/iKVx/nVPOTJX8DDKO177L99FU5ji9ZsqR/f/1+Za6K8+fPL1u2bPHixYMHD7a11XLhCzED4lcvKvdELVUQw+w7/eocFk5z96xqrHKj+1R3/C2Ye2i3astsL9YNbuFtXmE0X9TW4f9N7XE8989l3vvOaHYxNXWrV69OTk5u1qzZqFGjyj4aKQsaNapL9+/fl8v/bfvbrVu369evt6YTnP3bXWYtWRN1OSIiokmTJjExMcq5esYJx/GffvopJiYmMjJyyRKL69BhCRS52d/7tCfUZg12+JCaIpEv8mH7WWsZ2DLpS5aCgMcNfdXXwlWyDmzkMnspo1Ez/SbWBi8UJvfrqH4XcHcWb10apxaDfqW+F02t8rmv3mrftafhE+pPSkqKv7+/SCR68OBB27YWveCGrqBCqC80Gk0oFFpbWwNBpKal+fv7C4XC+/fvt2/fnuxoZXj06FHbtm0ZDEZ8fHz16tXJjoPomPRzYtqYgXiBQLVFWQhLf1axQki1d3Ces1Rz4SRDElw8nb1snuqhjCB+jUv/JpEt9WWPdC1aop3m7uF77g7FxnwubwwZMuT06dPDhg07duwY2VnMBBo1qi9yudza2hoAAMPmzp0rFAqHDBli/FUQAIKCggYNGqS6CYGYGwpF69ffYDeHRT5szf80O8VgNBprSpjWhZMMidlroPr9PzqGzfNmAcCmdC5HXtSJTZ6VyTuyh4R8+vHo0aMzZ87Y2NisWrWK7CzmAxVCvXvy5MnJkydtbGz++MNkBrCtX7/e1tb2xIkT0dHRZGdBdIzm6g4Sieb2niy7YDcHzf8oULzaYQwburevQcKWikJxmb1UvRh3cbJt62DDU+BbMv47leLgLvWuh6YLx/HQ0FCCIObPn4+u1ugQKoT6heN4SEgIQRBz58718/MjO055+fr6zp49myCIkJAQXNtSA4jpotgzKQ5VGmdIyKRW9QJ1lacqGI2a2Xfrpb5lkQ+bhmHHc/kJ6mufScR528xhyvnevXtjYmKUb0+ys5gVdI9Qv/bv3z927Fhvb++EhAQ7O7tiex89erR8+XJSgqlbvHhxUFBQsY0ikcjf3z85OXn//v3BwcGkBEP0JGflAsGlM8T/rx8q7xGeq+fZ2E7L5If6r77LCEL9HiHdp1q1iw8MF7dU8qzMlAGdcFGhaktkSv6RHH4rJuNIHY+i4zDMe88pRtMfSIioIwKBoG7dupmZmadOnRo8eDDZccwKmj6hRwKBIDw8HADWrl2rWQUBIDMz88aNGwbPVdz48eM1NypvQowYMWLBggX9+/dHc5XMiePgUQVXLxLyCvfaBgCKra3j8DE6j1RpNHcPp98n5O8uWjs61Mvpb07BE4H4Nq+ws6r3DUHkrov0OXrJeNY+q6jIyMjMzMzWrVsPGjSI7CzmBp0R6tG8efPWrl3bqlWrR48eYdqGFWRlZb169crwwYpp2rSpu7u75naCINq3bx8dHT1v3jwTusGJqMvMzNy+ffvevXv37NnTs2fRLILMGWOEz6JBLocKnhHS2C7Vrjw0zMT5ciIk4uSBXeTpqaoth3P4y1Lyfa1pN4qtfbZkDbOPSZ5LJSUlNWjQQCaTPXv2rEUL8tf0NjOoEOrL58+fAwMDZTLZkydPfvzxR7LjVNLLly9/+OEHGo327t27unUtaLFTs7Fr1y4Gg3Hs2LHx48erX09T5GYnD+isnERR/kJIYdh4bNxj82PxC+mkK7h5OWvBdNVDBQG9P6YniKRzvFkT/7P2mXO1C/cqtzIwuXr27HnlypXx48f/9ddfZGcxQ6Z6lcD4hYWFSSSS0aNHm24VBABl6wqpVDp37lyysyCVMWnSpNGjRzs4OBTbTnVx89y0j8KwAYDRrg4zPJ08rLTfKJnm6TjD08mOgmE2NqxJM42wCgKAfdeeNs2K3mhUDMJ92ACwI5OXLSuaSqHIz+Ps305CvrKUPiTt9u3bV65cYTKZy5YtM1gki4IKoV7cvXv30qVLTCZzxYoVZGepKmUzw4sXLxrD7UxEhxhNf/DcfphizxzlyZ7h6eSu0ThGaaqH0wwvlr2tnfOM+U4jtdxONhLOs5cApehHaMVkdHa0FSrwDekc9cN4x/fLvn81eLoSnT592tvb28/Pr2vXrllZWZoHyOXy0NBQAFi6dKmHh4fmAUjVoUKoe8rVGwAgPDzc09NwDRj1RNXePiwsTCaTkR0H0SVGkxbVzt22a92OwrDBtHf+wzBbO7pfTe+9pxwH/27ofBVhXS/QofdA9S2LfNhWGHY+r+CNsGjeJCGT5W1ZbfB02mVnZwcHB//999/KxqFaW1hs27btw4cPtWrVmjZtmuETWghUCHVvx44d7969q1mzZkhICNlZdEO54FlcXNzu3bvLPhoxKVQXN49N+7wPXWD2GUJ1dQMajWJjR7Gzw6ysKXZMu5+7eKzdUe3MLeuAhmQnLRt76hz1+3++1rTRbg44wIrUfPWhEML7twqf/GP4eJo+fvzo7e3drFkzAAgODr569WqxA/Lz85VXlTZt2vRvpypED9BgGR3jcDh16tTJy8uLiorq06cP2XF0Jioqql+/fiwWKzEx0cXFhew4SHnl5+d//fp1zpw5Xbt27dWrV2BgGRPh8cJCBScX5HKKg5NeVtbVM+6RPXmbinqPCRV4l7i0bJlig59rb3bRFCarGrV9Tl7DaCTPH8vNza1Vq9apU6caNGhw+PDhxYsXy2QyitoEj8mTJ+/atatz5863bt0iMafZQ2eEOrZ48eK8vLyOHTuaUxUEgL59+3br1o3D4URGRpKdBamAuLi4NWvWuLi4vHz5ct++fWUeT7G1pXtXo1evaYpVEAAch46mV6+hemhHpcz0YgHAmrR8EV70pV/6NYl/7jgJ+f7LxcXl3LlzGzZsCA4OxjCMxWKpV8EPHz7s3buXRqNt3LiRxJCWAJ0R6lJcXFzjxo0Jgnj16lXDhiZwKalC4uLimjRpguO4Wf50iNkQ/nMnc+Y41UMcYFBCxhuhZLqnU4ink2o7xcGx2oV7VCcWGRm1OHHixJ49e+7evava0rVr11u3boWGhqJCqG/ojFCXwsLC5HL55MmTzbJOBAQETJgwQTUUCEGMk127Trati5Z5oQCE+7AxgD1ZvFRp0WpTOJ/H+WuTthcwqNjY2O/fv9+8eXPRokXz589XbT937tytW7fYbLayOxWiV6gQ6kxUVNSNGzdYLNbSpUvJzqIvy5cvd3Z2vnv37sWLF8nOgiDaSSSS7137fpYVXQltamfdk2Unxol1af+dSnHmmDQpwfAJ1V2/fn3s2LHHjh3bvn17165dlRslEomyKK5cudLZ2ZnUgBYBXRrVDalU2qBBg0+fPm3dutW8Rzlv3bp1xowZNWvWjIuLQ8PYEGPz9evXrl27tmrVKv3F08K0lH213ZUt1jJl8i4f0kQ4cbyux4/2DNXx9t16ua/aQl5e7VauXBkeHh4YGPj69Wsa2SN6LAE6I9SNDRs2fPr0KSAgYOLEiWRn0a8pU6Y0bNjwy5cvmzaRf1kJQYo5fvx4u3btDh8+fPPZi0wFoZpB6EGnjXd3BIDlKfkKAgAAo9Ech452XWR0y9tmZmauXbsWADZu3IiqoGGgM0IdyMrKqlu3Lp/Pv379erdu3ciOo3d3797t1KkTk8lMSEgwg44BiDk5fvz4okWLLl686OTk9FOTJud97FWrR4lx4pe4tFSpvD/bvk2zJva/9KayjPGq46FDh/75558BAwacPXuW7CyWAhVCHRgzZsyBAwf69OkTFRVFdhYD6dOnz6VLl8aMGVOeEfkIYjASiaRVq1bp6elCoXDRwgXD30RLP31U7d2Ryd2bU8CXyUt5BdI5Ojoqm/U3atSI7CyWAhXCqrLM9RnMY20NxPyEhoYWFhbu3r374cOHI0eOXDlpfMsze5W7MAen39IEzz99bty4sbKZi3G6fft2SkrK7Nmz161bR3YWS4EKYZVY8op9Za62iCCG16ZNm1mzZvXr1w8AZs6cSafTwwhB4YNbzD6DrzpXGz15sru7e2JiouZyHMbj1atXLVq0sLTv1iQjkCo4evQoALi7u3O5XLKzGBqfz1feIDx27BjZWRDkX+vWrWvevPnVq1dPnjzp5eUVHR0tS0+VfE4UCoXVqlUDgIMHD5KdsWxjx44FgF69epEdxFKgM8LKE4lE/v7+ycnJ+/fvDw4OJjsOCfbv3z927Fhvb++EhAQ7O7uyn4AgekYQxNWrV//55x8qlTpw4EDVJdDw8PCVK1c2b978+fPn6m3MjFN2dnbdunV5PN61a9d++eUXsuNYALIrsQlbvHgxADRr1kyhUJCdhRwKhUJ5g3DJkiVkZ0GQEiUnJ9va2mIYFh0dTXaW8lLOoKhfv75UKiU7i/lDZ4SVlJKS4u/vLxKJHjx40LZtW7LjkObJkydBQUEMBiMuLs7Pz4/sOAiixaBBg86ePTtixIgjR46QnaW8pFJpw4YNExMTN2/ePGPGDLLjmDlUCCtpyJAhp0+fHjZs2LFjx8jOQrJhw4adOHFiyJAhJ0+eJDsLghT38OHDdu3a2djYxMfHK28Tqvv+/Xt8fDwpwdS1bt1ac/zOpUuX+vTpg9Y+MwSyT0lN0sOHDzEMs7Gx+fbtG9lZyJeSkqK8QXj//n2ysyDIfygUiubNmwPA8uXLtR6wYcMGsj+DAQBevHihNZ7yBuGUKVP0+Y+EoEujFYfj+E8//RQTExMZGblkyRKy4xiFyMjIiIiIJk2axMTEUKlUsuMgyL927do1efJkX1/fjx8/2traah5w4cKF3bt3Gz5YMVu3bq1Tp47m9vj4+MaNG+M4/vLlSzS/Xn9QIaywPXv2TJgwoZS3lgUSiUQBAQHfvn3bs2fPuHHjyn4Cgugfl8utW7duTk7OmTNnBg4cSHacSgoJCdmyZUuHDh3UlypEdAsVwooRCAR169bNzMw8derU4MGDyY5jRE6dOjV06FA3N7fExERHR0ey4yAIhIWFbdy4MSgoKDo62nR7PnA4nLp16+bm5p4/f17ZKADROWOfT2NsIiMjMzMzW7duPWjQILKzGJchQ4a0a9cuOzt7xYoVWg/g8/nLli0bMmTI8uXLBQKBgeMhlubjx4/btm2jUCibN2823SoIACwWKyIiAgBmz54tFovJjmOeUCGsgKSkJPN4a+nJpk2bqFTqli1bEhK0LHY6bty4pKSksLCwxMREdPkU0bewsDCZTDZ+/HjlYBmTNmnSpEaNGn358mXjxo1kZzFTJA/WMSk9evQAgPHjx5MdxHgpK1zPnj2Lbc/IyLCzs+PxeARB8Hg8Ozu7rKwsMgIiFuHy5csA4ODgkJGRQXYW3bhz5w4A2Nvbp6WlkZ3FDKEzwvK6ffv2lStXmEzmsmXLyM5ivFauXOno6Hj58uXr16+rb09KSqpevbpyppSDg4Ovr+/nz59JyoiYOZlMNmvWLACIiIjw8PAgO45udOzYsV+/fgUFBQsXLiQ7ixkyaCHs169fr169uFyu5i4ej9erV6++ffsaMk/5yeXymTNnAsDSpUvN5q2lD25ubuHh4fD/C1Oq7WKx2MrKSvXQ2tpaJBKRkA8AAPr27durVy8ej6e5i8vl9urVCw1JMGlbt25NSEioXbv21KlTyc6iS+vXr2cwGIcPH37+/DnZWcyOIU8/aTQaAGi9WJGVlQUAFArFkHnKT3lpvlatWmKxmOwsxk4ikSjXjtm0aZNqY1xcnKurK47jBEEoFAoXF5ePHz+SlVDZc1nrtdmMjAwAoNFohk+F6ER2draTkxMAXLlyhewsujd//nwAaNmypfKthOgKujRatvz8fOVIyE2bNllbW5Mdx9hZWVmtX78eACIjI3Nzc5Ub/f39nZ2do6KiACAqKsrd3R0ttIboQ3h4OJfL7dKlS/fu3cnOonuLFi3y8vJ6+vQp6uyoY4asuiZ6Rjhp0iQA6Ny5M9lBTImyNdTkyZNVWx48eFCnTp2GDRvWrVv34cOHJGZDZ4Tm6tWrV1QqlUajvX//nuws+nLgwAEA8Pb2FggEZGcxH1UthDiOR0RE1KhRo2nTpocOHSr9YFMshO/fv6fRaMrVosnOYkri4uLodDqVSn39+rX6dmN491a9EMbExPTq1atOnTo//vgj6jdrPNq3bw8AYWFhZAfRIxzHlWufhYeHk53FfFS1EO7fvz8wMDA9Pf3Lly/e3t6vXr0q5WBTLIRdunQBgNDQULKDmJ6QkBAA6NChA9lBiqtiIeTxeG5ubufPn5fL5ZmZmehujZE4ffo0ALi6unI4HLKz6NeTJ08wDGMwGF+/fiU7i5mgVfHKanR09JAhQzw9PQFgyJAh586da9KkSelPefv2bVpaWrGNHA5H9WeFQiEUCqsYTCcuXbp069YtNputHAmJVMjSpUuPHTt27969o0eP9u7dm+w4AAB2dnaqnuBv375lsVjFDsjLyyvzRY4cOdK+fXvlyFJ3d3edh0QqQSwWz507FwCWL1+uHCxjxlq2bKlcAG7u3LnK8o9UVRUL6erVq7t27SoSifLy8vr06RMcHFxa1aWVUXeVZ4QPHz40zM9eHhiGbd68WeuPEx8fHxsbW8V/QDMQGxsbHx+vdZexteBR3ptUnhGWovQzwhkzZvTo0aN169a+vr7Dhw8XCoV6+WdFKkI5u7dx48ZyuZzsLIaQmpqqXPvs3r17ZGcxB1U9IwwJCUlISPD39/f19WWxWJprS2qaPn268n+husLCwi1btqg+hphMZhWD6YRYLJbJZFobY966datHjx7169d/+fKlJS88pFAogoOD4+Pjr1y5oryMrE55R5BOpzMYDFLiFaP+VWzGjBmai4cIhcKtW7eW/iJ8Pt/KyuratWs2Nja9e/fevHnzggULdJ8VKbe0tLQ1a9bA/5v8kR3HELy9vefNm7dkyZLQ0NDY2FgL+an1SIdFtX///tu3by+t6praPULluida2xqJxeLatWsDwI4dO0jJZiS2b98OADVq1BCJRMV2ZWRkKL8Y3bx5k5RsJaniPcJFixbNnDlT+edly5aNGTNGLymRchs+fDgADBo0iOwgBiUSifz8/ABg165dZGcxeToYNZqcnCyVSk+ePOnm5paTk1PKwSZXCAmC6N+/PwD8/vvvmrvOnj0LAGw2Ozc31/DBjEF+fr6LiwsAnDt3TnPvyJEjAWDAgAGGD1a6KhbCly9fenl5ffz48du3b/7+/ocPH9ZbUqRsljxyxHLGB+lbVQvht2/fgoKC/P39Bw4c+Pbt29IPNv5CKJVKMzIy1G8zfPnyhcFgYBj29OlTzeMtfExpKeNCY2JiKBSKlZXVp0+fDB+sdFWfPnH06NEff/wxICBg9erVaNQoidBcAkuYMWIAaEJ9kdOnT9esWbNly5Y+Pj4XL15UbVfeAdLa1siSZxmWNFOQIAgcx4OCggBg4cKFpGQrHZpQbzbQ7HJL6CFgAKgQFpk7d64y29WrVwMCAlTbBQKBl5cXAGi9CGaxfWc0e8eoHDp0CADc3d2V6y4ZG1QIzYPqjXnkyBGys5BpwoQJANClSxeyg5gwVAi1+Ouvvzp16qS+5eDBgyV98czLy3N2dgaAy5cvGzAjyf7++28AYLFYmneFhUKhr68vAJTZaYgsqBCaB9SBWsm8+4wbhkEL4ZQpUyZOnKj1LIHP50+cOFHr6YUh3bx5s3Hjxi4uLgkJCerbcRz/6aefAGDRokWaz1KuTVG7dm0LWZtCIpHUq1cP/ru+hIpyvbTmzZsrFArDZyuPyZMnT5w4kc/na+7i8XgTJ06cMmWK4VMhFfL582flzftnz56RnYV8f/75p/IjSCKRkJ3FJKEV6ovj8/nr16//8ccfi21XDk6ztrbWHP0hk8kCAwMBYP369YaKSaZ169YBgL+/v1QqLbZLNbYoOjqalGyIhVB29hk1ahTZQYyCVCpVfjfdsGED2VlMEiqERVQXWPh8PgBodgwZMWIEAAwcOFDzubdu3QIAJpOp9cKvOcnKynJ0dASAq1evau4dMGAAAIwcOdLwwRDLcefOnZIm+Fqsy5cvA4CDg4PZfwTpAyqE/8JxvF27dnv37n39+nVYWFiLFi00j0lNTbW3ty9phniPHj0AYPz48foPS6Zx48YBQM+ePTV3KfsP2Nrafv/+3fDBEAshl8sbNWoEAKtWrSI7i3H59ddfAWDixIlkBzE9qBAWSUpKmjZt2oABA5YsWaJ1JAVBEMuXLweAwMBAmUxWbNenT5+sra0pFMqLFy/0H5YcyrHaVlZWmuvLy+Xyxo0bA8CKFStIyYZYiG3btgFAzZo1NZsZWbj4+Hg6nU6hUGJiYko5LDc399atW6mpqQYLZvxQhcS2HwAAIABJREFUIawYkUhUo0aNktoahYWFAUBQUJC5DmNTzt6dNWuW5q6dO3cCQLVq1VATakR/VM2Mzp8/T3YWYzRz5swyP4L69u3r4eGBGrOpQ4Wwws6cOaPsrJaXl1dsF4/H8/DwAIBTp06Rkk2vTp48CQBubm6a/Zw4HI6rqysAnD17lpRsiIWYMWNGSc2MEELtnXjmzBmtB5w+fbp9+/Zjx45FhVAdKoSV8fPPPwOAqvOyut27dwOAr6+vmZ0YFRYWKjv8/vXXX5p7Q0NDAaBNmzbmeiqMGANVM6M3b96QncV4Ka/NaP0I4nK5NWrUiI+PHzduHCqE6lAhrIzXr1+X1NZIoVC0aNECACIjI0nJpicREREA0KRJE8313sp5ZwIhnUQiuXXr1p07d7Tu5fF4t27devTokYFTlZ+ymRGa5Vk6hULRvHlzAFi+fHmxXcHBwcuWLSMIgtxCKBaLb926dffuXa17Sfk9RIWwkiZOnFhSZ7WHDx9iGGZjY2M2gydVq4Dev39fc69yrNqkSZMMHwypkPT0dACg0+la9z5//ly5opaBU5XTxYsXS2pmhBQTHR2NYVix8du3b992c3N7/PhxTExM3759Fy5cSNaaFampqQBgbW2tde/Tp08BoFatWoaMVMZS3UhJVq1axWazb9++ffXq1WK7goKCBg4cKBKJlD1WzMDcuXOFQuHgwYOVg2XUXbly5dq1a05OTsolwhFEH6RS6Zw5cwAgIiJCOVgGKUWbNm0GDBhQWFi4aNEi1caUlJT27dtv3LhxzZo1b968uXHjRmJiIokhjYshq66Z2bBhA5TQWS05OdnW1hbDsH/++YeUbDr0+PFj5Qmu5npvqn4WGzduJCMaUjGme0a4du1aAKhfv75mMyNEK9VHkNYeT+ReGkVnhGZl+vTpgYGBSUlJylXa1fn6+irnGISGhuI4Tko8ncBxXLna4pw5c5SDZdRt2bIlISHB399/6tSpZKSzdARB3L59Ozo6WrUlOjr6zz//PHnypEQiITGYbmVnZ69cuRIANmzYQKfTyY5jGnx9fZWj+bR+BI0cObJdu3akBKsiiURy+vTppKQk1ZYXL15s3br14sWLMpms8q9ryKprfm7evAkltDUqLCysVq0aABw4cICMaLqxf/9+APD29i4oKCi2KysrS9nzXmuvNUTfYmJifvjhhzp16gwbNky5ZcWKFe3atdu8efPAgQM7duyo+RQTPSMcO3YsAPTq1YvsICZGKBQqP4IOHjxIdpb/qPQZ4ZEjRwICAry8vFSns0eOHKlevfr69et/+eWXfv36VToSKoRV1b17dwCYMGGC5q6jR4+CES/LVyY+n+/p6QkAx44d09w7fvx4AOjRo4fhgyEEQfB4PD6ff+jQIVUhfPXqVWFhIUEQHA4HwzDNK/amWAhfvnxJoVCsrKyKLQiDlMfhw4eN8COo0oUwMzNTJpOpT4L09/dXNryUSCSurq4fPnyoXCRa5c8lEQAA2Lx58507d/bu3TthwgTlqGWVYcOG7dq16+HDh6tXr169ejVZCStt5cqVGRkZrVq1+u2334rtev369f79++l0unL9F8TwHBwcim1p0qSJRCL5+PHjvn37+vbta21trfWJcrm8bdu2mtsLCgpUf3727JlyRizpbt++jeP49OnT69atq7mXIAgcx6lUquGDGRW5XK5c7bWYESNG7Nix4+nTp+3bt2/atKnhg2lSfWJIpVKtv4cCgaCk57q7u6s/LCwsTEhIUI7gs7KyatmyZWxsbEBAQGViVa5+IupKaWsUGxur/D6bmJhISrZK+/z5s7J1qtb13pS/fGFhYYYPhqhTPyMkCCIxMbFPnz41a9bcv3+/5sHKM0IAYGijLJzKM8Jjx45V5tNEDxwdHW1tbV+/fq3547x+/TooKGjt2rX6++c1FX369Pn999+1dkhevny58haGkUhOTlaeEUKpv4elDJZRnRGmpqba2Nioto8YMULrCqnlgc4IdSAiIuLEiROPHj06e/bsoEGD1Hc1a9bs999/P3jw4Ny5cy9cuEBWwkoICwuTSCTBwcE//vhjsV2nT59+8OCBq6vr4sWLScmGlKROnTpRUVFcLrd69epdunTx8fHRPIZOp4tEIs3tL168UP2/btmy5b59+/SbtXwOHz784MGDyMjI8+fPF9uVm5v76NGjt2/fjhgxQnkN3zLduXPn4sWLTCbzjz/+KLZLJBLt3buXy+WOHj1a6+mX4bHZbC6XCwDW1tZafw+fPXvWsmXL8ryUi4uLWCwWCoXKWc65ubnFThkroHL1Eylm165dUEJbo8zMTOVVrBs3bpCSrRKU670xmcz09PRiu1S91nbv3k1KNkSd+hnh27dvlbMLJBKJs7Pzu3fvih1sivcIS3/79OrVCwDGjh1r+GBGQiaTNWjQAAC0nhkvXboUAJo2bapQKAyfrSRVnD6hfo+wVatWJ06cIAgiPz/fyckpJSWlcpFQIdQNVVsjZQejYpQ3CAMCAjQXbzJCcrm8YcOGAPDHH39o7o2MjIQSeq0hhiSRSCZMmNChQ4fatWtPmDAhMzNzzpw5zZo1CwsLCwoK6t27t+ZTTLEQEgSxatWqkt4+SUlJygv4z58/JyUb6TZt2qQsG5pjo1JSUpSnSg8ePCAlW0kqXQjfvXs3YcKEevXqtW3bds6cOQRB3Llzx93dffz48QEBAcotlYMKoc6U0llNIpHUqVMHALZt20ZKtgrZunUrlLDeW+m91hBDksvlt9QoL0V8+PDh9OnTjx8/1tr93MgLYWpq6iA1Y8aMUW5XvX22b9+u+Sxlx5lWrVpZYMP3vLw8Z2dnALh06ZLm3iFDhgDAb7/9Zvhgpat0IVSupKik6lWSk5Nz48YNzRVSKwQVQl0aOHAgAIwYMUJzl/IOB4vFys3NNXyw8lOt93bhwgXNvcOHDweAQYMGGT4YUnVGXghFIlHM/61du/bnn39W7VK+fdhstubbRzXJR3mJzKJMmTIFADp16qS569GjR8rv5d++fTN8sNIZYWcZVAh1qfS2Rl27dgWA6dOnGz5Y+U2bNg0AtE7HVvZaYzAYmr3WEJNg5IVQXZcuXf7++2/1Lcq3z4wZMzQP3rNnDwD4+Photn0wYx8+fKDRaDQa7e3bt8V2KRSKH374AQCWLl1KRrQyoEJo/sLDwwGgWbNmmnenlb+4VCpV8xfXSKjWe9P61lIOKVy8eDEp2ZCqM5VC+Pbt23r16hV7B5nu576emO43A1QIzZ+qrZHWzmqlXMowBt26dQOAqVOnau4qpdcaYkLEYrFEItG6C8fxUvYa0u+//651TLKJXgnUh3Pnzpn0tWJj+z1EhVD3jhw5AiW0NVLd3L548SIp2UoRFRUFJaz3JhAIlG+to0ePkpINsRxpaWne3t7KXnHFlD42ZOjQoQAwdOhQ/WckWemjh2bPng0ArVu3tsDRQ5WGCqHu4Tjepk0bAFiwYIHm3s2bN5c03JlEEolE2cJqy5Ytmnvnz58PAC1btkRvLUTf5s+fX8oVzlLePkY7W0Dn0HwSnUOFUC9iYmJK6qwmk8mUs/SMqjXUmjVroIT13j5//sxgMDAM09prDUF0qKCgwMvLS3MtFxVTnD+uW6jDgD6gQqgvo0aNAgCtK4Pcvn27pL4tpMjKynJ0dASA69eva+7t27cvAIwePdrwwRBEUylvn8LCwurVqwPA3r17SclmACb0wWJCUCHUFxP64jZmzBgA0NqLRNlrzd7ePi0tzfDBEESrUt4+x48fBwA3Nzcul2v4YPpW+qWmUs6VkdKhQqhHJnEpv5T13lS91lavXk1KNgTRqvS3j3Lt9ao03DJOpQ8+KKXXGlImVAj1qMzWUN27d//06ZPhg6n79OlT9+7dtX5qbNu2raReawhCrlI6q5nrWr7lGY6udTwtUiZUCPWrlOk+RtW0WjOMqtfa+fPnSYmEIKUofbZcKVf7TZRJT1A2fqgQ6l0pDSCM2fTp00vqtYYgxqCU/imq8V/Xrl0jJZvOldmySmvPHaScUCHUO1P8NVX1Wnvz5g3ZWRBEu9I7q5UyI8jklKeJscl91TYqqBAagslduNDaa00mk5nlSDzEdJXSWU3VI2Lz5s2kZNOhUpa1KeXmC1J+qBAagmndyr548WKxXmsCgWD9+vXVq1dv1qwZudkQpJhSOqtp/iabIuVCp7a2tqUsdKp1OB5SfqgQGoipDG7W+j06MzPzwIEDV65cQYUQMTald1YrpY+8SVAoFM2bNweA5cuXa+4tZYIWUiEYQRCA6J9cLm/atOn79+/HjBkzYMAAsuOU6OzZswcOHKhfv/6bN2/odLr6rufPn0+ePDk2NpasbAiiVURERGRkZNOmTZXzzdV3xcfHN27cGMfxly9fNmrUiKyElbZ79+5Jkyb5+vp+/PjR1tZWfVdWVlbdunX5fP6NGzeUtwmRyiO7EluQEydOMBgMsv+Hl43BYGgdkv7s2TN0RogYodI7q5nu+Gcej+fh4QEAp0+f1txbSq81pKLQGaHhjBs3bt++fV5eXo0bNyY7S4nevHmTnp4+duzYvXv3FtuFzggRo3XixIlhw4a5ubklJiYqJ06ocDicunXr5ubmnj9/vl+/fmQlrISwsLCNGzcGBQVFR0djGKa+KzY29scff6TRaO/fv1feJkSqhOxKbClMpdtFKc2r0BkhYsxK6axmij2SPn369L/27j0oqvL/A/hnWa4GKJd0ka8XkBCkhKBQMk0GNSszBicVyUzsQl6KlUKNMDURwns2ampeysoodZyhCZNQUVJKE1PBBd0KEBAQhVUQ3N3z++PMj0EWV4Vln7PnvF9/OefZP96cET77nPM8n8fW1tbKyurUqVPthoz3WoNOQCE0Ewvqf9jhwZ6ZmZkrVqzw8vLKyMhAA24QIJF1zX3xxReJ6O233zYcMtJrDToHj0bNocPnNmfPni0pKRkyZMiQIUPYxmtHo9EMHjy4srLy+++/59emE1Fqamp9fT3/7xkzZvj7+7MLCNCxWbNmbd++feLEifzGibZycnIiIiIcHR1VKlXfvn2ZxHtwhw4dGjdunLOzs0ql4l8TtmpsbPT39y8tLd2xY8cbb7zBKKDosK7E4tfhm/y5c+cGBwcrlUofH58NGzYwjNchI82rAATLeGc1SzlZ886dOwEBAUS0evVqw1Ejvdag01AIu53hqdl6vX779u2NjY0cxx05cmTQoEFMA3agtXnVkiVLWGcBeAhGOqtdvnzZ3t5eJpPl5+czyfaAVq9eTUQ+Pj6Ge46N91qDTkMh7F7Gd/tyHJefn+/r62vmVA/CSPMqAMEy3llt4cKFRDR8+HDDw5sEorq6ulevXkT0888/G44a6bUGXYFC2L2M9H/ivfbaa4sXLzZnpAc3ZcoUIoqOjmYdBOAhGOmsptFo+MObdu/ezSTbfb3zzjtENGbMGMMhI73WoItQCLvRfSdVy5cvHzFiBP+MVIDuO50FECYjndW2b99ORJ6engJ8/11QUCCXy/ndge2GjPdagy5CIewuxs+IaW5unj179tixYwV+noPhC04A4TNyjphOpwsNDSWi5ORkJtmMGD16NBEplUrDoc2bNxNRv379bt26Zf5goodC2F34ziwdLry8ffv28OHDFQpFSkpKWlpaWlqaYCeFrUtev/rqK9ZZAB6Ckc5qv//+u0wms7e3/+eff8ye654yMjKIyNXV9dq1a+2GjPdag667q0EtmIpGo0lOTiailStX8k8X2+I4bubMmUuXLnV3d3dxcXFxcWnXP0k4HBwcUlNTiWjRokWt+wgBhI///crJydm/f3+7obCwsGnTpt2+fTsxMZFJNkOtYVasWOHq6tpudMmSJVVVVSNGjOAXy4Dpsa7E4vThhx8SUVhYmGAXpz04vV4/cuRIIkpMTGSdBeAhGOmsVl5ezn9DPXLkCJNs7Xz66adEFBAQYHigkpFea2AqKISmZ6Rdp4WylEapAG0Z76y2bNkyIgoKCtJqtebP1lZ5ebmjoyMRHTp0yHDUSK81MBUUQtN7+eWXiWjWrFmsg5hSbGwsEU2cOJF1EICH8NtvvxGRo6OjYYNc/v23g4MD85nW5s2bZTLZpEmTDId+/fVXInJ2dq6srDR/MOlAITSx7OxsInJycqqoqGCdxZRam1dlZWWxzgLwEIx0VsvLyxPInrzff//dcOWO8V5rYEJoum1KWq02ODj43Llz6enp/GtCMUlPT1+wYEGHh9cDCJZarQ4ICGhubj558iS/ccJSrFmzJiEhwcfH5/z583Z2dqzjiBlWjZrSxo0bz507N2jQoPfee491FtOLj4/39fUtKiritzQBWARvb+/4+HiO495//30L+t5fU1PDr6BZv349qmB3w4zQZOrq6nx9fa9du3bgwIGJEyeyjtMtDhw4EBkZ6eLiUlxc7O7uzjoOwAO5efOmr69vZWXl7t27Y2JiWMd5IHFxcV9++eWYMWMOHTrEOov4oRCazJw5czZu3BgREcG/JhSr8ePHHzx4cM6cOfzadACLsGPHjtjYWE9PT5VKZbi1V2jOnj0bEhIik8kKCgr414RENG3aNK1WS0QODg67du1iGlBsUAhNo7CwMDAwkOO4M2fO8Cu2xaqoqCgwMFCv14v+JwUx0ev1YWFhf/zxR3JyMr9xQsjCw8OPHDmiVCrXrFnDX6mvr/fx8cnKyiIiGxuboUOHMg0oOgwX6ojJuHHjiGjevHmsg5jD3Llz6R7NqwAES5id1Qx12GutsLAwKCiIYSpxw4zQBPbv3x8VFeXi4lJSUuLm5sY6Tre7fv26r69vbW3t/v37+bXpABYhOjp6z549Li4uQv49rampqa+v37RpU1xcXOvFnJycGTNmBAUFNTQ0xMXFRUdHM0woPtasA1i8lpaWBQsWENGnn34q5N8uE3Jxcfnkk0/mzZuXkJAwfvx4e3t71okAHkj//v1tbW2vX79+/fp11lnuydHRUSaT+fn5tb347LPP7t2719/f/9KlS+PHj/fy8ho+fDirhOKDGWFXpaWlLVq0aMiQIWfPnrW2lsoXC51O9+STT547dy4tLY3/HgAgcFVVVYMHD25oaNixY8eIESNYx7mnTZs2rV27Nigo6NSpU3K53PADU6dODQsLe//9982fTbRYP5u1bFVVVc7OzkR08OBB1lnMjW9eJb4eOiBW06dPJ6IOO5kJSmNj48CBA4loy5YtrRfz8vJyc3M5jqutrR0wYMDhw4eZ5RMjFMIueeONN4goMjKSdRA2XnnlFSKaOXMm6yAA93HixAmZTGZnZ1dSUsI6y/3t2bOHiHr37n39+nX+Sm5u7tNPP+3t7d2vX7+VK1eyjSc+KISdd/r0af5MhuLiYtZZ2Lh8+TJ/zkZ+fj7rLAD3pNfrhw0bRkRJSUmtF2/evPnee+8FBgb6+vq2nXsJxHPPPUdECQkJbS+2tLSwyiNuKISdpNfrn332WSJauHAh6yws8S8IxXHyIogVv/1coVDU19e3Xnz77bdjYmIaGhpaWloMD4Vn7syZM3K53MbG5uLFi6yziB8KYSft3r2biPr06dP2V0uCGhoaPDw8iOjbb79lnQWgAxqNpm/fvkT09ddft16sr6/v0aNHXV0dw2D39eabbxLRhAkTWAcRPzTd7oympqaPPvqIiNLS0vjFMpLl5OSUkpJCRImJibdu3WIdB6C91NTUioqKkJCQtl1G1Wq1o6Mj30f+iSeeOHHiBMOE95KSktKzZ8/MzMxffvmFdRaxY12JLVJycjIRBQcH63Q61lnY0+l0/Ok2ixcvZp0F4C5qtdre3l4mkx07dqzt9WPHjrm5uZ05c4bjuO+++27QoEGMAt7HypUricjPzw9vB7sVCuFDKy0t7dGjh0wm41czA/f/zascHBwE3rwKpCYqKoqIXn/99XbX1Wq1i4sL/0W2vLxcLpcL80ttc3Pz4MGDiWjt2rWss4gZHo0+tISEhMbGxmnTpo0cOZJ1FqEICwuLjo5uamrC5noQjsOHD+/bt8/R0TE1NbXdkJeXl7e39xdffNHQ0LB27dpRo0ZZWQnxj6Gtre2qVauIaOnSpTU1NazjiBfrSmxhjh8/zk99/vvvP9ZZhKW8vJw/3ebIkSOsswBwWq2WP6IhJSWlww/8888/kZGRvr6+r7766pUrV8wc76G88MILRBQXF8c6iGihED4EnU731FNPEdGyZctYZxGipUuXElFQUJBWq2WdBaRu48aNROTl5dXU1MQ6S1cVFRXZ2NjI5fKCggLWWcQJvUYfwl9//TVy5Eh3d/eLFy86ODiwjiM4TU1Nfn5+tbW1x44dCw4OZh0HpKv1gJSffvpp0qRJrOOYQHx8/Pr168PDw3NyclhnESEUwodTVlZWWloq5I69bOXl5fXv379fv36sg4Ckia9siK+0CwoK4V0uXLjAcZyfn5/hORI6na6oqEgmkwUEBDDJZin4e+jv72/YOF+r1V68eBH3ELpVUVFRYGCgXq8/ffp0YGAg6zgms2nTptmzZ3t5eRUWFuLsM9NCIbyLjY2NVqutrKxUKBTthq5evapQKORyuVarZZLNUlhbW+t0uqqqqj59+rQbqqqq8vDwsLa2vnPnDpNsIAUvvPBCVlbWu+++y78mFA2dThcSEnL27NmUlBS+oQeYihBXDAMAdE5mZmZWVlavXr2WLVvGOouJyeXytWvXElFKSkpZWRnrOKKCQggAItHS0vLBBx8Q0SeffOLu7s46jumFh4dHRUU1NjZ+/PHHrLOICgohAIjE559/rlKp/Pz85syZwzpLd1m1apW9vf0333xz/Phx1lnEA4UQAMSgurp6+fLlRLRmzRobGxvWcbqLl5fX/PnzOY6Lj4/X6/Ws44gECiEAiEFSUlJ9ff2ECRP4PiwitmjRor59+54+fZo/DA66DqtG78KvGp0wYYKdnV27oebm5szMTKwavS9+1aiRe4hVo2ByBQUFTz31lFwu//vvv/ku1eL29ddfz5gxo0+fPsXFxRI/Cc4kUAjvwhdCIx9AIbwvvhAa/wAKIZjW6NGjjx49mpCQwLeoFj2O48LCwvLz85OSkvgHwtAVKIR34QthcXFx79692w3V1NQ89thjKIT3xRfCkpKSRx99tN1QdXW1r68vCiGY1g8//DB16tTevXurVKpevXqxjmMmp0+fDg0NtbGxOX/+vI+PD+s4lg3vCDvg5OTU04CTkxPrXJYE9xDMo6mpaeHChUS0fPly6VRBIgoJCYmJiWlubuZ/fOgKFEIAsGDp6en//vtvUFBQbGws6yzmlp6e7uzsvHfv3kOHDrHOYtlQCAHAUpWXl69cuZKI1q1bZ9jbVvQUCkViYiIRKZVKvLLpChRCALBUiYmJt27dmjJlynPPPcc6CxsffPCBj4/PhQsXtm3bxjqLBUMhBACLdOLEiT179jg4OKSlpbHOwoydnR3/4yclJV27do11HEuFQggAlkev18fHx3Mc9+GHHw4cOJB1HJYmTZo0duzYuro67KPoNGyfuEtpaSnHcf369bOyav8VQa/Xl5WVyWSy/v37M8lmKXAPwQy2b98+a9YsT09PlUr1yCOPsI7D2IULF4KCgojozJkzjz/+OOs4lgeFEAAsjEajGTx4cGVl5bfffjtt2jTWcQTh3Xff3bx585gxY7CCtBPwaBQALMzy5csrKyvDwsKio6NZZxGKlJQUNze37Ozsn3/+mXUWy4MZIQBYErVaPWTIkDt37pw4cSI0NJR1HAFZt26dUqn08fE5f/68YadfMELqM8KtW7dGRERERERg8bE5bd26dWwbuPnw4ObPn9/c3DxjxgxUwXbmzp0bEBBw6dKlL774gnUWCyPpGeHhw4ffeuuto0ePchw3evTorVu3hoeHsw4lCXV1dTdu3OD/HRMTs2DBgsjISLaRwCLk5OREREQ4OTmpVCoPDw/WcQQnOzt77NixTk5OxcXFCoWCdRyLYc06AEtlZWU9e/b08PCwsrLy9PS8evUq60RS4erq6urqSkTnz5+vq6ubOHEi60RgGQIDA2fPnu3l5YUq2KExY8bExMSEhIS4ubmxzmJJJD0j1Gg0zz//vE6nGzZsWG1t7c6dO21tbVmHkpbY2NjQ0NC4uDjWQQBAuiT9jvDy5cv19fVz5sxpaGjIzc0tLCxknUharl69evDgwenTp7MOAkJx7dq1iIiIex0xr1KpIiIi8B/GONzDTpD0jHD69On+/v4fffQRES1evFitVu/evZt1KAlJSkqSy+XLli1jHQSEoqKiwtPT09bWtrm52XD0zz//DA0N9fLyUqvV5s9mKa5cufK///3Pzs7u9u3bhqN//PHHsGHDvL29L1++bP5sgiXpd4R9+vQpKSnhOE4mk1VUVBgexgvdp7GxcefOnadOnWIdBACkTtKPRpOSkurr6wMCAvz8/Gpraz/++GPWiSQkMzNz8uTJWPIAAMxJekbo4uKyb98+IuInhazjSMvkyZMnT57MOgUAgLRnhK1QBQEAJEvSM0IAECCO444fP254/eLFi+YPY6HudQ+LiorMH0b4UAgBQFju3LkzcuRI1iksW0tLC+7hg0MhBABhkcvlixcvNrxeUVHx5Zdfmj+PJbK2tk5OTja8fuXKlS1btpg/j8BJeh8hAAgK9hF2HfYRdgIWywAAgKShEAIAgKShEAIAgKShEAIAgKShEAIAgKRh+wQACIWjo6NSqbS27vjvkkKhUCqVOHLWOCcnJ6VSaWNj0+Goh4eHUql0d3c3cyqBw/YJAACQNDwaBQAASUMhBAAASUMhBAA2ioqK2vbRvnTp0k8//XTy5EmGkSSiuro6Ozu77ZVjx46VlZWxysMcCiEAmFthYeFLL700atSolJQU/sqGDRtefPHFvLy8efPmzZ07l208EdNoNG+99dbTTz8dHR3NX8nKygoJCYmMjPzxxx/ZZmMIi2UwAL1nAAABsUlEQVQAwNwqKyubmppyc3N/++23b775hoj27dv3zDPPKBSK6urqvn37ajQaBwcH1jFFSK/XFxQUeHh4DB06tKamhojUanWvXr2WLl06YMCA+fPnsw7IBmaEAGBuHh4e3t7eba9ERUUpFAoisre3l8vlVlb409QtrKysgoOD2x5F7u3t7erqyjCSEOB/GwAIyIYNGyIjI+3s7FgHAQnBhnoAEIqMjIxdu3bl5uayDgLSgkIIAOxxHJeenp6RkZGdnc0/IwUwG/mSJUtYZwAAaWlpaVm1alVubq5Kpbpx40ZAQEBsbOy2bdtef/11lUqVl5c3cOBAZ2dn1jHFadOmTfv27cvPz9doNI888ohWq/3ss8+OHj1aWlqqVqvDw8NZB2QAM0IAMDcrKytvb+/W9TI2NjbR0dFRUVGtH7C3t2cUTfz8/Pzc3d1HjRpFRI8++qijo2NISEhISAgR3avLq+hh+wQAAEgaVo0CAICkoRACAICkoRACAICkoRACAICkoRACAICkoRACAICkoRACAICkoRACAICkoRACAICkoRACAICkoRACAICkoRACAICk/R8PKCDSnpXoQwAAALZ6VFh0cmRraXRQS0wgcmRraXQgMjAyMi4wOS4xAAB4nHu/b+09BiDgZYAARiDmgeIGRjYHDSDNzEIszcgAohkZiaW5GRgzmBhYExjYGBiZEhjZGZiYM5iYOIAmJTBzMrCwZjCxcDGwcjOIMLIxsLIwMzGKz4K6EQyAjjxgv3rVKhUI18H+oduy/VD2fgT7wP4J/VNUkcTtkdTD2A5Ac6BqDgDF1Q7A9CLYDgeQ1IDEHZDUg9liAHhKMASIpwgRAAABHnpUWHRNT0wgcmRraXQgMjAyMi4wOS4xAAB4nI2TzW6DMAzH7zyFX4DITsJHjgWqbpoKUsv2AJU47MJppz597VbUYa0ikiAS84v/jh0u03yd5ikDaafu6/cPns12GdsxMUII8OMQMTuCTKDZHz57aMdds1ja4bsfz0BWBkpfs7txOC4WghbIFCgN0OD/ycJZ5tBUdxByMjYEdPUb0DGYbyK9kFu0i5XLhMcyDjLBVfAB7lXvhauZewaYW1OEGqvyDRgYzDeRhIJuESdaOU35tHGcCXDfd6viP65DM/SdXgfPj9Wqy7LU2hL3SgvomXBaJt4JtRbD8wavOSdeBk2t7C40gXflOE38VZVJhFmAojNzYPyKzxafRNbLT8Hz7AZJaaXD4wYCnQAAAK16VFh0U01JTEVTIHJka2l0IDIwMjIuMDkuMQAAeJxtjj0KwzAMRq/SMQFbSI4d2+oFMpXuIUMJHYtLyZjD1zGlskuXT3o89DNPy0prN09L/ycp52nvNIFT2oCLAYM6awSfmcDEiDbzoTHXoWSxrfyOtq5RnyVlx8+B5nyvblt6XF/pyQhHe0nbHQKTgGEjMPAgENkKWHYChDwKOfaVIg5CyFFgZKqeIKbqC7+/AdzrXmHFQmbUAAAA5HpUWHRyZGtpdFBLTDEgcmRraXQgMjAyMi4wOS4xAAB4nHu/b+09BiDgZYAARiDmh+IGRkYGDZAYnGZjUADSLHCuA4hmZiGWRjcOF83NwMTAyMTAxMzAwprBxMKZwMLFwMqWwMrNwMaewcTGw8DOkcDOy8DEwcDBmcHEyccgwsjGwsnBzsYqvg/qBzDg/3LCd99DtzAHEKdQ2fjA4d439hCpA/sfuqk5QNgCB4KyKqHsAyB5VQjbwf6h27L9UPZ+BPsAiFZDErdHUg9jA81rUIWZCbTrAJK9ULYDkHZQQ1IDdoMYAOY+NQRem9V/AAABU3pUWHRNT0wxIHJka2l0IDIwMjIuMDkuMQAAeJyNU0tuhDAM3XMKX4DIzoeQ5QCjtqoGpJb2BrMYqWo30/vXBkGC6EQkQYqdl2fHftx/vn6v39cCZLx1r7c7rEN3Bfsxs0II8GkQsbiAbKA5P7300I6nZvG0w0c/vgM5WShziz2Nw2XxEDxDiUojhkqDUcbW6CtAhdOIV7UAtfIhoKmBVP0IaKCFkpSb3Vq58ABoZ0Y9ITl0HTyR/QfomHEljMc7XMU4VH7m4xT0nOse6CXFQ8h685hM7LChzDASplnmgNIas4+4B0pr1hzLTMXJSMmPQafuHArv0vAZynPfbaQ3i7EZ+i6KUaaJkiP+TBQWn4GN8rGsjCqKRExKxEBiU+y5ZZ+PjSWB6Ng/y5A66RKxTSbphuVLISk6CYlNaivphaSAllkprRNNvC4tSPp8sZf/mPfFH2qKxrFYhBa8AAAA2HpUWHRTTUlMRVMxIHJka2l0IDIwMjIuMDkuMQAAeJxdjr0OwjAMhF+FsUiJZTtJk7gjCxNiRwwIMaIi1JGHJw3QuCyXfD7/3Gl/vtK1O+3P25/u6ltFlano5tURBGMZQk6YzIAQCxJwzujNMJtoBjdLtVbOMmXXnv03GTH3bBw4/ykxxNpKkJYKh9KQciT6big3bb1s/1LZVeatuUzj/fgcH4Iwfw/jdAMioUZBuIEXp/pQfKMsQVle+kZJYgOW1AAlqyEhlcIJqRRRSMWgYqogvZAKQvx6Az4HeXjF7VU5AAAAAElFTkSuQmCC", + "image/png": 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", "text/plain": [ "" ] @@ -399,7 +399,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "-rw-r--r-- 1 richard richard 841K Apr 11 17:26 out.json\r\n" + "-rw-r--r-- 1 richard richard 842K Apr 11 21:43 out.json\r\n" ] } ], diff --git a/openmm-rbfe/OpenFE_showcase_1_RBFE_of_T4lysozyme.ipynb b/openmm-rbfe/OpenFE_showcase_1_RBFE_of_T4lysozyme.ipynb index 66ee263..52a9456 100644 --- a/openmm-rbfe/OpenFE_showcase_1_RBFE_of_T4lysozyme.ipynb +++ b/openmm-rbfe/OpenFE_showcase_1_RBFE_of_T4lysozyme.ipynb @@ -40,7 +40,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "4a6e22e9e06043f386b6c7c2f3071765", + "model_id": "de08168b6dea481a83d549420cf1777f", "version_major": 2, "version_minor": 0 }, @@ -52,7 +52,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dd582f5db1b74cb8954db2cacd50d698", + "model_id": "cdc5ba5194fa47209b20fc733f8c4151", "version_major": 2, "version_minor": 0 }, @@ -110,7 +110,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -207,17 +207,9 @@ "execution_count": 3, "id": "4096ce97", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" - ] - } - ], + "outputs": [], "source": [ - "from openfe.setup import SmallMoleculeComponent\n", + "from openfe import SmallMoleculeComponent\n", "\n", "# Create and SDF supplier\n", "# Extract the contents of the sdf file and visualise it\n", @@ -272,32 +264,33 @@ { "data": { "text/plain": [ - "{'atoms': [(6, '', 0, True, ''),\n", - " (6, '', 0, True, ''),\n", - " (6, '', 0, True, ''),\n", - " (6, '', 0, True, ''),\n", - " (6, '', 0, True, ''),\n", - " (6, '', 0, True, ''),\n", - " (1, '', 0, False, ''),\n", - " (1, '', 0, False, ''),\n", - " (1, '', 0, False, ''),\n", - " (1, '', 0, False, ''),\n", - " (1, '', 0, False, ''),\n", - " (1, '', 0, False, '')],\n", - " 'bonds': [(0, 1, 2, True, ''),\n", - " (0, 5, 1, True, ''),\n", - " (0, 6, 1, False, ''),\n", - " (1, 2, 1, True, ''),\n", - " (1, 7, 1, False, ''),\n", - " (2, 3, 2, True, ''),\n", - " (2, 8, 1, False, ''),\n", - " (3, 4, 1, True, ''),\n", - " (3, 9, 1, False, ''),\n", - " (4, 5, 2, True, ''),\n", - " (4, 10, 1, False, ''),\n", - " (5, 11, 1, False, '')],\n", - " 'name': 'benzene',\n", - " 'conformers': [\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '>f8', 'fortran_order': False, 'shape': (12, 3), } \\n@9ú^5?|î@\\x15NÙ\\x16\\x87+\\x02@\\x13\\x1d²-\\x0eV\\x04@:e\\x1e¸Që\\x85@\\x14KƧï\\x9d²@\\x0býó¶E¡Ë@;W\\n=p£×@\\x17p£×\\n=q@\\x077KƧï\\x9e@;ÖE¡ÊÀ\\x83@\\x1b¯\\x1a\\x9f¾vÉ@\\x0c\\x8dOß;dZ@;k\\x85\\x1e¸Qì@\\x1cÈ´9X\\x10b@\\x13l\\x8bC\\x95\\x81\\x06@:\\x7f|í\\x91hs@\\x19\\x84\\x18\\x93t¼j@\\x15àA\\x897KÇ@9:Ô,<\\x9eí@\\x12¾\\\\\\x91ÑN<@\\x14úû~\\x90ÿ\\x97@9÷´¢3\\x9c\\x0f@\\x10ð¾\\rí(\\x8d@\\x07\\x98üPH\\x16ð@;°bMÒñª@\\x16\\x86?\\x14\\x12\\x05¼?þRT`ªdÃ@<\\x92° Ä\\x9b¦@\\x1eC\\x95\\x81\\x06$Ý@\\x08¶\\x114\\x04êK@;Ò&\\x80\\x9dIR@ \\x1e\\x9e\\x1b\\x08\\x9a\\x02@\\x15\\x8b\\x92:)Çz@:/ùrGE9@\\x1aIº^5?}@\\x19û²þÅm]\"],\n", + "{'atoms': [(6, 0, 0, True, 0, 0, {}),\n", + " (6, 0, 0, True, 0, 0, {}),\n", + " (6, 0, 0, True, 0, 0, {}),\n", + " (6, 0, 0, True, 0, 0, {}),\n", + " (6, 0, 0, True, 0, 0, {}),\n", + " (6, 0, 0, True, 0, 0, {}),\n", + " (1, 0, 0, False, 0, 0, {}),\n", + " (1, 0, 0, False, 0, 0, {}),\n", + " (1, 0, 0, False, 0, 0, {}),\n", + " (1, 0, 0, False, 0, 0, {}),\n", + " (1, 0, 0, False, 0, 0, {}),\n", + " (1, 0, 0, False, 0, 0, {})],\n", + " 'bonds': [(0, 1, 12, 0, {}),\n", + " (0, 5, 12, 0, {}),\n", + " (0, 6, 1, 0, {}),\n", + " (1, 2, 12, 0, {}),\n", + " (1, 7, 1, 0, {}),\n", + " (2, 3, 12, 0, {}),\n", + " (2, 8, 1, 0, {}),\n", + " (3, 4, 12, 0, {}),\n", + " (3, 9, 1, 0, {}),\n", + " (4, 5, 12, 0, {}),\n", + " (4, 10, 1, 0, {}),\n", + " (5, 11, 1, 0, {})],\n", + " 'conformer': (\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '" ] @@ -432,8 +425,8 @@ "output_type": "stream", "text": [ "molecule A smiles: c1ccccc1\n", - "molecule B smiles: N#Cc1ccccc1\n", - "map between molecule A and B: {0: 2, 1: 3, 2: 4, 3: 5, 4: 6, 5: 7, 6: 8, 7: 9, 8: 10, 9: 11, 11: 12}\n" + "molecule B smiles: Cc1ccccc1\n", + "map between molecule A and B: {0: 4, 1: 5, 2: 6, 3: 7, 4: 8, 5: 9, 6: 10, 7: 11, 8: 12, 9: 13, 11: 14}\n" ] } ], @@ -466,7 +459,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -489,7 +482,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -512,7 +505,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -570,7 +563,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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GxtrZ2bX2cDDyRcrjCl6/QA6/f9LKGZ3WAF0FKrOBVwIA8DEGcj7VYyIANFgqY9QYVuoqwzVUeqgo0QCATmeZdFcZMlTVarTqsJEMrf85xYwDb3Pu36n++6akOI+hras+3lF9yixaEys5t/UVIQqcUVZWRqdCoZDJZNZboiwjIyMmJubp06fR0dFJSUkSiYR8adiwYXPnzk1JSUlISIiNjf3w4cOPP/54/36bKAiJwbRbkJbTzp079+zZ09pjwTQRqVSQ8k5ckMfQ1VPuM6BewyPlcgRJL3lx0TXPngjevwGpFAByBOIAgcYFcQdR0ZtPVhAA6AxgqoCmMXQbo0RnzhMk72JmAp3BMjFDxo89fBRdU1t+d9PWV4Q0Go20ggDQwDamqampqakp2vzkcrnPnz9Hi8WYmBgkb9G3b19VVdVjx45NntzKSucYDAYAbGxsjh49+ujRo9YeCKZpcCPvlezbLuHX0BgMIAggpDqrftJesAxoNElZKT/pBf9lPGn8pADva4TPqvnPOPx/OPwKMcp/TwYAYOtBh4FQnQ+l76HnVNDrA8JqBp2YPn6S4biB7OHWdE0txdxRW18RthypVFpZWXn27Fkmk7l06dJhw4Z5e3t7eHiQK0KRSOTu7k6j0bZt2yZrdDEYjFwpKyszMDBQUlIqLy+v5dTHtFkqrwTmHvktsrBskvYnz+7Tan53DXYXMzMAmiA95UR+xQpDrcN55WKCKBNLn1bXfDZ+AAAdlBiW6iovTWbmd7ImtLoC0CD1LsSdgG5jQdsUVHXV6bzvzJW8T55Q5E219RVhy6HT6WSWhZaW1u7du7dv366urk6+gclkZmVlqaurKzVxWxmDwbQEXV3dgQMHvnz5Mi4uzsbGRvEDePbsmUgkGj16NAAQBHH06FFUCrGgoODmzZtaWlpaWlpv376dPn16z549FT+8NogoN6fs6G9VHO7RvHLSEF4qqZ5LEHppqck1wj+Lql7xBOeLq6rqGD9rDRVLdZWeKkoA8EEtx0mrExdoAACGAwEAPsaCXm9lLaNpXQvU1RRtmL5+Q1iLBQsWnD17VvZKRkaGnZ2dUCjMyMjo3r17aw0Mg2mH2NjYvHz58tGjR61iCJ88ecLj8ZAhlEqlHh4eyBDq6+uPHTv2+vXrGzduHDBgwN27d79kCO/fv//s2bNZs2b99ddfDg4OY8aMUegNKJzqaxcJibjudZ6UsHiVzZX8z/h1ZjHZdJpzR63h6iqdWUwAoDEYrF79VK2sVQYPNbW0isgXzzySwOXUcNU7E6q6UFOmqt/jqP7HpScPKn5N0l4M4YQJE8gswzNnzrx69Yp8ycTEJCoqis1mm5qattLo2i8pBdzsUn5nHeXeRuqNzgLFfD3Y2NgcP368rbkJmUymtrY2h8ORSqWBgYFr16790jvt7e2fP39eUVGhrq6O5JG/bmqe/0OIxABQLpaeKvikYZbGF7HpNH0mQ5dJt1RTUaXTZumpv+EJpAR8Y6CFjB97+GiVIZY0ZRWyqRE9IPv4uKt/RT4KvB2hbZBWU7bx/ZHx5epM8T6awlPa2oshHDRoEDp48ODBzz//PHLkyBkzZqArdDp9yZIlrTe0dsqtxKK1f74p5wqVlehCMaGmzPBa3G/eSKPWHhdGodja2tLp9JiYGD6fr6Ki8u8foJrbt28XFBQAgGy0RFZW1p07d3r06HHkyBFVVdUPHz40XKB0yJAhI0eO3L59e9vPAzlz5szQoUOTk5PLyspWrlzJYrFqamqqq6s7dPgkSJaTk9O5c+d6I/MBAD5H4yvTab1VP63bHlfRAeBWXyM2nQ4ANBVV5d797cyHsoePVjEfSmN9MfCCxaR/+81Iq+OrOitV7AR4UcUh9FT5LxPYI8d+6SNyor0YQhJtbe0XL14UFRV5e3s3XokGQy0nw7PcLjzn5b2FjkO4AgkAcLJfLj9ZnlIweMcs7IxpR+jq6vbr1y8pKSk+Ph5tUSoYS0vLZcuWAYBUKg0ODkYXu3Xrtnr16sZ8/OnTpwRB/PPPP2/evJk+fbocB0oFr169Ki8vz8/Pt7W1/eOPPzgcjq6u7sOHDwMCAgIDA9F77O3tY2Ji9PX1621BeZAFP/k1iCRsOs1O65OP8EYZFwDYdDrTsGOHXYdUzIc1fklHZ7NZfQZYceMB4BmHDwD8hGeKN4RtqAyTYjA3Nzc2Ns7Ly4uPj2/tsdRGIBBcuXIlKysrJyfn5s2bsro5tSgqKkpJSQGA4uLiFy9eKHCMFPAhn+t26R2vshheXvjf1TeXeaU5HrfSEzKaoBmP+QqwtbUFgIcPH7ZK70ZGRhYWFhYWFubm5s34+MiRIzdt2jR27Ng1a9YMHz6c8uFRi7u7O5fLffv2raqqqrKycnV1dVNb0Jqz8Eu56jRVtv7mPapW1k3d2FS1tDJTUdJXYhSJJJkCUfNqE7aQdmcIaTQamrjdvHmztcYQGRnp4/O/opHz589HB9nZ2WPGjPHx8Tl+/LixsXFISMiXWkhOTkbjP3PmTFhYE8putQWO3csUiuuppQkAfKHkwO10BY8H07qgMBkFuwlLS0uzs7MV2WNb4MqVK999992IESMuX74sEonIcPqSkpKnn2lg/g0ASt3MdJb/qKGmtsLwfxl+M3XVzLQ11UbZqNk2p6q5qqUVDWC4ugoAxFXzBW9eSmt4zWinJbQ7QwgAyDvYioYwNzf3zZs35GlYWBjyT/Ts2bOqqqp79+76+voXL17s0qXLl1pAvx03btywsrISCAQKGDOFPH5XJpYQAAD8Cvhw69M/bhEASAl4mtKo+pyYrwYbGxsajRYTEyMUChXTY1FRkZ2d3dixY4cMGTJz5qcaBXQ6/ffff29eg69fv16wYMGYMWMWL1787t076kZKPahQwapVq7Zv366pqYkuZmdnX/nMvxZ81Vm2tuuW3d927UhX16SpqdPVNOwNdft9v9LQo5mZfypDhgGdYaWuAgDPOAJCLBa8orj6+r/S7nyEAGBra6utrZ2UlJSSktKm0oPi4uIOHTq0YsWK9+/fW1hYpKenk/WnapGYmJiSkjJs2LCioqLU1FQFj7OFiMkYazoDlD89isD49FWUSL5yhQdMLQwMDPr27fv27dv4+PgvfeEppKCgwMHB4c2bN3369OnduzdZ8olGozWvCGJOTs7MmTMvXrw4bNiw6OjoyZMnP3v2jIw9kRP+/v5ZWVnz588PDg7u37//tGnTWtKahYXF4cOH0TGSa24YjRnfqk+exX/xjzg/l6FvoDJ4KF1do9m909U1lHv3G85LBIBn1XwAqEmMVbWybnaDzaA9GkIlJaVJkyb99ddft2/f/umnn1plDMHBwaSTsqqqCh0MGjTIx8eHwWDY29tXVlbq6up+6eNGRkZubm7GxsZKSkoTJjRnO6IVsTTVep/PJQCApQHdPmePpX8S+hnQtflPFOY/io2Nzdu3bx89ejRq1KiUlJSnT5+OGzcOKSNSS3Z2tr29fWpq6pAhQ8LCwgwMDFre5rlz55YtW2ZlZQUAY8eO/fbbb/39/eVd6NvBwWHfvn33799nMpmtEvRHY7FUh1Nmq1QtrXokv9ZjMgpE4hyBWCU+lqqWG0l73BqFNrA76uTkFPMZcoNCRUVFR0dHU1OTwWA0YAUBwMjIyMzMDKWdamkpSI4PAJKTk8+dO5eUlHTlyhVvb29ZZfPGs258N1UlRr0vqaswXCfhbM52h6yb8NKlS1ZWVteuXaO8l4yMDFtb29TUVEtLy4iICEqsIACkp6f36tWLPO3Tp096utz93Do6OuPGjSsuLp4wYUJCQkJLmjIwMBgyZAh5amNjo/jKdCoWVjSAYerKABDL4QvevCT4NYocQDs1hFOmTGGxWNHR0SUlJf/+7jZMQUFBSEhIWFhYZaUigi0NDQ2HDx9+//59GxsbTU3N5j3wI3vqLBnTma2hA91l1rImtirKmhM60yYPpubnCfMfArkJnzx5IhKJOnTocP/+/YYngs3g3bt3Y8aMycjIGD169IMHD/T09KhqWV9fv7z8f47t0tLSL+UeUMjVq1fz8/NXr14dHR3dcI7jvzJ06NBNmzYBwLt37/bs2WNvb09OzRWGqqUV0BnDNT7FyxAiEf+1Qt2E7dQQampq2traSiSS0NBQRfZLEASPR1lA1M2bNydMmPD27dvo6OiRI0cqII9CV1c3NzeXzWYrKyvn5OT06NGjee14Lx3gNmcwu99UDRCyJDVK/5ygJV9dKHjtrfyY2gG3ZwoLC5sRH98qdOzYsVevXhwOJzExUSQSMRiM5m02fIkXL16MHTs2NzfXxsbmzp071P7QT5o0KTAwUCwWA4BQKPzrr78UUN9m4cKF69at09PTW7t2LVV1VZ8/f7579+4LFy78+1uphq6uodyzDwocjeXwAaAmQbFJFER7xdvbGwBmzZqlyE43bdpkaWmZnp5eVFREXkxLS2tGUwKBoGvXrtnZ2ej07t27dnZ21Izyyzx69MjX1/fixYtOTk7e3t65ubktaa2cK/T77cweu+8M2RoAENCzY8aE4YRUStVovz6Ki4v37dtHEERAQICPjw95vby8XCwWo2MOhyMSiQiCcHFxYTAY1tbWHh4e8fHx0rb9h7Wzs2MwGD179ty7d29iYiKFo42Pj0frv8mTJ/N4PKqalWXbtm3m5uYrVqwYPHjw/v375dGFAsjLywMApBWn+N6LD7mnWJjoMBkA8HBAl9yV3yqy9/ZrCD9+/Eij0dhsNpfLVUyP+/fvBwAWi/XgwYOWt/bu3TsLCwvyVCKRaGlpSSSSlresSPjv3qRamIzRVAWAkRoqWzvrPgzwa+1B/T+ysrLevXsnlUpjY2PLy8tbdzA8Hs/Dw0MikZSWlnp5eZWUlKDrampqv/32Gzp2cnJ6/PgxQRCLFi1iMP7nizUxMVmzZs2tW7cU9oVvPKdOnaLT6Ww2mxytkZHR8uXLr1y5UllZ2ZKWo6Ki0Ppv2rRpfD6fqgHXpaqqKikpicPhSCQSDw+PWbNmkVMTCrlw4cKpU6fQsVQqtba2prZ9FEUfFxdHbbONgRN5L9XCZII2GwAOdNNPG9FLyq9RWO/tdGsUADp37mxhYcHj8R48eKCA7s6fP//LL7/Q6fQLFy6MGzeu5Q0KBAJZnzbSBpRK609Ub7Mo9+6XqqFrraEKAEk84TJDzduB/q0yEtkwv2vXrj158gQdv3v37uLFi7///ntNTc2BAwdaZWwkqqqqdDqdTqdLJBKhUEg6ujQ1Na9evVrLZevv719RURESEuLs7Ny5c+fMzMxTp05NmzZNT09v/Pjxnp6eb9++bY2bqI23t/fatWsJgtixY0d4eLiLi0vXrl3z8/PPnj07Z84cXV3doUOH7t69OyEhgWhi8dRHjx5Nnjy5qqpq3rx5165dk1+10cTExBMnTtBoNDU1NTqd7uvre/36dXm4KkpLS4uKitAxQRCU5yyiqKVWUflRtRgOdHoPbX0AOAjdlxo6e5x/VsZRkI55+zWEoMDY0evXr69YsQIAfv/992+//ZaSNs3MzDIzM7lcLjp98+aNsbExkymvfJjQ0NCcnBx0nJeX14DqTVOJUtVhM2gqdFq1RPqSK6DnZFHVcpM4f/48eRwbG5ucnIyOe/fu/fHjxwEDBsTHx6O9o1akuLj4/fv3ISEhq1ev1tTULC0tRdcZDMZvv/22bt26Wu9XV1efNm2aj4/Px48fk5KSPDw8HBwcxGJxRETEli1b+vfv371791WrVt26dathPRH54enp+cMPPwDAsWPHtmzZ4uDgcOzYsaysrLS0NC8vLwcHBwaDkZCQsGfPnqFDhxoZGX333XfBwcFkxlED3LlzZ/LkyRwOZ9GiRQEBAfJ7NADAx8dn27ZtZAYeEo1rayU1GkOrqPwg6Jraf/SYf7b/Zno/p+Jesx+r9XWP5pi6Rt57VayI7hW29myDvHz5EgA6dOggFos5HI6Pj8/+/fsLCgqo7SUiIgJNRZF3h0J27NgxZ86cxMeo4hsAACAASURBVMTE6OhoKyur4OBgatuXZc6cOREREeg4Kipq2rRpVLXMi3uSYmEyTosNAHZaqgmDuwqzMqhqvPHo6uqSx5s2bfrjjz8IgpBKpTdv3ty7d29CQkJUVJSHh4fiB9YYunTpQhDEjBkzgoOD0daot7f3nTt36nWJFRUV+fv7z58/XzZyUk1Nbdq0aU+ePFHksHft2gUADAbj3LlzX3oPh8MhF7XkaJlMJun7rPdTISEh6KFbvXq1AvwFSLF68uTJ6NTPzw8Apk+fTnlHR48eNTY2HvkZPT09attHk10tLS157Os2jE9EFnvRLZgfAhYroctI6GYDY7bDglC1pXefZ7Zoe7wxtGtDSBCEmZkZAJDP//79+6mNKYiNjVVXVweAH3/8kcJmSa5cubJ69eoNGzZERUVJJJLo6Ojbt2/LoyP5GUKpWJRuO9i3ewcAGMBmpVqYlPv7UtV441FTU5v5mZ49eyJDSBBEXl5eZmZmTU3Ny5cvFf/r0EiQIczIyOjbt6+jo2N4eLiqqioAqKqqOjg4eHh4JCcn1/2URCKJj4/38PCwtrZGSdnh4eGKGbBUKkUlcBkMxoULF2RfEgqF7969q/dT5KJWtnCrqamps7NzUFBQdXU1etulS5fQ+m/Tpk1yvxOCID6HmWhoaKAwE6RiKg9zcvTo0T179qBjiURCuSEkCAIVJ//SDENOcAVijWV3YUEomNlDn5nwTSBM+wMMB4HVetrCUKudcp+ftXdD6OrqCgCbN28mCCI3N9ff35/Cxl+/fo3SoRYvXiy/mD0yBODx48cA0LNnT3n0MmfOnPnz52/ZsmXLli2LFi2i0BASBFGwzfWteTc1Bp0GEDWgy8flThQ23kh0dXULPrNu3TrSEP4nQIaQIIi9e/cqKSmFhobu2LHD0tJSVnOkb9++P//8c0REhEAgqNtCXl7e2bNn5RpOQiKVSn/88UcAYLFYV69erfUq8tmT5q2qqqpuC8XFxYGBgQsXLpTN2GOz2VOnTl24cCHyl7u5uSngXkhqhZmYmJgAQGJiIrW9KMAQoqJUhw8fprzlBghJKNRcHgbfBALbAObfggWhsCAUpvmCpjEsCFX+7k5JdT1fWgpp1z5C+OwmvH79OgBEREQ4OTlR1XJ6evqECRPKysqmT59+7tw5OckgLVy4UE9PLzc3FwBGjRplYGCQkpIipyCIYcOGTZo0adKkSUhNikLUbMezaLTRGioEwP3KGv7LREl5GbVdNAbDz8iGL/4nGDhwIDrYuHGjnZ2dkZGRu7t7fHx8YWFhUFDQ4sWLdXR0kpOTDx8+7ODgoKOjM378+GPHjsmWXzAyMlq2bJn8wklIJBLJ8uXLT5w4oaysHBwc/M0339R6Q0FBgYGBQUZGhq+v79y5cw0NDadMmXLy5EnZUCB9ff0FCxYEBAQUFhaSi1o+nx8aGhoaGkoQhLu7u4eHh7zvRZZa3rVWdLa1kFYZeWohVyASA6cANDoB7bNV0ugEvBIAQoVFTy+Ss9CMXM1s20ckEqmrqzOZzBkzZhw7duyff/6hJIemsLAQqS6NGzeupkaOQcDIkJMR1d9//z3IwRlJyHNrlCAICZebNqL3QRN9ABitqZpqYVIVIkd/Z+3eJRLiCz7CrwaxWBwfH79r165ay0QzMzMXF5fw8PB6l4nyGMbixYsBQE1NrYFtWNk9W9lS6WZmZs7OziEhIbVWrtXV1fHx8WFhYadOnWIwGAwGo951pFzx9/cHAEdHR3R67tw5AJg5cya1vYhEouDg4IqKCvKU2vaJz/u62trainQE+ERksBfeAkcf0O35aTm4IBTmXgNlLVgQqrHs3puP1XIdQHs3hIcOHYL/L9fJZrOtra1dXFyCgoKKi4ub0WZFRQUq8jls2DB5P5DoeZs0aRI6RUtbKysryjuSqyEkCCLvx+/jB3Vl0IBJoyUO7pq/wZna9r9ESkpK3759nzx5EhUVRV5MTU3Ny8tTzAAoJykpqWHzVlhY6Ofn5+TkJPu1V1NTc3R0RPGlchqYQCBA6z8tLa3GR+UUFxeTi1rZh9TBwcHLyyszM5P4bAhdXFwkEgmqjnv37l053cWXqBVmgtavurq61IbqxMbGAkCvXr0obLMuaF/3+fPncu2FRCoURv60WXXedZh3E9SNYJrvJ0M44ifoNhYWhKovuycSy1cOol0bQj8/PxqNRqPR9u7d6+vru2TJElnxXACg0Wj9+vVbvnz5uXPn6g03qAuPxxszZgwA9OzZs7CwUN63UFJSwmAwlJWVUd4xl8tls9k0Go3yn7N79+6Rbebn5//999/Utl95JTDVwgRpLHmZGqSN6iutkYsIiCw5OTnomZ89e7a8+1IYZD0d9Ls8Z86cc+fO5efn132nQCCIiIj4+eef+/TpQ35ESUlp7NixO3bsePr0KYVrAj6fjwpi6+joxMbGNqMFsVgcFRW1devWIUOGyC5qBwwYkJOTU1pa+ueffxIEsXnzZgDYunUrVSNvPCjyDuU7EgTRtWtXAHj58iWFXaA42x9++IHCNuuyZMkSAPDy8pJrLwipQJC/YWWqhcmQaYcZ82+B/X7Q6ga9poGZPej1ghnn2Uvvbg+qP3iKQuRiCH/66af169dfvnw5JydHHu1Two0bN1Bo2ZEjR2SvV1ZWhoeH79q1y8HBAYXekWhqajo4OOzatSs8PLxeeQ6hUDhlyhQA6NKlC5qrKoDRo0cDQFBQEDpFlclk9bcoobKy0sjIyMnJSU5RP6KiglRL021ddAHAUUct1cKE8yhCHh2RlJSU9OvXDy2gyYDDrwBkMNzc3Grtgvbr18/NzS08PFwoFNb9VHp6ure399SpU4cNG0Z+RE9Pz8nJycfHp4XrYw6H4+DgAAAdOnSgxDCQvk9tbW1dXV2xWHz8+HH0n4jUg0eNGtXyXprK0qVLZX9P0Cbw8ePHKewC/e8gP6j8QPtMCpCfFFZXnRo/5lbfTks6aOooMTXH72POD4G512DiUZjyO8wPYX9/12rnE4FI7gkwjTWEYrH45s2bYrH42bNnWVlZ5PUbN26Q657Y2NiCggKpVCq7iWFkZOTo6Ojh4REVFaUYP0RjePDggYqKCgCQIVj1IhKJkpKSfHx8Fi9ejJYOJEwms1+/fs7Ozn5+funp6QRBSCSSefPmAYCBgUEjl4+UcPDgQQBYuHAhOj1z5gwATJkyhdpe7t27BwAjRoygtllZchbPeDigCwBoMOjJ5t2K3OUY9cflclEN2AEDBpSWlsqvo9YlIyPDx8fHyclJVmZaV1e3AfNWU1MTHh7u5uaGZgkIOp1uaWnp5uaGsnSaNIaKigr0p+7YsWNSUhJFd/YJoVD49u1bgiDIJW9lZSWDwVBSUuJwONT29a8gTYYZM2agU/QkUrjZUFRURKfTVVVV5S2SJ6d93VpIuNzERTOfDTIeoqYMAAwaePY0sZ++n7XwltaKMM3l91S/v7sx8C1fqAjZyMYawvz8/K1bt3I4nKCgoJCQEPI6mjOi44ULF4aHh0skkoiICHd398mTJ9cqlaempjZu3Ljt27eHhoaWlZVRfzeNIy4uTkNDAwDWrVsne10gEDx+/LgBWd709PSAgIB169YNHjxYVsURALp169a3b1+0GUXujSgGVKFeS0sLzTMKCwvRZim17km0J7NhwwYK26xF2dmTqRYmvVRZAHC+h2GGgyUhkYu7XiAQTJw4EQDMzMz+u77AJlFTU3P37l0XFxcU5U+aNysrqz179iQmJmZmZq5fv57P53t4eJBB/2lpaT4+Po6OjmjWiDAwMHBycvLz82vMI1xWVoZijLt27ZqSkiLnu/wEKkuksJxIEhRmoqOjg+xHSkoKAOjr61O1iYIMLeVz3HqRx76uLJKqyo9LZqVYmMzVUwcAJo222EDjiIlB0b5tFdWCpynlzzMrFWMCEU3YGj1+/HhNTU1ycrKsITQ2Np4xYwbyGCFDWOtTaWlpfn5+zs7O/fr1q5VCYGZmtnjxYh8fn6SkJIVJ479//75Dhw5oCVVrvhMTE1NrqZeRkfGldjgcDpIacXR0RMmCOjo66urqlAhqNxU0eSeDWaytrQGAWqGZ8ePHA8CVK1cobLMWgtT3qRYmaztqAcAiA41UC5OaF/9Q3otYLEZJMh06dHj//j3l7bd9kHlzcnJCUg8AgOayBw8eJAgiMTGR/CKR8Hg8pALarVs38vllMBiWlpa7du36UmmLwsLCQYMGAYCpqSnaNVEMSDZ2+/btCuuRBP19Xrx4gU6NjY0BgKp1MFJnPHHiBCWtNYw89nVJxGWlOfOnpFiYTNP5ZAXXdNTaaax7ccXi1io+01hDWFFRsWzZsgcPHpw6dUrWEW1sbIzi7ng8HjKEDeTkFhYW3rhxY/PmzaNHj5adYwLA77//3tJbaQTZ2dlopuPo6Fg38vj+/fv1LvUWLFhw4sSJxMTEemMHysrK7t27l5CQgL70Dx8+VMCN1GLr1q0gI16DtKEXLVpEVfuotAUAyC+kEJE10/ZaHyMAMFRipFiYlHhRnAcilUqR6KuWlpbCguLaLFwuNyQkZM2aNdeuXSMaNISykCqgsprvHTp0WLx4cVBQEBnZn5+f379/fwDo06ePvL82tUBCuGPGjFFkp4haYSYLFiwAgJMnT7a8ZbFYjCbcqampLW/tX6F8X5dEXFqcPXfie3OTb/TUAUCVTjvfwzDVwiTv6F7K+2o8LQ2WMTY2Jghi586d27dvR4awR48ejVnqiUSi+Ph4Ly+vxYsXd+vWTQHbiUVFRSg6btSoUQ34D6qrq2st9WS3dq2trd3c3EJCQsgKOFu3bk1PTxeLxajK808//STvG6kLCqru2rUr+mujPRltbe16wyKaAdLRNzU1paS1Big5sjfFwqSjEhMAbvbplDXDppEfvHbt2tGjR7Oystzd3RtwbKD/IzabLZssgSEIorCw8JdffgkPDz9+/Hit8LEvUVlZeeXKleXLl3fq1Il8Rlgslr29/fbt201NTQFgyJAhsqU3FUNFRQWDwWCxWIovOFUrzMTHx4dccLcQpBvVp0+fljfVGCjf10WI8nOzZtq+M+82SVsNWUH/nh1TLUzKfI9R2EszoMYQ8ni8/v37jxkz5saNG7WWegYGBtOnT/fw8GjY/SZvKisrLSwsAGDQoEGNryonkUhevXp16tSpxYsXIwk+EjqdPmDAgNu3bzs5OW3fvj0lJSU6OhoATExM5Hoj9SKVSpEkMbnKQSafqn3a33//HWTiceQHL+FZqoXJDF11AOirruo52e7FP3GNCeIvKyu7ffv248ePDx8+/KX37927FwCUlJQoz/3A1FUBVVVVtbCwaK1QAJTIqwA/RXBwMErbuHXrlre3d1paGsiEmaAySR06dGi5OUG7PnJ10svy8eNHDQ0NfX19AwODWmv9ZiPKzcmaNjbZvNt4bTYAaDLoV3obpVqaVgSepWTMLYEaQ0gQxN27dwEgPDy81lJP1ng00v1GOTweb+zYsQDQo0ePlhSXKCgoCAkJQZkVyN5HRUW5urp6eXm9fPlSIpF07NgRAF6/fk3h4BuJs7MzAOzevRudurm5AcD69espaXzRokUA4O3tTUlrDSERJ8we30lbU1tDnfzaqKurkwvxL0V4VldX//bbb2Kx+EuG8M8//6TRaHQ6/a+//pLzPbRrSktLL126hNwE58+fb61hIA3hXbt2KaCvgwcPlpaWent7o71l5Hx59eoVehUtl1Foa0sYPHgwKCoCKCMjA+VEkl5kcq1/6NCh5t2LMDMtc/KI10O6jdZUBQBtJv1aH6NUS9OKv9pEIe6WGkJZNa+jR4/WDQzLzc0NCgpycXGxtraW1YwHAJSX5uXlJdfMCqFQOHXqVADo3LkzhdaXz+c/efKEz+dXV1eTLhCkV0vWClckKH3K3NwcnaLAn27dulHSOHoqFOBUq6ioGDJkCFq4Hzp0aOHChbWyVhgMxqBBg9asWePv75+WlkZ+cM2aNQEBAUlJSZs2baq77Xnt2jUGg0Gj0U6fPi3vW8AQnxffqNwuiSKzp5DEkq2trQL6Onjw4MmTJ/39/RcvXkx8njWSIS0opYoUQfxXoqOjf/nll5KSkl9//ZUMS8zLy0NVfxWgip6eno62tS0tLUtKSkiXsOyvt4mJSQOS6HURpH3ImDD89ZBuIzVUAEBfiRHat1Pq0O6KVFJsGIoT6h88eNBAPHpVVVVYWNju3bsnTJggm9gEABoaGg4ODkeOHLlz507L1+AkUqkUZbnq6+u3fFL2r6Aav8OGDZN3R3Xh8/koJwQZe3J52vIA6IKCAjQ3lIewoSxcLheJA3Tr1g0lnl6+fPngwYPp6ekhISFubm7W1ta1Nt4NDQ0dHR2RxMGXNF3JepD79++X6/gxJMhN0K9fP3Sanp4+ePBgcpamAEpLS+l0urKyslyVfgmCCA0NdXd3R96WsLAwgiD++OMPAJgzZ87ly5ePHj06derU6dOnP3r0qPFtHjhwwM/P7+DBg6RKBmqTzFCUH8nJycjJMnr0aCRWRVJdXY0KQ8q6hFVUVFCdrzdv3nypTX7y6ww788TBXc3VlAGgE4sZ0b9z2vAe1eFtyENBpSHk8/nod0p2qfeleA2xWJyUlFQrswIt/4G6zIqffvoJWdl//qE+EL8uPB5PTU2NRqMpRlInJyenuLi4pKTk8ePHYrF4zpw5IBPxvHLlSvg3xYDGcPXqVQBwcHBo8XgbQigUTp48GQCMjY3RlGXHjh3o2SNL26C3oY13JycnlAYju/FuaWnp4uLi5+dHaj7Iux4kpl6EQiF6EJDaBvploNPpZIiZAkCZG02yQJSQkpIyaNAgpCFASougwpCkOGrDHDhwIDg4+MWLFzt37kRXkEarvPcznj9/bmBgAAA2NjYNay2RLmEkzkX+aCNJdNnJBz/pZbrtkITBXQexlQGgC4sZ2b9LmlVPTuQ9ud5LU6HSEObm5o4fPx6tS2ot9Xbt2nX37t1aUwxZ8vPzr127tmXLllGjRtWqBWNoaDhz5syDBw9GR0c3aWdg586dAMBise7dU9wffebMmaCQbBCRSOTt7R0SEhITE3Pv3r3g4GCkf29vb4/ecPv2bQCwsLBofJsXL148ceJEQEDAyZMnyQU0ysqSq7tFIpGgHCkDAwNUlPXChQuvXr0qLy/ftGlTA5P69+/fnz9/fuXKlf3795ctUwAAPXv2nDFjBvo2Ll26VGGJqhiEvb09yCSz2traAsDNmzcVNgBU8rDlE8GmIpVKkYeSxWL99ttvv/zyi7m5uWwKdf/+/Tdt2vTgwYN6Fwnx8fGenp6vX7/29fVFwT5CoRAlL8lVtTE+Pl5PTw8AJk+eXCuq8enTp3fv3q33MSwpKQkKCnJ2dkb7T7Wsfmp8XProfrEDjXursgDATEUpemCXtFF9uU8fy+9Gmgf1WqP1LvVkZw11l3q5ubkfP37kcDihoaFcLpcMt2l41t/w18Lb2xsAGAwGtXnl/8qff/4JABMnTlRAX0jcQCAQbNy48fnz56WlpUwmU0lJCYXqoc1SGo2WnZ3dyAYlEsmhQ4cCAgLOnDlDrqFHjhwJ8pTzl0qlKNJHU1MTZdEEBQVt3LgxNDT0+++/79KlC0rcRvVAGtAxr6qqIvNetLW10RfGyMho9OjR8t7UxSCQW6SwsDAvL8/d3R1k5KEVoExUiytXrgCAnZ2dwnokCEIqla5btw4AlJWVr1+/Tl4vKioixVHJXzOy4kfDT+j9+/cBYODAgfIbdlRUFPJVOTo6vnjxAoWbZWVlIYuItpqQefPw8KhXP1IsFiPvZi2r36ujob4SAwB6q7JiBxqnW/fjxcm93HwzkHv1iby8vKtXr27YsGHkyJGyGbjkUi8pKSkwMNDPz2/Xrl0pKSl1Z3BkuI2lpWWtWT+5BxsfHy+bPRYQEECn02k02pkzZ+R9g7UoKSlhMpksFquB5S9VBAQE7N279+7du8HBwRcvXiQIYty4cQAQEBDw9OnT9evX29rarlq1SlYbtmGQISwtLRWJRGhbhs/nKysr0+l0Ch23tdiyZQt6zOruYolEokGDBtVd6i1ZssTHx+f169dfShkUiUQJCQlIAH358uVyGjlGluTk5KVLl1ZUVLi5ue3YsePGjRuyP9+o7nyT9idaSHFxMY1GU1VVVUCACUIsFqOEejabjfyF9b6n3sKQDUiib9y4EQDc3OQlvRsZGYncB/PmzXvw4EFERISnp2dpaek333yDYl89PT3rjvbnn3++f/9+vQFQtay+sa5OZxbzn0HG6TaDal4qVH6y8Si0DFO9S70PHz4UFBT4+fnt2LGDIIiGt+AqKiru3Lmzc+dOe3v7WnuwmpqaEydO3L17t7u7O9q5RtHMigeVYbp8+bK8O8rMzExPT+fxeKTYhJeXF5vNHjVqFIPBILfvGQxGv379GuNzDQkJOXbsWExMzOnTp5GKMYp6kN9s9Pjx4wCgpKR0+/btL72nqqoK1QORXeohNDQ0yMyKuumhSAfA0NBQrtrBGJIDBw7Ex8ffvHkzMjIyPDwcVQRDRT1rampUVFQYDEbjs3hbDpK2UYx4gkAgQCsnNTW1+/fvN+Yj6HfvS5Loubm56G1IQFFOklWhoaEosGPFihVk7uOff/65b9++GzdukEkghIx5q7cwpOyi9sWLF35+fgKB4PLly0jIaUxPs3TbwfykF/K4BUpozXqEHz588Pf3l0qloaGhmzdvPnjw4I0bNxpfBKvWHiz5f4NqJ23btk2ug28AVOx3wYIFiu+6uroaLbBUVFR2797t6upqZWVVN2tl9uzZR44cefr06b9GtHt6egLA6tWrmzqSqqqqwMBAgiCio6PPnTtX73vIepAoJbkxoLnU8ePH58+fj5LVSJhMprm5ubu7u+z7USD406dPmzp+TFPhcrlbtmxJSUnZuXPn/v37P378iPYnkH4b8XmCeOvWLYUNCe1SonQmqVQaHh4um3JDIXw+HwUHaGtrx8TENOPjYWFhrq6usvVQ6XT68OHDUbifpqYmVSpRsoSEhKCAjNWrVyMrGBcXt3Pnzo8fP3p5ea1fv77epNsvLWrJctDPnz9//PhxUFBQQUHBDz/8QKPR2Gw2N6ex+1KtQpsozJudnZ2WliaRSHJycpod1JCbmxscHLx+/XoWi0Wj0cj5lOKpVQ5CkZw4cQItAWXVsWUjLVFUGImSklLD7jf0ePv5NTnpNS8vb9euXaWlpfv27as3VvBL9SCb2guZWYEe6VryNy4uLgCwZcuWZneBaSRFRUXh4eHv3r1LSUlBEU+7d+8GGVWHHTt2AMDGjRsVNqSgoCAAGD9+PEEQp0+ffv36tTyUbrhcLpKk19HRkY1wbh7p6em1JNFVVFSUlZXRMrHeAsvN49KlS+gB3LRpE3nx9evX4eHhaCFYUlLyr3U6s7OzfXx8Zs6cKZt6b2Njc+DAgaKiomfPnm3ZsgWV5XnypC26BknahCFsOVKpdPfu3du3b6+qqpoxYwb8/wzWrKwsBZdGqlUOQjH4+/sjz+jZsw1JFqF6II30uaJgsOYV0Dlw4MDr1683b97s5uaG9sdIGlkPsklwudyHDx/WypNBgQZ9+/alqhdM44mMjASAIUOGoNOIiAhQbJZtYWEhWo6ggDJfX98m6V8XFha6u7uLRKKLFy+ePn26XsUiDodjZ2eHduBlNxJbDpfLvX37dl1BCWtr67179yYmJrYkEPqPP/5Azz5VrkeRSESWg7a0tNy2bVt0dLSPj89PP/20evVqAJCVXmmDfCWGkM/nb9iw4dixYw8ePEC6t5MmTUIvhYeHg5wrytalVjkIBRASEoLmd4cPH278p5BE57Zt22xtbdXU1GQfOW1tbRTyrq2t3YwC7tnZ2bNnz75///7GjRs3b94sK3/8pXqQ8kAkEqGg8PZZcal1QX5BMn2Qy+WyWCwGgyG/wKt3797Z2dnJLpvQciQmJubEiRPXr18/dOhQ41sTCATHjx/n8/l5eXk7duyoq6JSXl4+YsQIADA2Nv7w4QM19yADn89XV1en0WgxMTGoMKRsahlZGLKpbtdTp04hK1jLj0AV5IwBRWtfunQJFBVI32y+EkNIEEROTs7+/fvfvXtXUlKCKtOiuE0ul8tms+l0uiJLsNYqByFvIiMj0QKLzMBtBsjn6uPjs3jxYtLnitz4KNwGicS2sLhaA/Ug5cTChQuh9SKn2jlI45dMH0SVMuUkev7q1StDQ0P4/y5tVHvSysoqJCTk/fv3Tf3KIUNYVlbm7u5ea1uytLR02LBhANCtWzc5lUZCAs6yobZcLhcVhkSKpuQyseHCkLKgGm00Gu3o0aPyGHNd8vPzAUBdXV0ebk6q+HoM4aVLl27cuIGOkVIXKVA0bdo0APDx8VHYYOqWg5AfL168QLGUa9asobDZnJwcBwcHAOjSpYusfgQAdO7cee7cuV5eXs+ePWvSl5usBzlt2jSF5fYhR9Ho0aMV0x1GFuQXJNMHf/nlFwq342RJSEjQ19cHgHHjxpEbGM+ePdPU1CSLqTGZTGtraw8Pj/j4+Ma0WVpa6uLicvv27VOnTu3Zs0d2X6SgoGDgwIEA0Lt3b/nJSK1fvx6+XGG43sKQhoaGqFhEvelbHh4eyApSUiKx8aAgoNjYWEV22iS+HkMoy8GDB0EmbgIVmZwyZYoixyBbDkIqlZaVlTVjg/Ff+fDhA5oFL1iwgPIFFnrUo6OjuVxuVFQUCrdBPzcksuE2DVeek60HqchCcdXV1WiDjsJAA0wjQX5BS0tLdHrv3j20PqO2l7i4OGTtpk6dSgqgkEnitra2O3fuHDFihKxHvEePHi4uLnfu3GmGGGl2dnbPnj0BoG/fvnINykO9/GuYCYfDQSqgaPL9JauPJiUMBkPxVUHQj6Gnp6eC+208X6chrFWZtrCwEG2WNlIrnRJky0FkyLr7CAAAIABJREFUZ2fv2bOnqfrXkZGRHz58ePPmzenTp+t1q+Tk5KBCV+PHj6c8QrWyshL90er+UsiG29SrHFRX4qB59SCpAqmYNhxDhJEHPB5PWVmZTB9EbkImk0nhk/jo0SPkcp47dy65P/Hw4UN0cd68eeTeA6kHZmRkRH5jGxZMqUtGRgYKYEHFGai6i7qQpQ0bU48TIZVK4+Pjf/3115EjRzIYDPIeu3fvjp4+FoulYKUtRGBgoOKXIk3i6zSERJ24TaSBK5tUIG/IchDp6elZWVlHjx5tfCkWgiCkUmlERERQUNCHDx+ys7Prfra4uBgFAowYMYLD4VA6doIgiDt37qDVW8Nvq6ysRNnuDg4ObDZb1ihqamoimdkrV66gv38L60E2m9OnTwPA9OnTFd81BvkFScEEFF1ClWLfnTt3UN7wggULSIP3999/o4tkkngtJBJJfHy8h4eHtbW17DKRlI2WFaPx9fUlqwA+evTo77//trS0HDp06JdKY1KFl5cXtCAduZbV79ixYy3VN0WSl5cHABoaGm1W7PCrNYS14jZRYjgqGCY/SkpKZJ3VZDkIJKYqm6/TGDIzM5Gb08fHp1YicGVlpaWlJQAMHDhQTkXAkWR5k7K+RCIRGW6DMtlJWCxW586dGy/2Ri25ublIbUseMwZMwyC/IPnlR4IPW7dubXnLt2/fRjFizs7OpMGrmyTeMPn5+efOnXNycpIVLdLQ0Jg1a5avr29OTo6dnV3Xrl3RU3bw4MEDBw4UFxfLw81Ri4kTJwKAv79/8z4ukUjCwsI4HM7ly5d37drFZDIVLOtTix49egCAYqoANYOv1hDWitv88OED2meQ35SkqKiod+/e3333HdkFKgdhYWGRlpZ248aNxotfI06ePOns7Lxt27YNGzbIJuoKBAKUwNu9e3f5+b1Q9QBSFqQZZGZmBgYG/vDDDyhMtKnzAGoZPnw4AJDhVBiFgfyCZPrgo0ePFi1a1HJ9mcuXLyPJpLVr15Kzz3qTxBtJvYIp69ats7Oz27RpE4pERYawhSNvDDweT1VVlU6nN6Ay3zDZ2dmurq7o+PDhw0g6PzQ0lHxDeno6WVFcASxfvhzacPD2V2sI68Zt9u7dGwAiIyPl0R1ZWt3CwoL0f1y9elVDQwPNT42MjBwdHVG92ZaoAIvF4tmzZwNAp06d0tPTKRp+Pb2gfV1KDO3ly5cBYMyYMbIXW5iG0VRQzfSlS5cqslMMIeMXpFCGPjAwEBk82QBUf39/5BhreVRqTk6Or6/vrFmzwsLC7OzskpOThw0b9vTpU4UZwpCQEGhx9jOyOpWVlb6+vmiHbPPmzeilPXv2AMAvv/xCwVgbx4ULFwDA0dFRYT02ia/WEBL/P26TIAg3NzcAIGdJFMLj8VC+lKwPLCoqCvnMevfuXUsqWk1NzdbWdtu2bbdv326qp2Hfvn0AoKenJ1dDkpiYiFaclLRWXV2NIiZQWKlUKkXxqHISfqyXpKQk9HdrfOgBpoWIxWKkpI/8glSlD6alpaG14K+//kpelF+SuJ2d3fv37589ezZ06FBPT0/FGMI1a9ZAy3SXSktLFy9e/PTp09OnT5eXl6OURDJe99atWwBgbW1N0Xj/nZycHADQ0tJqmw/g12wIZeM2CYKIiYkBABMTE2p7EQqFU6dOBYDOnTtnZGSgi69evUIa7UuWLEH7NijSsoEajXUjLRGenp7knmpwcHBaWtqcOXPknZFz8uRJal2qyOFBimvPmzcPWqYy2hju3Llz7tw5Pp9/9uzZR48eoWSmx4/bXFHQr5U//vgDzTuRX5DC9ME///xTVkFJrkniyBASBLFq1aqBAwcqxhCamZkBQEuUS0UiUVlZGYfDQVHf1dXVSkpKZLxuRUUFg8FgsViKTGRCcQMKVrtsJF+zISTjNpF9kkgkSDmzqWkMDSCVSr///nsA0NfXJ0u6p6amoo5mzZpV7/RHNtIShbeRkJGW4eHh6DuqoqIybdo09EFHR8eWq/o2hgULFsD/12ttIb///jsAzJw5E50i1SUbGxuq2q+LVCrNyMg4f/78kSNHXr58uXXr1p9//hkAfv75Z/l1iiHJzs5etGjRokWL0tPT0UZfp06dgoKCKI/XQEnidDpdTsVHSUNYVlZmYGCgAEP45s0bADAwMKA2M9jKygpk4nVRQoVc9ZDj4uJOnDiRn58fEBDg7++/dOlSBUx/m8fXbAgJgkDutBMnTqBTVByLws0TVCRFQ0ODjIbKzc1FEx97e/vG+AIFAsHTp0+PHDkye/Zs2fQm+Fylr0uXLnPnzkVBKwozhChTisIZA4rbZLPZyLpXVFQg2claYtzUUlNTs2fPHi8vr8LCwj179qDasFTt92IaBq1Ijh07VlZWJqt+0iQ9sH9FAUniDx8+JL3+sbGxqLCGXEF6IEuWLKG22c2bN4NMvO6GDRsAAO1dy4nc3Nz8/HwvL6/i4mJ3d3ckbDJjxgz59dhsvnJDiDy0Dg4O6BTtjJM6Fy0EJRiwWKx79+6hKyUlJSh/0crKqnkB1rm5uUFBQS4uLtbW1iwWKy0trUuXLpmZmb169aqqqlKMIczNzUVrU2p385EwIyk7iQJfm1HgqZEIhcJZs2Z5eXk9efLE3d3dw8NDLBajKlRv3ryRU6eYWnA4HBR+rK+v7+rqamtrK1sd09jY2NnZ+fr16814WKRSqaurK3oAFZAfLBaLx44dy2azFZCBg8pZ1FsLsCUgV9HIkSPR6c2bNwFg7Nix1PZSi+PHj+fk5BQWFh4+fDgjIwMAdHR02mCh7K/cEJaWljKZTCUlJZQGhDZLaTRaUzMZ6oK8aAwGg1Rq4HK5KG18wIABlCTbonloly5dCILYt2/fpk2bFGMIg4ODQQ5q8b/99hsALFu2DJ2iP+A333xDbS8Ng/ax23hFmK+GiooK9ER07NiRjO0i9cC6dOlCWsSmqoBKpVJUdFdZWVlhKTFoJhcWFibXXsjIMsoT9quqqtCPIbLl5eXlSDqKx+NR2xHJ/v37jxw5Eh0dvXDhwsuXL3M4HKSE9eJFmytV/5UbQoIgUJlsVC2dIIhZs2bVqlvbDAICAlDlP9ItIRAIUDyImZkZtfKDyBAKBIJBgwb1799fAYYQ7feS0bZU8fr1awDo0KEDWmh+/PhRdrNUMVy/fh3koHWJqUtZWRnK3ezateuX6lkmJSV5eHg4ODjILhNNTU2dnZ2DgoJkZdgSEhLISKuKigo/P79Vq1ax2Wx5myVZNm7cCADbtm2Tay9Xr16FOrlGVDF06FAAIIVyUMaXnDLK6uW7774DAC8vL4X12Ei+fkOIlIrmzp2LTlNSUlrol8rIyEAODzJuTSwWo2ovHTp0oLzuHTKEBEE8fPiQRqMpwBAip7o8fmKQiHB0dDQ6ReI4Lc+tboDLly/L/p6imlw0Gi0xMVF+nWIKCwsHDRqErFpjsl1LS0uRHlinTp1Ii6iiooJUQN++fXvy5EklJSUU2ZGenj5ixAiJRKLgLW7kWJF3GRMUxyCnTQtky8lyFqi6BeVT3gY4e/YsiiJUWI+N5Os3hJmZmQCgrq7ekjT2Wvj5+ZGV/6RS6cqVKwFAS0tLHj+vAQEB5HFgYGDDFR5aDo/Hk1/1VLTWJFU/3N3dAWDFihWUd4Q4e/YsjUYbPnw4qfXD4/E6d+5sbGwMAEZGRk5OTl5eXlFRUW25Utp/jvz8/P79+wNAnz59mqpdUq8KqL6+/okTJ1auXDlw4EA+n48MoZwG3wCKSTlYsWIFm82WU/m2Wrb82rVrADBu3Dh59EUQhFgs9vDwkHUAp6amAoCurm5bcxN+tYZQKpVeuHDB29tbJBKhySlVOr+12LRpEwCoqqrKNUFNKpXOnj1bXV1drmGWBEE8evQIAIYMGSKPxh8+fAgAPXr0QKcvX75Ey2h5PBU3btxA4iNkuLZIJJo+fTqasqC8GhINDY3x48fv2rXr3r17FAqgtEMyMzORquSQIUNaOGkrLCz08/P79ttvf/zxx5MnT+7bt2/btm27d+9uLUNIfE45uH//PuUt3759mywg8/r161evXlHeBVHHlpeWltLpdBUVlWbUovpXhEIhShcms7/QRS0tLTabPWbMGA8Pj7YTtvbVGkKCID5+/Lhjx46qqioUY01t3VrE/v37UZ6DrIifnJgwYQIAyLuWGLqjtWvXyqNxsViMyhmSOZco1SQmJobajh48eIDkmEltDqlUumzZMgDQ09N78+aNWCxOSkqqV+KAwWD069dv8eLFPj4+SUlJdUP8IyMjSf2ggoICRbpY2jjp6enoP5TyEkXIEHK53N69e0dERLSWIURbGuRuEIV0796djI45fvy4PLpA1LLlSOOJ8km8QCD45ptv0KSTrKdYU1ODaqTLlqnp1auXq6trWFgYhTt2zeBrNoRlZWW//vprXl7e3bt3aTSampqatbW1m5tbSEgIJRFZ58+fp9FodDr90qVLLW/tX/H29lbA9rqbm5uysnKzNe//lSVLlgDA/v370SnyUlBbsjwuLg4t+NatW0deRFlTbDa73jKn+fn5ISEhSOIAWVASQ0NDR0dHJHGAJs4zZ858+PAh+mBkZKSCA1/bLMnJycjDN3r0aMpX1cgQEgRx48aNoUOHtpYhvHHjBsgn5UBhhrCWLf/hhx+Aal06Pp+Ptl50dHRIDSwul4um8rq6umFhYcgljIRHEKgwpJeXV2ZmJoWDaSRfrSGUSCSnTp369ddfP378iLZGZaHT6QMHDly1apWfn9+XQtoa5vr162jnTWERUHINs/z48SOp/Mnn88m4MspBbgnyhwwluffp04eq9t+/f4+KXSxcuJDccUUSwywWqzHb4zU1NVFRUZ6entOnT0d5hyTKyspubm7YENbl+fPn6G9lY2MjjxJFpCEkCMLR0bG1DGFZWRmdTpdHykH37t33799/7NixY8eOzZ49W36GENlyUtQJ5UrZ29tT1T6Hw3FwcEAuDzJNgsPhoORIQ0ND2V1fVPEDuYRld2XMzMxcXFzCw8Mprzf+Jb5aQ4jgcrmjR48GgJ49eyYlJTVm1l/vV1wqlaalpZEJ5nl5eeHh4VpaWnv37lXk7aDo55CQEMpbPnXqlKwUvY6ODuVdIFDcJp1Oz8vLI2Q2SykR7MjOzu7atStyS5ABMkjdjcFgoOKOTYWUOLC0tKTT6Z6enjNnzly9evWBAwcOHDiwevVqbAjj4+P19PQAYPLkyXJKSktKSiLjR3JyclqrwCxBEIP/r707D4iq6vsA/rt39oVh2EVQWdwXZEcFGUVwSRY1tdJos1wqNbPesp7SJ80sl8w0U3PByj0VoVxABAFlHRBFERHZ93WYfbvvHxcnHsWNZQaY8/mnO3eGe8+Ew3fuuef8ztix0IkpByUlJXv37j1z5szx48d/+uknXRels7Pzb7/9dvTo0aNHj7711lvdF4TkfUFdltfU1JBfr7skctrOHL158ya5s7GxkVwEasCAAfn5+U/62ZqamhMnToSHh7ddooDD4QQHB+/Zs0c387uwsHDlypW6n1q1alVBQUHnW96Xg1CpVM6YMYP8BTxyua1SqTIyMrZv3z5v3jzyAkKHSqV6eHisWLEiIiJCt5CsQqFoe/EXGBiYk5PT+Vn5L2r9+vUAsGjRoi4/st6CkCCI4OBgANi7dy/5MDw8HAC+//77Th62pqZm+PDhADBhwgTdRfOff/5Jzvjct29fJ49PEERjY2NDQ8OsWbPWr19/+vTp06dPr1+/XheEiYmJ+/fvP3369LZt277//nvdfcS+LTExkcfjkV8+DHubRz86OeVAoVCQsycJgti/f7/uH4neukbT09PJWe2fffYZWRKLLIalm9TUYQ0NDeTMq7YzRxsaGshCBA4ODs+ZWEqlMi4u7tNPPx09erTuzzKGYW5ubl988cWVK1fadk1Pnjy5S6bn99kg1Gg05JglKyurO3fuPP3F+fn55BTd0aNH6wZtk5ydncPDw4VCoZ2d3ZgxY8jh4GQQ6uV9/I+cnBxoMye9C+3evTssLOzPh0xNTbv2+G3t27cPAGbOnEk+PHXqVOc7Z5qbm8lRAC4uLrqyzpcuXSJnfHZtoeSndI3+8ssvJ06cWL58+apVq7p2tEjP1NTURK6ysnDhwu5b8rpH6fyUg+Li4q+//lqj0bQtP623IBSLxWQnWWho6IYNGwiCOHHixKlTpzo5XardmaNVVVXkYJxhw4aVlpZ24LBVVVURERHz5s0jv2zhOB4fH4+C8AWsWLECAHg83ouu+tHS0pKYmLhp06bg4GDyQw4AaWlpTk5Ox48fnzt3LmG4ICQers/S7oiPzti9e/fUqVN3PNStQVhdXU12zpBT3VtaWqKjo8lxKB2bR9HuepDXr1/ncDjQpspwV3lSEMpkMolE8vXXX//3v//9+++/9Vn0pDuUlpbu3r37kbUCtFptXFzc/v374+LiyF/WmTNnli5dqs9pYV9++eWePXsMlbudnHJw9+7d3bt3Hzx4MDs7u+0X9O3bt+t6lVNSUuLi4rqmue0hy7uMHDmyqz4a7c4cLSkpIQtojBw5krwP0hlyufzSpUtbtmzJycnh8XjjH+LxeCgIn4hcjpnFYiUkJHTmOGq1Oisra9euXVKp1MnJiSCIKVOm/PPPPwYMQrLQsG6l6a6iz65RgiDIewZ//fVX250tLS2rV6/OyMiIjIz86quvdGHzdM+5HmQXOnnypK7PvLi4WFeuLyUl5ddffxUKhREREbt37+4D8xFjY2PbLn6rVqtnzJjx9ttv79+//+233546dapBFln95ptvdu3a1R1T355TN0050I+UlJTXX38dx3EajaYbvN0ZxcXFj88cLSoqcnZ2BgB3d/eunfqck5PzpCvCqqqqd9555/bt2z///POLVvPvg0G4Y8cOeLiGUVcdU6FQkEGYl5c3ZswYgUBgqCC8cuUKOfanaw+r5yAk15B74403HtkfFxdHll3+7rvvnifAnr4e5KxZs3rmcti9glgs1mg0bS8a/vzzz1dffVX38NVXXz18+LD+GyYSiSIjI/WzHlm7yCkHvbd0e/2vP44aMgQAEhMTO3modmeO5uXlkRXVvby8urx0+FOC8JtvvtFF+4v2Lf/P/bA+4Pfff1+5ciWGYXv27CEvFLrWsGHDQkJCkpKSuvzIz2nixImWlpb37t3Ly8vrwsOOGjWKvNFNIueedx9ysm10dLRarX782fLy8kGDBrUdTv0ka9asOXTokImJyfnz50eMGAEAFRUVQUFBVVVVU6ZMOXbsGIVC6fLGGwmpVLp//36BQKDbk5aWRq6pRAoMDExPT9dzqwiCiI6ObmpqIgvVGsTixYuFQiG5vJ9KpdJoNIZqyQsjiLot3zTu/cm6oRrH8XfffXfz5s3kOsAdkJeXN3HixAcPHvj5+cXFxZEjh+/cuRMQEFBWVubv73/58mVzc/MufQNPJBQKa2trMzMzS0pKDh06RK5E+wK6KKd7hMjIyEeqanUVlUoVHh5ObkskkrCwsOcpJdxNyC5+cuBZ79W/f38qlTpx4sT169fHxcWRS8Ps2bPnyJEjt2/ffs7B3Ldu3XJ0dNStB9nY2EiObu/wepDIU3z44YcHDhzQPTx06FB3VGvq4cgJP7qH77///pEjRwzYnheg0dR881mBu8NWBysKBizKv1dBNjY24eHhJ06ceKH+/KysLDMzM4FAoKtrLxQKydlQ06dP76aJNCqVqu0wtLq6urZ3i8+dO5eQkPD1118fP378hQ7bd4Lw8apafRW5UItugc3eaM+ePRiGmZqa6j6KZGGzxYsXR0RE6Nauex66yJRIJL6+vtB160Eij9izZ8+SJUt0D5csWfLrr78asD0G0UuDUKtWVX2xssDdYeNACzIAF1nzfvMc8W7463Z2drqP4YsuDJmTk6O7WZuenk5e/wUHBxvwDm7H9JEg1FXV6qYimT2KWCxmMpk4jldWVnbJAU+dOrVjx4579+7t2bOnY7POX8jp06cpFAqGYd99993Ro0dXrFjh6elJXsrr2NnZzZ8/f/v27Wlpac+zNIRCoZg+fTp0w3qQiI5MJnN1dd28efO1a9c2b97s6ura6/7edZ5arTY3N3/wUHh4eM8PQq1SWfnpsgJ3h28epuBHtvwCd4eK5W9p5TKizcKQbT+GTk5O5MKQz9O5kpCQQP4Fnj9/fm9cy6UvBGF+fv7jVbW6VV1dXXevz/l0wcHBLi4uz/mt7ZkaGhq2bNmSl5d39+7drq06+LjY2FgGgwGPjTWQSCSJiYlkiQOyd0WHzWb7+vquWLHixIkT7S5o0HY9yC6pUIM8iVgs3rlz50cffbRz506yN9vYqNVqBoPx2kNDhgzp4UGoVSgqP36vwN3hMzszAMAAvrQ3L3B3qPxokfaxGxB1dXVkFVBbW1vdB5CsArpp06YnTciOi4vjcrkAsGDBggsXLuzYsePUqVM//PCD7p5Fz9frg7CxsZEcoRQcHKy3byLffvstORfVUNoWxb5x48YzKwY8nVwu37dvX1paWllZWZfPumsrJSWF/MAsX7786a+8f/9+REQEWdjskVEzTk5O4eHh27dvz8jI0Gg03b0eJPK4e/fuLV++3LDfBQ2ld3WNamXS8mWvF7g7fPowBdcNsChwd6has1z71ImY7S4MqbtMPHfunK6KUHR0NHlPavHixeR1SFNT05YtWwoKCk6fPq2PN9kVen0QEgTx008/+fr66u37aVpa2nvvvTd37tyO1UroEm2nN6xbt66Thb8jIiI2btyYnp6+a9eu7rvrc/PmTfIWQnh4+AvN7autrT137tznn3/u7+/PYrHahqKFhQVZVo3NYsWf+evZx0K6AlnhyN7e3tANMYBeFIQaiaR88WsF7g4f2vIBgILBD4MsC9wdqv/zkVb9AuUIKisrDxw4MG/evLZVQE1MTGbPnr148WIajQYAH3zwAfmhbmxs/OGHH8RiMQrCLnPr1q1du3YdPHiw7c7i4uJly5YFBQW99957un4w/U8X08O9tKfo2iDUg/v375OdLaGhoZ2pCVJbW7tw4cJ169aNHj2ajFUGlcql03YOsa38eHEXNhh5Cq1WS3Zf61YsMR4ajaZtfbWNGzeeP3/egO15Eo2ouezN2ffcHd625gEADcN2OlkVuDvUbPyS6Oj9I3KxiLVr1+r6abhcLoZhbZdRO3HixM6dO+Pi4g4dOvTjjz920bvpdj06CElr167VbTc2Njo7O0dHR4vF4suXLzs4OOgq+hgVMzOzzQ9NnTq1hwdhdXX10KFDAWDy5MmdH14RHR19+/btTz/9NHxa0IlhtsNZdADY7mh1f8II8s4/ogezZ88GgP379xu6IUg71PV1Ja/OyHd3mGfBBQA6hu12si5wd6jbvpHookJLJSUlP//8M5VKxXFcV9239+rRE+oJgiAvyXV7/vjjj5CQkJkzZ3I4nICAgEWLFpEVnI2Q00O6gqg9U3Nz8/Tp0/Pz8728vCIjIx9ZAOtFEQQhFoslEgmGYf/31dcPFOqXLbgAENskJeQyaYrBCh0YG3KifUJCgqEbgjxKU19bsXSB7O6dz4vrTtaLWTi219k6iM/mv7XUYuUaeI46Fc9jwIABH374obe3t1arTU5O7pJjGlCPDsKUlJSWlpaysjLdnvv375OLhpBGjRp17949QzTN8OY8RFZU6ZmkUmlwcHBWVtaQIUOio6PJAdadodFocByvr69fuHBhbln5fN9xQXw2ACSIZCqCkCTEdEWrkWcjg5As+GecsrOzDx06JBKJ9uzZQ07t7QlED+7/ONVfdO/uwvyq060paOPHY5kv+9hi+WddfrpJkyZBn/g+RH32SwyHrC/edg+fz29ubtY9bGpq0lsJH+RFqVSquXPnJiUlDRgwICYm5pF1HzuGSqXqeghcXFwaJXX2d24OZdHzZcrUFrkg8TJoNYCjsmrdzsXFxcLCorS0tKioyMHBwdDNMQBzc/MBAwbEx8dXVVW17bUyIHVFWfH7b1RUVS2XKDMkchaOrR9oMZ7Hsvz4P6YLuqVookAg2LhxYx8Iwh59Rfi4wMDAU6dOkQUqCYI4duxYUFCQoRtlAF9++aVuOyAggFwVukfRarVvvPHG+fPnraysLl26RK4F2uU4giAACDRlAUBss1TTUC+/mdUdJ0IegeM4uaxdH/gj2DHW1tZCodDMzCwwMHDt2rUGqTiqVGtPpFQuPXDrjd03fjyaeeO9t7Cq8nMNkivNUjaOTTfj2DFolv+3rptSEAAmTJhAo9GEQqFIJOqmU+hHLwtCX1/fadOmTZgwYdWqVRMnThw2bFhoaKihG2UAq1ev1m1PnDiRXAO6R/n444+PHTvG4/EuXLhAznDoDnTnobSBDoF8NgDENEkJAEk86h3VEyO/Tfj333+Td6yFQqGpqan+y7vfKBE5fhT/7r6cPZdLfk8q/yK6bCJv+TdK+/tylSkFX9HfbACDFrTpR9P5b3RfG7hcroeHh1qtvnbtWvedRQ8wgiAM3YYXVldXd+/ePXJdJHLBHaRH+eKLL7777jsWi3XhwgVyydzuU//jt41//DbxZlmVSh05vL/rsCEDz8Z36xkRklAo9PDwIFckN3RbjE5xncxlTWKLTE3U5kFpMqhkYD0KHCaxtKqQ7I2LKRWOHJb1tzu4gTO6uyWff/75999///nnn3/33Xfdfa7u08uuCEmWlpY+Pj6hoaH29vY1NTWGbo7BKJXKX375Zc2aNenp6eSCvT1BeXn5L7/8QqPRTpw40d0pCABsQRAGEPCwd1RVWqwqut/dJ0UAYOzYsXw+/8GDByUlJZ081JtvvimTycjtM2fOHDt2jNy+cuXKb7/9dvLkyQ0bNly+fLmTZ+lLVhy+LZVriPJ0yPgF+nvDkJlQlQ3pu2U4/R/Xz/uZmvXbskcPKQh9pWOgVwYhAOA4bmFBfS/2AAAgAElEQVRhodFooqOjDd0Wg6HT6ZMnT25qavLy8mpbQl7PNm3apBvZm5mZeenSpfj4+N9//z04OFgPZ2e5elDMzMne0dgmKaDeUX2hUChddZswPj5etzLlgwcP7t9v/SozefLkuro6Ho83a9YspVLZybP0GTKl5uKNWrWWgFvHwOtDsBkDZo7gvRwq0kDWAAA3P/iJPTFAP42ZOHEilUpNT09vaWnRzxm7Q28NQgAICwsDgMjISEM3xGDUarWdnd2AAQMkEokBm3HhwoWGhgZyu7i4ODk52dXV9ZVXXtHT6XEK2y9gnAmTS8HvyJRlSjWaRKE33X010NjY+PHHH6ekpHTT8XupB7UyBg0HABCVgplT616cCqaDQFQuozIfcLtlbFq7uFyum5ubWq3u1b+mXhyEISEhGIbFxMQYNgYMiCCIs2fPOjs7V1ZW8vn8u3fvGrpFhsGZFETHMH8eCwBim6TyW9ma+trOHFBx52bVqneLJrsW+o4snRvUfOwQ8fB6BWmrk0EolUovX75MDjgMDAwkp0vt2LFD94LCwsL9+/e/+eabKSkp6E6kDoOKa7UAAEBhgKbNhbJaATQmBcMYVL3+Ye8Dswl79DzCp+vfv7+Xl1daWlpsbCx5dWhsaDQauVo9AAwePNiALfnggw/IyfLV1dVubm56Pjt73ESMyQo0Zf/TKIltlr5lzZMkxvFmdfCStOnowdqfNxMKBQW0AKB8UFD98/eiM8fs9h3HeabP/HGj4u7ubmpqWlBQUFpaOmDAgOf5EalUKhQKk5OTY2Njk5KS5HI52akTGxtL/hPatm2b7n6hh4eHh4cHALz77rvd9iZ6HwcrVmt9GKuRUJEBg/wBAORNIKkC3gAGHfdy1us/VIFAsHnz5vj4eH2etGv14itCQL2j+iWTyQ4dOnT16lUASE5ObjtgetOmTUeOHDly5MiqVav03zCMyWJ7T5hsyqJhWLpY3qTWSuIvdexQsvRrDTs3/1ZSta+qkdyjJAi/9Huq4sKqzz7ouib3ERQKhZzDmpiY+JSXtbS0XLhwYc2aNRMmTODz+RMnTvz8889jY2NVKhWZc8gLoeDY+0GD2HQKjH0Tco9D9kG4fQqufAWu72BUprUJ02+oXsuM+Pn5USiU9PR0qVSqz/N2ob4QhOfOnTPIbFZjI5PJXnrppYsXL4pEotTU1MzMTN1TJiYmfD6fz+ez2WyDtI0tCDKh4F5choaABJFUlpqklXakw7xu2wZCLnt8P6FSKW5mKW7f7HRL+5on9Y6KxeLY2Nh169YFBQVZWlrOmDFj06ZN169f12q1I0eOJJc+r6mpycjIMM6pwJ20ds5gZxs208Iepm6Ffm7As4NJ/8UGTmRjqr9WuXVRPdHnZWpq6urqqlQqr1+/rtcTd51e3DUKAKNGjRo6dGh+fv61a9cmTpzYTWfJysrKzc318fG5cuUKnU5/6623uulEPZy5uXlKSgqLxdqwYYOPj09aWpqhW/Qvjn9gLU4J5LOvtchjmmRh5kpZSiInYPrz/rxWq7ibK758QXUvj9wh1morlWoAUD2cZ0uolNKUq4yRY7q+9b1Z2/tDYrE4JSWF7PNMS0tTqVTkaygUioeHh6+vr5+fX2Bg4ONl4pOSksgVmwFg0aJFvXFys56x6JRr6yZ8cCj3eHIZw3qYMi9Skb7bdszsg5xCl/4z9d8egUCQmZmZkJAwZcoU/Z+983p3EAJASEjI1q1bIyMjOx+Ec+fO/f3338mlX48fP65UKsPDwwGATqf369evpKRk8eLFmzdv7oJG905FRUVZWVk4jq9cubKlpaWoqIjcv2vXLkdHR3J70qRJY8eO1X/bKOYWzDGugfLU9aUNV0UyhZaQJMQ8MwgJpUKelSG5Giu5fF5dWw0At6VKJUEAQHyzrEShBgCtLgjVak1dXfe+jV7IwcGBxWLdvXt39OjRd+7c0baO4gAajebr6ysQCPz9/X19fXU516629xdNTdGN2OfCZVIilrpsnmEZ/cbic80PImX1kwqPOw+0UOTdYo521XNjBALBtm3beu94mV4fhGFhYVu3bj179uyWLVs6eai0tDRdF2tZWZlcLie3R40aFRUVtWTJkuTk5B5Y1VNvHBwcli1bpnuoK5w2atQo3U5zc3NDlUHnCIL638gczqLfkSlTxPLJV+MIjRqjtPMvXCtqkqZdk16NlcTHRJVVF8pVdgzqoWpRvVpbrVL7mDAFPFawGWdpP1MAUBKE4FYZAGAMBm3AQH2/qx6ptrY2JSWFHPCSlZWl1Wp5PF5ubi6VSnVzcwsMDPT19fX390eRpgfWg/oHWkh51Y2RAKliOQDIM1L0H4T+/v4UCiU1NVUmk5HXEr1Lrw/CCRMmWFlZ3b9///bt221XaOpCixYtsre3r62tvXr16po1a7rjFEjncSZPq9+xydvc8k55xccqR7zSLPC/Fx1p+dZs7YcffshgMFTlJZL4GGlCjCwrA7QasUZ7VSQ73ySJa5IpH/bFWdMog5m0J5wBY/v3ym6fLlFdXX316tWEhIT4+Pjbt2/rei+ZTGb//v0LCwtDQkKOHz/eG/8I9nYsd58xRYUsHCuUq+pUGrYwlf/WUj23gc/njxkzJjs7OzU1lewt7116fRBSKJSZM2ceOnQoMjKyw0HY0NBAfnsNDAzEcRwAKioq3nvvPfLZ/fv3kxsoBXsy2kCHv5xnHbUeCOUfN9fkw5iFp+60UO9lTvZ0cfpslVddmSLvFhBEg1qTIJKdb5Qmif7NPwsa5SUzjkSt3eRgiQOcqhfrblLhAM5MGs5kmYTOpdn1nSvC2tpaCwsL8l+7VCrVarWP916S4ZeUlJScnCwUCnXhx2Kx3N3dybGCDAbD3d09JCSkoKAApaBBsDx8qGeOeXCZSSJZmlhunZX+pL6QbjVp0qTc3Nz8/HwUhIYRFhZGBuGLBlVxcfHFixejoqIuXrwYExMDALGxseSfg61bt+q6RpFe4dLNunWMKUoaDhwbkFSDpJrAKCorl7jk66amFDNx1uVm6fkmiVCsIP+c4wCj2XRHJs2RQZtvaRLXLB1q158/M5hqazf/0B5CrSIUcgCg4pQ/XZw4AdMsPvnasG+wa/n5+SUlJVlZWQHAoUOHKioqNmzYAABVVVWJiYmPhx+bzXZzcyNHu/j5+TGZTADQaDTXrl2TSCQcDicvL6+6utrGxsaAb8o4sbwmAIA3l5kkkqWK5S9JJcq824xRLvpsA0EQc+bM2bBhA4fDAYDi4mJTU1M+n6/PNnRGXwjCqVOnstnstLS08vLyZ5bcJAgiMzMzMjLy3LlzOTk55E4ajZabm9v9LUW60bIDt2RaHADAzhvyo6D6JpgPhaZCdXPxqYqak6Jy8mVMHJtgwgowZU0xZVvRKIBhjBGjOZOmegmC6IOHka8xnfu6KPKYNPkqIZXQhw43CXuFOUbfVQL079NPPz179mxBQYFuj4mJiW7Ai5eXF432aKdxU1PT9evXV69ePX78+NjY2KtXr/aQJWqNCsXSmmY/yFt8FwDSWuQAIBOm6jkIZTLZa6+9pqs5vH79+pdeemnOnDn6bENn9IUgZLPZU6ZMiYqK+vvvvxcvXtzuazQazfXr10+ePHn69Gndb4vNZgcEBMybNy80NJTP52/atEmPrUa6jLqq4m5sYmUtG4ACAGDrDvlRICqFtJ9A0QIABIAJjTGdRwvks31NWEwcA5zCHOPGDXqJEzCDavPoSl44z5QfvoQfvkT/70WfcnNzyZkM5eXlGIbdv3+/oKCAy+WOGzeOnOrg7+9Pp9OfcoQNGzYMHTr03r17AoEgNjY2ISEBBaFBsDzHjS0tYuFYgVxVr9awM1P44e8ZulG9SV8IQgAICwuLioqKjIx8JAglEklcXNzJkyfPnTvX3NxM7rS2tp42bdq8efOmTp3KYDB0LxYKheR1PQAsW7YMTWbq4ZT386WJcZKrl+U5mXeYDlTrN6DqJpSnQUUGAIBaDlo1cG3Azofa33M5dv+dpgScxWZ6juMGzuRMCsK5JoZ+B/rQ3Nx89uxZPz8/uVyelpa2YMEC3b/5iIgIsoczNzfX39//q6+++uKLL9zc3J5/jdkff/yR3OgD1SZ7Naa7D+3scVcO43qLPF2ssMpKB60G8A6uFawVNTcdPSS5/I+msYFqZc2ZMYs/73WM+YwbwHK5/J9//iG3dRcbvUUfCcKQkBAKhXL58uWWlhYTE5OSkpILFy5ERUVdunRJt3rLyJEjQ0JCgoODfX19sfZKL1haWuq2DVUhBXkGrUaekyVJvCy5clFV/AAACuWqmGbpP6L0FnE8kN9dMAwYPFCIYOTL4PoOADBBMdCO2y84nD3eH3usf69va2homDdv3saNG1Uq1fLly/ft2/fhhx+ST/3www/kPcJffvmloqKiM0Vivb292Wx2bm5uTU2NtbV11zQdeW4sz3EA4M1lXm+Rp7XIp4tbFPl3GMNHd+BQiru5lcteJ+RyrUIOAJqGOlVxoejogf77jj99sJhKpdJVm6r73xm3t27dcnJyqqurs7Ky6pkjqvpIEFpbW48bNy45Ofm99967d++eUCgk91Op1MmTJ4eGhoaFhekmfSM9k6axQXz+rDwrHTBgunlzZ8yi8FtLkBBymTTtmiT2b8nVy9oWkRbgtlQZ1yz9p1FSIG8tX4JRaITVKLDzhgF+UHcbkjZBXWuZGA2dNevL9zmmjPZP3Kc5OjqeOnXKzs6uoqLC1ta2vr6+O85Cp9PHjRsXFxeXlJTUi+4M9RlUG1ua3YBxYvlPlZAmlgOALCOlA0GoFTVVLn1dWFm9v0a0w9GK3PlKTtEWJyts8YIBZ+KwJ3eVm5iYfPXVV+R2cXGxbn9paenatWu/+uqrv/76a+7cuQYpuPFMvT4IyZt/0dHR+fn5DAbj+PHjAMBisaZMmRISEhIWFoaGsfUK4vORtd+uIQiCkMsBQJIU37Bri8XHX2F0et6Z49cTEydzaAUy1cVmqVxLnG+U1KhaSx/wqfh4E1aAKQuzH/+V/dsynAEA0N8TcBrU3gFFM4vJWTx5sI1RpiAAZGZmNjU1yWQyDoezZcuWwMBAcr+3t7fu/p+dnV3bewQdIxAI4uLiEhISUBAaBNPdZ2xZCQPH7sqUjWoNR5gKr7/wkh2Nv+/TyiRqAuTaf28MSbVaQkuomxtaok7xXl7woseMiIhYuHAhh8Px9/d/0Z/Vm94ahGQ9+8jIyH/++aexsVG3n8FgnDx5MigoiLz5gfQK0uT42m/XbCusnM5nD2fRAeB2kyimSbri2zUAUCRWVEvl4eWNd+QK5cPPpz2d6sdjBZiy/HksKoZhTBbLw0pko92Sj8tVWi2VBTZjoFJIL7s2sZ/dD7MmG/LtGZSHh4ebmxs5X7Ct33//XbfdJauYkdW3ybV4kpOTUfUJPWN5+NCjTrlyGKkt8gyxwiIrHbRaeOz33g6tRnH3tjw7Q56dIY67AFotAGiB0GUh+R9CJhPHRD8pCKlU6qxZs3QPfXx8yLJ5crnc2to6NjYWx/GKigqxWIyuCLtAbW3t+fPnT548GRMTo1AoyJ1OTk7e3t5vv/32ypUr8/LyuFwuSsHeRKut+e//aWWyPKnS16T1F9es1ubLlKfrxSfrxUKJXPPw66kllTLPkjvTjEPmJdXKhi0I5AiCWJ7jMTr9vwChD5q/P5OXmHpf3N9VXCkcWXDqFxVTmTqGHmSASsQ9xOMp2B3GjRvHZDJv3rxZV1fn6+vbdpUuRA9YHuMAwI3DSG2RxzRLnWrqLn33bfiq1e0Pd3gYfuXXEm8nXR2La25LlQQQ5Up1pVLjwmZkSxTv3q8mX1usaF2VWlNb86Sz0+n0nTt36h7qqpEwmczFixc3NTUxmczc3NzOdzx0k94RhIWFhVFRUSdPniSXcQEAHMc9PDyCg4NfeeUVW1vbX3/91d7ePiwsLC8vLzIycvJk470C6HUUebntrnwEAHdkynSxHMdgIIPqxmEyceBRKEttTPkDHdgTA7hBM5ku7o985/VwND3xsU/F4h+Lldd8Ae42VssGDJTEx3CNOAj1g8Fg+Pj4JCQkJCUltb04QPSD2t+eYtt/aIMEAK42y6yolMUXTmzNz/vq4O/kZ4TQqJX5d2SpybLsdHlWulbcAgDJzdIbEsVPEuUDhapcqQYAOobtH2zjzmHudW4d9BSWV0FuUKwfnWj0nMiZ9T157UkDB2FLS8vx48d1y0+fPn3a09Nz4MCBAKDVarOysqKiok6cOHHnzh3yBUwm08/PLzg4eP78+ba2trrjuLi4AEBYWNj3339/9uzZ7du36/2tIB2kqijVbX9dUs+l4AAg1midmbR5FlxXDkPAY1ExjEmlUoeOYPtN5s8Iozk4P/2YbEGQdWbqGDYjR6q41iILSoojVCpjGy+qf5MmTUpISEhISBg2bBiGYYWFhU5OToZulLEgFHKQy6fx2XQcq1drNAA8qbjuSkz523PZkwLlNzLlwjStRAwANSpNqlie1iJPE8vvPxxrBgAUDDw5zPEmTHV7M8dwFttkep9dOdLAQdjc3Lxjxw5dEP75559cLlcoFJ47dy4qKko3BtfKyio4ODg0NJQsIvPIQTQaTXZ2tkgkItOxuLj4xo0bPbMnGnkczjUBrPWq7puBFl5cJgCktMj/qBUNZdGHsemM0a4mofM5gkCKhdVzHpM7eVr9tg2BfHaOVBHbJJ1i2iLPSmN5+3bXe0AAoM0ivT/++OOIESM6fBzyE627gMjPz7eysiLn/sfFxVVVVb388stHjhzx8PAgvwEjAFC3eZ1W3JIulnNwvFGraVZrDlSLvNh0+a0s+a2sWpUmQyzPkCgyxfJcqVIXdHQMs2NQhzBp7lxGg0r7f3ZmAJAjVfAo/3a0mFEpVAqFYmHJfWm2Id6ZPvS4rlGZTDZv3jy1Wg0Ajo6OISEhISEhAoHg8fJOOjiOL1u2jEKh4DgeHBy8b9++yMhIFIS9BXOMG6FSPPFpOtP2lz/xF5zWSe1vT3ceGii7ta2iMa5ZpiFAkhCDgrC7jR8/nslk3rhxo7Gx8fHVd5+fSCR65ZVXdMXe1q9f/+qrr86cORMA7O3ty8rKtm3b9sorrxw8eBAFIUldXdkSfYZQqfx4rAWWJruqmkwoeIg5J1Ms/09JfYZYXtDmyo+FY+4cpgeX4cFljGYzRGqtPYNazrdyHjee5zGOamtHXbt6rLlEd8MiYowjxdyi/95jfbhPxfBBWFlZ+dFHH5HbOTk5PB5v8eLF9vb2oaGhbRe6ewoMw3SfurCwMDIIv/66T5VI7sNwDpc3d2HL6aOPP4Ux2abzXn/RFCRxJk0dej/fgUErUqiyJQqf+BjLT9ZCe4UUkK7CZDK9vLzImt0hISHdcQonJ6cjR4688cYb586daztZzQgRcpkiL1eenSFNS5JlpoJaDQClCrWC0ALAH7UtB2pEuhebUHAvLtOby/DiMkez6RQMAwCa3UCmqwfT1Ys93n+w7b9VmgecTRD99Yc45m9tUxPFyspkxmyT0HlPmUHYB+gvCBsbGy9cuBAYGKhUKuPi4ubMmUPWM+Pz+QsWtA7JJatg79q1q8NnCQwMNDExycrKKi0tbbvsNdKTWSz/XJl/53u1hq1UABAAmDuXMdJ8INPV3fzDTzt2TI4gqHH/zsmmrIM1qthmqUdVhSL/NmPYc321QjpMIBAkJiYmJCR0MgglEslff/1FbpeUlJAbarV61qxZkydPbmlpIYcLdLa5PYyqpEhdV0O1tqHZD2r3BVqZVJGTJctOl9/IkGelE0olAJQq1BkSeaZYkSSSlSnVAIABqAiCTcHd2IwJPKYHh+HKYVAfhh/Lx5c51pPlOY7ar3+7Z8HZbH74Yn54+3Wb+yT9BWFpaen06dO3b9+u0WhWr16tK7TDYrG8vb3J7c50p5AYDEZQUNDp06fPnTv3wQcfdPJoiH5gNFr/X/7gnjnWdHivqrwUADgDBtq9uYQXNv+5JkK1hzFyDNWmX5BYfrBGdKlJ+pmdmSQ+BgVhdxMIBBs2bCBnE76QmpoacuHfa9eu/f333yqVqqioiHxKIpGQG1QqNTo6mtzuY/c+JJfP1275r7alBaNSCZWKwjez+nw9e2IAAGilUsXNh+EnTCNUKgAolKvSxPI0sTylRa4rLgEAfCruxWXmSZWlSvV2B6sAUxYAAIZxA19i+wWwvCY8XmIeAX0GoYuLS1JSkoeHx+HDh3/++efg4GB3d/fuOFFYWNjp06cjIyNREPYmOM57eUEH6lY8EYaxJwZ6VP1hRqUUK1T35aqRCTHmSz7qsuMj7ZkwYQKdTs/Ozm5qanrmcnQ1NTWpqanJycmxsbFt1z4UCoV8Pn/16tXkw+zs7O5ttKE1Rvxasnt7XmOzJ5dJ/i+4VtA4evUSC3cvjVh85noaBbSuHMbfDVKRRlOu1KSJ5RVKte7HzakUVw7Dg8vwNWGNZNNxgK0VjburmtPFcjIIafYDbTbtfMLJEQB9BuHZs2ejoqIWLVrk7OxMoVDI8k7m5ubkcqCk5cuXDx8+vJMnCg4OplKp8fHxz/NRRPowzqSg64cPODKpjWLNtorGkU3XFsZc9AiaZuh29WVsNtvT0/PatWvJycnk8JZHPGnV+7YL/44e3ZFq0b2UMv9O494dRc0tP1c2RwxpLSixtaJxI5XCTr/+QK4qVyjP1EtqVOpmjVb3UxZUytiH4TeKTccAAMcBCNASAODDZe6GZrLoKEalWa/bYoh31pvoLwiDgoLIwZ/jxo1TKBRk1yibzQ4N/XduCrmYSyeZm5v7+fnFx8efP3/+tdde6/wB21VbW5uZmRkYGHjjxg0Oh9P5/Ea6HMtz/Cgrc79mqVCsqFZpXDFsUE25oRvV9wkEgmvXriUkJOiC8Emr3nM4nPHjx5NrH06cOFFXdkQqlc6fP193wEmTJpFzi/ukpt/3gqKdUdNlSvWr+VWN6n+7PXkU3J5Ofc3KxJvLdGbSAABwCt3BienqyfLxY3tPkF5PrN3wOaGQu3MZVAy7JVWINVorN1emq6fe3k4vpb8g5HA4utX+unsljrCwsPj4+MjIyM4H4caNGxcuXDho0CAAEAqF6enpS5YsAYD8/HxTU9NDhw6NHj16z54927Zta3dpJ8SAMBqNM0EwqyFyd1VzjkQxnc9RJsXBwncM3a4+TiAQfPfddzExMSdPnoyNjU1KSrp9+7bu2bbh96SFf9ls9saNG3UPFy1apI92G4gsO4MgtABQpVL/WdtC7qxVaWxpFIWWsKRRrGkUPgUfZ8IypeAEwGvWfMawkcyxHkw3L7a3L84z1R2KOy2ENc6v7vu1xMWo0Wx6tkQhlCgmFxcCQaDx0k9n+OkT3WHWrFmrVq06f/68Uql8+hLbz3T58uUZM2aQQVhWVpaSkkIGoa+v79GjR728vGg0mlwu75p2I10t384xuUU2kk2/IVHQMZBnpmhFzW3/diBdqKKiIjk5+fz58xiG3bhxQ3dVR656HxgY6Ovr23bVCwQAQNN6zYcDcCitcUUBjIJhl0fZWdEoAIBRKPShD8PPxw834T3pYBRTM/PFK8UXo3y4zGyJIk0s92+oVxUXPrMYk5Hrm0Ho4OAwZsyYmzdvxsfHT506tTtOsXv37traWhcXl1u3btHpdIVCgSp990B+by+2/3OPFqPckNTsVlrF9V/s/vPVd8IFYwc+8U8J8kIKCwvJPs+YmJgHDx60fcqVwwi2s5l95PRYV9fnX/Xe2NAHD1dXVwKANY06y5xL7jxa1wIAVjQKbZCzxUdrWO7eONfkOQ9Ic3CmWtl4i2R7qptTWuQAIMtMRUH4dH0zCAEgLCzs5s2bkZGRHQ5CgiA0Gg0ALF26lMvlAkBdXZ1upGtoaKhCoTAzM7Oyspo2bRpKwZ4J55rcGztte78xVMa52oH+tZzh1+8Qv629/rpf/1/eHk3BUX9RR+jC7+LFi21ntZuYmHi5jPEounNfpjrdIB5nwnyLiw+0NEMp+BSmr70lE6aCVPn4UziLbfWfjSx37xc9JtPV06u6koLBTalCotFyhaldOR67L9LH+iwGQS6xFhkZSbRXQPYp1Gp1UlLSypUrBw4cSC7z+9NPP0VHR0dHR+vWXwYAOzs7JycnMzMza2trU1PU1dZDldTL5immN9UXqc0Gg/VoANAQmPTil38mV6z6/Y6hW9eDqFSqTz75RPcwIiJCKBS2fUFhYeHevXvfeOONQYMGOTs7v/nmm3v37i0uLibrAG/atCkjI6OpqelyYtL7I5xnmnMAILWldal0Pb+X3oU93p/jF2Btwgk25+h2zjLnWnA53KDgDqQgADA9fNg4PorF0BCQJVGgX8Ez9dkrQg8PD7IsoVAodHNzi4qKcnNze8rYs8bGxvPnz0dGRl64cEEkai1NdPXqVQBgMBjk6B50b6PX+fRInkyDEbIGULb8u7f+rkSh+S2+ZOV0B2ebjtRv63vUavXRo0e3bGkdZ5+UlMTlcvl8Pjna5cqVK2VlZboXW1tbe3t7k1Md3N3dHxkmxnL39qytoWCQgy5Hno/Nxu3UHT/YnIjAqDRymZQFDCZ/4SLzZR937IAsdx8A8DFh5kgVqWK5X12NqvgBbZBjl7a6T+mzQYhhWEhIyO7duyMjI7Ozs728vHbu3PnDDz888rKSkpILFy5ERUVdunRJqWztnRg5cmRISEhwcLCvr29gYKDe2450DS1BnBNWq7VPeFYLp9MrPw1G907ap1arx4wZI5VKyYc2Njb+/v7kaM/Hw68tpocPJ/afUSxGjlSRJVEI0OXIM+EUi4/W8N9cIktLVtdWU637sXx8KaYdL7NFdxpCMbfwbpbtq26dTSgTpqIgfIo+G4QAEBYWRgbhnDlz3nrrrbZzNnJzc6Ojo6Oioq5du0b2nVIoFF9f35CQkNmzZw8dOlT3yt27d7C1XK0AAA9sSURBVOtqlgoEAlTtvhdpEKuAePj3+v4lqPyfvj6FWptfKTFAs3qqhoaG4OBgcjsnJ2f69OmzZ8/GMEwgEAgEgiFDhjznccil0r1NmDlSRRp5OVJSRBvo0E3N7jMoZubcaV1UqRzDWO7enrW1FAxyJEqZlpBlpvJmv9o1B++L+nIQBgQE8Pn8nJyc1atX//rrrwRBJCUlRUdHnz59+t69e+Rr2Gx2QEBASEhIWFiYjY3N4wdpG4qmpqbodmAvYsKkqrUP7xA7T4UxC1u3T70CADiOWfEYBmqaIV26dCkvL8/W1vbWrVsYhr3//vvW1tYAwOfzIyIiyNesWrUKAP74448OHP/h5Yj0t2pIEyuAvBxBQahfTPdxJrHnR7Dot6TKLInCX4iuy5+mLwchjUabNm3a8ePH4+LiJBJJbGzs+vXryadsbW1DQ0PDwsICAgJ09SyQPoZBw10GcIVFonaf5dApU10s9dyknmDq1Kmurq4nTpyorq7WarW6Mhc4jltYWJDbnfpQYBjTzdurtpaCwQ2JQqYl5JkpvFmvdL7lyPNjefgAgBmVAgB/1IruSPPfS0tx9B5n6Hb1UH121Ghtbe3hw4fz8/NxHI+IiDh16lRTU5OTk9OKFSsSExPLysp+/fXXGTNmoBTs235YMIJNb2fsPo2CDbXlCIZb6L9JBldVVfXHH3+8/vrrAwcOnD17dkZGRpefguXuY0LBh7PoKoLIlihkmehyRN/ozkMLqEwHBhUAbkmVr1py//gF1d1+or52RXjz5s1z585FRkZmZGSQN/8wAAxgpa3Z/KCA8RGnDN1ARK+mjLJY9/KQdZqZMoVaN42G+fIBazNm1Ceexll2Ki0tjc1m37hxY9iwYdXV1eRqoAwG48yZM7rXfPbZZ51ZE428HPHhMnOlyjSxfHx1laq8hGbXZ+uF9kQYdkZDd2LSMIBqpVqqJbQVZc/+KWOFveg0ux5Io9Fcv349Ojo6MjIyLy+P3MlisQSjRvjVFEc2SNLF8i0OlrP7mTtcFuJsztOPhvQ9yfmN/zl5N7WgSaEibM0Yb/vbfR46mMNAs7y7DUEUBXpcKi5fcr/Gi8s8OrSf9dofTELnGbpZxqX56MGi79eF5VUUK9SvW5l8NGaYx+V0Qzeqh+rFV4QymSw2NjY6OvrcuXNVVVXkTgsLi5deeikkJGTGjBkstaooyEtFEOlieWyTdJY5V3b9KmfKDMM2G9E/36FmV75Ed0f0CMOYrl6e9fU4wA2JQq4lZJmpKAj1jOnuw6XgAabsgzUicyqF31TX8vcZkxlhHV7sug/rof9HTpw4odvOysoqKCjQPayrqzt8+PD8+fOtra1DQ0P37t1bVVXl6Oi4YsWKmJiYqqqqw4cPz5s3j8vlUvhmTBe3qXwOBnBVJFdoCUlCjCHeDYIYHZaHjykFH8aiKwnihkQhS79m6BYZHcaQ4TiTHWDKnm9p4sFlABC1335RMjdIVVr87B82Mj30inDp0qW60vXHjx93dnbGcTwqKio6Ojo+Pl6tVgMAjuMeHh7BwcEhISEeHh7tHoczaapNdsZINj1XqrzWIp+SeIXQqDFKD33XCNJnMD1ai5vckSlTxXKf6kp1ZTnV1s7Q7TIiTUcPESrF+UbJ+gEW5LIWsTUNWG1j0JuzBpyKpZgb40ixJ+mhV4SP0Gg0np6eH330UWxsLJVKDQwM3L59e0lJSUZGxrp1656UggDAmTQVAAJN2QAQ2yzViprkWaiXHEG6HWPICJzH9+YyAaC1uAkaO6pHmob6xl+2EBrNpWapFloHgtyTqe7LlFqJpP6njU//cWPTQ6+N1Gr11q1bye309HRnZ+cFCxaIRKKwsLBp06aRa0E8D9qAQXTHwYGy2z9VNl1ulq4HC0l8DMtzfLc1HEEQAADAcZarh3dDAw6QJVEoyNuEwS8bulnGQhJ/CZ4wKJpQq8Qx/1iv/QFwNF6slYGDUKFQpKenu7u7s9lsstoFWboFx3EHBwfyNeSenTs7OAmGLQga8aDAnk4tU6pzJArP+EuWn3zdRc1HEOSJmB4+/KuXh7Dod2XKHKliQmaqoVtkRJQF+VqZjNx+u6CazMRypfo1y9Z1DTUN9RRLawO1rscxcNdoTk4Oj8fbvHlzfX39t99+e+dO68o4OI6//FDbImcdwJkUBABT+K29o+rKcuW9vM63HEGQp2stOvqwd1RVXqKuqjB0o4wFzuUC1vrn/Vcn633ONvucbeY8XPgXNBqMyXriDxsfAwehl5cXlUp1dHTcvXv3m2++2R2nYI4aS7G0Jm8TxjRJAQCNHUUQPWAMG4mb8BhWwwFgh2ag25AtQZuFV27XG7pdRoHl4YOzW5cYY+AYE8eYOEZ9WEKCYm3z/EveGwMDB2FycvKBAwfGjx9vbW196dKl27dvk/u/+OIL3WumTp3q6enZ8XPgOMd/ijeXwafi9+WqQrlKEo+CEEG6nQbwFU4f/Dn8Q/D/j8ZvTQuFFV+Fh2zJ+OQIWhK527G8JlCsbQBv5z4hxmJbdHSlw77KwJVliouL6+rqTE1NBw8eLBaLMQzTlQDuQtLkK5Ur3lldVBfZIP7Mzuy9fvxBfydTbWy7/EQIguh8fSp/67l7UmkLZO4BcRUAgPlgcH2bw+Huemvkm/72hm5gH6cqKy5/c/btmvqh9NY8rFVpcBbLceZsq683GbhxPUxfKLH2TIRSWTTF/Z+Kmg8Laz24jONDbS0//8Z0Xrih24UgfVaLXN3v/ctShQbi14G9DwyeAUBA9iFQy8FzmaUJvXr3FNw4i73qkaahvv6njeKYvzEKTqjUFCsbi2Ufc1+abeh29Ti9Yx5hJ2F0Omu8vyODRsOwLLHiUI3o5P79hm4UgvRlV+800CgYKMXQVAiDpwMAAAajF0BRPAAo1Nrb5WKDNtAoUMwtrP+71Skpd8CpOIeYjEFRiSgF22UUQQgAHEGQioDRbLoWIFuqEDSUa1vaX6YOQZDOq2xSqDUESOuBZQG6GW1UBuBUUMkoGFbZqDBoA40JTqHa9MNNeIZuR89lNEE4cfIYE5Y9gwoARXL1N0XV4uR4QzcKQfosK4YGV8iAbQ6yhn/3apSg1QCNpSUIKx7dcK1DkP9hLEGI8/g/qRn9aFQc4K5MiQEmvRpr6EYhSN+kaW4cvOsTJYEB3QR4A8juUACAvDMw0A8AcAzGDEDD95GewliCEAA+ef/9pf1MR3MYKoIwsRp6IaOiqg7dpUCQLqZpqKtYsoB2J3NayRmWVgXjVkFxAsR8CpdWQ3MpuL3NYVDXzx1KaW9kP4IYhFGMGiWpK8tTZoUsFPcvLclkurxGHeCrpLMCXaz3vzfGxpRh6NYhSF+gqaupeD/8VHrWoRpRvlzpPGHZA/tAKc4AQgsYBoBxGNS53v0OLnFBI0aRnsOIgrCkXua64h9ReY7GYhgw+QAA0jqqpNJysGf2d34oCxGkk9SV5RVLF9YUFS68V5UvU7Fw7OCQfgX2gQcc5xXJKRhgo+y5X80eMte7n6FbiiD/o4euPtEdFu660YIxNffOA8O0NQibS9SFMfX9XBbtuxn9SSeK1yCI0VMVP6hYtlBaVbG6qC5fpuLi+JJ+pp5c5vR3A758NcTQrUOQpzGWe4T3q6WZRc1qop3uGJWGuHyrrroZDeZGkA5SPiioWPJaS2XFewU1V0UyDo6/bMl93YZv/dUm01ffMnTrEOQZjOWKUFjUTMOx1lVJCmOh+gYAQEtrLXwGjZJdLJrmYmWo5iFI76W4m1v5wRtNdXWLCqqFEoUVjXJ4SL8hbKb1us0mM9H0baQXMJYg/J87oSb9wXQAAAChheaS1ueN5VYpgnQl2a0b5cvfEjU2vFNQfUOisKNTDw+xceCybTbu4ARMN3TrEOS5GEsQjh3EU2sfZp3VSLAcDgCAUcggVKgIl4FoVhOCvBhJZurWV+bcbBKlixXFChWPgh8b2q8/l22zaRdHEGjo1iHI8zKWe4TDbDkj+nPx9qYuUSnYhCH8/mZM/bcKQXodQqPWipoJjVqWmVLz0TtT2ZQrzbJihao/nbrVwcrOlGe7/QBKQaR3MZYrQgA48oGr19fJLYOnEhzr1l08O9wpgMeiHlzqYtCmIUgvoCy4W7d1gzwrDTAArZbQaivkyjl3K+vVmpFseoApa4qdte2Og8yxHoZuKYK8GCOaRwgA+ZWS13Zm3a2UUHAcgFBribEDTY584OZgxTJ00xCkR5NevVz62YdVEok9jULuKVeq45ql35Q22DGoB537qTmcSQeOMUe7GradCNIBxhWEpDvlYmGRiIKDh6PpkH5dvw4wgvQx6pqq0jlT8hqa1pY2HB3aOh3+9XtVX9qbF8pVk3gsnpVV/12/04eOMGw7EaRjjKhrVGeEHXeEHdfQrUCQXqP5yAFCrW73qZlmHJzF7r/3GN1xsJ5bhSBdxRiDEEGQFyK9lkColAAg1mhTWuTkTpFGS25QzC1RCiK9GgpCBEGeQSuVkBvNGm1yS2tdimZ1axASmvYvFhGkt0BBiCDIM9AGOqorywHAjk5d3d+M3Jklaa1KSLMfaLCWIUhXMJZ5hAiCdBhvzgKMzW73KZzN4c1ZoOf2IEjXQkGIIMgzcKdMZw4bZcJkenP/rTvhxWWaspi0wcO4QTMN2DYE6TxjnD6BIMiL0sqkNWtWSDOuEwoFaDUYhYrR6Sx3b+tNu/AnXCwiSG+BghBBkOeluHNTHB+jqSyj2Npz/QMZo1BJJqQvQEGIIAiCGDV0jxBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxaigIEQRBEKOGghBBEAQxav8PAKByJwlIwSUAAAKNelRYdHJka2l0UEtMIHJka2l0IDIwMjIuMDMuMQAAeJx7v2/tPQYg4GVAAE0g1gLiBkY2BgUgzQKlOBg0gBQzE5sDmGZhh9DMMD46zc6AJg/mM0HFmZjh8hAaYT7UVhzGEpBmBJvCyDhYaG4GRgYGcQYGCQYGSQZGJgZGKQZGaaDvFZg5M5iYWRJYWDOYWNkSWHkU2NgzmNhkGNg5FNg5EzhkGTjkGDi5FLi4NZh5eBV45Bl4+TSYePkZ+AUY+BUY+BUZBMQSBAQzmASFEgSVGISEGYREMpiElRmEVRiEVRlERBNE1BhExTKYRNUZxDQYRJjYmFlY2dg52QSFRETFBMS/AZ3FCI9y47c9B1S1mw+AOFMlZx+QnqcFZn9zXXng+um5+0Hs90s6DvRfYd8HYvOsNz6wIe0dmP3n5pP9RvlK9iD2oaN8B/4EsziA2FMScg50LpYEs9fEtBzYGV0KZgdenHbgXOkysPpd844eeCFyEcxWzvlygGnLbzA7aeKyfX8SZ9mB2B37De2PZG4Gi3/Z0WAnlGsKNmcLF5fDqlMdYPG2pekO8uE2YLbq/0aH+Z1GYDf3ndng8OpxM8T9P/Y5qK6Shfgx96LD1qt9tiC2seNhh92xB8B6TxVPcbj65xWYbaZkd6D9byZYvffJXQfawyeC2Ysaaw/Ysm8Es9d8PXHg/jc3MPtnVdSBCFc+MDudffb++Xu8wO70LHc/oPdoLpgtqrX5wOLQVjD79aUPtg+vTwC7zTFK3kGdQQks7lf20v756pPgsLXe4+QgKvYMrIb96huHxEBGsPk3ZCY6vGCyArMDVc86hCRVgtn1sgyOf461gfVaPXrs0DBdDWxmhnubwyyzQDBbDABz9cM5jLwgLwAAA5h6VFh0TU9MIHJka2l0IDIwMjIuMDMuMQAAeJx9VstuJDcMvPsr9AMj8CVKOvqxWC8Cj4HEyT/knv/HFtW2uhdLZOxDi1NNFosPDQ+XeeOHEp8/X/7497+yP/LyADv9z/+cs/yjRPTwVuKhPH37/uNenj8en74sz+9/3z/+KsbFBO9Q4V+xjx/vb18WLs/lZtVIZ2vxhADEvVCl9Sk7jhzIYT66lptU9zYD8BtSy3u5tSpj9knlxlVU+tAEaYdP6cRq5UaVVdyy6K3cI+ag3toMpHlzyZAePrn2geBIv7pao5kAewAF5GgI0qvk3cUT4AigVp8KkQA0IqcMOIOkIQlXlqJ1qHbJ8mY6Eu+s1LhwRUGtZ1ryKpBX7i3IMdKJfDKkRPQOANLBsU53U86QGkiqKk1lAKDETTKJOAoECacpvmdkzqh6BmyoOVVpbGCMOqn1IRkwyiOVm09SeCQfPlKPUR6IzhOe8H3X1skyYJQHfesS1bkF226cxp5AttrEZl99SYq2z+ojtJDT+xgjmsRcG2eqS9THazP77MaIntEUWUDGcFFI2UiGpx515eM6YyYYUrZhWRnFymtwQzJDx+rQ5hjfDNoC2uCrU5fovGY8NdMdTfYavqYzaY+X0CTBJIH2gDrGd84x4yWmMXhk0HF4NVY08ho7kZl2CBrtdS0NZnZFC7B3DGa2Z+hwytGUDs0woLmmGIOVf5uoZwcSb+TdpAIk9hDm7dhdrNxGlpIqkOiMAZ+Gh4aZb5n4GnVCHdGfqG3M3dQxU5pRJq1k0nWuJTZ7s9RnVKnHPGJxhJwk1GYqUhQJ9cZAdl7tQm7p4tSokaNENKiFrgMbJEdGiQbMXYgjI3EVzZBGiyejg9whkpPMmQJ50eTpgslXeByzZbJ/u7/8coEdV9rT+/3lvNLiT857ax3tvJ3WuZ130Dr7edPgVPS8TuIo/bw11nmclwPD5fY+4Q8er5s+DDsatiNOZUfDojLE2O456CHmjs/xei87Hge/jim5bFQGi/OM/g5W1wXJ4KXXPbgM+2eBLNGC3GWtaXDlU8alI4w7VdHDoifGQm2kv9OVYBuWnXBcYovzTln6YdGds4xPyxkrdEVmsjFH5fDqVkZD2kh2S6GL88BcXYaWI3+5Tqcty85dVwfEjF2m7bBszto/LWf0AT+QTbYaGjKHkJuPBWcIeWZhq/fQA+cgBGdIa5tPdPy1v+P89XMPzw8/AZ/b54mhAYOsAAACiHpUWHRTTUlMRVMgcmRraXQgMjAyMi4wMy4xAAB4nGWSvW5bMQyFX6VAFwe4EfgnUpTRKUsmp3vQoQg6timKjHn4HslFzaLLtUiTh58O9fz45YVfTs+PX+5eTg/7d38eTp+e7i7XvPx4OT3989/f45++jxd8L7eMXPP/qe4E4/vh/RSNhTUOaU56nL0xcfDBrZMMP87WXJMViaF92EpwusihTXwkKrQ5p42DWmgPQok07p5oIR8+Vmwjx8CMrixogZhoynFPTVNHHmdqnqaymozYY2WkszGja6gFZFa1dJU1Som77D7m6Hncc2NlHM44xYhEjat1WhkoUHRf48y7C9TvrUkQ7oUcq7itXG8yMraYqOyR1ob5iAMa7lve0ZiJIoVVYwhS2gwW+ioSkbz2GWn2AwfKJNkUGQQHUQ4I3vOGBsUq6sapWyqd4SH+w01Zt1SwUoczSrQ5sSRcZ+3EFXdEJnACCqPb7arjqZowz4j2MOxNXRl7G6pxxWZ298MaSteu11DsKZHBH7GZRQnCglWG63ZXGG2QwZZiPxByseWjZxjLehBkErZsjIwOPuiKZawMKXxZmfTAJEww1w7AWItfMQkto+EJ1r6dT9qXcthMg5eBeHi+M93Qjl3TGq4YNRpR7JclroKHfXd8fXv9/vnX689JbR0vr2/f8Owm3yLhKSWSqbfIaNotYp+9RH160ZQZJdI5SqXOLJFNLjAskwuN8uSC0ydXnMkFRycXHMgUHKgUHPHJhUdicgGiKYVHdEp1x6ZUe/qUApRTqj9QqgbB2IIUUwrSmFKJIFSIlKYWJJ9aicbUQsQxtRCpTa1IKK5IObUurU+tW8OgAqVQLlCCt1Af0Xj/DUrecoPtcu+KAAACzXpUWHRyZGtpdFBLTDEgcmRraXQgMjAyMi4wMy4xAAB4nM2SW0iTYRjHn+/d9u3bSbe5kzPrM3NOO6mRpen2jjIKBFldFHbRPBArSkwoA42mQZkmYXaAKFR2EcgWXgUVuTctKTop3RidSAPDC03rJgrXt2crwRtve+Hl/3sO3/M+z8M3Oxj+CNLRweLZKN086QY4AURJCY8iT4gATklkhKeocmVcZX/tpaqEJXG0ScJPZP/icV2sn3h1Sbllwsukc2hy3P+qGuAA0oAjwK0ALl1aD5CVQFZJk4hytZ/IFT4F7ye80sfrRKXgJ0oRBJUoqH2qDFCtBrVG1GidMl2SqMuEpGQnSdKD3gD6NaDPAoPNZzD6iTFFNJp9KSYwOcCUDSYnmC1+Ys4Bi9VnyQWrzU+sqaJtLaTawb4O7OvBvgFMhJcreKWg5o1mi9VmsLoIxHpN/DIlgQtsqG2AxYwR5VUW+NaC7L3Uy5oGHMjvLSE2E70cifEJKf/i7IeHMVY8KWQ2F4/+Y3mTkTcyuTvG5bcFNl/9Gzlvspa1jtlojHfONzElOYkcld7St4YwZz5rmGUOfUb+sn2OpbVoMKd7on+wKv1FaYznPG7384kuzCnUjLimMxyY49NqaX1ZEfrLdvnplfZbrhhX7jtDaxbiPXfkFtNQshZn6Svy0nx6GLnnWZg2up5iDu8bpbJt8Vmaxx/TpPN1WLP9/jV642y8z8m6KdpcG59r/9Q7OlR9AHsISvuJjo5hTVXvMFOf6ozzp3I2PXYUuTv7NeuKlCKvnKlhh/qSkX/9OBf5/ohinUZpn57dN5HN1hATvG3IXVVvSwp6wtjbkZCD0k41+u+MT7jF/i3oLzZ+dQezA1gzZfN1mpa/B/mutJPKrUHkXO89yio8yD9PL9CcB/X4bbSgg3obNmHNl26FZ/x4A7KlIkzDe3cgvzo4SoOJ3ix/ADI702HZ8SVPAAAD/HpUWHRNT0wxIHJka2l0IDIwMjIuMDMuMQAAeJx9VsuOIzcMvM9X6AcsiA+J1HFnZpEJgvUAyST/sPf8P1JU2+pegIjtQzddTRZZFNnko/uNXkp8/nz/4+e/ZX/4/QX29j+/OWf5R1prLz9KXJTX77/9fi9vX99en5a3z7/vX38VtaKOZ1qhX7Hfvj5/PC1UPstNK88xm5Rbr53GmFpabetTdhwub4Ec3QgUcNUGvp4gJZC9NjKdvdy4jqadOUFqRO9VzGy2cqNK6io9QfYjevAUKrdWqbkMS5Cj3COm927WA9lZxTOfFj6pGmhS/N+pD5cE6AHkStKZqFCdRmYtAc4ASu02ZIzCVdzMM5LUgqXUCdzgIhVpu2VlR8Bn3ZkHgosaFMiQS6FR23RxBhIsTNLoEtGtdpkdAGRm03pWItJAtioiNlbGc46RIkOhVk2NYIaSMk3ShAZEh0cf3hSx++zOWTEp9AE3d17cjMXwcAIMfaQOnopqwXWzMbPGpLmA06c1e7SbeIYEoU/Ens0dx+i2WGjPasm0fPKYpBySDtWuWRdx6NOrSnNIjugd0JYlxHGCBvqRoPmzhSXlqQtJHcHDEw6HaQoMebRCHDZ6dNFIY4c8VlmYfRy97pQmbiu0xzjAma/KFiMkAXr5eJ7yIdGhvQ8b2TzgGVDkQ8I4smh75ZZ7xe8jys2NgI4ra00o84qBsbyqSXcKNefQPCnhw2sfpG0GlFWZs0KJBJRqg9qdUAAQ8VQkHIOPx0jyxlDBXaamTPsulaOrMe86mjVFDiBh5tFxCRqCbmLKkAbkmsFsEucJuVsKDJ1iHDLjEl2HKYcGzZBzIZ2k9bFWQdM8dQ2V0HaM+T+XXK6sWeU1RDoOcUTvGPANNxkyNOrVJlH38Nm4aVokDYmszs6Yw5HR1IY6ZUhdPEUnBiGK5A0rI3UZCnlFeza4RNO5UzprdCyWSn1izIEkWr6liYc+67APbMujOTzL+/v9/ZfVfSzz18/7+7nM48vnxl63fu5l3BU5l2/c8jxX7Pr7skjR1UXPbRm3st8l/Ljf0SbutfTLhtNwufG0/i7jsq4UMeyylBRBNlsKfgi6CWDDKKJuvhQEHefwsjAoaFwXg4bh5BAkKU7kZdAflk2cVxXxO+vIUTpUc0fiKGUQPJ/SwyI7P0xcDs50WoJwWHbOHIyD9s6a/bDIiZkPy6nj0s4R4TLhloV2cWSJP5HfZV7psuy8ZHVAZHyZPxQFkNPPOCx6nSeH5Xwqyowi8a6PBGdYdMfS4Iyy8a68ruYEqbMhoyNQyDMvXS0Ly66hLs7ohNPP4oy3rV0xXZzx9nv6sYfl9OMPy34qjtf1MMX9860a1y//AZ8kKmzBUpa/AAACx3pUWHRTTUlMRVMxIHJka2l0IDIwMjIuMDMuMQAAeJxlkrtuVEEMhl8FiWYjnYx8H89EVDRUCT2iQCtKCEKUPDy/zxJiiWLPjv/x2J8vnz58fnp/+fTh8935acenK18v798+3l0vj6dQv+vl9n386/3uqT+5yvXSo/UXt+9/t1Lf7/zm9+Xeh84547iP4R44PNzbkBXrwJVz4FBK+JQD/xQUDCUGs8oqyYTKR4cQMx84TCItJx/E0+AkI8hcznc21bOkFZZyPvRg45LETPR8WFB4yIMtzf5B6XFPgyk15j8sPXiozXkLTys1oczQWe/mcF1uhwyea/otX11C0awkpSyNiENHGjQoMtJBaSNTl70QARcYvnKdLqwujFRr8qxHPMhNHWHQiLRTmahfDkInPbJKo6GqaDOBdEWA7AFHCT+7gko8TEqbNis465patmYkVR2+vPqG/JlSgdCCyVEKuixVGKO1IKr0IlY9A2K1UUfIQq8RjmasLAXlINOt1boqzKJMr3kgg0X5SCy2Gm2YFTKPZKU4N4KsajVgOGZRe5Am/pLLao+YKFCED1PKqFyoEhhQ5mKw4xUJYVUesIbMtWvomZhilg9zLJfMkpYRGlRe7GyKOpaistNJVLCQ5ziqPzFy8lqYBnqCwDlUkhL2zLoHDHtdE7ae/ybGtt9mvs6KmNDO23bNEtQWeoblIE1/WZOZL7DQ7o4vv56/ffz5/GPTqOPj86+vQ+bmV4u2vBq8tfnltmat7c3a8Woo7dks3tmsvV4N29w4mDY3EObNHUU2N5jc3GDW5gajurnjIE3jQbWdB2EbUWzpRLqlEalv6US2pRExbhsSI1RngtmZ5pbGxDAbFOeWBsVra58Xbe1UubVRKZwblcG5Tw0TbVQGs1GJbG1UBrNRiW5tVOLbOlVsa1Rm2xqVwblTwblTwblT6bZG5b//AOHjqTJGxxApAAAAAElFTkSuQmCC\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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VqStSHQtlz6H0MVCowKmEW9vQ0yg0xggWxUZFyVZVyUKZwVBQoPczY9raK1kMY1rbUpVV0NOUHZ056Sn1MZcExYWKvUxVp85gDBjceW+utWAhfA8ojyI3Nzc1NdXGxqYDRnz69CmTyTQxMQGAxMREc3NzDQ2Nu3fvpqam9unTp7i4WEtLa/r098Qf+/v7jx07VkdHZ926dcHBwR3galv5K76guoEv4tSA6N+uLvXvGnkNmQW1N56WOw3W7lTvMDLE0dFx165dt27d6mxH5BERh10ffZFzLxEAmDb2KlO+okqke4rFj3MvmV+YX8oXptdzk+u4NzhUbsMp8coPAEBZB7QHgPZAVTr9KD9Grb8Z09aeZeOgNNSawlB679BMazumtZ2M3x/JYCF8P66urgcOHLh8+XLHCOG1a9d0dXWREB49etTLy0tDQ2PEiBH5+fkKCgovX760tLR87wtHjRrF4/HCw8PFJzFdjUvp7xp4QgCAmgIofgAAwC4DgAae8NpjLISfM/b29oqKiunp6XV1daqqqp3tjhzRmPOyaKk7wWGLOBwAYN+5XXnYV2frH8Kqqpr0lOqUxPKCtxUCYXGjILmOm1bPzeZKtB5UUALtAdDdDLjV8CoGdC2gW28KQ7WvroJxUKqyxufZfgcL4fuZNm0aEsIdO3Z0lg+hoaFPnjwZOHCgpaUlCklvTmZmJgDo6OjcvHmzoqJCS0urY338OA28f4/VG+uRBIKACwAiAup5gs7zCyNzVFRUrKys7t27d+fOnYkTJ8p6uMbGxry8vL59+wJAdXV1Q0NDjx49AODatWu9e/cuLi7mcDiTJk2StRudDsHlFHvOScgretrAXaqnDgCH80pMlRTHrVxQyhfuL67O5vLzePxSvlD8EhaVaqnMGKmmZK3M+HPAzxnKffgUBajMgVcxUJwGPUcqaff2nKn1KOuZZMWSzwkshO/H0dGxW7duqPFvx6Q97d+///LlywBw//59lM4vrowjFAppNNp7XzVt2jQKhaKqqjp16tSu2ex7RB/1B2+q+QDQfSCYTgQAqHoDACpKCjYmOHb0M8fR0fHevXu3bt3qACF89+7dzz//fOnSJQBISkpKS0vbsmULn883MjK6du0am81+7+ECADx9+vTJkyezZ8++cOFC7969P7T78qlQF3NJyGbzRcLGfwMhGwmiXCC0f1LwTkL8lGmUESpMW1UlWxWl/ky6ojKLYW6pZDFs1IBhLleJnHcN9d1MgK4MnCp6Y+0OS34fPdXPVQUBC+GHUFRUnDx5cmhoaGRkJCrhLWt++OGHOXPmAMC8efOa/OhDKggAamr/FGuQqQqWl5fHxcVpaGiUl5eLRKL58+e3/rUrJvQ6El/Ab3ZdkUqZaav/nhdgPiMcHR337NnTiceEioqKLBZLIBB8//33gYGB69evb17vbdCgQXfv3s3Nza2uri4uLv7UhZB9P5ngsAEgo4F3vLQWAB7U82Zpq/IIopsCbQCT3k2BaqXMcO+uqqSszDC3ZNk4KA0dxhg8lPJvelj6KOJiavFJnxNJmkYVJc9+Kb0wR6io4/h9Z74rGYOF8INMmzYtNDT08uXLHSOE0pCQkHD48GGBQDBy5MiVK1e2IJztQ1tbW1dXt6ioaO7cuSjVsvWY6rL+WmS+8BCXxxcIAYDPpnXrzVTVOT9FUUWJZD8xXQ0HBwcajZaamtrQ0KCsrCzr4VJTU1HdxOLiYtQ0hsvlJiYmDhkyJDEx0czM7L1VT3k8HpvNTk1NFQgEqOdiF+HIkSMODg5RUVHOzs6WlpY8Hu/JkyfDhg0DgLKysqqqqpY3q/QUaRbKDADI4fIpAFEDDXQVFWgsZYb5UJaNA9PWntF/ELzvF0KjUmbYGtj3zPZPLfEBeF5TyU1LkdF77CJgIfwgLi4uDAbjzp07ZWVl3bt3l9EoCQkJcXFxLi4u4miCadOmtWm4V69ebd68OSoqSk1NbePGjX5+fqiwOImUlJQoKCiUlpZu2rRpwoQJbX357BEGQ4zG7zr/LCnpacnNHdyK7E1Vvc2fzwUYQ66fmK6Gmpra0KFD09PT79692wHtzIYPH37u3DkAiIqKSktLAwAlJaW5c+e2/KqysjIzMzMbGxsGg1FXVydrJ1tJWlqagoJCeXk5AKB/Kysrt2zZEhUVBQDp6emJiYnvjWBg2dizk+Khhq1PV7BSZgDA3ToOAOgzmepzFmitWPde8WsO09rWJu4qANyv4/IL8wUlRQp6BuS9v64Frgr/QVRUVMaMGSMUCsVF9EknPT192rRpO3fuzMvLGzJkCLr41Vdf6ejotN5IZGTkggUL0B7punXrzp8/T7qfenp6JiYmy5cvX7hwYe/evdthwayHSsjK4ckaV2dRSwEguqQk6OSpRh6PbE9bC5fL5XK5QqGwqqqqs3yQE1DNXlnvjgqFwo8/6QMYGho6OTmpqakxGAxt7a4SxlxQUFBdXf3mzZtx48Y9fPiw9S9UmTyNymQqUKn0f7N06RSKApVCVVHp9v3KVqogAChZ2w5iMlRp1Fwe/x1fyM243+b38OmAhbAlpk2bBgAohoV0Xr16NWXKlNraWnd399mzZ7fbjmRsurKyMpvNJsnB/2BoaMhgMExMTIyMjNptRGm0UzdFKgCk1XP7CriJZ0+T5+AHOXjwoEj0T+Sqv78/epCQkPD7778fOHDgxIkTOTk5HeCG3OLo6AgyFsKMjIzBgwfn5+fb2tqiK3p6egMGDGiTETabffbs2cOHD7948UIGPraZL774wtPT86uvvmKxWMuWLUMXHzx4gNrm7Ny580MvpDJZBoGhY417LOuth64sMdZzNjIwCAyVTCX8KIyBgxWVldGa8n49l9OWlkyfHFgIWwLFZMbGxpKuLm/fvnV2dn737p2zs/Px48eladhmYWFx794/n9HU1FTxypIUOBzOsWPH0OOcnBwpF8dve5joKtDUaFSOiEiuYyvnZJHh40cICQkRC6G45oCFhQWXy+3Vqxefz8/Ly+sAN+SW0aNH02i0e/fuyWiKdvfu3XHjxj1//vzEiRPr1/9TymvYsGFtmlxyudxJkyZxOBxTU9NVq1bFxsaS7ieHwzl9+vSdO3fCw8PDwsJa8xImk6miomJmZiY+XrWysjp37ty5c+c2bNjQwgvppv2MIm5rrdqgPHaC8hhnzVW/GEUm0fu2bWZAoSkoWVjbqCoBwP06Lif9cz4mxELYEgYGBsOGDWOz2devX6+srDx37px4SSEN5eXlEyZMyMvLs7Ozu3jxIp1Ol8ba1KlTc3NzV61a5ePj88svv2zevFl6D8VwOBzxlzYvLy8hIUEaa0PGjptra+3STRkAsrmCHs8fS+9ha+ByuRwOh8PhiK/cvXtXU1OzZ8+eDAbDzu4Tq4LxaaGurm5ubt7Y2Hj//v3i4uJz586RKDO3b9+eOHFidXX1rFmzDh061G47Fy9edHJy+vbbb52cnI4dO7Z7926yPBTDZDJNTU0zMzOfPXuWnp4uzV5ua6AyWWoz5+r9cVhv7xH1WfPatBYUo2Rta6uiBAD367n8/FxB2Tuy3ewqYCH8COLdUU1NTRsbm4EDB0ppsLa2dtKkSc+ePTM3N4+JiZE+lI5KpYaFha1YscLJySk2NjY7O3vSpEniZZz0iEQiNpvNZrN5ZBzpKY+ZME1TGQAeNvAas1/wCzsiSM/T03PJkiVLlizh8//J4/jyyy9/+uknKyurVatWsdp1j8C0HnRMmJCQoK+vP2bMGLKWhlevXp00aVJdXZ27u/upU6cUFdtfwz03N7dPnz7osaGhYWlpKSkeSsJms/v161ddXe3h4UEQRDtCu1kslrggQM+ePYcPH062j01hWtmas+jKNGoOl1/KF3IffLbHhFgIPwISwqioKKFQePXq1fHjx0tjrbGxccaMGenp6aamprGxsd26kVOvKDs7OywsrKysjMFglJSUXLt2DcXOkUJWVpanp6enp2dbEyfei7Kjs5WyUjcFWh6Pn8Pls29dl97mRzl+/Pjff//9999/S3OvxLQbyWPCiIiIKVOmSG8zOjr6iy++4HA4ixYtCgkJkbJFmoGBQUFBAXpcXl5O1hdTEiUlpcTExJkzZ2ZnZy9evLgdFtTV1VesWAEAu3fvnjdvXq9evUh2sRmMQRaKLGVLZQYApNVzOQ8+32NCAvMxDA0NAeDrr78ODg4uLCxstx2BQPDVV18BgIGBwevXr0n0cP/+/QDw1VdfEQRRXl6uoKCgqKhYVVUlveWKigpnZ2f0+MaNG2vXrpXWokiUO9nuKy0VAFhr0K1w0WxpDX4MW1tbPp+PHltZWcl6OJlSWVkp/vdD/Pbbb8uWLYuKimpoaOgovz5CRUUFlUpVVFSMjIy8f/++9AbPnj2L5jTLli0TiUTSG6ytrbWxsUlISMjJyfnmm2/CwsKktyk7UEULX1/fDhir0HPOTwbdAGBOd9X8r5w6YMROAQvhR4iPj6fT6ZL5DPr6+q6urj4+PomJiTwer5V2RCLRggULAEBLS+vp06fkOpmXl0ehUFRUVDgcDkEQo0ePBoAzZ85Ib5l8ISSI0p2bAkx0AMBCmZE9zFRQ1dJtvd0IBIJZs2YdPnz46tWr4nvllStXZDFWx1BcXOzl5VVQULBx40Z0pb6+fvbs2RUVFQRBxMfHx8bGEgRhamqKPqhKSkpOTk4+Pj5ZWVmd6TdBoBUbWmYxmUwnJydfX9/c3Nz2WTt16hRa/3l5eZHoZElJiY+Pj5eXV0JCQmVlZURERGlpKSmWX716lZqaih7fuXOn3W9cTFBQEAB88cUXUrv2cSqP7j/bTx8A+iopZlv3FlSUdcCgHU/XEkKU2tXZXvyfhw8famhooM/c1q1bJ06cKC5phlBRURk/fvyvv/4aExPT8goMladhsVhJSUmycBXVhYqOjiYIYu/evQDwzTffSG+Wx+NdvnwZ/VFKSkoyMjKkt9lw59aTocZMKoUCkGzeszbyvPQ2myCuA6elpYV04vPg6NGj4n8JgqisrDQyMlq0aBFBEEFBQQEBAQRB3Lt3z9vb28bGRjIUuV+/fmvWrImNjeVyuR3s8/79+1FTz3HjxllaWko2+Bw8ePC6devi4+MbGxtbae3w4cPofZGrggRBlJeXX7x4EX2LUQPFkydPkmI5LCxs165d6PHGjRuln429fv0aADQ1NTvgbsl5cP+ZpTGDSgWA78cuDP0rmsfvQrdosuhaQujn56eiomJvb+/l5RUREdHyFpCsefnypa6uLgC4u7tLfuBycnKCg4MXL15sZmbWpG2viYmJh4fH4cOHMzMzJXdstm7dCgB0Ov3q1asy8nbLli0AsHjxYoIgsrOzAUBdXb31C9YWmDBhQrdu3RITE6U3hRA1Nr4eNXicOgsAfjPSKl67lCzLYmQ97egUKisrN23adP/+/U2bNqFVRWVlpZub29y5c5OSksRCKKasrCwsLMzDw0NTU1P8EWWxWGhBlpeX1wE+79mzBwAoFMq+ffvQldLSUuQVmmIilJWVXV1dDx8+XFBQ0IK1AwcOUCgUCoWyd+9e0l1FB5kXL14kCAJFjaIZhvSQLoQEQaB03sePH0tvqmV4bO70qdsUBkyjDZ5N+fKkqkeE/rLrD/NqZD1uB9NxQnjs2LGsrKz9+/eLdwb8/PwCAwPR4x9++IEgCHHeKIJGo1lYWCxfvvzkyZPkHqp9lIKCAmNjYwBwdnZuQU5KSkouXry4du1ae3v7JmWvdXR0pk2btnv37rVr16L3ItODh4yMDADQ1dVFmj148GAAQHtl0iAUCtXV1QHg7du3ZLj5DyVey3cZawPAGHXma3szEY/MZcq2bdvQtOOT3ghtDUgIi4qKHBwcjh49GhAQsHTpUrTGEh+LEgQhEAgSExPXr18/dOhQyambra0tKQdsH8LHxwep4P79+5v/VCAQpKWleXt7W1tbS3plZmbm5eUVFxfXZJkotubn5ycLb729vQFg9erVBEGgxNx+/fqRYjksLGp1+RoAACAASURBVMzc3HzGjBkzZswYOHAgKR9LVDfuvb9Ycll07DHT/TJM8ofe48DIAawWUb6J1FgUW1Ld0VsLMqXjhPDx48cpKSl37tzJzMxEVzw9Pc3MzJDC2dnZoYvFxcURERFeXl7NpUVPT098OIcOw2REWVkZSpOws7Orr68XXw8ODv7Qmo8gCD6fn5aW5uvr6+HhIVl+RVNTk0KhHDt2THYOI1Dxs5SUFIIgNm7cCADLly+X0iaq7dS7d28yHPw/dVcu3RvSk0YBOoXyyMKoIfEGWZZRMpmspx1dBCSEBEH4+vra2tr6+vqKvzIfWmO9e/cuODh45syZGhoarq6usvPt119/RX+IEydOSF7funVrSEhIk+O3kpIS5JXk0YOmpubMmTMPHz5cWFiIVIpGo/31118ycvjmzZsAYGlpSRAEn89H1ZqkCY4TI4sV4dGjRwFgxowZ0ptqgbxyNvPbq/BFCGj1gy9PgXsUmLuDxTzGvJjVf5Mc6NC5dJwQXr9+PTo6+tKlS7du3UJXPD09Q0NDp06dShCEnZ2dQCBo8hI2m3379u1du3a5ubk1qQGopKR09uxZWfhZU1NjbW0NAObm5k32Zj09PSV90NbWdnNz8/HxuX37NpvNbmInOzs7MDDQw8MDhbG0/hSk3fzwww8AsH79eoIg7t+/DwA9evSQcsqPdGXu3Lkk+fgPwrraHNu+1ioMADhg0r10+y+kmA0NDaVSqRQK5ciRI6QY7OKIhVAgEFhaWh48ePDmzZtr1641MzMTf0opFIq1tfWmTZvu3Lkj+RVrbGwsKiqShVcikWj16tUAoKioeO7cOckfVVdXo2hPKpVqbW3t5eWVmJgoee7A4XCuXbu2atUq1F9X/BaQNVLivz4Eh8NRUlKiUqnomBA1UCRlRFkI4cuXLwGge/fuMl3TByUUqCy4BrYrwXoxuEeDezTMvgyapuAebbI6XnbjdjwdJ4RPnz599uzZkydPXrx4ga54eno+fvx4zZo158+ft7OzO3PmjK6urqurq7e3d1xcXPM1HzqcW7lypbW1NZVKTU9PJ91JHo/n7OwMAKampsXFxU1+KrnmQxunYhQUFMzMzBYvXhwcHPzmzRuCIE6ePBkZGVldXT1o0CAAiIuLI93bJly/fh3tLBEEIRKJUNaHlL8ltANz8OBBknz8P4VL53j16AYA0zRV3jgNIz527P/w4cOzZ8/m5+dfvnz5vU+IjY1F66Hdu3eT7m3XRCAQvHr1Cj0uLi6WXGbl5uYePny4yRpLS0sLrbFkJIEEQQiFwoULF6KtaXTeJklNTY2fn9/EiROVlJTEXunq6n777bdnz55tEm6Wk5OD3gKDwVBWVhbHysoOBwcHAIiMjCQIAhXz9PT0lN5sVlaW+Ij99u3bZJ3y9OjRAwBID0GXxPfKG4ZHDFguBLs1/wihezRo9AL3aJ2lMr+hdSSdGSyDhLCmpsbBwcHKymrdunVN1nz29vao5fS7d++avLaysrL5ClJK2prnl5OTExISsmzZMgsLiyZ1Iuzs7DZv3hwYGJiamop2KVesWEGut83h8/koQh1NNZYuXQoAmzdvlsamiYkJAJASLNqE6tMnogYaAIAyjfrVELP9e/94+PBhC3/T6urqmJiYJ0+evHe1d/fuXVSj55dfyFlcfoq4ublt27YtNTVVcpXA4XDi4uK8vLwkl4kfWpBJiUAgQMG6LBbr2rVrLTyTzWbHxcWtXLlSckJJo9Gsra29vb3T0tJEIlFFRYWvr++jR4+8vLwAgJTUnZbZtGmTeKA7d+4AwMCBA6U3m5KSoqqqisIgSOSbb74BgEOHDpFrVpILQVEqs8/D2O3Qd8o/KugaCD1swT3afssd2Y3b8XS+EBIEcfr0aSMjI4IgCgsLw8LCxGs+SWnR19efOXOmr69vWlqaLIKGpczzq6urS0xM9PHxcXV1RWcbFy9e9PPzy8rKImuXsjWgHve///47QRBXrlwBAAsLi3ZbKykpAQBVVVXS5xwEQXAK8iabm6GbsvivrKysLI4ZbpL2UFhYiM6Hmgvh48eP0Qxg/vz5HfBL7po8evRI/GvU1taeOXNmcHBwk739ly9f+vr6TpgwQfL0feLEid99911YWJiUFRh4PN6MGTPQH/HGjTYc+mZmZu7Zs2fs2LGSdX969ux56NCh/fv3X758GdUmHT58uDTutYa4uDgAGDZsGEEQjY2NysrKFAqlpKRESrOo/C/pQhgYGAgAX3/9NblmxdRdufTMpp/mV8fBPQqMHWHAdLBaBDrmMPmAynfXzt1rumH2SdOZQpiXlyc+Wnvy5EmTn1ZWVkZHR2/atGns2LFNCnKqq6tPnjx527Zt169fr62tJcUZccB9cnKylKaEQqHkDYisXcrWgApkOzg4EATB5XLRtli7t2JQa0NxQj2JiEQitIGmrq5+8uTJgwcPzp49G60+JVct5ubmS5cuDQ4OfvXq1cWLF8PCwjIyMv766y/Jnb3s7Gx9fX0AmD59umSopLzBZrMjIiKWLl0qucZSUFAYPXq0j4/Po0ePJJ9cX1+PnmxiYiLeO22yIGvT6FwuFxUj1NDQuHv3bvveAvJq8eLFhoaGqqqqGRkZ+/btO3jwYENDA51Op9Fo1dXV7bPcSpoMhDoJNznmbAeop3xMTAwZPv6fZ8+eAYCenp4sJn9RWzed6m8wQ0ulB4vFnB5EcY+GKYfAeQ/MDFf+7uoMP5nfyjqYrpJHGB8fL17zJSYmNgktEQgEmZmZhw8f9vDwkNzhUVBQUFNTEx/OieNR24qs8/xI2aVsDXV1dejAH01jZ86cCQDtDjdHkwNvb28yXZSwzGKxdu3atXnz5ps3b/r4+FRVVZWUlERERHh7ezs5OUkeIwGA5PmxeP5UWFiIYmXHjRvX8aniXZacnBxfX18nJyfJxiY6OjoeHh5hYWHoLi8UCrlcbmNj46NHj5o/WVdXV/LJLdPQ0IBO1jU1NUmpoCYSidDsTVwlbsSIEbLQkuZIDrR9+3aQ+lDj3bt3VCqVyWTKouKdgYEBADx//pxcs9Vhfyeb9zRj0QFAgULxHjDQ7usARY8YBY8r+stu+F55I/zs9l26ihDu2rVL8q6npqY2YcKELVu2xMbGNl/zvX379uzZs6tWrZo6dWqTMsoGBgYzZszYt29fSkpKKwM1OyDgXvpdyhbIz8/fsmXLq1evTp8+ff36dVSfPigoiCCIkydPIpH4qBGRSBQQEPDu3bvTp08/fPgQXUQ3BdInB5J5fhwOx9/fnyAIHx+fJkrG4XCSkpL27Nkzbdo0yRJ3AMBgMEaOHLlixQoUW2hjY1NXV0euk58HNTU14eHhCxcuRHdMBJ1O9/b2jo6OPnXqlI+Pzx9//IFW2JILMsm5pr29vY+PT1pa2nuHqKurGzt2LNJO2eV3//LLLyCDajItD3T79m0AMDc3l8bg8ePHAWDKlCkkOfgfZs2aBQCHDx8m0WbV8YBnlsYjVJUAgEGlfKGlct7Rml9aIhSJGnjkH5F0EbqKEKI1n7hii+Rdj0ajmZmZNcneq62traysrKioePLkSWJioq+v78yZM7t37y75QkVFRWtr65UrV4aFhX2obOCpU6dQwL24bJUskH6XsmXQsdnx48dv3LgREBAAANOmTSMIoqqqSlFRUUFBoby8/KNG4uPjX758uW3bNnTAw+VyGQwGlUoldz9KPO04e/asSCTy9PQ8c+bM3r17+/Xrp6am5uTk1GTNJ+a958e6urq9e/f+nIqoyY7MzEwfHx8nJydFRcWjR482NDScOnXqzz//TE1NbV5/R/LJ4i9U7969Fy9eHBYWJp6bVlVVoW6ORkZGL1++lJ3zaCopzjbumIF4PB6LxaJQKGVl7S+wibTqwIED5Pn4fw4ePAgA7u7uZBmsCNz3ZKixgxoTAFhUyvn++rdcHSty35Blv8vSVYSwCYWFhefOnVuzZo2dnV2TNZ++vv6hQ4euXLkSHh6+c+fOgIAAyaTXJikWTV7YJNwmNjYWbQft2bNH1u9Iyl3KFnj37t2SJUvi4+PPnTsXGhpaWFhIoVCYTGZISMjWrVv79+8/c+bMGzdufLTcWnx8/PPnz9++fevj40MQRGJiIgAMGTKERFc/lOcnEonE3eDEq5YRI0b8+OOP58+fbx7rX11dfeXKlcmTJwOATLPCP0sqKirq6uoSEhK2bNly4sSJvXv3trCeLi8vDw0NnTt3ruQsk8lkuri47Nq1C6UG9erVKycnR6Y+19XVoSkdWTEBrRwILXYvXLjQPmsCgQDVt8vOzibVzX/IzMxEdzYSbIlE5X/+9mSo8UhVJQDQUqBFDTQomOMmqO7MOpcdRhcVQkkaGxtR9p54zXf8+PHKysrw8PA///zz1q1bH9q0KS8vj4yM3LBhg6OjY5Pmq926dbO3t0enUB0TcN/6Xcq2wufzKysreTyeeGtxwoQJqCaAZDlHyfVx83QUgiAiIyNv374dHx+Plq2o3OLSpaQVAv1onl9rYoYlz4/fvn0r2XMDI1OEQmFaWpqPj4+9vb34r8NkMvv27Utu+b0PYWNjAwAtZ2W0j9zc3L/++uvFixenT59+9eqV5ECohO+qVavaZxm1YCQlB+O9VFRUsFgsdXX1/v37o7p07YwXE4nK9nhnWBhZKTMAoLsi7YpZj7cLZwrr5eXE4RMQwiY8f/68srIyLi7Oz88vPDz80KFDrfnbS4bboPAKdHudMGFCxwTct2mXUkpCQkLQwmvnzp3Hjx///vvvm9cH79ev3/z5848cOZKZmfnedJTp06cDQHBwcCsH5fF4O3furK2tPXLkSPMK3eI8v1Ye89TU1Fy9etXb29vZ2RkVuxKzadMm8dMke25gOozY2NgjR4707NlTRpsc7+Xnn38GgA0bNsjC+NGjR0UiUVBQ0K1btyQHio+PB4ChQ4e2xkjzOwk6cfzpp5/I95ggSktLhw4dCgCSYWXdu3efO3duaGhoG+4zQsG7LT+nWxhZKDMAoAdd4cagHoWLZgu7TD/LDuDTE0LpiYmJiYqKQnv3CxYskPyRTEURdbcPCQmR3RAEQVy+fBl1a2tSob+2tjYuLs7b29vV1VVypQgAqqqq6HAuIiJCnEymp6fX1i2d8PDw9PT0tWvXXrp0SfK6lHl+AoHg4cOHBw8enDt3bu/evVHhD4Rkzw1Mx3D9+vW4uDhfX98///wTAL777jt0vba2NjY2VnbfoKioKACwt7cn3XJjY6Ofn9/r169LSkoCAwMlB8rPz0dh2B/VFR6Pt2bNmsrKyp9++kncfXDIkCEAcP36ddJ9LikpMTc3B4D+/ftnZ2ejmgmoQjJCsmZCS38UoeDdptUp5j0HMOkAYEhXSBhsWPTDd+TWwe/6yJ0QVlZWHj9+fPfu3Y8fPwYAHR0dlC2+b98+ExMTmfYr8Pf3BxnXyY2Pj0fTw5ZTNfh8fmpqqp+f3+zZs9G8XoyCgoK1tbWHhwcAaGlptWn0Q4cO3b9//48//pDc/JRpnl+TnhuYDqC8vPz48ePr169PT08HiYLsKNb02bNnMhq3urqaRqPR6XTJOvikUF5eHhcXl52dHRUVxWaz0UAKCgrjxo2zt7dXVVWdOHFiYGBg85qLTQgPD6+oqAgODkZNdQoKCtDWPemJPXl5eShe2szMrElZcFSXztXVVbJmQvfu3VGBhSY1E0SNjcU/Lblj3rOvkiIAmCop3jHvWbzmexEZ7ds+LeROCAmCiI6ORlUeUIAGyqBH1R9IKS34IZr0kScdcRvhZcuWtemFhYWFzTt+qKiooN1jccePj36fHzx48Pr167y8PPH3rQPy/JD9didxY9pKbW1tSEhIenq6UChEnzfU2lAWofxNQCffslhgNcHPzw/1IkU9yBA0Gm3kyJG//fbbgwcP3rvG2rhx47Vr14KDg1EI+uHDh0EGfeTfvHmDSk9YWVlJhrM20emGhgZUxE6yE45kzQQhu6Fo+bxbgw2NGAoAYMai3x/Ss2TDSpFAHqtSyKMQnj9/HsXXoBr569atIwgCTW8NDAxkujuK9vRlkRf8oTbCbaW+vj4+Pn748OEAwGQyJReLLBbL0dFxw4YNkZGRrTmBqKqqsrCwABnn+a1cuRLku8RoJ+Lq6ire7Sc9lL85P/74IwD8+uuvshuCIIinT5+itMvRo0fX1ta2aY0lCTplJ7cRyvPnz1Gt7eHDhzfJGjI2NhaXQaip+U/j3MePH/v4+Dg6OqJDE4SRoeE0I/3uijQAsFBmpFsYlf62/qO17z9X5FEIxaCT8L59+xIEIRKJ0CYhKaUxPgRqq7ZkyRKCIHJycuLi4kjR3Va2EW49qK9vcnKyZDpKk3Ab1JrxQ9VfGxoa7O3tAWDQoEEyzfO7ceMGyDIwD9MCv//+OwB8//33BEE8ffoUyArl/wCXL19G+iS7IdLT01HHt0mTJjVJZv3oGkvyu8zj8VRVVSkUSpN+kNLw9OlTdMowevToixcv7t69Oy0tDWVXFxUVoUN9BIPBcHZ23rdvn7jVD0JcMwGpKQBo0BXNWfSHFkalOzcRn129mNYj10IoEAjQhx4dbCxfvhwAZNrt5cGDB+hmgSq5NK+w+iGioqISEhIiIiLQ8YMkH2oj3G5qamqoVCqDwWiyhVtTU4PCbZycnJqko4hz4SMiIqqrq3k8Hipw07Nnz/z8fOldaoEmPTcwHQkqKC+eSqICQOLOUKRTWVmJPpnN6y2Qwv3791Ha35QpU1o+v3hvETvJBRmq301iMam0tLQmCu3n58fj8bZv3y4usdZyGQS0K9PQ0HDu3LmysjI/Pz+UBuo1sHe53y6y/PxEkWshJAhi3rx5AIBSyFGR+8GDB8t0xF69egHAvXv3cnNzDx06lJub25pX1dTUoEbE+/btk5x4ttBGuN2g4hojR45s4TmNjY0pKSn79u2bOXOmZPkuAFBQUECbtHp6erK7J0oi2XMD05EIBAJ0ioZSCVEXM5kWaUKHC/Hx8Twe7/jx4yTG5ty6dQsl6syaNav1bbSrqqrOnj377bffos88QklJCe3QkJWGm5SUhEpTubq6IoXevXv3rl27EhMT//rrr/j4+CbPLysrO3XqlLu7u2Q/cxaLNWXKlN27dxcVFfn5+ZWWlqJGTm4uLqQ4+Ukj70KIGiyMGDGCIIjGxkZ0+E9usajs7OwpU6aIa7ytWLECAJYtW3bjxo2jR4+2MoTk1atXhw4dCg0NPXfunLgpUstthNsNihtqU/s3yXAbOp2upqamrKx8504HdSyT7LmB6WBQfZ/Q0FDi37jouXPnym64VatWAcCWLVvy8/O9vb1bv/GIdu8/1FDsypUr6ETc3d293bHN4gUZOopDuyYmJiaLFy+OiIhod4hcQkICUuivv/66uUK3fBTy3jIIf/75Z15e3u3bt9Hdr1u3bjjoWt6FsL6+HuUJISGZPXs2+qCQZV8cNonOBQmC2LZtG5pEq6urt1BaswmvXr16+PDho0eP0tLS0Be1rW2EWw/Kd2x3Wam6ujo0bQ8PDxdf/FCtV1Jo0nMD05H4+PiIP94oJcnQ0JDcIaKjo8W7HRcuXACAsWPHNjY2CgSCVm4DvHnzZsOGDZmZmaGhoREREU1+GhkZiZKOFi9eTIokpKWlAQCdTtfS0hIvyJSVld3c3AICAlq5CYSIiYlBCj1nzhwps4+Ki4uDgoLmzJkTGBiYmJi4b9++2NhYtHIV19mXW+RdCAmCmDJling/5/Tp0wDg6OhIiuWqqiokCeKwSXGeX5OUdjqdbmdnt2bNmvDw8OalNd8LasuppaXV7uZT70UgEKDppzRLTFSezcPDgyAINpttYWGhrKwso3MdBFqXoM69mI7k7t27ADBgwACCIEQiEdqLI3Fm9vfff9NoNBsbG7SiqqiooFKpioqKZ86cOX78eOsLPqAveEJCwr179ySvnzlzBp2oLV++nKyIcbQynj17tuSCTDLWzMTEZOXKlXFxcS2v5yIiItDtYsmSJTJatM2fPx8AfH19ZWH8EwILIXHkyBH4t3ZzTU0N6swpTb15hGTYJEo2EOf5oWxFcWlNe3v75oXFxaU1xV+Vurq65cuXo65miYmJt27d+vHHH1NSUqT0swkonMfU1FQaIy9fvgQATU1NNIdFjUmbz8RJBE0Lpk6dKrshMO+Fz+ejmRPK7EY5A6gLmPQcOXIEbeiJK/MdOXKERqOh8Cgmk+nk5OTr6/vRNVZRUdHKlSvDw8PXrFmTlZUlvn7y5Em0jUlugyc0LWtSnrC0tDQsLMzDw0NyEqysrOzq6nr48OHme7ynT58mXaGbExQUBDJIdvzkwEJIlJSUoFA0VG9+woQJAHDixAlpbEqGTaJ045bz/Gpqaq5du+bt7T1hwgRxx3AEqn+2efPmlJQUHR0dlDB39OhRcvOTxBw4cEC8mJOG/v37AwA6xkcNThcuXEiCfx9A3HOD9LIjmI+CvjLiYC4AmD9/vvRmDx48iFZR27dvR1f8/f3RFScnJysrK8k11qBBg37++eebN2+2Ps4lMDAQqeyWLVuk91YMm81msVgtbNQLBIK0tDRvb+8mKUlmZmaocHZjY2NISIgsFLo5OTk5aM7aYceE/MKCmotnqk/9xb6f3HXSFrEQEsS/HWjRqRjKC5ZmiiQUClGVje7du6PI5jbl+TVpzSj+qoSFhU2fPn3q1KmZmZmyE0J3d3cACAgIkNLOunXrAGD16tXEv0dH4mp2MgI1DWhS5hTTAezYsQP+rWeEit716tVLSpvo6JFCoYh37dB+O4VC2bdvH7rS1jWWJBcuXKBQKJLWyAKVKh0+fHhrnpyXlxcQEDB16lRUkh6hpqaGvvW7dnVEVgNKi2x9Kle7EQn4pTs25IwY8Np+YI5d/9ejBue6OvByZNjGsvVgISSIf791aBqLmvuwWKyGdhVfF4lEixcvBgB1dfUHDx4QUuf5FRcXX7hw4aeffsrJyZk+ffrLly+dnZ1lJ4Qou+PRo0dS2klOTpa8IaLSiM0bwJIIuh2La0BjOoykpCS0JiMIQigUolS8NoWENAF9H2k0mvjQF12hUqnHjh1r/vzWrLHq6+ttbW1RmkdUVFRoaKiLi4u/v3+7nfwQKB3Z29u7Ta/i8/mJiYleXl4oG0pbW7uthRLbDco+2r9/v6wHKt2xId2m33I9jWyrXtlWvS4O0Pfppf1mzFBBZec31sZCSBAE8ezZM8kzLfRZjI2NbYcptBJiMpm3b98mSM3zKysrmz59OkEQv/76q7OzsyyEsLCwEE1IpV+6CYVCVOri8ePHBEGsWbMGAH7++Wcy3Pw/IpEoNDT0woULN27c2Lp1KwBoaWqWHfFjp90VsuWoiUznwuPxlJWVKRQK6nM5depUJpPZvvr1IpEI1VGj0WjiM7ZNmzahK605sEBrLDc3N8k1lpaWVkhISJ8+fb7++muCIIKDgw8cOCCjzUBTU1MAaOvhfVlZ2e7du+vq6gIDA2fMmAEyazjVHBQkIdNmAARB8AsLckYMuGPec6w6EwlhUB9dTz31HNu+ZX9sk+nQreE/7U/llgEDBvTv37+yshKtY/z9/Z8/f45S9NpEcnLynj17FBUVw8PDR40a1djYOGPGjPT0dFNT09jYWHTCLz3r169HO/ukc+fOHQCws7Oj0WhSmqJSqagQ5aVLlwBg2rRpAHDx4kWpffwPwcHBbm5ueXl5jx49+vrrrw0MDCoqKw9u3/rm+9lvRg8pmDmhbMeGuqgLjTkvyR0XIwmKeSYIIjExEQACAwOrq6vRGXmbIAhi9erVf/75J51ODwsLmzdvHrry22+/0en0s2fPohDHljEyMlq6dCmqcCReY1VUVPTs2bNfv37a2toxMTHomU2aP5PC8+fPc3JytLW1UbXe1qOtra2pqXnjxg0ul4uCy1BTXwDYtGmTnZ1dVVUV6d4ixowZg4YjCEJGQwAA+34yhUoBgFqhKKOBl9HAy+HyAYDg89m34mQ3bivBQvgPU6dOBQBUzHDkyJEo1qOt2Nvb+/n5nThxwsXFRSgUuru7x8XFGRgYxMXFSVYCbB+qqqqoZSiTyQwODrayspLSYHOQEI4cOZIUa0j80K/UwcFBW1s7Ozv7+fPnpBgHgD/++OO7774bNmyYtbU1n89//fo1jUbrqaW5paByyKO86VkFv96+F3zsaMb6VQWzJuZOtC1evbD6RCD3YRrR2NjElFBExD4p/yP69cG4vOdF9WR5KA/ExsY+e/bM0dER/r136+vrSxYeaz2HDh3y9/dnMpmXLl368ssvhULh999/7+fnx2AwwsLCUNZs61FQUHBwcPDx8UlLS3v16hWq/759+/bt27dzudx2uNcakMpOnjy5rSrL5/PLy8uVlZVRnSYajZaamspmswEgMTHx3r17aJIhC/r27WtgYFBWVkbid7M5BIct4gsAoEbwHyEEABGXI7txW0vnLki7ApcuXTp06BA655D+kB8hEokWLFgAAFpaWk+fPiXFppidO3dSqdRt28jfT7C1tYX27gk3h8vlorrDqNyoZDU76QkODkbBDj///HNCQkJRUdGoUaMAQI3B6KOk2OQm1JOhME1TZWtPraiBBi8se+WMHPj2uxlle7bUxUULqiqz3tb1WnVTbeE1xbkxrO+usr696n4gg9vYVeLZujJ8Pj8rK8vPzw9J4JAhQ6SxxuVyv/zyy5s3bxIEIRAI0AeGxWKR8oGsqalxcXEhCOLEiRPm5uYHDhyQ3mZznJyc4N86O22Cy+WmpaUVFxc/f/6czWZLNpz69ddfAeDHH3+Ugb//gAqJSB8i1wIVB3/Ptu7VfGs026pX4RIZdixpJVgIibdv3/7yyy9CoRCVDN6yZcvTp0+lTNz56aef0HcYNTskl8jISACwtrYm1yybzabT6VQqtbq6miybX3zxBQAcPHiQ+G81Oym5dOkSCi5HIX8i58n/xwAAIABJREFUkWjhwoUAoKGhEfuFc7Z178dDjc700/u5R7dx6ix12n9kkUWl9lZSHK/OWqKr/ksPzZThg7q5h1NG/ATOe8A9GtyjYfA3Sl+f/mb/A+n9lAfy8/P37t1bU1OjqKjIYDDmzp179uzZFpoTtQYej4fWfyoqKkgXpUcshCKRaMyYMbIQwvr6egaDQaPRWtOkrGUkG05dv35dFt93SQICAgBg9uzZMrJfF3Px1XDTPcbayc2E8PVIM3ZKoozGbT1YCIlr165t3Ljx5cuXampqqMs2/Ju9h9optPVbjaI26HT61atXZeFwk5UWWfB4vMjISHKjyU+cOAEAEyZMIAiioaEB5VdJWRb1xo0bqNyGeE0snnagqFRhfR07JakicF+h55yckQNfWPaKHGiwtafWNE0VQ7qCpChSALRUtWl9XWDIXBi96R8h7GkPU4NY313NeiurHoqfDTwe7+TJk1FRUSiVUNyx70PNiVoDl8tF5xQaGhok9lsWCoXog1dZWXnmzBkSa/OKQUfg9vb20puSbDjV0NCAqnxIOb1oARQtqKenJwvjNeGnAvvouWgoA8A32qq3BxsiIXw81CjVpl+5/25ZDNpWsBAStbW1WVlZqBnh0KFDZ82aJZZDhIKCgrW19Q8//HD69OmPao9AIHBxcaHRaJKVNklHcqUlPZGRkeKajbt27SJRv8vLyxUUFBQVFdF3GIXPSBPvev/+fVTHZPny5ejKtm3b0LTjvWGKIgGfl/2i5nzou1/X5LmNyrbqlWze8ycDjeEqSgZ0BQUUaq9hDEM8wGIeOPmAkw/omMPUIOb8K/uvtT8BQH6or69HxWl1dXWjo6ObNyfS0dFBzYlas9PQ0NCAdhd1dHRkVADTzs4OANoX1NoyKG/qt99+k95Uk4ZT6Ng+OjpaesvvRSQSoXIfpPcyqz4bfMhUpz+TDgBKVMoCHfU3jkNyJ9m+GW9duHh2w93b5A7XbrAQ/j/Pb8SIEeI8vybtFCR1UV9f39XV1cfHJzExEfWO2LJli7gxt7Ozc2Nj440bN2Tqs+RKS3ouXLggLt7h7e0dGRlJilnE6NGjAeDMmTMEQRw9ehQApkyZ0j5TL168QNvXc+fORbHvhw4dQuuPsLCw1ljgl5bU37qeuX3D87nTs4b3uTnIUHvsrzB2GwzxgD6TYJgnDPOEbiYwNUhhboxPRGvrWMotVVVVqBiFvr6+ZM1bcQNYyTmlgoKCvb09il4RP1MoFIozDmtra0+dOkWhUAwMDCQLoZGLl5cXAKxfv550yygzHWUPSw+K7kG1mTZs2AAA69atI8Xye5k5c6aUk9TmVB0PyBxqbMRQAABlGnWpnvqfwwbxS7tiZXx5F8LW5PnV19fHx8dv377dxcWlSQoEavGF9DIqKoogCBRHLmuarLSk5MKFCy4uLr6+vr6+vpMmTSJXCPfu3QsA33zzDUEQxcXFVCq1b9++7chTzM/PRzcaNzc3lO4ZGhpKpVIpFEr7vr1CdgMnI9VlVTjFPRqGeDTZGlVbeC06Q4YdMz4DKisrUTUfY2PjFopft9wtNj8/n0KhoIljUFBQQEDAsWPHZNrGEgV2knJWLcmjR4/g357bpBgUN5wiCOLatWsAYGNjQ4plMUKh8OzZs8nJyZGRkSgkx/3rrwVV5LQ1rQj488lQYwc1JgCoUKkHTXQOjLSsl8GONCnItRC2r59fTk6OZP2z8ePHe3l5xcTE2NnZNTQ0dIwQEv9daUnJhQsXFi1alJiYmJiY+N1335ErhNnZ2QCgrq6OastlZWWhO8WDBw9KS0vj4uJaU9uptLQUJbSMGTMGdSGIjY1FJ1K7d0t1xpD8opL13dUmQkiZFmS44gZfIKtKx58BJSUl5ubmANCvX79WnlVXVFSEhYUtXrxYspPz/v37HR0dR40axeVykRDK2vPa2lo0iSS3LG1MTEyPHj0WLFhAlkFxwymCIOrq6hQVFRUUFFA9ZFKorq7ev38/l8v19/cvKChApSX1umlkDzMpmONW7u/DyUhtZy1Qkah87/YnQ41HqCoBgLYiLWqgQcFcN2GNrM44pUd+hZCUfn4o+cbLyyspKSkkJGTjxo0dJoSSKy0pkenWKEEQgwcPhv9mZVRVVXl7ezc2NhYUFDQp0t+cmpoalDRpYWGBVsB3795FdUNIKUm8KyKbOSec8vV5JIT0mac0Fl55nE/aHefzIz8/H9XMGzhwIGo60SaEQmFKSsrmzZuHDx/+7NkzNze3v/76a+vWrR0jhMS/7VDIShMST0bZbHYrt+hbA2o4paSkhGZ+KLWJrPP7iooK9Ev47rvvHj16VFRUdPr0aRaLpcagmygpLtFVD+6r+8LS+M14q5J1y2ojzwtrWh1JLhKV7d78wMLIUpkBADqKtCtmPd4unCms79KhZ3IqhOTm+SEhFIlEkyZN6tOnDykefpQmKy1piIiIEK+rduzYQXocwcaNGyXDWxCoP9zJkydRp8YPwWaz0dq3T58+qJz/48eP0Qb1/PnzydqGSnxe6bIn1WD5ddM18av/flpSzSXF7GdJbm4uqiJmaWkpfbeyyspKNzc3kUg0fvz4zZs3d4wQrl27FgA2btxIijUrKyv0oLS0dNKkSaTYRKAZZGJiIvFv7UZSjjbfvXuHDiCNjY3/+OOPK1euXLhwARUZZlD/X6m1mwJtmqbyvt7d04YYZQ8zLZjjVhG4j5v1mGjhSycUvPNem25hZKHMAIAedIWbgwwLF38jbFfd5o5EToWQ3Dy/LVu2oNKCmZmZUnbyaxPNV1pdk3v37gGAmprajh07EhISGhoaOBzO7t27nzx5EhcX1/Jra2pqHB0dxd2ssrOz9fX1AWD69OlSNuzGtIPnz5+j+Jdhw4ZVVJBQKxkJIUEQDx8+1NTU7BghRJm4Dg4OpFiTnRCuWLEC/u1CFR0dDQAjR46U0mZxcfGgQYMAYMCAAagEeXFxMbqT9DUyih9sGNxXd4mueh+l/x/oUgEGs+hLdNXP9NN7ZdXrjdOwd7+uqYuLFtbVEgTxOL/Waec9+rwrFPeYnh7h6xxmoBjR3gzFJHPDopULRLxPYFopj0IoDriXUZ5fh/HelVYXZPXq1VQqFaXAoyBPMzOzxYsXBwcH5+TkfPTlHA4H7V0XFhb27t0bAMaNG4fidTEdSVZWFjreGzVqFFmHVXV1dZs2bUKPd+3aRfq2/Huprq6m0Wh0Or0dHWby8vLKy8ufPHmCZmYEQZiYmMyYMWPGjBlTp04lVwjPnTsHAE5OTgRJR5t5eXl9+vQBgKFDh5aWlkpeMTMzKyzI52Sklvv7FMxxy7bqFT/I8DcjrXHqLLpEQw81GtWcxfjFsNsmQ82rg3uGzf+BNTeCMm4HuEeBezR8eYrhsI5GU+zHpN8171nitVz0icxW5U4I2xpw35W5f/8+APTo0UOaHcLU1NSgoKBHjx6dOXMmIyODRPcQ4mnHunXrVqxYYWVlJVZERM+ePWfPnu3n55eWltbCIq+qqmro0KEodq7l3VSMLEhPT9fW1kbhG5/B79/S0hIA2prmxOfzt2zZEh4efvDgwZ07d6KLslsR5ubmUigUJSWl0NDQt2/folO9j26ifIjXr1+jeaS1tTWqfdP8ipjGt3nVZ4OLfvguZ8SAJ0ONA011vtFW1ZeoR0GlUAayGEyLOTAlEKgKYLsS3KPB3gusFimN3+FjP6VkwyqR4NNQQYIg/nNL+uw5ffr0ihUrKBRKQEAAypshnbi4uNzc3EWLFsnCeBOGDRvm5ORka2vL4/FQsZX2GYmKikLR7UVFRUhsyCIgIGDz5s00Gi0kJAQ1KwaAhoaGjIyM5OTkpKSku3fvFhQUnDlz5syZMwDAYrEsLS2tra0dHBzGjh2L7rwAwGazXV1dHz58OGjQoJiYGBUVFRKdxHwUPp8/Y8aM8vLyqVOnhoWFiSvIfLqMGTMmIyPj1q1b48aNa/2r/P39lZSUXr58OXDgQB6PJzv3ECjPvaSkBO1hLl26tKSkBB3QtpXnz5+PHz++qKjIwcEhOjpaTU3t+fPnTk5OhYWF9vb2MTExampqks9X7GGkPmue+qx5RCOPm5Gmfz/J5db1xjfZCTWcM+V1RY3CF9zGZ2wePDoFStrQ3Qze3ARDO/Raru7QC0wTr+2zQAb9PWRFZytxxyEUClHklZQB9y1QU1MTEhKCwkA6hjVr1qAHr1+/bl+X0ezs7ISEhHPnzhUVFfn5+ZHoW2vy/IRCYWZm5pEjR+bPn9+vXz/JTyaFQjEzM1u4cOGRI0dQpxjxSSGGdDIzM4ODgyULe5aXlwcEBOzZswd1aU5JSVmwYEFjY6PsfLh58yZZNdk/CuoO5ujo2NYXlpaWPnr0KCgo6MKFC+jK8ePH0QNyo0YRqK0HakTabjIyMrp3747eL1rNS15p/S5349u8zED/q99MTR9mGtZff+qgsVQTJ5h2HPQsYfwuMBmPVoTgHq3neV0ahzuez0oIa2pqoqKiUFEihEgkSktLu3r1KtoQr66ullFjd8TLly/PnDkjFqcOQFyKNyMjo305TOXl5bdu3eJwODdv3mzHkcmHaF+eX3V1dVxcnLe3t7m5ueQaV11VVVNZOX33drLcwzQnNTUV1TEhCKK6utrW1jYiIiIlJWXy5MnXrl2T9egNDQ3BwcEdNolEyQkMBgMlJ3RNXr58Oc/DA6SL60lLS9PS0gIAFxcXdG8UX5k8ebLk3bL1iDhsdkrS/l8OKH9zEb6+CP9r7z4DojqzBgCfO70AQwfpAqJiQ0BRqSpqLIixRaMxMU03yaY3P3XTTGI2ZmNMsafoGqPERIoaFZUiKs2KgjRBeh2m32n3fj+uYY0tCAMDzHl+XQfmnTMqnLlvOcd1NDx+CHwmwuDZTCIc+mZ6pwM2i36VCCsrK2tra9vbWFMUtWDBgnfffXf79u0TJ05kWsb3ABMeev1bQ4cOzc/Pz8/P37t3rwkP83ZR+zm/d999t3MjXLt27dSpU8uWLZsSNHKotRgAptiKKqaPe9DWbdQFWVlZzz//fHthsy1btrSXXy8vLzft0tc91dbW7t+//4UXXujuF2o3ceLE2bNnd+IcZI8xNDWcnzcVAIRCYedqzmVkZDBznnFxccz+sszMzDse6bQ6KSl86o//JcK5/wW+DQQ/J3zqj49+M01tIFXGiZsLp5WFDCwL9b25aHr31SbtV2uEnp6eP/zwQ1xcHPPHEydOODk5ffrppwDwyCOPPPHEE6dOneqBMJiq0D1DLpcz5Zfq6up67EUf7MqVKzNmzFCpVE8++eQnn3zS6XG4XO7IkSMHalVjtC2zClW5SlJbX68tKuAPHWHCaBFjwoQJtzdkrqqqYrZmAIC3t3dtbW13BzBgwIAFCxZ008r93VpaWpYtW/bUU08BwOXLlxsaGpgiU72Hoa6mduWSa4XXBSxCLBAEBgb6+vrGxsbGxsZOnz69I8vker1++fLlcrl8yZIlP/74I4fDSU9PnzVrllKpXLRo0a5du24vetcJrrb8d2f7/jupWOUWCgAgsIMxL7J5Iich8dqMgV0ZmSH9aUvxN1+qNCpvPhdouujyZduXnwl4Y7XksSe7Pvgd+s5iZgdkZmbq9fqWlhbmj0VFRSNHjmSuPTw8mpubzRdad3F1dV21atWqVauY+gBmV1ZWNnXqVKlUOmfOnB07dhC3bbx+KDU1NcxR+qiFjw0Rcn343DYDdUFFqtKOmzZgdE9eXl43btxgrisqKpjeLP2JTCZjTuYBQGlpKbMBu/fQV5bXPDP/j6uFT5c2kBQt0artbW3Ly8u3bdu2cOFCFxeXuLi4zZs3V1RUPGAQLpd78ODBN954Y9euXRwO58iRI9OnT1cqlUuXLt29e3cXsyDjX48Oen/+UNHIR62NGv6VPUTB3mEgTx1TK+azuziyrrxEuvWrvFbpEamaeSSxVXVFKm/ZtF5fc7PLgd+pXyXCqKioFStWDBkyhPmjg4NDe/LT6XQm+YdHD1BbWztlypT6+vrJkyf/8ssvdxyTeCixsbFTpkwZM2bMpLkLeH4BkyRCAEiVaVRpx0wXL7qvRYsW7du37/Dhw+fPn3/55Zdfe+01c0dkenq9vq2tra2tTa1WmzuWv9CVFdc8tyipqOzF8iYdTS9xsj46zOPG/v/m5eWtX78+PDycJMmUlJQXXnhh4MCBfn5+K1asSE5Ovucu1uHDh2/YsIHFYiUnJz/66KMajWblypU//fRTV3427/DmTN+mrVO3an4b3ZhJt1XEF++0Ljjb9WHliftovf4eXzAaFSm/dX38O3XTlGtvIJVKQ0JCSktLdTrd2rVrP/30U3NHZHpFRUXMhVqtNu+OSqlUytRtMvk5v5ZvN+wNcAUAbz63NNhHV40bR3tCc3Pzt99+++mnn3ZTX0DzKisrGzhw4LPPPvvss8/GxsaapImgSZBFBTcmjf6PjxObAABY4SIpCxukPPGXqoeNjY379+9/4oknbG1t23+Ti0Si2NjYjRs33l0Dfe/evUzme+WVV3788cfffvtt8+bNGzduNFWFQpqm61e/+qm3IwBMsxWVxwR1slr3bWpeeKI02OcbX6cAIe8RW/EjtmI/AXenv0tpsE/dmytMEvPt+nMipGn6+vXrK1euXLx48ZYtW0z4r47uoFKpwsPDAWDYsGEmqbx1O7Lg0vXRPnYcNgAcDXRv27PTtOOjB3j77be9vb37Xy4sKyubP38+c33gwIFekgjJq5fLY4LWeTkwM3UrXCRl4wKUafc9QW8wGPLy8t57772QkJDblyF8fX1ffvnl48eP63S67du3s1gs+LNCvVqt/vrrr7/66qu9e/d2vOXO35L9tvfkMA8AsOWwioN9tMWFXRyw4V+vlwYP/MbX6U03O6aj/Yuutjv9XUpDfZvW/8skMd+uX02N3i0gIGDz5s1vvvlmTk4O02ekX7p48eKePXuuX7++e/duswTw9ttvZ2VlDRw48NixY/b29qYdnB84gu/qOlEiBIDjbWpcJuxJ9fX1lZWV6enp5g6k/9Ocz6ldsXh3WdXamy00wCp3+3f83Qds/F4cHXu/p7DZ7JCQkPfffz8vL6+iomLLli2zZ88Wi8XMqeIpU6Y4Ojo+//zzFEWtX79+/fr1ra2ta9asmThxolQqLS0tNeGPqjBknBefM4DHaTNQpRqdJj+7iwNaTY0jRMK7Hyf4AnHsjC4Ofg8mT629UGJiIgCMGTPG3IF0F51O9/HHH5eXl/fYMaxLly613yUcP368sLDw0Ucf7b5+qo2frP7O1xkAgsT80lA/U/UORX9r586dADB37lxzB2JiSqXy7NmzzHVNTU1hYVfvYLpIlZVWNn7IW+52AEAArPWwL48aobmY14mh9Hp9ZmbmO++8w7Qcd3BweOmll5gvqdXqsrKyuro6pVIpk8lM+g7oimlh8fZiAHjf06HurX90dTiKql4+N2mI2w/+Lswd4TY/56NBA+tef84Uwd6pn98RMqZMmSIWi/Py8qqrq80dS7doa2ubNGlS+za/HnDu3Llz584x17///rtMJvvtt9+Y6r3dQRw9JdJGKGQRl1XaRq1OnZXW6aFoo4GSy4CmTRddf8ZUNklPT6coytyxmJJYLB437lZJMDc3t/YddmahPn2y/o0VX1XUf14jZRPwibfjcn/PAd/uEowK6cRoHA4nIiJi/fr1eXl5zz33XEtLi0QiYb4kFAp9fX1dXV3FYvEdNdW6ThA8dqyVAACylSR5PrurP2IEIYqOZROwrrp1c70MACY72o5e+LjL+m6Z2LOIRCgUCpkK7ikpKeaOpVsoFIqamprBgwcLBILKykpzh2N6wtDxYmvr8dZCCuCkTK1K78zsqKG2uu6Vp2+ED6uYNrY8IrDp41WUUmHyUPsZPz8/Ly+vlpaWa9eumTsW06Mo6siRI01NTfn5+QUFBWaJoXjvrvx/PrOqpGZTXRubgM+8HRcN8nbf9otguAmq/s6ePRsAemZmWxgcNtZaAAA5CtIgbdWVl3RxwKpTJ5KlqjJSX0bq2HZ23n+cdVq1juiezf8WkQgBID4+HgCYOdL+x9fXd968ee7u7kuXLvX29u6ZF922bRtzApo50d+tCB5PND5qiq0IAFJlas3ZdFr3cCWP9ZXlVYtn/vHHH7ReR+t0Wo3m+M//rVo8E3Ph34qMjISe+mXaw+Ryub+//4EDB1JSUnqm2sYdlH8kFn+85v/KGva3KNkE8aWP87xBA922/MwbZJo71MjISDabnZOT0wNHRISh4wbyuc5cdovBWE7qNee7tExIqdUH0jNPy0kAELAI8aTpbFsTbz64naUkwtmzZ3M4nJMnT8rlcnPH0vckJSV9+eWXAPDaa68VFhYyDz7//PMJCQkJCQnTpk3rgRjEMVMmSYRsAs7ISYVCqck981BPb1j9CqVSfFF9q9iCwkhtrW4xNje2bFrfDcH2K+2zo+YOxPRsbW21Wi2LxRKJRK2trT3wikrS+Fly2di1Z4a9k7ls9cHMDz/bWSc9rdBwCeJRe/EEPx/3nQk8v4C/H6hjJBLJqFGjdDpd+0JG9+F6+7LtHf83O9q1/TKa7MyBHOKaWssmYJCQJ5oQY5oo78NSEqGDg8P48eN1Ot0ff/xh2pHb2tooiqqoqGCax5p28F4iJCSksbHxwIEDYWFhRqPRLDGIIiY5CgWjRHwdTWcqHq7EjObcaW1x0d2LFrROqzj0O64XPlh7Iux//72ZVhKBgYFubm5Dhw7t7perbNYMfiPtw99Kc8vbrlUr9tzgzvN+q5Ql4RLEiwMkYZ5uAVv3cD1NPKPTc59jCEIYPLZ9dlSTf64rP1nqrLQzCg0FMFTIa6WAFxJmukDvwVISIXR5dpSiqE2bNjHXVVVVBw4cAACdTpeWlvb555+3tbV9//33pgq1t8nLy2OxWE5OTuXl5Tdv3gSA8ePHt5emnD9/fg/Mx7KsbQSjx8Yys6PMIYoHbt+gdVpNdlbz5x9UTh9f9cLSc21KqcEoM1D/vNH0zxtN/1d569YQjEZKpezu4Pu0gIAANze3xsbGoqKizo3QXsCMJMkrV64w14cPH7506RIAMK0ozcLZ2XnJkiURERFLlixp75fZTWgaZn6e16DQqS/th7T3IPMTKmu9Rq8rm/z1t6OCXh8f+uqxDGtf028368kbekFIWJiVAABylKSxtUVfWd7podRnM1oNFADMsBP/3+wZXGsTb+25U3dsRe2dSktLAUAikWi12k48XafThYWFMdc5OTkrVtyqblBTU7N7926VSvXzzz+bLNZexmAwPKB3fI9p2/tj6jB3ALDlsK6P9tZcuXD39xhlUsXxQw1rX8sbN+Tf3o6vDLANEvMlHBYAfODpMEzEY7ZiZ4/0jLIRlgb7lI0bRBsNPf9e+pZFixYBwObNmzv39PZmYeXl5fPmzaNp2mAwFBQUbNq0KTs7u/1HqX9LL2yxfuYPmPoFeEXC4ynw+CGIXAMBs1iLk+c9/mX3nQhqbzjVuY5LD0Vber0k2MeRywaA1GHu1U/Ek0VXOzPO9WslwT7OXDYAHB7qJv3vDpOHegcLuiP08/MLDAyUyWSZmZmdG4GiKJVKpVKpSJJkHpFKpR988IGLi8uJEyeYDVr9EpvNNmFxwk4Tx0xxE4rsxbZtBmq4MdLnraR5L//73xu+oChKX1Ml+/n72hWPV8SGXnl95fafdr1ZWLn6ZstXdW0XVVqZgfITcAWsOyuAE2yOaHwMsLpaILjfM/ldBZvNdnBwIEkyNTVVLBbX19ebauReK7dcptXR0FgAHmEABACA+xhoKqQI1gXHEWxbu256XXt7+xEjRmi12h4oLE7weCyCFSoWAEC2giSvXq55er5s308PO44q61SBWtuoNw7gcQKEPHHEpG4I9i/M/9utJ8XHx1+7di0xMXHy5Mkdf1ZBQUFSUtLzzz9fUVHx0ksvAUBzc7O7uzsA2NnZbd26tbvCRX+ltXOZF7BWrvodCn/Xk60NSjI5s+SUUTnu7Cn3ymtVpP6ETH2kTXVBqWXmTFkAA3jsMCuhLYe1xsMeCEL152yqiMWaYW9FiESOb79vvjfUZ3QuERYXF6enp8+YMaO+vp7pr6RWq4VCIQDo9fr09PQxY8bExMTk5ua6urp2R9i9Dw0E8ZeVM6YuWjd/FIuOjr506VJ6ejrz79gdSJL8NSFBvGNjeYuCBhoAcpTkY47WNKlp3fSZYHgQf9iojo+mzkpLk2kAYJJEyHX34nqboKnTg1lcIvz0008TExO/+uqrB3cIoijqwoULycnJ+/btY5ZGvLy8fH19f/jhBwDIzc1lKm6gnvT6fwtvsOwNnuFQ+DtUZYOVm94jWl7064rLan11dRl5q1Y9n0WEigUTJcJQK0GgtZAMHO08eZpD7HR9bfXT771ByaTA4Yq02iXDRrp8sonj6mbeN9UnDBkyxMXFpa6urri4OCDgQXsay8vLT58+nZWVdfToUeZI644dO1xdXRMSEgDgxo0bb731FgBwudzHHnuMecqYMWO6/x2Y31g/CZ/L1jmPgGv7YeBEAAKqzoDLSDaLiBjcjQcDACA6OnrTpk3dukzI5XLj/L0/uVmT2iS/qTUAQLbi1gEnSqtp/vIT9217gdWhCUhKLtNePp8u1wBAtI1QFNntt4NgaYlw7NixHh4eN2/evHjx4ujRo+/+BrVaffTo0aSkpJSUlPYWTk5OTnFxcX5+fj0bLPoLrVK1K+MmSRHgMAQEtqBpgbrzULDPqFMwWzjsOOxJEuFkiSjSRigSiYRjxoujYsUxU9n2DswInAHu3imn9RVlhqYGrqcPpsCOIwgiKioqISEhPT39jkRI0/S1a9fS0tLS09MzMjIaGhrav+Ts7BwVFdVjB1t7uYgAex8n0TXdYKPLKEj7ADh8YHFhzD/4bOLdON9ufeno6GgWi3X27FmtVsvn87uwaE3FAAAgAElEQVTjJVgs1o87dhxslBardc5ctj2HraaoqddqpkhEE2wEYeezK6aECkPHiyIni6Mms2wkDxhKfS5TqtVfUWt5BDHOWiAKj+mOgO9gWYmQIIiZM2du3bo1MTHx9kTY0tJy6NChlJSUI0eOKJW39hAOHDgwLi4uLi4uJiaGw+HQNP3RRx8xX/Lz83v22WfN8AYsj6G+Vn0mXZWRej2vkOW8EuovQU0O6JTAEULdeQAAKxeuW+hH7PI5AjXf3kE0IVocO1M0LpLg8e45INfHj+uDn2keWnR0NJMIn3vuOQAoLy9PTU1NTU1NS0trampq/zYXF5eoqKjw8PCIiIjg4GBm3mXmzJnMV62trWNiYswRvvkRBBx6K3TC+2dah81WB8SBopbVcIVn1GwaJxrq/vft5rvCwcHBycmpoaFh9+7dTk5OzP550yosLPw88XCNWufCZe8bPMCJw44oqCon9VtJ2dYGmR2HHWXTHF1eHfVHii2Pyx8cKIqcJI6azB8yHO6amVNnpaXL1UYaJtgIxEKRsJsPTtzS3btxepvDhw8DQFBQEE3TZWVlGzdujI2NvX0nSGBg4HvvvZeX15lyt8hUtKXXpT9srl4+vzRkYOow93fc7YKsREC0T60QAABCB5j5HTx+SLD44OWPP9ZcyKWx01a3YY462NjYxMfH39G1wN3dfcmSJdu2bWvvjonuR0kavkguCZn3jch5KACsDRjU+Mma7n7R5OTk8ePHA8CcOXO6oy5/XV3dsMBAAPATcM+M8CwN9jk7wtNfwA0U8Z5xtvEX/K8oGgtguIi3wkXyS4BrcbDPjdjQhrWvKY4fMirkNE1rS4pqVi4pCxk4214MAGs87Gtfedrk0d6TZd0RAkBMTIxIJLp48aK/v39ZWRnzoEAgmDp1anx8fFxc3IABA8wboSXQ5GTJ9+/S3ShjOzlbz3jUauajBJtDGw3k+RxV2nFVRqqhtrqE1B+Rqk7K1AVqHfMsgmDRToHgFQHu4+DIi6BpBa4IAAa42Iz4v/8z6xvqn4xGY1FRUVZWVmpq6okTJwCAJEnmJO6AAQMiIiJiY2PDw8OHDRtm7kj7DDGf/fos/0WH3t/AqfsSoELaQOZ3e82X2tpaBwcHACgpKTF5zdjKysrYyZNLy8qGCnk/DXKx57CrdYZlJfU3tYahQt5KV8kqD/sqrSFLoTkp02QpNAVqXYFat7VBZs9hh1k3TSqtmJx0QMLjcn389Ddv0Aa9kYIMuQYAYiRCQ0uzaaO9H4Lud9Ui7okkydOnTycnJ//666+1tbUSiUQmk9nZ2cXGxs6aNWvOnDkmr8WO7qfpk9WNyQf4Oi0zJ0LyBNYuLoLRY48lHaxvkQIBSa2qIo2uSX+rhI2EzZpgI5wkEdq7Dn9p4CsaggcAkLkOqs7CmBeEAY8kvTs+drij2d5P/8L0emUW/DIzMxWK/9ViFYvFKpXqmWeeWbNmjY+Pj/li7POkO74+9vkni4rrA4S8w4HuPsdy2Pbd+x/42rVrw4cPFwqFV69eNeG/XXFxcezkyVXV1cNFvB/9XW05rGqdYWlxfbXOMFzE/2mkr72VyGnNekNDrfp0mibvrEajyVeRJ2Wa1DZ1jc7ADMImYKiQN0kimiQRDRPxLqi0C6/XefI5p4Z5EAKBy4f/EU+ebqqA76ef3xG2tLSkpKQkJSUdPXpUpVIxDzo5OTU1NQUHB2dnZ/eG43EWRZGUoDx88NXCqvc87QfwOACw6HL5j/7qs5evHWlTp7apdX9+MnPncSJthJMkwkgbocDBSRw1WRwz1ZkX8MTOQqNGo3ML1Vad5VRlfWnTONn/vp1LUbva2lqDweDl5QUA169fd3Z2trO7dXbNYDBcunQpNTX19OnTp0+fbmtra3+Wr69veHj4oEGDAEAikbzyyit6vR6zYBcJgsNGiflCFlGi0bXoDS7nc61iu/d3fWBgYGBgIIvFevBu+YdSWFgYO3lybV1dqJVgh5+zFZtVRuqXldQ36I3jBvn98o/lDiNGi6NimdV6ycJltE5LXshzzTk9MT117Y3S6xpdulyTJtPkq0jmNnFTXZsth+XO4wBAtI0QAGiSbN74Kc8voLvX9ftnGqioqDh27FhycvLRo0f1+lu76gMDA2fOnNnS0rJ69eohQ4ZcuXJFoVC0/y5APaN121eU5s5C+DsaZVvqZcy1A4c9WSLy5LNXutry3L1EkZPEUbGC0DCCzQGAWQD1wW5H9hzJraxbTxDQcHmsi1R97rQ4ZkpPv5O+5vjx4zKZ7OWXXwaArVu3zpw5k8fjMbs9z549e3t3giFDhkRHR0dFRcXExLi53dpbu2PHDuacg1m6NPQzguFBPL5glJh/TkHmK7W+5891dyKUy+Xr1q2bM2cOAJSXlzc3N48dO7YrA164cGHa1KlNzc1h1oLtfs4iFqtQo3uypKHVYIyOjk5JSbGyunMHEMHjC8PChWHhDv98R19z0yk7a3RG6ors02pSe0iq/K1FVajRtRmoNoNOwCJ+blYUanSTJKJwtc4wL5br7iUMCxdFTn7APriu6JOJMD8/n2m+DAB5eXmhoaHM9dWrVxMSElJSUvLz85lH2Gx2eHj4ggUL5s6d6+npCQCJiYlWVlaRkZEnT548fPjwkiVLzPIWLBOt1xsbb9UQ+bpeJmYRANBmoKZIRFdUuhFi3nARb4JEfFniPGPhY5JpcTzfQXcPwueyZi+cOHLr68fF/FwlmSHXuKcfx0T4sKqqqpYvX97+R19fX2bBb+LEicxPyu1aWloqKysXLlzo4OBQVVVVUVGBN4VdQfB4ghGjx1Y2nFOQOUpyVl63LxO2trbu3r2bSYRXrly5fPlyJxLh3r17FQqFk5PTuXPndmzf3iqVxkiE3w505rOIArXuqdL6NgM1ffr0AwcOMGUTHoDr7sWd62UzdzFNai4nHSz66P2PrEQ1Wl1qm+bnZrmOogkC8pXafKX2c5B68TkxVa0xhdfDfv1ZIBIJRgWLIyeLJz3Ccfnrfg7KqLtRRikVPN9BrIesTdonE+GKFSvy8vLar7/88su9e/cmJyfX1NQwD0okkunTp8fHx0+fPr29OzOjoKDA3t4+Pj7+5MmTiYmJmAh7EsFm0XBrZmaRoxVTS/Ccghwl5v80yIUfONJ6xhxx7IzRTi4PHoclEglDxk2pbs5Vkqlt6vj0VKCMWCntdiRJ6vV6sVgslUqZjRIA8MMPPzD1BS9duhQfHx8ZGRkUFMTc/Dk5OT1gNB6Pt3z5ch6PFxERkZiYmJ6ejomwiwQhYePS0zbVQbaC1JWXGNuknaiyZmioV2eeMMrbuAPcRVGxLHH3HsNYvHjxf/7zn8uXL2/fvl2n002WiL72deIRRJ6SfLasUWmk4uLiEhISHuqoIiEQDomf+3lwSOPSOBcO0aA3QjPESET/9nY8q9BkKciTMvVNrWFXk3xXk1zAIkLEggmV9bHpaX4bPuQFDBVNiBFHTOSPHK38I7llwweUwUCw2bSWFMdMc1rzScf/QvpkIrzDL7/8smXLFgBwdnaeNm3aggULpk2bxrvP7fOLL77I5XK9vLxeffXVI0eOkCQpEAh6Nl4LxmLzA4ZqC68AgBOH7crlAACbAAAg+EL37b8Qgr/5INlOHDNlavrJT6pb0+Uasq2VvHxBEBTabXH3PefOnTt16lRcXNy5c+dmzJjh6+sLAMuXL2emRl9//XWCIDIyMjo4mrW1tbW1NQBER0czifDJJ5/svuAtgTAkLEjM57OI6xpdm95IXsgRT3y4vp6tW76U7dpGEwRNkiyRCD5d4/zhlw+YGsnOzmYK3dXW1j7yyCMPGzBFUR988IFEItm2bZter58kEX3n68wmIFtBPlfWqKaoRYsW7d69uxO7Lvh8Pt8/gPX0P6RbN6XLbhWUseWwpttbT3ewoSjjNbUuS6E5KVNfUGqzFJoshebzGqkDhz22vMntVNYo0YaZznYKve5EqyreXkwD3NDq6xN/jy4t8tiT0sF51D5ZdJum6YV/oml66dKla9euzcvLq6+v37VrV1xc3P2yIADY2tqKxWJvb++RI0cqlcq0tLQeDByB4xtrWQKhG4/D+XPR3ovPZQlFdk//o+NZEABE0VM8+NwAIU9hpLIVpCrtWPfE21cFBgaSJBkUFNTU1GTCHWH9uElvDxOMDObzBSNFfAogT0VqHrKNbdue72X/3fFmcTVNagDoZrn80+LqhtWvkFcu3O8pYWFhTCftt99++2GjNdTXKlIOzHWwmiAWDBLx4+2ttvg5swlIk2meLmtQU9Szzz67Z8+ervxPE4VFGGnqjEIDANESIcHlCseO9zly1mt3UtQb774yOXrfEPesEZ7rvR2n24nFLFaLwXikTbWzQf5WZfM/rlfnydWHpbe2Q1aQhuw2pb6uWpawu4Ov3tvvCOVy+cGDBwcMGCCXy8ViMfNBhiCI/fv3M98QEhIyYcKE9t54HRcfH3/p0qXExMROfDhCnSYYPcZp7fr3170LQFMakuByvx7iaR2/0O6Zlx5qHI6TC3/o8Nia1mKNLlWmnph2zOFVPEr4Pzk5OY6OjtnZ2c7OzhRFAcCgQYM0Gg3z1bCwsM7VuR41apStrS3TlpLZgIo6h+Dx+YEjxlU25CrJbAU58/xDJEJap5N+9zlFktc1t47Y6mkoJ/U0STZv+NDjp99NGShlbP78A3nifoLFtjYarPT6PT72VmwWAXBCpv5neZOOpleuXPntt9+yOlZK9H7UWel5Sq3cSA0ScD34XLedCYJhowCA7ejEDxxh9+w/jTKpS+5Zv+zTj6WnXqmqOdqmLiF1OUpSZqCOtannOlgBgJEGAGCqftMajSL5V9slz3Tk1Xt7IrSxsXn88ce//fbbCxcumDZjxcfHf/jhh0lJSd99950JtxSjv2X1yGxhWITqxBHdzXKOvZN44lSud2dqLYqjp8bm5X1XLzvepn6vqlJ3o5Q30PR9TfuoWbNmzZo1CwDCw8OZR27/sNhe8PphsdnsiIiIlJSUjIyMpUuXdj1OSyYMHTc28zQA5ChJbUmRUSZlSx60TKivuanJziIv5qrPZlAkSQFoKfq0XAMAUiPTVoXWFhbQRgOzxfp29vb2y5YtY66HDRvm7OzcwSCbP/9QnvRrqUy5p0mhpKhIG2GcnRgA/mhTv3KjyUjTb7/99mefffZQb/ye1KdPpd86Ry/iBwwV3NWtgi2xs4qdYRU7w2nVugHXr0ZmZ6kyTpCX8+u0+ky5ZqiQV6DWvl7RBACNemOYtQAAjNKWu1/onnp7IlSr1Zs3b168eHFbW9uNGzeYB2fMmNH+DbdfP5Tg4GAfH5+Kiorc3Nwu7iRGD4ttZ28zv6vblMQxUzy+/rctm92gN25vkI35euP8/3xjkvDQAzCb49PT0zERdpEweNxoMZ9LEIUa3cFmueaZp974cY/wr6cOmOSnyT6tyTubWVWnNlInZOpqnbFSq//Q08FIQ63OAADyW4kQAGgwGuGuRMjUxmOu/f39/f079JHRUFcjT9rfpFC9Xdn8hY+jM5f9WY1UaaQWO1qPshZ6uLosWvbk+vXru/bXAABgbG7UFl9La+84ET7xQd/NYvGHjuAPHWH71EqjtFXyw+YB+36q12iGi/hfDXQCgFMyzSW1FgA4Lh0trN/bEyFJkqNGjWpoaLh9q/eHH37Yft1eCLsTZs2a9c033yQmJpo2EVZWVlpZWbHZ7Obm5g7+h0OdwPMfLPD0Dr3ZktqmlhmppvPd3ncUAS4Tmg5/SKCAzR4p5uUrtWcUZEDumePTxj/yw35gs8lL+Zrs05rcswaZtESjy1aSmXIyR0EqqfaEB5fUWhGbWOhoDQANemOuUgsAbHtHgmey/hLqc5kAxDGZeo692IfPBYA33eyeLm1Y7Gjt6eF5MS3Z1tbWJC+kOn2qTqsv1uis2KwQK37HO06w7eztV76mOPgL/Dnt344lEkkWPNHBcXr7Zhl7e/vY2NjRo0d7eXmZfE2C+YjE1E7snNWrVzMXGo1m3bp1zHVNTc13331XUVFx4MABzV3/PMiEHKMmT7YRAkBSqypS0WxsbjR3RP3f6NGjbWxsSkpK2k8rPaz2w/s0TZMkyVyXlZU1NzdLpVKTF8PstRo/WgUEjBLxAYBPEJTR6KaUVT8+s2rhtPP/envHL/teuFg89nLVjMLaD6paT8rUSooSs1mjxfyVLpKDQ9xeHnBnEmIJRXZPv2DCCClZG63XtRkoO86ts0lWbJaaogEAjAZTZUEAUJ9JPyXXAECEtYAvseWPCOr4c1kikfMHX9iKxU862wDQADBUxJ3mbMcfGWI989GODtKJoPuN6OhoOzu7q1evlpSUdG6Eo0ePMhc6ne7kyZPMtZ+fn6enZ0BAgEaj0Wq1pokV3YtsaBCLIMRsVp3OUKujVRknzB1R/8fhcJh1x46fvrhDZGQkc1FeXs4sXNE03draun379q1bt54/f764uNhU0fZauuJC9Zk0oKhhIh4AXFBpg8SCo23ql8obQy/fnHi1es3NliNSldRgdOayp9uJ13jYHxg8IHHIgGd93SeEjY169W2uu+fOYT7MaI4c9meDPUVRkzt+D9QRHA9vlkDoK+AW/bkrp5zUe/I4AMDxMNltCW0waLJP3zo4IRGJxkfdvcb5YOKJU/2+2x0THAQcLsHlutnZT/jHywM2fd/BVsDQ+6dGuxWXy33kkUeYw/ivv/56J0agKOr69esA0N7F0Gg0fvHFFxMmTDh79qyTk9MDDnKgrhsyM461ceB2rV1ZU+V0uyWc36ymV+R/+cTQgU4ic4fWn0VHRx85ciQ9PX3x4sUmGZAgCDs7OzabzeVyhw0bVltbe0f73/6BNhp0xYWa7CzNxVxNTpaBJAvUuhqdkQAo0ugeL65r/04PHmeMlSDMWhBmJfDkc9gOTsLRYwRBoYKg0Ml/9vCzXf4P2wN7FId+N0pbhB5e7oueetiTiH9LFB4NAFMkwoRmxdYGmTOX/WuLcpW7PUsokixcZqpXIS/mkgr5WQVJAETZCEUTYjoxiGD0GM9fjtCkhtbpHtz4954spfvE/ezbt2/RokVRUVEdX/MwGo1nz55NSkpavHjx0qVLH330UQDQarX5+fntN4WoZ2j1VNjK/YVl13QOQ0EgAQBW0xUrr9FZ748f7mFt7uj6rXPnzo0fP37IkCGFhYUdfxZFUQUFBT4+PiNGjFi4cCEAyGSytra2/fv3UxS1b98+o9FoY2NTW1v71FNP9aUyFxSlyT1DXrkALJYgaIww+C8bDm5PfuT5HL1SWajR5SnJfJU2S65p3+TCJsCazRpvLQy3FoRbCz35HLajszAolEl+92xg2zNUp441rHnVoNHkq0ilkQoS8x2srESh41w37jRVSIdeWXE0Yf/XdW3uPE7KMI8Rqbnd3YvjbhZ9RwgAM2bM4PP5WVlZzc3Njo4P+ttvb+SUkJBQV1cHAGw2WygUMkuDMpmMyYioJ32WXF6il+gqM8Hag0mEVP52hfOm+RsvFG2IMnd0/VZoaKi1tXVRUVFtbW1OTo5YLJ4y5d4FTSiKKiwsZDoanjx5sqWlJSEhwdbW9qWXXgKAmzdvfv311wDAYrFMdXPZwwwNdbUvPmFsbKA1GhqAJRRyffxcN2wx1FaTF/PUOafJi/k6UnNJpc1RanOUZL6S1FD/u/cYyOeOtRY06Q0nZZrHHKzfcrcDguAPHub84Rc8v15xTyyeOHXAph+aPlkTVlNJcLjAYkmWPmv3zIsmTMxn0tJKNToAmGUnPsS1CerxLAiYCK2traOjo48dO3bo0KF7Vo1qampiGjkdO3asfZHfz88vPj5+7ty5x48f79l40V9sOVGpNt45pUHTUCPVXKlSjPDEm8JuweFwxo8fz/zUCIXCioqK2xOh0Wi8cOFCenp6enp6Zmbm7U2dvLy8NBoNh8Px9vYGAIPBYIboTYfWaWufWXiqpKxFZ4i3FwPAjxW1o5pbR82YYKSobQ3yi2ptjdZQqTVobtvt6cnnhIgFoVb8KBuhG48DABlyzUmZJltJAgDB5zut/qSXZEGGMCTM68BxSiGnNGqOk4tp700NDXVLWNrtSi0ARNoIa830xi09EQJAfHz8sWPHEhMTb0+EN27cSEpKYs5L3d7IacGCBXFxce29L3bs2MFcWFlZffXVVz0cOapvu7WGD/nbgCsEAFC3AACbRZQ1qDERdh/m42N2dvaECRNomjYajRcvXjx9+jRz8yeVStu/8+5e9gcPHmS+xOPx2ts89UWKQ78b2lqlekPzn02k63VGiUb/WVXrRbVW9+edHwEwSMBlFvzGWAkcuWyuu5cgKITj4S37+Xua1IRaUWwCrqi1KiPlFjefHzjCfO/pvljWNg/b0qEj1JknjrWpmwxGazarVmd46pXXTP4SHYGJEObMmfPSSy8dPXpUrVbfuHHjjkZOzB65BQsWzJs3z8PD447nBgXd2ubLZrNHjOiN/337N2shW64xAACEPA82HgAAf7wMAECDozVuU+pGzGlCZlH86tWr//rXv9r3iwGAv79/dHR0dHR0TEzM3U2dDhw4wFx4enpu3Lixp0I2PVVGKq1RA0CZVp8m0wDATa1hjJXgvEproGknLtuZyw4W85257H+42jId9QSjQoWh4ziut9K/7aInpd9/R+/ePkzIv6zWXlBp3cWW8unN2Nrc8tWnyiOJPBbBJYhoG+ECP0+nMePMEgwmQrC3t/f39y8pKfHw8Gj/JGtnZ9feyImpu496ofgQl1/O1urvepxgEWH+JjvkhNq197LPyMggCKKysnLnzp3Ml5he9hEREdOmTWNmPvs9+s+1EpWRajYYAUBDUTwW/OTvMljIs+Ww/pf8xkzguNyjuCvLxtbh1f/T5J0Lq5FeVmuzlWTswxQd7bsMdTXVS+Oy6xtDBdxptqJzIz3ylVraaKC0JEtohi3flpsIpVJpampqcnJyYmKiXC63tbWVSqWenp6DBw9etmzZY489hicfer/PFg85dLFR6hZM8/4sTOUVJeKztz0zgsvG+rGmodFosrOz09PT09LSsrOzb68RQdP0tGnTli9fHh0d3bkq3n2aICiUvJQPoBop4s93sAKAclIPAGG2VjZx8+z/+U4HWwwKQ8LGnsvZ3iDLUZLaq5cotYolEndv6ObWsPY1Si7/qKIpcYgbAFA07GpSTHaUtH7zueNb7/V8PBaXCCsqKhITE5OSkjIyMtrX6ocMGVJUVGRvb//+++/HxcXt3r0bs2CfMMCWn/Nh+GNfiwprlBw2AQCcsYu3PTNi3liL+6X8sHQ6HU3TTA9VjUbD5XJv76GjVqvPnz/PLPidPn26vf4L3NbL/sqVKxs2bAgICOh0Ce++zmbu47I9O+5+nCUUOby2mmXV0ZkkQXBYqNU2NgGXVTq13qC9ckEYFmHSSHsXQ1OD9tplmjLe8Tit1ysS9zu++a+ePytiKYnw6tWrKSkpycnJZ86cYY5Ostns8PDwuLi4uXPnDho0yM/Pr7y8/Pr164899lgX+4mgnuTnIspbF17aoK5oUtsIOSEDJWwW3gv+vT179qhUKuYYw6pVq+bNmxccHHzhwgUm+WVmZt5eFIlJfrGxsRMnTmw/ZZSRkbFhwwZLLjrKcXF1/njTtFX/NOq0QNMA8JKnI5/Pd/1ye8ezIAAIQ8KsudwhQt5Vte6iSuuWn93PE2H1TYLLo7VauZF6qbwJAPR/HmenjQZKIe/Eifgu6ieJMDMzMygoiFnMO3z4MNOSgjn5npCQ8Pvvv1dVVTHfKRKJJk2atGDBgtmzZ99eK2/27NkbN27UarUJCQnTp083y7tAnebvIvJ3wWoynVdSUjJp0qT2ORI2mx0SEsLsdomIiLCzu8cUX1hYmEAguHLlSktLi4ODQ8/G21uIo2P9fznS9tMW8kIuEIQkLML2yZX3XA58AJaVNX/Q4LE3m66qdTlKcmL+uW6Ktpdg2doBRQGADZv1ja8TAEgNxtcqmgEAaJowx7RwP0mE33777bp165hEuHbtWrFYvGXLliNHjshkMuYb3N3d4+Li5syZExMTw0wH3SE+Pn7jxo2HDx/u09vYEOq43377raysDAAyMjJmz54tFAoDAgKYDS+xsbH3TH634/P5YWFhzGHBOXPm9EjIvRHXy8dpbVdbEQmCw8Jy8n5olJ9TkNqrl2hSQwiEJgmvF+L5+BFWVqBW3fE4wWIJRocRXWhz32n9JBHeoaio6JdffgEAX1/fWbNmLViwIDw8/MHddyMjIx0dHUtKSq5fvz548OCeihShnnDq1Knc3NzY2NiMjIxXX32VeXDSpElPPfUUACgUCi6X29TUdM/PiA8QExPDHJy35ERoEsKQsDFWO1kAl1RajVZHXrkgHDPh75/WRxGE83uf17+x4hHbWzd/AhZrokRICIRO73744Kd2k/6TCF9++WWxWAwAtbW18fHxKpVqzpw5vr4dbX3OZrNnzJixa9eugwcPvvPOO90ZKUI9LTQ0NDExccSIEe0HZAHA1taWORprZWUFAA+bBQF7E5qOMCRMwuU4ctmNeuPm+rYJP3z/eD9OhACicZGu//72pX+9QRv0QLDERuOz3gNdPvuW6z3QLPH0vUSoUql+/fVXsVg8ffr0d999l6lVCACbNm1iuuCGhIS4urp2optEfHz8rl27EhMTMRGifiYnJ8fR0fHq1auFhYUKhcLa2trOzq69sPWAAQOYD5EPa/z48QKB4NKlS21tbSbsTmeBWDa2uWL7AGFdo17TbDBWF1wyd0TdThQ+0ed4LnntirGxnuvpwxs0xIzB9MnuE1qtdvv27Ww2m6KoF198EQAWLVq0bt269kR4+8fejlOpVI6OjjqdrqamxgIPRaF+jKZpnU4HAGq12sbGhs1mm2rkyMhIphj9rFmzTDWmZfp5+eL9SSmJrcpAES/eSdZxid4AAAX5SURBVPJxcZUJe82jB+t75wRkMtnq1aujoqK4XG5BQUFDQwMAxMfHt6/tM8senSAWiydPnkxRVEpKiqmiRag3IAiCz+fz+Xym7Z8JR46JiQGcHTWF2UuWveNuxwIoI/WL7ITqrDRzR2RB+t4dIUmStbW1AoHAzc2NmeQx4eDbt29//vnnZ82alZycbMJhEeqvUlNTp0yZEhoampuba+5Y+jajtLUiNmRpSb0Nm/W+p4OLgC+KiHF+f0PPH6qzQH0vEXarhoYGNzc3ZgcdlhhF6G+p1Wo7Ozuj0djS0iKR4K/szmv97ovybV+fbFPNc7ACgEKNrpUmYoYN8fz5MIGFrrpZ35sa7VYuLi5jx47VarWpqanmjgWhPkAkEoWEhBiNxjNnzpg7lj7M0FAn++8OucGQIb9VyrVSq78iVxvqa+W//Wze2CwBJsI7xcfHA0BiYqK5A0Gob8BDFF2nzjxJEwQAGGhaZqRkRorpOE1rNIpDv5s7uv4PE+GdmESYkpLS19tnI9QzMBF2nbGtldaSAFCo0X1eI/28RpokvVV4xShtMWtoFgET4Z2GDh06ePDglpYWnOpBqCMiIiK4XG5eXp5CoTB3LH0V18OLJRIBwAgRf52Xwzovh0WOtzqLcT19zBmZZeh7B+p7wPz58/Pz87lcrrkDQagPsLKyeuedd5giNahzRFGx8Mmaux9nicSSRU/1eDgWB+8I78HPz2/btm3jx48HgI8++sjc4SDUq5WUlBgMhhUrVlhbW6ekpPz444/mjqjvYYnEzh996Wxl9bTLrZ23o8WCR1wdxNGx4uhY88ZmCTAR3kNmZmZraytzjbtmEHowrVZbW1vLXMtksvafHfRQxNGxg3buCwsLAzYH2Gx3D48xq95z/uhLc8dlEXBq9N4aGxurq6sBQK/XmzsWhHq7ysrK/fv3A0B2draPj4+5w+mr+MNGuf/4G1BG2mDA+mo9CRPhve3evZvpxM2UcEMIPRiLxQKABzc7Qx3CYhM8U5bBQ38LE+G9vfHGG6NGjQKAjIwMc8eCUG/n7e09f/58ANBqtfjZEfU5uEaIEELIomGt0XsoKCjw9vZmao2ePXuW2T6KELoniqJ0Oh3T3dBgMFAUxcPamKhPwUSIEELIouEa4X3JZLLk5OQJEybk5eV5e3uHhYWZOyKEei+ZTHb48GF/f/+ysjIbG5sZM2aYOyKEOgrXCO+LxWItXrz4yJEj2dnZVlZW5g4HoV7NxsbGw8OjqKhowYIFxcXF5g4HoYeAifC+xGLxrl275s2b9/HHHx8+fNjc4SDUq8lkslGjRjU0NKSkpEycONHc4SD0EDAR3ldxcTFBEJWVlcnJyfiDjdCDicXiU6dOLVy4UKfT3bx509zhIPQQcLMMQgghi4Z3hAghhCwaJkKEEEIWDRMhQgghi4aJECGEkEXDRIgQQsiiYSJECCFk0TARIoQQsmiYCBFCCFk0TIQIIYQsGiZChBBCFg0TIUIIIYuGiRAhhJBFw0SIEELIomEiRAghZNEwESKEELJomAgRQghZNEyECCGELBomQoQQQhYNEyFCCCGLhokQIYSQRcNEiBBCyKJhIkQIIWTRMBEihBCyaJgIEUIIWTRMhAghhCwaJkKEEEIWDRMhQgghi4aJECGEkEXDRIgQQsiiYSJECCFk0TARIoQQsmiYCBFCCFk0TIQIIYQsGiZChBBCFg0TIUIIIYuGiRAhhJBFw0SIEELIomEiRAghZNEwESKEELJomAgRQghZNEyECCGELBomQoQQQhYNEyFCCCGLhokQIYSQRcNEiBBCyKJhIkQIIWTRMBEihBCyaJgIEUIIWTRMhAghhCwaJkKEEEIWDRMhQgghi4aJECGEkEXDRIgQQsiiYSJECCFk0TARIoQQsmiYCBFCCFk0TIQIIYQsGiZChBBCFg0TIUIIIYuGiRAhhJBFw0SIEELIomEiRAghZNEwESKEELJomAgRQghZNEyECCGELBomQoQQQhYNEyFCCCGLhokQIYSQRcNEiBBCyKJhIkQIIWTR/h98LNi5INrq5QAAAo16VFh0cmRraXRQS0wgcmRraXQgMjAyMi4wMy4xAAB4nHu/b+09BiDgZUAATSDWAuIGRjYGBSDNAqU4GDSAFDMTmwOYZmGH0MwwPjrNzoAmD+YzQcWZmOHyEBphPtRWHMYSkGYEm8LIOFhobgZGBgZxBgYJBgZJBkYmBkYpBkZpoO8VmDkzmJhZElhYM5hY2RJYeRTY2DOY2GQY2DkU2DkTOGQZOOQYOLkUuLg1mHl4FXjkGXj5NJh4+Rn4BRj4FRj4FRkExBIEBDOYBIUSBJUYhIQZhEQymISVGYRVGIRVGUREE0TUGETFMphE1RnENBhEmNiYWVjZ2DnZBIVERMUExL8BncUIj3Ljtz0HVLWbD4A4UyVnH5CepwVmf3NdeeD66bn7Qez3SzoO9F9h3wdi86w3PrAh7R2Y/efmk/1G+Ur2IPaho3wH/gSzOIDYUxJyDnQulgSz18S0HNgZXQpmB16cduBc6TKw+l3zjh54IXIRzFbO+XKAactvMDtp4rJ9fxJn2YHYHfsN7Y9kbgaLf9nRYCeUawo2ZwsXl8OqUx1g8bal6Q7y4TZgtur/Rof5nUZgN/ed2eDw6nEzxP0/9jmorpKF+DH3osPWq322ILax42GH3bEHwHpPFU9xuPrnFZhtpmR3oP1vJli998ldB9rDJ4LZixprD9iybwSz13w9ceD+Nzcw+2dV1IEIVz4wO5199v75e7zA7vQsdz+g92gumC2qtfnA4tBWMPv1pQ+2D69PALvNMUreQZ1BCSzuV/bS/vnqk+Cwtd7j5CAq9gyshv3qG4fEQEaw+TdkJjq8YLICswNVzzqEJFWC2fWyDI5/jrWB9Vo9euzQMF0NbGaGe5vDLLNAMFsMAHP1wzmMvCAvAAADmHpUWHRNT0wgcmRraXQgMjAyMi4wMy4xAAB4nH1Wy24kNwy8+yv0AyPwJUo6+rFYLwKPgcTJP+Se/8cW1ba6F0tk7EOLU00Wiw8ND5d544cSnz9f/vj3v7I/8vIAO/3P/5yz/KNE9PBW4qE8ffv+416ePx6fvizP73/fP/4qxsUE71DhX7GPH+9vXxYuz+Vm1Uhna/GEAMS9UKX1KTuOHMhhPrqWm1T3NgPwG1LLe7m1KmP2SeXGVVT60ARph0/pxGrlRpVV3LLordwj5qDe2gykeXPJkB4+ufaB4Ei/ulqjmQB7AAXkaAjSq+TdxRPgCKBWnwqRADQipww4g6QhCVeWonWodsnyZjoS76zUuHBFQa1nWvIqkFfuLcgx0ol8MqRE9A4A0sGxTndTzpAaSKoqTWUAoMRNMok4CgQJpym+Z2TOqHoGbKg5VWlsYIw6qfUhGTDKI5WbT1J4JB8+Uo9RHojOE57wfdfWyTJglAd96xLVuQXbbpzGnkC22sRmX31JirbP6iO0kNP7GCOaxFwbZ6pL1MdrM/vsxoie0RRZQMZwUUjZSIanHnXl4zpjJhhStmFZGcXKa3BDMkPH6tDmGN8M2gLa4KtTl+i8Zjw10x1N9hq+pjNpj5fQJMEkgfaAOsZ3zjHjJaYxeGTQcXg1VjTyGjuRmXYIGu11LQ1mdkULsHcMZrZn6HDK0ZQOzTCguaYYg5V/m6hnBxJv5N2kAiT2EObt2F2s3EaWkiqQ6IwBn4aHhplvmfgadUId0Z+obczd1DFTmlEmrWTSda4lNnuz1GdUqcc8YnGEnCTUZipSFAn1xkB2Xu1Cbuni1KiRo0Q0qIWuAxskR0aJBsxdiCMjcRXNkEaLJ6OD3CGSk8yZAnnR5OmCyVd4HLNlsn+7v/xygR1X2tP7/eW80uJPzntrHe28nda5nXfQOvt50+BU9LxO4ij9vDXWeZyXA8Pl9j7hDx6vmz4MOxq2I05lR8OiMsTY7jnoIeaOz/F6LzseB7+OKblsVAaL84z+DlbXBcngpdc9uAz7Z4Es0YLcZa1pcOVTxqUjjDtV0cOiJ8ZCbaS/05VgG5adcFxii/NOWfph0Z2zjE/LGSt0RWayMUfl8OpWRkPaSHZLoYvzwFxdhpYjf7lOpy3Lzl1XB8SMXabtsGzO2j8tZ/QBP5BNthoaMoeQm48FZwh5ZmGr99AD5yAEZ0hrm090/LW/4/z1cw/PDz8Bn9vniaEBg6wAAAKIelRYdFNNSUxFUyByZGtpdCAyMDIyLjAzLjEAAHicZZK9blsxDIVfpUAXB7gR+CdSlNEpSyane9ChCDq2KYqMefgeyUXNosu1SJOHnw71/PjlhV9Oz49f7l5OD/t3fx5On57uLte8/Hg5Pf3z39/jn76PF3wvt4xc8/+p7gTj++H9FI2FNQ5pTnqcvTFx8MGtkww/ztZckxWJoX3YSnC6yKFNfCQqtDmnjYNaaA9CiTTunmghHz5WbCPHwIyuLGiBmGjKcU9NU0ceZ2qeprKajNhjZaSzMaNrqAVkVrV0lTVKibvsPuboedxzY2UczjjFiESNq3VaGShQdF/jzLsL1O+tSRDuhRyruK1cbzIytpio7JHWhvmIAxruW97RmIkihVVjCFLaDBb6KhKRvPYZafYDB8ok2RQZBAdRDgje84YGxSrqxqlbKp3hIf7DTVm3VLBShzNKtDmxJFxn7cQVd0QmcAIKo9vtquOpmjDPiPYw7E1dGXsbqnHFZnb3wxpK167XUOwpkcEfsZlFCcKCVYbrdlcYbZDBlmI/EHKx5aNnGMt6EGQStmyMjA4+6IplrAwpfFmZ9MAkTDDXDsBYi18xCS2j4QnWvp1P2pdy2EyDl4F4eL4z3dCOXdMarhg1GlHslyWugod9d3x9e/3++dfrz0ltHS+vb9/w7CbfIuEpJZKpt8ho2i1in71EfXrRlBkl0jlKpc4skU0uMCyTC43y5ILTJ1ecyQVHJxccyBQcqBQc8cmFR2JyAaIphUd0SnXHplR7+pQClFOqP1CqBsHYghRTCtKYUokgVIiUphYkn1qJxtRCxDG1EKlNrUgorkg5tS6tT61bw6ACpVAuUIK3UB/ReP8NSt5yg+1y74oAAAMYelRYdHJka2l0UEtMMSByZGtpdCAyMDIyLjAzLjEAAHic1ZJtSFNhFMfP7ra7TTc33ZtzLm++NfMtX5qZzj2LFHpB0D4kpDh12cIiKrJSKIUQTaIX0UYMksTAULK+iGX6gCIGlUZ+MIVIC0ZhmBpRWtp2Zgl+8XMPPPx/5zzn/s957r1z/V3vwLNksL52erbJs2t4LHAeFayJGIwe4TMsQRWIfMr/G29UEWw4x5hZyzP8f+c+Xfdf67rBbpPjTco3acbDkMf739QfeABbAMLAcw8eA7ytwAv3vFuOL3EwfIFNIHQwQtYmlHKsyMGwESAScyKJTRwJ4iiQ+HF+/ka+VMZJo0EWYGRkcpArQL4N5EZQaG2KQAcTGMQFqmxBSlDGgHI7KGNBpXYwqjhQa2zqEE6jdTCaYE4bD8E60CWALhF0OyBEz4UkgT7UyOgNYEgGQwoYUkHFsHyBkBVJ2ECVWqNVaOsY8I6/9teNqJvp1+la6g3OOlvp4o1o5DB9J5X13Rrwsrm2gS47J595ufi5iU78FGLeFPthICJWYPHywh0xraj+hSwbt1O7O5h42f62muaFVCJ/4jfT/X2dWFPJDdL78hlkgXme1j7yxxr4+KA/L+KF2YuKIovl8fhNrCm/PpyVY4jGmmKFlHQy6Ziv2ucg7h5Xlpdf514m79OacDaZfjdJjJLiXexcHgktOI78fbCLZDaOYM1E0Ri5NM8ix00NEdPkSfS8299CzhPfnAHn3MS5uoxctjBFepOP4AxHq1aIyXkan21YEViXnDr0Tyxzk/TwQuRRaai1Y08qctuPg7Sh/wRy1sNB6rJfQw5taqDHqruRPyeN0gMtZuSl32XU1ShHLnxVN/BtmGDf1nYTHclzIRNJJ10tqUeuqxjPbG/r8t39STQZvuiH+ZKXMxY6nYb5uUm3pTehBj07Ym6Tofx85KdRDnIluw35Xk4vabxgRV7MaCQxh1LQ50u20KrYdQZ5tqCLlMbvRe45NUbeJPvmibyqsEaWD+G3s5eqrTPmevTJ6Am36pXdOINGn2I9PJuLec0fxQDtaBHSUs8AAARxelRYdE1PTDEgcmRraXQgMjAyMi4wMy4xAAB4nH1X224dNwx891fsD1gQb7o8xnFQF0VsoHX7D3nv/6Mz2hPtBiB6kocVd5akZkhKltGsP8vTwd+fr3/8+PfYP319gr3+z/855/GP1Vqfvh98OF6+/fb7+/H188vLT8vXj7/fP/86wo5wfFMP+RX75fPj+0+LHF+PZy8txqyTTzXGgL2Wun7HjqNERqkymtTjWUurGrMnSDs+iLQ+TOR4liLerHmC9DO6zl61Hc+1SG3dMp9xvDPmiFZdiAw188xno08pvXuPhvchTYclwE6gFoGb6IeU2RXYBDgItBI93O3QYmOKZh4nk7QyLQSpWRkeFplHEPigvXVlbPPpM0UugVqps3cxIHsD/VlwUUbvJWy0OZCmDO3QNEEakbWYGZwSMGfrGetCgWohlXiEkDatZqRLQHN4HH0IKYoZkeoolIe5DZ/Mrau2tIiE8lhpOn0EXddWp2TAsYATooy2qi2qeMrQRJJaZu2zypJ+zN4yhrQun9o6ipySNm81ZV2pTxS3Kn3VerTWPfXJBmooR/FoZwV3gTFB2kJKSA9ufeoUybaulMdLl8ptsIqiS0amUp5e0DLVCJxtdBsZsK3QeGt4b8XVZ09Dd9RQLzPUz36cbl3TfVOfUUxUXMm6YYS1dN/zkWUdaEiw7jYxl7IJQ31m6QGvfc0FQSulSDne6EorxVxSTnRvClVCwTuIZ6ZeXAYiZFAjlINrah0cdoGqS0ec+enVuw9UJXLF5nVm5FucuUZTY65a1HWkY8EaoYJpbdINQlXSmxUouvDtnJw2YqJa2HnpPITgb+fcbiQIu2syW4qcQMKM+Y8V0sCUi3TQeQWSR0VgsrPvcRCMTHunTpza6EljPeFBR8aS60IOEWvniVWjp0PJqRLaQ22AWhYBTt6aRqdIHDYdDUIOpKpauiFqFKWPOfqaDJVipj7bim7cOcfxqDIjKyanQmgQneheZNGH41TIgGPFdkyaGtS8YYRkR7VTH44aw01jSS6aMhSUZ5aKg0BjHQTot4z0kAU0Q0hZlY/TMFKXSx6c5BjrqH0WB5BpwQX1EQzs6RBzCWQo50ig395ff7m2nBeZl4/31+siw3963Va4tH3zsXO93/u5tut+gY+PvWzn0q+7wlrHdSXAx8d+jW6hx/v5TsOGY3pjdbTbWe2I0W8nsjDmji/8vB/jdr4K1nZ5YIbjmLfj0pnV/VgU5GVXjHkatk9dpDG522FmzFUuGhePMO6tqp0W26FxCikTlr1dZba07HSV6TLnnQ6vPbT4/aA4LVc+5BU70x39VA6f7l3YEh6f7nyM5MKil/gr54kd36aykADfO7V4WLYG1h6WKzpJBkl6fcWcYfG9d2POoE23HyfPtOy9O8sBRF77cvJMy/bjzBnU+ubQV84BLm/DSEitX57bw7L58f6wbH585QxWL8w8LXEfDEpBdH8Vq71wJ7h3uhzrza2hT8vmJ/xh2Wywk+99y/XPP17w/PQfAHxsOgA1wpYAAAMkelRYdFNNSUxFUzEgcmRraXQgMjAyMi4wMy4xAAB4nG2TvY5cNwyFXyVAmjVwVxD/RJFGKjeudtMbLoJByniDwKUfPoeaDYZIUswdiSKpj4fUl89fb3R7enn68vnrh/rdnj79/ILv66djOp+2rN+3G72823957YeV6fVfzrf7ydP/R9zP/hND+P704+mZxjQhv2RMYtLr4zMPEtV10QhnY1hkmJvqxUN20N0SYvCWsdVknahtstelY2+NDYsN8UUCCy+K8tGxbLkgs2gYDGvMcCcYfC2CwQeSOC6izU4nhMOnXM9z0ESsvyfeQhfYSde53UC/kQEYa/I6qdV1a1lisdyrwGKXhZXjnQcFXVhM2/vuxIiHk4wVQXIyEVWt8FJCke9UwQi0YWTzn/rX1AI1FggIG8py9XXBRItP/jlEpEqeKD2WLxSEJVvI5KpIDBqVDZF1K2qUkDJAmA0gHhZmSF8ibUMiZ17hZVjTiEo9qIIW1P20o5jwzxsmlMWhG1Ey10TtsAQ0iKOmTTqJY3qUTgh2LxcG6dEEc4G0mwidKNmm4VyBIKU1HPeS+y3oM+ShyYyUNlQmpgyX2FpuZfEdaDVi4FIDs6AS+nlX0IkxZz7CWI+ooeLVtBgTAsrRD80D58c95IwugCWiJs0Hy9yHWCXshDnGXMpn0yroQCcWxruaifEGEmHGSsjqAgwlIDQJpVOY1CsBJGbBS/XgIIh97pJZjYq1XctlOwYAb0NZqy/g47B6Yr61Gg41CCOAygHg98pl6Xk79bygJ00IX+/E/Bjw2ma9iz0JL+fD9dv3tz9+/evtz5yjli9v33+HT9Jjt5IfG095bHZqC1ppbee5HrtIf2xo5m47ymhhSY2DkxoHJTUQgWtDEU1qMDOpw2DbaASZGo/gmgaERA3IkhsQcXIjkp3ckEiSGxJpckMipOpMkdyYdCZ3jSB6F8mTGxTtlE4VKY1KKaVRKac0KpWURsUzpVGppjQqRqpGxSulK4V7GxV7am8epqNRcaQ2KpupjcootVEZpzYqxrZTaWqnstROtVIblXpao9Kd1qkkrWtlP/4GEbDeM7lsLFsAAAAASUVORK5CYII=\n", + "image/png": 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Rce/eva9fv378+NHFxeWX2sppMCi0Ixw+fLi2tvbXr1+/fPkib1vqQ1JSkomJyeTJk0eMGLFs2TIulyvT6QYMGPD69esePXqMHj1aU1NTGo00X19fOPDz88vPzx80aBBFNmJ+A5SUlOCh5PfdHo6JiTlx4sT+/ftfvXol67ny8/MnTJjg6OjIZrPLysrInh71QFtbG6S1EEJsNrt58+YU2fjbo9COkMViTZgwAdGbO/rvv//eunVrxYoVf/zxB5wZMGDA/Pnz6zHU0qVLt27d6uPjExgY2KZNmyNHjlBqqST+/v5dunT58uXLihUr+vTpIx5Zqjc7duw4f/68vIpYMPKC/iKKT58+Xbt2TU9Pj4xGKCsrjx07th5D+fv7L1q0SF9ff/To0SdPniTV/GVE69atw8LCtLW1W7Ro8fnzZ1Awrx+nTp0aMWIEQigpKSk2NnbhwoXUmfmbQyg2jx49QggNGzaMnukOHDiAEFJXV09PT5dyqMLCwk6dOpEv09PT9fX1pRyzdoRCYXZ2tkAgKCgoKCgoEIlE0o95+PBhhNDKlSulH4pa+Hw+HJSXl8vXkgZJQUGBkpKSsrJyUVERDdPFx8fr6uoyGAwXFxfpRxs7dmxgYCAcFxUV6erqQhWQjPj69evVq1etrKzu379vYWHx5csX6cdcv349Qmj//v3SD9UwUHRHWFpaqqamxmQyMzMzZT0X7EmwWKyHDx9KP1pCQkL//v3JlzweT1dXV/phaSYyMhIhpKOjIxQK6Z+9vLz83r175EtXV1cOh0MQxLdv327cuGFhYXHkyJETJ05ERETQb1uDB9SFKLkWaictLa1jx44IodGjR5eVlUk/YOvWrXk8HvnS0NAwOTlZ+mHpBDox9e3bV96G/CoodGgUIaShoTFmzBiRSCTrKM2dO3fMzc0ZDMaVK1dmz54t/YBt27ZNT08n000/f/7cpUsX6YetlsjIyIKCAjjOysqisI1L7969O3bsmJOTQwpe0AmXy4VHUuD06dM5OTkIobZt27Zp00ZZWbmysrJx48ZhYWH029bgoSc6mpuba2pqmpSUNHjwYHd3dzU1NenH1NDQEJdm5HK5jRs3ln5YOhk5cmTjxo0/fPiQlpYmb1v+A5fLvXv3blFRkZ2dnTS7oT+LojtC9F+Jmbi4uKtXr966dYvaKR4/frxs2TKRSGRpablixQpKxlRRUVmwYMHWrVsLCgqSk5M3b968adMmSkauiqWl5YcPH+DY39/f1taWwsHlK/FTLWlpaV27duVwOBs2bNDU1MTtomQBlMk+ffpUKBRyOJyHDx/a29tTmxLJ4XAmTJgQExNjYGDw9OlTslpXSkaNGkWK9UdGRjZq1KhZs2aUjCxBWFgYKZdRUFBw7949qkZWVVUdO3YsQRBPnjyhasyfwtDQkEwy2Llz5+PHj+H46tWrCQkJtra2f/31F4Xf94dgR4imTp3KZDJ9fX1LSkq6deu2YsUKDodD4fjBwcELFiwQCAR79+7dunUrhSOfPn26W7duK1eu3L1797Zt22bOnPnx40fSY/0ugCOUVxuQrKys9d8hFad0dXW/fPmybt26b9++9ezZc8iQIXKxrWHTo0ePrl275ubmvnnzRlNTc86cOcXFxRQ2Y+LxeFOmTHn37l3Xrl19fHwo9FWHDh1ycHBYtmzZhg0bli5dWosmsJS8evUKFN0QQjk5OWQJIyXAQkReoq+FhYXE9/orLpcLEj/v3r1LSEiIj4/ncDg0d8RT6IJ6oGXLloMGDQoNDX3+/PmMGTPc3NwmTpxI1eAfP36cNGkSl8s1MzOzsLCgaliAyWSuX79+7dq1TCYTIQQ9b6dOnSqLp6sHDx68f/8eIRQZGUnt+le8iKV2GQFZ0LRpUzMzMzgODg6GAxaLNW7cOIQQKB5gZMSkSZOsrKw8PT2HDRv25cuXnj17UjUyn8+fPXt2QEBAu3btfH19W7VqRdXICKFWrVq9fv06ISGBy+WeOXMmOzt7zZo1fD7/xo0bFM4iayZNmsRkMv38/Hg8njSZqPUmOztbWVkZIUTGmfX19S0sLJycnAYNGnThwoUePXrQZgx+IkTov9E5yMakZNj4+HhTU9OioqKZM2fKqHp3zZo1LVu2zMrKQgiNGzcOHm1lUVCop6fXo0ePHj16UO4b5FLEQqKqqqr/HbncDhQZ8YeSoKAgWHxIj1AoXLRokZeXl46Ojo+PT/v27SkZVpzMzMyXL1+2atVKRUWFzWZfu3bt7t27spAedXJyWrJkyZIlS3bu3EntyDo6OgMHDiwrK5NX8dKWLVs2bdq0adMm8qlXVVW1adOm69atGzhw4MaNG+fNm0ebMdgRIoQQPAI6OzuvWLGCxWJ9/vyZkLolQnp6uomJSXZ29tixY52cnGT0mJ+RkZGXlwcR9pYtW8IvmyxXp5AhQ4ZMnjx58uTJ/fr1o3xwuWwTYoUOuWNkZKSurh4XFzd+/Pi8vLyQkBAy+aveEASxevXqhw8famlpPXv2TEZPFZs2bTIzM4NfbPPmzYcMGVJRUSELjzJx4sQjR44cOXJk48aNlA8u35ZYt2/fvnfv3r1796haAEkDdoRIKBQePXoUIcTj8a5fv758+fI//vhDS0vLxMTk4MGDz58/r0fz3ry8PBMTk+Tk5CFDhri5uamqqsrAcISquJBfMPGkLowbN05NTS00NBQebWkgMjKyV69eSUlJN2/eJE9evHgRx0LpZMuWLWVlZUpKSt7e3jt27DAyMmrUqNGAAQMge+KH3VeqZfPmzdevX9fQ0PD09Ozbty/lNgMSLgRekhkfFKKlpaWnp6enp6erq0v54PBE/vjxY+nX/b89ci3ekD8ikQi6HzRp0sTBweHs2bOzZ8+W0J5XVlYeNGjQxo0bHzx4kJaW9sMxi4uLBwwYgBAyMDDIz8+Xqf3Z2dlMJlNVVbW4uJggiE+fPiGEmjdvXllZSeEs/v7+5BdPTk4OCwujcHAAHsrt7e0pH7kqsbGxLVq0QAitWbOGhunoh8vl3rhx4+vXr/I2pDYg1qeiouLk5PTgwQNzc/P+/fvDbjfAYrF69uy5evVqBweHlJSUuoy5Y8cOhJC6uvrLly9lanxOTg6TyVRXVy8tLSUIIioqCsmgHPbkyZNnz56F49jY2JEjR1I4OABx43fv3lE+ck2cP3++sLDwr7/+Iu9R1tbW/v7+tBlQLbJ1hC9fvhw+fPiOHTs8PDxyc3NlOlf9gKtRXV399evX4ufT09Ph4jQyMlJRURH3i7q6upMnTz5x4kRAQEBFRYXEgBUVFaampgihzp07Z2Rk0PAVhg4dihBydnaGl926dUMIUfvDysnJadSo0eTJkykcUwLIiJsyZYrspgC+ffsGF7+JiUnV/32/OOAPcnJyyIJugUAACXgACLWQMbqWLVuSv1VSK+dX4Ny5c7DE9PDwED9fXFzs6+t74MCBsWPHSsRRdHV158yZc+XKlejo6GpVjUBiUFlZGR5xZA3kEru7u8NLaMby5s0bCqfIzs5+8+YNyNaUl5fLYmXzzz//IIQOHjxI+cjVAl2Rhw4dSokuFYXUxxHm5OTcuHGDx+PZ29uTSg1JSUkXL16Er1dSUuLo6Eh8VxST+ClbWVmFh4fLRUlEgvPnz8OV4+npWcvHSktLAwICTpw4MXnyZAmZWg0NDSMjI/D0eXl5AoEAiuVbt26dmJhIz7eAXjZLliyBl1u2bEEIbdmyhcIpoEHH6NGjKRxTgqysLPEltozIzc0FidchQ4bIdCJZkJaWtnTpUh6Pd+DAAXIJHxMTw2Aw/Pz84KWenh5BEKGhoTNnztTR0RH/rTZp0mTChAlHjx719/enRGCl3ly6dAkhxGQynZycavkYn88PDw+H665p06bi36WqgyfHvHv3Lj3fAvzuqlWr4OWGDRsQQvv27aNwioqKCjabraWlJbvfKtQRDhgwQEbjiwOrH6qktailPo4wPz//1KlTpaWlly9fJkN/vr6+ysrK4P/S09OHDBkCn/T09Ny9e/eIESMkUvKaNm06ceJECwuLFy9eyOWW5ODgwGAwGAzGjRs3xM9HRkbWEgIVCoVRUVG2trZLlizp2rWr+DdiMplw62nWrBk0sKYH0Hlp1qwZhBr8/f3heZTCKbZv344Q2rt3L4VjVgWW2JSoQVYLh8OBHnIGBgYFBQUymkWmQKDs1atX4o6wT58+/fv3h6dbcIQkCQkJDg4Oq1ev7tmzp3i3ECUlpf79+5ubmz948EDW0XsJbt26xWQyGQyGra2t+Pn4+PirV6/GxMRU+1eVlZXh4eFWVlazZs1q2bKlhIPv27cvXMv0hNYBUAfU1dWF1T/09uvTpw+FU0A9T69evSgcU4Ly8vJGjRoxGIy6bPpIw/Xr1xkMBpPJvHPnjkwnqh/1DI3CBXnz5k1xRzh//nx9ff38/HzSEYpTWVn59u3b8+fPz5s3r23btuI/ZbgsN2zYcPfu3ZycHGm+Tx1xc3NTUlJCCJ07d07iLXiiQj8KgQJFRUVkJEddXV1NTU1DQ4OeyIw4UH736tUrgiAEAgFsgH369Imq8aFb6dOnT6kasFqgcfbSpUtlMXhFRYWJiQksEWjQlZUFJSUlZmZmUVFR586dc3BwgJMxMTGTJk06cODA0aNHiSqOUJz09PR79+6tX7/e0NBQfCuOyWQaGhrS8yBFXneWlpYSb8HjAji2sWPH1n7dVXXwzZo127Ztm+y/wX/o0KEDQujt27cEQVRUVIDQGoW6oydPnkQImZmZUTVgtUCG3dWrV2U3xe3bt2H1Y2NjI7tZpKE+jjA9PX3btm3h4eH79+/39vaGk76+vmvWrLG3t1+9ejU4wpycnOjo6JpCoOnp6R4eHpAqJr4ZcPv27fp/m7rh5+cHkoOHDx+u+u6NGzfGjx8v0Sq2UaNGo0eP3rdv39OnT8W3ZMjvcv369cTExFGjRiGEJJa6NLBt2zaE0L///gsvly1bhhA6duwYJYOXl5erqakxGAxZPzqAtCDlmT4EQQgEglmzZiF6Q9aUU1FRkZCQkJ2dnZiYmJqaCifBEfJ4vF69eiUmJurp6YWHhw8ZMmTr1q3u7u55eXnVDlVSUkIu4OBauHXrlqzt9/X1hbkOHDhQ9V0vL6958+ZJ5Kk1atRo7NixBw8e9PPzqxo3Ki8vf/jwYXh4OHRTOnHihKy/ggTr1q0T/zrwG6PwXg9ZnbK+JdrZ2SGEpk6dKqPx3d3dYfVz8uRJGU0hPZQly4AjFAqFRkZGbm5uQ4YMuXz5MkKocePGY8eOPXDggIeHR1UXApSWlr58+dLCwmLixIlJSUlUmVQtYWFhsHBbt25d7Z+sKayEEOrUqdPixYvJfftdu3Z9+vRJJBJdu3YNITRhwgSZfoWqBAYGIoQ6dOgAL2FLb/DgwZQMTkN8hgQyfSQSl2ohNjbWxsbG0dHx/v379+/fr/YzIpFo+fLlCCEtLa0PHz5QZ+wvAThCgiA8PT1nz56tp6d35syZmn6r1Y5QVlbm7+8v61y2kJAQkPrcsGFD7Z8k89T69+8vft2xWCwynAtxoyNHjqSmppaXlzs7OyOEjI2NZfoVquLl5YUQIpvAgLIM/O+QHpFIBBkJdcyYrTcZGRkMBkNdXZ3L5VI+OPnUQe3uKeVQ7AgJgoiIiAB5Rmtr63bt2lUbAnVyciKXtHTy5csX2MZbtGjRT2XrZGdnu7m5bdu2zcjISELAftWqVWvWrPHy8rp582Zubi6LxVJVVYVuPrQhFApBRAr2JkEzicFgfPv2TfrBT58+jcSSAmTKtGnTVFVVtbS0yKD0D9sBikQiCwsLPp9/8eLFaj8AsW42m032kGtIkI6QIIjp06e3bdtWPLerSZMm4r9VeWWrffjwAbJdli5d+lPpgtnZ2S4uLps2bRowYAA8VQAMBmPx4sXm5ua3b9+2s7MrKSlRU1NjsVg0p6aXl5c3btyYvNAkaiqkBEoy2rZtK/1QP2TgwIEIoSdPntTx81lZWVevXpSXoiMAACAASURBVA0PD/f29r5y5UpN7RjJ1c/69eupM1YmUO8ICYIwNzcn9whrCoGi/27C0dD+NDU1VU9PDyE0ZcoUaYJv5L794sWL27dvf+nSJQcHh1OnTkH+grGxMULowYMH1BleJ+Ch58iRI/By6tSpVAVpZ86ciRCSSCmSBb6+vqqqqiA/SMJms0eMGLF79+7Hjx9Xjc1WVlZaW1unpqbW5Aihy5KKisqzZ89kbb9cyMjIIL94cnLyjh07xN8lf6tz5szR1tYW/4clQzW+vr4yvfq+fPkC6S0zZ86U5roTd/Campq7du2yt7e3trZ2dXUlCALUSWgI8Eowffp0hNCVK1fgpURNhTRARG3hwoXSD/VDoIiiXbt2O3bsePz48Q9TybKzs4VC4YEDBzIzMx89elRtbiApSrxkyZJfoUagdihzhBwOh3yE53K5sbGxVT/D5XIDAgKqvSzZbLaRkRHEPWSxrMvJyYGMkmHDhlEbAZC4tk+dOkXbz1ccUJMZOHAgvKQwSAuSFpT0xa6FN2/ekIvHHwalyWeakJCQs2fPurq6enh4nDp1SqIqAG4lLBaL/nWJvMjNzY2IiKh2hS4SiaKjo21sbBYuXCgRqmGz2SNHjty3b9+LFy9AmYEqUlJSYPVpampKobvl8/kSre0vXryIEJo3bx5VU9QRe3t7JFb/KlFTIQ1//fUXonTHsSa+fPnSokULiUAXGVGvaU/d09MzMDCwpKTk+PHjVf1cXFwcxKhmzJhB+Za/LJBVQf3Lly9rD4GKRKJPnz7Z2dktW7ZMoucAg8GYMWPG8uXLr127FhMTI33pJYfDAYVMQ0PDmvYpqeLr168IIS0tLZrrl3k8noaGBhmlyc7OhiCtlPe1hIQEhJC2trZMC2Cjo6NhO6RqyJrD4Yjn5Yr/TiC9EJ5pqi5unJycIFFNpulwvxqQ+AA5JvAvI95LXRzxrTgyibR///7iei5S7l9kZWXBpW1kZCTrEqmUlBSEkKamJs0iCSDtRG6wSdRUSAMsIGra2aWK1NRUyH0dNWqUu7v7nj17hg8fLuEUO3ToAE4RMiEIgnj16tW6des8PDxWr15tbW0tsQWTmppKalbQEOqjBFk5QrIIAdUtBCp+v2Oz2eK1wOT9zsPDQ2IZWBd4PN6ff/6JEOrSpUtWVpbU3+zHQMk2WeMsO+zs7MLDw319fW1sbDIzMyFKc/nyZXgXah7qXrvK4/HCw8NLS0uh4xKchB7FsssoI8RC1lOnTq198VhZWRkdHX3lypXFixfD1UuipKRE3r4TExN9fHwgDv8rJ6rJAjs7O4neKaqqqsbGxrt27Xry5ElNl09eXp67u/vWrVtHjx4tEZru3r37ihUrbt68GR8f/1OW5Obm9urVCyE0aNAgap8ya0JfX5+e6668vLykpKSysjI9PZ0giEGDBqHvip3Ef2sq6k1ycjJCqFmzZjINKmZnZ4Mo+dChQ8VXKuIRdQkJEfHilmpv5tnZ2WTs7TfSrJCVI3z37h1kgUpIQtS+5QPw+fzQ0NBz585VK/sJHTru379fl0wQPp8/adIkhFCbNm1knY9KArJt5ubmsp4oISHB2dm5pKTExsYmIiICktbGjRsH71paWsIzVh1Hi4yMtLCwCA8Pf/z4MfkYAZsHsktMJ0PWI0eOlAhsWlhYXL9+PTY2tqbFdXJy8p07d9avX9+3b1+J5h6g3iCxYaY4ZGZmwsa8hHonk8kklwvil0NFRUVJSYlAIEhMTIT9C9iK09LSEv9XrbtgG4fDAbldfX39mko4KGfXrl1IrIhIdtjZ2d2+ffv58+fXr19/9uwZ7EOTGRISNRU/5OPHjzdu3MjPz7e0tCSVEWlYgBYWFoIouaGhYe2bggkJCdWuPmE/C3S1INJWWFjYp08fhFCfPn1kHXujFjpEt+u+5XP16lV/f39fX9+LFy+S9eDi6TY/JfspEomgok5bW7smxQpZEBISghBq3769rPX0kpKSnJ2dBQKBm5vbs2fP8vLylJSUVFRUIGf15cuXCKEePXrUfcCzZ89mZWU9ePDAwsICzhgaGiKqlUtJioqKyEsxPT1dKBQKhUJYRfJ4PPL/de0hUEAik0JbW7tTp06/mp6hXCgsLPT09Ny5c6exsbFEtlrnzp2XLl364cOHa9euOTo6njhx4smTJ2TeB0EQAoGAfASX2FasJfrK4/FGjBiBEOrSpQs9crtAUFAQolpTqVqys7Nv377N5/M3b94cExMTERGBEGrdujX83iRqKn5IeXn52bNn3717d/z4cVLeZc2aNag62QGqKC0thYhRt27dJOJkDx48CAwMrCl0l5iY6ODgsGrVKoh7kSgpKQ0cOBBCO7169aJt9UMVdHefyM3N9fDw2Llz5/DhwyW2fNzd3UH8AhZT1erA/lD2Uzzd5t9//0UINW7cWMowxc8iFAohwUTWVWuPHj06f/68m5vbuXPnYDtn+PDhCKHNmzdDS43mzZtPmjQJFgo/lJeMiIiYM2eOj4/P1atXraysCILgcDgsFktZWVkWBUY8Hg+s7dq166tXr27fvn38+PFbt27t2rUrJSWluLi4ppAAdAK5f/9+TaJQmZmZlOyPNjzEQ16Q0YcQCgoKys3NdXR0PHToEEEQ1apMAHURbMvIyIBGIm3btqUtBgMIhUJIT602U49CAgMDLSwsXr9+7eHhAdWrsCUWGhqalJRkbm7evXv3zZs3130ddvbsWT6fn5+fv2fPHjjTs2dPhFBISIgs7OfxeCNHjkQI6enpSRQpCoVC+GHURYQP9rPEKwI6duzYvHlziBj/XsizDZN4CLRNmzbZ2dmkIxQKhbVckADIfl65cmXp0qVQiE3CYDDgBqqmpvbixQt6vo444Ido03QnuXfv3sqVK+FZSqKGTF1d3djYeMeOHe7u7rXr2JHbEiCfWFUtT3rIkHXbtm1BkqqgoAB0+y5evJidnS3+4Xp0AvnZ/VEFRCAQREREXLhwoby8PDg4+PDhw0eOHHn58iW5x1w7NQm2gYPU0dGB7tY0s3TpUoTQqVOnZDpLTExMeHh4SUkJmcliZmampqY2aNAg8WT4H7bLAD5+/LhlyxbY6Qeh3fz8fEjAkUXiD5/Ph+6Jbdq08fb2trGxuXDhwv379/fv35+WlsbhcNauXauvry/RD6tv377kQqfaYUtLS2FrplmzZjWVFf7K/Fr9CN3d3a2trUNDQ62trX9WT0FC9pPBYGhpaZ05c0ZGptYOaLr37duX5nnFixCI70v4qp3eUN1qq6F5yObNm3/WDGdn50uXLj19+vTy5ctVb4gikQhuWGTIOiUlZdu2bcnJydeuXbtz504tD6C1hAQ+fvxIfuxn90cVHLizV1RU1H7LrglxwTYVFRVlZWXx+CqdPHz4ECE0YsQImufNzMxcvHgxrD7/+ecfU1NTuAwlLrfz58/XVNwijpubG0Jo1KhRlNspFArnz58Plx7sPZWVlR0/fjw/P3/VqlXiuVTi/bAkkkhrcfDwQPI7Klf8Wo5QepKSkuzs7HJzc6Ed0v79++ViBkhOIITojA7VUoRAiP2yqyZBNG7cWGLTGwCV6kePHtXDmLNnz27YsMHe3r5qeGfTpk1wywgPD4cziYmJvr6+b9++ffHiha+vbx03GMQ7gfTt21f8K8uriEVh8fPzs7a2jo+Ph0yxTZs2ib9bUwkH5ZSUlKiqqrJYLDr3qEQikZmZGfykyZ7V9dteBbZu3Vq/exfslbi6utra2la7ACWbkMOlV1RUdPbs2eLi4oSEhMePH9cUieXxeK9fv7awsBg3bhzc1khatWolXuCxefNmhNDOnTt/1nK509Ac4aFDh0pLSy0tLWHL2tDQEM4nJyfPnz9/1qxZtFkCCrw16X5Rzk/p5ohfpbAbIR4GgcTCmzdvwqq2fv1Zzp49e/jw4cLCQomM07179yKE1NTUoFeG7IC8cLkExhWQgwcPFhQUHDx4EJRpO3XqBOc/ffpkaGhIpwooCHDT2etnx44dCCF1dfWXL1/W9Jm6bK+Sznvw4MEIIR8fn5+1RCQSnTt3bsOGDVeuXKn6WAb+lc1mk1q+Hz58uHLlipubW0hIiLOzc12CAeK3Dj09PYkEghcvXiC6dImppaE5woCAADs7u927d5eXl8MmGSgj5OfnKykpKSsr09aIztHRESE0duxYGuaqpQihLmRkZFQrg8disVgsVj1k8CD5MC0t7datW+KyqyD/wWKx6veU+VPAo8nGjRtlPRGGIIjU1NSbN2+ePHmSzFiBoDf9KqBWVlYIoQULFtAzHdRO/LC5tzjp6en379/fsGGDoaGheNkPk8ns3bv3mjVrlJSUlJSU6pHqVVxcfOzYsUOHDmVkZEhknMI2h4qKCrXN1CQkFyorK6Fe7merTuVOQ3OE4eHhZ8+ehaf1OXPmIITOnz8Pb0GiVO1NsSmENtdLrW4Ol8t99erV0aNHoTxZHDab/eeff+7atcvDw6MeoSeyI+u1a9ekNLIu0FbEgiEI4s2bNzY2NvCrgA1g8kYMKqDQspsGQAtJU1OThqi4tbU1LOzq3c1RfHuVzKKHKljxXfw6/oZfv3794MGDrKwsFxcX8WL28+fPI7q0BmEP8sKFC7KeiFoamiMU5/bt2wih0aNHw0vo/EmnGiENrpfUzenatSu1ujmLFi1CCB06dKimdBuJGtDaRyN7ktGWvkRbEQtGgkePHiGE/vzzT3gJYYC5c+fSZgBE+2sJVFKCg4MDtRp+5eXlAQEBY8aMQQhJVHy2aNFi+vTpZ8+effPmzc9Kd968eZPBYDAYDHt7e0rsrB2465qamtIwF4U0ZEdYWFiorKyspKQEC1VQLWrUqBFt8nfirjciIuLEiRPQoaLukJZXm0hdtQiBQkCpKyIigjwjIYMnfqGKd52squP18uVLSDyjOXdp1apV4MvpnBQjkbECKqBNmjShTQV0+/btCKEtW7YQBPH58+dLly5Rrtjp4uICC7vTp09TOzLscTo7O9ek5wIF05Da9kPJSWdnZ4i+Qm0SDUAkTEVF5feq4m3IjpAgCFhekc1ZevfujRCirSOPuOt98uSJlZVVtf1KaiIoKGj37t1RUVFeXl5VqyplqpuTlZUFlte0/BSX/ezYsaP4hSqu2pyQkPDhwwdIUl27di21Rv4QT09PhFC/fv1onhcD+cZka3UDAwNEiwooEBAQgBDq3r07QRCOjo6enp4/JXrp5+dnZ2f39u1bW1vbalv0kUq2pAATVVRWVkKDw5pKaas2K4ZrjWxWLI63tzfYefz4cWrtrB0QyqAhD4BCGrgjvHDhAkJo9uzZ8HLfvn0IoX/++Ud2M0pE80nXC8vhuisQArCOy8jIuHTpksRbZBGCLHRzoOV33TN9UlJSQPazX79+4g1U4WERIfTXX3/R35OM7Jsqly7QigxsSs2fPx9e0qYCCggEAihs//LlS1FRUVJS0s96LLjuBALB0aNHJd4KCgrS0NBAssnDCgsLI114TWRnZ9ckOUn2ToqOjn7x4gWEYSRKWWjg5MmTCKFly5bRPK80NHBHmJKSwmAwGjVqBLmU4eHhSEwVkHKgXlW84wG43iVLlnz69MnW1vanngjT09M3b94cFha2bt06CdUoGFZFRaUeOdZ1AZqH/KzbBsRr3ps2baqjo9OiRQt51fNBV2Fra2u5zK6wQCyEzFiRtQqoSCTatGmT+KYg7HCfOXMmNDT0ypUrvr6+PzUgaJ5ZWVllZmaKn4+IiIDwxt9//y2Le8iZM2cQQitXrqzj50tKSnx8fPbv3z9q1CiJ3Qrw1vSHYQiCiImJgX3N30hipoE7QoIgQA0dkoZFIhEUt8riKUokEoFUrqamJqm2d/36dQ0NDRaL1apVq8mTJ0MVbR0rHNLT08PDwz9//gz/Jc+TuWqyCz4MHTqUkhgyj8eDYkTx9p4CgYA2v+jg4IAQMjExoWc6DAn0YALnJGsVUFi3NW/enNyXOn36NIPB0NDQqGO7DHECAwMPHz5869at06dPi18CUVFRIFgxa9YsGd3iZ8yYgRC6efNmPf5WXEhWS0tLW1u7ffv28moN36VLF4RQcHCwXGavBw3fEUIBDdkkZe3atQihvXv3Uj4RFK6pq6uT9aqRkZFQVSOht6SmpmZsbLx9+3Y3NzeJzYAf4uHhATlg9bta6kJ5ebmqqiqTyaxH98eqgMQPmU5taWmpra1NfxHL79UUpgEAZeakPp/sVEAPHjwI0RFyPy8hIUFXV1ei0Y2mpubEiROPHTv2UxWxJPHx8SBfPHXqVBkt40QiEfRhTUhIkHIoaESjrKwsfgkLBALaSok2btyIENq9ezc900lPw3eE79+/R2I9o0FI2sDAgNpZYDNSvK42Pj4e0venT58OPTyrNgQHapf9DA4OJnNhoqOjY2Jixo8fT3mumjiBgYEU/hNBZ7UxY8bASxqKWDIzM+/evcvj8YKDgwMDA6ElUL0rvTD1AzJWyHAoqICSNRVUAdcdi8WCLhAEQaSlpUHC86hRo2JiYkg9F/Erri6tFSSAIoQxY8bUQ7CijsTGxsLdgJLRoKqK1J3fs2dPixYtaFMBff78OUKod+/e9EwnPQ3fERLfe0a/efOGIAg+nw9R/ri4OKrGd3R0hKe0GzduwJn09HTIpRw9enTV5WcdZT+hEn/WrFldu3YFTcJ9+/bdvHlT1uEOUKw2MzOjZDQoYiGFBZKSkpCMk+k/fvwYHx9/+vTpM2fOXLhwAZ4YyMQNDD2IZ6wQslEBJSvk7Ozs4Exubi74vEGDBkmk79elWbF4DdKff/5JFt6sWrUqODjYw8OjpKSEKuOrcvXqVQrXiHAVL126FF7SowJKRozJ2yzNrbjqjUI4wvXr14s/p4P2AVWFNW5ubpAnSQ5YVFQEG5ODBg364ZUDkf0LFy4sWLAAxEJJWCzW+vXrZ82a9ffff0OjMnCElJhdC9OnT0cIOTg4UDXgqFGjkJj8IyTTe3t7UzV+VRwdHT08PJydnT09PT08PCAyRlsdGwaAhgxk9AJqKqhSAXV2dpaQaOBwOAMGDIBgRu3PeWSzYglNQfS9WfHz58/79OnTp08f2Jv/66+/ZNSbWpwlS5Yg6tSJ4fmSzFihQQX09evXtra2N27c2L9/v7+/Pwh7/S55agrhCH19fcV/BHfv3kUU9Wohc5TJxSOXyzU2Nkb1bdMsIft55MiRWbNmvX37tm/fvjExMfQ4wlatWiGEvn79StWAIP9IaouA7rbs8tksLS3XrVsXHBy8c+fOnTt3lpWV0eB6MVW5f/8+QmjkyJHwEmoqKFEBJSvkyPIGLpcLwcAuXbrU1DOvWqptVmxlZdWnT5+nT5+OGTNGJBLR4wghhkShEFLXrl3R94wVelRAk5KSrl+//vbtW39/f8hTGz9+vOymoxCFcIR8Ph9+BBClKSoqUlFRkV4IOCwsDIrk1q1bR040YcIEhFC7du1+tp9iVbhcblFR0axZsyIiIl69ejVmzJi9e/fK2hFCAyMdHR0Kx5TQ9Hn79i1CqE2bNrRt3cva9WKqhcPhqKioKCkpQVQ8MTERUaECGhgYCLUBZIVcRUUFXHdt27aVJhYHzYrPnz8fFxfXp0+f4uLixYsX3759mwZHmJaWBv84FOajQqmxRCRMdiqgsbGxJ0+eLC0tffnypZeXV15eHovFUlVV/S0kZv6TtdFQUVZWhuvk8ePHCCFNTc0RI0YoKSlFRETUe8y4uLjJkyeXlJQsXLgQduxFItHixYu9vLxatGjh6+srEeesB2w2W1NTE45HjBihq6sLUikyBdroQId3qmjfvn3v3r1LS0tfvXqFEOrfv3/btm3T09Mhj4kGpk2bhhByd3cnCIKeGTEIoSZNmgwfPlwgEECGWseOHXv27KmtrZ2amlrvMcPCwiZMmMDlcpcvX3727FmEkFAoXLRoEXndSQiS/RQsFqtPnz7m5ubwLIUQOnXq1PHjx7lcbr3HrCOQW2RkZCTej0JKoBM93PSqvqSc4uLijh07fv78mcPhCASC5s2bDxkypKKiws/Prx6jxcfHX79+/cmTJ05OTufOnSstLaXc4P8gb09MExClGT58OLyMj4+Xcp0ClXZk8z/xppc/KyhaO/BESBBEVlaWlpaWrJ8IoRRSooeL9Ozfvx+Jafr8888/CKF9+/ZROwvJ8+fPxf8viESitm3bIoScnJx+oyLfBgAkCS9cuBBeSpkpU1JSAvWIZOtpkUi0YsUKhJCmpub79+8psPg78ERIEIS1tbWSkpKsnwjXrVuHEDp27BiFY5IZK+Kt6OhUAT1+/DhCaPny5fX4W6FQ6O/vf+nSpd27dz9//tzV1ZVy88RRFEdIJq1VVeSrHykpKcuXLycbTJPNOckiQqoIDg4ma+BevHhBYbJrtcB2GuVp1u/evUNimj7Pnj1DMsuuDg0NbdSokaamJvlvVVFR0aVLl/bt2yOEGjVqRObl1jF1HlNvoClSs2bNfrZnQk14eHjMnz+fHO3ff/9FCLHZ7ICAAErGJ1mzZg1c3QKBYOHChR8/fqR2fAlAi5HybzF37lwkloAD26iyE+JIS0sT3++IiopCCOno6NQj0V0gEOTl5R06dCg6Ovr48eOy7rDd8B2hq6vr+fPnc3NzoS8aWeFAIZAF8FPNOeuBubm5ioqKTKfgcDgQ1qe8WIrU9AkLCyPE1qrS1w5LEB0dDfIf5EODQCAAobUmTZqALyRhsVi9e/f+559/HB0df7tWor8yIpHI3t5+7969fD6/R48eCCHKF4jEdwkLFRUVmcro37p1a+HChTJtWVNUVCSj6w7ag48bNw5eQk2FjFRAU1JS9PT0Fi5cKL7o0dbWVlVVNTIyOnjwoJ+fn3g7+9r58uXL1atXfXx8njx5cv36dVnXjDV8R1hUVLR///7U1NTLly8jhCZMmEDt+A4ODhJFhDLi0KFDCKFVq1bJbgp4UBs2bJgsBofgD9SBEAQxb948hNC5c+conCI1NRXc7dSpU8mQ9cqVKyF0BhFmqCeDZlKQ8UvSsmXLmmTwsrKyxPP+z507Jy/xqt+F5OTklStXikSibdu2QXSUWl9y7NgxWH16eHhQOGxV+vbti2Tcsgb2/smNGwqBcCiZsSI7FdC0tDTIeh01ahR57YAkMmQUAuJSBlLmKlJLw3eEZWVlLi4uAQEB/v7+qqqqbDa7lvvdz0IWEVJ7Q6+WDx8+wM1adrdgLy+vwYMH79ixQxaD+/j4IIT09fXhpZOTExLLrZeenJyc7t27S1yKcBeuKXTG5/PJ1HlQt6p6xTo4OKSkpERERIgngrdr105eMuK/BUKhMCMjw8rKKi8vb9euXVCWUA89l5q4dOkSQojJZNKg1Qfb2+vXr5fdFPBou2vXLlkMDtVcLi4u8FK8poIqqtUxgDgZi8Wys7OrS7sMCu2pBw3fEbq4uFy6dCkmJgbCYhIa7WpqakZGRlu3bnV1df1Z2c+wsDAoZqKt9SusuUJCQqgdtqysTLx8/u7duxwOh9opiCqaPlQVsQBFRUWwcjc0NCS3VMmHBpBc/yFfvny5efPm6tWre/XqJSGD5+bmhh3hT+Hh4eHh4XH79m1o496hQwfxf1LIz9ywYcP9+/d/quyPIIg7d+7AmKSgjEyB1kh6enqym+L169f//vtvUFCQLAY/ceIEEstYkaipkB4Oh9O/f3/0Xx2DGzduQJzs2rVr4h8Wb01DpsQD1SpNOjo6WllZwXFWVtakSZOoMluChu8ICYLIzc39448/EEJDhgwpLS2tt+xnREQEmZkWHx8fGRk5b948soiQBszNzZEMdJJyc3PJBzWCIIYOHSreLIJCJDR9oBm3o6OjlMPyeDzoBdq1a9esrCw4CfpbTCazfiqjxcXF5BXbqVOnd+/etWrVas532Gw26QjJbY+6738oCO7u7hAvgcZkpLJg1aC0+HVXtbo0JCSEXKRGRUWFhYW1bduW8sTmmhCJRG3atEEIUZ4vExQURKaxiEQiSqQGqhIdHY3EMlZABZQqJeFqdQwePXoERSC1x8n4fH5wcLClpeWUKVNgX5+kWbNmU6ZMsbS0vHTpEvmYkZaW1r9/f0rMrkrDd4TFxcW1CC+J3+8kZD/F0wuhItjc3Lxly5aQAm5jY3P06FE6Bd0JgoCKnD/++IPaYWlzhBKaPhcvXkQIzZo1S5ox+Xz+pEmTEEJt27YltSJdXV3hUjx//ryUNgMRERGjR4/O/k6bNm3AEaanpzs5Oe3fv//IkSOXL19+9eoVJdM1APz8/MDbVVskA0Fpsmml+HWno6ND9k4CVTxDQ8OJEyfCHy5YsCAgIIDmXiKwzUxtbQNBEC4uLmRBkVAobN26NbXjk0BTpNDQUIJSFdBqdQzc3d2VlZURQsePH/+p0RISEkAhHTTTEUI9evS4fPny7t27i4qKioqKYmNjsSOsJxUVFaampgihzp07/zACU1lZ+e7du5pkP+fNm2dubr5kyRJomwmOkJYv8R8LYekEEjlUkZub27Rp07+/o6OjIyNHKBEOTUlJUVZWnjZtWr0HFIlE0N9HW1ub7NHx/PlzCFlTeOeqZY/w+fPnx48ft7CwMDMzI21QcEJCQqD1WF221gQCwfv378+fPz979myQ9yNp3LixlZWVoaHh7NmzoZECOELZf4P/4ObmhmSQREabI4RIEtl7DmoqpFQBFQgEME6LFi3INpPk6ofMiasfKSkpt2/fvnXr1uXLl7t37z5lypQpU6aYmJhgR1gfBAIBNMNr3bp1Pe7sErKfZmZm5ubmnp6eI0aMCAgIkIsjJAhi4cKFiOq+brm5ud27d0/4Tr9+/WTkCAmCgHUJKQtAbkZGRUXl5eW5u7s/ePCg7ptGsOHRpEmT8PBwOANFhAihDRs2UGh2TY4wNTU1NjZ2165dO3fujIiIoGfX6hcnMjISsmOWLFlSj8Qu2LmA3kkMiB1sXwAAIABJREFUBuP27duGhoZxcXF//PEHh8ORiyPkcrnq6upMJpMMvFOCi4tL69atR3xHdo4Q8tQMDQ3hpaOjo5KSkjQ5cdXqGISGhkpITkrP5cuXcWhUKsj/VVpaWpA3Lw1cLjcnJwccYVRUVL9+/aytreXiCB88eIAQMjY2pnBM2kKjxPd8vxkzZoifrKiosLGxgZCXjY1NZmZmXYYCBVE1NTUyIBkVFQW34MWLF1Mbsi4vL09NTSVfkorkIpEoICAgKysrIyPj9evXWLYmLi4OnupmzJghfRF9ZmZmSUmJoaFhcXGxlZXV5s2b5eIICYKAGCC1uk60PRHy+XzITIG9Ay6XS8aWExMTeTxeeHh4tVkRNVFVx+Djx4/kpUdhWjttjrDBao1u37792rVrbDbb09MTmiJJA5vNbtGiBRzr6+uPGjXq+vXrUttYHyZMmKCmphYcHJyVlSUXA6Rk2rRpDAbD29ubx+ORJ1VUVCDxTEtLi8/nS8THquXSpUtHjhxhsVi3b9+G1ruJiYnjxo0rKCiYOnXq9evXJRqUS4mqqipUKAKw6YIQYjAYxsbGLVu21NXV/fPPPykUivwd+fbtm4mJSVZWlomJyd27dyFTRhpatWoFz/cIoXXr1gUEBIAoPP1MmTIFIUSD2K8sUFZWhsS0p0+fIoTYbDZsE6alpcFF1LRpUxcXlzpeMrt37z537pyKioqLiwvUZsTHx5uamhYUFEyfPv369esSGYjSoKurS+5SqaqqSn8nrxEZOVj5YmFhgRBSUVHx8vKicFh4IiQIori4uG3btnJ5IiS+L04l8pKlobKyUryOJzY2Vqat+8DnXbhwQfz56eDBg4cOHXr27FlkZOQPR8jPz2/evDmDwSCrPrKzs6sWEWLohPxfMGzYsNLSUgpHhidCgiDevHnDZDLl8kSYmprKYDDIDiqU4OnpuXXrVjgWiUQybRZ48+ZNhBCZc0QCKdz5+fl1TN4G8VhxHYPU1FQQOh87dqxM9XdkSgN0hDY2NgghJpN5//59akeOiIggt68+fvwor8wIW1tbhNDUqVPlMruUiESiwYMHgwS2NLKfUVFRV69ehWOyiHDgwIG/Rc+XhkdhYSGs1vv06UN5SuejR4/I1CQXF5c6Rs4px9DQECHk4+Mjl9mlJCcnh8ViqaioiLeH43A4ZmZm0dHR1tbWdVm7gPKUeElSdnY2SOgNHTqU2tUPzTQ0R+js7MxisRgMxpUrV+Rti6xIT09nMBjq6upU/fIgBaC0tLSkpISSAWth48aNqEqkEa4uAwMDMzOzn5X9rLaIEEMnHA5n4MCBCCF9fX0p+0v8ysCetLm5ubwNqQ9FRUXa2tpQEEnqubx//z4hISEnJ6eoqKgugwgEgtWrV5ML0MLCQlLFAgrMfl8alCP09fWFvHko4JU1ycnJ8orCDRo0CCHk5uYm/VBhYWFeXl5nzpy5d+/epk2bZPqNQC6VVEmun+ynOHw+f+LEiei/RYQYOuHxeCNHjkQIde7cOT09nYYZ5fXQHxoaihBq3749JaO9fv0a9lmysrJkoUguTklJyZAhQxBC2tra6urq4hcayGTb2NhER0f/VH5ZaWkpdC3t1q1bA1iANhxHGBISAn2r6Vmy5ebmzpw5k9p6vrrj5uZ27949qoTQbG1tHzx4kJKSsnLlStnlPYLoOYvFevDgQdV36y77Sf6JUCgEqRrxSiYMnZBqBm3atJFdprE4L1++JLsb0oxQKGzXrp2JiQkl111iYuLhw4dLS0tPnDhx+vRp6QesiYqKCmi8o6enl5ycXFlZSV5o2tra4hda48aNx44dC6vP2nf76F/9yJqG4wgPHz6MEFqxYgU9Ui8nTpxwdXWVlyM0MTEhY1CnTp2SRnr4/fv3ERERR48e9fHxOXjwIFX9GiX4WX3IWmQ/u3TpsmTJEltb2yVLlqD/FhFiaEYgEPz9998tWrT49OkTDdPxeLwzZ86QEn00U1hYSEpXEwTh5OQkjd5sWlra9u3b79y5c+bMGTMzMxndtfh8PuS7tmzZ8vPnz1U/QOq5gI4xCZvNJvfvJTZ9+Xw+NLunbfVDA7+HI8zKynJycpLYEistLX316pWvry/5/8nV1ZWeQi6RSHTlypX169e7u7vTMF1VevfuTaov7t27V5o67oqKivDw8IqKiuTkZBn9rH18fEB1vn76kCCDt3Xr1p49e4rL4DVr2lRVWenRlDHCEpwgIxPs7e2PHz8ufrsXCAR2dnZr1649cOAAqGoJhULagtJxcXGXLl2aM2eOXPJlYmNjxZuldO3atd5BWpFIZGdnt2fPnuTk5ISEBAsLC1k4QqFQuGDBAoiI1qW9Q00izCwWq2fPnqtXr3ZwcEhOToYwjLa2Nj2rH3r4PRzhx48fU1NTSYFagiA+ffrUu3fvPXv2HD582MDAwN/fn36ruFyuvFoQ9O7dOz4+HiT4tm3b9isLmpAhaym7zAgEgrNnz2ZkZCxbtmxCX8OhLZohhLqrK8f361Di/ZgqazHiCIVCGxsb8SDB7Nmzd+3aFR0d7ebmBmpE9FslHh6nEwodIQ2IRKI1a9ZAyOTt27c/++cg87Rly5Y+ffpI1IMyGIwmGhoBVlSKW8kdaSte6cHAwODu3bsQlQa2bdt26tQp0OuaPn364sWLoV0fnUh0dKKZ9evXw2PW58+foeveL0hUVNTEiRO5XO7SpUuPHj0qzVBQq56bmztq1Kg38bGr2zQanF/4tawyt1LY6LVvI9PJFJmM+X9iYmLU1dVJKYnY2NikpKSHDx8ihHr16pWdnX3hwgUrKyuarZLQAaaTr1+/Qn9phFBOTo68zKgLO3fuvHLlirq6uoeHB3Qd+CmaN28+derUqVOnxsXFubi4ZGRkqLCYno8eJWdmiUSiZU2U23reQxs2I+pq50nK3r0psD5VHhOJBAKWdosmM/9qusyMoar247+Ugt9DWeb8+fMBAQFcLpc8Ex4ePnr0aDg2MDDIy8sTf1cRcHBwcHd3d3d3B+nbX5CEhARTU9PCwsLp06dfu3ZNSqmX9PR0Ho9XUlKSl5fXbfDQRkzmkEZqIoReFZfxAl8SlZVUmY0BhELhvXv3KioqcnNz4UxcXJyBgQH5gd69e3/58kVO1smH1q1b//MdiWY1vxSHDh2ytLRUVlZ+9OgR6C7Vm27duqmqqg4dOrRtmzZ/qwi3tNKsJIjkikphQX55NPXPHkW3rn5dtzTr/VskECCEKvNyY69af1swSVRcRPlc4vweT4RQfCaOSCQSv7GyWCyRSESvUZjayMjIALWtMWPG3Lt3T3rtsTZt2uzZswchZGRkRPD5yWN8x2qxXxeXPS/izWleUv7+jfpgYyoMx/wPFot15MgR8TOampolJSXky5KSkl/ZGcgCDQ0NfX19OJZotv7rYG1tffDgQRaLdevWLSgukgY/P7+IiIihQ4c2btoMdepskplyLL3gNadMSBC8gBdqvftRYjNQHhleaGv1NCs/taJyS+umCCGOQLj8a4aXMitn7+ZWF2Soavl7PBFWRV9f/+3bt3CcnJzMZrNB+FxB6Nu3L3kdtm/fvi7inHSSn59vYmKSlJQ0ePBgNzc3KO6kEIaKCnvYnyaabCZCQSVlZSKC+8qX2ikwVenXr9/79+/JB0QnJyfYm8D8Ojg4OGzcuJHBYNja2s6bN0/6AceMGePo6PjPP/+sWLHi79Vr2qkqdVZT5ghF77kV3AA/6ccXp8DmrKi8rOp5opLPCw+pTEuhdrr/zvF7EhgYaGho6ODgcPfu3UGDBlFSWo6hBC6XO2zYMISQvr7+zwqn1Z3iJ67x/ToYaqgihGw66SSPH0LQ2CFZYfH09DQwMFixYsWIESNkWnX6C8Lj8T58+EC+DAsL+9W+vouLCyS2nDlzRhbjV2ZlxPfvuKqlJkJoZUvN+H4dKtO/UTh+olHP+H4djrfXHq+lcbmTzuVOOqc6aHdTV4nv1yHBuFfxY2cK55Lgd30iNDIy8vDwKC8vLywsdHR0nDZtmrwtwiCEEJ/PnzlzZnBwcKdOnby9vaEziyzQGD6aoaRkoslGCD3n8AQ5WRVfPsloLgzJpEmT3r9/v337dnd3d3Nzc4XqtqGurg5yo8DAgQN/qa/v6+u7YMECgUBw5MiRzZs3y2IKpZa6Kl17jNZURwi95PAQQtzAFxSOL/q+018qEqXzBel8QSZf+P09EVFRTuFcEvyujhAhpKenB3Wgy5cvP3bsmLzNkQMCgcDe3v7SpUthYWEXL16UtzkIIXTq1Clvb+9WrVr5+Pi0bt1adhMxGzdR6ztorBYbIeTH4QkJHB2lCSUlpW7dupmYmPTu3Ts6Olre5siB8vLyyMhIoVCYkJCQmpoqb3MQQig4OHjGjBkVFRUbN26ErXQZoTF8TD8NNS0lZkJ5ZXJFJS+ASkeorNsGDgzYKn/rNPlbp8kC7f814WKwWModu1A4lwS/sSMEKisrg4ODXV1d5W2IHFBSUhozZsy3b98GDRpUKae0SWiFAXh4eCxcuHD58uXe3t6dO3eW9dQaI026qCl3VFUuEojecyu4r7EjpI/evXuj37Y/n5R8/PixuLj4ypUrX758OXv2rLzNQRUVFfPnz+dyuWZmZtAmSXaw/xzDYqARTeChsKwsPETEoyxdv8ncJcz/SqGSMNls9T79qZqomvFlNzQ9jB07VkND4927d9++fZO3LXJAR0dHSUlJXl4QIQQ62oCDg0NGRsa1a9fgLilrNEaYIAZjtJY6Qug5h8ePi61MV8TfgFwAka3Hjx/L2xA5MGjQoMDAwD/++MPU1FQuebNBQUG+vv9b9hUVFdnY2Li7u2/YsOHSpUvU9qOuilrP3qzmLUZpshFCLzg8gs8vexNI1eCacxcrt9Hrqa4ypPH/qgbVmMy/tBsz1NVbHrdGTBkGon97R6iurm5qakoQhAJek3w+/+7dux07doyJieFyufJq3i0vlHTbqHTtMVaTjRDyLeIhhHj+z6UZsDwyPHvH2pQpw1Nnjso9vIOfFE+NoQ0RExMTNTW10NDQX7yuXBb4+/v36tUrOjp61apVvXv3pn8Z+uHDhzdv3sBxSUmJk5NT3759L1y4QGFr+BphMtlGI0c0UVdiMN6WVhQLRVzqoqMMJaUmcxb3YqvYZRdvSsrNFwjVlVhL9bu1tnZU6zuQqlmq5feoI6ydadOmubq6uru7r127Vt620IqKisrKlSvhWHwbn05KS0uXLVsGx+/evaN5do0RJv3jYrWVWakVgq/lleqvfDUX/F2/ofJOHy52vUeUlyNEIIQEad9KvD2ab9mvOXMBpSY3EDQ0NEaOHPns2bNnz56B+rniYGRklJ2dPX78+LS0NPRd80hx0Bg+urHHwwGNVENLygOLy6YE+CGRiCqJmfJPkUUCUXBxmQqTcaqDdqPx01oeOi0L/RoJfvsnQoTQ5MmTlZSUXr58WVQkW/UBRaa8vPzOnTugY/fixYukpCQ4z2azD3znjz/+oNkqjZEmTIRGNlFHCD0v4pW9DxNyCusxDueBY4nbffPY1Cje/zLTXHM555OyCs5YlL0LpdLiBgS0NVDAbUIWi9W6dWsVFZVOnTp16tSJjuewKjg4OJiampqami5atIjmqdWH/MlQUR39v+hombAgvzzmI1WDP/J8YpGWL0KotYrSxcyixuMm0+AFUcNwhM2bNx82bFhlZaW3t7e8bWmwlJSUjB8/3t3dPTMz8/nz53FxcXCeyWR2/A794quqPfSVWreF3NHnHB4SCXlBr392EEIgKLA+LSrjVYgIEfG/k5UEqiQIUXlZ3unD1NrcYJgyZQqDwfD29ubz+fK2pcHy5s0be3v7uLi4K1euiN/fFi1a5Onp6enpeePGDZpNYrLZ6v0Hw5bEq2KekCB4FFXWCwvyBvNLv5ZXIoSMm6gbNlKnVrmmFhqCI0QIQR2hu7u7jMYvKytzdHSMiYl5/PjxvXv3UlJkqXHwSwLNbzU0NI4ePdqxY8f09HR5W/Q/NIaPNm6srs5kfORWZFUKeK99fuKPRcLyD+G5FjtFZf/LfMupFKbxBWl8QaHgfwVMlYnxojIe5WY3ANq1a2dgYFBcXOzv7y+jKSIiIlxdXfl8vru7e3y8Im7ZGhoaNm/ePC4uLjw8vGnTpuR5FouloqKioqKirKxMv1Xs4WOYDNRCiVUkEJ3JKHrt4kzJsGXv3zIZqExEIIT4IsK0nyGzCU25SA3KET59+lTKxWlqauo///xDvpw7dy5oeRcUFMyaNevevXtTpkypqKiAvkIKRVJSUmxsrLq6+vbt27t06dKyZUs4L75KtbS0pH+rUmOEqRqTMayxOoHQS04ZL/g1wa+o/U+I8jKuv1/O/s1Jo/ulr5iT4/HIp6A0vrwSIfQgv+RCZtGFzCK//2vvzuOaONc9gD8zSUhIwr7KJqCIIAoKIoorm6gJoNYeW6ueWnvbW9fberp7Tnv6sVdbq7bW3tbl1HpqteIWEhcUEBFZRAVRUBFZRFzYt+zL3D+GRk6tFtmCzPP9+McwJJMnavgxM+/7vM3t4UdwOIbWll5/G8+nnro6+uGHHxo7JiYnJ3/55Zf0NpvNbmtr27t3L4fD2bt3bzdf5XnE4/EGDRr08OHDrVu3SqVSnU5n6ooAAARToixZpLc5BwAadfr0ouu6+z3wm7Eq/0JOq+qOWsciQGwjEI4Z1/1jdtJAGCwDAEOGDBkxYkRRUVFGRkZUVFSXj6NUKq9fv278srCwkP6f5+rqeunSpaCgIIqimpub7e3te6Do54qXl9frr79Ob3dcB6fjTIk+mDv4OF7wONLSKsq6LbVZkdKkeMleoczL4odPe/yR+oZ6RdbZtpRjytzMYw8bCxUaMxJONymr1DqVwfCqoyUALHe2ptu2Jda33VFrAYDSaVm2jPvn7iSRSLRu3TqpVNrNxZgqKyuN7bybmpqM1xv8/f2PHj0aERGRlpb2u1XxGCI1NfXmzZvBwcE7d+4cPHgw/Zfw6quvGtcYcHV1PX78eB9XxXZ2sff187nXmNuqSm9Rhgl5ivNnLF/o7t1KVX6eBYvUU9RIPnecBY8X9MyrR3XZwPm/lZCQUFRUJJFIuhOET5Kbm7tp06YXX3wxNTV1+vTpPX581GUEm82fMGVs0wniTn1GmzaQjLb6R/LUkOLRjtrYmKiAgADNrRvysynys6fU168BRZWrtaebFMlNikK5mr4hSAAECrhDeZwq9WO/brNY/AlTCUb+CO6M0NBQJyensrKy4uJif3//Hj/+m2++GRoaev/+faFQqFL1YoetfisyMjIyMhIAxo17dHrU8WY8SZIm+b383rCRcCZLQJK1Wn2IkCvPSOtmEBrkbepbNy62qQAgRMgFAAzCroiPj1+3bt2RI0e++eabLswqVavVBQUF1tbWBQUFxhWAjf2Txo0b9+uvv/ZgtagHVQXFzLs9kaj+xFBbLOdYyNtUick5KUPCbiev+UCg1VaWGQCKFZq0ZkVas+Kaov3iOYcghvE4M20EtTr9x262AFCvM1iy228WuJixzEiC5Jnbr/m7yd5Yv0eS5IwZM3bv3i2TyboQhBRF5efnW1paAsDbb79NT06vra2Njo6mH7Bjx46eLRj1lMB5L31y/GCNgXWqvmmf1YS8Sm/RydIFU72EvC5OJlEVXgaD/mKbGgBChDyOqzvbaVCPlvw0AycIQ0JC3Nzc7t69m5+fP2ZMZ8caNTY2pqSkSKXSpKQkhUJx/vz5oKCg9PR0+rvDhw/vrXJRD1Fo9OIz3CYWUG7jobYYqvNg0Bjt0LjaumsHmuzHydPSm+UnGuU12vbBL1YscoKluQ+PM5jLjrTin1UZXo6MdBIlECRr+bqPKMpAKeRAwCQnB1IgcP56F9vFzbRvsJ8Ti8V0EL777rudfIpSqTx//rxUKj1y5EhVVdXKlSsBYNOmTfRS27/++mt2dnYvVox6Ai8g6MSgKekWHuyaazfcI28Ivc7sL/nwSLn0nZAJw2z+/PmPUeXn6SmqQKEmAEKE3N6eQf87AycICYIQiUTff/+9RCL50yCsqKhISkqSSCQZGRnG+89BQUEPHjzo/UpRT/opo7pVqaeAALcwyN8F9y6AlQeUSKn7FxubKl7Vti9v5mrGnmRpHmFlPtnSnE0QLBtb/oQpgqhZo8ImEcaVHSdFyNOS1cWFhBmHNyqYPzkKL4r+qZiYGC6Xm5WVVVdX9/RrdNXV1fSI/9TUVKWy/d/F3d3d3t6+rq6uT4pFPSb9ZuO71nM1ViQom6BoP5BsucdEudv46Rsu5H8+aajTM8+kUuVfKFJq5HqDN49jx2bxgjAIuyo+Pp4Owo4NMDsqKiqSyWRSqTQrK4uiKABgsVjh4eFisXj27NnDhg0rLy8fNOjR+bibm5tJZsuiztBW31FkpP56TCenXAEACBJ41qBqgmv7gDIAAAWEjdWgxXxlpDXfz9wMADiuHvxJEcLoWbxRYx6fqEvyBRaiORaiOX3+Vp5jQqFwypQpp06dOnny5B9O7n78QwcA/v7+YrFYJBKFh4cTBPHuu+8aR2JbWlr2t4Wm0eOW7riqpFhw5SdQt0DIf4NeDblbQaNQDolc/e9i2Zpnu71HabWq4quX2tQAECzgAoB5354REsb/mgOAWq12dHRsaWkpKyvz8vKid+r1+uzsbJlMdvjwYWM3Tj6fHxERIRaLExISHB0dTVcyekYGg/pmkTwjVX76GN0LdKbtwpKHt6H6AjTcBqCAwwetEhz8wGMiuE+IMDzYfm8713cEf1KExXQxx9ME41oHvK1bt65cufIvf/nL/v376T3Gi5+HDh0yDgE1fuhEIlGvLtGFelvpQ0XgB+cUKh0ceQXifwSWGQBAazVkrocZW81Ad9v1tMXoMbygEO7wAOjEiA3VlUvVS154q6zmVJPii8H284a4e6Zc6swTe8qAOiPkcrnTp09PTEyUSqVLly5NTU2lt42t1xwcHGJjY+fNm0dfzzFttajzKLVKVXBJnpHSlnJcX1ejpaicVlVKsyKlSfFQ+1n7gzgCsPOBBwXAd4DoLwAINhgCfS09/5XDsnMwafkDXFxc3MqVK0+ePHn37t20tDSZTHbixIm2tjb6u4MHD54+fbpIJMIP3YBRUaswYxEKVSOYWbSnIAAIXUBeC3oNUZ1TcOOsX6oMAFi2dtwRgeZBY3lBIdwRgcQTpv+3Xr5YZWaXJ78LAGOFPN7o0L5MQRhgQQgAkyZNSkxM/PTTT//2t78ZJ9cHBATExcUlJCSEhIT09jIlqAukl2vWSUqL7rYCwAg3iw/jh8SNcQIAfWODIiNFnpGiyMmkVEqlgcpuVR5vVKQ2K1r17fOoLDlmCtfxusFTYFAwECw4uggUtdBYATZeZmacJUumsOwsTPneBjqDwfDgwQMHB4fa2lp3d3d6J0mS48ePp0/+Ro4cadoKUY+z4nMMFADXArQdFiPUtgHXAupvqTM3iAHcuexgAS+krjX43gOfc2kAQJrzzYb580aH8AJDzMeEkkILAKht0bz9c3Filgth+6pKu5zkWec6TQrqw4kTtAEShGVlZVKpNDExMSsriyTJpqYmgiCCg4NFItH8+fNx8Ge/RVHwX7sKfzlbrtARQLIBILek7qXND+e6Gv635WjS6ZR7Kk2wkLvrYctdja5IodH+diV/KI8TacWfZmUeLOStdI1KF45UEmwAANdQKD0J1TnmVq6Lw9wD3DAFe4VCoUhNTaUHv9y7dw8ABAKBRqOZPn26WCwWi8Ud77WjASbQw8JAAZAcsBkCVVngPgEA4NZxcAsDAJ6jH1F3o0qtq1K3HW1oAwAnDmuskBci5I1taPK5cpEEIFgss2H+Df7jo28HNmkJDcWC+hIAMDiO/KfjvJIHtrv69h09x0Go1+vPnTsnkUiSkpLKysronQKBgM/n19bWfvfddx2bpaH+6YfUyn1Z9xU534NrKLiOAwB4kK+oyjo09k2Liha2XHWwvm3dXR2dfiwCgoXcCCt+tBXfm8cBkuQFBAmmRB+cHL36jOLnzHugUSvdwqD0JFmVvXCQ/f+6hAP04qrWDFRZWUmPfElPT1er21vZeXt7BwcHJyYment7M3BZUAYyY5NrZnl9KSuThy6D3G+g9AQYtGBuB2OX8VnsL/yGR7coy9TaS22q862q3FbVQ61e1iiXNcoBQMAig/jcYCE3uOnC5tZpDQKDjiABAGqKAAAcRyhI7q/XVeKLDxNCnPrsHT1/g2WUSmVKSopMJpNIJA8fPqR32tvbz5gxw83NbdiwYa2trStXrnzhhRcSExNNWyp6OooCxzdT6to0cGHroyC8lwdVWaCog/uX6YdxCCJUyBWwyM897K3ZJGHG5Y0OEUyKFETNZDs8+qiU1yoOfvXv8stX/pW2U63Xpge4+cbMdP7qB5O8tYHEYDDk5+dLpVKZTHb58mX6JwZJkqNHjxaJRMHBwUePHt2xY8egQYNqampu3Ljh6+tr6pJRr9MbqPhNl9KL6+VqPRi0AASQbAGX9V+TXdb5tSgL8lRXLqoKLlFqFQBUqXUX5apLbeqLbSq6qW87ggVCZxA6wZAYyP8XyGtgxrdg4wUAgVa63BVeXN8RfbMM03NzRlhXV3f8+PHf3Yf39vYWiURisXjq1Kl0F77NmzfPnTt31apVJ0+eVKlUPB7PpFWjpymvVSi17bf6oKkSOPz2DQCwHUo0lI4yo3zMqLecrTNalO7WVm6xsfxJkYJpMaRA+PjRvBz4y+cF3Tu9vtyCc7JJe6ZZ4Z5zjlIpCZ55n72j505mZqafn5+dnR0AlJeXt7a2GpvHGi9+SqXS+/fv0zsFAsG0adPEYnFcXJxxkkNpaSlJkrGxsXv27JFKpRiETMAiCek7If86W7VBWna7Rk4SxCh3y09f8BGNdgQA83HhAEDpdZqS66qCi8KCi5552bObGwHggVY/m/2pAAAYCklEQVSX16bOaFHmqFj3FS3QWg2t1XD/MgABBAm3k8ExABz8iynLu6/EkXwBd2QQLzDEPGgsb/RY45TfHtdfgrClpUUkEhnXc5k9e/amTZu8vLzom38ymSw9PZ2e+U6SJH3zTywWBwf/wYUvDw+PwMDAgoKCM2fOzJgxo0/fBnoWrSod29iPqakc9BoAgNZ7wObCiPnCEfM+q/pmlB3FD5sUOimSP2HKn05vNx8dwrK2ibJuO9mkSGlSLnRQKnLPC6b0fO/ZAeOrr75677336CBMT0+/deuWpaXlqVOnpFLp6dOnjRc/vby8oqOjRSLR9OnTzZ7ww0gkEu3Zs0cmk61Zs6bv3gAyHYKA16a6vzbV/YkPYLG5fiO5fiOtXnoVALTVd5S55y0K8tzy88T37v5d7XrIIUp9JwsA4MFlUDYAZYASKZRIAUBrbrOCpwm3aA1uSB+ae54AIHjmXN8R7WNtRo8lLSw7vpbyUm6r9JC2opS0thFOmy6ckfBMqdlfgtBgMNTW1hq/rK+vv3r1amxsrHEBWB6PFxsbGxcXFxcXZ1wD6Hdyc3MNBkNzc3N8fHxBQYFEIsEg7M+8HPhKzW9nhJ5T/+PSKJurJqlx2751DBr5DAOpSRZ/4rSpdQdZBOS2qZr1BouzpzEIn8nMmTPpBVhYLNbTf+Ok3b9/XyAQnD9/ns7IzMzM+vp6OlkR6ojj6sGZ42E55yUA+GXHds3hY4TeDKw9Qa8GMgRKk2GYCPgOUFsEtcWgbDyhhBONcgCwY7MCBdxgITe4rjGwII9DEECyzDy9eUEhvMAQ3uixDVvWKXLOGZRKoCgAUF3Mbfjha5fv93I8PDtZW3+5R9jU1BQaGpqVlUV/GRcX9+23344fP14oFEZGRopEooSEBLo5b2fQ7UadnJzu3buHrWH6s9gNeanX6nQ53/zuHiFr/OoIf/tTHzxzdwl52skHf/vvBbce5LaqNns5JAzx8EzOBbKLjYAHvNmzZ1tbW7u6ugJAYWFhQEAAn8+/du2aWCyeMWOGra3tMx0tOjo6JSVl7969L7/8cu/UiwaUgPfOXa9uNVAAx5dDUzlEfwEOIwCAr1e9Wb7T9sGFi23qC22qe5pHy8LwSMKJw3IxY3vxOB5mnFAL7ig+F0hi/o37+4e1X6vfUN0YYyMY6+HmfuQMye9Us7f+ckYIAA8ePHjjjTfo7Vu3bllYWOTm5o4cOZLFeuafYqNHj/by8iovL8/Ly+u4fAnqb75fEhD04bkWp1GU4LeuWgJHwjlQyGP9sHREFw5oPn4yYcaNsuLntqpSmhTihnrV1XxeYF9PS+qHcnNzy8rKgoODr1y5IhAIZs6cSe8PCwujF46gl7j7+OOPu/wSIpGIHsiGQYg64+Cq0aHvn5ErFYbmSiA5YOsDAHwOMdFO97aVjfqa43yVEgAq1NqLbeq8NtWlNnWFWlup1lWqddmtKhLAz9ws3NI8wVZQqX40BqdGq1fo9frW1ub9u22WvNWZSkwQhPfu3Tt06JBarR48eHB9fX18fDw95cjV1fXQoUP0YyZPngwAQUFBXX4VkUi0detWiUSCQdifeTqYZ38yYfZm3r0mpU5PAQDbyXuQ74gjb4/xcnjmvr0AQJrzzUPDo9tOrbvbkN6i1FCUPP00BiEA2NraVlVVKZXKuLi4nTt3GvcHBgaGhYUBQGlpqbEHYdeIxeLVq1cfP35cq9VyntBDBCGj4S7C48o9r1XblVAGtt0wczAY2MQa8ZC1s31Y5Isdx9oMuZj9QlMjANxQam6rtD/VtNzX6mq0hiKlpkipoRfT1j26ukkBAKVWtiUn9d8gdHFxWb58+WeffXb8+PGQkBBj/5eeFR8fTwfh559/3hvHRz3Fz1V4/cvJ2bcaL5U3UwAhXlbjfWy60/9HMDXaLTPN19zsplJzoVU17Uyy3aoPeq7e55WXl9eBAwfi4uISExPnzOmVxuLe3t7+/v7FxcVZWVlTpkzpjZdAA4m+qXFQ+aWI6oYSgFk81at3v445csjcoX0Nk8fH2qjyL/Iu5VaeOPEl30xHQYFcbcMmr8g1Y4VcjYFaUto+m+6WSjvHTggA+qaGTlZigvtnWq1227ZtS5YsGTx48Jw5c5KTkwGAzWaHhoYaHzN69Gh+567tPsnkyZNtbGyKi4uNw21Qv0UQMGGYzYrpniune04Y1q0UBADBlCggSR9zDgD8XNeaVFhcfCalZwp9ni1ZsoTL5T58+LCpqcnY9uWDDz4wznaIjIxcuHBhN19FJBIBAE6rR52hKsgDisprUwFAHE85ZhDXmIKP47h6WIjmuP5jw1s5hVPSL0/4evuSZctjfIf+j4u1LZtlRhJ7fJzpPxMs2mdMsW2fti5YRyYIwjt37tjY2Fy5cuWVV145derUokWLAEAoFP7000/Gx3z99df0Dfwu43A49JBR/EwyDcvWXjdsRLyNEAAK5Wp7DuvemdOmLsr09uzZs2bNGnd392XLlhl3hoaG2ti0L6Pq4eHh5+fXzVcRi8UAIJPJunkcxASq/Isairqm0JAAowVcXqdbjLLsHYVRM+3/9g+X7fuIJ3RyJ815ghkJnTygCYJwyJAhCxYsmDVr1tChQxcvXtx7c97j4+MBQCKRdPM4jY2NxuGs0CFZCwsLv//++++++27fvn0fffRRTU1NN18I9RSn6Bl2HFLIImu0+uQmhV9VqakrYorx48fb29vfvHmz+1diUlIenccXFhbSn68HDx5s27Zt8+bNALBx40ZM3OeaMv/CFblabaB8zc0sWSRvdOifP+c/cVw9LMQvkDxzX/NHswbdzNhCDoe0tLF+sbNXOAby1IKZM2fSa2d3nKHYBbdu3dqwYYPxy9dff53eGDVq1KJFi+RyeXR0dE1NjXFlUWRymtHjrso1nlw2ANRq9aprBfr6bv0fQJ3EYrFiY2OhJ04KO16n3bp164ULFwDA2dl52bJlLS0tGRkZrq6uxiZT6LlDqZSakuKLbWoACBHyoKuL8dq/+4kgasZPAZ7EbzPl3hnqGjJsqOuuxM53lRrIQSgUCqdOnarX648dO9Ybx6+vr//hhx+WLVvW3NwcExPTzRF3qAc5Bo75a0jgGhcbAChVacFgaNjxjUEh/9Mnou6jbxP20rmawWDYsWPHyy+/fPbs2bt37964caM3XgX1AVXhZUqrpW8QjhVy2Y7O7EFduR1GsNiOn37lun2/5bxF/HETLWLjHNaudz94+pmO1o/mEfaG+Pj45ORkiUTy17/+tTvHuXnz5ieffEJvG38JraioEAgEaWlptra2ZmZmgYGB3SsW9ST+5IhxFbdH8M3CLHh6imo9sr9Ndsh29UdWLywwdWkDXGxsrJmZ2blz5xobG403ILvGeCWmsLCQvtNx+/ZtlUqVm5u7du3atra2qqqqHqgYmYIyP88AkC9XA8AYIZc35pmvi3bEHTGKO2JUl58+8INw2bJlycnJCoXimYahGgyGnJwciURSVFT097//3d7efurUqfS3vvnmG3ojODj4Ka2nkGlpblyr0+oX2Fu8aG8BAJROt6Wq7n+2fE6plNavLDV1dQOZlZXVxIkT09LSkpOT58+f/0zPvXHjBt1beMeOHQBg7ACenp5Ob/j4+Pj4+NDbQqGw+6N7kKmoCi7eVGpa9QZ3LtuZw+7addGeMsCD0MXFJSQkJC8vLyUlJS4u7k8fr1KpMjMzpVLpwYMH6eVGAWDRokV2dnbGIHxS02HUfyhzM1XXCut0+pRmJR2EAPBrXevKQYrG7zYJY0RsR+enHwF1h0gkSktLk8lknQlCvV6fnZ0tk8mSkpLoNqfw25A0Y6/gw4cP9161qO9Rel16ZmZmoxwAXMzY9zQ692cfKdODBngQAkB8fHxeXp5EInlKEDY0NKSmpkql0qNHj7a2ttI7PT09Y2JiRCIRdhB+7rQc3EsplX/4LQoM8pTjVi8v6eOSGCUuLu7tt98+ceKETqdjP2HNEOOHTiqVNjU10Tvt7OwiIiJEIlF8fPzGjRv7sGTUp5qvXHaidMlNCgCYasl3sLU18xpqwnoYEYQff/yxTCbT6/W/a1taUVEhkUhkMtnZs2e12vZWdf7+/vPmzROLxWPGjCEIAgAaGxvff/9947N27drVl/WjLtDcLqHbLOXL1YtvtfebaNIZAIBSqzW3cIRF7xoyZMjw4cNv3LiRnZ09adKkjt8yLqzW8UP3+MKiAPDtt98an7V06VI3N7c+qx/1uuIrSgP1QKMDAAIoKcdiuElXRxj4QRgQEODj43Pr1q2cnJzw8HAAKCoqSkxMlMlkly5doh/DYrHCw8PnzZs3d+7cxz9vNjY248ePN345a9asPisedQ3Bb5/KEiQw+z9vR3p78rW7AAAEQQj/YF1f1LNEIhF9w2/SpEnGi58SicQ4zpPNZoeHh4vF4oSEhD9cy3fu3LnGbewYPMDU5WanNSkVBkpAklySdPYPMG09Az8IAUAkEm3evHnbtm0HDhw4dOhQdXU1vZ9ebnvevHnx8fFWVlamLRL1IP7EqerSEgA1AQT7Pzu2keZ8fuhEUxXGHGKxeOPGjXv37i0vL09OTjbecXBycpo1a9asWbNiYmKE+BsJI1E6LfvaZWczFgBMtOQtdLBwfe1105Y0wIOwsbExJSWlqKiIIIh9+/bROz08PGJjY5++3DZ6rlm9uLh574+P7ydYJMvBiR+O/aB7EX3xUyqVEgRRW1t78OBBAPDz84uLixOJRBMmTMAlQpms+cCe+q1fUAo5AHhyOSFCHsEx4/qNNG1V/WVh3p5VUVGRlJQkkUgyMjJ0ukeLOi5btszFxUUoFAYFBdErPaGBSnkxu3r1a21KtQUY6D2NBMvB1tblx0McV3fT1jbwqNXq9PR0+uZfZWUlvZPFYun1+nnz5s2cObOtrU2v169atcq0dSLTqtv4z7IDe683Nk+0bO/5crihbY695aAtO/nh00xY2ID61ayoqGjDhg0TJ0709vZetWpVWloaRVHh4eHr16+fPXs2ALi5uWk0mpUrV6alpZm6WNS7zEPGex445TIzjmVpBWw2y8HR65XX3A+lYAr2oLq6usTExEWLFjk6OsbGxm7btq2ystLBwWHhwoUHDhzYvn07ADQ0NFRWVi5fvry5udnU9SJTUl251HJkf1lzy6/1jxrjfVHdCAbDww9XGxQKE9b23F8aNd6HP3z4sLHJGZ/Pj4iIoO/DOzo6AsCRI0eOHDkikUiioqIUCkXvdfpG/Qfbxc3xs82mrmIAKioqkslkUqk0OzubXtceAPz9/cVicceLn83NzW+++WZGRkZ0dHRJSUnHazOIgZp/2UWpVX/8PYNefibZYtbsvq2oA6ofa25u/uWXX4xf7tmzR6FQ0NtyuTwpKWnhwoXW1tbG90L/KpqUlKRSqX53KLlczufzSZLMzs7euXNnc3Nz370NhJ4rU6dONW6/9957ubm5FEUplcrTp0+vXLnSw8PD+Inj8XhRUVFbtmy5c+fOkw4FAPv37z927Fh9fX0fvQHUL1WKJ5eO8dw3zHkwl/OKgwX9R8giS8d4lo7xrN34qQlr69dnhA0NDV9//fVLL71Ef7lx48aQkJD09PSjR4+mp6cbl7YPCAiIi4tLSEgICQkhnrCoK32OKJPJrl69alw+AiH0uI6drKuqqu7cufP555+npKTI5e1dy11cXEQikUgkioyMfHrnQpFIlJ6efvz48Y6rjSKGYrffiXPnsl/6rd/T8UYFAABBECyOqeqC5+7SaElJyVtvvQUAJEkGBweLRKL58+cPHz68M8+Nj4+nZzJhECLUeZaWlmfPnpXL5caLn+Hh4U/6jfN3RCLRmjVrjh079ng7C8Q05qPH6aqqAMCCRRqXDyQJAACSL+AFmbJvc78eNVpRUREaGmqcwE63wH733XejoqJmzZplb2//TEerqakZNGgQh8Opra21sLDohXoRGgisra2DgoLo7evXr+/bt89gMPj7+7u4uHThaL6+viUlJZmZmXQ7C8RYmvLSu6+IL9Q17alt3erlQO8Mu1qVM8qDbe/oIcskntCNrw/0lzPC2tpaiUQSERHh7e19+PDhadOm0Qu4uLq6rl27ln5MTk4Oi8X697//3bWXcHR0HDduXHZ29unTp+fMmdNjpSP0fJLL5bt27WpoaBg6dCiXyw0PD6ejztzc3Ljaw4IFCwAgKiqqy68iEok2bdokk8kwCBnOzGuo3dtr/Td+uorNAqAACAD4v6HOJF/g/PUuE6Yg9J/pEwqF4tVXX92zZ8+VK1dSU1Pr6+vp/Vwu1/s33Z/8Ti9pJpFIulsuQs8/gUCwcuVKgiAoimpubu6l1d57dZ1e9Hyxmvuyzzc/+gcEEBwOIRAQHE54zHS3fce5viNMW1h/OSMcPHjwiRMnIiMjt2zZMmTIkOrq6qFDe74Z+Zw5c95//32ZTPaUpvgIMQRFUXv27JkzZ46zs7OZmdnOnTvfeecdAOh4093d3b2bjdAmTZpkbW197dq18vJyLy+v7haNnnPmYye4/3rSoJAbmhpZDk4Ex5RjZIz6yxnhiRMn0tLSCIJYu3att7e3ra0tALi4uPz446NGWb/88gu9v8t8fHx8fX0bGhoyMzO7WzFCz7na2lqSJG/cuHH//v1Dhw698cYb9P4zZ84YH7N+/frQ0G4tFMdms6dPnw54Uog6IPkCtotbP0lB6OeDZXrDe++998UXX6xevXrzZpxqjVBf+PnnnxcuXBgTE5OcnGzqWhD6A4wLwqysrPDwcE9Pz/LyclPXghAjNDQ0ODk5kSRZV1eHA7ZRP9RfLo32mbCwMGdn54qKiqtXr5q6FoQYwdbWdvz48RqN5vTp06auBaE/wLggJEmSnpiIY0cR6jM4dhT1Z4wLQsBJFAj1OToIjx07ZmzSjVD/wbh7hACgVCodHBwUCsWdO3fc3NxMXQ5CjODj41NaWpqdnR0WFmbqWhD6D0w8IzQ3N4+JiaEoKikpydS1IMQUM2fOBICTJ0+auhCEfo+JZ4QAcO7cuZqamtjYWIFAYOpaEGKEq1evNjQ0TJw4kcVi6XQ6iqI4/WYaGWI4Jp4RAsCWLVv8/PzoFNy5c+emTZtMXRFCA9xHH33E5/PpNSh27979z3/+09QVIdSOoUHY2tqq1+vpbZVKpVQqTVsPQgghU2Fuv82amprq6moAaG5uNnUtCDFCcXExfS+moqKikysaItQHmBuEmzZtsrKyAoBbt24lJCSYuhyEBr7U1NSioiIAKCwsHDt2rKnLQagdc4Nw/fr1I0eOBIBvv/0WTwoR6gMrVqyg82/nzp2VlZWmLgehdgy9R4gQQgjRGHpGGBISYmz+6+HhoVAoTFsPQgNedHS0g4MDve3r62tvb2/aehAyYug8QoQQQoiGl0YRQggxGkMvjdLUavX27ds1Gk1AQEBjY2N4eLi7u7upi0JoIGttbd29e3dbW1tYWFhJSUlkZOTQoUNNXRRiOkafEXK53BUrVigUirKyMpVKRZKM/ttAqA9YWFisWLFCq9UeOHAAAHAxCtQfMP1H/6lTp8LCwpYuXbpgwYLdu3ebuhyEBr69e/eKxWInJ6fFixcfPnzY1OUgxOxLow0NDVevXvXw8MjJybl27dqiRYtMXRFCA1x1dXVdXd2lS5cWL178448/vvbaa6auCCEcNYoQQojZmH5pFCGEEMNhECKEEGI0DEKEEEKMhkGIEEKI0TAIEUIIMRoGIUIIIUbDIEQIIcRoGIQIIYQYDYMQIYQQo2EQIoQQYjQMQoQQQoyGQYgQQojRMAgRQggxGgYhQgghRsMgRAghxGgYhAghhBgNgxAhhBCjYRAihBBiNAxChBBCjIZBiBBCiNEwCBFCCDEaBiFCCCFGwyBECCHEaBiECCGEGA2DECGEEKNhECKEEGI0DEKEEEKMhkGIEEKI0TAIEUIIMRoGIUIIIUbDIEQIIcRoGIQIIYQYDYMQIYQQo2EQIoQQYjQMQoQQQoyGQYgQQojRMAgRQggxGgYhQgghRsMgRAghxGgYhAghhBgNgxAhhBCjYRAihBBiNAxChBBCjIZBiBBCiNEwCBFCCDEaBiFCCCFGwyBECCHEaBiECCGEGA2DECGEEKNhECKEEGI0DEKEEEKMhkGIEEKI0TAIEUIIMRoGIUIIIUbDIEQIIcRoGIQIIYQYDYMQIYQQo2EQIoQQYjQMQoQQQoyGQYgQQojR/h9bCiZPu8bxdgAAAo16VFh0cmRraXRQS0wgcmRraXQgMjAyMi4wMy4xAAB4nHu/b+09BiDgZUAATSDWAuIGRjYGBSDNAqU4GDSAFDMTmwOYZmGH0MwwPjrNzoAmD+YzQcWZmOHyEBphPtRWHMYSkGYEm8LIOFhobgZGBgZxBgYJBgZJBkYmBkYpBkZpoO8VmDkzmJhZElhYM5hY2RJYeRTY2DOY2GQY2DkU2DkTOGQZOOQYOLkUuLg1mHl4FXjkGXj5NJh4+Rn4BRj4FRj4FRkExBIEBDOYBIUSBJUYhIQZhEQymISVGYRVGIRVGUREE0TUGETFMphE1RnENBhEmNiYWVjZ2DnZBIVERMUExL8BncUIj3Ljtz0HVLWbD4A4UyVnH5CepwVmf3NdeeD66bn7Qez3SzoO9F9h3wdi86w3PrAh7R2Y/efmk/1G+Ur2IPaho3wH/gSzOIDYUxJyDnQulgSz18S0HNgZXQpmB16cduBc6TKw+l3zjh54IXIRzFbO+XKAactvMDtp4rJ9fxJn2YHYHfsN7Y9kbgaLf9nRYCeUawo2ZwsXl8OqUx1g8bal6Q7y4TZgtur/Rof5nUZgN/ed2eDw6nEzxP0/9jmorpKF+DH3osPWq322ILax42GH3bEHwHpPFU9xuPrnFZhtpmR3oP1vJli998ldB9rDJ4LZixprD9iybwSz13w9ceD+Nzcw+2dV1IEIVz4wO5199v75e7zA7vQsdz+g92gumC2qtfnA4tBWMPv1pQ+2D69PALvNMUreQZ1BCSzuV/bS/vnqk+Cwtd7j5CAq9gyshv3qG4fEQEaw+TdkJjq8YLICswNVzzqEJFWC2fWyDI5/jrWB9Vo9euzQMF0NbGaGe5vDLLNAMFsMAHP1wzmMvCAvAAADmHpUWHRNT0wgcmRraXQgMjAyMi4wMy4xAAB4nH1Wy24kNwy8+yv0AyPwJUo6+rFYLwKPgcTJP+Se/8cW1ba6F0tk7EOLU00Wiw8ND5d544cSnz9f/vj3v7I/8vIAO/3P/5yz/KNE9PBW4qE8ffv+416ePx6fvizP73/fP/4qxsUE71DhX7GPH+9vXxYuz+Vm1Uhna/GEAMS9UKX1KTuOHMhhPrqWm1T3NgPwG1LLe7m1KmP2SeXGVVT60ARph0/pxGrlRpVV3LLordwj5qDe2gykeXPJkB4+ufaB4Ei/ulqjmQB7AAXkaAjSq+TdxRPgCKBWnwqRADQipww4g6QhCVeWonWodsnyZjoS76zUuHBFQa1nWvIqkFfuLcgx0ol8MqRE9A4A0sGxTndTzpAaSKoqTWUAoMRNMok4CgQJpym+Z2TOqHoGbKg5VWlsYIw6qfUhGTDKI5WbT1J4JB8+Uo9RHojOE57wfdfWyTJglAd96xLVuQXbbpzGnkC22sRmX31JirbP6iO0kNP7GCOaxFwbZ6pL1MdrM/vsxoie0RRZQMZwUUjZSIanHnXl4zpjJhhStmFZGcXKa3BDMkPH6tDmGN8M2gLa4KtTl+i8Zjw10x1N9hq+pjNpj5fQJMEkgfaAOsZ3zjHjJaYxeGTQcXg1VjTyGjuRmXYIGu11LQ1mdkULsHcMZrZn6HDK0ZQOzTCguaYYg5V/m6hnBxJv5N2kAiT2EObt2F2s3EaWkiqQ6IwBn4aHhplvmfgadUId0Z+obczd1DFTmlEmrWTSda4lNnuz1GdUqcc8YnGEnCTUZipSFAn1xkB2Xu1Cbuni1KiRo0Q0qIWuAxskR0aJBsxdiCMjcRXNkEaLJ6OD3CGSk8yZAnnR5OmCyVd4HLNlsn+7v/xygR1X2tP7/eW80uJPzntrHe28nda5nXfQOvt50+BU9LxO4ij9vDXWeZyXA8Pl9j7hDx6vmz4MOxq2I05lR8OiMsTY7jnoIeaOz/F6LzseB7+OKblsVAaL84z+DlbXBcngpdc9uAz7Z4Es0YLcZa1pcOVTxqUjjDtV0cOiJ8ZCbaS/05VgG5adcFxii/NOWfph0Z2zjE/LGSt0RWayMUfl8OpWRkPaSHZLoYvzwFxdhpYjf7lOpy3Lzl1XB8SMXabtsGzO2j8tZ/QBP5BNthoaMoeQm48FZwh5ZmGr99AD5yAEZ0hrm090/LW/4/z1cw/PDz8Bn9vniaEBg6wAAAKIelRYdFNNSUxFUyByZGtpdCAyMDIyLjAzLjEAAHicZZK9blsxDIVfpUAXB7gR+CdSlNEpSyane9ChCDq2KYqMefgeyUXNosu1SJOHnw71/PjlhV9Oz49f7l5OD/t3fx5On57uLte8/Hg5Pf3z39/jn76PF3wvt4xc8/+p7gTj++H9FI2FNQ5pTnqcvTFx8MGtkww/ztZckxWJoX3YSnC6yKFNfCQqtDmnjYNaaA9CiTTunmghHz5WbCPHwIyuLGiBmGjKcU9NU0ceZ2qeprKajNhjZaSzMaNrqAVkVrV0lTVKibvsPuboedxzY2UczjjFiESNq3VaGShQdF/jzLsL1O+tSRDuhRyruK1cbzIytpio7JHWhvmIAxruW97RmIkihVVjCFLaDBb6KhKRvPYZafYDB8ok2RQZBAdRDgje84YGxSrqxqlbKp3hIf7DTVm3VLBShzNKtDmxJFxn7cQVd0QmcAIKo9vtquOpmjDPiPYw7E1dGXsbqnHFZnb3wxpK167XUOwpkcEfsZlFCcKCVYbrdlcYbZDBlmI/EHKx5aNnGMt6EGQStmyMjA4+6IplrAwpfFmZ9MAkTDDXDsBYi18xCS2j4QnWvp1P2pdy2EyDl4F4eL4z3dCOXdMarhg1GlHslyWugod9d3x9e/3++dfrz0ltHS+vb9/w7CbfIuEpJZKpt8ho2i1in71EfXrRlBkl0jlKpc4skU0uMCyTC43y5ILTJ1ecyQVHJxccyBQcqBQc8cmFR2JyAaIphUd0SnXHplR7+pQClFOqP1CqBsHYghRTCtKYUokgVIiUphYkn1qJxtRCxDG1EKlNrUgorkg5tS6tT61bw6ACpVAuUIK3UB/ReP8NSt5yg+1y74oAAAKdelRYdHJka2l0UEtMMSByZGtpdCAyMDIyLjAzLjEAAHiczZFrSBRRFMfP3Nmd3dVZ9+E+srVt1NRN6YMVbZaPm680SawvEflhorBJgkiIAjFsTRONTCMzTClCWKmkh4Ep7d4+lZqmUgRBCbUQSBIIZfSAdo5bghD0sQuX/+/873/OnLnzKXD7LYSXEZZWWninh3cdJ4AUVk1E9OAJC08EiqrRLSr/u16uOlh2jjWJ+IT/c76oS/0jb/1L23+McRjjuP9Fo4EDWAEQB7ASOAKcC7j48C1IvEEhvEbWaBWiFWStKAk6hQirQKeXdAZZ7wb9ajBESVHRHl40SqIExhgPMZrAZAZTApgSweyUzRaFWKyyJQmssZLVrpBYG9jWgC0ZbClgd8j2VHA4FeLwgHMt2IjAa7SCziBYrHaH0+wkBNThIn//ePMFFiP6mFocKu1hl90e5L3iLZa0ozOocnlcC8sYHn2kct/oZublQgGV143MBQuyknNVrqm0so5iLVW5euIoe1/tQk73N7DDR04g573uZHu238R8zvknbL7rFXJm3hdW9JXDzLfQWCA+5UqOyuO9CbkLH+5i5uPJspzTcxsxUzWlpRUXG9Dfn3mA+mrXIxcNn6LtSjbO3GPppyF9C3Lb9DBdqE3A7zrXOEsL/V5kv+Y5HWq5tFXlB/mPaUfTIPYZam2nGW9CyAddhczboWDeezbAYLwV2TfiY5P6O8hpxc9YzbV85JJumd27KiJPwfXghLMUZy6oKmGT3T3IqRUDbN9YI3L5Li67vq8N5xz87qK9GRL6W8pmcovnX+A9b3iZRR/unsVM5bEZ6m5qxv73uz7TrroB9Au6xW3t0zvRvzH4k2b96Mdng5ve0frYROz5dOIMdStlyI5fwIPAovXKN/gAAAOselRYdE1PTDEgcmRraXQgMjAyMi4wMy4xAAB4nH1WS27kNhDd+xS8QAusP2s5tgdxEEwbSJzcIfvcH3lF2VQPQkTqhVj9VPXqLxoufKOnVtfvr7/9/U9bF78+Qd7/55eZ7S/pvT/9aPXQnr//8uu9vXx8e/6SvLz/ef/4oyk3FbzTG/2M/fbx/uNLQu2l3fQw8uxUT90lg1o/+rzassMnMtWja7vx4ezDY4OU9t5udki49Wg3OmhkOG+QeuoU9aG93QDIkdk3SGv3sjlGiGchVU14p9NLJx3DInr972pqO4eigHzwCOvW+OiDzWwDHAWUI9QzCqg8wGMDzCKpB4VHjCbHcMvccaR+Oh7hjhzRITKIdrZpJsgPFicqJCKlujNOXNbj8DQbWv5Ij8gdUgrZD8nBUpHhTKItshKEEAqpeNEcyM8uluD+Xoo6uQhsD/ZM3wErPeCWuAMau4r5LuFU6ZHDrEuUu4g989gBxwTm6DRmDQX8im2EEkg7VNJREKiRPpz7zm/ucMeQv0TxVJEYiyBsG2TlJw7hkTyReAcVt0NWA/mBIhI6/VBz3QFlArukIayESif3nedc2UHvjlIFYIwx//8v0NpbVXCPxF1PLlC61ekFtSNNe/eqUU0kYMszCoqJMQJtWy9Zpx67rCNxb1XFMmQg4EXFHHNkB82Tq/V0yBFRyujbQOH3VmlkiyGBGqAQ2dYIqnwqJQ1CR2GKmcdeJ5/+uzhrAsnEwlukAAmxkAXPKSeRY+e9KJAYGxIsiQeUNO4dsPKEPGK2YdDUkCNDZ+6QPlUmJhfKssadIEa7UpbKkqM92DCKatgyVG5pVpIwP8LYvHQqdYpdKUvlaFS9oUYrrgye25bTPnUmhuyYYxt1F9vCU5rI7kmjJgimOwp/B6wMYchaZzzCNhv1Hcvv99efVt25/J7f76/X8qubrw03j3btsXn2a1vNc1w7Cacm1+KpI49rv8xzXmuEoFKvZaGl8XEnlGBZrzqFiWUd811hY1mvbVA2l32q16Mt+zU8cJbH2UtgsQhhxGqxehylBF6yvhcwMafgCtEMWpF7GIBSXOnCzDhCuFxlOSWyuKKjuAjTcpeLbUmWw1x0i/NymeOUyKV5fEouPRVXeMYLc2YOr67ISIW2nF2hkMkZO3tZl8kZ7frYxjoly1OZFVAxeOhLmiFZnCU+JZf18Sm5rBdnhI1XfLTiDIk+9odWIC+/tDhDost6fUpUaHUxrB54rPg6f30q4vnpX1ER8xOUkVz/AAACl3pUWHRTTUlMRVMxIHJka2l0IDIwMjIuMDMuMQAAeJxdkjtvFTEQhf8KEs2NtLE87/FcUdFQJfSIAkWUEIRS8uM53ovIQOO1z87j8xl/+vD5iZ4unz58vnu6vD+/5/L+8u7x7uGm8/eny+M///5u/+S9fcD68KrwTb+tj//HE9Y3vy4xpi/Kg4cJqx5XH1OW8UFDlTyOqw7LORVCZO4j2WQ9ZDAbHVcZZlPymMNXMCOCx1xzERKmivkWVAIp6DFlynFFKVWL434OJVuEMkgXUnFkSa5cW+FJLoK0ZHAgWFayEFrxQtIZM4UsCKVokMSChl1axAZS0414jwoZcuunuCdD0yHqqVuba3cE+b0NCUfKrpYr/IxbChtQw9lzczgIJREkY5pvi7CzuXwH0Yp5SzPydeA7XdYtZgaa4OuiFGe3ZTppB+mSiDNtZkAxFKSZpxLhvu0XNKWzP4vT9hf8alAC3pvtIcLfyLNZqK+tKOd2E3UoHP9kpNvpkwzSQB3c0Py8qwHN2aAwsfDpHGfYLpy8Rwlj2CL3+CkEw8H8V048IPgYmM++BaPgvLmYMh2EhhewsIEy05m2kr4Ajw7GgqFeYwjn4q3g185ylGY7TRRexDvGw9h3HaW5Tcz9KmVtWxlPKXcWbg54zHWquWw/YizcY23I7b3ncXd8eXn+9vHn84+aY28fnl++Dp1Fryfm4naSkhZJpa8n8rJ2svLXk0hFO2lli5Ra7aRFDYa4qNEIjg3HijpOUcORooaDvIZDRQ2Ho6jxcBY1oFnceFiLuztW3O3x4ga0irs/qNQNouKGFMUNKYsbkSCzEQmVNCQv6USrpBFRlDQisZKOlCUdCbkNiWdJnxoadShU7lBZ2n3C0+hzW79+A4sufH7u5YJqAAAAAElFTkSuQmCC\n", + "image/png": 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\n", + "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -710,7 +703,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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", 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\n", + "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] @@ -807,13 +800,13 @@ "metadata": {}, "outputs": [], "source": [ - "from openfe.setup import Network\n", + "from openfe.setup import LigandNetwork\n", "\n", "# load a new network from this graphml representation\n", "with open('network_store.graphml', 'r') as file:\n", " network_data = file.read()\n", " \n", - "new_network = Network.from_graphml(network_data)" + "new_network = LigandNetwork.from_graphml(network_data)" ] }, { @@ -827,8 +820,8 @@ "output_type": "stream", "text": [ "edge 0 molecule 1: benzene\n", - "edge 0 molecule 2: benzonitrile\n", - "edge 0 mapping: {0: 2, 1: 3, 2: 4, 3: 5, 4: 6, 5: 7, 6: 8, 7: 9, 8: 10, 9: 11, 11: 12}\n" + "edge 0 molecule 2: toluene\n", + "edge 0 mapping: {0: 4, 1: 5, 2: 6, 3: 7, 4: 8, 5: 9, 6: 10, 7: 11, 8: 12, 9: 13, 11: 14}\n" ] } ], @@ -868,12 +861,19 @@ "\r\n", "Options:\r\n", " --version Show the version and exit.\r\n", + " --log PATH logging configuration file\r\n", " -h, --help Show this message and exit.\r\n", "\r\n", "Setup Commands:\r\n", - " atommapping Check the atom mapping of a given pair of ligands\r\n", + " atommapping Check the atom mapping of a given pair of ligands\r\n", + " plan-rhfe-network Plan a relative hydration free energy network, saved in a\r\n", + " dir with multiple JSON files\r\n", + " plan-rbfe-network Plan a relative binding free energy network, saved in a\r\n", + " dir with multiple JSON files.\r\n", "\r\n", "Simulation Commands:\r\n", + " gather Gather DAG result jsons for network of RFE results into single TSV\r\n", + " file\r\n", " quickrun Run a given transformation, saved as a JSON file\r\n" ] } @@ -893,9 +893,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING:root:Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n", - "{0: 5, 1: 6, 2: 7, 3: 8, 4: 9, 5: 10, 6: 11, 7: 12, 8: 13, 9: 14, 10: 4, 11: 15}\n", - "\u001b[0m" + "{0: 5, 1: 6, 2: 7, 3: 8, 4: 9, 5: 10, 6: 11, 7: 12, 8: 13, 9: 14, 10: 4, 11: 15}\r\n" ] } ], @@ -911,17 +909,9 @@ "id": "3b0dc398", "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:root:Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n", - "\u001b[0m" - ] - }, { "data": { - "image/png": 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CcwAgpVLhL6vdlq5hKGYl5W1aS4iLl0I+lyqPCyUcDJvv/dGMGIfhIRyfWqYOZwkGDhw4cODApUuX0ltPK5VKkiR79OjBdC7Tqe5XM82+sJMpK7yCUAPHsY5+TmWPQxCmOYwIYXt6aw5rcNlj3e0pgMg0ofac6cKzxxXP4k0fr9KUb14V/vG79pmf0oQEBSPd7Oj+EjSWk4vDqIkmT2eRMjMzKYoaNWpUr169Dhw4wHQcU6juhdCWxw6sbV/pt1tb4YPb1DBgHgQxEoxr5TxjvvaZie72nlz2M6nyeJ7WhC+Kyl390UadZi537U8UUfxd9kx+0b8SuTObNcXjo3/XzjPm49Y2Jk9nkby8vP7444/IyMjly5cvWLDg0qVLTCcyuupeCAFgbm9fG14lbxE78XB0RYhYCpuuPfmBxff8eTgW7ukIAGsy8guJ4stC+ZNHhed07xZrbooun5P9c1NzKCep1en5ABDu6WDLKv5ws/JvYtuzHwP5LBObze7UqZO3t3ezZs169+59+/ZtphMZHSqE0KeFex03AY5XuMWggFJEcv8p+YQRQcyWc/hiwIsX1fVxsm5lw8tVEVuzPnr+Ldy4ipRKTZ6uYiiVKm/jKu0z27PF6Up1QwF3gLPWkgkMc5m1GNCktnJLTEyk982XSCQ3btxo2rQp04mMrrpPlgEADIOjM5q3WHhTIq/Aw0Ieqfyq8HG7hD2yh1/zW7Q2XjwEMSCrBg3t+g0uOP7hoRoGEOHt9O3LjF05BYNcbGr991BNnZMl2rvFaZKOudMEQaSnp5suMYCnp6fOiYui/dtVqcX742ep1DuyxQCwyNtJu3O2bc9+vGYtjR/TgqWkpPTv3z8/Px8Azp07t2fPno0bN86cOVOtVo8aNapfv6p/MY061H9w+Vlen7X3pVIZvH8OOAfcGukYJBOCOAV4jjz7Go1kqftTN3AptVWDRt6/nULfN2lPnz7Nyspq2LChp6dnyVdv3LihUCiCgoL4fH7JVxHTIPKFKd92IQuLm0vPTc49lifp5iDY4utWPA5nWXfuyq3rZ+XXmN+qHS6wpk+npqb6+PiYMvDLly8bNGgAAHFxcT16/K9B83bB41e9Ts0vvPaXqzzXX57WTvrKTS0Oe/f+dH5RL0fr9bWLF4RgPL7Psb/YHjr+NiL6URSFVZv7XagQFruTmN977f0iOSFX6dt6lJf7oguWuzY3lkt9WErvuuhnu37fmSSjWROJRG/evAkMDGQ6CFIG0W+/5q1brjnMVRFdn6dLCHJ3XfcOdh9/R2HhON+aUiqtu3R3mhLO8fLJzMxs00bX4iKjuXbtmq+v77scSavWbcXWDTBJBtlxMb0PBkZRAkqpBlatzOuvrkdb4dj5hl7e3OLLR6fJ4Y5jp5oyLWKJUCH8iEiqWnAoYc+NdBzDihQfbRnDoigepeS8iBXH/z7KzXahVlcXlpOzzx9XcRvbEr+vegkLC9uwYcOaNWvCwsKYzoLoQ6nVqYO7q5Lfas5szRKvycivy+Oc8fdk67oOwNhsjM12CAl1HBECJr9Q2HghKTxiOSFKJb3awOsL0OnjVoIUBRfDIC+xfr3Ox+wz+OSHfbfZ7jV8jl/GeOj2A1IGdEPvIw4CzuYxjdM2Bq8b7v9NC/fargJnttpFXdBInjpcdG1n6qaDqus4Ru17X5AoL95LkxDm5e/axGBsc/Dy5ctNmzZhGBYcjLp+mzuMzXaZuRAA3shVy1KFYe/eO3HwWlac13LV77mFOt9CqdWkXJ6/Y0N2RBiQlezWUgkUBeN2PJm382/Vi5NkwCjdg95egrxEELikNJv47RdzxKwPDXidZy5CVVCDJMnSLnsoitLzanWACqEOTjac8V1qng4PfBvTOWdjp39zo04m/RyRHdtS9oaLQV0el6AgMlUIAM+kyiWpebOScg/9skGV/I7p4EyaOXOmSqUaP358QEBA2aMRpgnaB7PbdhiZmO3L44x0s9uTU9jFng8AMRkiUeldySiZVHr94ifbWxvVwiOvDt1Jl96IgaYjwMpOxwiVDB7vBwBoPkbOsU3huo6oOV2NsXjNWtp8VY32RtHv3LlzLBarU6dOOl+dN28ei8VavHixzlerAzRrtAy4nb1jSGhu1BIAOJYnOScqssIwPo7dKpSdzS9amiqc7+3ozWXPTnrvPHPykGO6elZUA2fOnDl37pyDg8OyZVWhrV01cbdxmy94sXRzhhk1HPbkFLS3498skG3IFC2uWerqWFImKzhxmNugET/A6FMxbyZJ159LlealgCQTnhyAJwdArQC1FOL3QcCID4OeHQKZEFz84IuOAKDE2G+57jFuvWNmjTH9LVzEQqFCWDb7QcMKjh9Uvk4Y4GwzwNlmZbrQjcP6SyxdmipsbWv1rZMNAEzwsN9z5e8+t68LvtT9nasKUyqVs2bNAoAlS5a4urqWOR4xE4kZmQ0EH3Z/bcDnvFOofqvn0etF+oHcgu9cbBrwS90YlpLL3y+dY+x4FGBjfRdJue5g5w19dn44m/4PvL5QXAUlmfDyJGAYBE4A+FD2ZDh3r/NXC93reev6tQhSEro1Wg44y2XWRzcNAm2s6vE4QjUh/a+pb30eN0mhzouOpNTVrivTxo0bExIS/Pz8pkyZwnQWpAIkcQ/Y/y37YWOYiqTq8jjfu9gSFESmCZnNBgD3BHVz2A6fnrV2gxotig8f7gBSBb7dwLm+9igCZ8WcSzJ6RKSqQIWwXPitvrTu/LXmEAcsoqYTANyVyN+rCADg4KCiKOW71wWx1WKPWo2cnJzIyEgAiI6O5nA4ZY5HzIdz6pt0mYL+OV2h9uSyASDM09GBjd8plP8lZnhnmRP2bWR4iatSh9pQ/5sPP2fFQdo/wOFD0+GfjFKqqQO30oyfEakiUCEsL+eZCzGtLkJBtnxfHkdJUuszRQCQrlDX4LABQLhtHSHKL/W3VDkREREikahXr17/+9//mM6CVAApKezCIv6RyN/IVQQF+94X/M/RGgDsWfj0Gg4AsDxNqGR0GuFtQX0SSn/IRxHwcAcAQKMhwNfxRFMkJbJECqOlQ6oU9IywvDhePvZDx4j2bNWc+bGm08jE7CO5hQOcrQ/kFvZ2tAYAskCcvy3GZe5S5pKaTlxc3K5duzgcztq1a5nOglSMKj3F2cb6x5rO0969l5NkC2veGLcPczKHudgdzpUkyJQT3+S0/q9VYQkU6KlShpBF/AU6umb/5/0LECWBTQ3w66vzdSsO/jpb6uGA2oUWS0xMnDBhQsnz//zzj+nDmBVUCCsgrW2XPnOXyNVqADiVLznp59nOlne7UP5DYvYAZ5uBzh+avIhjD9j1/55bz4/RsKYQGhpKEERoaCi9AxZiQciiIsCwbxytv3G0/uQlFgaDnG3WZ4r+LpD9XSBjJB4AQMa+MgZY2UPtLoDrviGPYSCRV7sH9vplZWVt376d6RTmCBXCCgho1frpof05WvPlNvu6dXue/l5FtLPhsTRztUkid+1Pnlur+MPCI0eOXL9+3c3NLSIiguksSIXhfH5pTQdJgJPCokKCDLSxamld2hUhzYjXhTucu+lbt5+XANlPIPkGNPoOcF2fYxQlsGLpOF+NtWnT5tixYyXPr1ixYvPmzabPYz5QIawY228GiGMPaFp427DwGTUcIlLyVqTnd7YX8P/r5ST793bRtYva82uqGJlMNnfuXACIjIx0cCgxtQ8xexwvH0qh+xHa0dzCJ1KFB4e9p64Hv+LtyQzlVN2+mezSm32Sajg7GQpSIfFPaNCn5OsKNVXHXWDEfBaIy+V6eXmVPG9jU91bFqPJMhWE4y6zFmsv1B3sYttYwM1Uqndmf9TRLS96OaWsss/qV69enZSU1KxZszFjxjCdBakM3M6e5eRc8nwRQcZkigBgvrcjg1UQANoUvcag9Nk6OBuajwUAeHIAFAUlX7e2Ynk56r+cRZAPUCGsMF7TFjbdi7+B4gCLvJ0xgK3Z4gxl8TMJVXqK+OAuJgIaXXp6elRUFADExMSwWOjuk6Wy7dkPK7HiZUOW6L2KaGFt1bPEs0MT61dwT0Ao9Y3wbgM1AkEpgce/ffIKh4UNaVvDiOGQqgUVwspwnjEP5xffdQm0serhaC0nqbUZHy2cyN/5i/p9tsnTGd2cOXOKiooGDx5c2taFiEWw+24kkB9dcqUo1PtzCnGARTWdynExaNzFFV9KXzoSkjIGBY4HnA2vz4Hoo51+2RQR1rO2EcMhVQt6RlgZbDcPh5EThFvXac4s8HK8JpaeEhYNcbFt9d+Mc1IqFW5a4/bjaoZiGsWdO3d+//13Pp+/atUqprMgn4VSKqiPi1lkmlBJUYNdbJsI9K06wKysnCaG8wNbGzkgbHunGBSbJ1WVXnHtakLd/8Gr0/BgB3y1gj7Ho5SDhXe9C+uDW/1S34ggWlAhrCSHESEFp46qMz7sXlGDyx7rbv9LpigyTfiHn6fmQrvwzDH7QT9YNaoiDRlIkgwNDaUoavbs2bVq1WI6DvJZ8tatAILQHN4qlF0RS61ZeGgNfbOfMB7fplsvhxHjjR8QejaC0XnP9lxPK1IQpQ5q+gMkX4fseEi9AzXbcSjCU5U/J/t47to0z82f3jJFEJ1QY97Kk1w6mz2vuPm1nKS+fp6eoVSv+sJlgHPxLCxe0xZeu2Krxkb4u3fvHjNmjJeXV0JCgrU1w8+QkM8hu38nY8JQzSFBUb1fZr6SKed5OY5zty/tXThfwA9s7R69A2OZ6Ds0QVJDNj46F/9eXy18dQbubwEbD27PjW6ULDZptQtRCAA1YnYKOqDumAAAKpWqsLCQw+HY2uroHy6TyWQyGZ/P5/OraftG9Iyw8my69eIHttEc8nAs3NMBAFZn5EuI4hVQ8scPJedPMpDP0AoLCxcuXAgAq1atQlXQspFE7pqPGmbte1/4Sqb0sWKPcNPV8w8Aw3DMimf33UiPdTtNVgUBgIVjR6a3mP2NL5/LYpU2i7VeT3CoDZIsr6e7TiWtpKsgAOSuWUYp9U63qTY2bdqkVqt1VkEA4PP5Tk5O1bYKAiqEn8k5fDHgxdMm+zjZtLTh5aqIrZ8spdiwipQxvIXx51u+fHlmZma7du2GDh1a9mjEjImPHVQmvtQcitTkpiwRAER4O3FL3LrABdYY14rXtr333j+cp80B3NQfGhgGS/rXexAZ1CPAlcdh2fI/KsM8UsWn1F5NBwBA9ssLcnnx/BpVWrL4yF4TpzVDp0+fDgsLa9Omjbr69cYpJ3Rr9HO9j5xf8MchzeEzqfLblxksDDvX0LOWVfHcdMdx05wmzWQioGG8ffu2YcOGKpXqzp07rVsbfZYEYjxkgTjl2y7aW8MvSc078L7wS1vevnoempMYhvGat+LW9bNqFCD4spPORYeml1uovPA49+7r/Nep+UUP77sqhU3kKUFFL32V2ZPe5lwSSQc426z6wkUzHhdY+5y4ynKuvm0ylUplkyZNXr16tX79+unTpzMdx0yhQvi5CGFeSv9gsrB4Se/c5NxjeZJuDoItvm6akxjXyufYX2xPS+0V2q9fv5MnT44ePXrXrqq5OLL6yF29VHxoj+YwUa765kU6AJz286yv1YzXYfh459AFpo9XfsIt0fm/btQcpirUPV6kK0kqtkGNAOviWa923w5xjVjJRECzsHr16jlz5vj7+8fHx6NGaaVBt0Y/F8vJ2XHcVO0zsz0dbVj4JZH0ptaGxZRSkbfhZ5OnM4wrV66cPHnSxsZm+fLlTGdBPovy3euC2I/mUi5PExIUDHe1066CLCdnx3HTTJ6uYhzHTGZ7eGoOa1qxR7vZUQA/pQm1v90XnDyieP7E9PHMQU5ODv1vFrUL1Q8VQgOwHzKa84Wv5tCFw5robg8AkWlCQuuCW3LprOyB5bU7oftLAMDChQtr1EC7dVi2vOhISutB0XmR9GaBzIGNT/X4aMmE05TZuI3uiRXmA7PiOU+drX1msoe9G4cVV6Q4JdRaiU+SuWuWlrbDeNW2YFei5ssAACAASURBVMECsVjcu3fvHj16MJ3FrKFCaAAYm+0StlD7zFh3u1pWnNdy1e+5H22Nkbd2GZClzwI3S1u2bHny5Imvry9dDhHLVfT3Zent65pDFUWtTs8HgLAajg7s4o8CqwYN7foMZCBfxdn06Mtr3kpzKMDxcE9HAIhKz5dpbZojj38g+etPBvIx6tGjR7t37+ZyuWvWrGE6i7lDhdAwBB2CBUGdNYccDJvj5QgA6zLyReripRSKhOcFJ46YPl5pdu7cOeE/b9++LTkgPz9/6dKlALB27VoeD21hbMEolSov5qM7279mFyQrVHV5nCEuHzUf+GQutFnDMJdZi7UnsvZ3tgmwtspWEds+mbm9bjklZ663IhNCQ0NJkpwxY0b9+miHnTKgQmgwLjMjMHbxxO6vHQTt7fhigtyQKdIeJty8RntmDbOOHDnStm3bkJCQkJAQnbc9Fy9enJubGxwc3K9fP9PHQwxIfGiPKrl4Q87c/0pFhLcTS2vJhM3X32ivjjV/Vn6NbXv11xxiABHeThjAjmxxmtYm+OrsTNH+HUwEZMahQ4f+/vtvNzc3eu0voh8qhAbDqVXHbvAI7TMR3k4sDA7kFiTIilf1EvnC/F9/MXk63TIyMjp37hwYGBgYGFhyOe2LFy+2bdvGYrFiYmIYiYcYCiHM055gCQCr0vMlBNndQdDervj/O2bFc5421+TpPpfz9Lm4dfFFbXNrqz5O1gqSWpX+8Sb4u7eoM9NNno4BMpls3rx5ALBixQp7+1L3CUI0UCE0JKeQGSzH4laidXmcIS62BAXL04Taw8SHdquSddyHNL2srKwFCxZ06NBh8eLFyhJ7cISFhalUqokTJzZp0oSReIihCDetJiWFmsOnUuVJoYSDYbO9HLWHOYycYIkrfFhOLo5jJmufmePlxMexc/lF/xTKNScphTzvlyq1A35pVq1alZyc3Lx581GjRjGdxTKgQmhIuK2d08SPVs3T0xBuF8ovi4t3lqHU6txos1iHcPny5bVr18bGxt69e5duMahx8uTJCxcuODo6/vjjjwylQwxDkfCs4FSs5pAC+CktjwQY526nvecD283DYUQIEwENwH7YOI5PLc2hO4c1oXjmdvEwyYVT8kf/mjydSaWlpdGzY1C70PJDhdDA7PoP4db31xw6sPFpHg4AsDxNqNSawC29eUV665rp432iadOmnp6e7u7uQ4cOvXfvnua8UqmcM2cOACxdutTFxaX0X4BYgLw1H81VPimUPJAoXP4rFRqfdNm0LBiH4zxjvvaZ8e723lz2C5nyaF7xpTBQVO6aZUCSn76/Cpk9e3ZRUdGQIUM6duzIdBaLgQqhoeEsl/BF2id+cLVtwOemKNR7cj6aI5P78You0xMKhRs2bFCpVCqV6syZM82bN9e8tG7dulevXvn7+0+cOJHBhMjnk1w8I3tY/BVHRlLRGSIAmOvlaMMq/ufPa9rCpnsfBvIZjnXnrwVtO2gOrfAPM7ejM0QFWpvgK14+LTxzjIF8JnHnzp3Dhw/z+fyff7bU7TsYgQqh4fFbtrP+6n+aQxaGLfR2AoBNWeIcVfEXc1XSm4Ij+xjI9x8WixUXF+fv71+vXj1nZ+f58z98oc7JyVm5ciUArFu3Du1GYdEohTxv40f9k7dkiTKU6sYCbl8nrSUTOO4ya3EV6BTmHL4I07oZ2NPRurUNT6gmfvl45nbexijtJ6ZVBr1YgqKoOXPmfPHFF0zHsSSoEBqF84z5GLd4t8MvbXnB9oIiglyX8dE0NuH29US+sMS7TcTe3n7Xrl2vX79OSkratm2bZpng/PnzxWJxnz59unfvzlQ2xCBEe7dpekcDQIZSvTungF5goP0v37b3wKrRO5rrW89uwDDtM4tqOrEw2Pe+8K1cpTlJCHPzd282eTqj271797///uvt7T179uyyRyNaUCE0Co5XTYcfxmmfoRvcHMuTPJYq6DM4X2D//Wjtad/m4NGjR3v27OFyuatXV4v5dVWYOjtLtG+79pkVaUIZSfV1smlpU7w3Ai6wdp4cbvJ0xuI0aSbLvngqrD+fO8jZVk1Ryz6ZuX1gpyolydThjKmwsHDRokUAEBUVhdqFVpTpGmxWN45jpxSePa7OzqQPfazYI93sdmSLF6fkRdV2s+kQ7DB8vNTeMTMxkdmc2iiKmjRpEkmSoaGhaDcKS5e3YaV2F8x/CuXnRVI+js30/GhbUcexU1kubiXebalwO3vHkBm5q3/UnJnp6fBnftHNAtn1Almn/xZNUipV3vqVHmu3MZPSCH766Se6XeiQIUOYzmJ5UBsmIyo8czxnSfF3bQlBtn+axmWzhQrz7Zrt4uKiVCpTU1Pt7HR3Kkcsgvzxw/QxA4GislTqM8IiGUmdEha9U6hmejpO9iieLMrx8qkZewnjcvX8KstDEqnf91K+TtCc2JVTsCJN+IUV53xDT47Wo9Aav+wVtKsKUyvfvHnTqFEjlUp19+7dVq1alf0G5GPoitCIbHt9W3D8d3n8ffow29ZBBulKCho0aGCe63sIgnj37h1BECkpKY0bN2Y6DlJZJJm7ZhlQVJpS/V1C5nBXu3cK1TuFyoXDGu320fcb55kRVa0KwoeZ2xmTftCcGOFqeyi38K1c9dv7Qu3/AnnRkfzf/9TeGdFCzZw5U6FQjBkzBlXBykFXhMalePEkbUQ/jGtlP2Tk9ycvXbp8efr06evXr2c6V6mmT5++cePG4ODgy5cvM50FqaTCk0dyls0FADVFJSvUrhxW12fpQjXxP0fBxtrFd0H5rb703HqAuZjGlTVzfNH1vzSHV8Wy8W+ybVj4pYZerpzir6Euc5bafzdC1y+wGJcvX+7atautrW1CQgJqlFY5aLKMcVn5N3Gdu8zn+OW/vetfunzZ0dFx8eLFTIfSh15Bf+XKlRMnTjCdBakMUlqUt3kt/TMbw+rwOBszRUI14cxmac+RAZzlMsus/yp+pk8udrvY8zvZ8SUlN8HfGk2I80u822Ko1eqwsDAAiIiIQFWw0lAhNDq7gcNIR+e5c+cCwE8//eTs7Mx0In0cHR2XLFkCAOHh4XK5vMzxiLnJ/3UjkZujOXwjV+1/X0j/O+/rWDxF2X7QMG7dBiZPZzoc7y/svx+tfWaBtxMbww7nFj6VFj+kJwvE+dvN9w5NmTZv3vzkyZM6derMmDGD6SwWDBVCU1i7dm1iYmLDhg0nTJjAdJayTZo0qUmTJm/fvkVNJyyOKj1F/Ptu7TMr0oRqiuKz8I2+rpruu7idvWNI1W+z7DhumvaE2Do8znBXWxIgMk2o/UBIfOQ37Zk1FiQ/P3/ZsmUAEB0dbWVlVeZ4pDSoEBpddnY2vd3RunXr2JbwWF7Td2n58uUZGRlMx0EqIC86ktLqInJVLLteIMMBYmq5tta6L+o0cSbLwVHXL6hSSi6RnFbDwYnNui+RXxAVFZ8lidw1y0wdTgtBEGUP0mXRokV5eXnBwcF9+lj29niMQ4XQ6ObOnVtQUPDtt99+/fXXTGcpL7oTr0QiQV09LYjs39tF1y5pDtUUtSg1FwBs2fj6zPx+LzPodmDc2nXtBgxlLKVp2fYeaNWoqebQjoWHetKb4OfLyOLLwk/+0xmJUChcsWKFl5fX9u0fNjqIjIz09fX18/P7/vvvK/ok4vnz56hdqKGgQmhcDx8+3L9/P5fLXbVqVdmjzcnatWutrKz27dun3ZUCMUPKN68KT8fmb1uXNX+a9vm9OYVZSqK2FedSQ6+99Tz21vMI83QAekNOS7gzYRg47jJrifY2qkNcbBsLuJlK9a4csfbATy6mjeHGjRs1atTo2rWrVCoFgPj4+C1btvzzzz8vX77Mz8//9ddfK/TbwsLC1Gr15MmTUbvQz4cKoRFRFDVjxgySJGfOnFmvXj2m41SMr69vaGgovcsMWmNjhiiFXPTbr0ndW6eP7J8btUS4fSOptW+thCA3ZYkAYKG3kxObZc/C7Vm4AMetO3erGkvIy4/XtIVN996aQxwgwtsJA9iaJc5QFrd/Kfl41eD69u07evRoJ6cPvbtPnDgxcOBAV1dXFos1fvz4Cs3T/uOPPy5evKiZ2oZ8pmrzxZAJBw4cuHnzpru7u6axgx6xsbGPHj0yQSpa8+bNBw4cqH/MwoUL9+3bd+fOnYMHDw4bNkz/YMSU5PH3s2dNJmRFlNYmatpsWPhmX7fLYmlne77mJMbhOE8v+69i1eM8fb70+l+aDeda2vC6OwjOi6TRGaI1tYrbbeb/utG217cm23AuIyOjbt269M81a9ZMT08v5xuVSqWlzEK3FKgQGotUKqUfsP3888/l2a7s1KlT+/fvN36uD4YPH15mIbS1tV2+fPmYMWPmzp3br18/tJOvmZD8eTxneQQll+kf1taW19aWp33GfthYzhe1jRnNTLHdPRxGhAi3FT9LW+DtdL1AdlIoGeJSvAU5vQTTbbGJnmIIBAKF4sMW/FKp1MamvPvvR0dHW9AsdIuACqGxrFy5MiUlpUWLFiNGlGvfikGDBjVoYLp1XU2bNi17EMDIkSO3bt167969VatW0RO1EWZJb119vzyCkstWpAlP5xfN9HQc5KzjA3RgQma6Ur2/nkdd3oeOkiwnZ8fRk00b1ow4jJxQcOqoOvPDVZcnlz3azW5zljgyTXjcz1PziKjwdKz9gKGmaUrl5+d38eJF+uf4+Hg/P7/yvCs7O5tuFxodHW0Rs9AtAtpizShSU1P9/PxkMtnff//dvn17puN8ljt37gQFBfF4vOfPn9eqVYvpONUakfc+pV8wKZUAwPzk3KN5kkXeTiPddNxv6PIsLVWhPu3v6c//sLuK2+JVtn0HmzSumZFcOJ29YLrmUEZS3Z+nZyjVUV+49Nf6MsFr2sJrV6wx2hSLxeJ///13y5Yt7u7uQ4YMCQgI8PPzW7hwoY+Pz+TJk48ePdquXbsyf8no0aP37NnTr1+/P/74w+AJqy00WcYowsPDpVLp0KFDLb0KAkC7du2+//57mUxGP5ZAGJS3bjmlUlTmnRhu1aCRoeNYGJvuvfktWmsONR2pojLyJQSpOS9//FBy4bQxAgiFwqNHj7q4uBAEcerUKXt7+6tXr2ZkZFy6dGnfvn3lqYIPHz7ct28fl8uNiooyRsJqC10RGt6tW7c6dOjA4/Fevnzp4+PDdBwDSE9Pb9CgQVFR0bVr1zp16sR0nGqKEOYm9+pAKT+sNqvYFSGO2QT3cF9VBduyV4gi4VnaD32B/LCAnQL4/lXWfYl8oof9LM/iHQbY7h41j13G+QKGYupGUVTHjh1v3rw5b948+u4oYijoitDASJKcMWMGRVHz58+vGlUQALy8vObMmQMAoaGhld4FA/lMRZfPYazK3q8jqaIbV429Ts78WTVoZNeneI4YBhDh7YQD7MwuSFKoNOfV2VkFR39jIqA+Bw8eLP8sdKRCUCE0sB07djx48KBmzZrh4eFlj7Ycs2fPrlWrVlxc3M6dO5nOUk0V3bpGysqYKaoHxuEoEp4ZMI+FcpoyG7ex1Rw2FnD7OtmoKGp1+oceFBiP7xgyw37ISIYC6iaTyRYsWAAAK1euRE2zDQ5NOjKkgoKCH3/8EQDWrl0rEJjXfZXPxOfzV61a9d1330VERAwePNjBwYHpRNWO6t2bkidvFMoKtJ5vaRSoS5wkCVXyO16T5sbIZkFYTs6OY6fmrS++tTjXy/GSWHpBJI3KyG8S2NKmRx+WwBEOH2EwZEnHjx+nZ6GPHGleFbpqQIXQkJYuXZqVlRUUFFTmEj1LNHjw4M2bN1+/fj0yMnLNmjVMx6l2SFlRyZPXxLJr4nJdJlIEQUoKDR3KItl/P7rgxCFV8jv60IXD6mrP/1uq2p4lhrOX4aw5tqTGMMza2joiIgLH0W08w0OF0GBev369adMmHMdjYmIwI8y9NgcxMTEtW7bcsGHD+PHjTbnqEQEAjM0peXKsu11PBx0bHUx+m5Ot+uhpLoazMNSpBwDoHXZCF2aFjaMPCWfXR+lSoTynbdu2mq1ezM3t27ffvn0bGxv77bffMp2lCkKF0GBmzJihUChCQkJatmzJdBZjadas2ZgxY3bs2BEeHn7mzBmm41QvbE9vdXbmJyc9OewAax3ljYuX+CrGYnE8vY2UzeJYd/xK8GUn2b+37QYM2yalki8tbdiw4Y0bN8x2iTq9NPn333+fOHFihw4dmI5T1aCrbMO4dOnSn3/+aWdnt3TpUqazGFdkZKSDg8PZs2fPnTvHdJbqhd+6PcbRcVFYTpRSYeWP2hQUc5m3zOf4FWLExKjodWD27ULp+XcURdFb4TMdp6pBhdAA1Gp1WFgYACxZssTDw0P/4PT09KNHjyYlJZkimRG4ublFREQAwMyZM1UqVZnjEUOx+aoHsNgAlVz4y63nj9vZGzaSReN4+bA9vefMmVNQUNC/f3/zbxdKr8h6+PDh3r17mc5S1aBCaAAbN2589uxZ3bp1p0yZon8kRVEjRoyYM2fO1atXTZPNGKZPn96gQYOXL19u2rSJ6SzVCLdOfa5vPcAq828W51s7jinjL2c19ODBg99++43L5f78889MZykbn89fsWIFAMyfP18sFpc5Hik/VAg/l1AojIyMBICYmBirsiYj7Ny509nZuUuXLiaJZiwcDoeeNbp06dL3798zHacacZ33E2bFK3vcxzAcZ39Ry7pTV2NEslyadqGzZs2ylHahQ4cO7dChg2bfbcRQUCH8XAsWLBAKhV27du3Vq5f+kVlZWZGRkevWrTNNMKP65ptv/ve//4lEosWLFzOdpRqxatTUYeiYim79hfEEHlGbjbGLtEX77bffbt265e7ubkGb6GIYFhMTg+P4unXrEhMTmY5TdaBC+FmePXu2c+dONpsdExNT5uApU6bMnj3by8vLBMFMIDo6msPh7NixIz4+nuks1YjT5HDrzl/jfEGYp+Of/p59nXQ3sdtd1/1Pf09fHhcXCGps3M3xqiK7/RmKVCqlH3VHRUVZ1kYtdGc3pVJJ73qIGAQqhJ9l6tSparV62rRpjRqVsbX/yZMn//rrLxaLtX379oSEhOvXr6emppompJH4+flNmTKFIIipU6eirdtNB8Pcfoq2Hz7O3ca6vjXPga37n3AtK04DR3trN3evnbG8ZlV2PU+lrVixIiUlJTAw8IcffmA6S4XRvb5PnDhx4cIFprNUEaj7ROXFxsYOGjTIyckpMTHRyclJ/+C///77zp07mjfWqlUrKiqqdm3LbhcuEonq16///v372NjYAQMGMB2nelEkPM9dvVTx/DEARSm0ejNhgFvbAEnaDx3rOHoSxuMzl9FMpaSk+Pv7l7NdaHx8vInvnZ47d67MHTl+/vnn+fPnN2zYMC4ujvMZi2oQGiqElSSXyxs2bPju3butW7dOmDChQu8dM2ZMhw4dRo8ebaRsprR169ZJkyb5+Pi8ePGiim2vahFU6anSaxel9++q01MplZJlZ8+t31DwZUdBUGeMi/aR0W3gwIHHjh0bPnz4vn37yhx85cqVr776ygSpNAiCKHMfNaVS2bhx48TExI0bN06dOtU0waowVAgrKTIyctGiRY0aNYqLi6voOtykpCRbW1tnZ2cjZTMlgiACAwPj4+MjIyMXLlzIdBwEKcPVq1eDg4MFAsGLFy/K0yhNKBTeu3fPBME0evToUZ5hJ06c+Pbbbx0dHV+9euXi4mLsVFUbKoSVkZ6e7ufnJ5FILl682K1bN6bjMKyinywIwpQq9r2tR48eFy5cmDp16saNG5nOYtnQZJnKmDdvnkQiGThwIKqCANClS5cBAwZopuEhiNmiJznXrFmT3grK0kVHR7PZ7C1btjx58oTpLJYNXRFW2N27d7/88ksul/v06VOz3avexN69e9ewYUOFQlGe2QcIwgjN3K6jR49WmUZpU6dO3bRpU3Bw8OXL5tg9ylKgK8KKoTe9pShq1qxZqApq1K5de+bMmWhHYMSc0RshtW/fvirNcP7pp5+cnZ2vXLly8uRJprNYMHRFWDF79+4dNWqUl5fXy5cvbWx0r2WunqRSqZ+fX2pq6t69e0eMGMF0HAT5yMuXL5s2bUoQxL179wIDA5mOY0gbN26cPn26r6/v8+fPy9zlEdEJXRFWgEQiWbBgAQCsXLkSVcFPCAQCes9Vejt/puMgyEfoZikhISFVrAoCwOTJk5s0afL27dv169czncVSoUJYAStWrMjIyGjbtq0l7kZhAsOHDw8KCsrOzl61ahXTWRCkGN0+087O7scff2Q6i+GxWCx6i8fIyMjMzE9bNyPlYepCeOfOnZCQkG3btul8de/evSEhIebZoujdu3fr1q2jN70tc9+H6gnDsPXr1+M4vmbNmtevXzMdR4erV6+GhISU1s5t27ZtISEhmg2AkKpBpVKFh4cDwI8//uju7s50HKMIDg7u06dPYWEhmrldOaYuhK9evdqxY8elS5d0vnr9+vUdO3Y8ffrUxKnKY9asWXK5fPjw4W3atGE6i/kKDAwcNmyYUqmcN28e01l0ePr06Y4dO65fv67z1UuXLu3YsePVq1cmToUY1YYNGxISEvz8/Kr2DizR0dFWVlZ79uwx8fL/qgHdGi2Xq1evHj9+3MbGBrUBKxO9nf+xY8dK+7qDICaTk5NDP7qmm6UwHceI6tSpQ7dXpKe1Mx3HwqBCWDaCIEJDQwFg/vz5np6eTMcxdx4eHnSDmLCwMLVazXQcpFqLiIgQiUS9evX63//+p2fYpUuXFixYsHPnToX29uWWJiIiokaNGnfu3Pn999+ZzmJhDFMIpVLp9OnTGzZs2LNnz8ePHxvkd5qPbdu2PX78mF4qR5/Jzs4ePny4v79/9+7dq/mNiMePH/ft2zcgIGDChAlisZg+SS+yfPbs2Y4dO5iNZ3B5eXlTp04NDAxs3br1jRs3mI6D6BMXF7dr1y4Oh7N27Vo9w3bs2DFp0qQ6dercvHnTopcY2traamZuFxUVMR3HkhimEEZERCQnJ9+4cWPKlCk9e/ZUKpUG+bXmID8/f8mSJQCwZs0aHo9Hn1y5cuVXX30VFxcXEhIyePBgRgMyiSTJgQMH9u/f/9atWyqVav78+fR5Kyurn3/+GQAiIiLy8vIYzWhgw4YNs7a2vnbt2vnz5wMCApiOg+gTGhpKEMS0adMaNGigZ9j27dvXrFkzduzYX3/99dGjRy9evDBZQoMbNWpUq1at0tPTV69ezXQWS1KxtgmluX79Or3BQa9evTw8PG7dutWlSxc940Ui0aNHj0qeFwqFmp8LCwsJgjBIvM+xcOHC3NzcLl269O/fX3NS04++fv36MpmMoWjM++effzAMGzlyJAAsXrw4ICBg8+bN9EsDBgzo1q3bpUuXFi9evHz5ckZjAgCwWCxbW1v6Z6FQqPOvn0gk0v9Lnj9//uTJkzNnzlS03whiekeOHLl+/bqrq+uiRYv0j7SystLcEfX09Hz16pW/v7/xAxoFjuPr168PCgqKiooaNWpUrVq1mE5kIShDGDJkSHh4OEmSL168qFev3v79+0sbuWfPnjIjbdiwgaKopk2bGv9PXzY7Ozscx+Pi4j75gyQlJU2aNKlevXrnzp0zyH9DS3Tw4MFevXrRP5MkyeVy8/LyNK/GxcXhOG5nZ8f0/0MAgKZNm1IUtWHDhjJH7tmzp7Q/7x9//NGoUaPevXvXrVu3S5cuSUlJxv4vjFSOVCqla8C2bdvKHHzu3DkPD4/Bgwe3b9/e09Pz2LFjJkhoVN9//z0AfPfdd0wHsRiG+WK7du3akJAQPz+/Fi1a8Hi8Mj/7/P39dd6LP336dHx8PP2znZ2dvb29QeJ9DoIgSJLUPP3ScHZ2Hjp0qL29/c8//9y9e/fqubIQx3HtnUVJkmSxWJpDsVhMkiRJkubw/1H772RAQEDv3r1Ljjl27Jj+22ISiYQgiF9++cXHxyciImLWrFlHjx41fFbks61evTopKalZs2Zjx44tc3CPHj2eP3+enJxcv379pk2bVoGrqKioqFOnTh0+fHjSpEmdOnViOo4lMGxdVSqV7u7ur169Km0AfUU4YMAAna/STdvpK0IzsXTpUgBo1qyZWq3WOcDOzu7NmzcPHjwIDg5OSEgwcTxGvH79un379rdv3753716dOnXok4mJiU5OTpoxdOM3AFi2bBlDMXWgrwhHjx6t81X6y5meK8KrV682b96c/vns2bMtW7Y0Skrk86SlpVlbWwPAtWvXKvTG48ePN27cmCAIIwUzJXoPHT0fXIg2w0yWEYlEycnJ2dnZ4eHhbdu2rVevnkF+rTmYPXt2rVq14uLidu7cqTk5d+7cU6dOSaVS+nGRh4fHpk2brly5Mnv2bAajmkxYWNjNmzd37tzZsmVLHo+3adOm7OzsxYsXjxo1SjNmx44dDx48qFmzJr2pR9UQFBSUm5t75MiRlJSU9evXd+3alelEiA70nMnBgweX82LoxIkTrVq1atu27aJFi/bs2YPjVWFR2Zw5c+gPrt27dzOdxRIYpJzeunWrc+fOnTp1mjNnjlgs1jPS4q4IKYo6fPgwALi6uubn59NnkpOTBw4c2KRJkwEDBty9e5eiqOzsbPoG4Pnz5xkNa3R//fUXANja2mZkZFAUlZCQMGzYsC+//HLWrFlFRUX0GLFY7OHhAQBHjhxhNOynPvOKkKKo58+fDxw4sHXr1vPnz5fL5caJiVTe7du3MQzj8Xjv3r0r/7tyc3O1H29XDYcOHQIANzc3kUjEdBZzZ+Bbo2WyxEJIURT91TI8PFzPGHqnaX9/f6VSabJgJqZSqZo0aQIAUVFReobRCy6DgoJIkjRZtvL4/EKImDOCIFq3bg0AixYtYjqLWejYsSMAzJo1i+kg5q4q3AQwgZiYGBaLRW9aWNqY0NDQ+vXrv3jxYuvWrabMZkqbN29+8uRJnTp1pk+fXtqYCema3QAAIABJREFU169fb9q0CcdxtDs5YmJ79+69d++el5fX3Llzmc5iFsrzwYUA2mKtnJo1azZmzBjNNvY6cbncqKgoAFiyZElubq4J05mIUChctmwZ/Le9b2nDZsyYoVAoxo0b17JlSxOmQ6q7wsLChQsXAsCqVavoyTJI8+bNR48erVQqZ82axXQW82biK9AnT56sWLHi+PHjOl89e/bsihUr7t+/b+JU5ZGdne3g4AAAf/75p55h3bt3B4ApU6aYLJjJTJ48GQC++uorPWMuXrwIAHZ2dpmZmSYLVn73799fsWLF2bNndb56/PjxFStWPHnyxMSpEIOgrwLbtWtnbjfkmaWZvqD/g6uaM3UhtGhr1qwBAD8/Pz1PAZ8/f87hcFgs1uPHj02ZzdiePXvGZrPZbLaeP5dKpWrUqBEArF271pTZEOTNmzdWVlY4jv/zzz9MZzE79HZr+j+4qjlUCCtAqVTSmxauW7dOzzC67VlwcLDJgpnA119/DQDTpk3TMyY6OhoA6tati6ZTIibWr18/ABg1ahTTQcyRQqGoX78+AMTExDCdxUyhQlgxZ86cAQAHB4ecnJzSxgiFQhcXFwD4448/TJnNeI4fPw4Ajo6Oubm5pY3Jy8tzcnICgNJuPCKIkVy+fBkAbGxs0tPTmc5ipk6dOkX/E37//j3TWcwRKoQVRjc2mzhxop4xGzduBABfX1+ZTGayYEaiUCjoHRJ++eUXPcMmTJgAAF27djVZMMSwAgMDW7RoIZVKS7708OHDFi1aDBs2zPSpyqRWq+klPStXrmQ6i1nr0aMHAEyaNInpIDoUFRW1aNGitK2abty40aJFiwkTJhgvACqEFfbixQsOh4PjuJ5JPVXpH+eKFSsAoGHDhiqVqrQxcXFxLBaLzWY/ffrUlNkQA6JnzxUWFpZ86fr16wDQokUL06cqU1X60mlUmukLJVsIMK6goAAAMAzT+erp06cBoHPnzsYLgJZPVJifn9+UKVNIkgwNDaX++/j4BIvFols1LV++PDMz07QBDSk7O5vuLLhu3To9vYc0jd/oyTIIYhr5+fn0bsBr167VtAtFdPL39588eTJBEGFhYUxnMTuoEFbGkiVLXF1db968eezYsdLGBAcH9+3bVyKR0GubLNS8efMKCgr69etHT5bR6ejRo9euXXNycoqIiDBlNgSh1+wGBwfTk2VoKpWqKvUGN6AlS5a4uLhcvXqVfupflWi3wSl5WDbjXWxWbVu2bAGAmjVrajbYLMnSp3Q/ePAAx3Eul6unnYhMJqPb1mzdutWU2RCDoz8QLOjWqOZeX3x8PH1GqVQOHTq0adOmAQEBQ4YMqc6rBdLS0gQCQWBgYGBg4NixYzXnN23aBAC1a9c2qzvJlb41ShDEsWPHgoKCatWqRVGUWq2OjY0NCgry9fWtUAB0RVhJ48ePDwgISE1NXbduXWljfH19Q0ND9d9ENVsURc2YMYMkyZkzZ+ppJ7JmzZqkpKRGjRqVp/EbghhQWFiYSqWaOHGipon3+fPnX716FR8fHxcXl5ycTM/xrp7S09PbtGlz//79+/fv//rrr5rzEyZMaNq06bt37+jFTpZOoVC8evVq8eLFarWaPkxMTFy0aBF9WH6GacxbDbFYrHXr1gUHB69YsWL48OE+Pj46hy1cuHDfvn137tw5ePDgsGHDTBzycxw4cODmzZvu7u7z588vbUx6ejq91bj+J4iIBdm7d2/J/fMSExM1P6empr569cq0oXS4ffv2hQsXHB0d6cZ7NFtb25SUlLdv33p5eeXl5dEtUKqnzMxMDw+P5ORkDMO0P53o6Qv0B1fdunWdnZ0ZDElr06aN5mftmq3x+PHj0t7L5/PnzZuXlpZGHwoEgnnz5qWkpFQ4RIUvYhEtdL+C4cOH6xmza9cuAPDy8pJIJCYL9pmkUin9j2f37t16hv3www8AMHDgQFPlQoyozM8K+tao+VxJ2NjYzJ8//5M/xYABA3g8Xp06dRYsWMDEf0VzceHChVatWg0fPrxhw4bat0ZprVu3dnV1Zfp/4AcPHz6kb43qp2fWaGpqqre3t+YwOTnZx8enQv+50Lf4zxIdHX3u3LnffvstJCSkffv2OseMHDly69at9+7di4qKome4mb+VK1empKS0aNFixIgRpY25e/fugQMHeDwevdU4UjX8/vvvJadfPn/+XDPny8fHJzg42OS5PvX27dukpKR79+5pnzx8+HBqampmZubNmzdDQ0MbNWo0dOhQAJDL5dVkTqnmT/r111/TE9xkMlnt2rXj4+MDAgLoMSKR6O3bt7m5uc2bN3d0dGQyLgAA2Nra0j9gGKZzFs/9+/eXL19u3BAVKptISfQHRGBgIEEQpY2hm4Xy+fwKNQtlSkpKikAgwDDs77//Lm0MSZJ047eIiAhTZkOMh/5AsJTJMkKhkL6td+LECc3JsWPHalburl69evjw4S9evOjUqdP48eMZimlSSqWycePG48aNE4lE2juP169f/+bNm5rD0NBQAGjfvr357E7+mesIP/+KEBXCz1VUVFSzZk0A2Lt3r55h9DfTwYMHmyxYpQ0aNAgA9G8jQjdY9vLy0vm5iVgiyyqE1H9tln19fTV72x46dMjf3//69eu3b98OCAjYu3dvYmIiPXP733//ZTatCdBrl+vUqSOXy2fNmjVmzJhjx47NmDGjWbNmmt0w6P1AzG1Z/ecUwitXrmzZssXZ2fnIkSPv3r27cuXK5s2bXVxcjhw5kpSUVM4AaNbo5xIIBJGRkQAwZ84cPXe6o6KirK2tjxw5Qn+mmK1bt27Fxsby+Xx6QxmdJBLJggULAGDlypU2NjYmTIcgxSZPntykSZO3b9/SBQAAvvvuu9WrVx8+fHjnzp1Lly4dMWJE3bp1y9z+omoQCoU//fQTAMTExFhZWS1btqxjx463bt3y9fW9ceOGZi7bzJkzVSoVPemd0bwGk5CQkJSUNG7cuAcPHgiFwoSEhOTk5LFjx9KH5f0tlSvgiDaSJIOCggBA//N5+gFhs2bN1Gq1ybJVCEEQgYGBALBs2TI9w+h5pG3btjWfWyvI56M/ECzoipD6b7ttW1vbjIyM0saIxWJ6+ujhw4dNmc3EJk2aBGW1C6UvrfT3DGAE41usoUJoGPfv3y9z7blUKqXXnm/fvt2U2cpv27ZtUI5dAng8HoZhd+/eNWU2xNgssRBSFNW3b18AGD16tJ4x5fmLbdGePn1KtwvV01a6nF3kGIEKYdVBT7Ds37+/njGHDh0CADc3t/z8fJMFK6dyfnH+9ttvAWDkyJGmyoWYiIUWwvLs30QQRMuWLQFg6dKlpsxmMt26dQOAGTNm6BlTnr7iTEGFsOrIysqys7MDgIsXL+oZ1qlTJwAIDw83WbBymjlzJgAEBQXpueF55coVQI3fqqgLFy6cP39e5317oVB4/vz5O3fumD5VecyZMwcA2rVrp+ev7s2bN+mZ28nJyabMZgL0jsdOTk562oVmZ2c7ODgAwJ9//mnKbOWkVqvPnz9/4cIFna/m5OScP3/eqNOdUCE0JHqxS6NGjfR0LHr06BGLxeJwOC9fvjRlNv3KM7lOrVbTe1mtWLHClNkQRL+CgoIaNWoAwIEDB/QMK890aIujaRe6efNmPcPGjx8PAL169TJZMMuCCqEhyeXyunXrWuJfyp49ewJASEiInjHmuV0vglAUtXPnTihr/6byLJC1OPSXb/3tQs3zy7dZQYXQwGJjY8tzm8Le3t58blNcunSJnnqXmZlZ2hihUOji4gIAx44dM2U2BCkPgiDoHR4WL16sZ9iiRYvoh516tr+wIJrHMaXdVKSZ7eMY84EKoeGV58H16tWrzeTBtUqlorvprlmzRs+w6dOnA0CXLl1MFgwxKy9evJg2bdrly5e1Tx48eHDQoEGaLkjMKs/+TZpNdHft2mXCaMZSngl6hw8fNtsJeuYDFULDK89UZoVCYSZTmek9lOvWravZnqOkko3fkGpl9+7dvXv3/vLLL6OjozUnU1JS6tatGxgYqH92mCl9//33APDdd9/pGfPbb78BgLu7u1gsNlkwY6gaS7bMBCqERmEpi1vz8vKcnJwA4MyZM3qG9ejRAwAmT55ssmCIGQoLC9MuhH369Dlw4EDXrl3NpxCmpqZaW1sDwLVr10obQ5IkvT/+vHnzTJnNsDR/ipL9N7SZ/yYeZgIVQqPIy8ujdwQ+ffq0nmHdu3f38/NjcNO/uLg4Pz+/7t276xlz6tQpAHB0dHz//r3JgiFmSLsQHjx4sHPnziRJmlUhpCiKbk+o/6P/wYMHZV5Lmbl9+/aVeV2blpZW5tcChIYKobHQnevpDXBLG5OTk8P4M0KlUqnnklShUNSvXx8AYmJiTJkKMUOaQpibm1uzZk16CqK5FcJy3gwcNWoUAPTr189kwQyoqKiIftK5Z88ePcMsaKN/xqFCaCwqlapx48YAsHr1aqazVB7da9Df35/xgo0wTlMIw8PDPT09Bw0aNGjQIHd3944dO6akpDCdrphm/yaRSFTaGM18y/Pnz5sym0FUvdZvjEOF0Ig0yxL07AhszjTLPM6dO8d0FoR5mkKYk5Pz5j9BQUF79+79f3v3HtbEmf0B/ISbgqiIFVwNBZGLBVHQgghYBbxUFy/lQdR6QRG6ruASAUnRLeC6wqq4otRCi6gPK2qLKKVo1Xqp1SqlXIpAEURAEQTkIhC5huT3xzy/PAoxICbzJpnz+Utm5o9vY81hZt73nK6uLtLpXvPRRx8BQHBwsIRroqKiBtyBJ4dEuyFv3779pmtEm0m++OILOrMpLiyEsuXm5gYAPj4+pIMMxebNmwFg6dKlpIMgwoqKimbOnGlhYWFpadlnCdj69eslfCOTkpubq6qqqqGhIWELuagny5dffklntnfk4eEBAOvWrZNwzfHjxwdsL4BehYVQtsrKyhR0Lmhubi61oKCkpIR0FoTemo+PDwC4ublJuOb8+fPUQjAJ7S/kyu3bt1kslpaWloSOqaKGc6dOnaIzm0LDQihzQUFBAODg4KBY0/sG83AJIbk1yP5NCxcuBIBt27bRFmzIRONC9+zZI+EyLpc7YAty1AcWQplrbW2lxhudPXuWdJbBOnPmzIDLDRCSc4Pp31RUVKSmpqaqqnr//n06sw1BXFzcYMaFDjiUCvWHhZAO33zzDQCw2WyFeGTf3t5uaGgIAAkJCaSzIDR0g9z84+fnBwAuLi60BRuClpYWfX19AEhJSZFw2YoVKwBg48aNtAVTDlgI6SCaCxoREUE6y8DCw8MBwMbGRrQlWSAQXLx4ceXKlQT3/iM0BINpB9HU1ES1v/j+++/pzPZWtm/fPuC40OvXr+O40KHBQkiTX3/9ldrWU1lZSTqLJKImVbdu3RIdDA8P37Jli5WVlZyMy0Bo8KgGgX//+98lXHP48OEB218QVFxcrK6urqKikp2d/aZr+Hy+lZUVAERFRdGZTTlgIaSPp6cnAHz66aekg0gioW3x4sWLsRAihSNqGS/heUZPTw9VRfbt20dntkFavHgxAPztb3+TcE1sbCwAGBsb47jQIWAJhUJAtHj8+LG5ubmKiopQKGSxWKTjiEEFEwgEJSUl1GvCVy1ZsmTbtm3Uv0mEFAiHwzl8+LCzs/ONGzfedM3169fnz58/cuTIkpISavuBnLh48aKbm9uoUaNKSkqoZXf9NTc3m5mZNTQ0XLhwgXpNiN4O4ULMJM+ePdPS0tLW1ib9dy6Jtra2lpaW2Am9eEeIFNQgx0ovW7YMALy9vWkLNqDu7m5qXturcz/68/f3B7lf7yPP8I6QPt7e3tRcN6oXonxatWpVRkaGt7d3YmJin1N4R4gUV1xc3NatWydNmvTnn38OHz5c7DWPHj2ytLTs6enJzMy0tbWlOaFY//3vf4OCgkxMTIqKijQ0NMReU1xcPH36dIFAkJeXRz3gRW+NdCVmCtHkFzlv1CJqhdN/HxLeESLFxefzp02bBgB79+6VcNmOHTtAbnaj19fX6+joAMDFixclXLZo0SIA8PPzoy2Y8sFCSAeBQDBnzhwA4HK5rx7n8XhyODAzJCSkz3dBRkbGzP+3efNmsvEQGhrqBaHk3QWi/mSnT5+mM5tYn332GQAsWLBAwjVpaWmA40LfGRZCOiQnJ8PrjVrKy8vt7e3NzMzYbHZ0dDTZeH3I1XcBQlLk7u4OABs2bJBwzbFjx0AO2l/k5eWpqqqqqakVFha+6RpRx4AjR47QmU35YCGUOVGjlsTERNHBU6dOJSUlCYXCuro6bW1teZvTJCffBQhJV3l5+fDhw1ksVmZm5puu6e3tpV4QhoeH0xitr7lz5wJAYGCghGv27dsHOC5UGrAQylxYWBgA2NjYvGmKpqGhYUFBAc2pJJOT7wKEpC40NBQGmmp7+/ZtU1PTH374gc5gr+Lz+QcOHDAxMWlubn7TNaKu4oo4W1jeYCGULdEUzV9++UXsBTk5OQYGBnL4Cx1OuEZKqa2tbcKECQBAPZJ5E3l4eS85g7e3NwAsW7aMtjxKDAuhbEnuJlNTUzN58uSMjAyaUw2ShC4zCCmukydPAsD48eNbWlpIZxkiHBcqXVgIZUhyf9GCggIrK6szZ87QH2yQxPYdRUjRCQSCWbNmAcCuXbtIZxkK0Sr0kJAQ0lmUhIrMNyoylUAgCAgIEAqFn3/+ef92ZefOnZs9e/aiRYtUVVVTUlIeP35MJKRkbDab2lbF4XB6e3tJx0FIOlgsVkxMDIvFio6OLisrIx3nrZ09e/b27dt6eno7d+4knUVJYCGUlcTExOzsbDabTU2o76O1tdXPz09VVTUnJycnJ6epqYn+hIMREhJiaGiYl5d34sQJ0lkQkhp7e/u1a9d2dXVRa2cUSEdHB5U5KiqKWiyD3h22WJOJtrY2MzOz2tras2fPrlq1inScd3L27Nk1a9bo6emVlpbiPzykNKqrq6dMmcLj8a5evbpgwQLScQYrIiJi9+7dNjY22dnZKip4JyMd+DnKxO7du2trax0cHKjFMgpt9erVH330UX19/b///W/SWRCSmokTJ3K5XADYvn07n88nHWdQnj59Gh0dDQAxMTFYBaUI7wilT9S697fffqMG0yu6vLw8W1tbVVXVgoICqpMFQkqgs7PTwsKioqIiLi5uy5YtpOMMbPXq1d9+++2aNWtOnz5NOotSwd8ppI/D4XR1dXl7eytHFQQAGxubjRs3dnd3BwcHk86CkNQMHz58//79ALBr167GxkbScQZw9+7d7777TlNTMyoqSnQwPT199uzZU6ZMmTNnTkdHB8F4io3wqlWl89NPPwHAyJEj5a1r2jsStbH48ccfSWdBSJqoF4QcDod0EEl6e3upX6wjIiJEBx8+fKivr5+fny8UCltbW8mlU3j4aFSa+Hy+jY1NYWHhgQMHlO/m6cCBAyEhIR988EF+fr66ujrpOAhJR1FRkbW1NQBcuHDBxMSEdBzxzp0798UXX7DZ7AcPHlC7ewGAy+WyWKz//Oc/fD5fTU2NbEKFhoVQmmJiYrZv3z558uSioqJhw4aRjiNl3d3dVlZWpaWlMTExAQEBpOMgJDUeHh4//vhje3s76SBvxGKxVFRUEhISNm3aJDro4eHB4/Fqa2sbGxvnzZt34sQJLIdDg5+a1DQ1NVHrKmNiYpSvCgKAhoZGdHT0smXLdu/evXbt2vfee490IoSkQCgUVlVVtbe36+vrU4Nw5dCzZ89aW1sfPnz46sHOzs4ZM2bs3buXz+c7OjqeO3du9erVpBIqNCyEUkO9b58/f76bmxvpLLKydOnSjz/++PLly2FhYV999RXpOAhJQXJyclZWlr6+fmlp6ahRo0jHES8nJ8fOzu7gwYObNm0yNTWlDhoaGo4cOZLFYqmrq0+ePLm2tpZsSAVG+B2lsigsLFRTU1NTU5O3gUpS9+eff6qrq6uqqlKv6BFSaO3t7e+//z4AnDhxgnSWAXh5eQHAJ598Ijpy7949Q0PDn3/+OSUlRVdX98GDBwTjKTR8RygdCxcu/OmnnwICAmJiYkhnkbmAgIAjR444OzvfuHGDdBaE3klYWNiePXtmzJjx+++/q6ioPH/+PD8/X3R23Lhx06dPJxjvVXV1dWZmZq2trVeuXFm4cCF18NKlS//73/+EQqG/v7+TkxPZhIoLC6EUpKamenh46OrqlpaWjh07lnQcmWtubjYzM2toaEhNTXV3dycdB6EhqqqqmjJlSkdHx61bt6h5Dvn5+aJn/gUFBcbGxqdOnSKa8TWRkZG7du2ysLDIz8/HdTHSRPiOVPF1dnZSS66/+uor0lnoc/ToUQCYNGlSR0cH6SwIDdHKlSsBYO3atWLPOjk5/fzzzzRHkqyrq4t6QXj06FHSWZQKFsJ3tXfvXgCgeqqRzkIfPp8/bdo0AIiMjCSdBaGhuHPnDjUu9PHjx/3PZmVlzZw5k/5UA0pNTQUAXV3dhoYG0lmUB7ZYeyd1dXX79u0DgEOHDjHqSYWqqir1NjQyMrKmpoZ0HITejmhcaGhoKLVYpo8DBw6IHaBGnLu7+8KFC5uamv71r3+RzqJESFdixbZhwwYAcHd3Jx2EjE8++QQAvLy8SAdB6O18/fXXAGBgYPDy5cv+ZysqKgwNDbu7u+kPNhhFRUUMWaNOG1wsM3TUzh41NbXCwkLRzh5GKS8vt7S07Orqunfv3qxZs0jHQWhQWltbzc3Na2trv/vuO+o1YR8cDsfAwEA+7wgpW7dujYuLc3V1vXbtGuksSoF0JVZUAoHA0dERAHbu3Ek6C0nUsGx7e3uBQEA6C0KDEhgYCACOjo5i/6dtaWlhs9kvXrygP9jgNTY2UgvU09PTSWdRBnhHOERJSUleXl5y3o2CBjwez9zcvKamJikpaf369aTjIDSAsrKyqVOnShgX2tDQUFVVZWNjQ3+2t3L48GEOh6OsnY1photlhqK9vf2f//wnAOzfv5/JVRAAtLW1IyMjASA0NJTH45GOg9AAAgICurq6fHx83jQu9L333pP/KggAfn5+U6dOffTo0ZEjR0hnUXhYCIciMjKyqqpq5syZ69atI52FvA0bNtjZ2VVXV1MLaBGSW9euXbt06dKoUaN2795NOsu7UlNTo1Zu79mz59mzZ6TjKDYshG/tyZMnhw4dYrFYMTExKir4AQKLxTp8+DCLxYqOjq6oqCAdByHx+Hw+h8MBgPDw8PHjx5OOIwWurq5ubm5tbW1hYWGksyg2/B5/a4GBge3t7evWrcPOfiL29vZr167t7OwMCQkhnQUh8WJjY4uKikxMTPz8/EhnkRpq6Nvx48d///130lkUGC6WeTs3b950cXHR0tIqLi4Wuw+Xsaqrq6dMmcLj8a5evbpgwQLScRB6TVNTk6mpaVNTU0ZGxl//+lfScaRpx44d0dHRDg4OVK8c0nEUEt4Rvp3KysoRI0a8qRsFk02cOJHL5Y4YMeLp06eksyDU186dO5uamubPn69kVRAAwsLC/vKXv9y9e/fbb78lnUVR4R3hW6uurtbV1dXU1CQdRO50dHQ0NTVNnDiRdBCEXlNUVGRtbQ0Af/zxh6WlJek40nfs2DFfX182m/3gwYMRI0aQjqN48I7wNW1tbQkJCcnJyWLPlpaWJiQklJSUYBUUS1NTs6SkJCEhobS0VOwFycnJCQkJbW1tNAdDDOfv78/n87dt26aUVRAAvL29bW1tnz59Gh0dTTqLYiK8oV/OlJWVAYCenp7Ys4mJiQDg6elJcyoF4unpCQCJiYliz+rp6QFAWVkZzakQk6WkpACArq5uY2Mj6Swy9Ouvv1LDNCorK0lnUTx4R4gQUlqilcx79+7V1dUlHUeGHBwcPD09Ozo6qK6H6K1gIUQIKS1qb6ulpaWPjw/pLDIXHR09YsSIM2fO/PLLL6SzKBgshAgh5STqdsSQcaFsNjs4OBgAOByOQCAgHUeRYCFECCmnzz//nMfjeXh4MGdjK5fLNTQ0zMvLO3nyJOksigQLIUJICWVmZiYnJw8bNiwqKop0FvpoampS/72hoaEtLS2k4ygMLIRi1NfXq4nj6+tLOppi8PX1FfsB1tfXk46GGEEoFHI4HKFQGBwcbGJiQjoOrVavXj1nzpz6+npqLAwaDOV/bj4EGhoazs7O/Y9XV1cXFhbSn0fhWFhYiN1Wf/Pmze7ubvrzIKZJSkr67bffxo8fz8Dmt1QT/A8//DAmJmbz5s1mZmakEykALIRi6OjoXL58uf/x48ePb968mf48Cmf79u3e3t79j+vr6+NNIZI1Ho+3c+dOYPC4UBsbm40bNx4/fnzHjh3ff/896TgKAB+NIoSUSmRkZE1Njb29PZPHhUZFRY0ePTo9PV3s7/SoDyyECCHlUVFRIRoXyuRRDHp6etRtcWBgYE9PD+k48g4LIUJIeQQHB3d2dq5fv37WrFmksxDG4XDMzMyKi4vj4+NJZ5F3WAgRQkri5s2b58+f19bWZtSWiTfR0NDYv38/AISHhzc0NJCOI9ewECKElEFvby+HwwGA0NDQCRMmkI4jF5YvX75o0aLm5uaIiAjSWeQarhp9jba29ooVK3R0dMSeNTIyWrFihZ2dHc2pFIidnV13d7eRkZHYs0uWLHnx4oW2tja9oRAjfP311/fv3580aVJgYCDpLHLk0KFD06dPj4+P/+yzz6ZNm0Y6jpzCwbwIIYXX3NxsZmbW0NCQmprq7u5OOo58+cc//hEbG+vi4nL9+nXSWeQUPhpFCCm8np6eBQsWODs7YxXsLyIigs1mOzk58fl80lnkFN4RIoSURFdX17Bhw0inkEf4yUiG7wiBx+Pl5uZqaGjY2dmpqOAtssxVV1cXFxeLfpwwYYKFhQXBPEghHDx4sKyszN/f39LSss8poVC4detWADhy5AiJaAqgrKzsyy+/NDU1FfsCNS0t7cqVK0uWLFm6dCk4MDMbAAAFHklEQVT92eQB07/3y8vLp02blpKSEhsb6+jo2NnZSTqR8quoqEj5f8HBwceOHSOdCCmAtLS0+Pj4J0+e9D8lFArj4+Pj4+Px0d+bPHnyJD4+Pi0tTezZe/fuxcfHZ2Vl0ZxKfjD9jvDChQtOTk6xsbEAYG5unpub6+DgQDqUknNycnJycgIAoVA4depUJowORwjJM6bfEVpZWV29evXKlSsFBQW9vb0ffPAB6UQM8sMPPxgZGeFzUYQQWUwvhI6OjoaGhqGhoTNmzPD19R0zZgzpRAxy8ODBoKAg0ikQQkzH9ELI5XIdHR1zc3NLSkqSkpJOnTpFOhFTZGdnv3jxQuzcR4QQohPT3xGWl5evWbMGAIyNjefNm/fw4UPSiZgiOjp6x44dTJ4PgIagsrLy/v37fQ7iHrBBevnyZf9PDwCeP39Ofxi5wvRC6OPjExQU9Pz585cvX6anp1+7do10IkZ4/PhxZmZmUlIS6SBIwVDbJNDQ5ObmTp8+nXQKecT0Quju7m5tbX3nzp1x48bl5OTo6emRTsQI6urqFy9e1NDQIB0EKZhVq1YZGxv3P47jJgbDwMBA7LDimzdvZmZm0p9HfjC9EAKAsbGx2H9aSHYmTJiA8wHQEHh5eS1evLjPQYFAgIVwMIyMjCIjI/sf53K5DC+ETF8sgxBCiOGwECKEEGI0LIQIIYQYDQshQgghRsNCiBBCiNFUIyIiSGdACKEBqKmpzZw5c968ebq6un1OsVgsNTU1FxeXuXPn4iQ1sVRUVMaNG+fi4iJ2H6Gqqqqpqamzs7ORkRHt0eQCDuZFCCHEaPjbE0IIIUbDQogQQojRsLMMQki+8Hi88+fPz58/n2o/lJ2dfevWrTFjxnh6empra5NOp5z4fH56erqxsbG1tTUAPHr0KCMjQ11d3d3dffz48aTTyRzeESKE5MjRo0dnz57N5XJzcnIA4OTJk/7+/sOHD8/JyXF0dMQ1DbJw7dq1Dz/8kMvlpqamAsDdu3ddXFw6Ojrq6upmzJjR2NhIOqDM4WIZhJAcaWxs1NXVXb58ua+v79KlS6uqqrS1tceMGSMUCjU1NSsrK5lwg0KzlpYWLS2tffv2dXV17dmzp7GxsaGhwdzcHABcXV23bNmycuVK0hllCx+NIoTkyNixY1/90cDAoKenJysr68KFC66urvr6+qSCKbHRo0e/+uPYsWNFfwttbW1jxowhEYpW+GgUISTXXr58+c0331y9etXW1hYnOdMpLS2tvb197ty5pIPIHN4RIoTkmo6OzrFjx3p6eiZPnrx8+XIbGxvSiRjhzp07HA7n0qVL6urqpLPIHN4RIoTkV1ZWVl1dHfVnFovV1dVFNg9DxMXFbd269fLlyxYWFqSz0AEXyyCE5MiNGzeioqL++OMPNps9Z84cV1fX4OBga2vr0tJSCwuL06dP49NRqaupqfHy8qqoqBAIBCYmJh9//HFQUJCtrS317vDTTz/dtGkT6YyyhYUQISRHeDxefX099edhw4ZNnDiRx+OVlJS8//7748aNI5tNWfX09FRVVYl+HD16dEtLi+hHHR2d/v1dlQwWQoQQQoyG7wgRQggxGhZChBBCjIaFECGEEKNhIUQIIcRoWAgRQggxGhZChBBCjIaFECGEEKNhIUQIIcRoWAgRQggxGhZChBBCjIaFECGEEKNhIUQIIcRoWAgRQggxGhZChBBCjIaFECGEEKNhIUQIIcRoWAgRQggxGhZChBBCjPZ/voSYMSuad88AAACyelRYdHJka2l0UEtMIHJka2l0IDIwMjIuMDMuMQAAeJx7v2/tPQYg4GVAAB4obmBkc9AA0swsxNKMDCCakZFYmpuBMYOJgTWBgY2BkSmBkZ2BiTmDiYkDaFICMycDC2sGEwsXAys3gwgjGwMrCzMTo/gskD4khx6wX71qlQqE62D/0G3Zfih7P4J9YP+E/imqSOL2SOphbAegOVA1B4DiagdgehFshwNIakDiDkjqwWwxAHcLMAOBEQOcAAABHnpUWHRNT0wgcmRraXQgMjAyMi4wMy4xAAB4nI2TzW6DMAzH7zyFX4DITsJHjgWqbpoKUsv2AJU47MJppz597VbUYa0ikiAS84v/jh0u03yd5ikDaafu6/cPns12GdsxMUII8OMQMTuCTKDZHz57aMdds1ja4bsfz0BWBkpfs7txOC4WghbIFCgN0OD/ycJZ5tBUdxByMjYEdPUb0DGYbyK9kFu0i5XLhMcyDjLBVfAB7lXvhauZewaYW1OEGqvyDRgYzDeRhIJuESdaOU35tHGcCXDfd6viP65DM/SdXgfPj9Wqy7LU2hL3SgvomXBaJt4JtRbD8wavOSdeBk2t7C40gXflOE38VZVJhFmAojNzYPyKzxafRNbLT8Hz7AZJaaXDd9fvDQAAAK16VFh0U01JTEVTIHJka2l0IDIwMjIuMDMuMQAAeJxtjj0KwzAMRq/SMQFbSI4d2+oFMpXuIUMJHYtLyZjD1zGlskuXT3o89DNPy0prN09L/ycp52nvNIFT2oCLAYM6awSfmcDEiDbzoTHXoWSxrfyOtq5RnyVlx8+B5nyvblt6XF/pyQhHe0nbHQKTgGEjMPAgENkKWHYChDwKOfaVIg5CyFFgZKqeIKbqC7+/AdzrXmGItXn8AAAA23pUWHRyZGtpdFBLTDEgcmRraXQgMjAyMi4wMy4xAAB4nHu/b+09BiDgZUAAAShuYGRj0ADSzCyMYJqREZ2GybM5kEbjMg+d5mZgZGBgYmBg0WBiZmFgZctgYuVKYOVmYGNPYONhYOfIYGLnZeDgTODgY2DhVODkymDi4mcQYWRj5eLkYGcTPwUyBMlXDgceuqk5QLgFB4KyKuHsh27L7CFsh/0I8QP7EeoPAOUPqELV2APV74epR7APAGkFNSRxeyT1MDbIPFWYmUDzDyDZBWU7gGg1JDVgN4gBAAJsNfYR/aCwAAABT3pUWHRNT0wxIHJka2l0IDIwMjIuMDMuMQAAeJyNVE1vgzAMvfMr/AdAdj6AHAtU2zQVpI3tH/TQyw5rL/33s6kgRlQZSZBi82I/5T243u6/559zBjI+uvfLDZZhuozzmFghBPi2iJidQDbQHF/eemjHQzNn2uGrHz+BSlkoc409jMNpzhC0kNsCpwGm8KHGik/NmeWogVcBVn5K26IOFZF7ArQrIBUmBLT1E6ATIO6o6IUjFf5fjiUDF1x8vcFVjFsa5wmKtXTehQwrjonehKuaiZJEmmYKKNrYbcstULRZSOaJm6RJnH1Q/xB8R/tSt0+UPPbdyqQP2zZD30XbyjTRnBLaaEFJ+Gg04sdHOzl2ShVNIyFRNAdJbKIHHOfqKDQJxCo9HWOCUo04JqfUcXJKq0CS8Oqyma1GCEVCRUq+5al0qe9I34jE80+A99kf3lPRvxf1HUUAAADTelRYdFNNSUxFUzEgcmRraXQgMjAyMi4wMy4xAAB4nGWOvQ7CMAyEX4WxSEmUy0+TuGJi6YTYqw6oYkRFqGMfnoYixYHF9qezzzf047kZ+vF42tuEae//FVs9rI20KnhhVUwBEJ20wiifoo55zhKUSUm7DaE8E3V9p6vdmqrDDFp0NpfPmqz25M8HWf9n8tdIZqejuC3z4/qan6RVHi/zclcgFNBkChiyBRy5ApZ8gUQtc9MUGHmKhTwlJm1/WYqWwGLAEFiQQGBJYAksSySwMHDrG3Qif6DxgJkNAAAAAElFTkSuQmCC\n", + "image/png": 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", "text/plain": [ "" ] @@ -979,13 +969,13 @@ "6. Carry out the necessary simulation steps (minimization, equilibration, and production)\n", "\n", "\n", - "The `RelativeLigandTransform` Protocol class in `openfe.setup.methods.openmm.equil_rbfe_methods`\n", + "The `RelativeHybridTopologyProtocol` class in `openfe.protocols.openmm_rfe`\n", "implements a means to achieve all the above with minimal intervention.\n", "\n", "Here we work through its usage for the benzene -> phenol binding free energy\n", "test case. As this involves both a relative binding free energy in solvent\n", - "and complex phases, the `RelativeLigandTransform` Protocol will\n", - "be used to build two separate Protocol `DAG` (directed-acyclic-graph) classes, one for each phase.\n", + "and complex phases, the `RelativeHybridTopologyProtocol` Protocol will\n", + "be used to build two separate `ProtocolDAG` (directed-acyclic-graph) classes, one for each phase.\n", "These `DAG`s (which contain the necessary individual simulations), are then executed to yield\n", "the desired free energy results.\n", "\n", @@ -1057,7 +1047,7 @@ "outputs": [], "source": [ "# First let's define the Protein and Solvent Components which we will be using\n", - "from openfe.setup import SolventComponent, ProteinComponent\n", + "from openfe import SolventComponent, ProteinComponent\n", "from openff.units import unit\n", "\n", "protein = ProteinComponent.from_pdb_file('inputs/181L_mod_capped_protonated.pdb')\n", @@ -1076,7 +1066,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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M2tnZqX2uYdywEH6BNC0lYWAX5WUSgQkZv6XntrPhHXD/7P+h68Ez5j6NCETUDz179rx48eLkyZN37dpFOgsyYOK4mPTQNcKIBxSHK8/PL9YW/71I8u2rJAqo817V6iififYeVGV5iM7DIiOBhfDLMndsEOzbVvRSKuv6MilHJv+ljlNH26IFiBYNm7rsPwWmdzIFAOfPn+/du7eNjU1MTIyzszPpOMgwyWUZ29Zn/3YQJGJaaYJoMVezC94IJZOUGqqx+Hy30+GcyjgJC5URPiP8MvsfpnKcqyleOnDY06vawf+XMSmOC59F5F36g0A+0iQSib+/PwAsW7YMqyAqG1oiSZkxPvvEIVok1FAFAaCLLX/S521F7cZPxSqIygML4ZdRFjyHqXOVj4ypbF3bgvteJDn6KVf5eMaWIHlhgW7Tkbd169aYmBh3d/dp06aRzoIM1cclfoVPH9P/37o5QSQNSRYMi03p8jKp08vEoTEpwUmCeKFE9YNcFze773/UbVh916hRo0aNGik/+VNITU1t1KiRr6+v7lPpM7w1Wjo0nfTjUGHkY8WB69mFP75Js2Kzrni7KK9ksv9xhsNPc0hEJOPTp08eHh5ZWVkXLlzo0aMH6TjIIOWeP/NpbSD9/5PIvWnZG5KzpDTNpqAql8OiIFksY17OcLab/tkKetp5/S7Lzt2JxNZPNE0zPSsyMjIcHByKjSYkJNSoUYPP5+fn55NIp6fwirB0KMpx7s+g1BKloy2vgw0vTybf8vlSiqxDe6TJiTrPR0xgYGBWVla3bt2wCqKyoYWFGeuXKarg4U85QUkCFsACF/vHDatfr+8a7uP6pKHbIlcHNlChKVm7UrOLPszm8Fq2JZMbGREshKVl7tWg2Cqln90cuBT1e3rui4Ki3X1psShjyzqdpyMjMjJy3759XC5369atpLMgQ5X712m5VMp8/VEiW5ckAIANNR1/dLK1/n+XJ0s264cqNhtrVQaA0JSsRPF/76c4nOzTx0ikRkYFC+FXqDRrIcvSSvGyhjn3+8rWig2yFfKunC988kD38XRv9uzZMplsxowZnp6epLMgvSOXy8+ePduhQ4du3boxRxISErp06eLi4lKjRo2dO3cyB3PCjiouB09l5InkdCtri2LbKjG62/Hb2fCkNP1b+n/P5mmRKPf0bxX/V0FGDgvhV2A7ONqNm6J8ZFZVu8pc9pM80d9Zn91wz9iwQnkJsFE6efLkjRs3KleuvGTJEtJZkD56+/btkydPhg8frpi18ccffwwZMiQpKenmzZvz589PSUmhRULx2zjFR+7lFgJAd7sS26T1sOMDwL1cRfcTWpqaJM/JLun9CJUG7j7xdexGT8o9d0qxR7YVmzWzqt2ShIzViYIONnzFHtmimFc5Z0/YDBxBLGgFEwqF8+fPB4BVq1bZ2dl98f3IBNWpU2flypV3795VHJkxYwbzRY0aNapUqZKcnFwpL5syM6cl/00HjRNKAMDDwqykP7MezwwA3hRNH6VY5jzx2ziLRtjMqLjz589bWVkVO5ienk4kjJ7DQvh1KC630oyA1HlFHe6HOVozjwn3f8ye5lxUEjJ3hFh168WytiERs8KtX7/+3bt3jRo1mjBhAuksyPC8fv06NzfX29tb9jxCeQ4a087ehlPinSo7DgsA8mVyGU2zKQoAaKBlWYKKj2x4xowZQzqCwcBC+NUsO3fn+7YvuH+LeclskD0iNnVXavYAB6tqZv99S2WCTMEv2yr5LSKXtKIkJSUFBQUBQGhoKO7qib5Wdnb2oEGDgoKCeDxeIUUp91BjAwWgaUmXjAYAYFHwWT9Mk2zn9EUhISF8lb04BALB4sWLieTRZ1gIy6KS/5LC4T1o2X9PAZtbWXxnZ3kpK39DctaGmo6Kt2X/9qvNwOHcGrUJxawoAQEB+fn5Q4YM6dixI+ksyMAkJiYOGTJk7Nix48ePBwC2gyNNF/WRseewCsTyDGmJz9eZIVv2Z5uGcio5lvR+UzZ+/Hi16wixEKrCyTJlYVa7rvWAz57/LXa157GoPzPzHucVbWJCS6XpG1frPF3Fun///rFjxywsLIKDg0lnQfru2bNnMTEx+fn5T548yc7OvnbtWvPmzbt169axY8cnT56kp6dzq9cCpX2tmUeAL5XWIxXzvEAMAN78ooeItKiQW8u9Iv8SyPhhISyjStP82bb2ipdVzTjjq9jQAKsSM5X7JBbcDi+4c13n6SoKTdOzZs2iaXru3Lk1a9YkHQfpu+3bt1+8eNHb2zsoKCg+Pj48PPybb76Jjo4OCgoKCgp6/vw5xeWa16uveP83NjwAOCfIV9tslAb4IzMPADrYFDW751avzeKrWWuBUOnhrdEyYtnY2U+ckR6yQnHkJ2e7s5n5LwrEZzPyBlYqmqyVvnGVW6t2FMcYvtUHDx58+PChi4tLQEAA6SzIAOzevVv5ZZMmTVTfYzN0jOhNDF1QAAD9HSw3p2TFFIr3pWVP/LyzNgAc/JjzskBsx2EN+v//L8rMzHbI6IrJjkwIXhGWnc3Q0WZ1PBQveSxqTjU7AAhOFuTJis5oJe/e5Jw4RCCftuXl5TFPF9atW6c6LRuhsrH6tjfb4r9LOks2a02NSmwKgpMEPydkKJZJvBVJln/IXJ2YyQJYVb2Szf8fEdJSiWUXbDSKygsLYdlRbI7j3J+Vj/RzsGpuZZEuke1K+2yFb+aezTJBpm7Tad/q1auTk5N9fX1HjRpFOgsyHhSHU3npOpbFf/Mbu9ryt9SqYsdhH0vP/e5Vkte/773/fd/tZdLhTzm2HNaW2pW72yndCJXT2ccPEImNjAl72bJlpDMYMK5LdVHUc0nCW+YlBVCPZ3YiPTcyX9zbwdKO89/SAloskhfkW7bvTC5pecXHx48ZM0Ymk4WFhbm6upKOg4wKt3otWWa6OC4GpFIAcLfgjnC0djPnWLNZlmyWsxmnmaX56Co2q6s7+vDNi31W9CLS6ts+bFvs6vAfiqLMzc27dOnyzTffcFSeyFAUZWNj07Vr1zZt2hCJp59wG6bykiS+/zDkW1pcNM9t3rv0M5l539nxt9euUvQ+Ftvt6DkzDy8CEbVh4MCBZ86cGTt27IEDB0hnQcaIpj8uD8j/53wZdvS07Nzdef3OighliAQCgZ2dHYVrK78G3hotL65rDdthY5WPBLjYW7FZf2cV3M4pLDoql30K+hkM87QjPDz8zJkzVlZWa9asIZ0FGSmKqrIs2GHmApYFj+JyNbzxoiDf/90n5f9I+eGXCh/cruiABoGm6X79+n3zzTdv374lncWQYCHUAvtJs9iORRd/jlz2ZCdbAFiVmClTqnzCyMf54ZcI5CsfmUzm5+cHAIsWLapWrRrpOMiY2Q4d7XYm3LrPYMqCx7KypsyKNx3Nl8mXfcj8IzP/guCzNvfpG1bSMqkOk+qp33///datW7GxsapL6ZEGeGtUO3LO/v5p5QLFSwlN93iV/E4kWe5WaVRla8VxjnO16mH/UBY8dX8GAXFxceHh4czXPj4+bduq2eN0+/bt06dPr1279suXLy0sLHQbEJkoWiwWPv9XHPtKkpKYey5MeX+J39NzFydkOHM5V3xcFG3uAaDywpU2g78nEVZfFBYWenl5vX//fu/evT/++CPpOIYErwi1w6bvEHOfhoqXXIqa52IPAJtSBFnSoqUU0tTkrKP7COQrwd9//338+HGBQCAQCGQyNX2tBAIBM50qJCQEqyDSGcrMjNesle2I8Y5zllRZul55aIijdQO+eapEuvfzudkZ2zfIsk26+3ZQUND79++bNGnCtK9DpYeFUEtYLEf/n5Wb/35nx29nw8uSyrelZim/UbB/hzQ1Wef51EtOTu7Tp09AQEBAQMA333yj+oZly5alp6d36tRpwIABuo+HEABYduzGb130w8kCWOLmQAHsSctOEhfdDpXnZAn2biURUC8kJiaGhIQAtsIvEyyEWmPRqJlVt17KRxa7OrApOPwpJ7awaE4pLSzM3BGi83TqpaSkxMXFLV++fO/evWJx8QaPUVFRO3fuZLPZoaGhROIhxKjkv0S5N1NTS/Me9pZCOb0h+bNLwJwTh8RvYnWeTi/Mnz8/Pz9/2LBhas9okWZYCLWp0uzFLF7Rvid1LbjDHa1lNKxK/Gw1fe6Fs8LIxzpPp8agQYO8vLyaNWt27ty5kSNHFhudM2eORCKZPHlyw4YN1X4cId0wq+VuM+izn8+FLvY8FnUuM/+Rcpt7mSx9wwqVTxu/e/fu/fbbbzweb926daSzGCScLKNlmbtDBXs2K15mSeVdXyVmSeV76lTpbFtUI829GrgeOqu8JSlZmZmZjo6O+fn5PN5/E3n+/PPPfv362dvbx8bGOjriNjeIMHlOdsKATsp78G5OydqakuXNNztbr5ryf6Sqofv4hty84mvJ5XJfX99Hjx4tXboUG6SUjb78IjYa9uOmcKq6KF7acVjTne0AYFVipljpnEMU9Tz3wlkC+T4XHR3NfJGQkGBra2tu/l/bDrFYPG/ePABYtmwZVkGkD1g2tvaTZisfmexk62LGeVUgDsvIUz6eHrKCVrnPb8R+/fXXR48eubq6Mv9nURlgIdQyytyi0vT5ykdGV7b24JkliKSHPuYoH8/YvFaenwfkyOXyadOmtW7detKkSf369QsNDWX9/wp18+bNsbGxXl5eP/30E8GECCmzHTLKzN1T8dKCRflXsweAkCRBrnKb+8T32ScOEshHQm5u7pIlSwAgODjY0hK3oyojvDVaIRa1aPj23bulbg5cigKAO7mFY1+nWbJZV7xdqnCLJnTZ/zDNYdpccjGBpunY2Ni3b982atSoatWqzMGPHz96eHhkZ2dfvHixe3ds7Y/0SOGju8lTinq+0wAjY1Mf5QknO9kyC5YYLL5l9bPX2JUqk8ioUwEBAcHBwa1bt75z5w62VSszvCLUvnPnzt2mzMMy8qT/P8doa83rbMvPl8lDU5SWUrBY8vxcIgkVKIry9PTs3r27ogoCwKJFi7Kzs/v06YNVEOkbXos2lh27KV5SAIGuDiyA/R9z3ookiuPygvzMnRtJBNSpN2/ebN68mcVihYaGYhUsDyyEWpaTk+Pn57f74KFiE2ECXR3MKOpUeu6zAhEAmPs0ctl/ynH+ckIxS/Tvv//++uuvZmZmzJokhPRNpTmByq3XfPhmAytZSWg6KPHzpRR/nBC9eq7zdDo1Z84ckUg0bty4li1bks5i2PDWqJZNnTq1cuXKy5cvNzc3/7dlXfOCoo6Ia5My96Xl2HE5NapX19tdY969e5eZmenv74+FEOmtjK1BWQd2KV6mS2TdXiXlyuS/uju1tynqX2jRqJnLvpNgpJdKV69e7dq1q7W1dUxMjPIdHVQGxXerQuVx79698PDwyMhI5qX9mMkFu4ruz/jYWnHS87Klsqdv4gkF/DKKojgcTpMmTUgHQahE9hOm5/51Wpb+kXnpyGVPcbZdnyRYlZh53qsa5/+VT/j0Sd4/F4q1uTAOilb4gYGBWAXLD68Italx48Y8Ho9Zfv7LL79MnjhxwcdYyfu3AMBt07HL+Rtxb98uWLBg8ODBpJOW6OTJk0FBQdhiG+m53D9PflxeND1b0eZ+qZvD6Mo2iuMcp6rVT1/Vnzb32rJ169aZM2fWrl371atXilVPqMywEGrTjRs3JJL/ntj36NHj3Llz7XkcwZ7NjnN/3njxyuLFi729vZ8+faq6bbT+kMlkTZs2ffbs2Zo1axYuXEg6DkIlkMsTxw0UvXyqOHAlq+Cn+I+2bNY/Pi72nKK52Q5T/OwnziQRsaIIBIK6detmZGScPXu2X79+pOMYAyyEFcXc3FwgEPD5fJDL0z598vDwyMnJ+fvvv7/99lvS0b4gPDy8S5cuVlZWMTExuAEh0lvCZxFJPwxW3ux6fFzarZzC0ZVtlroV7cZHmVtUD/tHuc2FoZs+ffr27ds7d+589epV0lmMBM4arSgikYjP5wMAsFjz5+MDIGcAACAASURBVM/PyckZOHCg/ldBAOjcufOAAQPy8vIWLVpEOgtCJbJo2NTqu77KRxa7OnAo6lh6Toxym3uRMGPbepVPG6pXr17t3r0bW+FrFxbCCvfkyZMjR46YmZkZUD9cZvfBQ4cOPXz4kHQWhEpUadYC5Tb37hbcEera3Of9/afw30c6T1ch/Pz8pFLp1KlTGzRoQDqL8cBCWLFomp49e7ZcLvf3969bty7pOKVVu3bt2bNn0zQ9a9YsvHmO9BanirPd2MnKR2ZXs7PjsO7lCv/JLig6StPpIStALi/+eUNz9uzZy5cv29vbL126lHQWo4LPCCvW4cOHx4wZ4+TkFBsba2NjU2z0/v37q1atIhJMWWBgoK+vb7GDeXl5np6eycnJhw8f/v7774kEQ+iLaJEwYXA3aXKi4sihTzkrPmS6mXP+9nYxU1pEWGVpsHXfISQyaodYLK5fv/7r16+3bt06ffp00nGMChbCClRQUODl5ZWQkHDgwIGxY8eqvuHs2bP6sPP7mTNn+vfvr3r8wIED48ePd3FxiY6OtrKy0n0whEoj7/JfaQtnKF7KaOgbnRxTKJ7nYj/ZyVZxnO3gWP1MOMvKmkRGLVi3bt3ChQu9vb0jIyO5XC7pOEYFC2EFCgwMXL16dbNmzR4+fMhSt/VgWlrao0fkH120aNHCyclJ9ThN076+vg8fPgwMDFy5cqXugyFUSsmThhc+eaB4eS9XOPp1qmqbe7txUyrNCCARsLzS0tKYmeeXLl367rvvSMcxNlgIK8qHDx/q1atXWFh48+bNdu3akY5TRvfv32/Tpo25uXlUVFTNmjVJx0FIPVHMq8Tv+4Jcpjgy5c3Hf7ILBleyWlejaENNist1O3GZW70mgYjlM378+AMHDvTv3//MmTOksxghnCxTUebMmVNQUDBq1CjDrYIA4OvrO3LkSKFQOH/+/C+/GyFCzD29bfp99vxvsauDGUWdzsh7mi9SHKQlkozNa3WeThOxWCz4v4KCArXviYiIOHTokJmZWXBwsI7jmQgshBXi9u3bYWFhfD5/9erVpLOUV1BQkKWl5cmTJ69fv046C0Ilcpg6l2VdNB/NzZwzroqNHGBVYqbyXa/865cL7t3UfbySHD161MvLq1u3bt26ddu7d6/qGxQzz/38/Axo5rlhwUKofXK5nFl7sHDhwurVq5OOU14uLi4BAQEAMHv2bJlM9sX3I0QE26GS/Y+fzaWc5mxbhcv+N190LjNf+XjGxlW0VKrbdCVKSkqaPn3648ePHz9+PGvWLNU3HDt27NatW05OTtjysOJgIdS+vXv3PnnyxM3Nbc6cOaSzaMe8efNq1qz59OnTffv2kc6CUIlsh43j1qiteGnJZvlVsweAoKTMQnnRZaE4/nXO6WME8qmTmprq7Oz87t277Oxs1dHCwkKmwdPatWttbW1V34C0AguhluXk5CxbtgwANm7c+F+LNcNnYWHBPJwIDAzMysoiHQch9Sgu19FvsfKRQZWsGlmap0lke9I+KzOZOzfKsgWgBywtLfft2+fv71+vXr0DBw4UG127dm1CQkLTpk3Vrr9C2oKzRrVszpw5mzZtatu27a1btyjj2hG0Y8eON27cmDNnzoYNG0hnQahEKTPHF9y5rnj5b75oaEyKOYu65O3iala08Yvt8LGO85bpPl5JIiIiOnXqlJmZyWb/t95DMfP8xo0b7du3JxvPuOEVoTbFxcVt376dxWJt3rzZyKogAISGhrLZ7K1bt8bExJDOglCJHOcEUko7nTWxNO9tbymU0+uTPrsEzD5xRBynRz/JNWvWzMvLU+zjBgBz584tKCgYMWIEVsGKhoVQm2bOnCkWiydOnNisWTPSWbSvcePGEyZMkEgkM2bM+PK7ESKEW7OOzZDRykcCXO15LOq8IP9hnrDoqFyWHrJC1+E+J5fLv/322127dt25c2fq1KmDBw9W7IZ9586dkydP8ni8NWvWkA1pCvDWqNZcuHChV69eNjY2MTExzs7OpONUiE+fPnl4eGRlZV24cKFHjx6k4yCknjwn+59vW6dmZvpa8dgUAMCWlKwtKVlePLOz9aqxlW7WOG/ca9mhK6mcAPDy5csjR468e/euZcuWU6dOZbabl8vlrVq1evz48fLly3/++WeC8UwEFkLtkEgkDRo0iImJ2bhxo5+fH+k4FWjjxo3+/v7u7u4vX740MzMjHQchNT58+NChRfOEjx8jG9XgsSgAEMrp7q+SEsXSNdUrDXUsajdq0bCpy69h5JKqt3fv3kmTJrm5uUVHRxvNnDt9hrdGtYN5cubu7j516lTSWSrWjBkzPD09maehpLMgpN6MGTNWrF/Poop+v1mwqHku9gCwITkrVyYHAIrDsRk4wnnTL8RSliA3N5e5CgwJCcEqqBt4RagFihuG58+f79mzJ+k4Fe78+fO9e/c27pvAyHAdP3589+7d165dszA3f+JTjbkiZIyITX2UJ2xjzevRyMeqc3e2YxWCOUvyxx9/XL16tU2bNrdv3za+OXf6CQuhFkyePHnPnj3dunW7fPky6Sw60rNnz4sXL06ePHnXrl2ksyBUJCMjo0mTJpcvX65Xr565uXn0T2Pkt/5RjIZnF/q9Ty+QyfX59x5FUZaWlseOHevTpw/pLKYCC2F5PX36tFmzZhRFRUZG+vj4kI6jI9HR0Q0bNpTJZA8fPjTKKbLIQI0dO1YqlTJNnVq3bn311AmXtQtosQgAWHz+zHzOX/8+8/Ly6tKlC+mkJbp69WpUVNTAgQPDwvTu4aWxwkJYXp06dbp+/bqfn9/GjRtJZ9EpPz+/0NDQdu3a3bx5E2/gID0xYcKE3Nxc5uuwsLBhw4Zta9tUcGCndY9+z5t/07XfACsrq5iYmGrVqpHNqYFi68HLly9369aNdBzTQKNyOHHiBAA4ODhkZGSQzqJrAoGgcuXKAHDy5EnSWRBSw8zMLD8/X5afL4x+KZVKGzZsCABr1qwhnevLmF1rfHx8JBIJ6SwmAWeNlp1il741a9Y4ODiQjqNrdnZ2K1asgP/vvEg6DkLFbd++3czMjMXnm3t67969+9mzZ7Vq1TKI1U2KFUpqN2ZC2ke6Ehswpgw0atRIKpWSzkKGTCZjHhCuXLmSdBaESpSZmeno6AgAYWFhpLOU1qlTpwDAwcEhPT2ddBbjh1eEZZSUlBQUFAT/78BJOg4ZLBYrNDSUoiimRz7pOAipt2zZsvT09E6dOg0cOJB0ltIaNGhQt27dMjMzV65cSTqL8cPJMmU0evToI0eODB48+OTJk6SzEDZ48OCwsLDRo0cfOnSIdBaEiouKimrUqJFcLo+IiGAeEypLTk6+c+cOkWDK2rZtqzp/5+XLl40bNwaAf//9t379+iRymQzSl6QG6d69exRFWVhYxMfHk85C3vv37/l8PkVRt27dIp0FoeK6d+8OAD/99JPa0TNnzpD+HQwAcObMGbXxpkyZAgBdu3atyO8QovGK8KvRNO3r6/vw4cPAwEC8a8EIDAxcvXp1s2bNHj58yGLh/XakL86dO9e3b197e/vY2FjmMWExjx49YjadJmv+/PktWrRQPZ6Zmenh4ZGRkXHu3LnevXvrPpiJwEL41Q4cODB+/HgXF5fo6GgrKyvScfRCQUGBl5dXQkLCgQMHcCttpCfEYnGDBg1iY2NDQ0NnzZpFOk4ZhYaG+vn51alT5+XLl8zeFEjr8OT96+Tl5S1evBgA1q1bh1VQgc/nr1q1CgACAgJycnJIx0EIAGDz5s2xsbFeXl4G3Qp/+vTp9evXf/PmzdatW0lnMVpYCL/OmjVrkpOTfX19R40aRTqLfvn+++/btm2blpbGTKZVlZ2dvXz58uHDh69ZsyYvL0/H8ZCp+fjxI7MsfePGjVwul3ScsuNwOJs2bQKAFStWpKamko5jnLAQfoX4+PhNmzZRFMWsGSAdR79QFLV582YWixUSEvL69WvVN4wbN+79+/ezZ89+9uyZQZ+hI4OwePHi7Ozs3r17M5NlDFrXrl179eql2J4JaR/hyToGZcCAAQAwduxY0kH015gxYwBg4MCBxY4nJiZaW1vn5eXRNJ2RkcHn8wUCAYmAyCRERESw2WwzM7Po6GjSWbTj9evX5ubmLBbr0aNHpLMYIbwiLK3w8PAzZ85YWVmtWbOGdBb9FRwcbGNjc/r06WI7UsXGxtapU8fS0hIAHBwcnJ2d4+PjCWVExm/27NkymWzmzJmenp6ks2iHu7v79OnT5XL5rFmzaJzhqG06LYQjR47s27ev2tvcQqGwb9++ffv2lUqluoxUSjKZjGlRuGjRIn3uW0+ck5NTQEAAAPj5+Sn/UwqFQuUJb+bm5oWFhQTyAUilUuYnTSgUqo6mpqb27dt35MiRug+GtOW33367efNmlSpVAgMDSWfRpqVLlzo7O9+9e5fp9Y+0SZeXn3Z2dgAQFxenOqTYOUUoFOoyUilt374dAGrVqlVYWEg6i74TiUTu7u4AsGPHDsXBp0+furi4MF9LpVJbW9u3b98Siaeof7m5uaqjcXFxAGBnZ6f7YEgrCgoKatSoAQB79+4lnUX79uzZAwCurq75+fmksxgVvDX6ZQKBYOnSpQAQEhJiYWFBOo6+MzMzW7duHQAEBgZmZGQwB+vXr29hYXHhwgUAOHXqVK1atWrWrEkuIzJawcHB79+/b9y48fjx40ln0b4JEyY0b948MTExJCSEdBbjosuqa6BXhDNnzgSATp06kQ5iSJgNRZnnGYyrV6/WqVOnQYMGHh4e9+7dIxUMrwiN2IcPH5jn0NevXyedpaLcvn2boigej/fu3TvSWYyHFgphUFBQ3bp1GzVqtGvXLs3vNMRC+OrVKy6Xy2aznz59SjqLIXnx4gWHw+FwOM+fP1c+rrb86JJWCuGdO3d69erl4eHRpk2blJSUikmKvtqIESMAYNiwYaSDVKyhQ4cCwMiRI0kHMR7lLYQnT56sXbv2u3fvPnz4UKdOHc1tlw2xEDKLkKZOnUo6iOH56aefAKBLly6kg3ym/IXw48ePjo6O58+fl0qlycnJFZYUfZ27d+8yl0qkHj/rTEJCAtPm/ubNm6SzGAlOOe+s3rp1a9CgQczT6VGjRoWFhbVr107zR168eJGVlVXsoPIcQrlcriedRy5evHjp0iV7e/vly5eTzmJ4Vq1adeLEiatXr544cUJPFjUrd8WLjIzk8XjF3pCYmPjFP2Tfvn19+vTp2bMnAFStWlW7CVHZKNYVzJ8/3+gfP7u5uc2dO3fFihWzZs16/PgxtrnXgnIW0m3btrVr1y4/Pz8rK2v48OFDhgzR8GbmilAzoVAYERGhg794KVEUFRwcrPavExMTg4tbaZp+9OhRTEyM2qHg4GC9asETERGhdtVEMZqvCMePH9+/f39fX19XV9cffvhBJBJVzPcVfYV9+/YBgIuLC9O0wegVFBRUr14dAPbv3086izEo7xXhpEmTXrx44e3t7eLi4uzsXJpSN2XKFNW3SSSSDRs2MF+z2Wxra+tyBtMKoVAokUgUt22VhYeHd+/e3cPDIzIyksMp77fRcEml0nHjxsXGxl66dKlz587FRpnbj1wuV09m27LZbMXX/v7+qi0os7Kydu3apfkPyc3NtbS0vHLlCofD6d69+549e6ZPn679rKjUcnNzmSWDwcHBzGQZo8fj8dasWfP9998vXLhw4MCBtra2pBMZOC0W1bFjx65bt07DGwzuGeGdO3dKmqAlEok8PDwAYMuWLUSy6YnNmzcDQJ06dVT/4RRT+G7cuEEkm1rlf0Y4a9asn3/+mfl63rx5s2fPrpCgqNTmz58PAK1bt5bL5aSz6I5cLm/fvj0ABAQEkM5i8LRQCN+/fy8Wi8+ePevo6PjhwwcN7zS4QkjT9PDhwwFg+PDhqkNnz54FAHt7+0+fPuk+mD7IzMysVKkSAPzxxx+qo8OGDQOAESNG6D6YBuUvhLdu3apVq9abN29ev35ds2bNs2fPVlhY9GVxcXFME84HDx6QzqJrT548YbFYZmZmJT2bQKVU3kKYmpraoUMHT0/PAQMGfPGBmf4XQrFYnJycLJFIFEc0X9Z89913ADB9+nQdZtQj06ZNA4DOnTurDmm4mCZLK8snfvnll+bNm9evXz80NLRiYqLS6tu3LwD88MMPpIOQwfQN6NevH+kghg0X1Bc5c+ZMnTp1Wrdu7erqeurUKcVxpq1MkyZNZDJZsY+8fPmSWWX47Nkz3YYl7+XLlxwOR+3fXSaTtWjRAgCWLl1KIpomuKDemFy9ehUArK2tTXYdS2pqqo2NDQBcunSJdBYDhoWwyMKFC5OSkmiavnLliru7u+K4onvhL7/8ovopZqKE2qsi4/btt98CwIwZM1SH9u7dCwCurq56OIUPC6HRkEqlDRo0AADNUxOM3tq1awHA29tbLBaTzmKosBCq8euvv7Zv3175yLFjxwCgSpUqWVlZxd6cmZnp6OgIAGfOnNFhRsJOnz7NPB9NT08vNpSTk8Osrjt+/DiRbJphITQaW7ZsAYDatWvrwy8NgkQiUd26dQFg69atpLMYKp0WQn9//ylTpqidWiIUCqdMmTJlyhTl53O6Fx4e3rhx40qVKr18+bLY0DfffAMA8+bNU/3U1q1bmf+QJrI3heI/3rZt21RH586dCwBt2rTRzyl8EomE+UlT+9vz06dPU6ZM8ff3130w9FUUE7VwshJN02fOnDHxiXvlhDvUF5ebm7tly5YmTZoU+z0eERFR0gQtU7tFo7gVo3rWopjC9/DhQyLZkInQMFHLNJn4xL1ywkJYRFH5mH5vqndBf/jhBwDo27ev6mdN56G94uH833//rTrap08fAJgwYYLugyHTYcqT1EqiYfIa+iIshEW6d+++e/fuiIiI+fPnN27cWPXOXlpaGtPB4eLFi6of79evHwCMHz9eJ2GJGTduHAD0799fdeiff/4xkbMBRBZe/aiFV8llhoWwSEpKysyZMwcMGDB37tzU1FS17wkKCgIALy8v1Qlab968MfqFvYoFvLGxscWGJBJJ/fr1AaCk1qwIaQU+DytJaZ6bxsfHnz9/PiEhQZfB9B8Wwq+jubNaQECAEbd6ksvlzNYiCxYsUB3V0GsNIW3BGZKaaZ5JGxoaWrNmzSlTpri6uqpdDGaysBB+tT/++KOkE1LFyoGjR48SyVahjhw5AgBOTk7Z2dnFhjIyMphT0T///JNINmQi1q1bh2vmNFBM3AsKCio2lJ+f7+DgEB0dTdP0o0ePqlWrht9DBSyEZcE8opg2bZrq0P79+8EYt4NRbPvy66+/qo5OnToV9G8PXmRkUlNTmYf02EVFg5Im7j148MDDw0Px0tnZOSoqSufp9BQWwrJ49eoVM2nt6dOnxYZkMlnLli0BQLFBgXFYsmQJADRt2lRtnzkOh8PhcHC6mv77888/T506pfYsTSqVnjp16tSpU6r/xHoC+2qWktr+q3/99Zevr6/ipY+PD6ltYWQyGfOTJpVKVUfz8vJOnTql43tLWAjLaMaMGSVN0Lp79y7Tb/rt27c6z1UhEhIS+Hw+RVE3b95UHWV6rc2cOVP3wdDXMpTuTqpwp4XSU7sjx927d729vRUvXV1dX7x4QSKdPnZ3wkJYRpo7q40cORIAhg4dqvtgFWHIkCEAMGrUKNUhpteag4ODaq81pIcMtBDi3ntfS3WPxqysLBsbG+Z+6evXrx0cHEh1wsJCaFS2bdtWUme1xMREZvOm69evE8mmRbdv32YucN+/f19sSDGFb/v27USyoa9loIXw6NGjzEQt1TYXSC3FxL1jx44pDgYEBDRr1iwkJKRBgwZr164llQ0LoVFRTNBS+yO1fPlyAGjcuLHa++CGQiaTNWvWDABWrFihOrpmzZqSeq0hXYqLi1u5cuWGDRu+uD7MEAuhYvuX/fv3k85iSPbt26e6CUx4ePiWLVtu3bpFMJhWCqFQKDxx4sSSJUt2796t9s/5KlgIy4WZoGVlZcXs36SsoKCgZs2aALBnzx4i2bRi9+7dAODm5pafn19sSHOvNaQzUVFR9vb2K1asWLVqlZubW0pKioY3G2Ih/Pnnn6GEDUGRBnq7LWj5C6FUKvX19R06dOiRI0fWrl2LhZC8/v37A8C4ceNUh3777TcAqFKlikAg0H2w8svOznZ2dgaA33//XXV07NixADBgwADdB0PKFixYMHHiRObrSZMmrV69WsObDa4Qap6ohTTTz4l75S+Ev/32W7t27bQYiaJpGlA5xMfH+/j4iESi+/fvMwsnlHXs2PHGjRv+/v4hISFE4pWHv7//xo0b27Zte+vWLYqilIciIiJatGjB4XBevHjBPCZEpCxevDg9PZ25dl+3bt2rV68OHTpU0pvt7e2zsrI8PDzMzMyKDcnl8levXgGAUChMTk729/ev0Nil9OTJk4SEhBEjRjB7gqoqLCzk8Xg6TqVvNHwTRo4cefz48erVqzPPOIjbsGFDtWrVLCwsAMDb25vFYhV7g1gsjo2NtbOzEwgEJf0hM2bMsLa2rlq16rt37/r27duhQ4fyxtJiUTVZGjqr/fvvv2w2m8vlMg0dDMjr16+ZGdiPHj0qNqS51xrSsfj4eBcXl1GjRg0cOLBu3brDhg3T8GbmirBy5crOKpycnJjfCUKhMCIiory/WbSHz+cfOnRI9e8SHR3doUOHMWPGVNi31mCMGTOmQ4cOan/JHDp0iM/nk/43LBIREaG4InRyclL9OaxcuTJ86Ypw0KBBnp6eO3fuPHTokLOz87Vr18r5DcQrQi3Izc319PRMSUk5cuTIqFGjio1OmjRp7969vXr1+uuvv4jEK5tevXpduHBh0qRJzKWGsiNHjowePdrJySk2NpZ5TIjIysnJuX//fvXq1Q8ePCiXy5nW8GoxV4RxcXF16tQpNpSXl2dtbQ0AQqFQKBQyz7+J+/PPPw8ePFivXr1nz55xuVzlobdv33p7e4tEonv37rVq1YpUQuKePHnSsmVLDofz8uVLd3d35SGpVNq4ceOXL1+OGzeO2SKNuC5dulhYWDBXhLm5uVZWVsXe8ObNG3d3d81XhBMnTqxXrx5z0yIgIEAsFm/atKlcscpZSBFDQ2c1xeZNFy5cIJKtDK5cuQIA1tbWqjMv8vPzNfRaQwRlZ2fXqFFD89mxwT0jFIlEnp6eALBp0ybV0YULFwKAr6+vUba5Lw25XN62bVsAWLRokeroxo0bAcDd3V2v/k3L/4xw7969PXv2pGlaLpd/991369evL2ckLITaoeistmTJEtXR9evXA0C9evUMosutRCLx8fEBgJCQENXRwMBAKKHXGiIiJSWlU6dOo0ePrlmzptrfhsoMrhDSNH3u3DnmN+PHjx+LDeXm5larVg0A1N47NQUHDx6EklvhOzg4AMBff/1FJFtJyl8ICwoKOnTo0L59+7Zt2zZr1qz8q0uxEGoNM0HLwsJCdYKW5rNafaPhLFIxhY/sOiRUzNu3b+/cuVOaycl6Xgg/fvw4e/bs7777bvr06cpLknr06AEAU6ZMUf3IgQMHAMDZ2Vm1Ehg9xXnAwYMHVUcnT54MAF27dtV9MM20so5QKpW+ePHi0aNHWjkjx0KoTRo6q2k4q9Urms8iNfRaQwZBzwth165dt27dGh0dHRAQ0KlTJ8XxqKgops19ZGRksY/I5XLmAeHixYt1G5a8RYsWAUCzZs1Ui8GLFy+YVvikGopqgJ1ljJzmzmoazmr1h4azSA291pCh0PNCKBKJmC/u3r1bq1Yt5aFZs2YBgHJ1VLh37x5FUebm5q9fv9ZFSv0QHx9vYWFR0u2Zbt26AcDs2bN1H+yLsBAaPw2d1TSc1eoJDWeRmnutIUNRrVo1Ho8XHx+vOpSXl8fj8Xg8nqIaEREXF7dx40ZfX9+TJ08qH1e0uT916pTqp77//nsAGDx4sK5ikjdo0CAAGD16tOrQyZMnQY9b4YtEIuYnTe12YPHx8Twer1q1arqMhIVQyxSd1Xbv3q06quGsVh9oOIvctWsXlNBrDSEtiouL27JlS5cuXQIDA4sN7dixAwCqV6+u+kOYmJjITMS/fPmyrpKSFB4eDgB8Pl/19kxhYWGtWrUAYOfOnUSyGSIshNr3+++/A0DlypVVJy9oPqslS8NZpKLX2okTJ4hkQ6ZGIBCw2exiVwxSqbRRo0YAsGrVKtWPrFy5EgB8fHyMvgU8fh+0DgthhWBa/vj7+6sOaTirJUjzWeScOXMAoG3btia7Wgvpxvr16zMyMmiafvjwobW1tepNWsWVkOo+G6ZzJYRXxlqHhbBCaOispvlsjhQNZ5Eaeq0hpF3bt293c3Nr0KBB9erVS7prMnDgQABQ21lNcVeDqaZGSSAQaLirNHr0aAAYNGiQ7oMZNCyEFWXixIkA0KtXL9UhDff3idB8FtmzZ08AmDRpku6DIRMkl8szMzM1vEHzbMmOHTsCgJ+fX4UFJGz27NkA0K5dO9XbM6Y5e1YrsBBWlLS0NGaqutrOahpmfOmehhl3ly9fBgAbGxvNu9whpEsGun6u/HA9ZQXBQliBmK2X1HZW03xWq0saziIVvdY2bNhAJBtCahloR5Xyww47FQQLYQUSi8UaOqstWrSod+/eapc261JcXFzv3r0NqGMvQrRh9tgsJ+y5WnGwEFYsDT+7etW0WjWMsf42QcZBcSfQRM7hNJ9VM7twqL1XjEoDC2GFM4jOaqqM+P4SMg6PHz9msVgmcle/NM9Z7t+/TySbEcBCWOH0v7OaKuOecYCMhobVAsY0z0vzzDsN60lQKWEh1AVmxnPHjh1JBymtrl27qs5BF4lERrw8CxmilJQUGxsbo1/588W1WFZWVsq7VqGvhYVQFzSvgdU3qquSs7Oz165d6+Li0rJlS7LZECpm1apVxt0LQnN3joYNGwLA6tWriWQzGlgIdUQ/O6up8sua6wAAFO9JREFUUtunKiUl5ejRo+fPn8dCiPSNUCh0d3c34u6AX+zXWKtWrcLCQt0HMyYUTdOAKh6zjdHTp0+///77wYMHk45TopMnTx49etTHxycyMpLD4SgP3b1718/P78GDB6SyIaRWWFjY4MGDHRwcYmNjK1WqpDyUk5Pj6emZmpp64sQJZltpw3LixIlhw4ZVqVIlJiaGeUyoIBAIPDw80tPTw8LCmMeEqOxIV2ITEhYWxkzuIv1vrglFURYWFmFhYar579y5g1eESD9p2EFs9+7dYJg7iCn2dNuzZ4/qqJ7v6WZYOBp/KyJtunDhglAodHV1bdq0KeksJYqIiEhMTLxw4QKeYyIDsmnTpsaNG2/btm3ChAn169dXHvrxxx/37Nnz5MmTDRs2LFmyhFTCMli/fv27d+8aN278ww8/FBuKiorasWMHm83etGkTkWzGhnQlNhUREREsFsvMzCwmJoZ0Fk3i4uKY+QUPHz4sNoRXhEifTZkyBUpY+Xr79m2Kong8np60uS+NxMRES0tLALh+/brqaPfu3QHgp59+0n0wo4SFUEe++eYbAJg3bx7pIF82d+5cAGjTpo3y/IJz586tXLnS3d39xIkTycnJBOMhpFZGRgbzgFBtLyTmAeGoUaN0H6xsRo4cCQBDhw5VHWL6Vdnb23/69En3wYwSTpbRhePHj48cObJKlSqxsbG2trbMwUePHsXFxXl5eTVu3JhsvGJyc3M9PT1TUlKOHz8+fPhw5uCaNWtycnKYr8ePH890e0JIr4SGhvr5+bm7u7948cLc3Fx56MOHD/Xq1SssLLxx40b79u1JJSyle/futW3b1sLC4tWrV8xjQgWxWNywYcOYmJjQ0FDmMSHSAtKV2PgVFBTUqFEDAH755RfFwZ9++ql58+YLFizw8PDQwy5Qe/fuBQBXV9e8vDzSWRAqLUVntZCQENVR5gFh06ZN9bwhp0wma9myJQD8/PPPqqPr16+HEnqtoTLDQljhli5dCgBNmjRR/u93+PDhgoICmqZv375ds2ZNcunUk8lkLVq0AIBly5aRzoLQV7hy5QoAWFtbq3ZWKygoqF69OgD8+uuvJKKV1v79+wHAxcVF9TQ0LS2NuaWkttcaKjMshBXrw4cPzBPvGzduqH3Dw4cP69atq+NUpXHnzh1mfsG7d+9IZ0HoK/Tq1QsAJk6cqDp0+PBhKGHzJj2Rk5NTtWpVADhy5Ijq6I8//ggAvXv31n0w44aFsGIxz9iGDx9e0htGjRq1dOlSHSb6CsOGDQOAESNGkA6C0FfQ0FlNLpe3a9cOABYsWEAk2xcFBAQAQOvWrVVb4URERLDZbDMzM9Vea6icsBBWoC9eVC1fvrx9+/bMPVI99MXLWYT0k7+/P6jMfGY8efKEWcgUGxtLJJsGb968YUr4gwcPVEeZmedz587VfTCjh4Wwoiges6m94BMKhZMmTerRo4fe3qJhqH3AiZCey8nJcXZ2BoDff/9ddXTcuHEA0L9/f90H06xfv34AMH78eNWh3377DQCqVKmSlZWl+2BGDwthRfnll19KmngpFApbtGjh4OAwb968gICAgICA3NxcIiG/SDHldd++faSzIPQVNHRWS01NZTZv+vvvv4lkU+vq1asAYGVlpbpOV3OvNVR+WAgrhOKJ9/Hjx1VHRSLRic8JhULdhyylY8eO4akoMjgymax58+YAsHz5ctXRNWvWAIC3t7fq5k1ESKXSBg0aAMDatWtVR5ctWwYAjRs3lkqlus9mCrAQVoh58+aV9MTb4MjlcmYB8vz580lnQegrKDqrqT6kF4lEdevWBYBt27YRyVbM1q1bAaB27dqqGyopHtWr7bWGtAILofZpaNdpoAylUSpCxQwdOhQARo4cqTp0+vRpALC3t09PT9d9MGWZmZnMxt1nzpxRHR0xYgQADBs2TPfBTAcWQu3r06cPAEyYMIF0EG1i+t/37duXdBCEvkJCQgKfz6co6ubNm6qjXbp04fP5xBenX7hwgc/nd+nSRXXo7t27zEXt27dvdZ7LhGAh1LJ//vmHaWxhZJ2pFS0tLl26RDoLQl9BQ2e1169fJyQkEElVTEJCwuvXr4sd1NxrDWkRFkJtkkgkzBPv4OBg0lm0LygoCAC8vLywySEyIIrOavv37yed5evs27evpF5rSLtY5W/bjRR27Njx/PnzOnXqzJw5k3QW7Zs9e7aHh0dUVNSuXbtIZ0GotHg8HjNHdOHChdnZ2aTjlFZubm5gYCAABAcHM5NlUMXBQqg1mZmZK1asAICNGzcW2wLGOJiZmQUHBwPA0qVL09PTScdBqLRGjhzZvn37tLS0tWvXks5SWqtWrUpJSWndujUzWQZVKNyPUGumTZu2Y8eOLl26MI8JjVX37t3//vvvadOmbdu2jXQWhEorIiKiRYsWHA7nxYsXzMIJfRYfH8+scbx37x7zmBAAPnz4sHXrVrFYHBoaSjae8cErQu149erVnj17OBzOpk2bSGepWJs2beJyubt27Xr+/DnpLAiVVtOmTceMGSMWi+fPn086y5fNmTNHJBKNHTtWUQVfv349ZcqUwsLCe/fukc1mnEg/pDQS3377LQDMmDGDdBBdmD59OgB07tyZdBCEvoKis5qez3xmeq2pnXl+586dli1bEkll3PDWqBacOXNm4MCB9vb2r1+/rlSpEuk4FU4gEHh4eKSnp585c6Z///6k4yBUWmvWrFm8eLG5ubmFhQXpLCUSCoUikWj16tWLFi0qNnT37l0/P78HDx4QCWbEOKQDGDyxWMxsIbZy5UpTqIIAYG9vv3Tp0hkzZvj7+3fv3l2ff6cgpMze3p6iKLFYLBKJSGcpEUVRFEXZ29uTDmJC8IqwvNauXbto0SJvb++nT59yOKZyYiGTyZo0afL8+fO1a9cuWLCAdByEviwnJ8fT0zM1NfXAgQPMhkf66ezZs+PHj69SpUpMTIydnZ3yEF4RVhTS92YNm37u56IbGnaNQUgPzZkzBwDatm2r/63wO3ToAAD+/v7FjuMzwgqChbBc9HaHT93QsI8oQnrl9evXTCv8R48ekc7yZf/++y+bzeZyudHR0YqDvr6+1atXr1y5cu3ata9du0YunRHCQlh2T548YfZkiI2NJZ2FjDdv3jC/XB48eEA6C0Ka9OzZEwAmTZqkOHL58mVvb283N7emTZs+efKEYDa1Jk6cCAC9evUiHcQkYCEsI7lc3q5dOwBYsGAB6SwkMROFjGPnRWSsrly5AgA2NjYpKSnMEblcPmjQoMePH9M0vWvXLl9fX6IB1UhLS2MeEBLfHMMUYCEso8OHDwOAk5NTdnY26Swk5eTkVK1aFQCOHDlCOgtCakgkEh8fHwDYsGGD2je8fPmyatWqOk5VGiEhIQBQr149bHNf0bCzTFkUFhYuXrwYANatW8dMljFZ1tbWq1evBoCAgID8/HzScRAqbuvWrS9fvnR3d582bZraN1y7dq1169Y6TlUaM2bM8PT0jI6O3r59O+ksxo50JTZIGnY4M0G4axrSWxkZGQ4ODgDw119/qX3Do0ePqlatGhUVpeNgpXTu3DkAsLOz+/jxI+ksxgwL4VfTvOe1acJ9tJF+mjx5MgB07dpV7eiNGzfc3d31/D9yjx49AGDKlCmkgxgzLIRfbciQIQAwatQo0kH0y8iRIwFg6NChpIMg9J8XL15wOBxmxwnV0Y0bN1arVu3ChQtv3rx58+aNVCrVfcLSiIqK4nK5bDY7MjKSdBajhZ1lvs6dO3fat29vYWERHR3NbHuNGElJSZ6envn5+devX2eWAyNE1rfffnvlypXZs2er7gmTl5c3YMAA5SMnT54s1sZFf8yePXvz5s2dOnUKDw8nncVIka7EhkQmkzVr1gwAVqxYQTqLPlq+fDkANG7cWG9PrpHpOHnyJAA4ODikp6eTzlJemZmZjo6OAHDq1CnSWYwTXhF+hefPn7dr187e3j4qKorH45GOo3cKCwu9vLwEAsHt27cbNGhAOg4yXSKRqH79+nFxcTt37pwyZQrpOFqwc+fOqVOn1qpV69WrV9jmXuuwEH6dtLS09+/fK3bLRMU8fPiwRo0aTk5OpIMgk7Z69erAwEAfH5/IyEjjaIXP3I56+vSp2u2ZUDlhIfxMRESETCZr1KiRmZlZsSGZTBYREQEALVq0IBHNYDx69AgAmjZtymaziw2JxeKnT5+y2eymTZuSiIZMQlJSUr169fLy8i5fvtytWzfScbTm2rVrnTt35vP50dHRbm5upOMYFSyEn7G0tCwoKHj//r3qRJjMzExmu0G5XE5RFIl0BoCmaRaLBQCK9VvKEhISatSowefzcek9qjhjxow5fPjwoEGDTp06RTqLlg0aNOj06dNjxow5ePAg6SxGBTvLIISMx/37948cOWJubr5u3TrSWbQvJCTEwsLi8OHDt2/fJp3FqGAhRAgZCZqmZ8+eTdP03Llz3d3dScfRvlq1as2ZM4f5a8rlctJxjAcWQoSQkTh8+PCDBw+cnZ3nz59POktFWbhwYbVq1Z48eXL06FHSWYwHFkKEkDHIy8tbuHAhAAQHBxtxK3wrK6u1a9cCwPz583NyckjHMRJYCBFCxmDt2rXJycnNmjUbNWoU6SwVa/To0a1atUpNTQ0ODiadxUjgrNHPMLNGO3furLpkVSKRMNt74qxRDRSzRrt168blcouNCoXC8PBwnDWKtO7t27fe3t4ikejmzZvMjtnG7f79+23atDEzM3vx4oVRPg3VMSyEn2EKoeb3YCHUQFEINcBCiLTOBNcVGPEqEd3DQvgZphC+efNGdb2qQCBgGqZgIdRAUQjT0tLs7e2LjX748KFOnTpYCJF2meZKc2PtG0AEPiNUg8Ph/K+9uwtpqo/jAP5Xs+VWI01hYbabZLGThjFJVhdFSrUlBQuqG4uiKCJqXvSCvaNBDZvikt6giIjoJqLZlTdCkmQQsVw7EpG9MZY7swM74jZ3novxRM/mEw8PPP52nv/3cyU7Xnw5F/vud875n39xjv/Hi5pmDc4hzI7p6Wm3280Ya2tr46cFGWOVlZUnT55kjLnd7lQqRR1H21CEAKBhN2/efPPmTWaBHXWW2ZZZLjkyMnL79m3qLNqGIgQArYrFYmfPnmWMeTweDvdk0Ol0maUUbW1t0WiUOo6GoQgBQKsuXLgwPj6+fv16l8tFnYXG9u3bm5qaJElqb2+nzqJhKEIA0KRQKNTb21tUVJS7AT1XvF7vnDlzfD7fyMgIdRatQhECgCa1trYmk8n9+/evXLmSOgslQRD27duXSqWOHTtGnUWrsHziL6LRqKqqZWVluYvhVFXNXIUvLy+niKYZ4+PjjLFFixblLjJJp9OSJBUUFGQ2tAL41/x+f3Nz88KFC0dHRysqKqjjEJMkqbq6WpIkv9/vdDqp42gPihAANCaRSNTW1oqi6PV6MQZleL3e1tbWZcuWvX37VqfTUcfRGFwaBQCN6enpEUVx+fLlhw8fps6SL44cOSIIwvv3769du0adRXswEQKAlkQiEYvFMjEx8ezZs82bN1PHySP9/f1NTU1Go1EURZPJRB1HS3ifCHt7e+12e0NDQ2dnJ3UWjnR2dtp+cf36depEoBmnT5+emJhwOp1owSyNjY0Oh0OW5XPnzlFn0RiuJ8KBgYG9e/e+fPmyqKhozZo1V65cwX3m2aEoytTUVObvTZs2tbe342WJ8E8EAoG6urrCwsJAIGCxWKjj5B1RFGtqatLp9OvXr2tqaqjjaAbX736MRCLz5s0zGo3FxcUmkykWi1En4oVer9fr9YyxV69eTU5ONjY2UicCbbBarT6f7/v372jBGVksljNnzlRUVFitVuosWsL1RDg5Oblly5ZwOGy32xVFuXv3bu4WevCf2rFjh9PpbGlpoQ4CAPzi+h5hKBT69u3bxYsXS0pK+vv7h4eHqRPx5ePHj0NDQzt37qQOAnmkvr5eEIRwOJx7SJZlQRAEQZj9VNqSOUuyLOceCofDgiDU19fPfqp8xvWl0Z6enl27drlcLpfLVVlZefXqVbvdTh2KI11dXYcOHZo7dy51EMgjwWBQUZREIpF7KJVKBYNBxpiqqtgT9O+oqpo5SzPuzZRIJILBYObGBPzEdRFWVVUFAoHp6enCwkJRFJcsWUKdiCOyLD969CgQCFAHAQDecX1p9Pjx4/Pnz6+trbVarfF4/Pz589SJOPL48eM9e/bgXWsAQI7ridBgMNy5c4c6Bad2795NHQEAgDHOJ0IAAACuJ0IAyE99fX25l83j8ThJGI168uSJwWDI+hAb2c+I63WEAJBvDAaDoii//590Oo2nRv+Oqqq5u8hl0ev1+FXxK0yEAJB3Ll++XFZWlvWhoihHjx4lyaNF3d3ducskJEk6ceIESZ58hokQAPJIZiIcGxtbunRp1iFJkjLXSzER/sbPiTAajeb+mPj06ZPZbMZEmAUPywAAANdQhAAAwDUUIQAAcA1FCAAAXEMRAgAA17B8AgDyyKVLl5LJZGlpae4hvV7v8XgYY3hk9DcKCgoyZ2nGLSZKS0s9Hg82Xs2C5RMAAMA1XBoFAACuoQgBAIBruEcIAJS+fv3q9/vT6bTD4TCbzdRxePHixYtft8VetWqVzWYjzEMLEyEAkBkbG7PZbPF4PJlMNjQ0jI6OUifihaIosT91d3cPDw9TJ6KEh2UAgMyDBw9u3LgxMDDAGNu2bZvD4Thw4AB1KL5IkrRixYpQKGQ0GqmzkMFECABkVq9e/e7du46OjufPn3/48GHjxo3Uibjj8/laWlp4bkGGiRAACMXj8a1bt5aUlAwNDW3YsOHWrVsLFiygDsWRqamp6urqwcHBqqoq6iyUMBECAJmOjg6z2fz06dMvX76kUqlTp05RJ+LLvXv31q1bx3kLMhQhABCKRqOZb2GdTme32yORCHUijqiq2tXV5Xa7qYPQw/IJACBz8ODB5uZmWZZ1Ot39+/cfPnxInYgjfX19ixcvrqurow5CD/cIAYDSjx8/BgcHE4nE2rVry8vLqeNw5PPnz8XFxSaTiToIPRQhAABwDfcIAQCAayhCAADgGooQAAC4hiIEAACuoQgBAIBrKEIAAOAaihAAALiGIgQAAK6hCAEAgGsoQgAA4BqKEAAAuIYiBAAArv0BckbGm6lFlfMAAAC2elRYdHJka2l0UEtMIHJka2l0IDIwMjIuMDkuMQAAeJx7v2/tPQYg4GWAAEYg5oHiBkY2Bw0gzcxCLM3IAKIZGYmluRkYM5gYWBMY2BgYmRIY2RmYmDOYmDiAJiUwczKwsGYwsXAxsHIziDCyMbCyMDMxis+CuhEMgI48YL961SoVCNfB/qHbsv1Q9n4E+8D+Cf1TVJHE7ZHUw9gOQHOgag4AxdUOwPQi2A4HkNSAxB2Q1IPZYgB4SjAEiKcIEQAAAR56VFh0TU9MIHJka2l0IDIwMjIuMDkuMQAAeJyNk81ugzAMx+88hV+AyE7CR44Fqm6aClLL9gCVOOzCaac+fe1W1GGtIpIgEvOL/44dLtN8neYpA2mn7uv3D57NdhnbMTFCCPDjEDE7gkyg2R8+e2jHXbNY2uG7H89AVgZKX7O7cTguFoIWyBQoDdDg/8nCWebQVHcQcjI2BHT1G9AxmG8ivZBbtIuVy4THMg4ywVXwAe5V74WrmXsGmFtThBqr8g0YGMw3kYSCbhEnWjlN+bRxnAlw33er4j+uQzP0nV4Hz4/Vqsuy1NoS90oL6JlwWibeCbUWw/MGrzknXgZNrewuNIF35ThN/FWVSYRZgKIzc2D8is8Wn0TWy0/B8+wGSWmlw+MGAp0AAACtelRYdFNNSUxFUyByZGtpdCAyMDIyLjA5LjEAAHicbY49CsMwDEav0jEBW0iOHdvqBTKV7iFDCR2LS8mYw9cxpbJLl096PPQzT8tKazdPS/8nKedp7zSBU9qAiwGDOmsEn5nAxIg286Ex16Fksa38jrauUZ8lZcfPgeZ8r25belxf6ckIR3tJ2x0Ck4BhIzDwIBDZClh2AoQ8Cjn2lSIOQshRYGSqniCm6gu/vwHc615hxUJm1AAAAL96VFh0cmRraXRQS0wxIHJka2l0IDIwMjIuMDkuMQAAeJx7v2/tPQYg4GWAAEYoG4QbGNkcNIA0MwuxNCMDiGZkJJbmANPMTNwMjBlMDKwJDGwMjEwJjOwMTMwZTEwcQBMTmDkZWFgzmFh4FFi5Gbh4GEQY2RhYWZiZGMWXQZ3LAHX/AXugk5dAuA72D92W7Yey9yPYB4B0gyqSuD2SehjbAUhA1RwAiqsdgOlFsB0OIJsTlFXpgKTeAUk9mC0GAHfELURNojWLAAABMHpUWHRNT0wxIHJka2l0IDIwMjIuMDkuMQAAeJyNk+FqhDAMx7/7FHkBS9JWaz+eemxjnMLm9gYHG4xtH/b+LDnRVE6KbcW0/tp/msTfj+v3z1cB0l76588/WJvtC17HzIgxwrtDxOICYkB7fngaoJtO7bLSjW/D9ArkZKD0LXuaxsuyQtABmQqlQYkGZ0uNBbQMognVDJKxMaJrdkDHYHmI9EKu4hntanNk5sQ6dTLDBXgEd693xzXMaXSsqWKDod4BI4PlIZJQ0CPiREKu13GmiYHI75E29TMn7mBM/MyQ56Hf1MlcOe049Fo5nh+r9SHTWquAuAdNtWfCaUJ5JzSaNs8bvGaHeBo1CbK70lDflNOA8ldVJhFmAZdEhx3jVxIFumEuvW16N5kvfxTbxT+Nz7CFrrt8xAAAALp6VFh0U01JTEVTMSByZGtpdCAyMDIyLjA5LjEAAHicbY4xCsMwDEWv0jEBW0h2HNvKBTK13UOGEjoWl5Ixh28cCrJDl48e//HRNM63hZZmGuf2T9Kel63RCN4pCyF6IjVoAqcMuBgw7HSUBCZG7NRQU2VmQDXYHIemK0+fJqXVp/o3pPNSqx5ret0/6c0I+bym9QmETAUZNkIdWwHHXeEROyHkXqBnX3gcBDxHAcNUfBGYii8sU/FF3L5g2GZIopgVAwAAAABJRU5ErkJggg==", "text/plain": [ "" ] @@ -1100,7 +1090,7 @@ "outputs": [], "source": [ "# Let's create the four ChemicalSystems\n", - "from openfe.setup import ChemicalSystem\n", + "from openfe import ChemicalSystem\n", "\n", "benzene_complex = ChemicalSystem({'ligand': benz_to_phenol.componentA,\n", " 'solvent': solvent,\n", @@ -1164,9 +1154,9 @@ "source": [ "# Settings can be accessed from the various classes\n", "\n", - "from openfe.protocols.openmm_rbfe.equil_rbfe_methods import (\n", - " SystemSettings, TopologySettings, AlchemicalSettings,\n", - " OpenMMEngineSettings, SamplerSettings, BarostatSettings,\n", + "from openfe.protocols.openmm_rfe.equil_rfe_settings import (\n", + " SystemSettings, SolvationSettings, AlchemicalSettings,\n", + " OpenMMEngineSettings, AlchemicalSamplerSettings,\n", " IntegratorSettings, SimulationSettings\n", ")\n", "\n", @@ -1185,7 +1175,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "constraints='HBonds' hydrogen_mass=None nonbonded_method='PME' nonbonded_cutoff= rigid_water=True remove_com=True\n" + "nonbonded_method='PME' nonbonded_cutoff=\n" ] } ], @@ -1205,7 +1195,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "constraints='HBonds' hydrogen_mass=None nonbonded_method='PME' nonbonded_cutoff= rigid_water=True remove_com=True\n" + "nonbonded_method='PME' nonbonded_cutoff=\n" ] } ], @@ -1216,74 +1206,30 @@ "print(system)" ] }, - { - "cell_type": "code", - "execution_count": 27, - "id": "511f57fd", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "SystemSettings(constraints='HBonds', hydrogen_mass=None, nonbonded_method='PME', nonbonded_cutoff=, rigid_water=True, remove_com=True)\n" - ] - } - ], - "source": [ - "# A complete set of settings is created via the RelativeLigandTransformSettings class\n", - "from openfe.protocols.openmm_rbfe import RelativeLigandTransformSettings\n", - "from pprint import pp\n", - "\n", - "# There are non-optional settings which need to be set\n", - "# we set them here\n", - "settings = RelativeLigandTransformSettings(\n", - " system_settings=SystemSettings(\n", - " constraints='HBonds',\n", - " ),\n", - " topology_settings=TopologySettings(\n", - " forcefield = {'protein': 'amber99sb.xml',\n", - " 'ligand': 'openff-2.0.0.xml',\n", - " 'solvent': 'tip3p.xml'},\n", - " ),\n", - " alchemical_settings=AlchemicalSettings(),\n", - " sampler_settings=SamplerSettings(),\n", - " barostat_settings=BarostatSettings(),\n", - " integrator_settings=IntegratorSettings(),\n", - " simulation_settings=SimulationSettings(\n", - " equilibration_length=2.0 * unit.nanosecond,\n", - " production_length=5.0 * unit.nanosecond,\n", - " ),\n", - ")\n", - "\n", - "# Individual settings can be inspected directly\n", - "pp(settings.system_settings)" - ] - }, { "cell_type": "markdown", "id": "a93d4daf", "metadata": {}, "source": [ - "The `RelativeLigandTransform` class can directly populate the above set of default\n", + "The `RelativeHybridTopologyProtocol` class can directly populate the above set of default\n", "settings through its `default_settings` method." ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 27, "id": "2da2945b", "metadata": {}, "outputs": [], "source": [ - "from openfe.protocols.openmm_rbfe import RelativeLigandTransform\n", + "from openfe.protocols.openmm_rfe import RelativeHybridTopologyProtocol\n", "\n", - "rbfe_settings = RelativeLigandTransform.default_settings()" + "rbfe_settings = RelativeHybridTopologyProtocol.default_settings()" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 28, "id": "85b38150", "metadata": {}, "outputs": [ @@ -1291,11 +1237,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "SimulationSettings(equilibration_length=, production_length=, minimization_steps=10000, output_filename='rbfe.nc', output_indices='all', checkpoint_interval=, checkpoint_storage='rbfe_checkpoint.nc')\n" + "SimulationSettings(equilibration_length=, production_length=, forcefield_cache=None, minimization_steps=5000, output_filename='simulation.nc', output_indices='all', checkpoint_interval=, checkpoint_storage='checkpoint.nc')\n" ] } ], "source": [ + "from pprint import pp\n", + "\n", "# Parameters can also be overriden after creation\n", "# In this case, we'll reduce the equilibration length to 0.01 * nanosecond\n", "# and the production to 0.05 * nanosecond in order to reduce the costs of\n", @@ -1322,13 +1270,13 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 29, "id": "d1829ab6", "metadata": {}, "outputs": [], "source": [ "# Create RBFE Protocol class\n", - "rbfe_transform = RelativeLigandTransform(\n", + "rbfe_transform = RelativeHybridTopologyProtocol(\n", " settings=rbfe_settings\n", ")" ] @@ -1354,7 +1302,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 30, "id": "b3237be8", "metadata": {}, "outputs": [], @@ -1382,19 +1330,19 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 31, "id": "dd35cb04", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[RelativeLigandTransformUnit(benzene phenol repeat 2 generation 0),\n", - " RelativeLigandTransformUnit(benzene phenol repeat 1 generation 0),\n", - " RelativeLigandTransformUnit(benzene phenol repeat 0 generation 0)]" + "[RelativeHybridTopologyProtocolUnit(benzene phenol repeat 2 generation 0),\n", + " RelativeHybridTopologyProtocolUnit(benzene phenol repeat 1 generation 0),\n", + " RelativeHybridTopologyProtocolUnit(benzene phenol repeat 0 generation 0)]" ] }, - "execution_count": 32, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" } @@ -1420,7 +1368,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 32, "id": "981cde0c", "metadata": {}, "outputs": [ @@ -1428,26 +1376,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "Warning: Cannot perform Hydrogen sampling with GPU-Omega: GPU-Omega disabled.\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Cannot find MPI environment. MPI disabled.\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" + "/home/richard/code/openfe/openfe/protocols/openmm_rfe/_rfe_utils/compute.py:47: UserWarning: Non-GPU platform selected: CPU, this may significantly impact simulation performance\n", + " warnings.warn(wmsg)\n" ] }, { @@ -1464,26 +1394,13 @@ " \n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 1009455B | 985.796KB | 0.963MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 1.240s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.065s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.349s\n" - ] - }, { "data": { "text/plain": [ - "{}" + "{'debug': {'sampler': }}" ] }, - "execution_count": 33, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" } @@ -1497,41 +1414,10 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 33, "id": "77accb06", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 56528B | 55.203KB | 0.054MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 0.071s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.038s\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -1549,10 +1435,10 @@ { "data": { "text/plain": [ - "{}" + "{'debug': {'sampler': }}" ] }, - "execution_count": 34, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1575,5241 +1461,55 @@ "\n", "In a more realistic (expansive) situation we would farm off the individual jobs to a HPC cluster or cloud compute service so they could be executed in parallel.\n", "\n", - "**Note: we use the `shared` argument of `execute` in order to set the directory where the simulation files are written to**" + "**Note: we use the `shared_basedir` and `scratch_basedir` argument of `execute_DAG` in order to set the directory where the simulation files are written to**" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 34, "id": "9abc4787", "metadata": {}, "outputs": [], "source": [ - "from gufe.protocols import execute as execute_DAG" + "from gufe.protocols import execute_DAG\n", + "import pathlib" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "id": "106ec948", "metadata": { "tags": [ "nbval-skip" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please cite the following:\n", - "\n", - " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", - " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", - " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", - " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", - " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 1009455B | 985.796KB | 0.963MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 1.191s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.065s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.351s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:minimizing systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Minimizing all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _minimize_replica serially.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: initial energy -127025.055kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: final energy -242814.674kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: initial energy -127026.826kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: final energy -238665.537kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: initial energy -127027.510kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: final energy -242045.497kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: initial energy -127025.411kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: final energy -239630.635kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: initial energy -127011.315kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: final energy -237904.062kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: initial energy -126875.169kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: final energy -242243.678kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: initial energy -126878.209kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: final energy -235929.772kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: initial energy -126878.342kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: final energy -240671.943kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: initial energy -126876.761kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: final energy -241571.384kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: initial energy -126874.699kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: final energy -239972.204kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: initial energy -126872.203kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: final energy -239023.773kT\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.105s\n", - "DEBUG:openmmtools.utils:Minimizing all replicas took 73.360s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:equilibrating systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 1/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.910s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.235s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:numba.core.byteflow:bytecode dump:\n", - "> 0\tNOP(arg=None, lineno=321)\n", - " 2\tLOAD_GLOBAL(arg=0, lineno=321)\n", - " 4\tLOAD_FAST(arg=0, lineno=321)\n", - " 6\tCALL_FUNCTION(arg=1, lineno=321)\n", - " 8\tGET_ITER(arg=None, lineno=321)\n", - "> 10\tFOR_ITER(arg=234, lineno=321)\n", - " 12\tSTORE_FAST(arg=6, lineno=321)\n", - " 14\tLOAD_GLOBAL(arg=1, lineno=324)\n", - " 16\tLOAD_ATTR(arg=2, lineno=324)\n", - " 18\tLOAD_METHOD(arg=3, lineno=324)\n", - " 20\tLOAD_FAST(arg=1, lineno=324)\n", - " 22\tCALL_METHOD(arg=1, lineno=324)\n", - " 24\tSTORE_FAST(arg=7, lineno=324)\n", - " 26\tLOAD_GLOBAL(arg=1, lineno=325)\n", - " 28\tLOAD_ATTR(arg=2, lineno=325)\n", - " 30\tLOAD_METHOD(arg=3, lineno=325)\n", - " 32\tLOAD_FAST(arg=1, lineno=325)\n", - " 34\tCALL_METHOD(arg=1, lineno=325)\n", - " 36\tSTORE_FAST(arg=8, lineno=325)\n", - " 38\tLOAD_FAST(arg=2, lineno=328)\n", - " 40\tLOAD_FAST(arg=7, lineno=328)\n", - " 42\tBINARY_SUBSCR(arg=None, lineno=328)\n", - " 44\tSTORE_FAST(arg=9, lineno=328)\n", - " 46\tLOAD_FAST(arg=2, lineno=329)\n", - " 48\tLOAD_FAST(arg=8, lineno=329)\n", - " 50\tBINARY_SUBSCR(arg=None, lineno=329)\n", - " 52\tSTORE_FAST(arg=10, lineno=329)\n", - " 54\tLOAD_FAST(arg=3, lineno=332)\n", - " 56\tLOAD_FAST(arg=7, lineno=332)\n", - " 58\tLOAD_FAST(arg=10, lineno=332)\n", - " 60\tBUILD_TUPLE(arg=2, lineno=332)\n", - " 62\tBINARY_SUBSCR(arg=None, lineno=332)\n", - " 64\tSTORE_FAST(arg=11, lineno=332)\n", - " 66\tLOAD_FAST(arg=3, lineno=333)\n", - " 68\tLOAD_FAST(arg=8, lineno=333)\n", - " 70\tLOAD_FAST(arg=9, lineno=333)\n", - " 72\tBUILD_TUPLE(arg=2, lineno=333)\n", - " 74\tBINARY_SUBSCR(arg=None, lineno=333)\n", - " 76\tSTORE_FAST(arg=12, lineno=333)\n", - " 78\tLOAD_FAST(arg=3, lineno=334)\n", - " 80\tLOAD_FAST(arg=7, lineno=334)\n", - " 82\tLOAD_FAST(arg=9, lineno=334)\n", - " 84\tBUILD_TUPLE(arg=2, lineno=334)\n", - " 86\tBINARY_SUBSCR(arg=None, lineno=334)\n", - " 88\tSTORE_FAST(arg=13, lineno=334)\n", - " 90\tLOAD_FAST(arg=3, lineno=335)\n", - " 92\tLOAD_FAST(arg=8, lineno=335)\n", - " 94\tLOAD_FAST(arg=10, lineno=335)\n", - " 96\tBUILD_TUPLE(arg=2, lineno=335)\n", - " 98\tBINARY_SUBSCR(arg=None, lineno=335)\n", - " 100\tSTORE_FAST(arg=14, lineno=335)\n", - " 102\tLOAD_FAST(arg=11, lineno=336)\n", - " 104\tLOAD_FAST(arg=12, lineno=336)\n", - " 106\tBINARY_ADD(arg=None, lineno=336)\n", - " 108\tUNARY_NEGATIVE(arg=None, lineno=336)\n", - " 110\tLOAD_FAST(arg=13, lineno=336)\n", - " 112\tBINARY_ADD(arg=None, lineno=336)\n", - " 114\tLOAD_FAST(arg=14, lineno=336)\n", - " 116\tBINARY_ADD(arg=None, lineno=336)\n", - " 118\tSTORE_FAST(arg=15, lineno=336)\n", - " 120\tLOAD_FAST(arg=5, lineno=339)\n", - " 122\tLOAD_FAST(arg=9, lineno=339)\n", - " 124\tLOAD_FAST(arg=10, lineno=339)\n", - " 126\tBUILD_TUPLE(arg=2, lineno=339)\n", - " 128\tDUP_TOP_TWO(arg=None, lineno=339)\n", - " 130\tBINARY_SUBSCR(arg=None, lineno=339)\n", - " 132\tLOAD_CONST(arg=1, lineno=339)\n", - " 134\tINPLACE_ADD(arg=None, lineno=339)\n", - " 136\tROT_THREE(arg=None, lineno=339)\n", - " 138\tSTORE_SUBSCR(arg=None, lineno=339)\n", - " 140\tLOAD_FAST(arg=5, lineno=340)\n", - " 142\tLOAD_FAST(arg=10, lineno=340)\n", - " 144\tLOAD_FAST(arg=9, lineno=340)\n", - " 146\tBUILD_TUPLE(arg=2, lineno=340)\n", - " 148\tDUP_TOP_TWO(arg=None, lineno=340)\n", - " 150\tBINARY_SUBSCR(arg=None, lineno=340)\n", - " 152\tLOAD_CONST(arg=1, lineno=340)\n", - " 154\tINPLACE_ADD(arg=None, lineno=340)\n", - " 156\tROT_THREE(arg=None, lineno=340)\n", - " 158\tSTORE_SUBSCR(arg=None, lineno=340)\n", - " 160\tLOAD_FAST(arg=15, lineno=343)\n", - " 162\tLOAD_CONST(arg=2, lineno=343)\n", - " 164\tCOMPARE_OP(arg=5, lineno=343)\n", - " 166\tPOP_JUMP_IF_TRUE(arg=188, lineno=343)\n", - " 168\tLOAD_GLOBAL(arg=1, lineno=343)\n", - " 170\tLOAD_ATTR(arg=2, lineno=343)\n", - " 172\tLOAD_METHOD(arg=4, lineno=343)\n", - " 174\tCALL_METHOD(arg=0, lineno=343)\n", - " 176\tLOAD_GLOBAL(arg=1, lineno=343)\n", - " 178\tLOAD_METHOD(arg=5, lineno=343)\n", - " 180\tLOAD_FAST(arg=15, lineno=343)\n", - " 182\tCALL_METHOD(arg=1, lineno=343)\n", - " 184\tCOMPARE_OP(arg=0, lineno=343)\n", - " 186\tPOP_JUMP_IF_FALSE(arg=10, lineno=343)\n", - "> 188\tLOAD_FAST(arg=10, lineno=345)\n", - " 190\tLOAD_FAST(arg=2, lineno=345)\n", - " 192\tLOAD_FAST(arg=7, lineno=345)\n", - " 194\tSTORE_SUBSCR(arg=None, lineno=345)\n", - " 196\tLOAD_FAST(arg=9, lineno=346)\n", - " 198\tLOAD_FAST(arg=2, lineno=346)\n", - " 200\tLOAD_FAST(arg=8, lineno=346)\n", - " 202\tSTORE_SUBSCR(arg=None, lineno=346)\n", - " 204\tLOAD_FAST(arg=4, lineno=348)\n", - " 206\tLOAD_FAST(arg=9, lineno=348)\n", - " 208\tLOAD_FAST(arg=10, lineno=348)\n", - " 210\tBUILD_TUPLE(arg=2, lineno=348)\n", - " 212\tDUP_TOP_TWO(arg=None, lineno=348)\n", - " 214\tBINARY_SUBSCR(arg=None, lineno=348)\n", - " 216\tLOAD_CONST(arg=1, lineno=348)\n", - " 218\tINPLACE_ADD(arg=None, lineno=348)\n", - " 220\tROT_THREE(arg=None, lineno=348)\n", - " 222\tSTORE_SUBSCR(arg=None, lineno=348)\n", - " 224\tLOAD_FAST(arg=4, lineno=349)\n", - " 226\tLOAD_FAST(arg=10, lineno=349)\n", - " 228\tLOAD_FAST(arg=9, lineno=349)\n", - " 230\tBUILD_TUPLE(arg=2, lineno=349)\n", - " 232\tDUP_TOP_TWO(arg=None, lineno=349)\n", - " 234\tBINARY_SUBSCR(arg=None, lineno=349)\n", - " 236\tLOAD_CONST(arg=1, lineno=349)\n", - " 238\tINPLACE_ADD(arg=None, lineno=349)\n", - " 240\tROT_THREE(arg=None, lineno=349)\n", - " 242\tSTORE_SUBSCR(arg=None, lineno=349)\n", - " 244\tJUMP_ABSOLUTE(arg=10, lineno=349)\n", - "> 246\tLOAD_CONST(arg=3, lineno=349)\n", - " 248\tRETURN_VALUE(arg=None, lineno=349)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=0 nstack_initial=0)])\n", - "DEBUG:numba.core.byteflow:stack: []\n", - "DEBUG:numba.core.byteflow:dispatch pc=0, inst=NOP(arg=None, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack []\n", - "DEBUG:numba.core.byteflow:dispatch pc=2, inst=LOAD_GLOBAL(arg=0, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack []\n", - "DEBUG:numba.core.byteflow:dispatch pc=4, inst=LOAD_FAST(arg=0, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack ['$2load_global.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=6, inst=CALL_FUNCTION(arg=1, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack ['$2load_global.0', '$nswap_attempts4.1']\n", - "DEBUG:numba.core.byteflow:dispatch pc=8, inst=GET_ITER(arg=None, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack ['$6call_function.2']\n", - "DEBUG:numba.core.byteflow:end state. edges=[Edge(pc=10, stack=('$8get_iter.3',), blockstack=(), npush=0)]\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=10 nstack_initial=1)])\n", - "DEBUG:numba.core.byteflow:stack: ['$phi10.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=10, inst=FOR_ITER(arg=234, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack ['$phi10.0']\n", - "DEBUG:numba.core.byteflow:end state. edges=[Edge(pc=246, stack=(), blockstack=(), npush=0), Edge(pc=12, stack=('$phi10.0', '$10for_iter.2'), blockstack=(), npush=0)]\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=246 nstack_initial=0), State(pc_initial=12 nstack_initial=2)])\n", - "DEBUG:numba.core.byteflow:stack: []\n", - "DEBUG:numba.core.byteflow:dispatch pc=246, inst=LOAD_CONST(arg=3, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack []\n", - "DEBUG:numba.core.byteflow:dispatch pc=248, inst=RETURN_VALUE(arg=None, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$const246.0']\n", - "DEBUG:numba.core.byteflow:end state. edges=[]\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=12 nstack_initial=2)])\n", - "DEBUG:numba.core.byteflow:stack: ['$phi12.0', '$phi12.1']\n", - "DEBUG:numba.core.byteflow:dispatch pc=12, inst=STORE_FAST(arg=6, lineno=321)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$phi12.1']\n", - "DEBUG:numba.core.byteflow:dispatch pc=14, inst=LOAD_GLOBAL(arg=1, lineno=324)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=16, inst=LOAD_ATTR(arg=2, lineno=324)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$14load_global.2']\n", - "DEBUG:numba.core.byteflow:dispatch pc=18, inst=LOAD_METHOD(arg=3, lineno=324)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$16load_attr.3']\n", - "DEBUG:numba.core.byteflow:dispatch pc=20, inst=LOAD_FAST(arg=1, lineno=324)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$18load_method.4']\n", - "DEBUG:numba.core.byteflow:dispatch pc=22, inst=CALL_METHOD(arg=1, lineno=324)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$18load_method.4', '$n_replicas20.5']\n", - "DEBUG:numba.core.byteflow:dispatch pc=24, inst=STORE_FAST(arg=7, lineno=324)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$22call_method.6']\n", - "DEBUG:numba.core.byteflow:dispatch pc=26, inst=LOAD_GLOBAL(arg=1, lineno=325)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=28, inst=LOAD_ATTR(arg=2, lineno=325)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$26load_global.7']\n", - "DEBUG:numba.core.byteflow:dispatch pc=30, inst=LOAD_METHOD(arg=3, lineno=325)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$28load_attr.8']\n", - "DEBUG:numba.core.byteflow:dispatch pc=32, inst=LOAD_FAST(arg=1, lineno=325)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$30load_method.9']\n", - "DEBUG:numba.core.byteflow:dispatch pc=34, inst=CALL_METHOD(arg=1, lineno=325)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$30load_method.9', '$n_replicas32.10']\n", - "DEBUG:numba.core.byteflow:dispatch pc=36, inst=STORE_FAST(arg=8, lineno=325)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$34call_method.11']\n", - "DEBUG:numba.core.byteflow:dispatch pc=38, inst=LOAD_FAST(arg=2, lineno=328)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=40, inst=LOAD_FAST(arg=7, lineno=328)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_replica_thermodynamic_states38.12']\n", - "DEBUG:numba.core.byteflow:dispatch pc=42, inst=BINARY_SUBSCR(arg=None, lineno=328)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_replica_thermodynamic_states38.12', '$replica_i40.13']\n", - "DEBUG:numba.core.byteflow:dispatch pc=44, inst=STORE_FAST(arg=9, lineno=328)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$42binary_subscr.14']\n", - "DEBUG:numba.core.byteflow:dispatch pc=46, inst=LOAD_FAST(arg=2, lineno=329)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=48, inst=LOAD_FAST(arg=8, lineno=329)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_replica_thermodynamic_states46.15']\n", - "DEBUG:numba.core.byteflow:dispatch pc=50, inst=BINARY_SUBSCR(arg=None, lineno=329)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_replica_thermodynamic_states46.15', '$replica_j48.16']\n", - "DEBUG:numba.core.byteflow:dispatch pc=52, inst=STORE_FAST(arg=10, lineno=329)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$50binary_subscr.17']\n", - "DEBUG:numba.core.byteflow:dispatch pc=54, inst=LOAD_FAST(arg=3, lineno=332)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=56, inst=LOAD_FAST(arg=7, lineno=332)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states54.18']\n", - "DEBUG:numba.core.byteflow:dispatch pc=58, inst=LOAD_FAST(arg=10, lineno=332)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states54.18', '$replica_i56.19']\n", - "DEBUG:numba.core.byteflow:dispatch pc=60, inst=BUILD_TUPLE(arg=2, lineno=332)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states54.18', '$replica_i56.19', '$thermodynamic_state_j58.20']\n", - "DEBUG:numba.core.byteflow:dispatch pc=62, inst=BINARY_SUBSCR(arg=None, lineno=332)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states54.18', '$60build_tuple.21']\n", - "DEBUG:numba.core.byteflow:dispatch pc=64, inst=STORE_FAST(arg=11, lineno=332)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$62binary_subscr.22']\n", - "DEBUG:numba.core.byteflow:dispatch pc=66, inst=LOAD_FAST(arg=3, lineno=333)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=68, inst=LOAD_FAST(arg=8, lineno=333)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states66.23']\n", - "DEBUG:numba.core.byteflow:dispatch pc=70, inst=LOAD_FAST(arg=9, lineno=333)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states66.23', '$replica_j68.24']\n", - "DEBUG:numba.core.byteflow:dispatch pc=72, inst=BUILD_TUPLE(arg=2, lineno=333)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states66.23', '$replica_j68.24', '$thermodynamic_state_i70.25']\n", - "DEBUG:numba.core.byteflow:dispatch pc=74, inst=BINARY_SUBSCR(arg=None, lineno=333)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states66.23', '$72build_tuple.26']\n", - "DEBUG:numba.core.byteflow:dispatch pc=76, inst=STORE_FAST(arg=12, lineno=333)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$74binary_subscr.27']\n", - "DEBUG:numba.core.byteflow:dispatch pc=78, inst=LOAD_FAST(arg=3, lineno=334)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=80, inst=LOAD_FAST(arg=7, lineno=334)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states78.28']\n", - "DEBUG:numba.core.byteflow:dispatch pc=82, inst=LOAD_FAST(arg=9, lineno=334)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states78.28', '$replica_i80.29']\n", - "DEBUG:numba.core.byteflow:dispatch pc=84, inst=BUILD_TUPLE(arg=2, lineno=334)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states78.28', '$replica_i80.29', '$thermodynamic_state_i82.30']\n", - "DEBUG:numba.core.byteflow:dispatch pc=86, inst=BINARY_SUBSCR(arg=None, lineno=334)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states78.28', '$84build_tuple.31']\n", - "DEBUG:numba.core.byteflow:dispatch pc=88, inst=STORE_FAST(arg=13, lineno=334)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$86binary_subscr.32']\n", - "DEBUG:numba.core.byteflow:dispatch pc=90, inst=LOAD_FAST(arg=3, lineno=335)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=92, inst=LOAD_FAST(arg=8, lineno=335)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states90.33']\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.byteflow:dispatch pc=94, inst=LOAD_FAST(arg=10, lineno=335)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states90.33', '$replica_j92.34']\n", - "DEBUG:numba.core.byteflow:dispatch pc=96, inst=BUILD_TUPLE(arg=2, lineno=335)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states90.33', '$replica_j92.34', '$thermodynamic_state_j94.35']\n", - "DEBUG:numba.core.byteflow:dispatch pc=98, inst=BINARY_SUBSCR(arg=None, lineno=335)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_energy_thermodynamic_states90.33', '$96build_tuple.36']\n", - "DEBUG:numba.core.byteflow:dispatch pc=100, inst=STORE_FAST(arg=14, lineno=335)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$98binary_subscr.37']\n", - "DEBUG:numba.core.byteflow:dispatch pc=102, inst=LOAD_FAST(arg=11, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=104, inst=LOAD_FAST(arg=12, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$energy_ij102.38']\n", - "DEBUG:numba.core.byteflow:dispatch pc=106, inst=BINARY_ADD(arg=None, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$energy_ij102.38', '$energy_ji104.39']\n", - "DEBUG:numba.core.byteflow:dispatch pc=108, inst=UNARY_NEGATIVE(arg=None, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$106binary_add.40']\n", - "DEBUG:numba.core.byteflow:dispatch pc=110, inst=LOAD_FAST(arg=13, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$108unary_negative.41']\n", - "DEBUG:numba.core.byteflow:dispatch pc=112, inst=BINARY_ADD(arg=None, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$108unary_negative.41', '$energy_ii110.42']\n", - "DEBUG:numba.core.byteflow:dispatch pc=114, inst=LOAD_FAST(arg=14, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$112binary_add.43']\n", - "DEBUG:numba.core.byteflow:dispatch pc=116, inst=BINARY_ADD(arg=None, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$112binary_add.43', '$energy_jj114.44']\n", - "DEBUG:numba.core.byteflow:dispatch pc=118, inst=STORE_FAST(arg=15, lineno=336)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$116binary_add.45']\n", - "DEBUG:numba.core.byteflow:dispatch pc=120, inst=LOAD_FAST(arg=5, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=122, inst=LOAD_FAST(arg=9, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46']\n", - "DEBUG:numba.core.byteflow:dispatch pc=124, inst=LOAD_FAST(arg=10, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$thermodynamic_state_i122.47']\n", - "DEBUG:numba.core.byteflow:dispatch pc=126, inst=BUILD_TUPLE(arg=2, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$thermodynamic_state_i122.47', '$thermodynamic_state_j124.48']\n", - "DEBUG:numba.core.byteflow:dispatch pc=128, inst=DUP_TOP_TWO(arg=None, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$126build_tuple.49']\n", - "DEBUG:numba.core.byteflow:dispatch pc=130, inst=BINARY_SUBSCR(arg=None, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$126build_tuple.49', '$128dup_top_two.50', '$128dup_top_two.51']\n", - "DEBUG:numba.core.byteflow:dispatch pc=132, inst=LOAD_CONST(arg=1, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$126build_tuple.49', '$130binary_subscr.52']\n", - "DEBUG:numba.core.byteflow:dispatch pc=134, inst=INPLACE_ADD(arg=None, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$126build_tuple.49', '$130binary_subscr.52', '$const132.53']\n", - "DEBUG:numba.core.byteflow:dispatch pc=136, inst=ROT_THREE(arg=None, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix120.46', '$126build_tuple.49', '$134inplace_add.54']\n", - "DEBUG:numba.core.byteflow:dispatch pc=138, inst=STORE_SUBSCR(arg=None, lineno=339)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$134inplace_add.54', '$_n_proposed_matrix120.46', '$126build_tuple.49']\n", - "DEBUG:numba.core.byteflow:dispatch pc=140, inst=LOAD_FAST(arg=5, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=142, inst=LOAD_FAST(arg=10, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55']\n", - "DEBUG:numba.core.byteflow:dispatch pc=144, inst=LOAD_FAST(arg=9, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$thermodynamic_state_j142.56']\n", - "DEBUG:numba.core.byteflow:dispatch pc=146, inst=BUILD_TUPLE(arg=2, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$thermodynamic_state_j142.56', '$thermodynamic_state_i144.57']\n", - "DEBUG:numba.core.byteflow:dispatch pc=148, inst=DUP_TOP_TWO(arg=None, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$146build_tuple.58']\n", - "DEBUG:numba.core.byteflow:dispatch pc=150, inst=BINARY_SUBSCR(arg=None, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$146build_tuple.58', '$148dup_top_two.59', '$148dup_top_two.60']\n", - "DEBUG:numba.core.byteflow:dispatch pc=152, inst=LOAD_CONST(arg=1, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$146build_tuple.58', '$150binary_subscr.61']\n", - "DEBUG:numba.core.byteflow:dispatch pc=154, inst=INPLACE_ADD(arg=None, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$146build_tuple.58', '$150binary_subscr.61', '$const152.62']\n", - "DEBUG:numba.core.byteflow:dispatch pc=156, inst=ROT_THREE(arg=None, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$_n_proposed_matrix140.55', '$146build_tuple.58', '$154inplace_add.63']\n", - "DEBUG:numba.core.byteflow:dispatch pc=158, inst=STORE_SUBSCR(arg=None, lineno=340)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$154inplace_add.63', '$_n_proposed_matrix140.55', '$146build_tuple.58']\n", - "DEBUG:numba.core.byteflow:dispatch pc=160, inst=LOAD_FAST(arg=15, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=162, inst=LOAD_CONST(arg=2, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$log_p_accept160.64']\n", - "DEBUG:numba.core.byteflow:dispatch pc=164, inst=COMPARE_OP(arg=5, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$log_p_accept160.64', '$const162.65']\n", - "DEBUG:numba.core.byteflow:dispatch pc=166, inst=POP_JUMP_IF_TRUE(arg=188, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi12.0', '$164compare_op.66']\n", - "DEBUG:numba.core.byteflow:end state. edges=[Edge(pc=168, stack=('$phi12.0',), blockstack=(), npush=0), Edge(pc=188, stack=('$phi12.0',), blockstack=(), npush=0)]\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=168 nstack_initial=1), State(pc_initial=188 nstack_initial=1)])\n", - "DEBUG:numba.core.byteflow:stack: ['$phi168.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=168, inst=LOAD_GLOBAL(arg=1, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=170, inst=LOAD_ATTR(arg=2, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$168load_global.1']\n", - "DEBUG:numba.core.byteflow:dispatch pc=172, inst=LOAD_METHOD(arg=4, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$170load_attr.2']\n", - "DEBUG:numba.core.byteflow:dispatch pc=174, inst=CALL_METHOD(arg=0, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$172load_method.3']\n", - "DEBUG:numba.core.byteflow:dispatch pc=176, inst=LOAD_GLOBAL(arg=1, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$174call_method.4']\n", - "DEBUG:numba.core.byteflow:dispatch pc=178, inst=LOAD_METHOD(arg=5, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$174call_method.4', '$176load_global.5']\n", - "DEBUG:numba.core.byteflow:dispatch pc=180, inst=LOAD_FAST(arg=15, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$174call_method.4', '$178load_method.6']\n", - "DEBUG:numba.core.byteflow:dispatch pc=182, inst=CALL_METHOD(arg=1, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$174call_method.4', '$178load_method.6', '$log_p_accept180.7']\n", - "DEBUG:numba.core.byteflow:dispatch pc=184, inst=COMPARE_OP(arg=0, lineno=343)\n", - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$174call_method.4', '$182call_method.8']\n", - "DEBUG:numba.core.byteflow:dispatch pc=186, inst=POP_JUMP_IF_FALSE(arg=10, lineno=343)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.byteflow:stack ['$phi168.0', '$184compare_op.9']\n", - "DEBUG:numba.core.byteflow:end state. edges=[Edge(pc=188, stack=('$phi168.0',), blockstack=(), npush=0), Edge(pc=10, stack=('$phi168.0',), blockstack=(), npush=0)]\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=188 nstack_initial=1), State(pc_initial=188 nstack_initial=1), State(pc_initial=10 nstack_initial=1)])\n", - "DEBUG:numba.core.byteflow:stack: ['$phi188.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=188, inst=LOAD_FAST(arg=10, lineno=345)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=190, inst=LOAD_FAST(arg=2, lineno=345)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$thermodynamic_state_j188.1']\n", - "DEBUG:numba.core.byteflow:dispatch pc=192, inst=LOAD_FAST(arg=7, lineno=345)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$thermodynamic_state_j188.1', '$_replica_thermodynamic_states190.2']\n", - "DEBUG:numba.core.byteflow:dispatch pc=194, inst=STORE_SUBSCR(arg=None, lineno=345)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$thermodynamic_state_j188.1', '$_replica_thermodynamic_states190.2', '$replica_i192.3']\n", - "DEBUG:numba.core.byteflow:dispatch pc=196, inst=LOAD_FAST(arg=9, lineno=346)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=198, inst=LOAD_FAST(arg=2, lineno=346)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$thermodynamic_state_i196.4']\n", - "DEBUG:numba.core.byteflow:dispatch pc=200, inst=LOAD_FAST(arg=8, lineno=346)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$thermodynamic_state_i196.4', '$_replica_thermodynamic_states198.5']\n", - "DEBUG:numba.core.byteflow:dispatch pc=202, inst=STORE_SUBSCR(arg=None, lineno=346)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$thermodynamic_state_i196.4', '$_replica_thermodynamic_states198.5', '$replica_j200.6']\n", - "DEBUG:numba.core.byteflow:dispatch pc=204, inst=LOAD_FAST(arg=4, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=206, inst=LOAD_FAST(arg=9, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7']\n", - "DEBUG:numba.core.byteflow:dispatch pc=208, inst=LOAD_FAST(arg=10, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$thermodynamic_state_i206.8']\n", - "DEBUG:numba.core.byteflow:dispatch pc=210, inst=BUILD_TUPLE(arg=2, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$thermodynamic_state_i206.8', '$thermodynamic_state_j208.9']\n", - "DEBUG:numba.core.byteflow:dispatch pc=212, inst=DUP_TOP_TWO(arg=None, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$210build_tuple.10']\n", - "DEBUG:numba.core.byteflow:dispatch pc=214, inst=BINARY_SUBSCR(arg=None, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$210build_tuple.10', '$212dup_top_two.11', '$212dup_top_two.12']\n", - "DEBUG:numba.core.byteflow:dispatch pc=216, inst=LOAD_CONST(arg=1, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$210build_tuple.10', '$214binary_subscr.13']\n", - "DEBUG:numba.core.byteflow:dispatch pc=218, inst=INPLACE_ADD(arg=None, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$210build_tuple.10', '$214binary_subscr.13', '$const216.14']\n", - "DEBUG:numba.core.byteflow:dispatch pc=220, inst=ROT_THREE(arg=None, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix204.7', '$210build_tuple.10', '$218inplace_add.15']\n", - "DEBUG:numba.core.byteflow:dispatch pc=222, inst=STORE_SUBSCR(arg=None, lineno=348)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$218inplace_add.15', '$_n_accepted_matrix204.7', '$210build_tuple.10']\n", - "DEBUG:numba.core.byteflow:dispatch pc=224, inst=LOAD_FAST(arg=4, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=226, inst=LOAD_FAST(arg=10, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16']\n", - "DEBUG:numba.core.byteflow:dispatch pc=228, inst=LOAD_FAST(arg=9, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$thermodynamic_state_j226.17']\n", - "DEBUG:numba.core.byteflow:dispatch pc=230, inst=BUILD_TUPLE(arg=2, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$thermodynamic_state_j226.17', '$thermodynamic_state_i228.18']\n", - "DEBUG:numba.core.byteflow:dispatch pc=232, inst=DUP_TOP_TWO(arg=None, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$230build_tuple.19']\n", - "DEBUG:numba.core.byteflow:dispatch pc=234, inst=BINARY_SUBSCR(arg=None, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$230build_tuple.19', '$232dup_top_two.20', '$232dup_top_two.21']\n", - "DEBUG:numba.core.byteflow:dispatch pc=236, inst=LOAD_CONST(arg=1, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$230build_tuple.19', '$234binary_subscr.22']\n", - "DEBUG:numba.core.byteflow:dispatch pc=238, inst=INPLACE_ADD(arg=None, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$230build_tuple.19', '$234binary_subscr.22', '$const236.23']\n", - "DEBUG:numba.core.byteflow:dispatch pc=240, inst=ROT_THREE(arg=None, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$_n_accepted_matrix224.16', '$230build_tuple.19', '$238inplace_add.24']\n", - "DEBUG:numba.core.byteflow:dispatch pc=242, inst=STORE_SUBSCR(arg=None, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0', '$238inplace_add.24', '$_n_accepted_matrix224.16', '$230build_tuple.19']\n", - "DEBUG:numba.core.byteflow:dispatch pc=244, inst=JUMP_ABSOLUTE(arg=10, lineno=349)\n", - "DEBUG:numba.core.byteflow:stack ['$phi188.0']\n", - "DEBUG:numba.core.byteflow:end state. edges=[Edge(pc=10, stack=('$phi188.0',), blockstack=(), npush=0)]\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=188 nstack_initial=1), State(pc_initial=10 nstack_initial=1), State(pc_initial=10 nstack_initial=1)])\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=10 nstack_initial=1), State(pc_initial=10 nstack_initial=1)])\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=10 nstack_initial=1)])\n", - "DEBUG:numba.core.byteflow:-------------------------Prune PHIs-------------------------\n", - "DEBUG:numba.core.byteflow:Used_phis: defaultdict(,\n", - " {State(pc_initial=0 nstack_initial=0): set(),\n", - " State(pc_initial=10 nstack_initial=1): {'$phi10.0'},\n", - " State(pc_initial=12 nstack_initial=2): {'$phi12.1'},\n", - " State(pc_initial=168 nstack_initial=1): set(),\n", - " State(pc_initial=188 nstack_initial=1): set(),\n", - " State(pc_initial=246 nstack_initial=0): set()})\n", - "DEBUG:numba.core.byteflow:defmap: {'$phi10.0': State(pc_initial=0 nstack_initial=0),\n", - " '$phi12.1': State(pc_initial=10 nstack_initial=1)}\n", - "DEBUG:numba.core.byteflow:phismap: defaultdict(,\n", - " {'$phi10.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0)),\n", - " ('$phi168.0', State(pc_initial=168 nstack_initial=1)),\n", - " ('$phi188.0',\n", - " State(pc_initial=188 nstack_initial=1))},\n", - " '$phi12.0': {('$phi10.0', State(pc_initial=10 nstack_initial=1))},\n", - " '$phi12.1': {('$10for_iter.2',\n", - " State(pc_initial=10 nstack_initial=1))},\n", - " '$phi168.0': {('$phi12.0', State(pc_initial=12 nstack_initial=2))},\n", - " '$phi188.0': {('$phi12.0', State(pc_initial=12 nstack_initial=2)),\n", - " ('$phi168.0',\n", - " State(pc_initial=168 nstack_initial=1))}})\n", - "DEBUG:numba.core.byteflow:changing phismap: defaultdict(,\n", - " {'$phi10.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0)),\n", - " ('$phi12.0', State(pc_initial=12 nstack_initial=2))},\n", - " '$phi12.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0)),\n", - " ('$phi12.0', State(pc_initial=12 nstack_initial=2))},\n", - " '$phi12.1': {('$10for_iter.2',\n", - " State(pc_initial=10 nstack_initial=1))},\n", - " '$phi168.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi188.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))}})\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.byteflow:changing phismap: defaultdict(,\n", - " {'$phi10.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi12.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi12.1': {('$10for_iter.2',\n", - " State(pc_initial=10 nstack_initial=1))},\n", - " '$phi168.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi188.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))}})\n", - "DEBUG:numba.core.byteflow:changing phismap: defaultdict(,\n", - " {'$phi10.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi12.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi12.1': {('$10for_iter.2',\n", - " State(pc_initial=10 nstack_initial=1))},\n", - " '$phi168.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))},\n", - " '$phi188.0': {('$8get_iter.3',\n", - " State(pc_initial=0 nstack_initial=0))}})\n", - "DEBUG:numba.core.byteflow:keep phismap: {'$phi10.0': {('$8get_iter.3', State(pc_initial=0 nstack_initial=0))},\n", - " '$phi12.1': {('$10for_iter.2', State(pc_initial=10 nstack_initial=1))}}\n", - "DEBUG:numba.core.byteflow:new_out: defaultdict(,\n", - " {State(pc_initial=0 nstack_initial=0): {'$phi10.0': '$8get_iter.3'},\n", - " State(pc_initial=10 nstack_initial=1): {'$phi12.1': '$10for_iter.2'}})\n", - "DEBUG:numba.core.byteflow:----------------------DONE Prune PHIs-----------------------\n", - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=0 nstack_initial=0):\n", - "AdaptBlockInfo(insts=((0, {}), (2, {'res': '$2load_global.0'}), (4, {'res': '$nswap_attempts4.1'}), (6, {'func': '$2load_global.0', 'args': ['$nswap_attempts4.1'], 'res': '$6call_function.2'}), (8, {'value': '$6call_function.2', 'res': '$8get_iter.3'})), outgoing_phis={'$phi10.0': '$8get_iter.3'}, blockstack=(), active_try_block=None, outgoing_edgepushed={10: ('$8get_iter.3',)})\n", - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=10 nstack_initial=1):\n", - "AdaptBlockInfo(insts=((10, {'iterator': '$phi10.0', 'pair': '$10for_iter.1', 'indval': '$10for_iter.2', 'pred': '$10for_iter.3'}),), outgoing_phis={'$phi12.1': '$10for_iter.2'}, blockstack=(), active_try_block=None, outgoing_edgepushed={246: (), 12: ('$phi10.0', '$10for_iter.2')})\n", - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=12 nstack_initial=2):\n", - "AdaptBlockInfo(insts=((12, {'value': '$phi12.1'}), (14, {'res': '$14load_global.2'}), (16, {'item': '$14load_global.2', 'res': '$16load_attr.3'}), (18, {'item': '$16load_attr.3', 'res': '$18load_method.4'}), (20, {'res': '$n_replicas20.5'}), (22, {'func': '$18load_method.4', 'args': ['$n_replicas20.5'], 'res': '$22call_method.6'}), (24, {'value': '$22call_method.6'}), (26, {'res': '$26load_global.7'}), (28, {'item': '$26load_global.7', 'res': '$28load_attr.8'}), (30, {'item': '$28load_attr.8', 'res': '$30load_method.9'}), (32, {'res': '$n_replicas32.10'}), (34, {'func': '$30load_method.9', 'args': ['$n_replicas32.10'], 'res': '$34call_method.11'}), (36, {'value': '$34call_method.11'}), (38, {'res': '$_replica_thermodynamic_states38.12'}), (40, {'res': '$replica_i40.13'}), (42, {'index': '$replica_i40.13', 'target': '$_replica_thermodynamic_states38.12', 'res': '$42binary_subscr.14'}), (44, {'value': '$42binary_subscr.14'}), (46, {'res': '$_replica_thermodynamic_states46.15'}), (48, {'res': '$replica_j48.16'}), (50, {'index': '$replica_j48.16', 'target': '$_replica_thermodynamic_states46.15', 'res': '$50binary_subscr.17'}), (52, {'value': '$50binary_subscr.17'}), (54, {'res': '$_energy_thermodynamic_states54.18'}), (56, {'res': '$replica_i56.19'}), (58, {'res': '$thermodynamic_state_j58.20'}), (60, {'items': ['$replica_i56.19', '$thermodynamic_state_j58.20'], 'res': '$60build_tuple.21'}), (62, {'index': '$60build_tuple.21', 'target': '$_energy_thermodynamic_states54.18', 'res': '$62binary_subscr.22'}), (64, {'value': '$62binary_subscr.22'}), (66, {'res': '$_energy_thermodynamic_states66.23'}), (68, {'res': '$replica_j68.24'}), (70, {'res': '$thermodynamic_state_i70.25'}), (72, {'items': ['$replica_j68.24', '$thermodynamic_state_i70.25'], 'res': '$72build_tuple.26'}), (74, {'index': '$72build_tuple.26', 'target': '$_energy_thermodynamic_states66.23', 'res': '$74binary_subscr.27'}), (76, {'value': '$74binary_subscr.27'}), (78, {'res': '$_energy_thermodynamic_states78.28'}), (80, {'res': '$replica_i80.29'}), (82, {'res': '$thermodynamic_state_i82.30'}), (84, {'items': ['$replica_i80.29', '$thermodynamic_state_i82.30'], 'res': '$84build_tuple.31'}), (86, {'index': '$84build_tuple.31', 'target': '$_energy_thermodynamic_states78.28', 'res': '$86binary_subscr.32'}), (88, {'value': '$86binary_subscr.32'}), (90, {'res': '$_energy_thermodynamic_states90.33'}), (92, {'res': '$replica_j92.34'}), (94, {'res': '$thermodynamic_state_j94.35'}), (96, {'items': ['$replica_j92.34', '$thermodynamic_state_j94.35'], 'res': '$96build_tuple.36'}), (98, {'index': '$96build_tuple.36', 'target': '$_energy_thermodynamic_states90.33', 'res': '$98binary_subscr.37'}), (100, {'value': '$98binary_subscr.37'}), (102, {'res': '$energy_ij102.38'}), (104, {'res': '$energy_ji104.39'}), (106, {'lhs': '$energy_ij102.38', 'rhs': '$energy_ji104.39', 'res': '$106binary_add.40'}), (108, {'value': '$106binary_add.40', 'res': '$108unary_negative.41'}), (110, {'res': '$energy_ii110.42'}), (112, {'lhs': '$108unary_negative.41', 'rhs': '$energy_ii110.42', 'res': '$112binary_add.43'}), (114, {'res': '$energy_jj114.44'}), (116, {'lhs': '$112binary_add.43', 'rhs': '$energy_jj114.44', 'res': '$116binary_add.45'}), (118, {'value': '$116binary_add.45'}), (120, {'res': '$_n_proposed_matrix120.46'}), (122, {'res': '$thermodynamic_state_i122.47'}), (124, {'res': '$thermodynamic_state_j124.48'}), (126, {'items': ['$thermodynamic_state_i122.47', '$thermodynamic_state_j124.48'], 'res': '$126build_tuple.49'}), (128, {'orig': ['$_n_proposed_matrix120.46', '$126build_tuple.49'], 'duped': ['$128dup_top_two.50', '$128dup_top_two.51']}), (130, {'index': '$128dup_top_two.51', 'target': '$128dup_top_two.50', 'res': '$130binary_subscr.52'}), (132, {'res': '$const132.53'}), (134, {'lhs': '$130binary_subscr.52', 'rhs': '$const132.53', 'res': '$134inplace_add.54'}), (138, {'target': '$_n_proposed_matrix120.46', 'index': '$126build_tuple.49', 'value': '$134inplace_add.54'}), (140, {'res': '$_n_proposed_matrix140.55'}), (142, {'res': '$thermodynamic_state_j142.56'}), (144, {'res': '$thermodynamic_state_i144.57'}), (146, {'items': ['$thermodynamic_state_j142.56', '$thermodynamic_state_i144.57'], 'res': '$146build_tuple.58'}), (148, {'orig': ['$_n_proposed_matrix140.55', '$146build_tuple.58'], 'duped': ['$148dup_top_two.59', '$148dup_top_two.60']}), (150, {'index': '$148dup_top_two.60', 'target': '$148dup_top_two.59', 'res': '$150binary_subscr.61'}), (152, {'res': '$const152.62'}), (154, {'lhs': '$150binary_subscr.61', 'rhs': '$const152.62', 'res': '$154inplace_add.63'}), (158, {'target': '$_n_proposed_matrix140.55', 'index': '$146build_tuple.58', 'value': '$154inplace_add.63'}), (160, {'res': '$log_p_accept160.64'}), (162, {'res': '$const162.65'}), (164, {'lhs': '$log_p_accept160.64', 'rhs': '$const162.65', 'res': '$164compare_op.66'}), (166, {'pred': '$164compare_op.66'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={168: ('$phi12.0',), 188: ('$phi12.0',)})\n", - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=168 nstack_initial=1):\n", - "AdaptBlockInfo(insts=((168, {'res': '$168load_global.1'}), (170, {'item': '$168load_global.1', 'res': '$170load_attr.2'}), (172, {'item': '$170load_attr.2', 'res': '$172load_method.3'}), (174, {'func': '$172load_method.3', 'args': [], 'res': '$174call_method.4'}), (176, {'res': '$176load_global.5'}), (178, {'item': '$176load_global.5', 'res': '$178load_method.6'}), (180, {'res': '$log_p_accept180.7'}), (182, {'func': '$178load_method.6', 'args': ['$log_p_accept180.7'], 'res': '$182call_method.8'}), (184, {'lhs': '$174call_method.4', 'rhs': '$182call_method.8', 'res': '$184compare_op.9'}), (186, {'pred': '$184compare_op.9'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={188: ('$phi168.0',), 10: ('$phi168.0',)})\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=188 nstack_initial=1):\n", - "AdaptBlockInfo(insts=((188, {'res': '$thermodynamic_state_j188.1'}), (190, {'res': '$_replica_thermodynamic_states190.2'}), (192, {'res': '$replica_i192.3'}), (194, {'target': '$_replica_thermodynamic_states190.2', 'index': '$replica_i192.3', 'value': '$thermodynamic_state_j188.1'}), (196, {'res': '$thermodynamic_state_i196.4'}), (198, {'res': '$_replica_thermodynamic_states198.5'}), (200, {'res': '$replica_j200.6'}), (202, {'target': '$_replica_thermodynamic_states198.5', 'index': '$replica_j200.6', 'value': '$thermodynamic_state_i196.4'}), (204, {'res': '$_n_accepted_matrix204.7'}), (206, {'res': '$thermodynamic_state_i206.8'}), (208, {'res': '$thermodynamic_state_j208.9'}), (210, {'items': ['$thermodynamic_state_i206.8', '$thermodynamic_state_j208.9'], 'res': '$210build_tuple.10'}), (212, {'orig': ['$_n_accepted_matrix204.7', '$210build_tuple.10'], 'duped': ['$212dup_top_two.11', '$212dup_top_two.12']}), (214, {'index': '$212dup_top_two.12', 'target': '$212dup_top_two.11', 'res': '$214binary_subscr.13'}), (216, {'res': '$const216.14'}), (218, {'lhs': '$214binary_subscr.13', 'rhs': '$const216.14', 'res': '$218inplace_add.15'}), (222, {'target': '$_n_accepted_matrix204.7', 'index': '$210build_tuple.10', 'value': '$218inplace_add.15'}), (224, {'res': '$_n_accepted_matrix224.16'}), (226, {'res': '$thermodynamic_state_j226.17'}), (228, {'res': '$thermodynamic_state_i228.18'}), (230, {'items': ['$thermodynamic_state_j226.17', '$thermodynamic_state_i228.18'], 'res': '$230build_tuple.19'}), (232, {'orig': ['$_n_accepted_matrix224.16', '$230build_tuple.19'], 'duped': ['$232dup_top_two.20', '$232dup_top_two.21']}), (234, {'index': '$232dup_top_two.21', 'target': '$232dup_top_two.20', 'res': '$234binary_subscr.22'}), (236, {'res': '$const236.23'}), (238, {'lhs': '$234binary_subscr.22', 'rhs': '$const236.23', 'res': '$238inplace_add.24'}), (242, {'target': '$_n_accepted_matrix224.16', 'index': '$230build_tuple.19', 'value': '$238inplace_add.24'}), (244, {})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={10: ('$phi188.0',)})\n", - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=246 nstack_initial=0):\n", - "AdaptBlockInfo(insts=((246, {'res': '$const246.0'}), (248, {'retval': '$const246.0', 'castval': '$248return_value.1'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={})\n", - "DEBUG:numba.core.interpreter:label 0:\n", - " nswap_attempts = arg(0, name=nswap_attempts) ['nswap_attempts']\n", - " n_replicas = arg(1, name=n_replicas) ['n_replicas']\n", - " _replica_thermodynamic_states = arg(2, name=_replica_thermodynamic_states) ['_replica_thermodynamic_states']\n", - " _energy_thermodynamic_states = arg(3, name=_energy_thermodynamic_states) ['_energy_thermodynamic_states']\n", - " _n_accepted_matrix = arg(4, name=_n_accepted_matrix) ['_n_accepted_matrix']\n", - " _n_proposed_matrix = arg(5, name=_n_proposed_matrix) ['_n_proposed_matrix']\n", - " $2load_global.0 = global(range: ) ['$2load_global.0']\n", - " $6call_function.2 = call $2load_global.0(nswap_attempts, func=$2load_global.0, args=[Var(nswap_attempts, replicaexchange.py:321)], kws=(), vararg=None) ['$2load_global.0', '$6call_function.2', 'nswap_attempts']\n", - " $8get_iter.3 = getiter(value=$6call_function.2) ['$6call_function.2', '$8get_iter.3']\n", - " $phi10.0 = $8get_iter.3 ['$8get_iter.3', '$phi10.0']\n", - " jump 10 []\n", - "label 10:\n", - " $10for_iter.1 = iternext(value=$phi10.0) ['$10for_iter.1', '$phi10.0']\n", - " $10for_iter.2 = pair_first(value=$10for_iter.1) ['$10for_iter.1', '$10for_iter.2']\n", - " $10for_iter.3 = pair_second(value=$10for_iter.1) ['$10for_iter.1', '$10for_iter.3']\n", - " $phi12.1 = $10for_iter.2 ['$10for_iter.2', '$phi12.1']\n", - " branch $10for_iter.3, 12, 246 ['$10for_iter.3']\n", - "label 12:\n", - " swap_attempt = $phi12.1 ['$phi12.1', 'swap_attempt']\n", - " $14load_global.2 = global(np: ) ['$14load_global.2']\n", - " $16load_attr.3 = getattr(value=$14load_global.2, attr=random) ['$14load_global.2', '$16load_attr.3']\n", - " $18load_method.4 = getattr(value=$16load_attr.3, attr=randint) ['$16load_attr.3', '$18load_method.4']\n", - " replica_i = call $18load_method.4(n_replicas, func=$18load_method.4, args=[Var(n_replicas, replicaexchange.py:321)], kws=(), vararg=None) ['$18load_method.4', 'n_replicas', 'replica_i']\n", - " $26load_global.7 = global(np: ) ['$26load_global.7']\n", - " $28load_attr.8 = getattr(value=$26load_global.7, attr=random) ['$26load_global.7', '$28load_attr.8']\n", - " $30load_method.9 = getattr(value=$28load_attr.8, attr=randint) ['$28load_attr.8', '$30load_method.9']\n", - " replica_j = call $30load_method.9(n_replicas, func=$30load_method.9, args=[Var(n_replicas, replicaexchange.py:321)], kws=(), vararg=None) ['$30load_method.9', 'n_replicas', 'replica_j']\n", - " thermodynamic_state_i = getitem(value=_replica_thermodynamic_states, index=replica_i, fn=) ['_replica_thermodynamic_states', 'replica_i', 'thermodynamic_state_i']\n", - " thermodynamic_state_j = getitem(value=_replica_thermodynamic_states, index=replica_j, fn=) ['_replica_thermodynamic_states', 'replica_j', 'thermodynamic_state_j']\n", - " $60build_tuple.21 = build_tuple(items=[Var(replica_i, replicaexchange.py:324), Var(thermodynamic_state_j, replicaexchange.py:329)]) ['$60build_tuple.21', 'replica_i', 'thermodynamic_state_j']\n", - " energy_ij = getitem(value=_energy_thermodynamic_states, index=$60build_tuple.21, fn=) ['$60build_tuple.21', '_energy_thermodynamic_states', 'energy_ij']\n", - " $72build_tuple.26 = build_tuple(items=[Var(replica_j, replicaexchange.py:325), Var(thermodynamic_state_i, replicaexchange.py:328)]) ['$72build_tuple.26', 'replica_j', 'thermodynamic_state_i']\n", - " energy_ji = getitem(value=_energy_thermodynamic_states, index=$72build_tuple.26, fn=) ['$72build_tuple.26', '_energy_thermodynamic_states', 'energy_ji']\n", - " $84build_tuple.31 = build_tuple(items=[Var(replica_i, replicaexchange.py:324), Var(thermodynamic_state_i, replicaexchange.py:328)]) ['$84build_tuple.31', 'replica_i', 'thermodynamic_state_i']\n", - " energy_ii = getitem(value=_energy_thermodynamic_states, index=$84build_tuple.31, fn=) ['$84build_tuple.31', '_energy_thermodynamic_states', 'energy_ii']\n", - " $96build_tuple.36 = build_tuple(items=[Var(replica_j, replicaexchange.py:325), Var(thermodynamic_state_j, replicaexchange.py:329)]) ['$96build_tuple.36', 'replica_j', 'thermodynamic_state_j']\n", - " energy_jj = getitem(value=_energy_thermodynamic_states, index=$96build_tuple.36, fn=) ['$96build_tuple.36', '_energy_thermodynamic_states', 'energy_jj']\n", - " $106binary_add.40 = energy_ij + energy_ji ['$106binary_add.40', 'energy_ij', 'energy_ji']\n", - " $108unary_negative.41 = unary(fn=, value=$106binary_add.40) ['$106binary_add.40', '$108unary_negative.41']\n", - " $112binary_add.43 = $108unary_negative.41 + energy_ii ['$108unary_negative.41', '$112binary_add.43', 'energy_ii']\n", - " log_p_accept = $112binary_add.43 + energy_jj ['$112binary_add.43', 'energy_jj', 'log_p_accept']\n", - " $126build_tuple.49 = build_tuple(items=[Var(thermodynamic_state_i, replicaexchange.py:328), Var(thermodynamic_state_j, replicaexchange.py:329)]) ['$126build_tuple.49', 'thermodynamic_state_i', 'thermodynamic_state_j']\n", - " $130binary_subscr.52 = getitem(value=_n_proposed_matrix, index=$126build_tuple.49, fn=) ['$126build_tuple.49', '$130binary_subscr.52', '_n_proposed_matrix']\n", - " $const132.53 = const(int, 1) ['$const132.53']\n", - " $134inplace_add.54 = inplace_binop(fn=, immutable_fn=, lhs=$130binary_subscr.52, rhs=$const132.53, static_lhs=Undefined, static_rhs=Undefined) ['$130binary_subscr.52', '$134inplace_add.54', '$const132.53']\n", - " _n_proposed_matrix[$126build_tuple.49] = $134inplace_add.54 ['$126build_tuple.49', '$134inplace_add.54', '_n_proposed_matrix']\n", - " $146build_tuple.58 = build_tuple(items=[Var(thermodynamic_state_j, replicaexchange.py:329), Var(thermodynamic_state_i, replicaexchange.py:328)]) ['$146build_tuple.58', 'thermodynamic_state_i', 'thermodynamic_state_j']\n", - " $150binary_subscr.61 = getitem(value=_n_proposed_matrix, index=$146build_tuple.58, fn=) ['$146build_tuple.58', '$150binary_subscr.61', '_n_proposed_matrix']\n", - " $const152.62 = const(int, 1) ['$const152.62']\n", - " $154inplace_add.63 = inplace_binop(fn=, immutable_fn=, lhs=$150binary_subscr.61, rhs=$const152.62, static_lhs=Undefined, static_rhs=Undefined) ['$150binary_subscr.61', '$154inplace_add.63', '$const152.62']\n", - " _n_proposed_matrix[$146build_tuple.58] = $154inplace_add.63 ['$146build_tuple.58', '$154inplace_add.63', '_n_proposed_matrix']\n", - " $const162.65 = const(float, 0.0) ['$const162.65']\n", - " $164compare_op.66 = log_p_accept >= $const162.65 ['$164compare_op.66', '$const162.65', 'log_p_accept']\n", - " bool166 = global(bool: ) ['bool166']\n", - " $166pred = call bool166($164compare_op.66, func=bool166, args=(Var($164compare_op.66, replicaexchange.py:343),), kws=(), vararg=None) ['$164compare_op.66', '$166pred', 'bool166']\n", - " branch $166pred, 188, 168 ['$166pred']\n", - "label 168:\n", - " $168load_global.1 = global(np: ) ['$168load_global.1']\n", - " $170load_attr.2 = getattr(value=$168load_global.1, attr=random) ['$168load_global.1', '$170load_attr.2']\n", - " $172load_method.3 = getattr(value=$170load_attr.2, attr=rand) ['$170load_attr.2', '$172load_method.3']\n", - " $174call_method.4 = call $172load_method.3(func=$172load_method.3, args=[], kws=(), vararg=None) ['$172load_method.3', '$174call_method.4']\n", - " $176load_global.5 = global(np: ) ['$176load_global.5']\n", - " $178load_method.6 = getattr(value=$176load_global.5, attr=exp) ['$176load_global.5', '$178load_method.6']\n", - " $182call_method.8 = call $178load_method.6(log_p_accept, func=$178load_method.6, args=[Var(log_p_accept, replicaexchange.py:336)], kws=(), vararg=None) ['$178load_method.6', '$182call_method.8', 'log_p_accept']\n", - " $184compare_op.9 = $174call_method.4 < $182call_method.8 ['$174call_method.4', '$182call_method.8', '$184compare_op.9']\n", - " bool186 = global(bool: ) ['bool186']\n", - " $186pred = call bool186($184compare_op.9, func=bool186, args=(Var($184compare_op.9, replicaexchange.py:343),), kws=(), vararg=None) ['$184compare_op.9', '$186pred', 'bool186']\n", - " branch $186pred, 188, 10 ['$186pred']\n", - "label 188:\n", - " _replica_thermodynamic_states[replica_i] = thermodynamic_state_j ['_replica_thermodynamic_states', 'replica_i', 'thermodynamic_state_j']\n", - " _replica_thermodynamic_states[replica_j] = thermodynamic_state_i ['_replica_thermodynamic_states', 'replica_j', 'thermodynamic_state_i']\n", - " $210build_tuple.10 = build_tuple(items=[Var(thermodynamic_state_i, replicaexchange.py:328), Var(thermodynamic_state_j, replicaexchange.py:329)]) ['$210build_tuple.10', 'thermodynamic_state_i', 'thermodynamic_state_j']\n", - " $214binary_subscr.13 = getitem(value=_n_accepted_matrix, index=$210build_tuple.10, fn=) ['$210build_tuple.10', '$214binary_subscr.13', '_n_accepted_matrix']\n", - " $const216.14 = const(int, 1) ['$const216.14']\n", - " $218inplace_add.15 = inplace_binop(fn=, immutable_fn=, lhs=$214binary_subscr.13, rhs=$const216.14, static_lhs=Undefined, static_rhs=Undefined) ['$214binary_subscr.13', '$218inplace_add.15', '$const216.14']\n", - " _n_accepted_matrix[$210build_tuple.10] = $218inplace_add.15 ['$210build_tuple.10', '$218inplace_add.15', '_n_accepted_matrix']\n", - " $230build_tuple.19 = build_tuple(items=[Var(thermodynamic_state_j, replicaexchange.py:329), Var(thermodynamic_state_i, replicaexchange.py:328)]) ['$230build_tuple.19', 'thermodynamic_state_i', 'thermodynamic_state_j']\n", - " $234binary_subscr.22 = getitem(value=_n_accepted_matrix, index=$230build_tuple.19, fn=) ['$230build_tuple.19', '$234binary_subscr.22', '_n_accepted_matrix']\n", - " $const236.23 = const(int, 1) ['$const236.23']\n", - " $238inplace_add.24 = inplace_binop(fn=, immutable_fn=, lhs=$234binary_subscr.22, rhs=$const236.23, static_lhs=Undefined, static_rhs=Undefined) ['$234binary_subscr.22', '$238inplace_add.24', '$const236.23']\n", - " _n_accepted_matrix[$230build_tuple.19] = $238inplace_add.24 ['$230build_tuple.19', '$238inplace_add.24', '_n_accepted_matrix']\n", - " jump 10 []\n", - "label 246:\n", - " $const246.0 = const(NoneType, None) ['$const246.0']\n", - " $248return_value.1 = cast(value=$const246.0) ['$248return_value.1', '$const246.0']\n", - " return $248return_value.1 ['$248return_value.1']\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 0\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: nswap_attempts = arg(0, name=nswap_attempts)\n", - "DEBUG:numba.core.ssa:on stmt: n_replicas = arg(1, name=n_replicas)\n", - "DEBUG:numba.core.ssa:on stmt: _replica_thermodynamic_states = arg(2, name=_replica_thermodynamic_states)\n", - "DEBUG:numba.core.ssa:on stmt: _energy_thermodynamic_states = arg(3, name=_energy_thermodynamic_states)\n", - "DEBUG:numba.core.ssa:on stmt: _n_accepted_matrix = arg(4, name=_n_accepted_matrix)\n", - "DEBUG:numba.core.ssa:on stmt: _n_proposed_matrix = arg(5, name=_n_proposed_matrix)\n", - "DEBUG:numba.core.ssa:on stmt: $2load_global.0 = global(range: )\n", - "DEBUG:numba.core.ssa:on stmt: $6call_function.2 = call $2load_global.0(nswap_attempts, func=$2load_global.0, args=[Var(nswap_attempts, replicaexchange.py:321)], kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: $8get_iter.3 = getiter(value=$6call_function.2)\n", - "DEBUG:numba.core.ssa:on stmt: $phi10.0 = $8get_iter.3\n", - "DEBUG:numba.core.ssa:on stmt: jump 10\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 10\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: $10for_iter.1 = iternext(value=$phi10.0)\n", - "DEBUG:numba.core.ssa:on stmt: $10for_iter.2 = pair_first(value=$10for_iter.1)\n", - "DEBUG:numba.core.ssa:on stmt: $10for_iter.3 = pair_second(value=$10for_iter.1)\n", - "DEBUG:numba.core.ssa:on stmt: $phi12.1 = $10for_iter.2\n", - "DEBUG:numba.core.ssa:on stmt: branch $10for_iter.3, 12, 246\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 12\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: swap_attempt = $phi12.1\n", - "DEBUG:numba.core.ssa:on stmt: $14load_global.2 = global(np: )\n", - "DEBUG:numba.core.ssa:on stmt: $16load_attr.3 = getattr(value=$14load_global.2, attr=random)\n", - "DEBUG:numba.core.ssa:on stmt: $18load_method.4 = getattr(value=$16load_attr.3, attr=randint)\n", - "DEBUG:numba.core.ssa:on stmt: replica_i = call $18load_method.4(n_replicas, func=$18load_method.4, args=[Var(n_replicas, replicaexchange.py:321)], kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: $26load_global.7 = global(np: )\n", - "DEBUG:numba.core.ssa:on stmt: $28load_attr.8 = getattr(value=$26load_global.7, attr=random)\n", - "DEBUG:numba.core.ssa:on stmt: $30load_method.9 = getattr(value=$28load_attr.8, attr=randint)\n", - "DEBUG:numba.core.ssa:on stmt: replica_j = call $30load_method.9(n_replicas, func=$30load_method.9, args=[Var(n_replicas, replicaexchange.py:321)], kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: thermodynamic_state_i = getitem(value=_replica_thermodynamic_states, index=replica_i, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: thermodynamic_state_j = getitem(value=_replica_thermodynamic_states, index=replica_j, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $60build_tuple.21 = build_tuple(items=[Var(replica_i, replicaexchange.py:324), Var(thermodynamic_state_j, replicaexchange.py:329)])\n", - "DEBUG:numba.core.ssa:on stmt: energy_ij = getitem(value=_energy_thermodynamic_states, index=$60build_tuple.21, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $72build_tuple.26 = build_tuple(items=[Var(replica_j, replicaexchange.py:325), Var(thermodynamic_state_i, replicaexchange.py:328)])\n", - "DEBUG:numba.core.ssa:on stmt: energy_ji = getitem(value=_energy_thermodynamic_states, index=$72build_tuple.26, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $84build_tuple.31 = build_tuple(items=[Var(replica_i, replicaexchange.py:324), Var(thermodynamic_state_i, replicaexchange.py:328)])\n", - "DEBUG:numba.core.ssa:on stmt: energy_ii = getitem(value=_energy_thermodynamic_states, index=$84build_tuple.31, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $96build_tuple.36 = build_tuple(items=[Var(replica_j, replicaexchange.py:325), Var(thermodynamic_state_j, replicaexchange.py:329)])\n", - "DEBUG:numba.core.ssa:on stmt: energy_jj = getitem(value=_energy_thermodynamic_states, index=$96build_tuple.36, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $106binary_add.40 = energy_ij + energy_ji\n", - "DEBUG:numba.core.ssa:on stmt: $108unary_negative.41 = unary(fn=, value=$106binary_add.40)\n", - "DEBUG:numba.core.ssa:on stmt: $112binary_add.43 = $108unary_negative.41 + energy_ii\n", - "DEBUG:numba.core.ssa:on stmt: log_p_accept = $112binary_add.43 + energy_jj\n", - "DEBUG:numba.core.ssa:on stmt: $126build_tuple.49 = build_tuple(items=[Var(thermodynamic_state_i, replicaexchange.py:328), Var(thermodynamic_state_j, replicaexchange.py:329)])\n", - "DEBUG:numba.core.ssa:on stmt: $130binary_subscr.52 = getitem(value=_n_proposed_matrix, index=$126build_tuple.49, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $const132.53 = const(int, 1)\n", - "DEBUG:numba.core.ssa:on stmt: $134inplace_add.54 = inplace_binop(fn=, immutable_fn=, lhs=$130binary_subscr.52, rhs=$const132.53, static_lhs=Undefined, static_rhs=Undefined)\n", - "DEBUG:numba.core.ssa:on stmt: _n_proposed_matrix[$126build_tuple.49] = $134inplace_add.54\n", - "DEBUG:numba.core.ssa:on stmt: $146build_tuple.58 = build_tuple(items=[Var(thermodynamic_state_j, replicaexchange.py:329), Var(thermodynamic_state_i, replicaexchange.py:328)])\n", - "DEBUG:numba.core.ssa:on stmt: $150binary_subscr.61 = getitem(value=_n_proposed_matrix, index=$146build_tuple.58, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $const152.62 = const(int, 1)\n", - "DEBUG:numba.core.ssa:on stmt: $154inplace_add.63 = inplace_binop(fn=, immutable_fn=, lhs=$150binary_subscr.61, rhs=$const152.62, static_lhs=Undefined, static_rhs=Undefined)\n", - "DEBUG:numba.core.ssa:on stmt: _n_proposed_matrix[$146build_tuple.58] = $154inplace_add.63\n", - "DEBUG:numba.core.ssa:on stmt: $const162.65 = const(float, 0.0)\n", - "DEBUG:numba.core.ssa:on stmt: $164compare_op.66 = log_p_accept >= $const162.65\n", - "DEBUG:numba.core.ssa:on stmt: bool166 = global(bool: )\n", - "DEBUG:numba.core.ssa:on stmt: $166pred = call bool166($164compare_op.66, func=bool166, args=(Var($164compare_op.66, replicaexchange.py:343),), kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: branch $166pred, 188, 168\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 168\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: $168load_global.1 = global(np: )\n", - "DEBUG:numba.core.ssa:on stmt: $170load_attr.2 = getattr(value=$168load_global.1, attr=random)\n", - "DEBUG:numba.core.ssa:on stmt: $172load_method.3 = getattr(value=$170load_attr.2, attr=rand)\n", - "DEBUG:numba.core.ssa:on stmt: $174call_method.4 = call $172load_method.3(func=$172load_method.3, args=[], kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: $176load_global.5 = global(np: )\n", - "DEBUG:numba.core.ssa:on stmt: $178load_method.6 = getattr(value=$176load_global.5, attr=exp)\n", - "DEBUG:numba.core.ssa:on stmt: $182call_method.8 = call $178load_method.6(log_p_accept, func=$178load_method.6, args=[Var(log_p_accept, replicaexchange.py:336)], kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: $184compare_op.9 = $174call_method.4 < $182call_method.8\n", - "DEBUG:numba.core.ssa:on stmt: bool186 = global(bool: )\n", - "DEBUG:numba.core.ssa:on stmt: $186pred = call bool186($184compare_op.9, func=bool186, args=(Var($184compare_op.9, replicaexchange.py:343),), kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: branch $186pred, 188, 247\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 188\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: _replica_thermodynamic_states[replica_i] = thermodynamic_state_j\n", - "DEBUG:numba.core.ssa:on stmt: _replica_thermodynamic_states[replica_j] = thermodynamic_state_i\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.ssa:on stmt: $210build_tuple.10 = build_tuple(items=[Var(thermodynamic_state_i, replicaexchange.py:328), Var(thermodynamic_state_j, replicaexchange.py:329)])\n", - "DEBUG:numba.core.ssa:on stmt: $214binary_subscr.13 = getitem(value=_n_accepted_matrix, index=$210build_tuple.10, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $const216.14 = const(int, 1)\n", - "DEBUG:numba.core.ssa:on stmt: $218inplace_add.15 = inplace_binop(fn=, immutable_fn=, lhs=$214binary_subscr.13, rhs=$const216.14, static_lhs=Undefined, static_rhs=Undefined)\n", - "DEBUG:numba.core.ssa:on stmt: _n_accepted_matrix[$210build_tuple.10] = $218inplace_add.15\n", - "DEBUG:numba.core.ssa:on stmt: $230build_tuple.19 = build_tuple(items=[Var(thermodynamic_state_j, replicaexchange.py:329), Var(thermodynamic_state_i, replicaexchange.py:328)])\n", - "DEBUG:numba.core.ssa:on stmt: $234binary_subscr.22 = getitem(value=_n_accepted_matrix, index=$230build_tuple.19, fn=)\n", - "DEBUG:numba.core.ssa:on stmt: $const236.23 = const(int, 1)\n", - "DEBUG:numba.core.ssa:on stmt: $238inplace_add.24 = inplace_binop(fn=, immutable_fn=, lhs=$234binary_subscr.22, rhs=$const236.23, static_lhs=Undefined, static_rhs=Undefined)\n", - "DEBUG:numba.core.ssa:on stmt: _n_accepted_matrix[$230build_tuple.19] = $238inplace_add.24\n", - "DEBUG:numba.core.ssa:on stmt: jump 247\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 246\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: $const246.0 = const(NoneType, None)\n", - "DEBUG:numba.core.ssa:on stmt: $248return_value.1 = cast(value=$const246.0)\n", - "DEBUG:numba.core.ssa:on stmt: return $248return_value.1\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 247\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: jump 10\n", - "DEBUG:numba.core.ssa:defs defaultdict(,\n", - " {'$106binary_add.40': [],\n", - " '$108unary_negative.41': [],\n", - " '$10for_iter.1': [],\n", - " '$10for_iter.2': [],\n", - " '$10for_iter.3': [],\n", - " '$112binary_add.43': [],\n", - " '$126build_tuple.49': [],\n", - " '$130binary_subscr.52': [],\n", - " '$134inplace_add.54': [],\n", - " '$146build_tuple.58': [],\n", - " '$14load_global.2': [],\n", - " '$150binary_subscr.61': [],\n", - " '$154inplace_add.63': [],\n", - " '$164compare_op.66': [],\n", - " '$166pred': [],\n", - " '$168load_global.1': [],\n", - " '$16load_attr.3': [],\n", - " '$170load_attr.2': [],\n", - " '$172load_method.3': [],\n", - " '$174call_method.4': [],\n", - " '$176load_global.5': [],\n", - " '$178load_method.6': [],\n", - " '$182call_method.8': [],\n", - " '$184compare_op.9': [],\n", - " '$186pred': [],\n", - " '$18load_method.4': [],\n", - " '$210build_tuple.10': [],\n", - " '$214binary_subscr.13': [],\n", - " '$218inplace_add.15': [],\n", - " '$230build_tuple.19': [],\n", - " '$234binary_subscr.22': [],\n", - " '$238inplace_add.24': [],\n", - " '$248return_value.1': [],\n", - " '$26load_global.7': [],\n", - " '$28load_attr.8': [],\n", - " '$2load_global.0': [],\n", - " '$30load_method.9': [],\n", - " '$60build_tuple.21': [],\n", - " '$6call_function.2': [],\n", - " '$72build_tuple.26': [],\n", - " '$84build_tuple.31': [],\n", - " '$8get_iter.3': [],\n", - " '$96build_tuple.36': [],\n", - " '$const132.53': [],\n", - " '$const152.62': [],\n", - " '$const162.65': [],\n", - " '$const216.14': [],\n", - " '$const236.23': [],\n", - " '$const246.0': [],\n", - " '$phi10.0': [],\n", - " '$phi12.1': [],\n", - " '_energy_thermodynamic_states': [],\n", - " '_n_accepted_matrix': [],\n", - " '_n_proposed_matrix': [],\n", - " '_replica_thermodynamic_states': [],\n", - " 'bool166': [],\n", - " 'bool186': [],\n", - " 'energy_ii': [],\n", - " 'energy_ij': [],\n", - " 'energy_ji': [],\n", - " 'energy_jj': [],\n", - " 'log_p_accept': [],\n", - " 'n_replicas': [],\n", - " 'nswap_attempts': [],\n", - " 'replica_i': [],\n", - " 'replica_j': [],\n", - " 'swap_attempt': [],\n", - " 'thermodynamic_state_i': [],\n", - " 'thermodynamic_state_j': []})\n", - "DEBUG:numba.core.ssa:SSA violators set()\n", - "DEBUG:numba.core.byteflow:bytecode dump:\n", - "> 0\tNOP(arg=None, lineno=1319)\n", - " 2\tLOAD_GLOBAL(arg=0, lineno=1319)\n", - " 4\tLOAD_ATTR(arg=1, lineno=1319)\n", - " 6\tLOAD_METHOD(arg=1, lineno=1319)\n", - " 8\tCALL_METHOD(arg=0, lineno=1319)\n", - " 10\tRETURN_VALUE(arg=None, lineno=1319)\n", - "DEBUG:numba.core.byteflow:pending: deque([State(pc_initial=0 nstack_initial=0)])\n", - "DEBUG:numba.core.byteflow:stack: []\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:numba.core.byteflow:dispatch pc=0, inst=NOP(arg=None, lineno=1319)\n", - "DEBUG:numba.core.byteflow:stack []\n", - "DEBUG:numba.core.byteflow:dispatch pc=2, inst=LOAD_GLOBAL(arg=0, lineno=1319)\n", - "DEBUG:numba.core.byteflow:stack []\n", - "DEBUG:numba.core.byteflow:dispatch pc=4, inst=LOAD_ATTR(arg=1, lineno=1319)\n", - "DEBUG:numba.core.byteflow:stack ['$2load_global.0']\n", - "DEBUG:numba.core.byteflow:dispatch pc=6, inst=LOAD_METHOD(arg=1, lineno=1319)\n", - "DEBUG:numba.core.byteflow:stack ['$4load_attr.1']\n", - "DEBUG:numba.core.byteflow:dispatch pc=8, inst=CALL_METHOD(arg=0, lineno=1319)\n", - "DEBUG:numba.core.byteflow:stack ['$6load_method.2']\n", - "DEBUG:numba.core.byteflow:dispatch pc=10, inst=RETURN_VALUE(arg=None, lineno=1319)\n", - "DEBUG:numba.core.byteflow:stack ['$8call_method.3']\n", - "DEBUG:numba.core.byteflow:end state. edges=[]\n", - "DEBUG:numba.core.byteflow:-------------------------Prune PHIs-------------------------\n", - "DEBUG:numba.core.byteflow:Used_phis: defaultdict(, {State(pc_initial=0 nstack_initial=0): set()})\n", - "DEBUG:numba.core.byteflow:defmap: {}\n", - "DEBUG:numba.core.byteflow:phismap: defaultdict(, {})\n", - "DEBUG:numba.core.byteflow:changing phismap: defaultdict(, {})\n", - "DEBUG:numba.core.byteflow:keep phismap: {}\n", - "DEBUG:numba.core.byteflow:new_out: defaultdict(, {})\n", - "DEBUG:numba.core.byteflow:----------------------DONE Prune PHIs-----------------------\n", - "DEBUG:numba.core.byteflow:block_infos State(pc_initial=0 nstack_initial=0):\n", - "AdaptBlockInfo(insts=((0, {}), (2, {'res': '$2load_global.0'}), (4, {'item': '$2load_global.0', 'res': '$4load_attr.1'}), (6, {'item': '$4load_attr.1', 'res': '$6load_method.2'}), (8, {'func': '$6load_method.2', 'args': [], 'res': '$8call_method.3'}), (10, {'retval': '$8call_method.3', 'castval': '$10return_value.4'})), outgoing_phis={}, blockstack=(), active_try_block=None, outgoing_edgepushed={})\n", - "DEBUG:numba.core.interpreter:label 0:\n", - " size = arg(0, name=size) ['size']\n", - " $2load_global.0 = global(np: ) ['$2load_global.0']\n", - " $4load_attr.1 = getattr(value=$2load_global.0, attr=random) ['$2load_global.0', '$4load_attr.1']\n", - " $6load_method.2 = getattr(value=$4load_attr.1, attr=random) ['$4load_attr.1', '$6load_method.2']\n", - " $8call_method.3 = call $6load_method.2(func=$6load_method.2, args=[], kws=(), vararg=None) ['$6load_method.2', '$8call_method.3']\n", - " $10return_value.4 = cast(value=$8call_method.3) ['$10return_value.4', '$8call_method.3']\n", - " return $10return_value.4 ['$10return_value.4']\n", - "\n", - "DEBUG:numba.core.ssa:==== SSA block analysis pass on 0\n", - "DEBUG:numba.core.ssa:Running \n", - "DEBUG:numba.core.ssa:on stmt: size = arg(0, name=size)\n", - "DEBUG:numba.core.ssa:on stmt: $2load_global.0 = global(np: )\n", - "DEBUG:numba.core.ssa:on stmt: $4load_attr.1 = getattr(value=$2load_global.0, attr=random)\n", - "DEBUG:numba.core.ssa:on stmt: $6load_method.2 = getattr(value=$4load_attr.1, attr=random)\n", - "DEBUG:numba.core.ssa:on stmt: $8call_method.3 = call $6load_method.2(func=$6load_method.2, args=[], kws=(), vararg=None)\n", - "DEBUG:numba.core.ssa:on stmt: $10return_value.4 = cast(value=$8call_method.3)\n", - "DEBUG:numba.core.ssa:on stmt: return $10return_value.4\n", - "DEBUG:numba.core.ssa:defs defaultdict(,\n", - " {'$10return_value.4': [],\n", - " '$2load_global.0': [],\n", - " '$4load_attr.1': [],\n", - " '$6load_method.2': [],\n", - " '$8call_method.3': [],\n", - " 'size': []})\n", - "DEBUG:numba.core.ssa:SSA violators set()\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.453s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1122/2662 attempted swaps (42.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.600s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:02:02.404464, at Mon Oct 31 10:17:38 2022 (consuming total wall clock time 0:02:33.005580).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 2/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.298s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.241s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 988/2662 attempted swaps (37.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.543s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:01:28.716848, at Mon Oct 31 10:17:33 2022 (consuming total wall clock time 0:02:27.861413).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 3/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.157s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.239s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 902/2662 attempted swaps (33.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.400s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:58.364157, at Mon Oct 31 10:17:31 2022 (consuming total wall clock time 0:02:25.910391).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 4/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.507s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.250s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 936/2662 attempted swaps (35.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.761s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:29.077179, at Mon Oct 31 10:17:30 2022 (consuming total wall clock time 0:02:25.385897).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 5/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.016s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.275s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1200/2662 attempted swaps (45.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.295s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:00, at Mon Oct 31 10:17:30 2022 (consuming total wall clock time 0:02:25.606120).\n", - "DEBUG:openmmtools.utils:Equilibration Iteration took 29.295s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.117s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:running production phase\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.274s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 1/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1244/2662 attempted swaps (46.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.701s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.261s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 1 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.119s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.868s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.837s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:12:20.117363, at 2022-Oct-31-10:30:22 (consuming total wall clock time 0:12:50.955586).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 2/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1260/2662 attempted swaps (47.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.441s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.265s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 2 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.344s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.056s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:11:40.331653, at 2022-Oct-31-10:30:12 (consuming total wall clock time 0:12:41.230057).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 3/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 770/2662 attempted swaps (28.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.594s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.289s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 3 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.110s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.348s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.235s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:11:08.343457, at 2022-Oct-31-10:30:10 (consuming total wall clock time 0:12:39.481202).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 4/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 868/2662 attempted swaps (32.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.018s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.281s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 4 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.096s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.344s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.648s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:10:34.141374, at 2022-Oct-31-10:30:06 (consuming total wall clock time 0:12:34.930207).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 5/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 820/2662 attempted swaps (30.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.187s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.300s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 5 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.105s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.349s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.844s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:10:02.551180, at 2022-Oct-31-10:30:04 (consuming total wall clock time 0:12:33.188975).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 6/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 854/2662 attempted swaps (32.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.775s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.300s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 6 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.102s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.347s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.428s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:09:33.390690, at 2022-Oct-31-10:30:05 (consuming total wall clock time 0:12:34.461434).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 7/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 800/2662 attempted swaps (30.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.237s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.297s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 7 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.096s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.332s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.871s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:09:07.571104, at 2022-Oct-31-10:30:11 (consuming total wall clock time 0:12:40.515422).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 8/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1036/2662 attempted swaps (38.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.173s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.343s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 8 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.132s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.371s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.892s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:08:38.158439, at 2022-Oct-31-10:30:13 (consuming total wall clock time 0:12:41.997704).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 9/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1198/2662 attempted swaps (45.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.059s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.304s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 9 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.342s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.710s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:08:11.649440, at 2022-Oct-31-10:30:19 (consuming total wall clock time 0:12:48.202251).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 10/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1188/2662 attempted swaps (44.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.208s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.295s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 10 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.344s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.853s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:07:44.112668, at 2022-Oct-31-10:30:24 (consuming total wall clock time 0:12:53.521113).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 11/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1006/2662 attempted swaps (37.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.541s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.356s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 11 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.105s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.350s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.253s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:07:12.301529, at 2022-Oct-31-10:30:23 (consuming total wall clock time 0:12:51.967015).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 12/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1024/2662 attempted swaps (38.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.361s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.346s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 12 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.097s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.333s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.044s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:06:40.521211, at 2022-Oct-31-10:30:21 (consuming total wall clock time 0:12:50.233098).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 13/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 922/2662 attempted swaps (34.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.026s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.350s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 13 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.337s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.717s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:06:08.706383, at 2022-Oct-31-10:30:19 (consuming total wall clock time 0:12:48.138298).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 14/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 972/2662 attempted swaps (36.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.121s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.387s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 14 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.134s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.373s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.886s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:05:38.109140, at 2022-Oct-31-10:30:19 (consuming total wall clock time 0:12:48.429864).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 15/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 788/2662 attempted swaps (29.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.233s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.375s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 15 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.106s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.344s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.960s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:05:08.190162, at 2022-Oct-31-10:30:21 (consuming total wall clock time 0:12:50.475404).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 16/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1084/2662 attempted swaps (40.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.443s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.398s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 16 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.109s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.354s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 33.201s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:38.712590, at 2022-Oct-31-10:30:25 (consuming total wall clock time 0:12:54.201640).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 17/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1030/2662 attempted swaps (38.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 33.534s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.407s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 17 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.109s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.359s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 34.306s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:09.317511, at 2022-Oct-31-10:30:30 (consuming total wall clock time 0:12:59.117222).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 18/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1016/2662 attempted swaps (38.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 33.504s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.382s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 18 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.102s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.377s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 34.268s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:03:39.360919, at 2022-Oct-31-10:30:34 (consuming total wall clock time 0:13:03.431853).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 19/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 770/2662 attempted swaps (28.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.105s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.407s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 19 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.109s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.364s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.882s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:03:07.564919, at 2022-Oct-31-10:30:32 (consuming total wall clock time 0:13:01.520494).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 20/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1052/2662 attempted swaps (39.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.470s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.389s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 20 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.121s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.363s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.228s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:02:36.046702, at 2022-Oct-31-10:30:31 (consuming total wall clock time 0:13:00.233509).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 21/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 894/2662 attempted swaps (33.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.314s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.397s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 21 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.101s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.338s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.055s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:02:04.808719, at 2022-Oct-31-10:30:31 (consuming total wall clock time 0:13:00.054491).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 22/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 882/2662 attempted swaps (33.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.366s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.378s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 22 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.114s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.364s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.115s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:33.731434, at 2022-Oct-31-10:30:32 (consuming total wall clock time 0:13:01.095285).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 23/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 968/2662 attempted swaps (36.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.082s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.411s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 23 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.103s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.361s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.861s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:02.628591, at 2022-Oct-31-10:30:34 (consuming total wall clock time 0:13:02.857389).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 24/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 954/2662 attempted swaps (35.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 33.673s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.446s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 24 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.130s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.407s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 34.532s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:31.448530, at 2022-Oct-31-10:30:37 (consuming total wall clock time 0:13:06.213248).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 25/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1130/2662 attempted swaps (42.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 34.339s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.412s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 25 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.126s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.375s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 35.130s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:00, at 2022-Oct-31-10:30:41 (consuming total wall clock time 0:13:09.897467).\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please cite the following:\n", - "\n", - " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", - " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", - " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", - " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", - " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 1009455B | 985.796KB | 0.963MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 1.361s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.079s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.396s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:minimizing systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Minimizing all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _minimize_replica serially.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: initial energy -129037.204kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: final energy -238293.969kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: initial energy -129038.772kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: final energy -239609.215kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: initial energy -129039.323kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: final energy -241611.937kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: initial energy -129037.018kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: final energy -240282.562kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: initial energy -129022.761kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: final energy -237167.602kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: initial energy -128886.417kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: final energy -239368.944kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: initial energy -128889.181kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: final energy -239002.343kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: initial energy -128888.967kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: final energy -237957.209kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: initial energy -128887.065kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: final energy -241928.833kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: initial energy -128884.619kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: final energy -241536.823kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: initial energy -128881.828kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: final energy -239345.683kT\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.109s\n", - "DEBUG:openmmtools.utils:Minimizing all replicas took 74.139s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:equilibrating systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 1/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.823s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.247s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 742/2662 attempted swaps (27.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.074s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:02:00.300407, at Mon Oct 31 10:34:59 2022 (consuming total wall clock time 0:02:30.375509).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 2/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.731s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.246s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 964/2662 attempted swaps (36.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.980s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:01:28.585587, at Mon Oct 31 10:34:56 2022 (consuming total wall clock time 0:02:27.642645).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 3/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.988s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.284s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 930/2662 attempted swaps (34.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.277s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:58.890074, at Mon Oct 31 10:34:56 2022 (consuming total wall clock time 0:02:27.225184).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 4/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.942s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.281s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 928/2662 attempted swaps (34.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.228s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:29.391095, at Mon Oct 31 10:34:55 2022 (consuming total wall clock time 0:02:26.955475).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 5/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.646s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.254s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1114/2662 attempted swaps (41.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.904s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:00, at Mon Oct 31 10:34:56 2022 (consuming total wall clock time 0:02:27.470073).\n", - "DEBUG:openmmtools.utils:Equilibration Iteration took 29.904s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.116s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:running production phase\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.252s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 1/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1140/2662 attempted swaps (42.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.924s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.259s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 1 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.095s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.791s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.980s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:12:23.555986, at 2022-Oct-31-10:47:51 (consuming total wall clock time 0:12:54.537486).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 2/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1092/2662 attempted swaps (41.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.973s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.266s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 2 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.115s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.359s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.603s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:11:48.270064, at 2022-Oct-31-10:47:46 (consuming total wall clock time 0:12:49.858766).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 3/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1064/2662 attempted swaps (40.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.819s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.282s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 3 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.103s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.364s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.472s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:11:15.152214, at 2022-Oct-31-10:47:43 (consuming total wall clock time 0:12:47.218425).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 4/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 810/2662 attempted swaps (30.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.573s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.288s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 4 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.112s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.358s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.225s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:10:36.794207, at 2022-Oct-31-10:47:34 (consuming total wall clock time 0:12:38.088341).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 5/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1016/2662 attempted swaps (38.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.107s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.300s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 5 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.337s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.750s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:10:04.190229, at 2022-Oct-31-10:47:31 (consuming total wall clock time 0:12:35.237787).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 6/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 880/2662 attempted swaps (33.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.682s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.278s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 6 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.101s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.338s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.304s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:09:34.287104, at 2022-Oct-31-10:47:32 (consuming total wall clock time 0:12:35.640926).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 7/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1114/2662 attempted swaps (41.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.934s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.303s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 7 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.110s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.347s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.589s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:09:05.004802, at 2022-Oct-31-10:47:33 (consuming total wall clock time 0:12:36.951113).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 8/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 934/2662 attempted swaps (35.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.240s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.288s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 8 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.100s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.337s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.870s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:08:38.116521, at 2022-Oct-31-10:47:38 (consuming total wall clock time 0:12:41.936060).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 9/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1144/2662 attempted swaps (43.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.217s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.319s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 9 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.121s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.371s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.911s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:08:10.193385, at 2022-Oct-31-10:47:42 (consuming total wall clock time 0:12:45.927164).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 10/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 964/2662 attempted swaps (36.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.190s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.308s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 10 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.143s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.398s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.904s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:07:42.962999, at 2022-Oct-31-10:47:48 (consuming total wall clock time 0:12:51.604998).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 11/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1060/2662 attempted swaps (39.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.259s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.383s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 11 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.110s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.352s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.999s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:07:12.274450, at 2022-Oct-31-10:47:48 (consuming total wall clock time 0:12:51.918660).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 12/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 920/2662 attempted swaps (34.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 33.351s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.469s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 12 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.136s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.401s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 34.227s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:06:45.030835, at 2022-Oct-31-10:47:55 (consuming total wall clock time 0:12:58.905452).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 13/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 896/2662 attempted swaps (33.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 34.008s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.422s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 13 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.129s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.385s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 34.821s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:06:17.263622, at 2022-Oct-31-10:48:02 (consuming total wall clock time 0:13:05.965878).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 14/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1016/2662 attempted swaps (38.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 34.755s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.431s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 14 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.111s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.377s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 35.570s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:05:49.073752, at 2022-Oct-31-10:48:10 (consuming total wall clock time 0:13:13.349435).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 15/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1144/2662 attempted swaps (43.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 36.061s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.477s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 15 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.152s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.419s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 36.964s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:05:20.829376, at 2022-Oct-31-10:48:18 (consuming total wall clock time 0:13:22.073439).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 16/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 952/2662 attempted swaps (35.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 36.738s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.451s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 16 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.131s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.400s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 37.594s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:51.847957, at 2022-Oct-31-10:48:27 (consuming total wall clock time 0:13:30.688768).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 17/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1134/2662 attempted swaps (42.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 35.856s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.367s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 17 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.338s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 36.566s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:21.369283, at 2022-Oct-31-10:48:33 (consuming total wall clock time 0:13:36.779010).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 18/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1246/2662 attempted swaps (46.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.877s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.393s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 18 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.133s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.400s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 33.677s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:03:49.090699, at 2022-Oct-31-10:48:34 (consuming total wall clock time 0:13:38.181069).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 19/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 868/2662 attempted swaps (32.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.031s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.386s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 19 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.115s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.358s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.781s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:03:15.750344, at 2022-Oct-31-10:48:32 (consuming total wall clock time 0:13:35.626435).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 20/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1050/2662 attempted swaps (39.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.781s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.486s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 20 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.147s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.409s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.681s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:02:43.140284, at 2022-Oct-31-10:48:32 (consuming total wall clock time 0:13:35.701418).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 21/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 964/2662 attempted swaps (36.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 34.024s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.487s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 21 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.131s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.408s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 34.928s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:02:10.951461, at 2022-Oct-31-10:48:35 (consuming total wall clock time 0:13:38.446632).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 22/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 918/2662 attempted swaps (34.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 34.571s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.461s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 22 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.151s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.428s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 35.467s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:38.586509, at 2022-Oct-31-10:48:38 (consuming total wall clock time 0:13:41.554242).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 23/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 872/2662 attempted swaps (32.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 35.828s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.481s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 23 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.140s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.409s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 36.726s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:06.060658, at 2022-Oct-31-10:48:42 (consuming total wall clock time 0:13:45.758221).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 24/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1426/2662 attempted swaps (53.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 35.639s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.401s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 24 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.109s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.354s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 36.399s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:33.170872, at 2022-Oct-31-10:48:46 (consuming total wall clock time 0:13:49.271806).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 25/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 898/2662 attempted swaps (33.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 34.606s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.398s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 25 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.133s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.386s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 35.395s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:00, at 2022-Oct-31-10:48:48 (consuming total wall clock time 0:13:51.498585).\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please cite the following:\n", - "\n", - " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", - " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", - " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", - " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", - " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 1009455B | 985.796KB | 0.963MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 1.321s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.066s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.354s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:minimizing systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Minimizing all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _minimize_replica serially.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: initial energy -129420.641kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: final energy -238737.438kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: initial energy -129422.041kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: final energy -236313.696kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: initial energy -129422.381kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: final energy -240792.306kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: initial energy -129419.944kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: final energy -241420.879kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: initial energy -129405.481kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: final energy -238163.440kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: initial energy -129269.003kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: final energy -241650.614kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: initial energy -129271.904kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: final energy -238343.709kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: initial energy -129271.858kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: final energy -240350.716kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: initial energy -129270.134kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: final energy -236440.634kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: initial energy -129267.866kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: final energy -236416.758kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: initial energy -129265.222kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: final energy -240265.322kT\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.129s\n", - "DEBUG:openmmtools.utils:Minimizing all replicas took 73.786s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:equilibrating systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 1/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.672s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.240s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1020/2662 attempted swaps (38.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.914s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:01:55.658964, at Mon Oct 31 10:53:00 2022 (consuming total wall clock time 0:02:24.573705).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 2/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 27.663s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.234s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1066/2662 attempted swaps (40.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 27.900s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:01:25.223919, at Mon Oct 31 10:52:58 2022 (consuming total wall clock time 0:02:22.039865).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 3/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 27.817s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.241s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 940/2662 attempted swaps (35.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.062s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:56.586066, at Mon Oct 31 10:52:57 2022 (consuming total wall clock time 0:02:21.465166).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 4/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.049s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.247s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 818/2662 attempted swaps (30.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.299s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:28.294930, at Mon Oct 31 10:52:57 2022 (consuming total wall clock time 0:02:21.474648).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 5/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.349s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.250s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 874/2662 attempted swaps (32.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.603s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:00, at Mon Oct 31 10:52:58 2022 (consuming total wall clock time 0:02:21.783662).\n", - "DEBUG:openmmtools.utils:Equilibration Iteration took 28.603s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.108s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:running production phase\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.247s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 1/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 928/2662 attempted swaps (34.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.715s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.256s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 1 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.100s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.793s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.769s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:11:54.484674, at 2022-Oct-31-11:05:22 (consuming total wall clock time 0:12:24.254869).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 2/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 920/2662 attempted swaps (34.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.134s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.270s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 2 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.096s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.377s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.786s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:11:24.935325, at 2022-Oct-31-11:05:22 (consuming total wall clock time 0:12:24.494918).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 3/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1074/2662 attempted swaps (40.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.479s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.271s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 3 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.090s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:10:57.450383, at 2022-Oct-31-11:05:25 (consuming total wall clock time 0:12:27.102708).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 4/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 872/2662 attempted swaps (32.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 27.853s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.275s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 4 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.337s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.470s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:10:20.157654, at 2022-Oct-31-11:05:16 (consuming total wall clock time 0:12:18.282922).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 5/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1172/2662 attempted swaps (44.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.240s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.284s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 5 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.097s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.865s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:09:47.967608, at 2022-Oct-31-11:05:13 (consuming total wall clock time 0:12:14.959511).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 6/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1026/2662 attempted swaps (38.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.682s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.280s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 6 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.097s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.334s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.302s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:09:18.272103, at 2022-Oct-31-11:05:12 (consuming total wall clock time 0:12:14.568556).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 7/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 798/2662 attempted swaps (30.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.607s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.286s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 7 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.233s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:08:51.082524, at 2022-Oct-31-11:05:16 (consuming total wall clock time 0:12:17.614616).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 8/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 920/2662 attempted swaps (34.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.827s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.294s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 8 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.334s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.460s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:08:23.616140, at 2022-Oct-31-11:05:18 (consuming total wall clock time 0:12:20.611971).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 9/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1104/2662 attempted swaps (41.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.530s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.298s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 9 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.102s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.339s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.172s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:07:56.749683, at 2022-Oct-31-11:05:23 (consuming total wall clock time 0:12:24.921379).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 10/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1090/2662 attempted swaps (40.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.244s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.300s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 10 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.886s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:07:30.091637, at 2022-Oct-31-11:05:28 (consuming total wall clock time 0:12:30.152729).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 11/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1006/2662 attempted swaps (37.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.830s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.350s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 11 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.334s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.519s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:06:59.470625, at 2022-Oct-31-11:05:27 (consuming total wall clock time 0:12:29.054688).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 12/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1046/2662 attempted swaps (39.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.247s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.350s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 12 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.334s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.937s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:06:28.400861, at 2022-Oct-31-11:05:25 (consuming total wall clock time 0:12:26.924733).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 13/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 894/2662 attempted swaps (33.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.944s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.357s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 13 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.097s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.643s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:05:58.310768, at 2022-Oct-31-11:05:24 (consuming total wall clock time 0:12:26.480767).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 14/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1204/2662 attempted swaps (45.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.501s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.360s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 14 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.340s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.209s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:05:28.730022, at 2022-Oct-31-11:05:25 (consuming total wall clock time 0:12:27.113686).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 15/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 878/2662 attempted swaps (33.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.267s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.360s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 15 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.097s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.969s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:59.570649, at 2022-Oct-31-11:05:27 (consuming total wall clock time 0:12:28.926621).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 16/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1006/2662 attempted swaps (37.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.166s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.366s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 16 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.101s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.346s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.883s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:30.698523, at 2022-Oct-31-11:05:30 (consuming total wall clock time 0:12:31.940341).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 17/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 958/2662 attempted swaps (36.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.949s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.365s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 17 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.337s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.656s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:04:01.835720, at 2022-Oct-31-11:05:34 (consuming total wall clock time 0:12:35.736626).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 18/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1042/2662 attempted swaps (39.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.435s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.368s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 18 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 33.144s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:03:32.741394, at 2022-Oct-31-11:05:38 (consuming total wall clock time 0:12:39.790692).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 19/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 936/2662 attempted swaps (35.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 27.801s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.373s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 19 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.103s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.341s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 28.522s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:03:01.760289, at 2022-Oct-31-11:05:35 (consuming total wall clock time 0:12:37.334536).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 20/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1136/2662 attempted swaps (42.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 28.524s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.376s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 20 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.339s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 29.245s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:02:31.205747, at 2022-Oct-31-11:05:34 (consuming total wall clock time 0:12:36.028734).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 21/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 844/2662 attempted swaps (31.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 29.434s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.377s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 21 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.104s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.341s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.158s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:02:00.949469, at 2022-Oct-31-11:05:34 (consuming total wall clock time 0:12:35.934183).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 22/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 792/2662 attempted swaps (29.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 30.198s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.377s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 22 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 30.915s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:30.805081, at 2022-Oct-31-11:05:35 (consuming total wall clock time 0:12:36.709008).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 23/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 850/2662 attempted swaps (31.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 31.106s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.379s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 23 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.095s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.331s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 31.820s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:00.671990, at 2022-Oct-31-11:05:36 (consuming total wall clock time 0:12:38.399870).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 24/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1042/2662 attempted swaps (39.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 32.051s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.383s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 24 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.098s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.335s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 32.774s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:30.437691, at 2022-Oct-31-11:05:39 (consuming total wall clock time 0:12:40.942265).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 25/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 968/2662 attempted swaps (36.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 33.134s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.386s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 25 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.099s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.336s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 33.863s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:00, at 2022-Oct-31-11:05:42 (consuming total wall clock time 0:12:44.370978).\n" - ] - } - ], + "outputs": [], "source": [ "# Finally we can run the simulations\n", + "complex_path = pathlib.Path('./complex')\n", + "complex_path.mkdir()\n", "\n", "# First the complex transformation\n", - "complex_dag_results = execute_DAG(complex_dag, shared='./complex')" + "complex_dag_results = execute_DAG(complex_dag, scratch_basedir=complex_path, shared_basedir=complex_path)" ] }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "id": "820aaf86", "metadata": { "tags": [ "nbval-skip" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 56528B | 55.203KB | 0.054MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 0.071s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:minimizing systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Minimizing all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _minimize_replica serially.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please cite the following:\n", - "\n", - " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", - " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", - " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", - " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", - " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: initial energy 7824.310kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: final energy -14389.463kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: initial energy 7823.331kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: final energy -14274.754kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: initial energy 7822.354kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: final energy -14353.540kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: initial energy 7821.401kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: final energy -14523.337kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: initial energy 7820.472kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: final energy -14414.472kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: initial energy 7819.567kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: final energy -14315.365kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: initial energy 7820.909kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: final energy -14663.298kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: initial energy 7822.511kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: final energy -14264.755kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: initial energy 7824.371kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: final energy -14323.100kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: initial energy 7826.491kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: final energy -14498.850kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: initial energy 7828.869kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: final energy -14303.902kT\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Minimizing all replicas took 14.654s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:equilibrating systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 1/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.394s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.077s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 968/2662 attempted swaps (36.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.475s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:17.900908, at Mon Oct 31 11:06:22 2022 (consuming total wall clock time 0:00:22.376136).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 2/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.191s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.067s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 944/2662 attempted swaps (35.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.262s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:13.108336, at Mon Oct 31 11:06:22 2022 (consuming total wall clock time 0:00:21.847226).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 3/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.210s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 992/2662 attempted swaps (37.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.282s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:08.681947, at Mon Oct 31 11:06:22 2022 (consuming total wall clock time 0:00:21.704866).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 4/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.218s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.066s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 944/2662 attempted swaps (35.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.288s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:04.327921, at Mon Oct 31 11:06:22 2022 (consuming total wall clock time 0:00:21.639607).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 5/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.229s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 730/2662 attempted swaps (27.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.301s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:00, at Mon Oct 31 11:06:22 2022 (consuming total wall clock time 0:00:21.613586).\n", - "DEBUG:openmmtools.utils:Equilibration Iteration took 4.301s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.015s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:running production phase\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 1/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 702/2662 attempted swaps (26.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.247s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 1 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.061s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.383s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:45.238146, at 2022-Oct-31-11:08:11 (consuming total wall clock time 0:01:49.623069).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 2/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1068/2662 attempted swaps (40.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.246s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.076s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 2 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.360s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:40.624775, at 2022-Oct-31-11:08:11 (consuming total wall clock time 0:01:49.374756).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 3/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 846/2662 attempted swaps (31.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.234s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 3 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.341s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:36.025339, at 2022-Oct-31-11:08:11 (consuming total wall clock time 0:01:49.119703).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 4/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 730/2662 attempted swaps (27.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.175s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 4 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.284s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:31.256525, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.638720).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 5/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 876/2662 attempted swaps (32.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.182s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.067s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 5 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.030s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.284s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:26.683064, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.353831).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 6/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1026/2662 attempted swaps (38.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.189s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.076s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 6 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.013s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.035s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.306s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:22.271992, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.252621).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 7/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 760/2662 attempted swaps (28.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.206s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.073s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 7 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.008s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.029s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.313s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:17.905780, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.202472).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 8/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 932/2662 attempted swaps (35.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.217s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 8 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.013s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.035s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.330s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:13.585235, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.213581).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 9/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 752/2662 attempted swaps (28.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.223s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 9 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.329s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:09.262337, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.222402).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 10/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 918/2662 attempted swaps (34.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.222s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 10 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.333s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:04.943289, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.238814).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 11/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1204/2662 attempted swaps (45.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.182s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 11 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.290s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:00.569341, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.159538).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 12/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 828/2662 attempted swaps (31.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.180s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 12 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.289s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:56.206394, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.089220).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 13/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 856/2662 attempted swaps (32.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.202s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.076s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 13 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.317s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:51.879772, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.082858).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 14/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 906/2662 attempted swaps (34.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.206s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 14 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.315s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:47.553063, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.075144).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 15/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 784/2662 attempted swaps (29.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.205s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 15 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.029s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.313s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:43.225600, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.064000).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 16/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 892/2662 attempted swaps (33.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.206s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 16 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.316s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:38.901404, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.059455).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 17/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 748/2662 attempted swaps (28.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.218s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 17 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.327s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:34.582530, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.070407).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 18/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 800/2662 attempted swaps (30.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.239s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 18 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.349s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:30.271705, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.113233).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 19/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 978/2662 attempted swaps (36.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.171s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 19 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.277s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:25.933670, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.056958).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 20/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 818/2662 attempted swaps (30.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.177s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 20 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.284s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:21.602324, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.011621).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 21/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 716/2662 attempted swaps (26.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.193s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 21 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.302s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:17.278990, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:47.993688).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 22/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 980/2662 attempted swaps (36.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.214s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.073s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 22 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.326s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:12.960597, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.004972).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 23/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 690/2662 attempted swaps (25.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.187s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 23 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.299s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:08.638979, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:47.987241).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 24/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 998/2662 attempted swaps (37.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.238s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 24 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.343s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:04.320666, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.016645).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 25/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 752/2662 attempted swaps (28.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.249s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.077s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 25 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.016s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.038s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.370s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:00, at 2022-Oct-31-11:08:10 (consuming total wall clock time 0:01:48.069395).\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 56528B | 55.203KB | 0.054MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 0.070s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.013s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.036s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:minimizing systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Minimizing all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _minimize_replica serially.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please cite the following:\n", - "\n", - " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", - " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", - " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", - " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", - " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: initial energy 3413.675kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: final energy -14514.682kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: initial energy 3412.134kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: final energy -14529.871kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: initial energy 3410.614kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: final energy -14481.227kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: initial energy 3409.119kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: final energy -14462.353kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: initial energy 3407.647kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: final energy -14494.012kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: initial energy 3406.199kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: final energy -14626.501kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: initial energy 3408.334kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: final energy -14035.892kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: initial energy 3410.728kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: final energy -14664.470kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: initial energy 3413.381kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: final energy -14531.540kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: initial energy 3416.293kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: final energy -14553.051kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: initial energy 3419.464kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: final energy -14551.805kT\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Minimizing all replicas took 14.634s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:equilibrating systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 1/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.592s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 968/2662 attempted swaps (36.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.666s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:18.667766, at Mon Oct 31 11:08:51 2022 (consuming total wall clock time 0:00:23.334707).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 2/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.380s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 946/2662 attempted swaps (35.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.455s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:13.685684, at Mon Oct 31 11:08:50 2022 (consuming total wall clock time 0:00:22.809473).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 3/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.393s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 876/2662 attempted swaps (32.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.467s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:09.062137, at Mon Oct 31 11:08:50 2022 (consuming total wall clock time 0:00:22.655342).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 4/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.399s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 984/2662 attempted swaps (37.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.469s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:04.516267, at Mon Oct 31 11:08:50 2022 (consuming total wall clock time 0:00:22.581333).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 5/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.402s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 938/2662 attempted swaps (35.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.475s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:00, at Mon Oct 31 11:08:50 2022 (consuming total wall clock time 0:00:22.541381).\n", - "DEBUG:openmmtools.utils:Equilibration Iteration took 4.475s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.014s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:running production phase\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 1/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 942/2662 attempted swaps (35.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.398s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 1 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.061s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.534s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:48.865986, at 2022-Oct-31-11:10:44 (consuming total wall clock time 0:01:53.402069).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 2/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1020/2662 attempted swaps (38.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.413s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 2 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.522s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:44.221012, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:53.283709).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 3/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 970/2662 attempted swaps (36.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.396s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.074s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 3 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.511s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:39.570930, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:53.148785).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 4/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1120/2662 attempted swaps (42.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.359s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.067s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 4 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.016s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.037s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.469s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:34.760526, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.810150).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 5/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 754/2662 attempted swaps (28.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.396s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 5 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.014s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.038s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.509s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:30.247866, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.809832).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 6/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 948/2662 attempted swaps (35.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.398s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 6 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.505s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:25.722874, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.793256).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 7/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 814/2662 attempted swaps (30.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.402s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 7 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.510s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:21.215447, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.799232).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 8/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 756/2662 attempted swaps (28.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.402s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 8 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.507s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:16.699054, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.792727).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 9/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 852/2662 attempted swaps (32.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.435s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 9 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.018s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.047s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.557s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:12.273432, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.927238).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 10/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 758/2662 attempted swaps (28.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.438s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.076s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 10 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.553s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:07.816299, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:53.027165).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 11/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 864/2662 attempted swaps (32.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.384s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.076s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 11 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.498s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:03.272323, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.986291).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 12/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 970/2662 attempted swaps (36.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.363s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 12 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.473s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:58.705965, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.896087).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 13/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1010/2662 attempted swaps (37.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.393s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 13 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.502s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:54.180836, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.876743).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 14/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 876/2662 attempted swaps (32.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.383s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.067s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 14 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.487s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:49.647110, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.834342).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 15/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 902/2662 attempted swaps (33.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.416s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.073s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 15 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.040s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.535s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:45.150720, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.876801).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 16/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 776/2662 attempted swaps (29.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.415s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 16 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.521s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:40.640720, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.890889).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 17/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1136/2662 attempted swaps (42.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.415s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 17 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.522s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:36.129538, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.904806).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 18/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 704/2662 attempted swaps (26.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.423s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 18 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.530s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:31.620421, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.930076).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 19/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 804/2662 attempted swaps (30.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.383s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 19 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.490s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:27.095713, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.898805).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 20/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 780/2662 attempted swaps (29.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.396s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 20 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.503s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:22.577864, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.889318).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 21/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 862/2662 attempted swaps (32.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.393s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 21 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.500s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:18.060003, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.875019).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 22/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1230/2662 attempted swaps (46.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.404s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.074s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 22 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.031s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.515s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:13.545481, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.879006).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 23/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 966/2662 attempted swaps (36.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.409s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 23 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.516s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:09.030708, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.883854).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 24/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 702/2662 attempted swaps (26.4%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.434s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 24 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.015s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.037s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.547s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:04.516873, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.921835).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 25/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 878/2662 attempted swaps (33.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.416s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 25 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.529s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:00, at 2022-Oct-31-11:10:43 (consuming total wall clock time 0:01:52.939261).\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:creating hybrid system\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "INFO:openmmforcefields.generators.template_generators:Requested to generate parameters for residue \n", - "INFO:openmmforcefields.generators.template_generators:Generating a residue template for [H]c1c(c(c(c(c1[H])[H])O[H])[H])[H] using openff-2.0.0.offxml\n", - "DEBUG:openmmforcefields.generators.template_generators:Generating parameters...\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:creating hybrid system\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:setting force field terms\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:adding forces\n", - "INFO:openfe.protocols.openmm_rbfe._rbfe_utils.relative:DONE\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistatesampler:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatesampler:CUDA devices available: (['0', ' NVIDIA GeForce GTX 1660 Ti'],)\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Serialized state thermodynamic_states/0 is 56528B | 55.203KB | 0.054MB\n", - "DEBUG:openmmtools.utils:Storing thermodynamic states took 0.072s\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:minimizing systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Minimizing all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _minimize_replica serially.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Please cite the following:\n", - "\n", - " Friedrichs MS, Eastman P, Vaidyanathan V, Houston M, LeGrand S, Beberg AL, Ensign DL, Bruns CM, and Pande VS. Accelerating molecular dynamic simulations on graphics processing unit. J. Comput. Chem. 30:864, 2009. DOI: 10.1002/jcc.21209\n", - " Eastman P and Pande VS. OpenMM: A hardware-independent framework for molecular simulations. Comput. Sci. Eng. 12:34, 2010. DOI: 10.1109/MCSE.2010.27\n", - " Eastman P and Pande VS. Efficient nonbonded interactions for molecular dynamics on a graphics processing unit. J. Comput. Chem. 31:1268, 2010. DOI: 10.1002/jcc.21413\n", - " Eastman P and Pande VS. Constant constraint matrix approximation: A robust, parallelizable constraint method for molecular simulations. J. Chem. Theor. Comput. 6:434, 2010. DOI: 10.1021/ct900463w\n", - " Chodera JD and Shirts MR. Replica exchange and expanded ensemble simulations as Gibbs multistate: Simple improvements for enhanced mixing. J. Chem. Phys., 135:194110, 2011. DOI:10.1063/1.3660669\n", - " \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: initial energy 3341.917kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 1/11: final energy -14544.019kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: initial energy 3340.845kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 2/11: final energy -14324.677kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: initial energy 3339.796kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 3/11: final energy -14552.917kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: initial energy 3338.770kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 4/11: final energy -14421.202kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: initial energy 3337.768kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 5/11: final energy -14128.653kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: initial energy 3336.790kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 6/11: final energy -14613.027kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: initial energy 3337.466kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 7/11: final energy -14745.327kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: initial energy 3338.401kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 8/11: final energy -14557.050kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: initial energy 3339.595kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 9/11: final energy -14555.424kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: initial energy 3341.048kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 10/11: final energy -14588.659kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: initial energy 3342.760kT\n", - "DEBUG:openmmtools.multistate.multistatesampler:Using FIRE: tolerance 1.0 kJ/(nm mol) max_iterations 10000\n", - "DEBUG:openmmtools.multistate.multistatesampler:Replica 11/11: final energy -14251.469kT\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Minimizing all replicas took 14.785s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:equilibrating systems\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 1/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.315s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 990/2662 attempted swaps (37.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.387s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:17.548892, at Mon Oct 31 11:11:25 2022 (consuming total wall clock time 0:00:21.936115).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 2/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.146s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 834/2662 attempted swaps (31.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.219s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:12.911737, at Mon Oct 31 11:11:24 2022 (consuming total wall clock time 0:00:21.519562).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 3/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.101s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 742/2662 attempted swaps (27.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.173s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:08.521378, at Mon Oct 31 11:11:24 2022 (consuming total wall clock time 0:00:21.303444).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 4/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.186s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.074s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 848/2662 attempted swaps (31.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.263s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:04.262147, at Mon Oct 31 11:11:24 2022 (consuming total wall clock time 0:00:21.310734).\n", - "DEBUG:openmmtools.multistate.multistatesampler:Equilibration iteration 5/5\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.210s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 938/2662 attempted swaps (35.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.282s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion (of equilibration only) in 0:00:00, at Mon Oct 31 11:11:24 2022 (consuming total wall clock time 0:00:21.331745).\n", - "DEBUG:openmmtools.utils:Equilibration Iteration took 4.282s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.015s\n", - "INFO:openfe.protocols.openmm_rbfe.equil_rbfe_methods:running production phase\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:Storing general ReplicaExchange options...\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing >\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 1/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 918/2662 attempted swaps (34.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.232s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.076s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 1 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.014s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.064s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.379s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:45.134583, at 2022-Oct-31-11:13:14 (consuming total wall clock time 0:01:49.515190).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 2/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 740/2662 attempted swaps (27.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.222s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 2 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.036s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.334s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:40.262573, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.981058).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 3/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1016/2662 attempted swaps (38.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.230s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 3 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.010s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.337s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:35.773705, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.833756).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 4/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1020/2662 attempted swaps (38.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.188s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 4 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.014s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.042s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.304s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:31.182551, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.550656).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 5/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 904/2662 attempted swaps (34.0%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.198s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.073s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 5 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.311s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:26.731079, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.413849).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 6/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 920/2662 attempted swaps (34.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.211s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 6 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.008s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.317s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:22.342558, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.345471).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 7/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 758/2662 attempted swaps (28.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.206s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 7 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.035s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.314s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:17.964530, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.284069).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 8/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 946/2662 attempted swaps (35.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.215s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.073s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 8 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.327s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:13.628557, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.277290).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 9/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 776/2662 attempted swaps (29.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.210s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 9 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.319s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:09.282271, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.253549).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 10/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 774/2662 attempted swaps (29.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.226s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 10 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.017s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.038s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.339s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:04.970477, at 2022-Oct-31-11:13:13 (consuming total wall clock time 0:01:48.284128).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 11/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1036/2662 attempted swaps (38.9%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.189s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 11 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.035s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.300s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:01:00.603835, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.221134).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 12/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 974/2662 attempted swaps (36.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.184s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 12 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.008s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.029s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.286s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:56.231997, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.138455).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 13/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 898/2662 attempted swaps (33.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.193s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 13 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.306s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:51.890028, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.104225).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 14/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 828/2662 attempted swaps (31.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.174s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.068s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 14 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.281s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:47.534636, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.033263).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 15/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 892/2662 attempted swaps (33.5%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.186s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 15 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.012s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.295s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:43.198154, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:47.995385).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 16/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 974/2662 attempted swaps (36.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.217s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 16 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.009s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.326s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:38.883441, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.009557).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 17/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 694/2662 attempted swaps (26.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.243s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 17 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.038s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.355s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:34.581120, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.066000).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 18/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1054/2662 attempted swaps (39.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.243s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 18 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.015s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.038s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.356s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:30.272418, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.115779).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 19/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 722/2662 attempted swaps (27.1%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.183s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 19 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.014s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.035s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.296s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:25.939698, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.082074).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 20/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1084/2662 attempted swaps (40.7%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.191s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 20 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.033s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.302s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:21.611965, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.059826).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 21/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 804/2662 attempted swaps (30.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.209s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.071s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 21 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.032s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.317s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:17.289123, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.057016).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 22/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 814/2662 attempted swaps (30.6%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.205s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.075s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 22 not on the Checkpoint Interval of 50. Sampler State not written.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.319s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:12.966980, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.058165).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 23/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 1018/2662 attempted swaps (38.2%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.218s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.072s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 23 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.035s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.331s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:08.645710, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.071380).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 24/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 900/2662 attempted swaps (33.8%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.231s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.070s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 24 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.011s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.034s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.341s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:04.323758, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.093939).\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration 25/25\n", - "DEBUG:openmmtools.multistate.multistatesampler:********************************************************************************\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.replicaexchange:Mixing replicas...\n", - "DEBUG:openmmtools.utils:Mixing of replicas took 0.000s\n", - "DEBUG:openmmtools.multistate.replicaexchange:Accepted 648/2662 attempted swaps (24.3%)\n", - "DEBUG:openmmtools.multistate.multistatesampler:Propagating all replicas...\n", - "DEBUG:mpiplus.mpiplus:Running _propagate_replica serially.\n", - "DEBUG:mpiplus.mpiplus:Running _get_replica_move_statistics serially.\n", - "DEBUG:openmmtools.utils:Propagating all replicas took 4.249s\n", - "DEBUG:mpiplus.mpiplus:Running _compute_replica_energies serially.\n", - "DEBUG:openmmtools.utils:Computing energy matrix took 0.069s\n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:mpiplus.mpiplus:Single node: executing \n", - "DEBUG:openmmtools.multistate.multistatereporter:Iteration 25 not on the Checkpoint Interval of 50. Sampler State not written.\n", - "DEBUG:openmmtools.utils:Storing sampler states took 0.008s\n", - "DEBUG:openmmtools.utils:Writing iteration information to storage took 0.029s\n", - "DEBUG:openmmtools.multistate.multistatesampler:No online analysis requested\n", - "DEBUG:openmmtools.multistate.multistatesampler:Iteration took 4.352s.\n", - "DEBUG:openmmtools.multistate.multistatesampler:Estimated completion in 0:00:00, at 2022-Oct-31-11:13:12 (consuming total wall clock time 0:01:48.125912).\n" - ] - } - ], + "outputs": [], "source": [ - "# Next the ligand transformation\n", - "solvent_dag_results = execute_DAG(solvent_dag, shared='./solvent')" + "# Next the solvent state transformation\n", + "solvent_path = pathlib.Path('./solvent')\n", + "solvent_path.mkdir()\n", + "\n", + "solvent_dag_results = execute_DAG(solvent_dag, scratch_basedir=solvent_path, shared_basedir=solvent_path)" ] }, { @@ -6834,341 +1534,14 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "id": "fd1332db", "metadata": { "tags": [ "nbval-skip" ] }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistateanalyzer:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatereporter:analysis_particle_indices != on-file analysis_particle_indices!Using on file analysis indices of [ 0 1 2 ... 37553 37554 37555]\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistateanalyzer:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatereporter:analysis_particle_indices != on-file analysis_particle_indices!Using on file analysis indices of [ 0 1 2 ... 37553 37554 37555]\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistateanalyzer:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatereporter:analysis_particle_indices != on-file analysis_particle_indices!Using on file analysis indices of [ 0 1 2 ... 37553 37554 37555]\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistateanalyzer:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatereporter:analysis_particle_indices != on-file analysis_particle_indices!Using on file analysis indices of [ 0 1 2 ... 2209 2210 2211]\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistateanalyzer:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatereporter:analysis_particle_indices != on-file analysis_particle_indices!Using on file analysis indices of [ 0 1 2 ... 2209 2210 2211]\n", - "WARNING:openmmtools.multistate.multistatereporter:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "WARNING:openmmtools.multistate.multistateanalyzer:Warning: The openmmtools.multistate API is experimental and may change in future releases\n", - "DEBUG:openmmtools.multistate.multistatereporter:analysis_particle_indices != on-file analysis_particle_indices!Using on file analysis indices of [ 0 1 2 ... 2209 2210 2211]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Checking if we need to unbias the restraint...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Trying to get radially symmetric restraint data...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Retrieving end thermodynamic states...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Isolating restraint force...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:No force of type could be found. The restraint will not be unbiased.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Reading energies...\n", - "DEBUG:openmmtools.multistate.multistatereporter:read_replica_thermodynamic_states: iteration = [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling effective timeseries...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Could not find t0: Online Analysis information was never written!\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Equilibration data:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: number of iterations discarded to equilibration : 1\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: statistical inefficiency of production region : 1.0\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: effective number of uncorrelated samples : 26.0\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling uncorrelated energies...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing free energy differences...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing covariance matrix...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Deltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 -1.386 -2.275 -2.813 -3.799 -5.230 -2.853 -0.406 2.055 4.377 6.284\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.386 0.000 -0.889 -1.428 -2.413 -3.844 -1.467 0.980 3.440 5.762 7.670\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.275 0.889 0.000 -0.538 -1.523 -2.954 -0.578 1.869 4.330 6.652 8.559\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.813 1.428 0.538 0.000 -0.985 -2.416 -0.040 2.407 4.868 7.190 9.098\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.799 2.413 1.523 0.985 0.000 -1.431 0.945 3.392 5.853 8.175 10.083\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 5.230 3.844 2.954 2.416 1.431 0.000 2.376 4.823 7.284 9.606 11.514\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.853 1.467 0.578 0.040 -0.945 -2.376 0.000 2.447 4.908 7.230 9.137\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.406 -0.980 -1.869 -2.407 -3.392 -4.823 -2.447 0.000 2.461 4.783 6.690\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.055 -3.440 -4.330 -4.868 -5.853 -7.284 -4.908 -2.461 0.000 2.322 4.229\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -4.377 -5.762 -6.652 -7.190 -8.175 -9.606 -7.230 -4.783 -2.322 0.000 1.908\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -6.284 -7.670 -8.559 -9.098 -10.083 -11.514 -9.137 -6.690 -4.229 -1.908 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:dDeltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 0.025 0.072 0.147 0.196 0.204 0.204 0.208 0.221 0.250 0.310\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.025 0.000 0.051 0.132 0.184 0.193 0.193 0.198 0.211 0.241 0.304\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.072 0.051 0.000 0.089 0.149 0.159 0.159 0.165 0.181 0.216 0.284\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.147 0.132 0.089 0.000 0.072 0.085 0.086 0.099 0.125 0.172 0.252\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.196 0.184 0.149 0.072 0.000 0.020 0.035 0.064 0.102 0.157 0.243\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.204 0.193 0.159 0.085 0.020 0.000 0.034 0.066 0.105 0.160 0.244\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.204 0.193 0.159 0.086 0.035 0.034 0.000 0.035 0.078 0.139 0.231\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.208 0.198 0.165 0.099 0.064 0.066 0.035 0.000 0.045 0.114 0.213\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.221 0.211 0.181 0.125 0.102 0.105 0.078 0.045 0.000 0.074 0.183\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.250 0.241 0.216 0.172 0.157 0.160 0.139 0.114 0.074 0.000 0.117\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.310 0.304 0.284 0.252 0.243 0.244 0.231 0.213 0.183 0.117 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Checking if we need to unbias the restraint...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Trying to get radially symmetric restraint data...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Retrieving end thermodynamic states...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Isolating restraint force...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:No force of type could be found. The restraint will not be unbiased.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistateanalyzer:Reading energies...\n", - "DEBUG:openmmtools.multistate.multistatereporter:read_replica_thermodynamic_states: iteration = [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling effective timeseries...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Could not find t0: Online Analysis information was never written!\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Equilibration data:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: number of iterations discarded to equilibration : 4\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: statistical inefficiency of production region : 1.493607521057129\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: effective number of uncorrelated samples : 15.398958206176758\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling uncorrelated energies...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing free energy differences...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing covariance matrix...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Deltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 -1.450 -2.499 -3.342 -4.577 -6.057 -3.615 -1.122 1.418 3.995 6.463\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.450 0.000 -1.049 -1.892 -3.127 -4.607 -2.165 0.328 2.868 5.445 7.913\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.499 1.049 0.000 -0.843 -2.078 -3.559 -1.116 1.377 3.917 6.494 8.962\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.342 1.892 0.843 0.000 -1.235 -2.715 -0.273 2.220 4.760 7.337 9.805\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 4.577 3.127 2.078 1.235 0.000 -1.481 0.962 3.455 5.995 8.572 11.040\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 6.057 4.607 3.559 2.715 1.481 0.000 2.442 4.936 7.476 10.052 12.521\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.615 2.165 1.116 0.273 -0.962 -2.442 0.000 2.493 5.033 7.610 10.078\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.122 -0.328 -1.377 -2.220 -3.455 -4.936 -2.493 0.000 2.540 5.117 7.585\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -1.418 -2.868 -3.917 -4.760 -5.995 -7.476 -5.033 -2.540 0.000 2.577 5.045\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -3.995 -5.445 -6.494 -7.337 -8.572 -10.052 -7.610 -5.117 -2.577 0.000 2.468\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -6.463 -7.913 -8.962 -9.805 -11.040 -12.521 -10.078 -7.585 -5.045 -2.468 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:dDeltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 0.027 0.084 0.162 0.200 0.211 0.211 0.219 0.234 0.254 0.305\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.027 0.000 0.061 0.145 0.186 0.197 0.197 0.207 0.222 0.244 0.297\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.084 0.061 0.000 0.091 0.139 0.152 0.153 0.166 0.186 0.212 0.271\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.162 0.145 0.091 0.000 0.057 0.074 0.080 0.105 0.136 0.172 0.241\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.200 0.186 0.139 0.057 0.000 0.023 0.047 0.087 0.125 0.165 0.237\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.211 0.197 0.152 0.074 0.023 0.000 0.046 0.088 0.128 0.167 0.239\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.211 0.197 0.153 0.080 0.047 0.046 0.000 0.045 0.088 0.133 0.215\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.219 0.207 0.166 0.105 0.087 0.088 0.045 0.000 0.046 0.096 0.189\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.234 0.222 0.186 0.136 0.125 0.128 0.088 0.046 0.000 0.054 0.160\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.254 0.244 0.212 0.172 0.165 0.167 0.133 0.096 0.054 0.000 0.121\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.305 0.297 0.271 0.241 0.237 0.239 0.215 0.189 0.160 0.121 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Checking if we need to unbias the restraint...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Trying to get radially symmetric restraint data...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Retrieving end thermodynamic states...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Isolating restraint force...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:No force of type could be found. The restraint will not be unbiased.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Reading energies...\n", - "DEBUG:openmmtools.multistate.multistatereporter:read_replica_thermodynamic_states: iteration = [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling effective timeseries...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Could not find t0: Online Analysis information was never written!\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Equilibration data:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: number of iterations discarded to equilibration : 10\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: statistical inefficiency of production region : 1.0\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: effective number of uncorrelated samples : 17.0\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling uncorrelated energies...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing free energy differences...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing covariance matrix...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Deltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 -1.388 -2.261 -2.829 -3.735 -5.113 -2.754 -0.320 2.162 4.601 6.760\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.388 0.000 -0.873 -1.441 -2.347 -3.725 -1.366 1.068 3.549 5.989 8.148\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.261 0.873 0.000 -0.568 -1.474 -2.852 -0.493 1.941 4.423 6.862 9.021\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.829 1.441 0.568 0.000 -0.906 -2.284 0.075 2.509 4.991 7.430 9.589\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.735 2.347 1.474 0.906 0.000 -1.378 0.981 3.415 5.896 8.336 10.495\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 5.113 3.725 2.852 2.284 1.378 0.000 2.359 4.793 7.274 9.714 11.873\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.754 1.366 0.493 -0.075 -0.981 -2.359 0.000 2.434 4.915 7.355 9.514\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.320 -1.068 -1.941 -2.509 -3.415 -4.793 -2.434 0.000 2.482 4.921 7.080\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.162 -3.549 -4.423 -4.991 -5.896 -7.274 -4.915 -2.482 0.000 2.439 4.598\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -4.601 -5.989 -6.862 -7.430 -8.336 -9.714 -7.355 -4.921 -2.439 0.000 2.159\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -6.760 -8.148 -9.021 -9.589 -10.495 -11.873 -9.514 -7.080 -4.598 -2.159 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:dDeltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 0.032 0.097 0.178 0.231 0.244 0.242 0.248 0.262 0.290 0.356\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.032 0.000 0.072 0.160 0.218 0.232 0.230 0.237 0.251 0.280 0.348\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.097 0.072 0.000 0.099 0.174 0.190 0.188 0.197 0.214 0.248 0.323\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.178 0.160 0.099 0.000 0.098 0.117 0.117 0.131 0.157 0.201 0.289\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.231 0.218 0.174 0.098 0.000 0.029 0.043 0.080 0.120 0.175 0.272\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.244 0.232 0.190 0.117 0.029 0.000 0.044 0.084 0.125 0.179 0.274\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.242 0.230 0.188 0.117 0.043 0.044 0.000 0.042 0.088 0.150 0.255\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.248 0.237 0.197 0.131 0.080 0.084 0.042 0.000 0.048 0.117 0.233\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.262 0.251 0.214 0.157 0.120 0.125 0.088 0.048 0.000 0.074 0.201\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.290 0.280 0.248 0.201 0.175 0.179 0.150 0.117 0.074 0.000 0.137\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.356 0.348 0.323 0.289 0.272 0.274 0.255 0.233 0.201 0.137 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Checking if we need to unbias the restraint...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Trying to get radially symmetric restraint data...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Retrieving end thermodynamic states...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Isolating restraint force...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:No force of type could be found. The restraint will not be unbiased.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Reading energies...\n", - "DEBUG:openmmtools.multistate.multistatereporter:read_replica_thermodynamic_states: iteration = [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling effective timeseries...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Could not find t0: Online Analysis information was never written!\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Equilibration data:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: number of iterations discarded to equilibration : 7\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: statistical inefficiency of production region : 1.2979750633239746\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: effective number of uncorrelated samples : 15.408616065979004\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling uncorrelated energies...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing free energy differences...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing covariance matrix...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Deltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 -0.749 -1.257 -1.839 -2.717 -3.646 -1.597 0.206 1.642 2.413 2.144\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.749 0.000 -0.508 -1.090 -1.968 -2.897 -0.848 0.955 2.391 3.162 2.893\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.257 0.508 0.000 -0.582 -1.460 -2.389 -0.340 1.463 2.899 3.670 3.400\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.839 1.090 0.582 0.000 -0.878 -1.807 0.242 2.045 3.481 4.252 3.982\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.717 1.968 1.460 0.878 0.000 -0.929 1.120 2.923 4.360 5.130 4.861\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.646 2.897 2.389 1.807 0.929 0.000 2.049 3.852 5.288 6.059 5.790\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.597 0.848 0.340 -0.242 -1.120 -2.049 0.000 1.803 3.239 4.010 3.741\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -0.206 -0.955 -1.463 -2.045 -2.923 -3.852 -1.803 0.000 1.436 2.207 1.938\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -1.642 -2.391 -2.899 -3.481 -4.360 -5.288 -3.239 -1.436 0.000 0.770 0.501\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.413 -3.162 -3.670 -4.252 -5.130 -6.059 -4.010 -2.207 -0.770 0.000 -0.269\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.144 -2.893 -3.400 -3.982 -4.861 -5.790 -3.741 -1.938 -0.501 0.269 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:dDeltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 0.036 0.101 0.165 0.181 0.191 0.201 0.231 0.288 0.381 0.496\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.036 0.000 0.068 0.134 0.151 0.162 0.174 0.209 0.271 0.369 0.486\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.101 0.068 0.000 0.069 0.090 0.104 0.123 0.170 0.244 0.350 0.473\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.165 0.134 0.069 0.000 0.025 0.046 0.083 0.148 0.231 0.343 0.467\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.181 0.151 0.090 0.025 0.000 0.022 0.074 0.144 0.229 0.342 0.466\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.191 0.162 0.104 0.046 0.022 0.000 0.071 0.143 0.229 0.342 0.466\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.201 0.174 0.123 0.083 0.074 0.071 0.000 0.081 0.183 0.310 0.444\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.231 0.209 0.170 0.148 0.144 0.143 0.081 0.000 0.113 0.260 0.409\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.288 0.271 0.244 0.231 0.229 0.229 0.183 0.113 0.000 0.166 0.342\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.381 0.369 0.350 0.343 0.342 0.342 0.310 0.260 0.166 0.000 0.214\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.496 0.486 0.473 0.467 0.466 0.466 0.444 0.409 0.342 0.214 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Checking if we need to unbias the restraint...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Trying to get radially symmetric restraint data...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Retrieving end thermodynamic states...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Isolating restraint force...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:No force of type could be found. The restraint will not be unbiased.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Reading energies...\n", - "DEBUG:openmmtools.multistate.multistatereporter:read_replica_thermodynamic_states: iteration = [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling effective timeseries...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Could not find t0: Online Analysis information was never written!\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Equilibration data:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: number of iterations discarded to equilibration : 8\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: statistical inefficiency of production region : 1.0\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: effective number of uncorrelated samples : 19.0\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling uncorrelated energies...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing free energy differences...\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Complex dG: 3.8525873030019717 kcal/mol, err 0.11626215732671775 kcal/mol\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing covariance matrix...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Deltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 -0.730 -1.169 -1.637 -2.461 -3.356 -1.398 0.361 1.743 2.320 1.616\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.730 0.000 -0.438 -0.907 -1.730 -2.626 -0.668 1.092 2.474 3.050 2.346\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.169 0.438 0.000 -0.468 -1.292 -2.187 -0.230 1.530 2.912 3.489 2.784\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.637 0.907 0.468 0.000 -0.824 -1.719 0.238 1.998 3.380 3.957 3.252\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.461 1.730 1.292 0.824 0.000 -0.895 1.062 2.822 4.204 4.780 4.076\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.356 2.626 2.187 1.719 0.895 0.000 1.957 3.717 5.099 5.676 4.971\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.398 0.668 0.230 -0.238 -1.062 -1.957 0.000 1.760 3.142 3.718 3.014\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -0.361 -1.092 -1.530 -1.998 -2.822 -3.717 -1.760 0.000 1.382 1.958 1.254\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -1.743 -2.474 -2.912 -3.380 -4.204 -5.099 -3.142 -1.382 0.000 0.577 -0.128\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.320 -3.050 -3.489 -3.957 -4.780 -5.676 -3.718 -1.958 -0.577 0.000 -0.704\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -1.616 -2.346 -2.784 -3.252 -4.076 -4.971 -3.014 -1.254 0.128 0.704 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:dDeltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 0.033 0.093 0.159 0.177 0.186 0.195 0.223 0.282 0.382 0.492\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.033 0.000 0.063 0.134 0.154 0.163 0.173 0.205 0.268 0.371 0.484\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.093 0.063 0.000 0.077 0.100 0.111 0.126 0.167 0.240 0.352 0.470\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.159 0.134 0.077 0.000 0.027 0.044 0.076 0.135 0.220 0.339 0.460\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.177 0.154 0.100 0.027 0.000 0.020 0.065 0.130 0.217 0.337 0.459\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.186 0.163 0.111 0.044 0.020 0.000 0.061 0.127 0.216 0.336 0.458\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.195 0.173 0.126 0.076 0.065 0.061 0.000 0.074 0.177 0.310 0.440\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.223 0.205 0.167 0.135 0.130 0.127 0.074 0.000 0.113 0.265 0.408\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.282 0.268 0.240 0.220 0.217 0.216 0.177 0.113 0.000 0.174 0.344\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.382 0.371 0.352 0.339 0.337 0.336 0.310 0.265 0.174 0.000 0.205\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.492 0.484 0.470 0.460 0.459 0.458 0.440 0.408 0.344 0.205 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Checking if we need to unbias the restraint...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Trying to get radially symmetric restraint data...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Retrieving end thermodynamic states...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Isolating restraint force...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:No force of type could be found. The restraint will not be unbiased.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Reading energies...\n", - "DEBUG:openmmtools.multistate.multistatereporter:read_replica_thermodynamic_states: iteration = [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", - " 24 25]\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling effective timeseries...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Could not find t0: Online Analysis information was never written!\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Equilibration data:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: number of iterations discarded to equilibration : 4\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: statistical inefficiency of production region : 1.2276790142059326\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: effective number of uncorrelated samples : 18.734539031982422\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Assembling uncorrelated energies...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing free energy differences...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Done.\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Computing covariance matrix...\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:Deltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 -0.717 -1.100 -1.525 -2.384 -3.327 -1.298 0.551 1.951 2.492 2.054\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.717 0.000 -0.383 -0.808 -1.666 -2.610 -0.580 1.269 2.669 3.209 2.771\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.100 0.383 0.000 -0.425 -1.283 -2.227 -0.198 1.651 3.052 3.592 3.154\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.525 0.808 0.425 0.000 -0.858 -1.802 0.228 2.077 3.477 4.017 3.579\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 2.384 1.666 1.283 0.858 0.000 -0.943 1.086 2.935 4.335 4.875 4.437\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 3.327 2.610 2.227 1.802 0.943 0.000 2.029 3.878 5.278 5.819 5.381\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 1.298 0.580 0.198 -0.228 -1.086 -2.029 0.000 1.849 3.249 3.790 3.351\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -0.551 -1.269 -1.651 -2.077 -2.935 -3.878 -1.849 0.000 1.400 1.941 1.502\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -1.951 -2.669 -3.052 -3.477 -4.335 -5.278 -3.249 -1.400 0.000 0.540 0.102\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.492 -3.209 -3.592 -4.017 -4.875 -5.819 -3.790 -1.941 -0.540 0.000 -0.438\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: -2.054 -2.771 -3.154 -3.579 -4.437 -5.381 -3.351 -1.502 -0.102 0.438 0.000\n", - "DEBUG:openmmtools.multistate.multistateanalyzer:dDeltaf_ij:\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.000 0.038 0.100 0.175 0.196 0.207 0.210 0.234 0.295 0.390 0.476\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.038 0.000 0.068 0.149 0.172 0.183 0.187 0.214 0.280 0.379 0.466\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.100 0.068 0.000 0.088 0.114 0.128 0.135 0.171 0.250 0.357 0.449\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.175 0.149 0.088 0.000 0.031 0.051 0.072 0.130 0.226 0.342 0.437\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.196 0.172 0.114 0.031 0.000 0.022 0.059 0.125 0.224 0.341 0.436\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.207 0.183 0.128 0.051 0.022 0.000 0.059 0.127 0.225 0.342 0.437\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.210 0.187 0.135 0.072 0.059 0.059 0.000 0.075 0.188 0.317 0.418\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.234 0.214 0.171 0.130 0.125 0.127 0.075 0.000 0.126 0.271 0.383\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.295 0.280 0.250 0.226 0.224 0.225 0.188 0.126 0.000 0.162 0.302\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.390 0.379 0.357 0.342 0.341 0.342 0.317 0.271 0.162 0.000 0.176\n", - "DEBUG:openmmtools.multistate.multistateanalyzer: 0.476 0.466 0.449 0.437 0.436 0.437 0.418 0.383 0.302 0.176 0.000\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Solvent dG: 1.147969963054243 kcal/mol, err 0.13666481503768743 kcal/mol\n" - ] - } - ], + "outputs": [], "source": [ "# Get the complex and solvent results\n", "complex_results = rbfe_transform.gather([complex_dag_results])\n", @@ -7203,7 +1576,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.10" } }, "nbformat": 4, From d179e8243c6ee0300b14d4ef8383f7f861c14adc Mon Sep 17 00:00:00 2001 From: richard gowers Date: Wed, 12 Apr 2023 09:27:04 +0100 Subject: [PATCH 09/14] env updates for v0.7.1 --- .binder/environment.yml | 13 +++---------- 1 file changed, 3 insertions(+), 10 deletions(-) diff --git a/.binder/environment.yml b/.binder/environment.yml index bceb41b..fdd5f86 100644 --- a/.binder/environment.yml +++ b/.binder/environment.yml @@ -2,7 +2,6 @@ name: openfe-notebooks channels: - jaimergp/label/unsupported-cudatoolkit-shim - conda-forge - - openeye dependencies: - MDAnalysis - click @@ -29,12 +28,6 @@ dependencies: - python==3.9.* - rdkit - typing_extensions - - gufe==0.7.* - - openfe==0.7.* - - ## needed for perses - - openmoltools - - cloudpathlib - - dask - - distributed - - openeye-toolkits + - gufe>=0.7.1 + - openfe>=0.7.1 + \ No newline at end of file From 866cc1c93538a8bbb02d4daa0cbd51e15e3f3dab Mon Sep 17 00:00:00 2001 From: richard gowers Date: Wed, 12 Apr 2023 09:45:08 +0100 Subject: [PATCH 10/14] changes from #40 --- networks/ligand_networks_for_developers.ipynb | 36 ++++++++++++++----- 1 file changed, 28 insertions(+), 8 deletions(-) diff --git a/networks/ligand_networks_for_developers.ipynb b/networks/ligand_networks_for_developers.ipynb index 0bac65b..4b15dcb 100644 --- a/networks/ligand_networks_for_developers.ipynb +++ b/networks/ligand_networks_for_developers.ipynb @@ -96,9 +96,8 @@ "# It includes a parameter to remove the middle atom (if True) or the final atom (if False).\n", "\n", "import networkx as nx\n", - "from typing import Dict\n", "\n", - "def my_mapping_function(molA: Chem.Mol, molB: Chem.Mol, remove_middle: bool) -> Dict[int, int]:\n", + "def my_mapping_function(molA: Chem.Mol, molB: Chem.Mol, remove_middle: bool) -> dict[int, int]:\n", " # here remove_middle takes the place of whatever parameters your method takes \n", " \n", " # always be sure to consider the case that your mapping function fails\n", @@ -566,7 +565,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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", 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", 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" ] @@ -678,7 +677,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -866,7 +865,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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" ] @@ -934,7 +933,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -950,10 +949,31 @@ "plot_atommapping_network(new_network)" ] }, + { + "cell_type": "markdown", + "id": "d838ef4b", + "metadata": {}, + "source": [ + "## Writing a ligand network to disk\n", + "\n", + "If you want to save a ligand network to disk in order to transfer it elsewhere, you can do so with the `to_graphml()` method." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "a04df5eb", + "metadata": {}, + "outputs": [], + "source": [ + "with open(\"network.graphml\", mode='w') as f:\n", + " f.write(new_network.to_graphml())" + ] + }, { "cell_type": "code", "execution_count": null, - "id": "a0c364c1", + "id": "960e68f0", "metadata": {}, "outputs": [], "source": [] From ba4b68a46cbb030523f8fb79b176ede5b27398eb Mon Sep 17 00:00:00 2001 From: richard gowers Date: Wed, 12 Apr 2023 09:48:32 +0100 Subject: [PATCH 11/14] removed old setup notebooks --- setup/Lomap_OpenFE_Comparison.ipynb | 284 ------------------------- setup/SystemCreation.ipynb | 214 ------------------- setup/openFE_AtomMappers.ipynb | 317 ---------------------------- 3 files changed, 815 deletions(-) delete mode 100644 setup/Lomap_OpenFE_Comparison.ipynb delete mode 100644 setup/SystemCreation.ipynb delete mode 100644 setup/openFE_AtomMappers.ipynb diff --git a/setup/Lomap_OpenFE_Comparison.ipynb b/setup/Lomap_OpenFE_Comparison.ipynb deleted file mode 100644 index a038051..0000000 --- a/setup/Lomap_OpenFE_Comparison.ipynb +++ /dev/null @@ -1,284 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "b8205f11", - "metadata": {}, - "source": [ - "## Comparing Lomap and OpenFE usage\n", - "\n", - "This notebook demonstrates how to recreate the behaviour of Lomap2 using the OpenFE package.\n", - "\n", - "Firstly Lomap, which just takes a path to where the molecules are held:" - ] - }, - { - "cell_type": "code", - "execution_count": 49, - "id": "52e2f9e8", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0. 0.20189652 0.90483742 0.74081822 0.81873075 0.77880078\n", - " 0.52204578 0.22313016 0.54881164 0.54881164]\n", - " [0.20189652 0. 0.22313016 0.27253179 0.24659696 0.23457029\n", - " 0.67032005 0.27253179 0.67032005 0.67032005]\n", - " [0.90483742 0.22313016 0. 0.81873075 0.90483742 0.86070798\n", - " 0.4965853 0.24659696 0.60653066 0.4965853 ]\n", - " [0.74081822 0.27253179 0.81873075 0. 0.74081822 0.70468809\n", - " 0.27253179 0.20189652 0.4965853 0.27253179]\n", - " [0.81873075 0.24659696 0.90483742 0.74081822 0. 0.95122942\n", - " 0.30119421 0.27253179 0.67032005 0.30119421]\n", - " [0.77880078 0.23457029 0.86070798 0.70468809 0.95122942 0.\n", - " 0.52204578 0.25924026 0.63762815 0.52204578]\n", - " [0.52204578 0.67032005 0.4965853 0.27253179 0.30119421 0.52204578\n", - " 0. 0.33287108 0.81873075 0.95122942]\n", - " [0.22313016 0.27253179 0.24659696 0.20189652 0.27253179 0.25924026\n", - " 0.33287108 0. 0.40656966 0.33287108]\n", - " [0.54881164 0.67032005 0.60653066 0.4965853 0.67032005 0.63762815\n", - " 0.81873075 0.40656966 0. 0.81873075]\n", - " [0.54881164 0.67032005 0.4965853 0.27253179 0.30119421 0.52204578\n", - " 0.95122942 0.33287108 0.81873075 0. ]]\n" - ] - } - ], - "source": [ - "import lomap\n", - "\n", - "lomap_mols = lomap.DBMolecules('./molecules', output=True, radial=True)\n", - "\n", - "strict, loose = lomap_mols.build_matrices()\n", - "\n", - "strict = strict.to_numpy_2D_array()\n", - "\n", - "print(strict)" - ] - }, - { - "cell_type": "markdown", - "id": "75d32699", - "metadata": {}, - "source": [ - "### OpenFE version\n", - "\n", - "Now moving onto recreating this in OpenFE.\n", - "This will involve a few more steps than with Lomap, as various tasks (such as reading files) have been made explicit.\n", - "This is slightly tedious, but allows the process to be much more powerful as with each step explicity, these components can be switched out and a wide variety of different techniques combined." - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "id": "d06e63f0", - "metadata": {}, - "outputs": [], - "source": [ - "import openfe\n", - "import os\n", - "from rdkit import Chem\n", - "import numpy as np\n", - "import itertools\n", - "\n", - "# load molecules in the same order as Lomap did\n", - "fnames = [os.path.join('molecules', lomap_mols[i].getName())\n", - " for i in range(10)]\n", - "\n", - "smallmols = []\n", - "for fname in fnames:\n", - " if 'mol2' in fname:\n", - " m = Chem.MolFromMol2File(fname, removeHs=False)\n", - " else:\n", - " m = Chem.MolFromMolFile(fname, removeHs=False)\n", - " # OpenFE lightly wraps rdkit molecules\n", - " # to make them hashable and immutable\n", - " smallmols.append(openfe.setup.SmallMoleculeComponent.from_rdkit(m))" - ] - }, - { - "cell_type": "markdown", - "id": "e44f6e5c", - "metadata": {}, - "source": [ - "### Simple Mapper usage\n", - "\n", - "To create the scoring matrix in OpenFE, first the `mapper` and `scorer` objects must be created.\n", - "These are separate objects representing the separation in responsibility between proposing and evaluating an atom mapping.\n", - "The `LomapAtomMapper` class and `default_lomap_score` function implement the default Lomap atom mapper and scoring function respectively,\n", - "and so are used here for the back to back validation.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "id": "7551f09d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Score is 0.7981034820053445\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# LomapAtomMapper wraps the lomap.mcs.MCS object\n", - "mapper = openfe.setup.LomapAtomMapper()\n", - "scorer = openfe.setup.lomap_scorers.default_lomap_score\n", - "\n", - "molA = smallmols[0]\n", - "molB = smallmols[1]\n", - "\n", - "# mappers can return an *iterable* of mappings,\n", - "# for our case just grab the first with `next()`\n", - "mapping = next(mapper.suggest_mappings(molA, molB))\n", - "\n", - "print(f\"Score is {scorer(mapping)}\")\n", - "\n", - "mapping" - ] - }, - { - "cell_type": "markdown", - "id": "f60b42b4", - "metadata": {}, - "source": [ - "### Creating the score matrix\n", - "\n", - "To fill the scores matrix, all possible combinations of molecules are iterated over (here using `itertools.combinations`) " - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "a79d3ebe", - "metadata": {}, - "outputs": [], - "source": [ - "mapper = openfe.setup.LomapAtomMapper()\n", - "scorer = openfe.setup.lomap_scorers.default_lomap_score\n", - "\n", - "scores = np.zeros((10, 10))\n", - "\n", - "for i, j in itertools.combinations(range(len(smallmols)), 2):\n", - " molA = smallmols[i]\n", - " molB = smallmols[j]\n", - " \n", - " mapping = next(mapper.suggest_mappings(molA, molB))\n", - " \n", - " score = scorer(mapping)\n", - " \n", - " scores[i, j] = score\n", - " scores[j, i] = score\n", - "# set diagonal for easier comparison\n", - "for i in range(10):\n", - " scores[i, i] = 1.0" - ] - }, - { - "cell_type": "markdown", - "id": "000f3c98", - "metadata": {}, - "source": [ - "In OpenFE higher scores are more dissimilar and unfavourable, in contrast to Lomap where low scores are \"worse\".\n", - "The default Lomap scorer in OpenFE is implemented as `1 - Lomap`.\n", - "We can then verify that the scoring matrix we have produced is identical to that of Lomap's." - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "id": "c8d438da", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[0. , 0.20189652, 0.90483742, 0.74081822, 0.81873075,\n", - " 0.77880078, 0.52204578, 0.22313016, 0.54881164, 0.54881164],\n", - " [0.20189652, 0. , 0.22313016, 0.27253179, 0.24659696,\n", - " 0.23457029, 0.67032005, 0.27253179, 0.67032005, 0.67032005],\n", - " [0.90483742, 0.22313016, 0. , 0.81873075, 0.90483742,\n", - " 0.86070798, 0.4965853 , 0.24659696, 0.60653066, 0.4965853 ],\n", - " [0.74081822, 0.27253179, 0.81873075, 0. , 0.74081822,\n", - " 0.70468809, 0.27253179, 0.20189652, 0.4965853 , 0.27253179],\n", - " [0.81873075, 0.24659696, 0.90483742, 0.74081822, 0. ,\n", - " 0.95122942, 0.30119421, 0.27253179, 0.67032005, 0.30119421],\n", - " [0.77880078, 0.23457029, 0.86070798, 0.70468809, 0.95122942,\n", - " 0. , 0.52204578, 0.25924026, 0.63762815, 0.52204578],\n", - " [0.52204578, 0.67032005, 0.4965853 , 0.27253179, 0.30119421,\n", - " 0.52204578, 0. , 0.33287108, 0.81873075, 0.95122942],\n", - " [0.22313016, 0.27253179, 0.24659696, 0.20189652, 0.27253179,\n", - " 0.25924026, 0.33287108, 0. , 0.40656966, 0.33287108],\n", - " [0.54881164, 0.67032005, 0.60653066, 0.4965853 , 0.67032005,\n", - " 0.63762815, 0.81873075, 0.40656966, 0. , 0.81873075],\n", - " [0.54881164, 0.67032005, 0.4965853 , 0.27253179, 0.30119421,\n", - " 0.52204578, 0.95122942, 0.33287108, 0.81873075, 0. ]])" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "1 - scores" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "id": "b0a90b3e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "True" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "np.allclose(strict - (1 - scores), 0)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/setup/SystemCreation.ipynb b/setup/SystemCreation.ipynb deleted file mode 100644 index 3fd106e..0000000 --- a/setup/SystemCreation.ipynb +++ /dev/null @@ -1,214 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "f48f4932", - "metadata": {}, - "source": [ - "This notebook demonstrates preparing a ligand-protein system for OpenMM using the OpenFF Sage forcefield for the ligand and Amber99SB for the protein. \n", - "\n", - "This is a stripped down version of the \"tookit_showcase\" from OpenFF Toolkit." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "4abde019", - "metadata": {}, - "outputs": [], - "source": [ - "import openmm.app\n", - "import openmm\n", - "from openmmforcefields.generators import SMIRNOFFTemplateGenerator\n", - "\n", - "from openff.toolkit.topology import Molecule\n", - "from openff.units.openmm import ensure_quantity" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "f2a4464e", - "metadata": {}, - "outputs": [], - "source": [ - "# load both the Protein and Ligand structures\n", - "# OpenMM is used to correctly interpret the protein structure\n", - "# OpenFF is used for the ligand to prepare for the upcoming forcefield parametrisation\n", - "\n", - "protein = openmm.app.PDBFile('3l9h_prepared.pdb')\n", - "ligand = Molecule('./chembl_1078774.sdf')" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "c1f70793", - "metadata": {}, - "outputs": [], - "source": [ - "# Load protein and water force field parameters\n", - "omm_forcefield = openmm.app.ForceField(\"amber99sb.xml\", \"tip3p.xml\")\n", - "# The TemplateGenerator caters for the missing parameters caused by the ligand molecule\n", - "smirnoff = SMIRNOFFTemplateGenerator(forcefield=\"openff-2.0.0.offxml\", molecules=[ligand])\n", - "omm_forcefield.registerTemplateGenerator(smirnoff.generator)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "c24016c8", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([[ 12.81459999, 0.80800003, 0.0689 ],\n", - " [ 11.91940022, -0.14740001, -0.75419998],\n", - " [ 10.46850014, 0.366 , -0.77810001],\n", - " [ 12.47529984, -0.2208 , -2.17810011],\n", - " [ 11.88860035, -1.579 , -0.1851 ],\n", - " [ 10.85159969, -2.45409989, -0.54689997],\n", - " [ 10.79469967, -3.74349999, -0.055 ],\n", - " [ 11.81169987, -4.20359993, 0.76440001],\n", - " [ 12.88070011, -3.36919999, 1.1329 ],\n", - " [ 12.88199997, -2.0552001 , 0.67629999],\n", - " [ 14.02859974, -3.87409997, 1.98640001],\n", - " [ 13.80760002, -3.44849992, 3.33260012],\n", - " [ 14.87580013, -3.7894001 , 4.21649981],\n", - " [ 15.07149982, -5.31809998, 4.25670004],\n", - " [ 15.23139954, -5.90089989, 2.84360003],\n", - " [ 14.11810017, -5.40789986, 1.8901 ],\n", - " [ 12.71850014, -6.04879999, 2.12649989],\n", - " [ 12.76119995, -7.5479002 , 2.03509998],\n", - " [ 13.24530029, -8.18929958, 0.8854 ],\n", - " [ 13.32120037, -9.57759953, 0.82969999],\n", - " [ 12.92739964, -10.31200027, 1.94770002],\n", - " [ 12.97550011, -11.66499996, 2.09100008],\n", - " [ 12.43039989, -9.66860008, 3.04439998],\n", - " [ 12.05770016, -10.44390011, 4.06949997],\n", - " [ 12.32260036, -8.30210018, 3.12100005],\n", - " [ 11.74339962, -5.53609991, 1.17820001],\n", - " [ 12.53250027, 0.88330001, 1.11500001],\n", - " [ 12.86839962, 1.81690001, -0.3389 ],\n", - " [ 13.81429958, 0.41060001, 0.0939 ],\n", - " [ 10.13119984, 0.32910001, 0.2282 ],\n", - " [ 9.78540039, -0.19939999, -1.39839995],\n", - " [ 10.29360008, 1.36450005, -1.13310003],\n", - " [ 13.47259998, -0.65890002, -2.22469997],\n", - " [ 12.52509975, 0.73290002, -2.6947999 ],\n", - " [ 11.78849983, -0.8617 , -2.70530009],\n", - " [ 10.05770016, -2.16400003, -1.21179998],\n", - " [ 9.96360016, -4.37650013, -0.32870001],\n", - " [ 13.71100044, -1.43970001, 0.98470002],\n", - " [ 14.95680046, -3.43969989, 1.60819995],\n", - " [ 15.79819965, -3.31590009, 3.87330008],\n", - " [ 14.66450024, -3.39910007, 5.2118001 ],\n", - " [ 15.94480038, -5.56930017, 4.85820007],\n", - " [ 14.21319962, -5.77799988, 4.74779987],\n", - " [ 16.20000076, -5.6072998 , 2.43330002],\n", - " [ 15.25450039, -6.98859978, 2.89639997],\n", - " [ 14.43599987, -5.62919998, 0.87050003],\n", - " [ 12.36260033, -5.78329992, 3.12590003],\n", - " [ 13.56330013, -7.58650017, 0.0421 ],\n", - " [ 13.69449997, -10.04459953, -0.0712 ],\n", - " [ 13.16170025, -12.20320034, 1.31040001],\n", - " [ 11.93050003, -7.84789991, 4.01690006],\n", - " [ 10.88700008, -6.06949997, 1.10500002]]) ]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ligand.conformers" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "628798e0", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning: Cannot perform Hydrogen sampling with GPU-Omega: GPU-Omega disabled.\n" - ] - } - ], - "source": [ - "# Combine structures and solvate\n", - "modeller = openmm.app.Modeller(protein.topology, protein.positions)\n", - "modeller.add(ligand.to_topology().to_openmm(),\n", - " ensure_quantity(ligand.conformers[0], 'openmm'))\n", - "\n", - "# solvate it in 0.15 M NaCl solution\n", - "modeller.addSolvent(\n", - " omm_forcefield,\n", - " model=\"tip3p\",\n", - " padding=4.0 * openmm.unit.angstrom, # 4.0 not enough, but works quickly\n", - " ionicStrength=0.15 * openmm.unit.molar,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "2a824d39", - "metadata": {}, - "outputs": [], - "source": [ - "system = omm_forcefield.createSystem(\n", - " modeller.getTopology(),\n", - " nonbondedMethod=openmm.app.PME,\n", - " nonbondedCutoff=9 * openmm.unit.angstrom,\n", - " constraints=openmm.app.HBonds,\n", - " rigidWater=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "96ef01a5", - "metadata": {}, - "outputs": [], - "source": [ - "# Create a Simulation from our newly prepared System\n", - "# Construct and configure a Langevin integrator at 300 K with an appropriate friction constant and time-step\n", - "integrator = openmm.LangevinIntegrator(\n", - " 300 * openmm.unit.kelvin, 1 / openmm.unit.picosecond, 0.002 * openmm.unit.picoseconds\n", - ")\n", - "\n", - "# Combine the topology, system, integrator and initial positions into a simulation\n", - "simulation = openmm.app.Simulation(modeller.getTopology(), system, integrator)\n", - "simulation.context.setPositions(modeller.getPositions())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/setup/openFE_AtomMappers.ipynb b/setup/openFE_AtomMappers.ipynb deleted file mode 100644 index 7d65f62..0000000 --- a/setup/openFE_AtomMappers.ipynb +++ /dev/null @@ -1,317 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "7ec68925-2b50-4d6f-9d09-4901e19ad77e", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "4022db68-4b97-461c-b8cd-cef265158903", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[0. 0.20189652 0.90483742 0.74081822 0.81873075 0.77880078\n", - " 0.52204578 0.22313016 0.54881164 0.54881164]\n", - " [0.20189652 0. 0.22313016 0.27253179 0.24659696 0.23457029\n", - " 0.67032005 0.27253179 0.67032005 0.67032005]\n", - " [0.90483742 0.22313016 0. 0.81873075 0.90483742 0.86070798\n", - " 0.4965853 0.24659696 0.60653066 0.4965853 ]\n", - " [0.74081822 0.27253179 0.81873075 0. 0.74081822 0.70468809\n", - " 0.27253179 0.20189652 0.4965853 0.27253179]\n", - " [0.81873075 0.24659696 0.90483742 0.74081822 0. 0.95122942\n", - " 0.30119421 0.27253179 0.67032005 0.30119421]\n", - " [0.77880078 0.23457029 0.86070798 0.70468809 0.95122942 0.\n", - " 0.52204578 0.25924026 0.63762815 0.52204578]\n", - " [0.52204578 0.67032005 0.4965853 0.27253179 0.30119421 0.52204578\n", - " 0. 0.33287108 0.81873075 0.95122942]\n", - " [0.22313016 0.27253179 0.24659696 0.20189652 0.27253179 0.25924026\n", - " 0.33287108 0. 0.40656966 0.33287108]\n", - " [0.54881164 0.67032005 0.60653066 0.4965853 0.67032005 0.63762815\n", - " 0.81873075 0.40656966 0. 0.81873075]\n", - " [0.54881164 0.67032005 0.4965853 0.27253179 0.30119421 0.52204578\n", - " 0.95122942 0.33287108 0.81873075 0. ]]\n" - ] - } - ], - "source": [ - "import lomap\n", - "\n", - "lomap_mols = lomap.DBMolecules('./molecules', output=True, radial=True)\n", - "\n", - "strict, loose = lomap_mols.build_matrices()\n", - "\n", - "strict = strict.to_numpy_2D_array()\n", - "\n", - "print(strict)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "141678d7-e47b-48ab-a823-f7963e5bb973", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "5741762f-4200-4c20-898e-86535edcc70f", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/riesbenj/Programs/miniconda3/envs/openfe/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for type is zero.\n", - " setattr(self, word, getattr(machar, word).flat[0])\n", - "/home/riesbenj/Programs/miniconda3/envs/openfe/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n", - " return self._float_to_str(self.smallest_subnormal)\n", - "/home/riesbenj/Programs/miniconda3/envs/openfe/lib/python3.9/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for type is zero.\n", - " setattr(self, word, getattr(machar, word).flat[0])\n", - "/home/riesbenj/Programs/miniconda3/envs/openfe/lib/python3.9/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for type is zero.\n", - " return self._float_to_str(self.smallest_subnormal)\n" - ] - } - ], - "source": [ - "import openfe\n", - "import os\n", - "from rdkit import Chem\n", - "import numpy as np\n", - "import itertools\n", - "\n", - "# load molecules in the same order as Lomap did\n", - "fnames = [os.path.join('molecules', lomap_mols[i].getName())\n", - " for i in range(10)]\n", - "\n", - "smallmols = []\n", - "for fname in fnames:\n", - " if 'mol2' in fname:\n", - " m = Chem.MolFromMol2File(fname, removeHs=False)\n", - " else:\n", - " m = Chem.MolFromMolFile(fname, removeHs=False)\n", - " # OpenFE lightly wraps rdkit molecules\n", - " # to make them hashable and immutable\n", - " smallmols.append(openfe.setup.SmallMoleculeComponent.from_rdkit(m))" - ] - }, - { - "cell_type": "markdown", - "id": "ae7c9bc1-3b8e-4943-a046-18fbb35ed6de", - "metadata": {}, - "source": [ - "# Atom Mappers\n", - "## Lomap Mapper" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "12ded123-49f3-4b55-9426-9da5caa3debc", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Score is 0.7981034820053445\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# LomapAtomMapper wraps the lomap.mcs.MCS object\n", - "mapper = openfe.setup.LomapAtomMapper()\n", - "scorer = openfe.setup.lomap_scorers.default_lomap_score\n", - "\n", - "molA = smallmols[0]\n", - "molB = smallmols[1]\n", - "\n", - "# mappers can return an *iterable* of mappings,\n", - "# for our case just grab the first with `next()`\n", - "lomap_mapping = next(mapper.suggest_mappings(molA, molB))\n", - "\n", - "print(f\"Score is {scorer(lomap_mapping)}\")\n", - "\n", - "lomap_mapping" - ] - }, - { - "cell_type": "markdown", - "id": "24030a1a-68e3-4723-92c6-bd16cfdc237e", - "metadata": {}, - "source": [ - "## Perses Mapper" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "0af3a289-7710-4c9a-aade-cebc196bbd75", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Molecules do not appear to share a common scaffold.\n", - "Proceeding with direct mapping of molecules, but please check atom mapping and the geometry of the ligands.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Score is 1.0\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# LomapAtomMapper wraps the lomap.mcs.MCS object\n", - "mapper = openfe.setup.PersesAtomMapper()\n", - "#scorer = openfe.setup.lomap_scorers.default_lomap_score\n", - "\n", - "molA = smallmols[0]\n", - "molB = smallmols[1]\n", - "\n", - "# mappers can return an *iterable* of mappings,\n", - "# for our case just grab the first with `next()`\n", - "perses_mapping = next(mapper.suggest_mappings(molA, molB))\n", - "\n", - "print(f\"Score is {scorer(perses_mapping)}\")\n", - "\n", - "perses_mapping" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "d751e147-c264-49d8-9fe2-68ee5aab59c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{10: 0, 24: 13, 25: 14, 26: 15}" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "mapper._atom_mappings.new_to_old_atom_map" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "2568d3eb-e884-4b9d-bf24-c284a492dae9", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "14" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(lomap_mapping.molA_to_molB)" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "b92ed8ee-5d06-4256-8e1f-9a155fbca702", - "metadata": {}, - "outputs": [], - "source": [ - "generic_mapping = openfe.setup.LigandAtomMapping(molA, molB, mapper._atom_mappings.new_to_old_atom_map)" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "5e3cf703-4fd9-42ff-9496-837aa69f36bc", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "generic_mapping" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "aa20a84a-0d7a-4b8f-9eee-76980c889add", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python openFE", - "language": "python", - "name": "openfe" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.12" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From f4478c1994f7b23c3e248cf0e65b13b51d93b668 Mon Sep 17 00:00:00 2001 From: richard gowers Date: Wed, 12 Apr 2023 10:17:51 +0100 Subject: [PATCH 12/14] add py3dmol to env --- .binder/environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.binder/environment.yml b/.binder/environment.yml index 84acbeb..cc640a2 100644 --- a/.binder/environment.yml +++ b/.binder/environment.yml @@ -29,3 +29,4 @@ dependencies: - typing_extensions - gufe>=0.7.1 - openfe>=0.7.1 + - py3dmol \ No newline at end of file From 9ea4988a309391e870314b842a43552e5be0f6b2 Mon Sep 17 00:00:00 2001 From: richard gowers Date: Wed, 12 Apr 2023 10:58:22 +0100 Subject: [PATCH 13/14] update benchmark_demo for 0.7.1 --- openmm-rbfe/benchmark_demo.ipynb | 205 ++++++++++++++----------------- 1 file changed, 93 insertions(+), 112 deletions(-) diff --git a/openmm-rbfe/benchmark_demo.ipynb b/openmm-rbfe/benchmark_demo.ipynb index 5341169..66e6f55 100644 --- a/openmm-rbfe/benchmark_demo.ipynb +++ b/openmm-rbfe/benchmark_demo.ipynb @@ -18,15 +18,7 @@ "execution_count": 1, "id": "77759687", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" - ] - } - ], + "outputs": [], "source": [ "import openfe\n", "from rdkit import Chem\n", @@ -142,7 +134,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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2vVhfXx8cHBwWFoZ2Y1cjk8nYbPb169eJA8e1a9da9HUjugXiAQUAiGolDWPGjDl+/Djx2s7OLjMzc968eQBgYGAwaNAgon7Jz8/vtY6P0dHRn332GYZhBw8enD59unZuAPGmIk1Nlj56ADgOAIEcZrxAfKKq4bpAcqtBInkR/LOnUwI5rEBDljebQcaAZMBhDfJjDRvFDggi6en/LQvD9CdO0584TYvq9SBD+PHHHxMvPvjgg9LSUnNzc82PfHx8rl69GhQU1E2q9SDodPrnn39eVVVF5Dpl3U8OGuYHanVnnuYQumfq1Kl37twpKyuLjY2NjY0FACqV6u3tPXToUF9fX19fX2tr62YfiY+Pd3BwcHJyAoCMjIxevXq98847gwcP/uijj7rhBhA6QK2SpqeK/romuhGjKMwHgGK5MrZefLVejAE8k8ifSuQA4Mygjjdij+Sw+rFoAECxsmGPCGIHBDF4gzCyjixUj5tQr1ar16xZEx0dnZSUhAbQdxtqVd2Jw4LjB9RSCYZhOA4Gk2YYf7aKxEI11G8cV65cOXPmTNMTYVZWFnH4Ky0tJeqXUlJS7t27p5mdCwBWVlbEMdHb23vQoEE0Gs3X11cqld67d49Coaxbt87R0XHevHkk9AD0nwOXSaVpKaKbsY2xV1TVlWqARyJZjEAcWy/mvwj+YQA4wCxT/WVWhuZUMgBQLKz1xk1iDxvF8PSGru+v2YweZwgBYMiQIUlJSREREVOmTOluXXoiuEpZ9ukH2cnJbIXUiEIGAKkaL8axPnZ2tiEXyBxUSf1mIZVKxWKxpv91WVmZpaXlq62AGxoa7t69e/v27du3b9+9e1coFGp+pK+vv3HjxnPnzrm4uPTv3/+LL74gDCE6C/7rUOTnSDPScKWS5uzKcPdsarFUgjpxwnVxQpz49g21WCRT4/dF0niBJLpOVPEi+Mchk3wNmCM5zCKZck9Z/TvGeju4pgCAUenU3/807eUEAGVlZcbGxkSev87oQa5RDZMnT05KSrpw4cKbZghVKlVSUpKbmxuLxXr48GH//v0ZDEaL7xQKhQqFwsTERC6XFxcX/7sqEOqO/Cp7lPp7YbknmzHZmA0ARXLlhsKaM3Rq1fovLff83t0KIl6CwWA0/T1srQWovr5+UFAQEV9QqVRPnz5NSUkhzouZmZkmJiYA8NVXX02ePBlFBLud8vJyuVxuZmb24MEDHx+f9lSFKivKK77+TP7sMWAYrsYxMolkaGy5/VcSx1B8M1aUECe9fxdXqQQq9W2hJE4giRWIG1XPhz9Y0yjDDZgjOcxhBkwqBgBYjlSxp6z+ulCswnEKmVzNG1LyLDvpeIiPj49KpRo8eLCODWFPPBE+efLE3d3dxMSkvLz81U5sOqCwsDA8PFxTobx169bFixebmJg8efJELpeHhYXp6+tPmDDhwoULa9eubVHCkSNHaDRacHDw/v37y8rKvvnmGx2q3znUqrwRnmqx6JuiWk82nTCE2VLFhsKa0y6WGINhHxZDsbbtbi0R2qS8vJzFYo0dOzYsLOzWrVuhoaF9+vRpeiK8ceNGdnY2g8GYO3du96raE8BxfOvWrSwWa/DgwQwGIzY2dvXq1W1/RFVbUzxrbHxBiTkZ+jDpAFCnVMUKJNPNDECtBoASuTJBKIkXSG4KJcoXNsWZQR3FYb3FYXrrMTAAsrEJYCR1gxCXywBg9JOSXKnilIvlII5er+upEsB+/vnngICAZ8+e6evrz5gxo4v/GV6iJzro+/bt6+LiUlNTc+fOnW5RoLq6+sqVK5pvIyIiCD9S3759TU1Nra2tuVzusWPH7OzsWpNA1Bs8ffrU0tKSw+HoQGdtIS/IgxfelIci2ZU60ZU6UYJQQlzByBRp+oNWP4z4d2JpaakpWJoxY4ZQKLxx40bTNygUimfPnkkkku7QrscREhJCHMp9fX1lMlnTOT+tUfXDRpWw/ma96JnkeZCvVqk+V9P4TCTdVVY/MbN0REbxusKaeIEYAPz0mRvtjG/2s43ua7PKxsjXa4Dxgk9tT1zkXkvmRt8xXrqSCH+M4rAAIKZeDCplycMHGzZsmD59OofDGTp0aFZWVhfef0v0REMIABMnTgSACxcudLciL/Ho0aN169YNHDiQz+dPnDixsLCwtXdmZWXl5+fX19dXVVXdvXv3X/QXBJfLAHv+WydUqSsVqkqFqvbFRBVcrSaeFhH/YX799df79+83vXLv3r3169fn5eV1l0o9Cg6HEx8fn5mZGR0dHRUVRTQbaQNcKhHfiMEVLcyUD69t/KWsPlMiZ5KwkRzWDq7pvf52x3pbBFsYOfoMMflsjX14nN2ZKOOPv6T39QAMAxLZcO4CbvwDiq1doMYQAghuxo8bN66qqqpXr17V1dUrV3a8NL5j9ETXKAAkJCQMHz7cycmJz+frfvUHDx6MHj1a0/L0zp07qampvXr1qqurq6uro9PplpaWWVlZzs7O1CZd+Jry5MkTiUTi5eVFIpEqKiosLCx0qH6nUIsa80d54wp5y65Rtp713j8Ynt7drSZC+5w4cWLKlCnE+JeIiAhHR0dPz+edIbOzs2NjY/v16zds2LBu1bEHIRAIpGKxgJ/FJmGW/TzaSFKTZ2WWLJypbmzYUlybKZHb0ygA0KBSVyvVX9kYhdc2BnJYQ/QZVAzD6AzmIF/28EB2QBDZ2LSN1Wt+/q425LDfo6Iqheqym3X/AQNsT13W/k22m56YLAMAfn5+FhYWOTk5jx8/dnd3170Cnp6eZ8+eJV6/9dZbxAsjIyPN9Jm2y8ybtmTUpRWMi4srLS2dNWvWn3/+SafTp06d+roSSGw95iBf8d2E1n7K8EATkv+baKr1t23bduDAgZMnT2p+1Lt37969e3eTXj0SHIerFyT7f6YqFHISqUAuo7v1M1u3TTMXSSKRhIeHz5kzR61WHw2PCHpR8z7CgEm4NAtlygMVAk823ZNNJ7HY7LdGs4YHsvwCSMx21aSxRgTVhxwOMGCG1TTGCsSuz54oSoqoNq0Gg7qaHuoaJZFI48ePh+7zjpLJZIMXdKaUSiAQJCQkJCcnE5MauxpnZ+fa2tr8/PyRI0empaV1zJ1g9vV3ZJbeIH2mI+P5c5ghmTTakAUkzHLrHlRZ/5+nqqqqoKDg4sWL3a1Iz6Vy06qMHVueVlapJSK1qAFXyK/dul0y7x3pwxTiDQcOHHj69CkAhISEZJaWY/jz/E8LKtmJQXViUO3pzzcvRqfbHj9v/s1OvcDx7bSCAMAc4E02NAoyZAFAbL0EAMQJcdq9x9ei5/7RmTx5MnSHIZTL5doSFRMT4+/vf+XKlePHjw8ePDg7O1tbkluDyWTW19cT46uMjIxeLSZrDxRLa5tjERO9+vU34gBAmkj2bXEtBgBqnGLRcmo+4r8EsfXCw8O7W5EWSE1NTUpKAoDbt2837Q/QjIaGBsJOAMC/bppNY2yUKC7qdlVdWPXf3ZW/yKtUi8XlXy7C5bLU1NSGhobGxsbs7Oz09HSMTMYnTMcYLfTPw8gUursntZfzaytBIrP8R/rqM5kkLEMsK1coRX/FdOamOknPNYSjR49msVjJycnFxcU6WzQiIsLd3Z1Go/3666+aiydOnLCxsXldUUql8uOPPz5//vy2bdv27t27bt26VatWaVXZFoiMjFQoFLW1tfPnz2exWB0+hlIdetmHxVj/egxjMquVqit1okt1IgAQJcRrVd92o1ZJ7t+pP3lEcPqo9GEK9MjAuc7w9/c3NTXl8/kaW6J73n//fc3r3377jUggx3FcpVJFRUXl5uZeunSpjS78Dx8+jIiIAICwsLAjR47oQGEtUndkr1oibvFHuEwmun7NxsZm/PjxdDpdT09v9uzZdDrd6KOP6Y7Os6xNh+g/N4dWNPIaezOyoaHFd7s7pgY7IIhBwvwNmDhAXL1E+iBJLRR08JY6Tc81hEwmMzAwEMfxy5d1FKS9evXqrFmz+Hx+fHx804hInz59OjDmtKCggMPhEJ0bAWDSpEkJCS0H3rSFPI8/09Fu7eTx/V16R0ZGLl68uLV6/3aBYQyvQXojx/rrM5kkLF0kK1coRTeuaU/f9iJ7/DB/wrDyLxbV/vJDzd7vyz79oPDdQHluNgDU1dXl5uYCQFFRUUlJie51+09CJpO7NzABAE3rl9LS0oj/XAzDzM3Ny8vLrays2o5Z+vv7U6nU6urq3Nxce3v7Ns6Obxw4Ls957jrKlMhDqhqILyIIqBaLpGn3zc3Nvb29t2/fbmVlRbwwMrewPvLnwHkLLfX1MLYeSU+PzWD4T5xkeyaaYm7Z1nKtwxo6HGMwidzRWIEYV6nEt65r6SZfm55rCEG33tGkpKRp06bJ5fJly5YtW7as8wLFYjHhoiSgUqlKpbKLcoClGWmF74wsDp5cuWllxaol+YHe1Ts249rw8bJHBDFImJ8+EweIF0ikKbp+KpQ9e1y6ePapx1nFdfW4XI7LZM9q6yPSMkrmvasoLrh79254ePidO3d+//33ffv2NW0bhugM3RWYaBu1Wh0fH29gYFBVVXX//v026owfP36cnZ1dUVHh6OiYnZ1dU1OjSz07iSbgRydhBmQS8aUJcuDKlvc1RqOZfP6/XjfSbY+eszkUyo1Ptdz+C9nYpONqMJisQb6jOCwyBncapEKVuhu9oz3aEE6YMIFMJsfHx3f1JMKMjIzx48c3Nja+//77u3bt0opMR0fH/Px8TQXho0ePnJ2dOxa0axtJyt3SJe+Fpj1SScTqBqGqoSG9Tnj/1LGyj+fiqhZKi14L5tARGI0e+DxmLsaVSvHtG//4KS1S8fUytVQaIxBXvWiHWChTJArFaomocvOaQYMGZWVl2dnZSaXShoYGdCjUFmPGjGEwGElJSeXl5d2igFAo9HvB+fPniYskEmnWrFmbN2+2tbX97rvvhg8f3trHzczM1qxZ4+TkNH369L1791padvBU1A1gGNnqeSDGkU6dZMwmvoi/HBiTRXdrq6wQo1BoTi40F7f258W0AWtEkCGFxGMzlDieKJSIb//VXWXEPdoQmpubDxkyRCaTXb16FQASExPDwsKI11okNzd39OjRtbW1kydPPnLkiLZsFZvNnjdv3oIFCx4/fpyUlLRkyZJ169ZpRXJTcLmsYvUnuETyQ0md+sVx87pAcrdGIH32WHD2eCflk1gs5iC/QA6LjMHdBmmjSi26obunQnlOlqqyvOWIoFoty0i7dz1+4MCBpaWl06dPNzAw+Hf1dH2TYbPZo0aNUqvVkZGR3aKAgYHBrRc07TnMYDCYTCaJRDIyMiJKHlvE3Nzc0dGRCA3osrVTYmLioUOHysrKTpw4cfDgwY65ZDmzPmwx8wUAMMD1Asd1TsfXgD08EEhkjXdULRZL7ndP5lGPNoTwsovG39/fwcFBT09Pi/IrKyvHjRtXVlY2cuTIM2fOaLe1KTHbfdeuXSdPnvzhhx+IZuJalA8A4oR4vJXNhkvE9ScOdX4JdkCQIYXkxWbIcTxBKBEnXteK07U9yPP4QCITr38qrfssr+qzvKqDFc99sxid3tdQf/jw4T4+PhQKZeXKlU190YhO8mZ6R1+Xurq6K1eunDt3TjfeAjc3N2Nj48ePHz99+tTLy6u1hhttw5kZTHNy6cvRH27wtzlcbMEBANOVG0gGuut2TTY2YXgMIIoorgskChzX5XNwU3q6IXznnXfgRTIkAMTHx/v5+WlLuEAgGDNmTFZWlo+Pz/nz5zuVWtIKs2fP3rNnz8qVK4cNG+bl5TVkyJCHDx9qUb4sM0MtERGvd5XV7yyt21lad6fhebKoqqYal3W2fpE9IhBIpEAOE54/FYokKTp6KsRodHjRIH+eucE6W+N1tsYzTF6cA3CwtLLq06cPhmH9+/fXcTv8/zyTJk0ikUgxMTFdHZhohhYDvWlpaf7+/ikpKXl5eePHjyfySLsUQ0NDhUIhk8mWLl2anp6empraASEYmWJz8LTP5CnDTA0Bw/JlimwhAcQAACAASURBVE9yKx+J5QCAsVs9BHcR7BFB9nRKbwa1QaW+1ygV34jtlpztnm4InZ2d3dzc6uvrExISKioqmnZs6SRisXjChAlpaWkuLi6RkZFtuFk6Q1xcnJmZ2eLFiwFg8ODBoPVH7CZ+3KH6DF99pq8+046uzXMt2diU4e6peSpU4rjOYuZkDkeTR25CIVtQyRZUsiHl+abAFXKas6tuNOmBWFhYDBo0SCaTEQPudcOdO3e4XO65c+eaPi9u27bt7bff7oC0lStX7tq1a/369StXroyMjFyxYoVS2dmoedtcunSppKTE0tIyKiqqvLy8V69eHZODMZjm3+x0uHST7txHn0yKrRfH1otF3ZGuwn5rDAC8yBKQKKsqanZ+p6qp0rEaPd0QAsDQoUMB4KOPPtq1axeGYdXV1R0QolQqm2aOKRSKadOmJSYm2tnZxcTEmJuba03dlxkwYIBUKr1+/bpQKOwKXxPdzYPEfD41frAeY4g+Y4g+w5b23BBSTM0wuhaOueyAIAc61ZlBFarU9xpl4hsxOngqVFaUV6xdrmplIRKdYfDOLK3cHaI1dOwdTU1NHTduXF1d3fXr15tW7hoZGTGZLcfM2ubBgwea/oh2dnaGhoYFBQXa0bUlZM8ejyIrF7n28jDmzJ8/f+3atZ30UpBNzQ2mvWdCIXuy6XIcT2yQihNbDYV0EVR7LtXKdqwhe4qx3kgOEwAEZ/8onDRCEH5al2r0dEPI5/MvXbrEZDILCgq2b98+adIkMzMza2vrGTNm7N69OyUlRa1W/6MQqVS6YcOGe/fuEd+q1erg4OCoqCgzM7OYmBh7e/uu09/ExISYpRIdHR0YGKivr5+amlpUVKQt+Sz/kVgrNY4kJosTvEg7q4wIgr+fCsXKqgpZZoZWJLeGqr6ubGlwY0lxcHb575XC1dZGzozn4RZvNmOJgwXNycXk8/91qQ4IIkslMjKyqw9SAMDn88ePHy8QCKZOnbp7dwdrwJuC47harW7aH5FKpXZRQaE8N7tw2ujS+TOrd2yq+mFj8dxJxe+/oyzVQicQ1oggwDDN1lM3NkhT73VebPtRlpcqqyvdWbTZpvp/CSVfFVSHVQpUUkntT982RukuftyjDWFpaeno0aOrqqoGDRoUHh7+9ddfjxgxgsVilZWVhYWFLV++fODAgaampm+//faWLVv++usvkUjUohxioG5KSopKpcJx/JNPPjl79qyBgUF0dLSra5f71jSP1XQ6PSgoCMdxLXZxxGg0ix/2A4n0Pxtj8ot811Eclq+pId3Nw3BmsFZWofVypjr0CtKU1gJ0aWW9WtRY9ukH4tzsZXlV9xqlRyuF1jQKm0zG6HQSk2nM0fda8LH1kTCMhlJjupY+ffq4urrW1NTcunWrSxcqKSkJCgoqLy8PDAw8deoUmUzuvEwMw1xcXNLS0ohv6+vrS0pKOuyrbAN5Hr/kg6nhD9LKhEK1SISLGnMFwqi7d4vnTlRWdLb4hGJmQe/rQeRtEvPidZyuUvPTN6BWxQvE3xXXTjRiL7DgPBDJNhbWqKWSqm3rdVdNgfdU6urqiCkwgwYNamho0FxXKpUZGRkHDhwIDg5u9mtNJpP79u27aNGiY8eOZWRkaD5SUlLy448/btmyRSAQfPXVVwDAZDJv3LihmxshJklxOByZTHbs2DEAIMyhFskb5nHIycKKRlluZcjncXMG96766Vu1XK7FJap3bc3mcS2oZAC45GZdOGOMFoU3RS2VlCycmc3jvmuiBwBGFPLVvjY5Q/uI799RlBYryktxlaqLlka8CtEXcMWKFTiOZ2RkhIaG/vHHH9pdoqqqioj9Dx48uOlO7zxXrlzx8vKKjIyMj48fM2bMzp07tShcQ+Hs8XxvxwAO85yrFZ/H5fO4B5zMp5no5QxyLvn0g87Lrz28l8/jOjKoAHDKxTJ/vC+uVndebHtQy+U5g134PC6PTQ/v8/zunno5WNMoSf3tcod5iBLidKPJaxjC+vr6Cxcu4Dh+/fr1wsJC4qJUKg0NDRWJRMS3165dk0qlBQUFK1eujIiIKC8v17rGWkEkEhHZoe7u7jU1NTiOq1SqIUOGLFiw4OjRo0+fPtW8s6Sk5OLFi2vWrPHz82vWCM3KymrChAnbt29PSEi4detWWlranj17AIBKpUZGRurydoh9HhsbW1NTQ6FQqFRqXV2dtoTLiwv5PO4SSw4ALLHk8HlcKf+ZtoRrkKQl83ncWab6ALDMypDP48pysrS+Cq5Slq36mM/jfmhuAABsMimijxV/oFNjXJT210K0g8TERADgcrnEtxUVFSEhIVqULxKJfH19AcDDw4PY6dolLS3t22+//eqrr+Lj45VK5fXr16uqqrQoX56fk+vnxudxXzWEfB43Z4iLsr62k0vI+M/4PO5CCw4AfGhuwOdxpU8fa0X5f0RelJ/r787ncY0o5McDHIi74/O4/gbMsy5WOYNdqo8fzMvLI95cW1srFou7SJPXMIT5+fnffPONQqGIiIiIinr+h6OqqopEIq1Zs4b41tPTs6Ki4o8//mhqLaZPn75r16779++r3oxnbZlMNnbsWACws7PTWPRHjx41NXIGBgaBgYEbN26MiYnR/Os3NjbGx8d/++2348eP1wwOJGCxWO7u7hiGkUgk7e7k9kAcQz/77DMcx0eMGAEAp0+f1pbwhisRfB53kB4DAA46mecFDtSW5JdQqfICfQ47WQBAXxaNz+Pm+DiVr1qirKrQ2hJqdcXm1Xwe9zMrQwCgYtgfzhZ8717CC6FaWwLxmqhUKqIty6NHj3Ac3717t0Qi0ZZwmUw2ZswYAHByciotLdWW2GY0NjYmJyfjOD579mwA2L9/vzaFx17JHe5BGMIgQ9YsU/1ZpvrDDJiEIcwd3l/y4F7nVymYEnDWxQoAbGkUPo9b89vPnZfZHhSV5TlDXPk8ri2Ncr+/vcYQ8tj0y27WOb59wr5aefbs2RMnTshksvnz50dHR3eRJq8RI3RwcGAymRQKxdn5paEbHh4e8fHxGRl/Zzd4e3uvX79+5MiRenp6TeNtxsbGY8eO/eabb2JjY3VcPKRBpVIFBwdHR0ebm5vHxMTY2T0fBenq6nr79u2ffvpp6tSpVlZWQqEwNjZ28+bNQUFBHA5nyJAhK1asiIqKcnV1Xbdu3eXLl2tra3Nyco4dO7Zo0aK+fftKJJLHjx8bGxsvX758zpw5Or4pTZgQx3GtZ+JJHz5Q4vBILMMABrDpDC8fbUl+CQzD6DRfAwabTHoilhfLlbhKJboRVzRjjKJYO5l4Nbu2NVwIDalq2FtWT8bg516m/gZMkxVr9SdN14p8RAdoOhlUpVKZmJhoq9xWpVLNmTPn6tWr1tbWMTExVlZdMuGrpKTE1NR07NixSqUyKCgItJ4ES/67VGm8Eft9M/33zfSJ7EoAAMBBGz062MNGeenRzajkYrkySyIX6ypMiFGouFIBAIP1GTGC54VMpXJlpULVi0HFMNLED+aZm5ubm5sfOHCga/+utt9mPnv2bNq0aSkpKdu2bfvyyy+Ji1VVVV5eXjdu3PD391er1cSJUOOXaxpva1aiR8TbgoODDxw4kJGRodaJV1qtVi9cuBAAOBzOgwcP2nhnSUlJaGjosmXL/Pz8mrVvaOoRlclkxPurqqomTJgAAOvWrdPBjTRDrVYT6eCpqal5eXkAYGBgoNGtkxTNHh/hZg0Ajgwqn8etO3FIK2KbIQg7QTwbjjVkA8AGO+Pnj4cDHQtnjOl80KL24G4+j/sT14wEgAFsczDl87i1B3ZpRXlEZyDGGOnp6Wm2lVQqfV0hUqk0IyND43NSq9Xz588ndnpqaqq2VX4JIiHu+vXr1dXVZDKZTqcLBAJtCVdUlOUO6dO6a9RVJRZ1fhXx/bt8Hne6iR4ArLA25PO48uLCzottG1WDsGj228QdJXrYerDos0z1l1hyXJi0I84WOQMdi+ZMuHbt2o8//lhTU/Pbb79t3Lixi6Kw+Gu5RluEMIQ4jgcHBx89etTT05PP55NIpBY9ooR1Wb58+eDBg1+1Lu++++7OnTuTkpI6e0+ts3r1agBgMpk3b95s/6eEQuG1a9c2bdo0ZswYAwODpmrr6emNGjUqJiYGx3GiSamHh0eXqd8WixYtAoBNmzbhOO7h4QEAV69e7bxYlUiU4+O0zs4EAIi9J0lv6wGiw+SNGURsiZ+4pgDgq8/Q+ElyBveu+2N/Z2IhgrATfB73oJM5GQMA+J+NEZ/Hrdq2Xov6IzpGZWWlq6trswa8LBbLz89v2bJloaGh7Qy5bdiwITExUeNWXblyJSEnISGhK9XH8Rd/VZYvX47juL+/PwCEhmrT2V722Yc5g5xaSJYZ4lr5zRrtrKFS5o30OuhkDgAeLDqfx60/reWUpWaopZLi+dOfeXHX2ho98XIgcmQu9LE+7WKZ6mnP93HKCxggLy5ITk6OiYnJzs7GcbyhoaG6urqL9NGaISwvL+/Tp4+zs3N0dDSL9VJjciMjo/Hjx3/77bfx8fGNjY3EB+Vy+f3793ft2jV9+nQzMzPNmwcMGNDZe2qF7du3AwCVSr18+XJn5DT1iBIbmBAol8uJ+tasrC7I8vgniKmKxP8F0X37k08+6bxY8d1EPo873ogNAFvtTXKGuKi1dNBsikpQlzPEhdjk9/vbkzCgYNgME70dXNM4dxuNRSyYOLxi/QrBuVMy/rP2nxEboi7wBzqGuloxSRgALLU05PO45WuXo+zQbkcgEHh7exOPjw8ePGi2rTQ4OjoGBwcTT9Utuo5UKtXMmTM3bdpEhPy3bNlC7PQrV67o4C6I2g8i32fHjh0AMGfOHC3KV9bW5A7v96er1R0PO2Ij3PGwu+TBLZg6SiXSwnGQoGLDFxkDHJgkDANI9LAtWaLNW2iGWqEoXfZR9ovkuNGGLOK+cn375o7onzPEpeTjuYqykq5T4FW0ZghxHN+7dy8AVFRUNPWINmvY32IFglqtzszM/P333+fPn//dd991UqUW+eOPP4hMFi1mkeA4Xl5eHhERUV9fT3w7a9YsAOi683sbSKVSootbbm5ucnIyANjY2HTe4Vx7YBefx7WkUgAguq9N8UfTtKJtM5TVlTlD+xCbYamlIQCwmtQpm1LJIzmsZVaGx3pbZLxILcsbxStbsaDuj98kqclq2UueNLVCIYw4U/zRtPxxQwsmDc8Z6BzpZm1AJgHAe6b6fB639POP1ApFV9wIov3IZDIiqObk5FRWVtb0RwKBICYmZuPGjYGBgc16vjTNYhM1MQNffPHF0aNH7927t3//fgAgkUhnzpzRzY1o8n3S09Ozs7MBwNDQUK7V4qKSxbNv9rOlYtgIAyafx+X7OFVuXatFK4jjeGNcFJ/HJTodbrYzyRnkrBLUa1H+36jVFRu+1CSiM0jYaRdL/kBH4eVwacZDcUqSsq6zebAdoLOGUCwWHz16lHitVCp37ty5bdu2NioQmvXvbxpv60BgoJ1EREQQYx927eramNDp06cBYMSIEV26SmtMmzYNAPbs2aNWq21tbQHg/v37nZRZuvT9m/1sAYBDJmXxuNV7tmtF1eao1TmDnPk87kY7YwAgY/CFtdH/bIyCDFmm1Jdqn2kYxmPT51sY7HM01zwg5/g4FU4LqtzylfDSOemTR4XTAnP8+2rOkXHuNoSQMYasZ17c4nlTtRJW0T1CofDXX39VqVQ3b95s6tvft2+fZrtdvnw5NzdXqVS+9957P//88927d7X7F1lbKJXKd999FwCsra1zc3OJi2PGjPHz81uzZs3Fixc1TjCFQqF5quZyuU1/GSgUiuap+v79++np6eHh4WQyGcOwAwcO6PJ2iHjkt99+i7+oZYqL014BnEqVG+D5cy8zAHiLw+TzuA0xnfJptbyIWJQzxPV7B1MAGGbA5PO49SePaH0VHMerfvyGz+OutzUGAAqGHXKy4Hv3Evx5sivWaj9aLqhXKpWa7tKtVSBcv369tQoEExOT4cOH//HHH1qs+ImLiyOs75YtW7QlszXq6+tpNBqZTNZuLVE7OXHiBACMGjUKx/GPP/4YANav71wYTKXKHe6xq8kObLx+TTu6vgxx7vyJa9o0k0XzddvDbm8vsw/MDfqxaM2ynM2p5HFG7HW2xuf7WGdpYoq8XprP3vKwIzqj+ukzn3g5FM1+W9Ug7Ipb0AECgeD777+Xy+UVFRU7duzQXHdwcBg+fDgRif/www+joqIePHig+SeiUqne3t5EvK2ysrL71P8bTSaLoaFhWloacVEulzc9/JFIJA8Pj8WLFx87dowIERHk5uaGhIQsXbrU09OzWYMYa2tr4nl327ZtOr4jopfTwIED8Re1TMuWLdOWcFn2Uz6PG2xmAABfWhvxeVxlTZf8eSn9/KP7/e3JGFAw7IGnfY6PU8HkAPH9u1pcombfj3wedwfXFAPAAL53MOXzuHVH92lxiY6hZUMoEok0FQhNf0epVOrgwYOXL18eFhZWUvK38/fVeJuGpoGBDhcgJiUlEfMFP/30Uy3d4j9AeHuOHTumg7USExPr6urKysrOnj0rFArr6uqoVCqVSq2trY2OjgaA/v37v5ZAhUKB47hUKq2trZXJZMQOfN/MAAC+sDbie/dS1mg/WF0fepyI/xOZLF/bPs8X3eZgOtaQvdbe7MK44VnDPYiL6QPsz7hYrrIxGslhccgvmUUWieStR19swTnoZJ7iac/ncZP72xEdRAew6Y8GOOSO6K+sfiMsQYfZvXs3ccJrZgiXLl1KnIEIQ1hVVfX7779/9NFHbm5uzbaVq6vrvHnzDh069PjxY92kar/Kl19+STz4JiYmNr1eXl5+8eJFwiParIjCwsJiwoQJrz5VJyQkbN++fcKECcbGxoQtHD16tO7viAhMYBhWWFh49+5dALC3t9fWP6/gz5N8HtedRQOAk70tC6YEaEXsq5R8PJfP4/roMQBgTy+z54+Vvm6NWjqA1p85xudxf3ux09faGvN53KofNmpFeCfpwhZrr1uBUFBQsHHjRn9//2HDhjULDJiYmEyYMGHr1q03btwQtdsznpGRYWJiAgBz587VWS3/L7/8AgBTp07t6oUaGhr27NmTmpqakZHB5/N//fVXHMeJXvgnT57UZO7k5OS0U2B9ff2nn36amZm5a9eu0NDQkpISQVgIn8ftx6IBQEhvy4J33tL+XVyJ4A90PNnbkk7C4EVPGf4gp5yhrmNN/x78TaVSef3cl7w99tC7b6dOHEFs0Wde3Etu1pvsTCYbszUDMQjIGLgxaZY0CgD0YdIeeNrzedz8MYO1rr8uqa+vX758OeEX/fTTT4XC50dbBweH6urq3r17V1RUEIYwLy9P8SIIqom3TZgwodksdX19fcJnc/HiRS22Imqbb775BgBoNJqmKUeLKBQKIpkuODi4Wdt6CoVCnHGPHTuWn59PvF+lUm3duhVeeER0DzHZlPBdE8cAbZVtVGz44tEABzIGZAzSB9hXbPhSK2KbIU6+nevrxudxv7IxBoBJxmyNWyXXr2/nn4CFlyP4Ax1PvNjpRLPGig1f6KydW9voqNfoP1YgbNiwISoqSpNT2s7AgCa68CqFhYXE/pk0aZJCh5kRxcXFGIax2WwtNshojZiYGGKzffvtt0TPhV27dgHAjBkzcByfOXMmAPz882s0ibh06VJmZmZoaOj+/fvv37/ffAduXKld/UU343IGOV96kckyx0yfz+OWLpolCD9df/rooz9PHzl8eP78+a96C1ycnOaMDtw5bVLcxLeyfZw0qXQHncwXW3C89ejEZrOlUQwppNsvQom5/n21q/8bgoODg1qtPnz48Lx58whDyOVyW6xAUCgU9+7d27Vr18yZM4kocjPr8tlnn50+fbrrovX79u0DADKZ/LoFBtnZ2ceOHVu8eHG/fv2aDnwAACcnJyI9ralHpGvUbwuinRZxHm1ay9R5CiaPONHbEl4UNgjOndKK2GaUfBJMbJN4d1sA0CeTMr2eJ6blDHQsW7FAkprc4RSzxr9icnycwvtYsckkAJhrps/nccu+WKhWvik5a93TdLs1j+jt27dbfH/Tw2UbDT81h0uiOAkAAgICdGCQmsHj8QCgq9uNqtXq9evX79u3Lzo6+tChQ8ePH8dxPD8/nzDDw4cPJ07Vffr0aRpiaRvCEGZlZRUWFu7cubNgUhfuQMnDlFy/vrEvMlkmGbOzeNyShbOIFNAjR46sXr26uLh4165dq1atavtYM3LwoP+9O/n41PGpPs7E1s0Y4LDP0RwDYJKwRy8STfMn+GtR/zcHwhCq1Wo/P78BAwaEh4c3m3mCYZibm9v8+fN///33zMxMjcuuxT66dDq9iwzhqVOnSCQShmEHDx7sjJyGhgaNR5TIM9AEBTUeEW3o+3pUV1dr2vw2rWXqJMqaaj6Pu8LaEAA+MDfg87iyLuj0i+N47lsDNEdASxqFgmE8Np1I1X7oaa95lCz+cFr1nu2NN2JVgva6EMT37+QMcY1xtzGhkAFgsrFeFo9bsmh2s2Tv7qX7p08QFQhffvnl8OHD27MDGxoaiOZnY8eObfY3UU9P76233lq1apWbmxsAeHp66szh05TNmzcDwMKFC3W2YlNj379/f3iFFkMszVAqlaGhoVevXuXz+aGhoeKy0q7bgbKszNwAz0QPWxsaBQD8DZiZXg6Fs8aphH+35Dh+/Dhhwjdv3qxUKjXXFQpFcnLy7t27WzjWkMnubAbROFjj1D3oZM7ncXOGulbv0XUOhW4gDCGO4+np6RQKhXA5vm4FgiaLbeVKLZ/7Ca5du0bkrH3//fdaFKtUKokBnMS3TT0iumf48OEAcObMGU0tk6ZhdIdpvH6Vz+OOMGACwO5eZrkj+ndR/WtuQH9i13xnb4IB6DUJwJMxcGZQpxjrbbE3ieprk/285ZNT4cyxlVvXCSPD5UX5L8lSqYQXQovem5A7rF9egGfOIOeEfrbWNAoAjOSwnnk5FAVPUokau+IuOkz3G8JO0uLh0tzc3M7OrrtmXxAjyiwsLHTfZFylUhGzpQwNDVevXv3DDz9MnjzZ3Ny86d9BOp3u6+v7j+NBGuOjicZOALDr9XdgZGRkXFycXC4/cuRIs1a58sL8vCCf5P52TgwqAHix6Y8GOBRMCWiaC3f37t1Tp07hOH7hwoXr16+3sdCrxTn92c+70iy3MgSAGab6fB43d3i/9j/D/rto6nuIj4/fsWMHEWjQlLd2PtDQSe7cucNmswHgf//7XxctQZCfn088EHedd7cNfvrpJwCYPXs2juNEccjevXs7KZMYT2ZIIQFAQj/bUm3MXWqRgokj+DzufsfnmSzLrQw1gQbay4EJMyp5JIe1ysbojIulZmREXuDA0s8/qvntZ9HNuKIP3snx+7t+6d6Lnc4jdvo7b3VFzl0n6X5DKBKJDh06RLQK3LdvX2cKD4qLi8+fPz9y5EgAmDRpEo7jVVVVhw8fvnTpkvb0bRfEIMPWPL1dhFqtJoITr3ZS1fiWvb29m4VYWhsP0nwHfjbvtZQRiUQ7duwIDQ0NCwsrLi7WXFdUlOVP8E/ztCeOa65MWoqnff64IYrS4qYf37Jly4EDB1JTU7/77rsDBw5ogsetERcXl5qaKhQKd36//WSgb46/O5/HjXSzBgATCjmLx63Y0CUHnTeQwYMHa/5zidTrZu188/LyiAqEAQMGNKtAYLPZffr0WbFiRUpKirYi6+np6YQD84MPPtBBnirxINh2Jk4X0XQy6PHjxwEgMDCwkzKLP5wW1dcGACyoZD6PW3toj1ZUbYbwQiif1+t4b0vC5hFFGpqvp14O5/tYr7M1HmfENqa89AtDwbB+LNoH5gZ7e5kl9bdr+iniK83Tnsh3JXLW8t/2U5SX/bNCOqf7DaFAIPj555+JkbZSqXTz5s0dk3P+/Pl9+/b9+OOPRNdpfX19qVQaEhICACNHjtSuzv/I559/DgCa6VS6oZ2dVIVCYbMQiwY9PT1NRfPjORM1OzDHx7nym/+Jbv31WkV4O3bs+OWXX65fv7569WriirK+tnBaUMYAB2Kokz2dcsfDLm+klyy3vVHMFmloaHj48OHKlStzc3O/+OILXK1ujIsqmDKSz+MS2aRhrlYFk0Z0Zol/ERcvXvzyyy99fX2bNa+wsLCYMmXKjh07EhMTNQemVysQmhrFV8vbXxc+n090XZkyZYpuctY2bNgAAEuWLNHBWrdu3Xr06FFNTc3Ro0efPXuG43i/fv0A4Nq1a5rJoK+VuSOTyRobGyUSSU5OTk1NjVomyxniusXeBAAmGLH5PK44KfGfpbwmjdev5fg4nXO1Ito5vW/2PLJwsrdlPxbtA0vDfSN9k94J5A90JK5fd7fdwTVtT11vxgAHohjDgU6962HH93GW5/G1rr9W6H5DiOP4wYMHBQIBUf3d4RrwkJCQQ4cOEZE5Ik4WHR1NJJJRKJSu69baIvHx8QDQp08fna24e/dueP2ZwEql8sGDB3v37n3vvfccHBya/k6TMbCiUgBgAJt+c8jfjo52Nvz866+/Vq5c+eTJk23bth06dAjHcbVEXPzhu8+8uGMNWYR9/aufba6/u/Rxeudvv6GhgejroSmwk2Y+4vO488wNAGCxBafrsgzeWDQVCNOnT2/mHm+xAkGpVIaHh8+ZM2fmzJnNRq2RSCR3d/dFixb98ccfxF/89lBSUkK4RkaNGqUzX2VKSgoAWFtb6+D0ee/evcOHDxcUFAiFwo0bN+I4vnbtWgBYunQpjuMBAQEAQHj428mePXtCQkKio6OPHTuWk5MjeZjC53HfNdEDgPW2xvyBTloPrYmTb+cMcb3iZk34fqYY62XzuDk+Trl+fZc7WDT9HbCytBzn77vhnQkRU0ZnDnneEDHF0/6Qk8UnlpzB+gyil68GAzKJaM1oRaPc7GfL53FzavC68gAAIABJREFUBjlpty2cFul+QygQCObPnx8eHn706NFdu3bFx8d3TI5MJquoqCDax6xfvx4APv74YxzHCU+pjoflKpVKooSxaau5roPopIph2B9/dKpnfFlZGRFvGzrAk3CS6L/whLQWGCj7YlHd8YOStOQWm3GrGhuEkeE1u7ZW/7KjaM7E7BejXgwppOi+NjmDe4vuvMYYkNYoLy+fPHlySEhIcXFxcHCwprFc/gT/kN6WAODEoPJ53Nojv3R+rX8vWVlZbVQgBAcH79u3r2lyR9Py9mbpNhwO59WmUc2oq6sjvJSDBg1qaGjQ0U3iuFqtJoaM3runhYm1bVNbW3v48GEcxy9cuEAU8t67dw9etPn9+eefAWDmzJntF1haWhoSEnLv3r1Dhw6dPHmy7vhBPo/bi04FgIg+VkXvTdCu/tKMh7n+7jf72VrRKAAwisN65uVQOGdi4/VrDTGXq2/fvHnjL+JBytTUtOkvAIvFGuo1YMmYwEOTxzwYOVBT1xvV12aLvckUYz2ikYUxhcwmkaL7Pu+bn+PnpijvqvHInaT7DaG2KCkpOX78OFFlfP/+fc1TIXFUmj59uo71mTt3LgD88MMPXb2QppPqa5UMtk3d8YMZAxyI7eGjzzR6OTBAJ2ED9RiLLDgHnMzv9f+74WfRnIlVP2wSXjpHdI5viL6YO8ydCNfxvbl8Hne+hQEAMElYqKsVf6BTY6x2hgMoFIra2lqiG05tba0mE7Jq+4ZnXs/DnNf62hQFT9bKcv8BXq1AIGitRWfT8vZmnoMWD5cikcjPzw8A3N3dtdgusZ0sXboUANauXdvVC504cWLBggV37949cODAnj17cBzXtPn19PQkmuPQaLRz586100GakpKyc+fOx48fP3v2bNOmTWUrlyT3tyOqgJ56OVR9v0GLyssL8vICvZP62zkyqAAwRJ/xeIBD4fTRxLyz8+fPb9myJT4+PiQk5Ouvvy4sLGyjC1gvB4fZo4O+mzoxMsBH0+kw0cPWgkoGAM30qJzBvbtido1W+O8YwqZongqTk5MLCgowDNPT09NxQWFYWBgA+Pn5Ed8mJiY2ayilFTSdVAnHoLYo+3Lx8x3IYFQe3deBhp/5QT78F6XuxNfnVoYAQMWwo84WfO9ewkt/alHhFhHfTeDzuJON9QBgjY0R37vXmxmo716ICoRffvllzpw5mZmZ7fkIn88/fvz4kiVLPDw8mh0uHR0d33vvPSI20atXr6b9FHXGtWvXAKBfv366XxrH8SVLlsArNJtD3tpnb9++HRMTIxQK4+LiGhsb80YP+s3JnLBSfB63IeqCtpRUlJfmv+2X6mnfl0UDgP4s+kNP+/xxQ5sOP0pOTr5w4UJVVdVHH32kyUAmqKqqIlxHr3YBM6SQR3JYROIM0R/1Y0sOn8flD+xV0mUpr53nv2kIcRz/5JNP4MW8+AEDBgCAboaTaWhoaGAwGCQSiRgxs3nzZmJYWjtRKpW//fZbenp6Wlrar7/+mpyc/Op7uq6Tal6QzwEncwAYMcwfx/Haw7+ULJyZ4+tGmLSHnvbHelssszIcyWEZvNzwk03+u+Hngxd1uHwed7OdCQCQMdjby4zP49ad6FRJdTtRKxS5AZ6/OJoBgLcenc/jdnuT+/8eLR4u7e3tDQwMdBMXeBW5XE5o0i2TQWfMmAEAdDr9xx9/DAsLa3HqjqWlpaYNSGsP6PLiAj6Pu9iCAwCfWHL4PG6zzOq2uXHjxpEjR3AcP3bsWLM4pbK2pmDqKE0mC5dOvethlzeKJ8//ux3jo0ePDh8+rFari4qKLl++fOvWrdYWerU4h0UmPfNy4PO4x3pbAEBvBpU4Dsrzu6o+p/P8Zw1h03nxGzduBIDFixfrWIdx48YBwJEjR4gOWCtWrHitxLn09PRLly4JBAK1Wr1hQ3OvSNd1UpUX5Wt2YFP/klqpkPGfCc6dqli/omDisBeBAYfzfaw32BlPMGJbvdLwsy+LFmxmMNfMgBgrsdXehOjYpLOmEuVfL0sfYE8nYSSAOx52r1sE0hNQKBQHDx7Mzs4uLCzcu3dvZ0JrFRUVSUlJq1atghdxstra2n379m3f3jXTu1pn9uzZAPDTTz/peN1vv/0WWuqk2toccmh9PIgwMlzTAvuQk8XrdspVqVQ7duzIzMxcv359UzOmamwoem/CUy8HokTYkkq50c82d7iH9OlL59Tz58+HhoY+fvw4NjY2JCSkaUeLFrl06VJUVJRCofh5587vp03OGdqH7+341MuB6Iwf625TOGPMa+mvY/6zhrBp12liKo2VlZVu2u1rUgN+++03AJg0aZJYLP7zzz9XrFghex0XeWZmJlEBeeTIkWY+K00n1YkTJ2o9K73pDmwjB1VRVtIQdaHqh41F703IeeEFvd2k4aemDpdFImGEc5Jo1DTMXV6Qp12dW6Ph6iVNW4BtDqY5Q1zetJYW3Y5KpUpISIiPj9+wYYNSqSScKB3g4cOHP/744+rVq9VqtY2NDQCkpqYStUxEaZ121W6bM2fOAMDw4cN1uSgxE5hMJp89e7btdxLxtrbremOWzn/i9XxkfAqvV9mXiwRhIbLsp+3va7Fjx46kpKSjR49+8803RCWuWiopWTAji8edYMQGAGMK+Wpfm5yhfSQPOpVYJJPJ+Hz+hg0bioqKampqvvrqK1ludvnqT/g87iRjNgB8ZWPMH+j4Js9+aRbu+e9ApVLHjBkDABcvXvTy8uJyuWVlZcTo9i4lPj6ey+USUYopU6aQSKTo6OhTp07Z2tpu3ry5WaPUtomNjU1OTj558mR2dnZRUZHmelVVVVBQUGFhYUBAQGhoKJEpo0Wk6SlKHM8QyzCAoUOHtvY2iqW13thJpqs22Z68xL2RbnMkzHTlBsdxE4PsbVbZGJ11sUrxtD/lYrnC2kgFOA4w1pANAJkS+f7CSvxlT1HXwfILwKjUQA4LAGLqxbhcLr59UzdL/1sgkUhEmEetVhNTbTsmh0ql1tfXE5bv7bffBoALFy5wudx+/foJBIIbN25oUed/ZNy4cTQa7datW9XV1bpZMTw8/NNPP8UwbP/+/YR3tA0cHR3ff//93bt3379/v6qqKjIycu3atSNGjGCxWGVlZWFhYcuXLw/69cigh0USNW5Mp2XrGVfHXa3atq5o5ti8ER4lH02v2fu96GacWihobYmkpKSysjJjY+Pq6moDAwM2mw1qVcW6FZIH97YW10bWifTIpN+dLZz1mJY//Mrw8unMvdNoNAsLCyqVamtrKxAIHBwcaL2czb/9mcRiE1svViAGtVqUEN+ZVbqW7rbEXcipU6cAICAgAMfxTz/9FAC+/vrrLl1RE7QjGjbGxsZSqVRNCRcRGCCSzjucuSMQCIim3l3XSbVw5thzrlYA4Gpu2pHPq1Sim3E5g100AULi8ZMYP0ZUuN+9q81pn21T+knwXQ87EgCDhD0a4FCxbrnOlv63sH///j179ty+fXv37t1//tnBJCaFQlFYWEgcKJt2nW5aWqdLRo8eDQCdLChqJzExMVrppKpUKjXxtl5WlgCgR3s+wI5o+DnLVH8H1zTqRUECf6BT4axxVds3NFyJkBe3kIKgEjUKIs5UblpVvn5F0Qfv8Hncjy05AMAgYadcLPnevYQXwzqj8P/ZO/O4qMrvj597Z9+HfZFlxBUQxAFXUBBZDEFwTUtzzVLLrTIzM7UsLdP0a2pmGZpLbongyiIqrigoKIYOO8gOs97Z7/39cXHiZ2bCzOCS7xd/DMPMeZ4L3Dn3Ps85nw9JQ0NDRETE5s2br1y5Eh0dbXJjrVk8Jy/Ag44gFASu+btXL5hh/lhW4mVOhK394lNTUwHA19fXesMVFhaSOY/ctDPpK4aGhsbFxT3SiMNkMkNCQhYvXnz06NHa2tqnH4XsBunRo4eV3MaNSoUkqMtSN1sAeGtIcLvjVL03pah/N/J0JT3u+3GZErHoLWcbAPjkk08sOOcnQxr/9uYwAGCrl2NxqH+73WRe8QTUavWJEyfIPgGT6nRxcXHr1rqOnM8PP/wAAKNGjbL2QKYzff78+ZaKqSnIK4sbQmptD3Zx7MWmU/7/jbozjRpjw1nmZnukp8ufD/2SSiL7Ppg/vXnnVnVuNq7VYFcuFA/xLwr2NV2Sfu5uS+bUH7wcJWKRdM/PlprwY1Gc+EMiFg3mswBgrad90cCeRuxVQ/2zwOQXb+1Cskc27fLz80nBqsmTJ7d2vWmr4CdBENOnT8/MzCQfr1+/vqCg4Ntvvy0rK7PGURAEobp8XiIWkcuYWxe+3+44RllzWUJY0aCeErEot7cHDUEoCGT7u+/p7wsAPj5WtwZMTk7+9ttvi4uL927/camb7RxnIQCMseNKxCIsu0M1YP+bjB07FgA2bdpkaq0zCR10DKQzKJvNJvtKL168eODAAYtvVebl5ZFnumWVVBu3bZCIRWQfXvoAn+JQ//wAz/3dnUnBz0f6elkoEshlkIKff/X1DuhhEkUjv74T2ZM1a2s87a0nW9oao1xW1K8rWTEeKWRLxCJlxql/f9uz4CVPhK394q1XSPaI/WFRUZGrqys80RNYLpebDPbIoh4TPB7PpPHY3Nzs4uIiFovJpdTx48c/WUfUfFqfgbf+MKvVD1djjf/7pjjEVyIWhfBZAPCtyL5wQA8boRAAnl6pq33odLr79+/v3r172bJlN8dGkytCQipa2Mez/tt26tm+4unZvXs3PPSLnz17NgC0Wz2x3QQFBQHAsWPHCIL47rvvamtr25SrDh8+fODAgdLS0k2bNu3fv//vL5BIJKQZvcWVVB/MmZzZy438j70vFtV9ubR2+Qdlo4aaslprwc9HNnXdGdQEW+4jfb0/dmmxlVjqZisRi+rXdNDfouqdNy75uZucQWuXf9Ax47aVlzwRtvaLt1Ih2SObdrW1tWRSHDp06FNuBLbeGPDx8Wn9P/3zzz97eHisXr2abJ8YP378+XPnLDv/R6iaPcl0BuqqLdANbZTLivp2XeFuBwDRQrZELBo/bCgArFu3zvzgT0ClUq1Zs0alUi1btuze+q+Xu9uSUlX7ujtL+nap+2pZm7qyXtFWWvvFnzp1CgD8/f07eA5kM8OMGTMIgjh+/Pinn37a1gWhdevWkRmOlBJtjUlJNTw83MJKqkZj8RC/70QOABAuYEvEItX59JafKOTYlazGbRuqZr9ZNLBF8DPb3317F8d3nQV9uUzm/xf8FFLRcAF7vB2PLOGe5yKUiEWSfl3VNzvo7ly6b6dELPJjMwBgexfHkqF9nh9X+ta8tFWjJJ06derTp49KpUpPT4+JiWEwGJYtJFOr1XFxcTk5OV27dj19+jSCIMOHDy8sLOzbt29SUhKpsfSvUCgUUtF4165dd+7cqaio+P333+fPn9+3b99BgwYBwKIFCw7t3XNt1VLs0jlNQb6lJv8YcKP2zq0bSi0ABHJZVAfHf33Hv4Ly+Mw+fSOFbATgvFytxYkIGy4AJCUlmR/8CXz77bcsFquwsNDf3/+PqrpYG84w4cMCNqNR8cf+inGRqnNpVp3DfxmhUBgSEqLX60+ePBkeHi4UCvPy8siaUmtTU1NDPoiPjweA5ORko9HY2NiIomg7SqypVOoff/xBbrKYkMlkI0aMKCkp6devX1JSEsOiVdA6SSGuVOSoNAAg5jAAQRh+fcgfoVweq3+w7TsLXLf81vlcXqedh+0WfuoWNSKyi+hDV5t93Z1v9fYwCX660alSA54hww40KigIjLPnznMRAgDKYBiqqyw44SfACY0EBIkQsgAgVYoZZc2aWzkdM3SbeMkTITw8GZKSkng8XlhYmNFoTElJsUhkvV4/bty48+fPu7m5paam8vn8uLi43Nzcbt26JScnk8UC7cDNzW38+PHff//9tWvXevbsiauUNSMHfwjKD7/7HlcptTevW2Tyj0V7/68zMMjZHqFYpjGDExbpRKP4sOlqnLik0ATXlTMYjEuXLtXX11sk/mP5/PPP582b16dPn3Hjxk1S1trSqBGClrMRAAijAVer65bO05VIrDeH/zimU49Go5E1nMeOHbP2oPn5+b6+vkuWLAEAPz8/d3f3urq62bNnOzs7z58/n7yHe0pIv6qLFy/euXNHJvurUQHDsNjY2Js3b/r6+p44cYIsFLcg6lvXASCn5XqUQfP0oght/v4yhEZj+ouFk2Y6f7tVdPqq56krTmt/sJ04za//wIlONutE9pm93M71clsvcnCjU9U4MYDLAoA7mG5dWX0VprHsnP8JqksnereeZBNFhkxtJAA7l9oxQ7eNZ31LanVa+8Vv2bIFABISEswPi+P41KlTAcDe3r6goECn08XExACAm5ubSXrYIrjZ2ZILIMOFbEca5UB/3yeYH5kJWWDpzaIDwOERYZYKq39QadIafd2eJxGLogYNBIBffvnFUkM8AdmNq0UhPhKx6J5YZE+jAMBf1eeBnavmTO6AOfw3KS0tJWV+NRrNnj17AGDo0KFWHdG0aTd69Gij0ZiXl8fhcMhn4KHg56xZsxITE58g+PlktFrt8OHDAcDd3d1KNWu1yxbkBXhQEKAiSH6AZ+3KxW2NYFTITIKIErFomZstAMTYcCRiUYwNBwA2rO04rR+y7MCTQQOAA8+rM+jLf0fYu3fvzp0719bWXrt2LT4+HkGQM2fOYBhmZtgPPvjg119/5fF4J0+e7NGjx1tvvXXixAkHB4fU1NRH5PnNBGG0rK9+6marNBLG5kbtn7ctGL812rwclRG/p9FREUTs28tSYVuuCoVsAEiXYThAlKsjWH91FACMRuOkmW8vulthIAgUYDCPBQDfVjXfwXRGggCCUF+5UBrdv3rBDOmv2zQ3rxN6vbWn9N/B09PT399fqVSePXt2xIgRdDr9/PnzjY2NVhqurq4uJiamuro6PDx8z549JSUlUVFRKpWqc+fO77//flBQEIIgBQUF27dvnzJlSq9evdzc3F5//fWNGzdeu3ZN/9R/9+zs7LNnzzo5OWVkZJCF4hZHcysnV6U1EuDLprNQhOkvbmsElMu3nfsRymKT35IbE5myvzYmjp06beFJ/zOcsEgACBewACBNiukry3RF9zps9KfEwqIkzydxcXGbNm1KSkr6+uuvx4wZ4+HhoVar2Wx2uwMuX758w4YNdDr90KFDQUFBCxYs2L9/P5/PP3nyZM+ePS04cwDIunwZFkzVl5U40ijrRPb+bIYqM5Xh7WfZUUg0t27cxLRGAnpz6HwPS6ZzTmik9/0/3ejUSp3hlko7RF5nuiIx5w/xZAiCmDFjRnJunpCKPtAJOtGplTqDHZWSIcMyZBgLRXzY9CAOI1CGiWtqhBcyAABlsRk+/sw+QUx/MdNPjPIFjwbFcexqlvrWdUSrpXXpwRkahXIsvDL2MhEfH3/r1q2kpKThw4cPHjw4PT395MmTpEOZZZHJZNHR0ffu3SM37RobGyMjI2tqaoYNG3b8+HFyDw/DsJycnBs3bly8ePHs2bNkO9OBAwcAgEaj+fv7BwcHh4SEhIWFtZYDfe+99zw8PBYvXgwAiYmJdDo9JSXFwcHhEe9iS2FsatBXlZPromIOAwCYvQPbEUc4caruz3zV2dM4hnWiU3uy6HfVuqtKTbgNl05vJq9ISLFiK1FRUfHHH38wGIx+/frtUuKRQvbOOvlpKba4k40q8wy9S3frDd0envUtqRW5du3aL7/8snv37vT0dADw9va2SFiyJYNCoZAaHEuXLgUAFotl6vazOA0bVkvEoi5MGgAke7uWvz7cGqMYGuslYhG5nT7NkS87aEkrY01B3iN+8UH+fgCQlGQxZ5m/88EHHwAAi07/vYfLvYd+TDwKGilkkws1JlCA7iz6BHveN572ab6dTGtKZXFDaj9bKDu8VyspJHBcW3y/dOSQ4sG+ksDOErGoKMS3eLCvBc1xXj5a+8Vv2rQJAMaOHWvxUVQqVUhICAD4+vo2NDQ0NDSQ1df9+/f/J09gHMfv3LmzY8eOadOm9ezZs7WwHIIg3t7e06dP//nnn4uKiqKiojw8PAoKCgiCWLNmjQVdPx+LMu2EqQn9f50disN6P7246GOiZZwqHxctEYvedxECwBv2PIlYFD6gPwDs2rXLgtP+J8hq2xUjIgv7iMj2x9M+nSomj+yAodvEy5wICYL48ccfd+3aZfKLz8jIMDOgXq8fMmSIyQuePLcpFMrhw4ctMd/Ho87NlohFr9vzAGC+i1AiFj1WTslMmnduNZ2Bm70cVFnm/q7+Hzhe+trA1n7xn46OA4Dp06dbcpRWrFq1CgDodPqeuGESsWiaYytPYLFIIhZd8XPf3sVxnoswmMd6pOicR0GDeax5LsLEbk63A1pkO4qDfYv6dd3s5bC7mzP5zHV/jznOgqJB3srU41Y6ihed1n7xVnIG1el0pM0LuWmnUqnIWutevXo9vSewTCYj+3ojIiJaL1EsXLgwKipq+/btoaGhOI6vWbNmvZXbfhrWr74nFvEoKABk+bk9mGeBE6R8XFRST1cAcKRR7otFa8aOBIAxY8aYH/nJ7N+/nzQz+er9uRKxaJQtFwA+6mQjEYsqJieoLlm3E6xNvMx7hFKpdObMmffv38dxnM/nu7u7h4eHu7q6xsXFrV27NisrS6vVtjUmlUo9efLkkSNHpkyZsnv37vnz5yMI8tNPP40ePdoah0DC9BdTbO3/kq8FwM5bsu5fX1lWMWFE07bvACCIywjiMvtwmEaZ1IJDAIJwQiP7cplCKlqk0Rdr9OEGFQCkpKQYjUZLDgQAAFu3bl2+fDmFQtk8Mrp/VdGmaunOOjkNQbZ4OZLLTQBgT6OECzlktrvV2+NoT9dlbrYJtlxXOlVhxC8q1JuqpVPu1/a+VZbw54MvKpsOV9VWYpoCTFeibdlPUuP4WZma0KjrvviE0LX5f+m/AIIgI0eOBICkpCQPD4+AgAClUpmRYTHxZRzHJ0+efPLkSXJ73tnZefTo0ZcuXfLy8jpz5gyp+fI08Pn8iIiIFStWpKamSqXSK1eubNiwYdy4cREREQAQMmigC4f1w+TXpbt/Ul+7aKnJPxbNrev31TqFEe9EpzrTqMzebd4g/Duc0EhfNt2NTq3TG/MxbaiiDkGQ06dPazRWrB3Nz8+vrKysqakpLi6munQqNuBklUCaFAMA7Z2btR+92/DNCutNoG0860xsRe7evbtt27Zbt26RYvA8Hs9kHErCYrGGDBmyZMmSY8eO1dfXtyn41atXyZ6kjRs3Wmn+rald8VFBH08OBQWAzF5uVbMmWiqyvra6ZGjvbV2djvV0JW90Lvu5r3S3Kwr2VuflWGoU4nF+8V29vAAgKyvLgqMQBLF3714URREE2TA+obUn8GYvB9OaZ/Eg75LwPpqCPH1djSL1eP03KyrejDNJUl3yc/9fZwdStqP1paIfmzHHWbDKw4582YVebt4sukQsKh7sp7qQbtmjeGlo7Re/YsUKAJg1a5ZFIuM4PmvWLADg8/k3btwwGAzjxo0DAEdHRwvqFoWLA87063nZz70zgzbbWfC52IrqgLhWWzSg+yoPOwAYacuRiEXY9cvmh9XcvikRiyY58OChx2+Arw8AHD/eQSsZ9Ws+k/Ttkt/KGbRFBy7YR370MZI9Hc/LnAiJv50qxBOdwLy8vCZPnvxYwU+CIE6fPm2yic/Pz8/JyVm4cOGKFSs65kCUZ89IxKJoIRsAVrjbSYK6GJqbLBK5ev70on5dJzvw14ta8sQJb9e+XKZELCqNDbFgqwau1xeH+rf2i38vLgYAPvroI0sNQRDEmTNnyMqIFWPjJWLRdyIHUl/xa097iVhUMXFE5VsJD+ZOlv6eaFQ9qv9rVCnVudnNO7c+mD+9ONSf/G3c7O2xs6sTuYL6rrNgjrOgM4PWn8fsz2P24TDIRFjUv3vzbzsqKioIgjAajQ8ePLDgEb3otJb5zc3NhYe9TOZH/vjjj8nL2XPnzuE4PnPmTAAQCAQ5OZa8gIsY0P+0TyeJWLTc3VZIRT91s9VXVVgwfmvITRByCfFzd9uivl3+/l/aHnC89LUBv3Z1AoDuLLpELFqSEGvBK5Ino2tsuNmvRX9/qIAFDw26Wy5JQ/1xNdYB03gyL3nV6CeffLJ9+3YWi5WcnEwKoXl5eZFmYAAgl8uvXbuWlZV18eLFS5cuFRcXFxcXkxqJPB6vf//+wcHBgYGBgwcPFgqFW7duzc3Nzc/P5/F4GRkZKpVq/fr1HXYg7IGDURY7Qsg+LcXSZNgkBx52MZM3YpSZYXGVEruSRRgMj/+pTKotyGP49jZzFBKESmUPCh1yMomBIrlKbb3eGE4jNgP88ccf33zzjUWGuHLlyqhRo7Ra7YKEuEnFtzJk2MdlDTjAkk424+y4gnGT7ZesesLbUTaHGRDEDAgSwruAG3UlRZpbN3g3s23v3h5cfJ98zfoHzVMd+a/bcwGgRm+cXVQHAEChXKuogoKCAwcOyGQyPz8/HMf/1ZTuPwKNRhs+fPi+ffuSk5MXLVrUuXPnkpKS7Ozs/v37mxN206ZNa9eupdFoBw8eHDJkyOLFi3fs2MFms5OTk/v06WOpyQMAyuOjBB/06jft+YcalQCgupAheP0tCw5hQnPrBgDkqMiSUSa9py9qkZpqBGGHhA+o2SOgoPfUulKtfiihXgOQlJS0devWR+4HLM7CWW+fvl3+axcHZxp1mIB9Vqb+qVYuoFLEHIYDjYIr5CWD/WgiL2ZAELN3EEvcj+rqZtX5PJaXORE+cqr8/QXkxgC5DaDX63Nzcy9fvnzp0qWLFy9WVVWlpaWlpaUBAJVKnT9/PgBERkaSjRMdfCAAgDCYrP7BQ9NOUxC4otDIjDjnXKr5iVBfUYowGIReBwC76+XpMgwAFEac/CmB47ri+5ZKhADACY1kn04ewGWek6sz5eoxZffs+DyJRHKD0SWxAAAgAElEQVT37l1vb28zg+fn58fExKhUqjejIuZWFuSqtPNL6g0EMcdZMNNJwB0eb794RRvCoRR6l+70Lt35oycCgLGhrnxcJC6XAwCCABVBAMBkAYAADJ04acvR5M6dOxcWFp49e3bKlClmHs7LRHx8/L59+5KSkhYtWvTuu+82NTWZTDrbx65duxYsWIAgyPbt20eMGLFmzZpvv/2WRqMdOnRo8ODBlpo2yeEjRxTfLMeO/4EDscBF6M2mq86dsVYizLvRaDCWafUsFOnJojH929M48Vg4oZHyw3uH8FnJzaoMmXp6VUlnT4+SsnLzr0iezPLly384/AcDRSq0BmcaNQ/TsSloiVY/t7gOABxplEAuM5DD6CXPDyi6Rz2yDwCorm7M3kFM/z7M3kGMbj0A/X9WG4Dj2KVM7OI5o1xKdfPkRcVapBPjpU2Ej5wq//p6Go3Wr1+/fv36kTnvwYMHZL9RVlZWdna2s7NzUVHRrFmzZs+eTS7vdDyc0EhhZmoQl3lVoTkvV8dfPkfotAjdLJFDhEYnCIJ8PNK2xU66WKvfXC0DAARFERrd/JmbYAeHIRRqhJB9Tq5Ok2Lj7LihDPQIwKGtmz/b9INMJpNIJK6urllZWRQKpU31R0VFRdHR0c3NzXGhgz+XlRWq1DMltWqceMOet8jVhtU/xHHFt2DGlS/F3tFu3pLG774EaH7kRwiVxggIut0ki42N3bt3r1KpnD9/fnJycr9+/do93MtBZWVlSkpKc3Pz3LlzTX7xZEOeOSQlJZE62hs2bJg6dWpiYuLSpUtRFN21axdZPmpZuFwuEhaFHf/jg9KGE82qVR52rjeu4nLZY3pMzUaTl0sq/fbhMChIe1rp/wlW30EomxMhVCU3q1Kl2HRHfnQvn21l5UlJSdZLhD/88MMXX3xBQdH1XZz6chnrHjQfaFAwUGSMHbdGZ7yJaev0xpPNqpPNKgDgUtAADkPMYYjl6oCKcu7JowCAstj07j7MPkHM3kHM3mJCo61+f4qhpgrHMCAIoFJlv+3gDR9pv/RLM/UgX86qUdOpsn79elIIra2QxaVr1qzJyspqamp6++23AYBCoWzatIn02rbwjJ8CdmgEoJSW2lEphmOYOvuSmTFpnl4I3nL/J6BQXOhUFzrV4aHbGWEwmNR+LYL2Th5B4JECNgpwUaFW40Q0n9Gfx7Q5k6QtvHP//v1Tp07Z29tHRETcuXPn6cM+ePAgMjKyuro6rF/fb/UNFUpsmqRWZsSjhOzP3e2Y/mLn735EaLR/D/RE+AkTOOHR0zycR9hwyGccaZSt3u5UZ1enrzZ27dq1vLz8gw8+WLRoUWVl5bx588wc7iXAzc2tW7duBoOBz+eHhoYajcbvvvvOfFGn7Oxsg8GwcuXKBQsWHD161HSmT5gwwSLT/jusgUMQOmMIv0UbhTAYsEuZFh9FmXbC2NSQS66LcpnQ3lb6x4LQ6exBoaF8Fg1BclSaZoNxGBMBa6o7/fbbb/PmzUMQZN3o2GgeY1e9fFuNjIogmzs7rvW0T+zmlOPvcdKn07ci+wn2vK5MmsqIZ8nVm6qlUyW1gXnlwwuqPixt2FtRm3/1cvOv22oWziwNF5fFhVzJy/+5pBrIT2CD4f275YpTxxpWLzNzti9hIszIyJgwYYLBYFi1atWCBQvMD8jhcASClqu/QYMGeXt779u3z/ywbYUisGH694kUsgHgnFytJwhVprnytQiVKpg4DX2sSwaVxhT3o3VyN3OIv8Dx2s8XAY7b0yh+HIYGJ7Lk6mEC9p5uzq9x6XUrFwcFBTEYDBqNVlVVRTrJPQ1SqTQmJqakpKSvv/9mhrZRoZxyv7Zebwzmsb7v7MDq1sNl4y8mrSmzQBDHVet7rlhjL/ICChWhUelCYa+pM932n6AIbHg83tChQ9lstpOTU3h4OM3svPsSUF9fHxwcTFoU6XQ6V1fXNWvW8Pl8X1/fd955Z9euXcXFxe0I++WXX6alpS1fvjw9PX3ChAlGo3H16tXkQo6VQFlsVr/gCAGb3JhQGnHzT73WGBvrK6eNrlu+EAACuYwEW24wj0nh8alOLhYchRMWyaWg/XlMIwFnZereVcU2QmFBQcG9e5YXPEtOTp42bRqO4ytHx8UV5x1tUn5R0YQAfOlhR9bLAAAFgW5sxmgHwZcedqd8Ol30c9/s5TjNkR/AYaCASDT6o03KZeWNrxVUDcqvmFtc90udPFeO1euN9zV/6eFdUWiMGrXy9DGdpNCcCb9siTA7OzshIUGj0cydO/ezzz6zxhDffPNNUVGRNSL/K5ywKDc6tQeLrjTiVxUaVWYqPLyfazc2s+YzevhOdXcYxGtJh+4M2mciZ6qdg9MXltwN1f55h3h4NxApYAPA0Salkby1JgjdvYIz40Zc/+NQadqplGPHoqOjnyYm6QNw69Ytn+7df7SjamTSKfdrKnWG3hzG1i4OHHeR65bdll3C4r6W4HH0rNeFfFF6rijjpt37SyyTZV9G2Gz2vn37Ro0a9eGHH547d66pqcnX17e14GeXLl08PDwmTpy4adMmsv/hKSMPGzasqalp9OjRWq120aJFpLqTVeGERQqpqJjD1BHEebkau3iW0OksEhlXY5VTx1y8mp1c0wwAEQL2OpH98WaVEVM1J261yBAk7JBwhEqNfNiOjOq0UUFisIIlSGZm5vjx4w0Gw+Ix8W+U5KXLsI/LGgiApW62Y+1a9AhRFhvl8l037+p86U+3347Zf7jc67W4EZ3dPnWzPdTDJT+gpa/3NRuOHZVSrzeelmJfVTZNvl+DP241jtDrlafMOgrkmazyWYl79+4NHjy4rq5u0qRJiYmJlq2GOnPmjFgstre3B4CsrCwulxsQEGDB+E+DvqKsPCFswwPpDzXSNx14K93tOv16hGn26iVhMDT/vFm252fAjQAogRt4MQl285eiPL5Fpk2iPJNS/+UnuEoJAEealCvKm7QEzkBQbzYtiMMM5DJ8WAwWBeGgCMFi8/3FTPKrdyDKfbyhlU6jiYuOOnP+gpuz88FeHtyG2in3a3JU2u4s+t5uzvaurq47DljyjvYV7WL16tXLli2j0+lHjx597bXXVCpVbm4uuft++fLl1hrcHA4nICAgMDAwJCRk6NCh5Llm4uLFixUVFeT6Z2VlJdnjlJqaunXr1tYCaVbC2NRQGt3/52rp11VNI20560UOLpsT2QMfU4LXVpq2rJP+9suhB/X3NLpPOrUoAATllV/390AZLPeks1QHJ/NHIXnw7pully6E5FcwUeSav8eVrv5vH0oOCQm5cOGCpYa4detWWFiYVCqdOWL4ktp7V2TYjKJaLU7MdxG+7yJkePvSXNyAxmAFDeAOH4myOY+8XV9Vrsm9rr2br7l1Q1t4h7zQL9boc1XaHJXWSBDhAvbyikZPRsuO4C2VrqCPJ0p6va378caNG0FBQUVFRSKRiEKhPDq5J/AMWzcsS3l5OSkGHxcXR5pKv5SUj4n4o6cLADjRKPfFooZNa82PaTAYCIIgjEZNZbmuvBS3zm9PlZVRHOwtEYuSerpyKSgACCn/70qFgkBPFv1NB946kX2Gr9s/CX6S0WRH9v7YqzMC4ECnpvdyv9vHkxSHc2dQL/m5lwztoy1qmxf5K6zB1q1bAQBF0d9///2xLyD7emfNmuXj4/NIPiP7en/88cfbt2/jOL5y5UoWi0X28l64cMEamqVPpnLq6AxfNwDgUdC7fTzrvlpmkbAlkUESsWitp/0MJ77pf15IRSViUfHAntK9lrQqI/3ie7HpALC9i+OdIb2ZTCaKojU1NRaJf//+fScnJwAYNyz8ft8uR3q6kBogbzrwJGJR9cKZbbKnNyoV2JWsxm0bHsyfXjzEj/zNbPFyHG/PM/2ibKmUe2KRJLBz3aqP9+zZ8/7771dXV7/77rtSqbRNM39Jqkbr6+sjIyPLy8sHDRq0f//+dvhQtxUMwxgMRtsuOiwBOzSyV4nEhU6t1hluY9reR/YwvHtxwqKQ9h6yWq1OTEy8d+/ee++9l5mZWVtb+8knn1h2ziQISsHV6jKtfkZRrdKIRwvZmzo7NhmM+Zj2hlJ7XaW5jen+VOv+VOv21CsAwJ5G8WczerHpgQp1UEUZ4/gfAIByeQxff1yl1N//cxid+MrTvheb7kajLCitvyBX21Epv3R1cuJxnTf8RPfqZo2jeMXTc+TIkffeew9BkK1bt/5TV2Xrvt76+vrLly+TTb3Xr19v3dfbt2/f2NjYmTNnzp0799Ilc2vE2gcnNNIjL6crkybR6LOV2iHnUmHJKjDzZhTH8aaWe+KTzVgB1rLcaiAAAHCtxsytr0fghEY2rFsVIWDfxnRpMnW4QDbIzSVDUnLixIlp06aZGbyysjIyMrK2tnZY/36rFJVlmOZtSZ3KiMfbcj53t2MFDXRas7lNtZ0oh8vqH8zqHwwAgBur35+GXX28vh3KYst7+qvlag8PD2dnZ7JlvE28DHuEcrl8+PDhhYWFvXv3Pn78uPVsfUyoVKoJEyZYY5P5X+GERiAA0x35izvZONOouFxev+KjivjQdjuts1is4OBgvV7v5eXl4uJipVUmbWFBzZL3avXGKfdrG/TGYB5rQ2cHCgIONEq4gP1RJ5vfu7uYBD/JjYEGvTFDhpGCn/4mwc/y6qIL57S3b+FaLQCMs+P2ZNE/q2g82aziUdBfuzl5cTku63+yYLndK9pHWlraG2+8YTQav/76a1Ld6V9xcHAYOXLk2rVrL1y4oFAorl+//v3330+ePNnT05NsM/Xz8wsKCvrhhx+sPPfHQ/rqmQQzDfW12oJ8c4OiKFBb6qqihewdXZzILyp5FiLIP+0LtA+qSyeamydZcJcuxYwEDNUrAODQ/743M3JDQ0NUVFRpaWn/gN4baVijGptyv7bRYAwXsNd62jO7ezuv22ZWrxdKsVu0DGE+LgKKUOzstd5+RqPx6tWrVVVV5eXlba3DehnuCHfv3p2Tk9OjR4/U1FShUNgBI27fvv3NN9/sgIH+DpaVCQiSo9Ju7OxAXsUcrqrj1zZGTR/nfjiVYmv/L+//GyqVysHBQSAQVFRUhIWFWXC3wIS+sqz6/amNUulb92sqdYYADmNbF0f6w4SLsjjMPoHcqJHaP28H3rrRq/DOVBwHgDq98YZSc12lvaHUFGC625ju9sPrZVMfbhCXmdKsOtCgYKHIT10cvVl0TtSIlkvIVzw7rl69Skr8zJs3jxRCaytUKjUwMDAwsOWCBsOwdevWAcCXX37Zv3//r7/+2pLTfTpooi40T69I1d1tNbI0GfaZu63qXCrD19/MsAzvXpq8HABAEWD8fxcUlM1hBQ00J/iWLVs8PDzs7Ozy8/NramqWTJpoqKnswaK7M6gVWsMtTDuMzzrNZw2R10t/TxS+3k4VCIVC8dprr929e7dXz54/ClCsuWnK/doqnaEPh7GxswPLs7PrD7vMLzigd+nu+Nna8JWLQwR/aYWnBojoNjYuP+yidfLw6eUXHx8vFAoTEhK43La5hD7viVCj0VRUVHTr9ugyl1KpVKvVpH/m3Llz9Xr96NGjW9tpWg+9Xi+TyYqLi2k0mvl6KG3CUF8r++0nIIhrCg1BEOSyTJnWYG8kcLWq6X/fOny+tq0xWSzW6dOno6OjbW1tDx06NHnyZAvPua6mes5keV3tdEldkUbfg0X/uasTi0YFnEAQoHl6CafN4cUkAILw4sYAAI6ptPk31Tez2XfznXOzX1MqAEBuxHNV2lyV9oZSc0ula92HS0cQ0lYiiMsEAFwht+z8X9FWbt++HRMTo1Qq33rrre+/N/dWg8S0zGNjY7Ns2bKVK1f26NHDIpHbBCc0wr+s2JlGfaAz3MV0AZlnbOd8YGZM2zkfVC+cCaB89AcIitrYsoNDzQk+evTotLS02NhYOp2uUCga1i4nDDgARAjYO+vkaVJscScbUoO0+X9r+bFj2uEyrdPpxo4de/36dS+RaKc7H+pqpktqizT6niz6jq5OPBcX1y2/teMC/bFwo+M6d+netGWd+vplHFNT7Ow8hyfYzHzPlGXJHUrT9dPT87wnwvT09MbGxsLCwtjYWPKZxsbGqVOnqlQqHo9XVVW1Y8eOgIAAi/QLPiU0Gm3FihXV1dUczqMlT9ZGlXmG+IelS0KvV6amtCMRoihq0nCxeBY0ypqr576FVZbPLanPx7QeDOrOrk62tnauO37/pz08lM0xbQwQRoOu8K4m7wb31g3bW9dDa2sAwEgQhWr9DZUmV6W9INeocDyYxyQrZQAArGDq9Iqnh3SybWpqio+P//nnn62x0j558uSdO3daPOzTwAmLlO7aPlTA2tegSJNhPkX39OWlNA+ROTFZfQcJp7wT/fOWIZja9OTv3u4UPt9106+PCoy1lzNnzny8YH7pkllA/JUIU5pVM5z4dlQKAOBqTeXEEezB4QxvP1Zgf6pLp6cJazQa33zzzTNnzrg6O+/u2YlVUzmtqO42pvNk0HZ2dbK1s3Pd8ttThnpK6F17OK//yYIBSZ73RDhixIjDhw+7uPzVWPrpp5+GhYWR5uOXL1+ePHnyrVu3rK0b+3daT6nDMJSXEpqWs2XivRryQbXe+I6TAAAIgw7HMMuo9FoCQqOuWfi2uuj+wpKGLLnakUbZ1c25TZUsCIXK8PFj+PgJJkwFAEN9rfbu7YY1y3xqa3zY9MkOcEulHVNYfU+tJwAQAIROZ1hOleoVbaWuri4mJqa6ujo8PNziNWtjx45lMpkAgCBIYmJiVVWVBYM/JUw/McXOIUKu3tegSJVi81yEqgvpwjdnmBnW9u35TN+Axo1rdCUShIICSgmIG2E372Pzb6SuX7+uUql0Ot2QIUOMdbUInU7o9QBgT0M5KILheP+8ir82GjCtT1U5+UlKc/Nk9ib7l4LoXbo9Nh8TRsPM8eMOHTkq4HJ3irvZV1fMK63PVmqcaJTEbk5OQoHL5kSaqIuZh9AxPO+J8ODBg42NjcOGDTM9c+LEifz8lj3qgQMHUiiU0tJSLy+vZzTBDgUV2gCFCkYDAOzr7kxBEAD4vrrFQZcgAH3sZvKzgNDraz58V33rxrLyhlNSFZ+C/tzVyZ3Dcl63rd2VLFQHJ6qDExgNtZ8tItQYAPhzGE40ygOd4U+1zptFR1CUHzfOosfxiqdFJpNFR0ffu3evX79+R48eZT5WrsgMfHx8TI89PDzIXqmOBkU5g8MHNuznUNC7al2lzsA6l2p+ItRqtRDQ1/33k1qlQlpX5+RlseRhWkgLDg42NjfhOh0AVOoMU+7XqXDCkYKyUKT1RgOPgoo5jD4chlihCSgvYZOl2mw2vVuL4CcrIBDlCwFAeSblxueLD1+/x6agv3gIvWoqPilvOC3FbKiUxG7O7lyO84YdDG8/Sx2ItXneE6Gbm5uNjY1MJjNVwchkMh7vr0oqW1vbhoaG/0giZPULRn7dSmD6x/6U0bOXpRZSzAU31i1bgF0+v7aq+WCjkoUiP3Vx8uawHL/83vweZM7QaM6gUNXFc4QGQwCGCtj7GxSpUsybRRdMm0N1crbIEbyiTZASPzdv3vT19T1x4kTrM9RKyOVyFEXbWhNhPuzQSPrR3wfzWKekqgwZ5pZzrfF/awUTpprT9n7w4MHc3NyFCxdu27YtNDQ00nKJsDW4Qo7geKPBOF1SW6M3iDmMxG7OdAQp1upvKDXXldrbmFai0Z+Tq8/J1QBAQaAzgxbEZQZylb2aZN1uXQcAQCl0kRfCZOruF7rpdXu7OTUb8N5M6urKpkONShaK/NjFsSub6fTlBlagFU0tLM7z3j4xcODAiIgIT09P0zMeHh6m0lgcxyUSyX8kCwIA068Po5s3Qn2MIwTKZNnNt0r/X5shiPrVnyrTTvxQI91RK6MhyA9ejoE8psOnq7kRMe2Oevfu3eXLlwPAzz//vN+1mzH+dYTJBASJeCgZBQCUvwlVvKID0Ov1Y8eOzcrKcnd3P3HihJ2dnbVHxHF81apV1hOMfgLs/sEIlT7Rgfupm22EgA0EIfttR0XCUOXJ9k8mJCSkqalJIpHgOH716lXCCmpfhpoHD959Q6HTT5PUFmv03iz6jq5OLBShINCNSZtgz1snsj/l0+mSn/v2Lo7vOAkCuQwKIBKNfn+D4qPShtcKqgbmV8wqqvuxuulSXr7iTh7p3ebNog/iMTdVS3fWycmaNTGX4fj5N5xhlrcBsSrP+x3h35kzZ84HH3yQmJjIZrPXrFkTEhLyiBTTy43zuh+rpo1JEwPF0HJf+K6zkMJg2rz3EUv8XPj+NG5aIz/6+94GxYYHUgoC34nsh/BZdvOW8BNeNyest7c3KX1eUFAgEAg4M2c6v/dBzUezB10+z6GgBZiuUmdgZaYK3phuoeN4xdNy48aNs2fPOjk5paend8yK5e7du8eMGdM+zW4zUWWcJghDmlT9hgPPlU4FgCvNyqtKzYLVn1Ds7Fn92ty6QxBEQ0ODWCxGEMTFxaW+vl6n0zEYltzjMEqbH8x9S1n94O2iujsPK1n4D3WdEBab0a2n7ZwPdJJC7s3rLtcvh0ubAUCNE3cw7W1Md0OlvarQ1OuNGTIsQ4YBABVBerJogVxmEIdRoTNsqpZSENjQ2X4wn0X39OLFtsFA7TnhxUuEs2fPZjAYr7/+uk6nCwkJ+eWXX571jDoUiq2d+4FTvN92yI/sM9bXInSGTe9A29mLmL06Wvj0sUh/3Srdtf20FFtZ0YgArHK3i7Hh2EyfK3zrqVqq/xWCIPh8vo+Pz927d12HDROMm6y+cmEwj3lKip2VYW652UZpM0VoY5GxXgEAGo3m+vXrQUFBj+z51dbW1tbWdu3alc1mDxgw4MSJEzY2Nn9vc7ISNBrt9u3b1dXVHTOcCcJgaPjmczDi5Vo99tC/WoHj1ToDrlbXffmJ57HzbY2JIAiCIIGBgYMGDWKz2RwOx7JZEFcpq9+boi6RvF9SZ6pkcWDSESYLcJzq7imcMJUXNxZQlNV3kGDiNHgo+Km5dV1w83pQiWQqQQBAhdZAyj+17utNBOBSUARgtYf9cCEHAIxKhQUn32G8wKLbTU1Nx44d4/P5bXJwfck4efKkh4dH9+7dL168GBYW1vETwOWy5p1bFCeTcGkzQqXiGvUFGTarqE5PEB91snnHScAf84bD0tXmD1RaWvrTTz/FxcXl5eXp9frx48c7ODgQWk1JuPiPB/UfljYE81iJ3ZwcV63njRhl/nCvILl69SqTyTx+/LjJ4UGn002ePLm5ublLly5ZWVkffvjhlCntbMQ2B51O19TU5OzcoVvCmpvXq+dPw5XKGZLaeS7C3hwGAKTJsHQp9rWnPcpiue0/SXPz/Nc4HQah1VS/PxW7cXVJWcPhRqUNlbK/u3NXAc9lc+JTLiDhKqX29i31zWzNreuamzcIrQYApAY8R6XJVWnPydT3NHpXOoVUYQUAtdA2feRb1dXVb7/99t69e6lUqpUkGy3Li3dHaOLChQvTpk3r16/ffzYRSqVSb2/vLVu2zJo16/Tp0x2fCPXlpVUzxsplco5RDwCEXqfGCSca1Z5GiRKy33EScIZGOyxZZZGxRCLR6tWrAWDAgAGmJxEGk90/OCz9DAWBq0qNzIhzzqW+SoQWpH///jk5Oa1v9bZs2WJra/v7778DgFwuDwgICA8Pd3fvaJcPOp3ewVkQAAy11fDwxuHT8kY2igCAzIiLOQwAQKg0Q23Nc5QIcWPtZ4vUN65+Vdl0uFHJoaC/dHXsQlayPPU2SmvBT8Jo0JcWN25aCxczwwXscAF7rrOwb155hdZQozc406gAiF1v8fz58z///PMzZ87MnDnzp59+MhgMHSD+bCbPe7HME4iKimKz2dnZ2ZWVlc96Ls8GoVBYW1uLomjXrl07oELhEQiDoXrOJEVjY8ytEtOTn5Y31OoNST1dlrnZsvoHO321ydqFrJzQSCEVDeIyDQRxXq5WXz5H6LRWHfE/RXp6+qFDh1pLdZB2uORjPp8fERFx8eLjpZBfPihCW+ShCtpqD7sDPVwO9HD5qFPLUjyBG5+jZXmCqPtyqSr95MaHlSw/dHbw4zAdP/uaEz68fSERCpXepbvD0tUoo6Vej4kiwTwWAZAhUwMAwmQIJ89KTExMSEhwcHCorq7W6XTPfxaEFzoRslisiIgIgiCOHz/+rOfybKivry8oKNBoNJWVlXl5eXl5eR05uirtuFEmJYUqHsGWSqHa2Dt/tx2hP6bA1bKwBw8DlNJSOyrFcAxTZ5vrToAr5KpzafIj+7DL58m1oP8sfn5+M2fObN2lIJfLW3dH8Pl8mUz2LKb2DGD498H1/+gejFBp9M5dO3I+T6Dx+68USQd+q1f872ElSwifZbdgKW+kuY22VCdn+yVfIMwWLSeTCjkAsIMGNjq4SKXS7OzsuLi427dvjxgxwszhOoYXIFc/gfj4+GPHjiUlJb3zzjvPei7PAAcHh4kTJ06ZMsVgMGzcuNHiLcxPRnk+HcdUAKAniPualhJWxcMKAkCRjrFup9jaMf37RGmurK5sypSrdQShykxlBw9tZzjc2LR1g3TPzwiNBjgBCAKE0fb9JYLxkwGgtrbWwcFBrVbX1tba29vz+ZY0Ln4+cXR0dHR0bP1Mt27d7ty5Y3K6yc/Pf1E+7MwHZbGFU9+VJW5Dkb/8lxCy4IXFspu/BDpc4uqxNG/fKP1tR1KTalVFIwLwpYf9cCHH9p0FwkkzLRKfFzeWYudQv3qpob42QsCmIHBZoVEacS6L7ebmNn/+fPJlpLvWC8GLnQhjY2MpFEpGRoZCoeiAHt7nEDL50el0uvXvvR4Bb24iHyiNxK91LVLXxZqW62VCrX7826wAJyyq083rPVn0P9W6qwpNWGaqwydftu8jqW75B0WnT1xvlMbYtLQk7mtQJGxYbWxqqB8ac9VJztYAACAASURBVO/evatXr3p5eTk5OQUEBPwXEuHfmTdv3uuvv96jR49u3bodOHBAKpWGhpolDP1iYTvjPUNZ8Q5IJbWNAGCYkBPhbCcYO4kXb1aDkKWQHdzd9OP3GTJscVk9DvBJJ9txdlzBuMk2s+ZbcBT2oFDP4xebd/wPtq4Xc5jZSs15uTr24llCp+uAdSCL81xcv7QbR0fHAQMGaLXa06dPP+u5vPykpqZu3rz5+PHjBEHMnDnzoh4ARQDAhoqu9rAjv3pzWs4BqmP7hTbaCicsCkxLNDLM2NSguXOrHXGwy+eVmamVCuXRJpXpyd31CoUKk+7a3pmGEgRha2vr4eFRU1Nz48YNS83/xcLf33/Pnj3bt2+fNGlSZWVlWlpaxyv9PktQ1PHL753Xbmb1HYQKbSi29uzgMNf/JdotWPosZ4UbDbXVRlmz8lRSwzcrclTa+SX1RgLmOgtnOPG5ryXYL15hjWH5oycCippELXAMU9+4Yo2BrM0L/x8cHx8PAM9EY+K/RmRkZEVFhb29/W+//RYbG8vqOwhlsB77SoTJ4pq9FfH00Nw96Z27kmdjqhQjAFSZqQqF4osvviguLj558uSmTZv+PW/hePP2jabL/Ecg9Pqb2zf7+vrW19c7ODgMGjSooKDA4gfyoiAWi3fs2LFv3z53d3dLGS29WLCDh7pu29Pp5JWsKfNrp81j9ulbVVV18uTJjp8JLpfVr/6keLBfxZiI0uj+tcsWAo7/r1qqxok3HXgLXYXsweGOK7610potxdae2SuAdPo9K1PrCUKVmWqNgazNC58IR40aBQApKSl6/eMVOJ+e8vJyk6S9wWDIzs4mH585c2bjxo0nTpxISUlZv359ZmammQO9oFRWVi5ZsiQrK0sikeTm5hZp9OxBQ6gMVh/OX/2/XZl0IZ1GsbUXTujQ3jJ2aKQvm+5Cp9bpjbcxLXYulcfjDRgwQKFQXLly5fXXXz9w4MBj30hoNarz6fWrl5YOH0D6owJAoVr3WXkj+VVHFkfgRqfqspKSkoULF3p6etbU1CxatKjDju75RK1Wz549++uvv1apVP/+6peR5ubmuLg48l8rMTHx9u3b1h6xrKwMAFQqlUQiAQBjQ13F69GKlD9wjbpCJpdptEAQALDFy/HjTjafu9sx/cVOX/8PsWbdJic00oNB7cqkKYx4tlKLnUuFF7A3/YVPhF27du3Zs6dUKs3KyjIz1O7du00fl1Kp1GTOFxUV9cYbbxQXF8fGxiIIEhISYuZALyhNTU2HDx+ePn36ypUr58+fP2bMGMcvN9qGDtvo49FyvYnAe11cA3v5dvrlIMLo0ModTlgkAhAuYAFAmlStK5Hoy1qaOubMmXP58mWTaDuJUdasSDlS+/Hc0ojAmoUz6w/tPV9a/kVl01RJLQC4MaiTHHjkl5DS0v5BpdEjIiIcHR2FQuHQoUM7flP2ecPJyalv375qtTo19YW8CTAfJyena9eu8Xi8AwcOuLq6PnjwAMcfU0RtKYqKilauXAkAK1euvHjx4sWLF2uWvGdobLzWJBtx98F3D5rnFdfPKqpTGHEWirztJGB26+my8Rdr16xxwiKhVe2oob5WW5Bv1RGtwYtdLEOSkJCwZs2apKSkoUPbWyv4ROrr63ft2jV37ly5XM7n81+Ithhr4O/v7+/vTz62tbW1tbUFAKe1P2gL8pVnknVlxVR7J/bgcE7I0I6vnWP69qbYO0bK1XvqFWkybKGrsPnQbzd0VAdHR39//8rKyunTpwOAvqocO5+uupCuuX6FMBqlBjxDhqXLsPNytRpvuYydYMfjoGgPVkueo6MIACB0OqvvoA4+qOef+Pj4q1evJiUlJSQkPOu5PAOKi4slEonRaBwwYEB9fX1hYaFVh+vSpYuvry8AUCiUixcvDnB31f55W6PXLS5r+LWrkyeDBgCbqqU/1MiWdLJBqDTXH3ahfIFVpwQANFEXmqdXpOruthpZmgz7zN1WdS6V4etv7XEty8vwmR4fH79mzZqjR49u2LDBTEfsnJycvXv3AoBSqTQ9mZaWRnbuc7ncceNe2d09Cumd+4wngaKcweED6vfxKWihWlehNXj8njiBL7BfvJIbGOjHoKgO/FqemaovLQKASp0hS67OkKnPy9WGh8s4XZm0YQL2UAHrsf9CCErhj5rQoUf0IpCQkLB06dLk5OQXQj3E4nh5ec2ePZt87OHh0Vp2wBooFIrKysqysjKNRjNq1KirKcdCcOK+Ru9Gp5JZEADG2HHfLqpb0skGgDC1+lkbTmiEf1mxM436QGe4i+kCMs/YzvmgY4a2FC/D/26/fv1cXFzKysry8vJ69+7d1rcXFxcnJSWRyixqtbq5uRkAWm97TJw40YKzfYWVwDEVFUEG81nHm1VpMmwag2psbqpbtqD+iyVkv+N9jf5ksypDht3GdORbKAgEchmvCTnRQrYLnQoAVHvHxob6Oa2kuz5zs7XhcBw+W0N1fOV0+Cje3t7du3e/d+/epUuXhgwx12nyFf/KpEmTAGDp0qVVVVUDfHs0XzghM+Bcyl8LMHwKKjMYAQCh0XGlEuV0hF8jJyxSumv7UAFrX4MiTYb5FN3Tl5fSPEQdMLSleBkSIYqisbGxP/30U1JS0tMnwjt37hw8eDAlJSUnJ4cgiICAgLFjxwYHB8+dOxcAGhoa/mu+Fi802j9vq86lAkCEkN2SCB35AGAwGHNljSel2OlmrOahJggLRQbyWK/ZsIcJ2HwKCiiF6deHM2QYZ2g0zbOzfdoJ2+9W4XI5QqEQBsMQTy+HT1ezAgc8afj/MCNHjly3bl1SUtKrRGhteDye6abTwcFBWV0KLLaHWlei+atOUKLRk3eHhEFPsesgfzqmn5hi5xAhV+9rUKRKsXkuQtWFdOGbMzpmdIvwMiRCAIiPjycTIend+k/odLqzZ88ePXo0OTnZVCAqEAhee+21hIQEshDrFS8izT9tIrRaAAjjs2gIcl2pOdakPC/XpMswk9iNHZUymM+KsWEP5rNoCIIwWay+AzlDIjhhURTbv5RaORExnIgYfWUZrpBTbO2pTi7P5pBeEOLj48lE+N133z3rufy3YA0KBYPBg0H1YNC218recuA3G4xrq5qnO/IBRVn9Q6xaLPr/QFHO4PCBDfs5FPSuWlepM7DOpb5KhM+AiIgIHo+Xk5NTWloqEoke+alKpcrIyDh48OCxY8dMuoiOjo7R0dHjxo2Ljo4mKwDT0tJotJaldhaLNXXq1I47gFe0C13RPWXaCexChvZuPgA80BnSZBiHgsiNxKLSBvI13iz6MCE7QsD2ZdMRAIqNLXtQKCdiBHvA4CdIYDxHHgLPN4MGDXJycioqKrpz5w5ZytFuamtry8vL+/btS3574sSJ6OhoCoVy+fLlu3fvAoDBYNBoNJ06dRozZowFpv6CQxHYCKfNliX+uKmzw9Ya2dtFtXwKOtOJHyFgIxSK/aLPOnIy7NBI+tHfB/NYp6SqDBnmlnv9xXIGfUkSIYPBiIyMPHLkyPHjx8m1TQAoLy8/depUcnLymTNndLqWbSEfH5+4uLjY2Njg4OBHyiIiIiJMjzkczpIlSzps/q9oA7hRk5erTD2uOnvKUFsDAPc1etI7O0epJUtfUAA7KmW2syBCyHajUwGA1smDPTicGzmC6S9+TgQhXw5QFI2Jidm5c2dSUpKZiTAnJ2fPnj2//fYb+e2777579+5dDoczcODA7OxsJyenO3furFq16vPPP3+VCElsZ76PNzVB8oFFrigBxF217rQU68KkdfcQ0Tw7d+RM2P2DURY7Usg6JVWlSbG3HPhYVgYv9oX5M70kiRAA4uPjjxw5kpSUFBYWlpKSkpycfOnSJdJ2mEKhBAcHx8XFJSQk9OjR41nP9BX/iKG+1thQR7Fz+HtlCq7G1NmXVWnHVefScKXCSECuSnNWpj4jxUq0LXsk5OZfEJfxbVUzhuMT7XkMFEFQlBMV57T6vyiA0jHEx8eTidBk3mtZKisr33vvvRUrVhgMBgzDKBTrGnu9SCCI/ccreHFjmrdvUF04u6tOfrBRSUeQzuWlhgeVVFe3jpsIg8nqHzI0/RQVQa4ptTIjzji4hx0S/qLcFL4kiVCv1/N4PBRF09LSevXqRT7J5XKHDx8eHx8fExNDNr294rlFnX2p/uvPDDVVCI1B6HVURyf7xSvZg0INNQ+wS+dU59PUVy4Qer0aJy4r1CeasQwZJn+4+WdLpQzhs4YJWGECNgtFAOB4s+oOpruk0AwVsIBGt5kx95ke3EtOVFQUh8MhnUHd3Mz68C0sLNy4cSP5WKFQkA8MBsOOHTtmzJhBoVD27t07b948c2f8csHw8XNe/1NpZN8IGXawUZkmxeY4C1QXMgSvd6j5Aycskp95ZjCfiQAiM+CC2zfLYgbyR46zW/TZ8y/D/WInQgzD0tPTU1JSjh49WldXBwAMBoNOpyckJMTFxcXExHA4nGc9x1f8O4rkQxVfL7vdKA/kMsial+w/73WfN53v6GyofQAATQbjObn6ZDOWJVfrHnb+uTOo4QL2a0K2mMtEARAmC/Q6wmgEgAgB+w6mS5NhQwUsXuxoule3J4z+CjMhnUGTkpJSUlLefffdtr69sLDw6NGj9fX1w4YNY7FYLi4t1UmmOz+RSDRr1izy8cyZljESetlAKeyQocFNh1koko9pa/QG1rkzHZwIcTUGAME81lRH0pWFuFAvdT2w17ekyHXrb8/5fsQLmQgfPHhA2hCePXtWq22xI/fx8XF1dU1LS4uPj9+1a9ezneErnh7Dg8qGNZ9Vy5VfVzUd6tHyIbjuQfMyN4RTXp4uw05KVa03/3qx6eEC9ggbThcmDQBonp05YVGc0Aimn1h29PemjWsITBkhZG+slqbLsC/ADmW9uhiyOvHx8UlJSUlJSU+ZCHEcz83NTU5OPnjwIClfTqPRBgwY4OHhMX78ePI1H374oRVn/NLBCY1UpBwJ4bNSpVi6VD3pxlVcLusAZRkSY3NT06Y1ALC5RvowEUKqDOtvxLveyVMcO8hLeC48qv6JFykRFhcXk2eOafMPRdHAwMDY2NgePXoUFBTMmDGjc+fOp06d0ul0r6QgXxSk+38lb+MeoUSrf7+4nnzMQpEQPmuYgB0uYNlSKYCijB6+7MHhnCHDGN5/idoIRk/kx4+rX/2pd9IBNzq1UmfIU2n7Zp6xW/hMLXL+A4wcOZJCoZw9e5aUIfynl6nV6rS0tGPHjh07doxcwgEAOzu7ESNGjBw50lSz/Yp2wBo4BKEzIgTsVCmWJsPedOBhlzK5w+M7ZnTlqWME/nitbVytku75+VUi/Bf0ej2O4wxGi4OBUqnkcv9SQzAajZcvX05JSUlKSvrzzz/JJ1ksVnBwcGxs7Pjx400LKevWrROJRH5+fvn5+ZmZmVFRUR18IK9oH5obVwi9HgAaDcZ9DS3bQnV6o4hB68Wme7PoEUJ2MI/FRBGEzmD2CeIMHsaJiKE6PN7vEKFQBeMmKZIORAjZv9bJ02RYQGWZrvj+q9VRq2JnZzdw4MCsrKzTp0//XYawqakpPT09OTn56NGjpp0/T0/P6Ojo2NjY4cOHkymwuLi49bs+/vjjV5ezTw/KYrP6BQ/LTKMgcIX0i89MNT8RElqNvrIcodFpbu6A/mOZkuZ2LqHVAIAWJz6vaCSfzFZq+3OZAKCvKDNzGtbm2SfCnTt3lpaWfvXVV+S3IpGooaGBvHJMSUk5duxYTU0N+SM7O7uYmJi4uLjhw4f/kx99fHx8fn5+UlLSq0T43KLVau/fv+/j42M0Gm/evCnUaMndAxQQJtrS0IIAIABHe7oCAMoXsvsNYg8exhka9TSSUYyevahOLhEKza918lQp9qGrjepc6qtEaG3i4+OzsrKSkpJMibC0tJTcODx37pzJJc3Hx2fcuHFxcXFisfiR/iUvLy8vLy/Tt6Y+qFc8JZywSGFWhqX84o1NjfXfrsDOnkYYTMAJQAjBpLdtZsxFKI/JGsjDu3kagkywb/lwbjY89OJAzZKA7gCefSL8O4mJibNnz1ar1eS33bp1S0hIGDly5MCBA/+pcjo3N5cgiMLCwvj4+C+//DIpKWnz5s1mCnC/wkpcv37dYDBs27atubk5JiYG7dYTyooBwIaKjrJtyXOHG5UAAAgw/QNdt+9vm0YGgnBCI/vW7BJS0SKNvlijZ2am2kybY/kjeUUrRo0a9dFHH6X8X3t3HtfUme4B/D3ZcxKSsITdDaEKCLJIlSJLWUQQiO0Udx3tvfUztYNOrTO9M+1tre3c1tvOtbej01brrdbaxTqtARSRAAoWFZBdUUERBdmX7HvO/eNgyih1YQtynu9fSQjJQz+Nv5xz3vd5srOrqqqOHTuWnZ1tHYZM7l9KT0//zW9+M8JlpeABeNHxXf/1RrwQL1PpZHJNsr1Ge/E8Hj6c1nemjraWNakX2zpmM+kcoxEh1G40NX3+99ll510//Wr/lweam5s3bdq0b98+i8XyzjvvcIOfVslyCK2GhiHfu5Nb7BkDC2RYPrNH628cIxMiCHt6esjOESRfX1+tVmv95vgoPd2Dg4ODg4MRQgRBeHp6trS0VFRUjHUzeDA8ERERO3bsWLhw4cGDBxkMRkTQAvfifKTT3/9MGgd3eOWPw+gUxYtJkB/5KlqAS3tV+XKN16VqU2c7dM0eO+RRvr29fV9fH/lJRAiJRKKkpCSJRJKUlPSAC4dgtNAdnDj+cxO0Ze+39lrnxQ8vCNtff8Wi6N/e1Lnby5lsSXFGrr2lV77OrZYf/Cwp6bm33npLIBBcu3bNxcUFIcRLSOne9dchLxLSuLj9i78f0R829iZEEF64cGFwo8KwsLC2tjZX1+H8s4VhWGpq6qeffiqVSiEIJ6aSkpL4+PjS0lIPDw8fH59eBmO2ZJnDj9+lO+qsz3nOgS+24/ETUrih84fxFpzQBTQ7QYJILe1VyeSal1yEmrOFgudhisgo6+vrk8lkWVlZ1uaFHA6HwWCsWrUqJSXF2rwQjBteTMLU2kpvDrNRZyxT6aPO5KH/2IEe89yYofGq4Vo9YRpiCZtFq+37ap8wbUVkZKRMJlu5cmVNTY1SqbSzs3Pd+Y+2117aOU1sffJyRztHO5yfkMKLjr//pSYUjCCGXuozFg4cOFBXV7dly5YDBw54eHhs2LABw7C9e/cOvkbo5OTU3d09knfJzc1dvHhxYGBgdXX1aFQNRplGo2loaJgzZ45Op7t9+/bs2bMRQfR9+Y++L/ZgTAZhMGBMFmEyitZtdNi4Zdjbjzr+srkzRxpWc9toIX4OmDItJt7tExgnMjqam5vJ/UtFRUXWi39BQUGhoaH79++fOnXqzZs34cKETRiaGm+/kPDRnb7P2uXrxIK3pjh4fiV9xDG5hNmkr61S5R1XnvjJopAjhJZeuZNkz3Ng0BFCZSqdmEH/o4c94uLl6zYbcd7y5csPHTokEomef/558hX09bWdO143Nt/AWGxkMWM0hv2mrcL0tY+bxONvXI8I169ff/Dgwaamptu3b9vZ2ZlMprFYMB0bGysSiWpqapqammbMGNeGe+BR4DhOTsvi8XizZ89GCCEMs3/xFeGyddryc+beHrrInjsvfIRboHjRCXhuVrgd57Rce1qhXVb2s0WjpuGwp3D4Ll269GvNC4ODgw8fPrxv376TJ0/eunWruro6KCjI1vVSEWuG9+POi7co5JrSnzVFMrJ5oYkgLqh0sn7tSic+Quj+BKPRsOcTE5jTvBBCL7744uAfsX0Dpnx7wtTVYWq9rWOyMHdPob1DR0eHWCymwYZ6q8LCQqFQ6O/v7+vrKxQKW1tbp0+f7u7uPvi/UURExAjfhclkJiYmfv/991Kp9A9/+MMIXw2MGxrfjhczamt98YgYjMmMF+Kn5dq8fk26I19TUsSPTxqt15809Hp9b2+vdRtSS0uLq6urddy8df/Sjz/+2NDQQD6I43hsbCzZvNfZ2Zl8sKamBsOwJUuW7N27VyqVQhCOp+bm5mnTprW1tRmNxnvmxc8tODnkvPiHNi8UMWgIoWR7HnmN0EIQt/QmhBBhNDLcHrTiiSF2YYhd/v7hhzQazdHRkcPhNDU1/fnPfx6Tv3yUjGtK19bWdnZ2KpVKf39/Jycncl5SSkrK4LZJUql05G8kkUhG66XAE4rGt+OGLogX4jSESpRarYXQnDll66Imotra2sETxyQSSUtLi0ajycrKWrdunZOTU2Rk5M6dOxsaGsRi8dq1azMzM3t7e7OysjZu3GhNwcG/juCjN74qKip27dql0Wh279594MABTWAohtCzQi5CSCbXGJsamxPnK/55GBEEQkh/9VLv5x+3rEppXhLR9de/NBXkfX2nd0NjR0j1rY3XO4/1qhRmiw+H+bKrcLEIv/+9MCaTH5/80C0ZJ0+ejIyMRAg1NTWtWLHC2v9rwhrXI0Jrt9z7RwaOruTkZBaLVVxc3N3d7eQ0ojHNLS0tarXaOrMiPz8/Li4OIVRcXFxXV8fn8x0cHBobG+fNmzfyY1kwuvCYBKfzxQE8drVaf1ahTTx7mjCbhtwFBQarrKxcsWKFdXKZv79/Wlra0qVLw8LCfu3KX1tbm4uLS2VlZVxcnJ2dXWVlJVyYGDchISEFBQUsFstsNqvV6lbEENPp8SLcOi/e1N3Z/bf3+g/tQxgy3mpGCN3Wmx7avBBh2AuOdnb0gYMlX5zlzmHTBSLH1x40/JzU2dl569at+vr6WbNmXbt2zTxU66gJZXL+oyAUCqOiomQyWU5Oztq1a0fyUrm5udeuXdu5cyd5d/ny5eRansjIyKCgoM8++2zFihUFBQWxsbGjUDcYVbzohO6dbycI8Wq1XibXJCj6dZVl3Hnhtq5rwunr6ysqKiJvq1Qqf39/LpcbEBCQkpKyfPlyX1/fh76Cm5ub9YOWmJh49OjR7OzsjIyMMSwa3NXV1dXa2trd3b1q1aoff/xRLP0GISLcjmOdF+/JYhB6ne5WMzm5LE+uuaEbWOLEoWHP2HFjhdw4IS5m/tK8kJ+wxCLvX//n31s0WsJkRBg2l8Nlz/Jz+WD3o0xWWrduHUIoJiZmxowZMpnstdeGODc7oUzOIEQISSQSmUwmlUpHGIS/pre398svv9y0aVN/f//WrVuzs7MDAgIe/mtgHDGcXdmz58RrKz6605cv15gJQn7kK05AMMbm2Lo0m9FoNDiOEwSh1+s5nIH/Dt3d3Tk5OeRthULBYrFaW1uHPblFIpEcPXpUKpVCEI6bNWvWIIRoNNrLK5arNywlzBYWhpHz4k/1q6ezmQVybb5c02UcODITMWgxAjxOyI0WcnEa7deaF047UaKtLDdcv0pjc9hzglgzn3qsqp566imEUFLSE3BhftIG4XPPPbd58+bc3FydTmf9wA9PTU3Nvn37yNvW3Sbl5eU4jpeUlPD5/NraWmvLfDCh8GISvOtrZ3FZ7ix6v9lCLzh18+dQ0cbN9us2Tvwl3aMuMzOzqanJy8vr/PnzbDY7PT2dPNrz8fF5//33yeecOnUKITSS+WXJyckMBqOoqKivr8/e/smYy/pEE4vFYrHY2HqLW1sm/+FrwmBACHUbzXZ0DCH0QUvf3UZnaAabmSDC44R4MJ9NG9y8MDZx6AXVNDo3dP7w9vI+WSZtEHp4eAQFBVVWVhYUFCQnJz/urzc0NBw7dgzDMHt7exqNdv82j8G9TMPD4WzbBGVRqXQW4t9dBAPN2wiLrKM7YM//zGy5KX7jfVtXN97S0tLOnTvX09Oj1WpdXFzKy8sf5bTn43JwcIiMjCwsLMzJyVm1atWovz74hcWsrSzXnMlTn84ztt5Cdy/+Fcq151VaM4FoCBEI+XJZCSI8VojPwVkIIabnNF5MAh4Vzw2e94A+2pQyaYMQISSRSCorK6VS6SMGoXVGmrVNorOz83vvvTdnzhzrsjqYkfYEMbY0y3/4SmW2fN4ut3YxPdKtEtBpLjmZ/EWp3LBnbFvhOCsrK7t+/fqaNWtiYmKysrLIM1cODg5RUb904UpISBj5OGuJRFJYWCiVSiEIxwKh12ku/KwpzlefkZl7uiwIVar0+XJNXr+mST9w8Y9Nw6IF3Jt64w2d8SUXYZoDDyGE0eju+49wAkNsWv5ENK6dZcZZVVVVcHCwi4vLnTt3HrCdU6fT5efnS6XSrKws66QLe3t7ckZaf39/Y2OjdbHMyBvfgHHT+/ed/V9/0aXVr2loP+nnQT74u+ud/+4imMfn4BHPUq3XTEFBQU9Pj6+vb0dHB5fLfeaZsfoe0NzcPH36dD6f393dbZ2wNurMZjPZhd9kMjEevyHtBGWxKKRH+r/+wnT7JoEwtreP6MXf8+OTEULm/j7N2UJNcb6m5LRFo9FbiHK1rkCuzelTdw66+Bdux40VchOEOJ9OO9CpeK+lN9me98kMMaLRuPPC3T/92qZ/3gQ1Wf7vGUpQUNCMGTOamprKysrmz7/3NLd1RppUKlUoFOSD1hlp1jaJFy9enDp1qvW3tm7dOm71gxHSVl0kTCaEULfJvLO1j3yw4e56Of3VSzarzEasa5vnzJkzpm80bdq0wMDAmpqa06dPJyYmjuSl+vr6ysvLExISyLs5OTmRkZF8Pv/y5ctnz57t7OwMDw+/evXq/PnzJ0FvYcJobNu8vquygmvQ0jEMIaS8crn3zVcd/m8Pxubo6qqQxdJvspxTavPl2jy5Rm0euPznyWIsFHBjhdwoAZeBkUPMCIRQggj/a0vvablWbyHYDLr4zf+y5Z83gU3mIEQIpaamfvLJJ1Kp1BqEzc3Nubm5WVlZubm5g2ekpaampqSkRERE3LNT6p5P11/+ArPOnxjY3SlofBotXjiwO7hKfXdvL/UWy4wniURSU1MjlUpHGITNzc07d+60BuH27du/+eYbPp/v5+fnlaX8LwAAEFlJREFU7e29c+fO/Px8Ly+vrq6u0ajaxnr+9319TcWW+ubNbqK5PDZCqEihze/ved9oaDGYZP2aQrn2gkpnunsaz5vDTLbnxQpxf5yFIcRw8+BFx/OiEix6Xdfbr1l0Wg+EZnNZ9VrDBZXu2SkOzAd2hKGySR6EEonkk08+OXbs2OrVq3/44Yfs7OyKiorBbRLT09Off/75KVOm2LpSMPrYwU/raiuR0cyhYaH8gRN0QnKDMIY4fo/UiRgMj0Qieffdd6VS6Z49e8aiAbdCodi3b19GRsaePXuWLVu2a9euxYsXj/q7jCeLUqH48RtiqCYsr9zozO3XkLeZGBYp4MYL8XgR7sKkI4RYXj54VBwvMo4zN9T69Q4/eV5x7Ej3f78dL8LrtQZZvyZK0Kerq4ILhEOazEFoMBh0Oh2bza6vr7eeCxIIBNYZaSKRyLYVgjElfH6l4vAXQ/4IY3NFv904zvVQSkhIyJQpU27fvl1eXh4WFjaSlxo8nebOnTvWB+3s7PLy8jIyMo4dO/a73/1upBXbmq6mAmOxyCBs0hsZGIYQuq03IYS8Oawimjbcjptkj8cLcTs6DdHonIBgXlQcL3Yxc+r0+18NY3OEy9cpjn4dr7n097Z+mVzzDnJUn8mDIBzSJAxCtVpdUFDwww8/3DMjTSKRCASCyMjIlSthLh0lMFzdHf+4Xbdz+zaPX3azrXcWeIkEwmVrOXPn2bC2SQ/DsLS0tD179kil0mEE4aVLlzIzM2tra//0pz/Z2dmR40oQQjg+cIo7MDAwMHDgmJ7sY/IkIgjCerhsUSmRZeCcZ4FcW602IIRaDEYnBv0lF0GGm5CBYRiHyw0L58cv4UXH0+wePuuYF53gd6PBjcVoM5hqNfrQ03mOGa+P3Z/z5Jo8QXjr1q2srKxjx46dOXPGevEvMDDQx8fnn//858KFC5ctW+bq6pqfn08217BttWB8CJYuZziJE//6BqFUIjoNEUSEndBhy+uC5+DL0JiTSCRkEL733nuP8nzr/qXvv//+ypUr5IPLly93dHRcsmQJeXfHjh1jVe6402q1Bw4cuH79+vr160tKSkrzTr2BBoLw35wF5DVCmVyT36/h02mIRhf/5wd2i9Me2vB6MDwmoe/Lf8QJuV93KWX9msCb143NN8gJSmCwJz4IHzAjbenSpbNmzVKpVMePHy8pKWlvb58xYwaTybTGJKACfGHs9JxzhhsN5q5OA4cjDAhSqjV9fX1cLneELYfAg8XExIhEorq6uoaGBh8fn197mkajOXXqlFQqzc7Otu5NEovFKSkpEonEOh9q8uFyuREREZcvX54zZw6TyfT08KB9/I5FrR7iqRhieXkL0l543Lfg+M9lOLvGK7Rfdyllcu1Wd3v1GZloHVwUuNcEDUK9Xq/X6wWCgWP/rq4usVhs/al1RtpPP/107do18kEulxsXF5eamiqRSFxcfmmXx+fzY2JiTp48aTQaT5w4wefzhcIRTXwFTyKWl09pd9/1y9c6CosDAgJqampcXV3hJPmYYjKZixcv/u6777Kzs1999dV7ftrT03P8+PHs7OycnByVSkU+OGPGjNTU1NTU1OjoaLKdU1dX16ZNm6y/tXXr1hHOk5k4NBqNq6urSCQym81SqXTbtm06Ed7x+isBPLaAMbDv2YlBf4rLwjhc57f+ezjvgWH4wmcXdLQJ6LRrWsNNvZFzOg+CcAjEhCSVSjds2GC96+rqajAY1Gp1ZmbmPVPQnJyc1q5de+TIEZVK9Wuv9umnnyKEJBLJuNQOJq79+/d/9dVXBEHs2rVLr9fbupzJ79tvv0UIRUdHWx+5fv36xx9/HB8fP3gLvJ+f39tvv11eXm67Sm3AbDYfOXKksLCQIIiqqiryQeXJzBsL/W48M7sxZHpjyPTrC/1vRAVqzhcP+13UZwsaQ6an2PMQQm94OjTO8zJ1d45K/ZPJBD0ivF92dvbKlSutAx59fX0lEolEInn66acf0DWGlJaWtmnTplOnTqnV6pG3jwJPqGvXrr3wwgsfffSRUqnkcrmsx7nWAoYnKSmJxWKdPXs2Pz+/uLjY2rwQIcThcGJiYlJSUl544QUPDw/b1mkTNBotPT2dvG1dDcRPTOUuWKg6la2rraLR6ey5ofxFKUN3xH403LAIGs6LF6mz+9R5/ZoNzgLN2UI7CQwJ+BcTNwh7enrKy8vJ20ajMSQkxGQyhYaGPvqMNCt3d/ewsLDS0lKZTEZO0AYU5OTkdOLEiYyMDJVKBSdFx4FOpysuLhaLxa2trfHx8eSDDg4OycnJEolk8eLFfD7fthVOTHShvTB9rTB9dObHYSwW/kxUdO5xJoZdVOv6TGbemTwIwntMiCC09gwcrLGx8dChQ+RtrVbr7u7e1dU17KkuEomktLRUKpVCEFKWg4PDihUrbF3F5Hd/80I2m02n09esWcNgMGbOnLl58+bJ0xr0SYBHL7KT5cy345xVaAvl2t+cP2vRamhcWDn/i4ecVBwH+/bt27ZtW3t7e3t7e3h4uHVJ5/z58//3LnLVzEhmm5H5l5mZaTabR6VsAMBgN27c2LVr17PPPuvs7Lxs2bJDhw4plcrQ0NBt27YZDAaE0KuvvhoUFOTv73/+/HlbF0stvMhYjMFIEOIIIZlcQ+h12gtnbV3UxGL7IHzppZd8fHwIgti7d6/1jPmo8/f39/Hx6enpOXfu3Bi9BQAUdOnSpe3bt8+bN8/b23vr1q2nT59GCEVERHz88cfNzc3l5eUffvhhSEiIRqMpKSmh0WgYNpkn3kxMNDsBJygsXoRjCBUrtFoLoT6dZ+uiJhbbB2F2draPjw+dTscwrLS09PLlywghFxcXa9sIhFBcXNxDV8Q8VGpqKkJIKpWO8HUAmPRMJlNjY6P1bnNzs1artd41m81nz57dsmXLlClT5syZ884771y8eBHH8ZSUlIMHD3Z3d1t/Sj6fPB9z7tw5nU53+fJlGGQ9/ngxCS5Muj/O0lqIc0qt+nQeYYLt1L+w8bczgiC++OILgiCSk5M9PT3r6ur8/PxGnnlDKioqio6O9vb2bmhoGIvXB2DSaG1tTU5Orq6uJu+mpKS8+eabAQEB9zQvRAg5OzsnJiamp6dbJ5fdr6amZu7cuc7Ozm1tbWP06QYPZmprbU6N3H2nb3e7/I/u9v/mIsA4XOGqFx1eysBYYzUw8glCodMUZrPZzc2tq6vr0qVLfn5+ti4HgInr/iBcv379qlWrrJfw586dm5aWJpFIQkJCHmW4hJeXV1NTU0lJCRwO2gSh0zbFhlT3KpyYNHcWAyGkMlu6acynZs3y+L8fYOEMhb6d0el0smMhnB0F4KGUSmXOXV1dXZ6enlOnTo2IiPjggw+uXLlSVVW1Y8eO0NDQRxyxlJaWhuCjZzvdf3uXMJn2d8qb9ANfZeo0ho+a2o3N13v+Nnnatw4bhYIQ3b1WAZ9GAAYjO3wqFIq+vj5rzwqtVnvxLvJEKDkU/vXXX581a9bjvgV89GzIolYps39EZtP9PyL0euWJYxaVcvyrmlCotZtn0aJFOI6Xlpa2trZSs5kFAPc4c+ZMR0dHW1vb/Pnzq6qq3NzcyNBydnZ+8803yeeQGx5G0osnKirKycnpypUrV69eHUaOgpHQX6nDWCzCoEcIlan0CpMFIdSgGzg0pLFY+vpabtgztizR1qh1RIjjeFxcHEEQx48ft3UtAEwI0dHR4eHhbDZ7wYIFSqUyJSVlLN6FTqcnJSUhOCi0BUKvR2jgDHafydxuNLcbzb2mgR3VBIERep3tqpsQqBWECE7RAPCv6uvrDx8+vHr16t7eXicnJ7LHE4/HI6/qkeLi4gaPfxke+OjZCnOaF2E0kLcXifANzoINzoLForv9S01G5vSZNituYqDQqlFSZ2enm5ubn59fdXU1rOQGoKys7ObNm1OmTPH09LS3tx+7rvQqlUosFhsMhtbWVldX1zF6FzCkltUp+mv1W653LHPiR9hxEULnlbqvu5S7ZzqzfGZP+faErQu0McolAYfDyc3Nra2tJVOwoqLCZBriGjIAFBEWFpaenr5gwQJPT88xnc3C5/NjY2MtFgtcmBh/zjv+h8bhRAq5bsyBdSGuLHq0kEvjcl3e3WXb2iYCygVhRUXFZ599Zr27cuVK69ZgAMCYgrOjtsKa+ZT73u9W+D3l7WiPMRgYg+HlYL/Cz8f98+9Y3rB2iWKrRgEANiSRSF5++WWZTAaTQccf2zdgWlaRtuy8vuEyQojt48cNW4Bo9479oSYqBmFLS8v3339P3lYqqb6BBoBx4+LiEhYWduHChby8vKVLl9q6HOqh0bnzI2hB8/bv398nO73c0eXo0aMODg4bN260dWU2RrlTowghnU7XcRdMZQJgPMHZUZtjs9nR0dE9PT11dXX+/v4wkAdRMwi9vb033yUSiWxdDgAUQh4IZmVlwSI1W7FYLDNnzuTz+UuWLHF1dYV/AxE1gxAAYCu+vr4+Pj7Ozs537tyxdS0UZTAYDh8+PGvWLIVCUVFR8dvf/tbWFdkeffv27bauYVyZTCYWixUcHEzelcvlUVFRTCbTtlUBQBEmk2nq1Km7d+8WCoUIoYsXLzKZTFg4M54YDEZISEhgYCCO4/PmzYM9nYiCG+oBADakVCoDAwObmprIu6tWrVq/fv2iRYtsWxWgODg1CgAAgNKouH2ClJmZeeXKlejo6KKiIqFQuGbNGhyn+nRKAMaBUqncvXs3ebuhocG2xQCAqByEaWlpfn5+586d0+v1FovlEeeLAgBGiEajOTo6krdHMtoJgNFC3SBsb2/PzMzMyMgwm82lpaU///xzfHy8rYsCYPLj8XgrV64kb2dlZdm2GAAQlYPwxIkTOI5XVVU1NTXJ5XJYQwwAANQEq0YBAOPHYDB8/vnnGRkZ5N2ffvpp7ty5Xl5etq0KUBwEIQAAAEqD7RMAAAAojbrXCAEANnTmzJm6ujqhUKhWq2k02tSpUxMTE21dFKAoOCIEANhAdHT06tWrOzs7lUplfX29QCCwdUWAuiAIAQA20N3dvX///pdfflmhUOzYsaOwsNDWFQHqgsUyAAAbOHny5M2bN318fAQCQVVVVUpKipubm62LAhQFQQgAAIDS4NQoAAAASoMgBAAAQGkQhAAAACgNghAAAAClQRACAACgNAhCAAAAlAZBCAAAgNIgCAEAAFAaBCEAAABKgyAEAABAaRCEAAAAKA2CEAAAAKVBEAIAAKA0CEIAAACUBkEIAACA0iAIAQAAUBoEIQAAAEqDIAQAAEBpEIQAAAAoDYIQAAAApUEQAgAAoDQIQgAAAJQGQQgAAIDSIAgBAABQGgQhAAAASoMgBAAAQGkQhAAAACgNghAAAAClQRACAACgNAhCAAAAlAZBCAAAgNIgCAEAAFAaBCEAAABKgyAEAABAaRCEAAAAKA2CEAAAAKVBEAIAAKA0CEIAAACUBkEIAACA0iAIAQAAUBoEIQAAAEqDIAQAAEBpEIQAAAAoDYIQAAAApUEQAgAAoDQIQgAAAJQGQQgAAIDSIAgBAABQGgQhAAAASoMgBAAAQGkQhAAAACjt/wEiIYHM5qWnzwAAAo56VFh0cmRraXRQS0wgcmRraXQgMjAyMi4wOS4xAAB4nHu/b+09BiDgZYAARiDWBGItIG5gZGNQANIsUIqDQQNIMTOxOYBpFnYIzQzjo9PsDGjyYD4TVJyJGS4PoRHmQ23FYSwBaUawKYyMg4XmBoWpOAODBAODJAMjEwOjFAOjNND3CsycGUzMLAksrBlMrGwJrDwKbOwZTGwyDOwcCuycCRyyDBxyDJxcClzcGsw8vAo88gy8fBpMvPwM/AIM/AoM/IoMAmIJAoIZTIJCCYJKDELCDEIiGUzCygzCKgzCqgwiogkiagyiYhlMouoMYhoMIkxszCysbOycbIJCIqJiAuLfGCGxDQaaxm97DqhqNx8AcaZKzj4gPU8LzP7muvLA9dNz94PY75d0HOi/wr4PxOZZb3xgQ9o7MPvPzSf7jfKV7EHsQ0f5DvwJZnEAsack5BzoXCwJZq+JaTmwM7oUzA68OO3AudJlYPW75h098ELkIpitnPPlANOW32B20sRl+/4kzrIDsTv2G9ofydwMFv+yo8FOKNcUbM4WLi6HVac6wOJtS9Md5MNtwGzV/40O8zuNwG7uO7PB4dXjZoj7f+xzUF0lC/Fj7kWHrVf7bEFsY8fDDrtjD4D1niqe4nD1zysw20zJ7kD730yweu+Tuw60h08Esxc11h6wZd8IZq/5euLA/W9uYPbPqqgDEa58YHY6++z98/d4gd3pWe5+QO/RXDBbVGvzgcWhrWD260sfbB9enwB2m2OUvIM6gxJY3K/spf3z1SfBYWu9x8lBVOwZWA371TcOiYGMYPNvyEx0eMFkBWYHqp51CEmqBLPrZRkc/xxrA+u1evTYoWG6GtjMDPc2h1lmgWC2GAB32sM6cR0lKAAAA5h6VFh0TU9MIHJka2l0IDIwMjIuMDkuMQAAeJx9VstuJDcMvPsr9AMj8CVKOvqxWC8Cj4HEyT/knv/HFtW2uhdLZOxDi1NNFosPDQ+XeeOHEp8/X/7497+yP/LyADv9z/+cs/yjRPTwVuKhPH37/uNenj8en74sz+9/3z/+KsbFBO9Q4V+xjx/vb18WLs/lZtVIZ2vxhADEvVCl9Sk7jhzIYT66lptU9zYD8BtSy3u5tSpj9knlxlVU+tAEaYdP6cRq5UaVVdyy6K3cI+ag3toMpHlzyZAePrn2geBIv7pao5kAewAF5GgI0qvk3cUT4AigVp8KkQA0IqcMOIOkIQlXlqJ1qHbJ8mY6Eu+s1LhwRUGtZ1ryKpBX7i3IMdKJfDKkRPQOANLBsU53U86QGkiqKk1lAKDETTKJOAoECacpvmdkzqh6BmyoOVVpbGCMOqn1IRkwyiOVm09SeCQfPlKPUR6IzhOe8H3X1skyYJQHfesS1bkF226cxp5AttrEZl99SYq2z+ojtJDT+xgjmsRcG2eqS9THazP77MaIntEUWUDGcFFI2UiGpx515eM6YyYYUrZhWRnFymtwQzJDx+rQ5hjfDNoC2uCrU5fovGY8NdMdTfYavqYzaY+X0CTBJIH2gDrGd84x4yWmMXhk0HF4NVY08ho7kZl2CBrtdS0NZnZFC7B3DGa2Z+hwytGUDs0woLmmGIOVf5uoZwcSb+TdpAIk9hDm7dhdrNxGlpIqkOiMAZ+Gh4aZb5n4GnVCHdGfqG3M3dQxU5pRJq1k0nWuJTZ7s9RnVKnHPGJxhJwk1GYqUhQJ9cZAdl7tQm7p4tSokaNENKiFrgMbJEdGiQbMXYgjI3EVzZBGiyejg9whkpPMmQJ50eTpgslXeByzZbJ/u7/8coEdV9rT+/3lvNLiT857ax3tvJ3WuZ130Dr7edPgVPS8TuIo/bw11nmclwPD5fY+4Q8er5s+DDsatiNOZUfDojLE2O456CHmjs/xei87Hge/jim5bFQGi/OM/g5W1wXJ4KXXPbgM+2eBLNGC3GWtaXDlU8alI4w7VdHDoifGQm2kv9OVYBuWnXBcYovzTln6YdGds4xPyxkrdEVmsjFH5fDqVkZD2kh2S6GL88BcXYaWI3+5Tqcty85dVwfEjF2m7bBszto/LWf0AT+QTbYaGjKHkJuPBWcIeWZhq/fQA+cgBGdIa5tPdPy1v+P89XMPzw8/AZ/b54lcqAOrAAACiHpUWHRTTUlMRVMgcmRraXQgMjAyMi4wOS4xAAB4nGWSvW5bMQyFX6VAFwe4EfgnUpTRKUsmp3vQoQg6timKjHn4HslFzaLLtUiTh58O9fz45YVfTs+PX+5eTg/7d38eTp+e7i7XvPx4OT3989/f45++jxd8L7eMXPP/qe4E4/vh/RSNhTUOaU56nL0xcfDBrZMMP87WXJMViaF92EpwusihTXwkKrQ5p42DWmgPQok07p5oIR8+Vmwjx8CMrixogZhoynFPTVNHHmdqnqaymozYY2WkszGja6gFZFa1dJU1Som77D7m6Hncc2NlHM44xYhEjat1WhkoUHRf48y7C9TvrUkQ7oUcq7itXG8yMraYqOyR1ob5iAMa7lve0ZiJIoVVYwhS2gwW+ioSkbz2GWn2AwfKJNkUGQQHUQ4I3vOGBsUq6sapWyqd4SH+w01Zt1SwUoczSrQ5sSRcZ+3EFXdEJnACCqPb7arjqZowz4j2MOxNXRl7G6pxxWZ298MaSteu11DsKZHBH7GZRQnCglWG63ZXGG2QwZZiPxByseWjZxjLehBkErZsjIwOPuiKZawMKXxZmfTAJEww1w7AWItfMQkto+EJ1r6dT9qXcthMg5eBeHi+M93Qjl3TGq4YNRpR7JclroKHfXd8fXv9/vnX689JbR0vr2/f8Owm3yLhKSWSqbfIaNotYp+9RH160ZQZJdI5SqXOLJFNLjAskwuN8uSC0ydXnMkFRycXHMgUHKgUHPHJhUdicgGiKYVHdEp1x6ZUe/qUApRTqj9QqgbB2IIUUwrSmFKJIFSIlKYWJJ9aicbUQsQxtRCpTa1IKK5IObUurU+tW8OgAqVQLlCCt1Af0Xj/DUrecoOWayupAAACzXpUWHRyZGtpdFBLTDEgcmRraXQgMjAyMi4wOS4xAAB4nM2SbUiTURTHz/M8255tbm5zby4zn2bTpWU0yQ/W9C4wqUCQCsOMVpDOUvoQJhSRUvkCoQmaoKiQZWSGhgplst00Sy0KI1G/FGn0Yi/6wcRoZduZFfjFrx04/H/nnHv/9z6XZ8Z9+xX4QgmBYHwZ58tNvixiJCD4VLQkUrD6hGMlBFXEB5T7Uy9XHpbNsWaX+iz3dx7Qf/5Lpy6zW2G8wnIGS4b5XzXI//SrAMIAVgPDAhMOzBrfIwmczMVyIqdI7GLFEqdYIUh4FyuJAF4q8DKnVADpWpDJBXmQlVMoBYUZlMFWVqkClRpUkaBaB2qjU61xsZoQQaNzhmhBawFtFGijQad3sTor6A1O/XowGF2sIVQwxkCoCUyxYNoApo2gYyWcSCzhZRKNTm8wqo02NvCPYMTFf7lELbHnqb+oMdXTsMYY5PmUm3TsSYPHzzPXymjlS97tZ0V7PO3I/orsnXjrsZ0wJ/u5byCYeveIiJ+rnXm0vNmE3JZ1gd47UICcPnKFPitowfU9jQP0g24EOTJvjrJdP5CPVLW4vYfrkvxc5tmc/DC3E/tzd4uSQvK3oE+XXE5ah8uwX3I9h0RkbEO2LBaTpnIb3nlr73aiN7xDXtQdIon9afhdFU87yPTUeezX5I+Q7tEKu5/jHf3k/kGKPsMnq8modxo5cXKKFNVG4bnj3aNkPLoYOcGcREt/5qLn7qEeWppRhXy1+Cy183eQ274N0tfzO5C/n86k+1OCkXP4ek9T7y702VmYSuMmG5D1MZ20ed9F5E8vZu1vxi7jPR2ZESQazNhPO/Ux+f2tIXz/51kc+RV2HD1rsxtI4TEbcl/+GdJtr0GeWXATS2s48rlwcHgfleBeV2oJqUtIR8+FQcaRuLcS+cHEDfJ5/ihyYfljMsu1Ixt+A34P2nSH0QTQAAAD83pUWHRNT0wxIHJka2l0IDIwMjIuMDkuMQAAeJx9Vs1unDcMvPsp9AIriL+SjrEdxEURG2jdvkPufX90qM+WNghbJ4cVd0QNOfxZGi56o4cSf388//7jn7L/+PkB9vY//+ec5W9prT18L/GhPH799ttreXr/8vhpeXr76/X9z6JetONOK/Qz9sv72/dPC5WnctOqTaZZfMIDjXCttvVX9jt8IYf66FJuXN1tBuAXpJS3crPKY/bZyo0qC/chCVIvn9wbiZZbqyTsmr1u5TXeHK2bzUCqm3OG9PBJtQ88zvgeybY2E2APIINcG4zwavPu7AlwBFCqT0GSANTWvGXAGSQVQbgQF6lDpHMWN7Ur8E7SjApVCKo9yyUtgbxStyBHCCfiyZAcr3cAEA6OdbqrUIaUQLYqbMIDAGlknKWIQiCkcKrge0LkBNUzoEHzVtlIwRg6ifbBGTDk4Urmswk8Nh8+Uo8hD5JOE57wfRfrTTNgyIO6dQ51bsG2K6VvT5CU2pS7zFUjs5tmcXNbj+swVFlojyJxzyTn0Meqsc6+ar0JWinTnKOBvJrqR90GzywgVEwACW3YIunWeHimOOuK3GVG9xCSbkMzwTnU6ZUYlenIvjeeM+1xX097H6ojkipOlHLs5SWaB0EPGas7zDE6MugIqIFdb52j6k1pSqY5auwlfE2nJj0uoUAjtmTGtIA6RsecY8YlamPQyKB0eVWSHmqi5ZlnWp3CAcXAIiIXVAB5x1DIkHI5pWgIDNqK4ZCrBPorfptjjA4kbuSVjJy8wEzo9WtukpCNNCQHEoNowKfig2HeWJZ8CZ1QGegNlFX0/JQxU5pjuWzGpm0Jio5Lh7aESgiYRr8WAUZt14ymhkiQk4d1DeXd5T8WQWhkEL4jSeFTXYwynhoSDZg7N4qI2IUlK2aVxZOmM4YJetjHtJSmLpfQHO2OG4rNkQ5OtcWS1QTfCyQVeM+AoQ+KU8xidyEapD/z+PX1+ad1fC3ox7fX57Og4x+fLbyO/ezadR5no67zPHsTpyJnOa7j3Q5c5/0DYeAsRc9C0/B4v7fCYHfbCafidztI8cZmR0EPb+73Ka73sulS8IM4Oz4KhqNs/lBOg9X9uCfwkuNyXoZNmlfSgtzdkJbgSieNK48wnltyWWQHh/HKQZiOJdiGZQccK3lx3iFzvyxyMOPDcjCRV0TG+/VLOVzdmZElPK7uVEgkFxbeccniPBHx3bChSIAcP/ZhOX78suj9lNBIEu/8SHCGRc9bwRlp4514jTzDovuWRjkgkScujTyHZedQgzNSq8fP4owfVjtjujhjqh4//mE5fvqHZd+KXrrvnDh//izG54d/AaZWHlZluvPUAAACxHpUWHRTTUlMRVMxIHJka2l0IDIwMjIuMDkuMQAAeJxlkjtvXEcMhf+KgTQScDUYvodcuHLjSk5vuAiElI4CQ6V/vM/MFqKTZnfIy8fHQ379/O2FXh6+fv72+PLw5dN5nJ+7pzk+PXz88vh8j+R/fg/+f94fz/h9fvfw3X9+6T99Pvx8WGPO4OtpDnZhievmw1RdrjmmZ6jYdbNhrBnXE40pU3J70mPl9cQDsUbXTRHOugvtLOLrJmMqh+6syDDbHl3mfj3JYJmOZjymsen2eIitXSdpRezKgf+TlbwMHh3uEmt7nFIXEJETU3cdMs8LfL58bVtXrnXxMCH260ZjseQZVFJAftugKryTdNKGgQZGSoSsJbob7Wg24d1KJhmfPKKw3GOREB63PeCKRIyL2tweVJhhvtupmzOqA59jkmwfCbtunw1eGacYC5+WOpb6OgK4n/KOxMzcItFcC9LipSThO4iZ856nWI1tmWbm5EORMdddXXM6/ZbEPFqaUsoplU7QEN8wKckpFSTToIzMeTh9YGYcBWFEzAhP4AUUQrbrvY6n4DYg/ZynmWJOF7oETSXu2ESOA8CSRZYfIMOeEh58iMOM00BhXIaHy1GXCWkogy1hZpyIS9KGWTgZ3Q5KZ0YE+0rfN0yTghBhk3efGITRAlV9yv6O68UYkEocC78tgOGC4NDIHYHFqImgpKXwzmAxk8uwz2S5Hq+/3l6///nj9d+aYz+fX9/+xlKK3i2m4mbNkhY5S98tirJmrfJmZcW7JVGrWauyWVnUYMiLGg1ZUcMRmI1HENyBpKgTaVFH4qLOhK8NCoUblBY3JinuAhU3IqjVgDiLG5DM4gYEq/GwFzcejuLGw6u4AWVJFwlb6SJRSUOKkoa0SjoRlzQikZKG5CVdIZTtRIjtEnFpQ2KYXSQt7Wdkpf2OpLRBKYIblNrPXzAfntIBckYRAAAAAElFTkSuQmCC", "text/plain": [ "" ] @@ -157,26 +149,26 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "id": "61ad110a", "metadata": {}, "outputs": [], "source": [ - "from openfe.utils.visualization_3D import show_3D_mapping" + "from openfe.utils.visualization_3D import view_mapping_3d" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "id": "a2bf6ba1", "metadata": {}, "outputs": [ { "data": { - "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", + "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", "text/html": [ - "
\n", - "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", + "

\n", + "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", " jupyter labextension install jupyterlab_3dmol

\n", "
\n", "" ] @@ -282,16 +274,16 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "show_3D_mapping(mappings[0], show_atomIDs=True)" + "view_mapping_3d(mappings[0], show_atomIDs=True)" ] }, { @@ -304,34 +296,34 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "d2b59d8a", "metadata": {}, "outputs": [], "source": [ - "from openfe.protocols import openmm_rbfe\n" + "from openfe.protocols import openmm_rfe\n" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "id": "6b667256", "metadata": {}, "outputs": [], "source": [ "# generate default starting settings\n", "# these are relatively sane, but can be tweaked\n", - "settings = openmm_rbfe.RelativeLigandProtocol.default_settings()\n", + "settings = openmm_rfe.RelativeHybridTopologyProtocol.default_settings()\n", "\n", "# tweak to hearts content\n", "settings.simulation_settings.equilibration_length = 6 * unit.picosecond\n", "settings.simulation_settings.production_length = 6 * unit.picosecond\n", - "settings.sampler_settings.n_repeats = 1 # number of completely independent runs, variance is std of means of these\n", + "settings.alchemical_sampler_settings.n_repeats = 1 # number of completely independent runs, variance is std of means of these\n", "\n", "# put Settings into Protocol\n", "# the settings are now \"locked in\"/read-only and can't be further modified\n", "# this is deliberate, the Protocol is now a fixed record of what you will do/have done\n", - "prot = openmm_rbfe.RelativeLigandProtocol(settings)" + "prot = openmm_rfe.RelativeHybridTopologyProtocol(settings)" ] }, { @@ -351,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "id": "85f2ab07", "metadata": {}, "outputs": [], @@ -405,17 +397,25 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "id": "4c137a1f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rm: cannot remove 'work_rootdir': No such file or directory\r\n" + ] + } + ], "source": [ "!rm -r work_rootdir" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "id": "b1e76dd6", "metadata": {}, "outputs": [], @@ -457,23 +457,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "5c929f53", "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: './work_rootdir/18629-1_18634-1/solvent/results.json'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgufe\u001b[39;00m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mjson\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m results \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(\u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m./work_rootdir/18629-1_18634-1/solvent/results.json\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mr\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m=\u001b[39mgufe\u001b[38;5;241m.\u001b[39mtokenization\u001b[38;5;241m.\u001b[39mJSON_HANDLER\u001b[38;5;241m.\u001b[39mdecoder)\n", - "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './work_rootdir/18629-1_18634-1/solvent/results.json'" - ] - } - ], + "outputs": [], "source": [ + "# NBVAL_IGNORE_OUTPUT\n", "import gufe\n", "import json\n", "\n", @@ -510,14 +499,6 @@ "source": [ "results" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "09fa637f", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -536,7 +517,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.12" + "version": "3.10.10" } }, "nbformat": 4, From d0f2da228b9fb232599a5c603c1460292877a786 Mon Sep 17 00:00:00 2001 From: richard gowers Date: Wed, 12 Apr 2023 11:23:36 +0100 Subject: [PATCH 14/14] benchmark_demo skip final cell --- openmm-rbfe/benchmark_demo.ipynb | 189 +++++++++++++------------------ 1 file changed, 76 insertions(+), 113 deletions(-) diff --git a/openmm-rbfe/benchmark_demo.ipynb b/openmm-rbfe/benchmark_demo.ipynb index 66e6f55..7e9b422 100644 --- a/openmm-rbfe/benchmark_demo.ipynb +++ b/openmm-rbfe/benchmark_demo.ipynb @@ -149,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "61ad110a", "metadata": {}, "outputs": [], @@ -159,16 +159,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "a2bf6ba1", "metadata": {}, "outputs": [ { "data": { - "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", + "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", "text/html": [ - "
\n", - "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", + "

\n", + "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", " jupyter labextension install jupyterlab_3dmol

\n", "
\n", "" ] @@ -274,10 +274,10 @@ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -296,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "d2b59d8a", "metadata": {}, "outputs": [], @@ -306,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "id": "6b667256", "metadata": {}, "outputs": [], @@ -343,7 +343,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "id": "85f2ab07", "metadata": {}, "outputs": [], @@ -397,25 +397,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "id": "4c137a1f", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rm: cannot remove 'work_rootdir': No such file or directory\r\n" - ] - } - ], + "outputs": [], "source": [ "!rm -r work_rootdir" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "id": "b1e76dd6", "metadata": {}, "outputs": [], @@ -462,42 +454,13 @@ "metadata": {}, "outputs": [], "source": [ - "# NBVAL_IGNORE_OUTPUT\n", + "# NBVAL_SKIP\n", "import gufe\n", "import json\n", "\n", "results = json.load(open('./work_rootdir/18629-1_18634-1/solvent/results.json', 'r'),\n", - " cls=gufe.tokenization.JSON_HANDLER.decoder)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4444350c", - "metadata": {}, - "outputs": [], - "source": [ - "results['estimate']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7051229c", - "metadata": {}, - "outputs": [], - "source": [ - "results['uncertainty']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "262f10f6", - "metadata": {}, - "outputs": [], - "source": [ - "results" + " cls=gufe.tokenization.JSON_HANDLER.decoder)\n", + "\n" ] } ],