From 2c834d866ccf31978f218c554e9fdb3e0a0696e6 Mon Sep 17 00:00:00 2001 From: Steven Walters <40771905+swalt826@users.noreply.github.com> Date: Fri, 14 Sep 2018 10:06:01 -0700 Subject: [PATCH] modified landslide fire tutorial - fisher creek --- ...andslide_model_for_fire_Fisher_Creek.ipynb | 2345 +++++++++++++++++ 1 file changed, 2345 insertions(+) create mode 100644 landslide_model_for_fire/Replicate_Landslide_model_for_fire_Fisher_Creek.ipynb diff --git a/landslide_model_for_fire/Replicate_Landslide_model_for_fire_Fisher_Creek.ipynb b/landslide_model_for_fire/Replicate_Landslide_model_for_fire_Fisher_Creek.ipynb new file mode 100644 index 0000000..d596794 --- /dev/null +++ b/landslide_model_for_fire/Replicate_Landslide_model_for_fire_Fisher_Creek.ipynb @@ -0,0 +1,2345 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n", + "\n", + "
\n", + "\n", + "# Reuse a published regional Landlab shallow landslide model to explore changes in forest cover at a subcatchment within the study area
\n", + "### You will explore how to reuse model code and data in a subregion within a larger region where the model was developed \n", + "* Load data from a regional Landlab landslide model (Strauch et al., 2018) developed for the North Cascades National Park, WA USA, published on HydroShare.
\n", + "* Define a geographic subset (Fisher Creek watershed) within the study region.\n", + "* Explore landslide probability sensitivity to fire by adjusting the cohesion parameter for Fisher Creek.\n", + "* Save results to a new HydroShare resource.
\n", + "\n", + "The shallow landslide model you will is based on a spatially distributed Monte Carlo solution of the infinite slope stability model. Detailes of the model and the study site are described in Strauch et al. (2018).\n", + "\n", + "Strauch R., Istanbulluoglu E., Nudurupati S.S., Bandaragoda C., Gasparini N.M., and G.E. Tucker (2018). A hydro-climatological approach to predicting regional landslide probability using Landlab. Earth Surf. Dynam., 6, 1–26, 2018. \n", + "https://www.earth-surf-dynam.net/6/1/2018/\n", + "\n", + "
\n", + " \n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## To run this notebook:\n", + "\n", + "Click in each shaded cell below and \"shift + enter\" to run each code block. Alternatively, you can run groups of cells by clicking \"Cell\" on the menu above and selecting your run options from the pull-down menu. This is also where you can clear outputs from previous runs.\n", + "\n", + "If an error occurs, click on *Kernal* and *Restart and Clear Outputs* in the menu above." + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Introduction\n", + "\n", + "** 1.1 Infinite Slope Factor of Safety Equation **\n", + "\n", + "This equation predicts the ratio of stabilizing to destabilizing forces on a hillslope plane. The properties are assumed to represent an infinte plane, neglecting the boundary conditions around the landslide location. When FS<1, the slope is instable.\n", + "\n", + "![image.png](attachment:image.png)" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C* can be calculated by the ratio of the sum of root cohesion, Cr, and soil cohesion, Cs, to the product of soil depth, density, and gravity." + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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+ } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Relative wetness is the ratio of depth of water subsurface flow above an impervious bedrock to the depth of soil. " + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "R: recharge rate to water table (m/d)\n", + "T: soil transmissivity (m^2/d)\n", + "a: specific catchment area." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "** 1.2 Monte Carlo solution of the FS equation **\n", + "\n", + "Below the Monte Carlo approach used by Strauch et al. (2018) is illustrated in a figure. At each node of the model grid variables for soil and vegetation are generated from triangular distributions. Recharge to water table is obtained from hydrologic model simulations by selecting the largest daily value for each year. Probability of shallow landslide initiation is defined as the ratio of number of times FS<1 to the total sample size of FS calculations." + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Module overview for a Landlab Modeling Toolkit Landslide Introduction\n", + "\n", + "This Jupyter Notebook runs the Landlab LandslideProbability component on a 30-m digital elevation model (DEM) for Fisher Creek, a subwaterhsed in the larger study domain developed by Strauch et al. (2018), using _data driven spatial_ recharge distribution as described in the paper (https://www.earth-surf-dynam.net/6/1/2018/).\n", + "\n", + "\n", + "To run a landslide demonstration using the paper data and approach we will:
\n", + "1) Import data from North Cascades National Park (NOCA: study area)
\n", + "2) Review data needed as input for the landslide model
\n", + "3) Create a RasterModelGrid based on a 30-m DEM - subset to Fisher Creek watershed
\n", + "4) Assign data fields used to calculate landslide probability
\n", + "5) Specify recharge option as _data driven spatial_ and access Python dictionaries to generate recharge distributions
\n", + "6) Set Number of iterations to run Monte Carlo simulation
\n", + "7) Run Landlab LandslideProbability component
\n", + "8) Run the model again to simulate post-fire conditions,
\n", + "9) Display and visualize results of stability analysis
\n", + "10) Save Notebook and Results back to HydroShare
\n", + "
\n", + "\n", + "The estimated time to run this Notebook is 30-60 minutes with 20 minutes of computation time. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.0 Methods" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2.1 Notebook Setup and Preparation\n", + "\n", + "To run this notebook, we must import several libraries.\n", + "The hs_utils library provides functions for interacting with HydroShare, including resource querying, dowloading, and creation. Additional libraries support the functions of Landlab." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1.1 Install Python packages \n", + "The CUAHSI JupyterHub server provides many Python packages and libraries, but to add additional libraries to your personal user space, use the cell below. To request an Installation to the server, visit https://github.com/hydroshare/hydroshare-jupyterhub, create a New Issue, and add the label 'Installation Request'. Uncomment the lines below to install the library. Python2 and Python3 kernels are both available. If you are a new Landlab user on Hydroshare you are advised to run the code block below in your first run of this tutorial, then comment them out." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#!python2 -m pip install xarray\n", + "#!python2 -m pip install plotly\n", + "#!python2 -m pip install geopandas\n", + "#!python2 -m pip install rasterstats\n", + "#!python2 -m pip install affine\n", + "#!python2 -m pip install seaborn\n", + "#from __future__ import absolute_import, division, print_function, unicode_literals" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2.2 Import Python libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#Import standard Python utilities for calculating and plotting\n", + "import six \n", + "import os\n", + "import matplotlib as mpl\n", + "mpl.use('agg')\n", + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import warnings \n", + "warnings.filterwarnings('ignore')\n", + "import numpy as np\n", + "import pandas as pd\n", + "import pickle as pickle\n", + "from datetime import datetime, timedelta\n", + "import geopandas as gpd\n", + "\n", + "#Import utilities for importing and exporting to HydroShare\n", + "from utilities import hydroshare\n", + "\n", + "# Import Landlab libraries\n", + "import landslide_probability\n", + "from landslide_probability import LandslideProbability\n", + "from landlab import imshow_grid_at_node\n", + "from landlab.io import read_esri_ascii\n", + "from landlab.io import write_esri_ascii\n", + "from collections import defaultdict\n", + "from landlab.plot import imshow_grid\n", + "from landlab import CORE_NODE, CLOSED_BOUNDARY\n", + "\n", + "# Import general tools\n", + "import time\n", + "st = time.time()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.1.3 Connection with HydroShare\n", + "After importing libraires, we now establish a secure connection with HydroShare by instantiating the hydroshare class that is defined within hs_utils. In addition to connecting with HydroShare, this command also sets and prints environment variables for several parameters that will be useful for saving work back to HydroShare." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding the following system variables:\n", + " HS_USR_NAME = swalt826\n", + " HS_RES_ID = 70b977e22af544f8a7e5a803935c329c\n", + " HS_RES_TYPE = genericresource\n", + " JUPYTER_HUB_IP = jupyter.cuahsi.org\n", + "\n", + "These can be accessed using the following command: \n", + " os.environ[key]\n", + "\n", + " (e.g.)\n", + " os.environ[\"HS_USR_NAME\"] => swalt826\n", + "\n", + "The hs_utils library requires a secure connection to your HydroShare account.\n", + "Enter the HydroShare password for user 'swalt826': ········\n", + "Successfully established a connection with HydroShare\n", + "This is a basic Unix folder structure.\n", + "Data will be loaded from and saved to:/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/\n" + ] + } + ], + "source": [ + "hs=hydroshare.hydroshare()\n", + "homeresid=str(os.environ[\"HS_RES_ID\"])\n", + "homedir = os.path.join('/home/jovyan/work/notebooks/data', str(homeresid),str(homeresid),'data/contents/')\n", + "print('This is a basic Unix folder structure.')\n", + "print('Data will be loaded from and saved to:'+homedir)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If you are curious about where the data is being downloaded, click on the Jupyter Notebook dashboard icon in upper rigth corner to see a File System view. The homedir directory location printed above is where you can find the data and contents you will download to a HydroShare JupyterHub server. At the end of this work session, you can migrate this data to the HydroShare iRods server as a Generic Resource. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.2. Import spatial data \n", + "Strauch et al (2018) pre-processed the data for the North Cascades National Park Complex case study and is on HydroShare as [Regional landslide hazard using Landlab - NOCA Data](https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/). Here the first task is find this resource on Hydrohsare. We will click on the link to see the published data repository on HydroShare and collect the resource ID of the data. The resource ID can be found in the \"How to cite\" box, and it will be the series of numbers and letters following \"hs.\". Here's the copied resource: http://dx.doi.org/10.4211/hs.a5b52c0e1493401a815f4e77b09d352b citation. Now we copy this ID and introduce it as \"Data_ResourceID=\" in the code below. \n", + "\n", + "#### 2.2.1 Set DEM data download variable name\n", + "To learn more about this data visit [Regional landslide hazard using Landlab - NOCA Data](https://www.hydroshare.org/resource/a5b52c0e1493401a815f4e77b09d352b/)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "Data_ResourceID='a5b52c0e1493401a815f4e77b09d352b'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.2.2 Download Data\n", + "We will execute the next cell to download data from HydroShare iRods database to your personal user space - this may take a few minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This resource already exists in your userspace.\n", + "a5b52c0e1493401a815f4e77b09d352b/\n", + "|-- a5b52c0e1493401a815f4e77b09d352b/\n", + "| |-- bagit.txt\n", + "| |-- manifest-md5.txt\n", + "| |-- readme.txt\n", + "| |-- tagmanifest-md5.txt\n", + "| |-- data/\n", + "| | |-- resourcemap.xml\n", + "| | |-- resourcemetadata.xml\n", + "| | |-- contents/\n", + "| | | |-- Data files/\n", + "| | | | |-- ASCII_header.txt\n", + "| | | | |-- cohesion_max.txt\n", + "| | | | |-- cohesion_min.txt\n", + "| | | | |-- cohesion_mode.txt\n", + "| | | | |-- cont_area.txt\n", + "| | | | |-- dict_coeff.p\n", + "| | | | |-- dict_uniq_ids.p\n", + "| | | | |-- elevation.txt\n", + "| | | | |-- exclud_mask.txt\n", + "| | | | |-- frict_angle.txt\n", + "| | | | |-- HSD_dict.p\n", + "| | | | |-- ksat_mpd.txt\n", + "| | | | |-- landslide_type.txt\n", + "| | | | |-- Re_annualMax_ex.txt\n", + "| | | | |-- sample_area.txt\n", + "| | | | |-- slope_tang17d.txt\n", + "| | | | |-- soil_depth_model.txt\n", + "| | | | |-- soil_depth.txt\n", + "| | | | |-- transmis_model.txt\n", + "| | | | |-- transmis.txt\n", + "| | | | |-- vic_idsnoca.txt\n", + "| | | | |-- VIC_latlon_key.txt\n", + "| | | | |-- wetness.txt\n", + "| | | |-- Driver and related codes/\n", + "| | | | |-- __init__.py\n", + "| | | | |-- landslide_probability9Jun17.py\n", + "| | | | |-- NOCA_runPaper_LandlabLandslide.ipynb\n", + "| | | | |-- vicDataProc.py\n", + "| | | |-- Results/\n", + "| | | | |-- prob_fail3000_Model_SD_LT.txt\n", + "| | | | |-- prob_fail3000_Model_SD.txt\n", + "| | | | |-- prob_fail3000_SSURGO_SD.txt\n", + "\n", + "Do you want to overwrite these data [Y/n]? y\n", + "Download Finished \n", + "Successfully downloaded resource a5b52c0e1493401a815f4e77b09d352b\n" + ] + }, + { + "data": { + "text/html": [ + "Found the following file(s) associated with this HydroShare resource." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Data files
Driver and related codes
Results" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "These files are stored in a dictionary called hs.content for your convenience. To access a file, simply issue the following command where MY_FILE is one of the files listed above:
hs.content[\"MY_FILE\"] 
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "This is the location on the HydroShare JupyterHub server where the data has just been downloaded:\n", + "/home/jovyan/work/notebooks/data/a5b52c0e1493401a815f4e77b09d352b/a5b52c0e1493401a815f4e77b09d352b/data/contents/Data files/\n" + ] + } + ], + "source": [ + "hs.getResourceFromHydroShare(Data_ResourceID)\n", + "data_folder = os.path.join('/home/jovyan/work/notebooks/data', str(Data_ResourceID),str(Data_ResourceID),'data/contents/Data files/')\n", + "print('This is the location on the HydroShare JupyterHub server where the data has just been downloaded:')\n", + "print(data_folder)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " ### 2.3. Review data needed as input for the landslide model\n", + " This section loads metadata associated with the Landlab component. \n", + " To view the code source for this component, visit [Landlab on Github](https://github.com/landlab/) or [Download the landslide_probability.py python file](https://github.com/landlab/landlab/blob/master/landlab/components/landslides/landslide_probability.py)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check the list of data inputs that the component needs." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['soil__density',\n", + " 'soil__internal_friction_angle',\n", + " 'soil__maximum_total_cohesion',\n", + " 'soil__minimum_total_cohesion',\n", + " 'soil__mode_total_cohesion',\n", + " 'soil__saturated_hydraulic_conductivity',\n", + " 'soil__thickness',\n", + " 'soil__transmissivity',\n", + " 'topographic__slope',\n", + " 'topographic__specific_contributing_area']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(LandslideProbability.input_var_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Review the details of what each variable represents." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'topographic__specific_contributing_area': 'specific contributing (upslope area/cell face ) that drains to node',\n", + " 'topographic__slope': 'slope of surface at node represented by tan theta',\n", + " 'soil__transmissivity': 'mode rate of water transmitted through a unit width of saturated soil - either provided or calculated with Ksat and soil depth',\n", + " 'soil__saturated_hydraulic_conductivity': 'mode rate of water transmitted through soil - provided if transmissivity is NOT provided to calculate tranmissivity with soil depth',\n", + " 'soil__mode_total_cohesion': 'mode of combined root and soil cohesion at node',\n", + " 'soil__minimum_total_cohesion': 'minimum of combined root and soil cohesion at node',\n", + " 'soil__maximum_total_cohesion': 'maximum of combined root and soil cohesion at node',\n", + " 'soil__internal_friction_angle': 'critical angle just before failure due to friction between particles',\n", + " 'soil__density': 'wet bulk density of soil',\n", + " 'soil__thickness': 'soil depth to restrictive layer',\n", + " 'soil__mean_relative_wetness': 'Indicator of soil wetness; relative depth perched water table within the soil layer',\n", + " 'landslide__probability_of_failure': 'number of times FS is <=1 out of number of iterations user selected',\n", + " 'soil__probability_of_saturation': 'number of times relative wetness is >=1 out of number of iterations user selected'}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "LandslideProbability._var_doc" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Check the units of each variable." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'topographic__specific_contributing_area': 'm',\n", + " 'topographic__slope': 'tan theta',\n", + " 'soil__transmissivity': 'm2/day',\n", + " 'soil__saturated_hydraulic_conductivity': 'm/day',\n", + " 'soil__mode_total_cohesion': 'Pa or kg/m-s2',\n", + " 'soil__minimum_total_cohesion': 'Pa or kg/m-s2',\n", + " 'soil__maximum_total_cohesion': 'Pa or kg/m-s2',\n", + " 'soil__internal_friction_angle': 'degrees',\n", + " 'soil__density': 'kg/m3',\n", + " 'soil__thickness': 'm',\n", + " 'soil__mean_relative_wetness': 'None',\n", + " 'landslide__probability_of_failure': 'None',\n", + " 'soil__probability_of_saturation': 'None'}" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "LandslideProbability._var_units" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will establish a RasterModelGrid based on a DEM for assigning our variables to.\n", + "Nodes are the center point of grid cells or pixels that are 30 m by 30 m in this example. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.4. Create a watershed subset using a regional RasterModelGrid" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The shapefile that you will download is a GIS point file with a table containing a grid_code for each 30m DEM cell in the Fisher Creek watershed. We import this table to generate a list of locations we want to keep \"Open\" as active nodes in this Landlab application for Fisher Creek. \n", + "\n", + "Here we will establish the watershed domain for our modeling study. In step 2.2.2, we have downloaded the DEM of the entire NOCA study region of Strauch et al. (2018) as well as other input data. This includes NoData resource that was created using a mask of Fisher Creek will be downladed and used to set no data nodes as inactive nodes (e.g., -9999). This step will establish boundary conditions. In your final run when you output modeled probability of landslide initiation, you will notice gaps in the model results at some ridge tops and peaks. This results from excluding glaciated areas and bedrock.
\n", + "This might take a few minutes as the park is large (2,757 km2).\n", + "\n", + "This shapefile resource was uploaded to HydroShare to generate an interoperable Notebook. To learn more about this data visit [Boundary and Nodes for Fisher Creek\n", + "](http://www.hydroshare.org/resource/e2e3a56690354b20af292ff8db519ee5)\n", + "\n", + "#### 2.4.1 Get shapefile of Fisher Creek watershed\n", + "The shapefile table contains Landlab Gridcode value and Albers Conical X and Y values, spatial projection is WGS84. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Download Finished \n", + "Successfully downloaded resource e2e3a56690354b20af292ff8db519ee5\n" + ] + }, + { + "data": { + "text/html": [ + "Found the following file(s) associated with this HydroShare resource." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Fisher_HUC12_watershed_WGS.cpg
Fisher_HUC12_watershed_WGS.dbf
Fisher_HUC12_watershed_WGS.prj
Fisher_HUC12_watershed_WGS.sbn
Fisher_HUC12_watershed_WGS.sbx
Fisher_HUC12_watershed_WGS.shp
Fisher_HUC12_watershed_WGS.shx
Fisher_node_id_WGS.cpg
Fisher_node_id_WGS.dbf
Fisher_node_id_WGS.prj
Fisher_node_id_WGS.sbn
Fisher_node_id_WGS.sbx
Fisher_node_id_WGS.shp
Fisher_node_id_WGS.shx" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "These files are stored in a dictionary called hs.content for your convenience. To access a file, simply issue the following command where MY_FILE is one of the files listed above:
hs.content[\"MY_FILE\"] 
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Data_ResourceID='e2e3a56690354b20af292ff8db519ee5'\n", + "hs.getResourceFromHydroShare(Data_ResourceID)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/jovyan/work/notebooks/data/e2e3a56690354b20af292ff8db519ee5/e2e3a56690354b20af292ff8db519ee5/data/contents/Fisher_node_id_WGS.shp\n" + ] + } + ], + "source": [ + "Node_path = os.path.join('/home/jovyan/work/notebooks/data',str(Data_ResourceID),str(Data_ResourceID),'data/contents/','Fisher_node_id_WGS.shp')\n", + "print(Node_path)\n", + "NodeID_shpfile=gpd.GeoDataFrame.from_file(Node_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Print a sample portion (bottom) of the table\n", + "* Col 1 = FID or POINTID\n", + "* Col 2 = landlab grid code for NOCA region\n", + "* Col 3 Point_X - the Alber Conical Latitude (original dataset)\n", + "* Col 4 Point_Y - the Alber Conical Longitude (original dataset)\n", + "* Geometry of lat/lon in WGS coordinate system of this GIS point file for launching in HydroShareGIS App" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " POINTID GRID_CODE POINT_X POINT_Y \\\n", + "81211 0 2466288 -1834530.0 3077940.0 \n", + "81212 0 2466289 -1834500.0 3077940.0 \n", + "81213 0 2466290 -1834470.0 3077940.0 \n", + "81214 0 2464104 -1834500.0 3077910.0 \n", + "81215 0 2464105 -1834470.0 3077910.0 \n", + "\n", + " geometry \n", + "81211 POINT (-120.8642431518081 48.53020052341004) \n", + "81212 POINT (-120.863854850779 48.53027103583014) \n", + "81213 POINT (-120.8634665489001 48.53034154719415) \n", + "81214 POINT (-120.8637508752399 48.53000770708789) \n", + "81215 POINT (-120.8633625748339 48.53007821808954) " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "NodeID_shpfile.tail()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make an array using Grid_Code column to develop a node based mask. No output from this command." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "filtercriteria = np.array(NodeID_shpfile.GRID_CODE)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Load spatial landslide model inputs from ASCII textfile (ArcGIS raster conversion) into Landlab grid\n", + "\n", + "Create Landlab RasterModelGrid using DEM grid with elevation - this takes approximately 60 sec for the North Cascades National Park (NOCA). " + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['topographic__elevation'])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(grid, z) = read_esri_ascii(data_folder+'/elevation.txt',name='topographic__elevation')\n", + "grid.at_node.keys()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot the elevation grid of NOCA" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "imshow_grid(grid, 'topographic__elevation', limits=(0, 3000))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The original NOCA dem dataset has nodes closed outside of NOCA.\n", + "All nodes insides of NOCA are open. To close all all nodes except those inside Fisher Creek: \n", + "* Close all nodes in the landlab RasterModelGrid.\n", + "* Open the Landlab RasterModelGrid only within the Fisher Creek watershed.\n", + "* Assign Core Nodes (these are the only nodes where computation will be executed)." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "grid.status_at_node[grid.nodes.flatten()] = CLOSED_BOUNDARY\n", + "grid.status_at_node[filtercriteria] = CORE_NODE" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "# In case the DEM has no data values inside the subset area, set boundary conditions closed.\n", + "grid.set_nodata_nodes_to_closed(grid.at_node['topographic__elevation'], -9999) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot where the watershed is witin NOCA and zoom in." + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32000, 47000)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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dsfD5PB8UkU73ei5wlKo+nNs+o34fVS2LyDxq/ZCw/HX6T2BNEZmmqgv7OzGv0XjGGncA761bvue27U3jMGecrXlz4P7c6lYWHGxmrhXioAsuIAwCKknC777xjQHH/+2n3+Pen30frSRonKKxQqqklYSkKyYpJ2icmLCJm/IbeIaAmTyXizqbKiL35ZZ+TajOMb8vtcEvt2E+lz0wE+ybMDPx9NyYZv2QNBj3Wt32PvEajWdM4UwKt+bX5X5Yc1V1qYh8CLMr3wC8iD2xHQ88C1yS27WVBQcHnKsVtBWLVJKEJE379NXMPXUWYSlCKykaBmiqaJKSxilJjxMoIgSJklWoksBXEho+tJG5bFGzJlN3fV8BXKuql1RnVT0pN2yuiMzBrs+vu6X3BBpM22B9/fumG595jcazOvIcZss+C/gjcBLwJ2DX/E1fVV/DotJCLC/mZKzg4El1852OBSDMxITXRBoUL2xyrhVi/7PPphRFCFCuVLjum9/k90cdVd1+y3HHoRUTKpooGqck3TFpOSEtJ2hi9wwRW1AnhJKWmHY8fTDUPBoRWRPTlp9lAKe8q1LxV5rzQ06m1g9Jg3HZ+4Y+xprz9EU1W4svfliLL6rZGgZ7XV3/rW/RXalQDEP2baDV3H7ySQTFkCAKCDsigvaIqL1ANL5I2BYRliL7214gLIZE7QXCUoG11/5Qyz7T6kZfv4XttttC//znn9esW3PNHQcsqulMtHOAtYGdVPU/A52DiPweGK+qu7n3lwJvVtWdcmOKwBvA0ap6jgsGmAfsqao358adAHxdVacMdFyv0Xg8Y5B9fvADimFI4h4kbzz6aP7w7W8DptWICEFkP39NFVLLUM80m6QnJunJtBynAa1gNJunMUOpDODyX36LBb/s1aSQWRvrW1Pvh3xHXbLyR4AS8Ac7P30K+Be1fsjAvfeNzzye1ZkgCNj3Bz/gum9+k73POKO6PgoCpBAgUUDQZlpNUAjMXBYGBIWQoBASlsKqb8b7aIaThj6agfgJFtxyBBb59c7ctgexwJbTMGH0DBYIMBNL2MnniV0JHIf5Dk/Awu1/BFyRy6EB8ydeJiLzMfPbFzAh95lmTtYLGo9njLLPD36w3Lo/zpxZfS2haTVSCM2MVooICgEEgoQBEgaEpZCgGIEIEgQsXHgL06Y1XXnE0xRDEjQfdH/PbrBtY+AVzFl/GtaobAkWJPNRV/rIjqxaEZE9scZnv8GqBvwKq5ZBbtwvXZuNb2OVyh8FPuzyzwbECxqPZwxz7ZFHkvfDZq9FsNtQKKbNRIFpLSJI4MxqYmYdZ9sh6amgBW8+Gx4G972q6vQmhu3d5FzPAx9tYtzPgJ81M2c93kfj8Yxx8sEAe5x+uitX48xkTnORMEAip+EEYgJJzX+jqTqfTUwae0HTepQ0jWuWsYYXNB7PGKY+4uzmY44BzGwG9ObJOBdM6hz/acVyatJyQtxZIe6qoImSxlb7zNNKFCsAnl/GFl7QeDxjnBtyeTR7nH569XWaVQRIlbd+7H9IyymkVh1AEydsumMTMqmSVBLSStpbosbTElRB07RmGWt4QePxjHE+/P3aqjtpmlrYctn+pj0xD/7qx7atkpKUnZmsHJN0myajFXfzS5WkHPv2AS1FTZPMLQPRTD8aN27AHjK+H43H42kZmWaTpCnqTGNJZ4W0kpL2xKQ9MUm3W9fd65PR1NoPpOWEpBybFuQrBbQOpapJVpeBGbAfTTM9ZFZWPxpfGaDF+MoAtfjKAK2hVdfVjUcfzd5nnMHNxxxDFIYUSwXC9oigLSRsiyz6LAyQAKsK0GGVASQKidpdxYBSZOHQxZD1Nti3Fae1WtDXb2Hbt26mN11fG4q+/vSP9VsZQESm1rcKEJErsAoBG7v3TwB/VdWD3PsA+Dvwd1X9nFv3aeAyavvRfAILcd48y6VpZq7+8BqNx7MasfcZZ3Dj0Uezx+mns/vs2ZR7KsRLyyTLKlSWlEl7EtNqul1FgJ7EqjjnfDaVpT0k5Zi0kngTWivIRfdly4C7DNCPZhA9ZHw/Go/H03pEeh+sd589G4DbTjiBILZgAAmtYkBaTtDUboJJV2zJm6WIqKMAna6fW6q8+Ow1SBSw7nofGYmPM+pRtFWmyHw/mmZ7yPh+NB6Pp7XMOfZYojBkzrHH1qzb7Tvf4V3HzyJeVrFqzt3WkyZeWiZe0mNaTo9FqaWVtDfsOfPlVFJefP7aEfxko5ghaDT1NOhH02wPmZXSj8YLGo9nNaJYKtDWVqQQ9naefv93v9s7IFF2nXkiSWeFyus9xMvKJF2xNUdzN8G07ISOEzhpxRXh7I554ZlreOlFb04bLA0ETdONz/rqR5NNXT+8wXrfj8bj8bSOd590MuH4AtG4ArefdCJAjXbz7lNOYe4ps9j1uJPY+ajjzUfTE1vYbU9MvKxM3FUhrSRVbSZ2fpt4WdmFRMe8MP9qXph/NS8+57WcAVHQJK1ZcI3PcssFjXbtpx9Nsz1kVko/Gi9oPJ7VjJ2PPM4iywoBt594IlHQ+DYw99RZ7HL0CZZv0xWbWa2zgsZK2hNbm+dyQtqTUFlSJu6smGZTjok7y8TdFdJyr5bz8st/WLkfdNSgQzKduVyXG4Ai8CFVXZbbnPlTZtTtNgN4NedTebx+jOtHs0lujmbn6hMvaDye1Yy7zzkNKQYWujy+QNgWcdsJJ1S3v+vEWfb3ePub9JiQiZeaMEmdTybpcWaz7titS2xdT0LF+XoqnRXirrL5dLq7efG5a70vpw5VLCk2twzEQP1oBtFDZqX0o/GCxuNZzXjn12ZajkxbRDTOcmXypjSwDpwZVV9MjwUIVN7oMe2ly2qgVZb1kPTEaKIuaCAm7qxQWdxTTQiNu2184v6+MP9qXnzW+3MyhqDRZP1ovoPrR5NbSm7MLOCLInK8iLwXuAgTTKfn5rkS01h+JyJ7u7yac2ncj2agufrEJ2y2GJ+wWYtP2GwNw3Fd3f/zM3u7avbEJMsqJF0x7z75lD73mXvKrKoWFHYUCIoWVBBEQlCMKEwoEhRCkp4YRIiqCZ8BaG8xz7BomRVBIet7EyFBSBCUEAkJw3GsscY7Wv2RR5S+fgvbbLGJXnNp7Xe+2Q6fHyhhcz6Wzd+IjVV1vhv3JayHzIZYNv9RqnpL3VwbYMLl/eT60ahqZ924Aefq83y9oGktXtDU4gVNaxiu6+qBK86G1CVjdlbMD7Os0q+wuf2kEwnarElaULJunBIKQTEkmlAibI9Iu2OkEBC1FwhLLl0vsF43EgaExRBN1bp4RqHNFVlHT1sKRNF4pkzZdTg+9ojQt6DZWK++uPb7fvNOB/QraEYb3nTmGdO4goFLRURdh8BsvYjIsSLynIh0icjtIvK2BvtvKSK3iEiniLwoIqeISFg3pmVzrWy2/8wRloQ5rkA0vkjYHhGOKzD31Fk1424/sdes9u6TT2HXmScSLylTea2HyuIeKm/0UH69m57/LDPTWpdVF4i7KlSWlom742rVgbRi6y00OiWtmFkuK+IZd1VIkzJpWmbRoltX6vcxIrQgj2ZVx1cG8Ix1vo8VHxxXt/4YrCXtUZiN+khgjohsraoLAERkDWAOljm9L7Ap8EPsAe34YZprpRMUQvNIBwESCpWgh6z11txTZiGhsOtxJzH3lFnVQAGgRuu5Y/bJULHotKQrJmgLicYVSbsrBMWQsL1ArEpQCAmTFETsuIBEAYkqYVtUfR8UQirJEoJCyKJFtzF16m4r6+tY6SiM+SKlI2Y6E5EDgYsbbPqKqp7nxggwE/gKMBW4FzhcVR+qm2tL4BxgJyym+0LgZFVNcmNaNtcAn2vsPY6sACNpOhORdwHXAt/FBM4EVV0qIm3Ay8APVfUUN3YcMB84X1WPd+tmAkcDG6nqYrfuaMwxuo6qLm7lXAN8lmG/rv5xw0+Jl5QtWmxxD8lSKzOz63EWGPDXM76DVlIXGZWCWt5NPXfMPpnA+WWCYkBQigjbI2sRXYoI2yI0TQmLEUEpQgTbVgghEMJSaD6cQCiMKxIWzG8TBB0UCpOYOHGr4f4qhoW+fgtbbz5dr7rgpJp1M95zkDedtZj3YTf1bPldblv2pPg9YB/syXSOiKyTDcg9KSr2pHgK8E3g5LrjtHIuzyqOM0mdg/0P6wsQ7gxMpLZI4DLgepYvOHhznRD4FdAOZI/YrZxrRHj0D5YLKEHAdp8+woREIOZ/KYbc+f1TufPM2QSFkKAUVs1rQSnk9pNPqjGrgQmmrFJAvKxCvKxM5Y0ey7VZWq5Go2WRaHFXhaRs5jSNLWzaTGuWk1Pp6iJJuoGUJOnijTceGYFvaXhpkLA5puhX0IjIb9zNdzi5V1Xvzi3/ccduw4TDaap6rqrOweK2Ffhqbv9DsR/rx1X1T04bOhk4UkQmtnouz4qzkq6rQ4E2ems/5ZmB9cv9d936f1KblDaDukKCqvos0EltwcFWzTUihKXIhE3uedsc/AE7H3U8Ox91PGF7RDShSDSxRDSpZP4cV2EgaI9M4OTCo991/Cx2nXkiuxx9Akmn9bhJuirES3psWVYh6bQw6Oo21zI67rQlraTEnRW7+aYpSdJJkiwjSZbw+usPNfgko5Qh5NEAiMhmInK+iPxdRBIRubXBmPnOP5lfFjQY1zJfZCMG0mg+DjwqIvs0M1mL8U+dY5dhva5EZAqWX3CkqlYaDFkDWNrAHPoa0OEyo7NxzRQcbNVcI8KM9x5E2F4giAL+ccNPCUqRaTMlu8/87SffI+ooUJhYpLhGG4UJRaLxRQoTS0QTSxQmFokmWCDB3FNn1QgcgF1nnmjO/q64V7h0x5ZX0+USO5eUq43X4s5yb8BAxaoPJF0V4u5OKj1vUKm8Rhz3a2kcdQwxGGArLJfmX27piyuotRrtnd/YSqtQXwwkaN4DLAOuEZGLRGTCQBMOgXkiEovIEyLy5dx6/9Q5dnkPw3tdzQb+pqo39jOmr0KC9duaKTjYyrl6N4gcIq6oYqPtrWTGbl80LcaVo7HmZ2JVBCILEojGOeGSEzSFCUUKk9soTHbrx+UETi7pc9fjTmKXY04kjVN2OuJYqw7dWan6hNKemLjLhJDGqQmZnqRa1ibuqlDpLFNZVibu6iFJlrBw4Zzh/lpWCqo6VNPZ9aq6oaruj+W19MVLdVajB+q2t9Iq1JB+BY2q3gG8FctC/QLwDxlEn+gBeAmTjp/HpOPfgPNE5Btuu3/qHKMM53UlIlsBBwEni8hkEZkMZP3PJ4lIO/b/nNAgtHgy0JnTgvoqODiJ2oKDrZqrBlW9ICuq2Gh7qwmKIZGrEiChEHYUKExqI+owp31QCCmML1Gc1G5+mraI4hrtFCaViDqKRBNKFCaXzOHvunDeMbv2oXjXmabt7PSNY0m6YtNWuu1vWk5Ind8mqwidlhMqztSWCR1Nlbi7QlzpZOHCpvIFV3mGotG45mOtoJVWoYYMGN6sql3A10TkKqzswB9F5Hzg7j7GXzrQnG7czViv6oybxEonHC8iZ2fDGuy6Sj11gj15An2W8fYsz3BdV1hZjAJwV4NtzwP/h5kSQmAz4Inc9nqNtlHBwQ2xUOl8wcFWzTWivGXnL/DkvZeBQtph+S6IS6AsBFXNJihaOLQmSlAISMoJKCShEC+ruKZpELhfzV9PP4W0bPfEfHj0zt86njvPnI0kCkWFOEULofkpUsD5KrQQkFZSgkIAHRB3VZBAiNoLpGE3Cxf+mTAcx5pr7rjyv7RWoKDxsAYAHCQihwNdwJ+Ab6rqM7ntM4A/15yS6rMikllyrqd/q9AnBzqBpvNoVPVWEdkVeAj4slvyZDfjZm8IjbgS+AQwndyTYp0mskJPnS2YazlcCe8LwIc3D5ZhuK7uAN5bt25PrHTG3sBTwDPAYkz1PxWqlXD3wf0fHTcBR4nIBFVd4tZ9EvvB3ube39nCuUactJyAQNhRgC4hjVOkEBAWIyQ0A0gQBUhHgTROCYqhjXEZ/UEhQNMQESEN7KemaYCEKarKHbNProZLg1WSBkzghAKpgoaoAqqkiRK2mUkvTC38mVSd0AtAKoTFFNWYV165A5Eia665w0r/3lYI9znrmFpnMr2gr1YBA3At9vD2PLAFcBKFRTRYAAAcz0lEQVQwV0S2UdU33JiWWIVUtdzXSTQtaETkfdiT51TsB9ToibFVKK19UhwzT51jjVZfV66X+q11x5juXs5V1aVu3enACSLyGr1JlgEWEp1xHnA4VnDwe1jp9FnAmZmZQVW7WzXXSPPUg1eQ9MQEWHkYjVPe+tHDePi6n1R1fxFBQkFCi0pLYyu4ufWHDuUf1/+0OpeGqdU3C4BESQtWAUAD4Y7TTqma0DIygXPXj76LpkqQKCK2exynhB2RHdd1+QzbIhKpQKponBC2K6oxYTie1167lyiawIQJo8itmi6n0SxqhclUVY/IvZ0rIndiD3VfBM7KD22w+1CtQssxoKBxNu0zsETHF4E9VPVPA+03RPbDch6ewXw4/qlzjLKSr6tGnI7dx2YCU4D7gA+o6svZAFV9zfmOzsXMB68DP8IExHDNNWJsst1nmHf/5Wz6zs/yxNxLTLshEy4BQWhCJnCZ+4nzr2Q+hW32+Qr/uOGnSGCaUKiKRCFJd0xQSUgrIWk5QXqSavJnXrsB893ceeZs3vm1mfztJ98jKCeW1BkAqYUBB1Fgx3SlW4LENKCwpFBcAqSkaYXXX38AkQKQMmnStixd+hTjx2+ycr/UJlCFNF45hhBVfUREngC2z61upVWoIf0KGhHZGbgE0wR+gWXSv9HfPs3ibPP3AA9j2sYn3XK4c3K17ElxLD11jgWG87pqhFp720vq1ikWnTZ7gH0fw5KK+xvTsrlGmqijwLwHLifpiglLEY/ceL4VvgxNuARFK6KJCElXBU0VCXoTcCQIkLYAcZFTYVsBiYS0HCLlxMxjqkhoprV6UxqYdnPnmbPZ8bBv87f/Pd36taRKECtBnBKWQhCIRQhTJatuYgU7BU2XIEGB7N4nEvLqq/cQx2/wyit3AimqCaoVrK0LJEk3mrrxQcjaa9dEAA8z2kijWQkHrdJKq1BDBgpvvh2LNPiYqn6hxTeDJ7DooKuwBj5bAgeoav7Gfzr2452JdZKbSIMnRWB37Eu4HgvL+xFmi2SY5hq1/OuOS0b6FGB4ryvPCrDRFp/oraAcWjmYLXY/2ASMEzJBwUxruDFBIaxWF9h67y+z9V5fJixFFMaV3N+i5d64tgJBu7UYyBqv3fn9U5c7j8yUlsbWQjrttoKbWSHOpDs2n1AxNA3L+Y+SroprPV0mTbuJ4yVUKq8Tx69RqbxKmna690uIy51UyouJK0tIk7LrDpqQVPp9OG89TqPJL8OFiGwNbA7cn1t9E7BHXZpBf1ahbK7MKjRg87N+a52JyG+x2mP1JTw8fbCqBgP87hvfYO1112TytmszbbvprLXWB1fKcRvVd/LX1eBZmdfVUw9dwSZv+wz/uuMSc/S7sv5huwmNqKNIZWmPlY7pcQmVrsPmVnscwiM3nV81r5n2QzUxM8uTAXqrN/fEJM73ssvRJzQ8p7vPOa23BE6bhVYXJpaqiaaZEMxqpoWliLBYIKn0Hk/CwDne06rZDUAECMTGiVhodiFknXU+3NLvta9aZ1tO31AvPenrNevecdC3Bqx15m70mer1TezhLXsovhELivkc9mD9IqZ9HI/1nHlbrubeGlix10ewZMxNgDOBs7JafW7cTJYvHrsjsFX+gb0RA+XR7O9vBmODj//oR647YjdLXxjZf6m/rlZdnnvyKkSEf935c1TNHJXd1AsTSs5fUrCbfhRYH5liaIKoGPHYnAvZeq8vs+UHvoSmylt2+YIZaUSsYKYTEtEEl/w5rkDokjzDtqihdgPWFTRrJ2BdPBNXS63sWkqnxMvKpHGKpurqpJmQSSsJ60//GHGntaKuLOmxxM9lZSt5k1UoKCeksbUrSCsJCxbcsFK+cwU01pqlSdbCrEG/Bd6JWYWy92sBz7m/ZwF/xITQn4Bd866AVlqF+sI3Pmsxq6pGA3DHaaegScq4jSczcbMpdKw3kfU23HdYj9nXU5xncKys6+q5J68i7rSb92bv+BxP3H6x1TUrRYROa8gamVWW9tjNP1GSroqZgBK74W/5gS/x2JwLKYy3XOi0nJgQiFPSJCV0YdFJlwmLbEzSZWVotJywyzEnLnd+d59zGmFbVO2hE5RCovYCQTEywVcKCcLANJ1CQNqTQCCu1bRpNtUABidIJRAQ154gW9znDAoh66zbmkpJff0WtthoA/35MYfXrNvxsG/76s2e0cmuM0+kMKnE9p/9OgvmPMXLf32Gx2+9iGcf/+1In5pnFSEzd2Utlzd/9xetNYDTFCQKCMISheIaLvvfyv9nlQDCYkjYVuCft1yY03qcgMr8PFHgBFeERNbuOeooELYXqpqNFEP+evryLQhMs0lJ48Q0mUyzcfXRks4KaZKSdFvDtaQnprK428xzXTHx0nLVhBd3VUyz6axYozZXzDPujqvVpJOemBefv3Z4v3Rdzas3e8YeE2dM5fHbLmbiVtNY9vTrzHjPQSy6/wXm3XfZSJ+aZxVg/Y0+aoKgGFYDR9KK00YSp5GkZdK0h7BUJHRlazJBI1FA1O5K0BTs9hK22ba37HqgM50VXOMzE0LRuKILDjAhE3YULNKt2Lj56I6HfRstp2jFBQb0JFaEs9PMX1lL6qqJrKu3kGfsfEVZsc7Mv5R0V6olb9KepDcwwNVhe/G5YRQ2OmTT2ajBd9hczWibNo4gCkjLMWvtthH3XXIm23/269x70Q945rFfU5jQhiaW8b3ueh8Z6dP1jABmSurtgAn2xJ3GKUk5MXNTqZswHIcWU6hYCRpii0LLnPxv3ukAnnrwCqfx2FxvfucBzLvvsqrpKmovIFFgjvia+2sRpMKdZ86uRqBl3HX2d3vrgsUJKVhJnHytsHzYswhpkiIi9jmSlLd+5LCaOR/5/XmkNg1Vb4Li5gkZXg+DjkktJo/XaFYzNnnbZwBYY8u1CUsRU/5rPR75/Xm846Bv8cpDC1h47/OsP/1jvPbogP49zxhlg033q4Y3A7xl1wNd/TG7C6dxytprf4gkWUYYdlSjvsKilYqR0N5Dr0koKITMu/9yADZ9++dsfGYucxpP1FEgaiv0VoaeWCQshcsFCOx0xLF2HpXUwoGzgpw9SW/Pm86K+Xs6zYSWurYEaTlZTsgAbP2hQ808GKcWSNAT94ZKu3XDhbpaZ/mlGaS5fjRN9ZCRYe5H4zWa1RCznUeMW28ibzz5CkvnvcZDv/kxhYltbLXnITzz+G8gVR7/y0XMeO9BI326nhFAwoCwLeLfd11a9c1kiZkbbPJxXnz2GpJKjIbLkCAkANJUCUIBDZD2yASLKtO3/hTPPPZrgqj3uTZztmdzhsWQtJJWBZwV6lQTFj29ieh3njm7Os8OhxwFwH0X/5BUFUlSV/9MkADe9onDeeg3P+ZtnzBHe/71w1f/r2lSqpDCWz96GFt/6FAAHrnx/Gr4c7aEpYj5j/yK6Vt/qvVfthPeQyDrR3M3UOxjTNZDJh+SPEdEtlbVBVDTj+YxrB/NpsAPMUXk+MHM1Rde0KyGvGnz/Xn99YdY/PK/2fxdBwLw9N9/Sdhe4JHfn8f4aRswZcrOPHH7xSN7op4R4aUXrrPKzaoEpZBNt/8sTz14RTVX5fmnfsd6b/pozT4LFtxggQIiECRobFn7aZzy1ENXoAqF8UXmP/rrav6KuugzTZSkkvSuVzN3Vc1YubL5IlZJOjOz3XPeGaSVlB0OOYr7Lv6hVYKWXm0sEyzZ679fdW7Vl5SZBjVJeeSm89E4ZZt9vsLWe1td10f/cIEJGsUdL+KZx37NRlsOWKx40DTbVbOO61X1WgARuRKrF1ilvoeMW3cXMB/rIZMJkXw/msXAn1wfmlkicoaqLh7EXA3x4c0tZlUOb27E0w//Ek2UTbYzk9rjt17EjPe0Tovx4c2tYWVfVy+9cJ05yCsJG834hD2ItEW8afP9ef7p37HBxh9fbp+XX/4DIgFJpduc7S6IAAHBcmiyEOc3zdif+Y/+utpuIE3Sam+aytIea+/cnVB5vZt3Hj4TgDu/fypBmyVsEgokJgQyzaYvHvzl2VaQMwpdlFuRggvZ1iQ1E1zW/8YFPWzz4a9U939szoXVSLmoo1D9HgZLX7+FGeutpxccfHDNut2+851BhTdngkZV35Nb9z7gFmALVX08t/4iYFtV/S/3/nbgRVX9VG7Mm7Cakx9R1eubnasvvI9mNWfjt36aiRtuUH0/efNpI3g2nlWFddf/iCukGTDvgcvBZdwveOl6gijkhflXL7ePaoxIRBAV3U25SNRuEWlhm5nJgihA6dVo0krqEiYtf8ZaPMdUlpaJl/RUhQyAFANrwOZCqLPX911yJvf//MzlzueBy8/iwV/9GMnCq13OTWF8sfq3tEY7xUklipPaKIwvEbUVCItW4y1jy/cfXBVEWT7O80//rmXftQKJas3SIkaiS3FDvKDxMHXqu6uvW5Wc5hn9hKWomsQIVCO7Cm3jLUS5HlWmTXsfUTSRqDCBsNBm4cttVuNMnSktLSc8ec8vqtpDWkkta39Z2UKSl/SQLKvU+GbuOvu7vbk6HQWi8dYJNFvC9ogHf3k2D1xxNg9cbtXvt//s19nuU4f3hpEFYomdLk9IUyVsL1Bao6PaWTRfk+2Rm3LC5gNfqp6rpjpUU1dDVJUkTWuWFjESXYob4gWNx+NpyDrr7lNtEbDxNp+ynJVyDChh2M7LL/+eBS9dXx0vQcDChXOAlCiaTBiOI4jCao5L3FWplozZbIfPWyRboi4yTYm7YipLeki7Yt75tZlWxfkHp3LX2d+1AJa2qCpUoo4ihUlttK01nvZ1JhC2F6yszYQiYXuBh37zYx64whr1vm3/rznhYNFjadn5g0Rc9emAtslr0DZ1HIWJbRQmlCiMsxyhrFgomB8lC/HOIsMaaXZDIUmSmgXX+Cy3DLWD70h0KV4OHwzg8Xj6JMvuB3vy1kRJkm6KxbUIgnbStJP//CfryC6AIFIkCArEcQVN02qZ/+qSpDx+28VWekasd001q78rrrZ9vuvs71ZrrWXVoyUMLIQ6kGpV6PIbPUTjitZKoBBWhUnc1RuSvO1+X+Xh636SEzgJEgYk3eICDBKKpalEhU56osXmVwqFypKe6hzqohM0tm6hzz15FQD/+c8fV6hIraqSLm8ua0Xjs5HoUtwQr9F4PJ4+WXf9jxAUQp7915W9dcFUSdNOomgixeI6hOE4wnA8YdhheTVBkSBoR6RgwilNLfGxWmLFEi3jrgqVxd30LOqkZ1En8dIyO/7PMez0jWO5+5zTzJw1oURxSgelqR0UJrURFHvDrNWZ8oqT2yhOLLHl+w+mMKFovpaOIlF7xN+vOheAh6/7CUFkgioTfBZ8kLUV6CFJliJi/pvixDbC9gKF8SUe+9PPAIt4s3IxdtysvE7W02ZFGCbTWb6HTJ4V7VLc31wN8YLG4/H0S1iykjKRS66UIEA1RrVMoTCZYnEqQVByQqaNICigmiAS9N6c07TGVJZWUtJuqz2W9sSkPQk7Hvbt6jGtJYAzlZVCM421Fwgil0Oo6vq4WFRbNK7IvPsuQ0Ss2GYhQKKAbff7Kv+4/qfmK+ooELVZcdDNdvg8m2z3mWo157izTFLpJkmWEBUmuPBnmyMoRjx68wVs+cEvuUObVpOUEzRJmTZtxXrZKZCmac3SIprtITPs/Wi86czj8fTLuut/xErmq1pyZlAiCEpkz6kiBfPHBBYgEMdLSdOllHsWOXNbSpoJmCTtDX2uJGz3aWtpf98lZ3LPBd9H49SFMBeqLQWqhTldgqWZrkzISCUliNJqZBtgDvtq7gvVfjrROHP0A8x74HI23f6zZrpLTGgAaFGh2AkKQRQSllI0jiyvKEfVjJa2IChAlXgIwqWuH836wEQR+W/3/kZV7RyBLsUN8YLG4/EMSBi2M23a7ixcOAeRyJnLOsy/IQUmTtyUpUufIkmWkSSdxPFi87uUTaBUtZhyUm2Wlk+mDEuhNSAruQrPpcic/u29vW+kEFAsBNY8LVUb7yLZwpLVaMvmzldBltCizSS08Oqoo0jcWWb+o78m6Y7NB+VK2kBsyZ5W7s0qIhSCmrpvgJ2Pq0KwolhO6pAEVtaPJk/2fmMsmfJ0TBjMBKYA99Ggs7CI7A6ci/WjeR3rRzOrbu4B5+oLbzrzeDwDMm3a7oBpL+aTUFRj2tvXB2Dx4kdJkmVUKq+Rpp1oWqnWRqvW8qoGBKRsu99Xa+bf7tNHEI4rEo0vuvyYXl+MBGKdNUsRm779c5TWbLeeOIGw2Y6fB6hqOJts95mqFpVPRs/aPUsYIKFQnNhmwQTjiyZoAuFNM/Zng033s0ABF21nn1mqAREz3ntQ1U8jobD+RrUVEoaCqhInSc3S5H7zVVX6WOa7Maqqs1V1A1VtV9V3qeqDDeZ6TFXf58asq6on1IcyNztXI7yg8YwpROS/ReROEXlFRLpF5AkROT4f5y8i80VE65blajW1stBgM3ONBqZO3Y0pU3YhizBbunQeYK0DkqSTqVPfjaozA+Wf9sW0hMzUlDnpAR668lwevvp/zRfTFrHtx/+HbT78Fbba45BqOHHSZUUun/6H1RuLOkzTmffA5dV5wrYCT//9l8sF2or0NjaTwKLMwmgcbRPWpjR5XNXp//xTv+P5eVex/vSPWXmctoIJHJdomrVNAGc6S7UmvHtFGKZggFUGL2g8Y40pwF+Ag4G9gIuA47Ae6HmuAHbKLXvnN+YKDSpWaPAUrC/7yXXzZIUGv4c5RpdihQbXGcJcowi7YY8fv6kTLCZUFi26jeXu9Lly/Ra51qutPHTluYQly5EJ2yyZsj4rP+muVPvIJF0VnnroCqfhhFUtJQitv03YXqjWORMRmyuwfBnrMRMTd1dI4mWkaTdRNIniuPEU2ya7GmghLzxzDbjouiAKesOrCyH/uvPnuag3arSmoaJ4QePxjCpU9XxVPU5Vr1bVv6jq9zAh8zmRGoP6S6p6d255oG6qfKHBP6nqeZhgONIVHFyuaKGqzsGichQrNNj0XKON7KtcuvQpRAIyU1re2pKFQ0voqgtI5tuwwpgPX/OT2orOUejaRtcmSm61xyGWF+NK1GQtAaoBA8WQpGJ10jROqpFiBMLWe3/ZlCkR149Gq34dUKZM2YW11vqg+Z0KBaK2NqK2IlGxnTAaR1CKKEwoUZxYojCxRFiKePNOB7jwaCtJs6IMY2WAVQYfDOBZHXiFvsuo98VewM1Z1I3jV5jmshvmNN0ZmAj8JhugqstE5Hq3//GDmGtUMWnStn1ue/nlG2tXSOaQD50ZS6pRXFn0GWrjgjAgmNhG3FnhsT/+rBpSvNWelhj/z1sudP1uLBggKEYWcNAdu0rLLiotSdl6ry+bwHLVnINiSNQWVQVf5ncCnDnQePXVu1hzzZ1qPsKCl64nTXqqDdxQXFuDhJdeuI5111+xJoFjUbjk8RqNZ0wiIqGIdIjIrljo5k+11s5xkIiUReQNEblSRDaqm6KVhQabmWvMsPbae1vNPHUl+6smM4FALNTY5eTY34KV5ne+lKDg2kG3RTw258LaMjBpLg8nay+dpNX1m27/2Wo9MjABlQm3TJ8NimG/Nf3qhQxAUk4IiyFB0frSZAEEEkrVlDZU1IU355eBEJEDG/gZVUQOzY1ZZfyHXqPxjFWWASX3+lKsWVPGtVizqOeBLYCTgLkiso2qvuHGtKTQoKqWm5xrzJH1rHnu31eiag51cf6SahSae//YnAt7qwa4Ypeo5cCkccpjcy6s9qXJetSkceoKZFpiZX1Oy2N//FkWs2CVCVxvGQtjHhxZdNmLz19LEghpnCBYz5wVLUSr0HSkWQPehyVWZjyVe93KpmcrhBc0nrHKzkAHsANwIpYjcBiAqh6RGzdXRO4EHgK+CJyV29bKQoPNzFW70QopDrWY4iqDRAHmxZFq2RgNA2ubHKdmUgukqtGggCrR+CJJdww9MRIIaTlmi90PbniM5/59JWkoPPXgFabppEoKbPG+/8fjt1kDvyzcekVCktfbYF8WvHS9tQtoVTl/1RWpBnCvqi6tX1nvP3TrhtT0bKgnlsebzjxjElV9QFXvUNUzMdPZV0Rk0z7GPgI8AWyfWz2oQoN1Y4ZStLDReV2gqm9vQXHFEWWDjT9eLekSuN4w9tqW0EV0RR3WuyYoBATFkOlbfdKivaKAzd91YJ9CBmDDN/+3tQAoWoh0cZJVYH5i7iWExdCiyESQaMVveeusuw9ReztRW4FCR9sKz5dpNIPNoxmAhv5DzB+4V25cX/7Ddsx/2BK8oPGsDmQRZRsPMC7/iNrKQoPNzDWmCQrm39hws/1MkIRBtVdNtdSMEzBBMSR0mfgbb/MpNtvh800dY8PN9iMsRRRdmf9ofJFoXIE373RAtahnWk4sfHkFWXvtPSkUp7D22nsPPHgAVjDqbJ6IxC5f7Mu59auU/9ALGs/qQBZS9HSjjSKyNbA5cH9udSsLDTYz15hmvQ32Zb0NzeyUCZlM+wiKrtSM02zCguWzDIUNN9sPcMU2sbDmJ+/5hYt8662Z1gqmTNm5JfMoUEmSmoWB+9G8hPlfPo9db38DzhORb7jtrWx6tsJ4H41nTCEif8Ccm49iT3S7YMmRv1bVeSLyIeBzwA3Ai9hT2/HAs8AlualaWWhwwLlWF9ZZdx8WvHQ9qoq4gpahiLUScCHOEgQrLgzEEi432e4zPPXgFUgUmJbTXiAqTGrJZ2kZrgRNHf32o1HVm4Gbc6tuEpEScLyInJ0Na7BrS/yHg8ULGs9Y417gQGA6EGNRODOxmz3Ac1gxwrMwv8krwB+AY/M3/VYWGhzEXKsFWZTWwoV/RoJuNEzQNHAVkV1SZzB0QbP+9I9Z50tV5j/6a1ccMxwwrHmkyCoDtIArgU9g134rm56tMF7QeMYUqnoCZlLoa/vDwO59ba8b+xgWPtrfGAVmu2WF5lrdyPdxWbjwFkBJ0zIiESuaxlHoGEfYVgYgXtYDIqy3wb4rNOdwoaqZuaxlU1LrP3wit21E/IfeR+PxeEacadN2Z9q091fbD+Sz9ofCWmt9kHXW+TAiEVFHsdqrZlWkhbXO9gMWAc+wivkPV91v3+PxrHZMndqyiFrAosNWdVSVSjy4mmkichVwD/Awprl80i2Hq1U5XaX8h17QeDwezwiiqpQHbzp7AjgI2BBz3D8GHKCqv8iNWWX8h9Ky7FYPACLiv9AcqrriLQg9/roaA/T1W1hr4kTd7+21AWbn/eUv94/2RN08XqPxeDyeESRVpTxI09lowwsaj8fjGUka59GMKbyg8Xg8nhEkVaVnjGs0Pry5AWOlv7vH41n1UaASxzVLM4ym+5TXaOpYWf0ZPB6PByzqrKdSGXhgjtF2n/KCZnlWSn8Gj8fjAUjTlK5yebC7jar7lDedLc9K6c/g8Xg84PJoKpWapQlG1X3KC5rlWa36u3s8npElVaWzu7tmaYJRdZ/yprPlWS37u3s8npEhTRI6lywZ7G6j6j7lBU1jBtWfoa63+1Jqq6Wuzmw00icwhsiKJXpGJ33+FrrT9ObHlyyZWre6TUTuy72/QFUvqBsz7H1kWoUXNMsz6P4M7gKovwg8npahqtNG+hw8w4OqDqXy50rpI9MqvI9meVb7/u4ej2eVZ1Tdp7ygWZ7Vvr+7x+NZ5RlV9ylfvbkOlwj1GPAIkPVnOBM4S1VXuUQoj8ez+jHa7lNe0DRARLbE+jPshNk7LwRm1fXe9ng8nhFjNN2nvKDxeDwez7DifTQej8fjGVa8oPF4PB7PsOIFjcfj8XiGFS9oPB6PxzOseEHj8Xg8nmHFCxqPx+PxDCte0Hg8Ho9nWPGCxuPxeDzDihc0Ho/H4xlW/j+qUlTZjWY8AQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure('Limit NOCA computational nodes to Fisher Creek extent')\n", + "xticks = np.arange(-0.1, 0.8, 0.4)\n", + "ax1 = fig.add_subplot(221)\n", + "ax1.xaxis.set_visible(False)\n", + "imshow_grid(grid, 'topographic__elevation', limits=(0, 3000),plot_name='NOCA extent',\n", + " allow_colorbar=False)\n", + "ax2 = fig.add_subplot(222)\n", + "ax2.xaxis.set_visible(False)\n", + "imshow_grid(grid, 'topographic__elevation', limits=(0, 3000),plot_name='Fisher Creek extent')\n", + "plt.xlim(36000, 53000)\n", + "plt.ylim(32000, 47000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Confirm the size of the grid, nodes located every 30 m." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "7029145" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.number_of_nodes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Confirm the size of the core nodes where we'll run our model, a subset of the nodes for the watershed." + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "69434" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.number_of_core_nodes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.5. Attach data (e.g., soil and vegetation variables) to the Landlab rastermodelgrid\n", + "This will be used to calculate shallow landslide probability and set boundary conditions. THe data we attach in this step was downloaded in Step 2.2.2. \n", + "\n", + "#### For each input below\n", + "1. Load data from ascii text file\n", + "2. Add this data as node variable to the Fisher Creek grid\n", + "3. Set boundary conditions\n", + "\n", + "For the entire NOCA extent, this takes ~60 sec to load each file using the CUAHSI JupyterHub server. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.5.1 Load slope\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "(grid1, slope) = read_esri_ascii(data_folder+'/slope_tang17d.txt')\n", + "grid.add_field('node', 'topographic__slope', slope)\n", + "grid.set_nodata_nodes_to_closed(grid.at_node['topographic__slope'], -9999)\n", + "grid.set_nodata_nodes_to_closed(grid.at_node['topographic__slope'], 0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.768809\n", + "0.3057313\n" + ] + } + ], + "source": [ + "print(np.max(grid.at_node['topographic__slope'][grid.core_nodes]))\n", + "print(np.min(grid.at_node['topographic__slope'][grid.core_nodes]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.5.2 Load contributing area" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "(grid1, ca) = read_esri_ascii(data_folder+'/cont_area.txt')\n", + "grid.add_field('node', 'topographic__specific_contributing_area', ca)\n", + "grid.set_nodata_nodes_to_closed(grid.at_node['topographic__specific_contributing_area'], -9999)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.5.3 Load transmissivity" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "(grid1, T) = read_esri_ascii(data_folder+'/transmis.txt')\n", + "grid.add_field('node', 'soil__transmissivity', T)\n", + "grid.set_nodata_nodes_to_closed(grid.at_node['soil__transmissivity'], -9999)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.5.4 Load cohesion (mode, min, and max) \n", + "This takes ~4 minutes because 3 cohesion fields are provided to create more flexibility in how cohesion is distributed on the landscape with different vegetation." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [], + "source": [ + "(grid1, C) = read_esri_ascii(data_folder+'/cohesion_mode.txt')\n", + "C[C == 0.0] = 1.0 # ensure minimum is >0 Pa for use in distributions generation\n", + "grid.add_field('node', 'soil__mode_total_cohesion', C)\n", + "grid.set_nodata_nodes_to_closed(grid.at_node['soil__mode_total_cohesion'], -9999)\n", + "\n", + "(grid1, C_min) = read_esri_ascii(data_folder+'/cohesion_min.txt')\n", + "grid.add_field('node', 'soil__minimum_total_cohesion', C_min)\n", + "grid.set_nodata_nodes_to_closed(grid.at_node['soil__minimum_total_cohesion'], -9999)\n", + "\n", + "(grid1, C_max) = read_esri_ascii(data_folder+'/cohesion_max.txt')\n", + "grid.add_field('node', 'soil__maximum_total_cohesion', C_max)\n", + "grid.set_nodata_nodes_to_closed(grid.at_node['soil__maximum_total_cohesion'], -9999)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.5.5 Load internal angle of friction" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [], + "source": [ + "(grid1, phi) = read_esri_ascii(data_folder+'/frict_angle.txt')\n", + "grid.add_field('node', 'soil__internal_friction_angle', phi)\n", + "grid.set_nodata_nodes_to_closed(grid.at_node['soil__internal_friction_angle'], -9999)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.5.6 Set soil density values\n", + "In this example, we assign all nodes a constant value." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "grid['node']['soil__density'] = 2000*np.ones(grid.number_of_nodes)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.5.7 Load soil thickness" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "(grid1, hs) = read_esri_ascii(data_folder+'/soil_depth.txt')\n", + "grid.add_field('node', 'soil__thickness', hs)\n", + "grid.set_nodata_nodes_to_closed(grid.at_node['soil__thickness'], -9999)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.5.8 Load observed landslide inventory. Class 1-5 are landslides, 8 is no landslide mapped for later plotting" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-9999., -9999., -9999., ..., -9999., -9999., -9999.])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(grid1, slides) = read_esri_ascii(data_folder+'/landslide_type.txt')\n", + "grid.add_field('node', 'landslides', slides)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.6. Specify recharge option as _data driven spatial_ and access Python dictionaries to generate recharge distributions \n", + "Recharge in this model represents the annual maximum recharge in mm/day generated within the upslope contributing area of each model element. This corresponds to the wettest conditions expected annually, which would provide the highest pore-water pressure in a year. Details of this approach can be found in Strauch et al. (2018).\n", + "\n", + "#### 2.6.1 Select recharge method from the component" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [], + "source": [ + "distribution = 'data_driven_spatial'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.2 Load pre-processed routed flows dictionaries \n", + "These contain HSD_id and fractional drainage at each node and recharge dictionaries. HSD is the Hydrologic Source Domain, which is the VIC hydrologic model data in this case study at ~5x6 km2 grid size. The 'pickle' utility loads existing dictionaries." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "# dict of node id (key) and HSD_ids (values)\n", + "HSD_id_dict = pickle.load(open(data_folder+'/dict_uniq_ids.p', 'rb'),encoding='latin1')\n", + "# dict of node id (key) and fractions (values)\n", + "fract_dict = pickle.load(open(data_folder+'/dict_coeff.p', 'rb'),encoding='latin1')\n", + "# dict of HSD id (key) with arrays of recharge (values)\n", + "HSD_dict = pickle.load(open(data_folder+'/HSD_dict.p', 'rb'),encoding='latin1')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2.6.3 Combine dictionaries into __ordered__ parameters\n", + "This sequence of parameters is required for _data driven spatial_ distribution in the component. Recharge is this model is unique to each node represented by an array. " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "HSD_inputs = [HSD_dict, HSD_id_dict, fract_dict]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 2.7. Set Number of iterations to run Monte Carlo simulation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The landslide component employes the infinite slope model to calculate factor-of-safety index values using a Monte Carlo simulation, which randomly selects input values from parameter distributions. You can specify the number of Monte Carlo samples, but the default is 250. The larger the Monte Carlos sample size, the longer the program runs, but the more precise the probability of failure results become.Strauch et al. (2018) sapmple 3,000 times for each parameter in each model grid." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "iterations = 100" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Results" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.1. Run the Landlab LandslideProbability Component in Fisher Creek for pre-fire conditions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.1.1 Initialize Pre-fire\n", + "To run the landslide model, we first instantiate the LandslideProbability component with the above parameters, as well as the grid and number of samples we specified before. Instantiation creates an instance of a class called LS_prob. \n", + "\n", + "No outputs are generated by this command as it is setting up the recharge and instantiating the component." + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [], + "source": [ + "LS_prob = LandslideProbability(grid,\n", + " number_of_iterations=iterations,\n", + " groudwater__recharge_distribution=distribution,\n", + " groudwater__recharge_HSD_inputs=HSD_inputs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.1.2 Run the Pre-fire model\n", + "Once the component has been instantiated, we generate outputs from running the component by calling the component's 'calculate_landslide_probability' method using the class instance (e.g., LS_prob). The cell below runs the model; in the following section we will assessing the results. These calculations will take a few minutes given the size of the modeling domain represented by core nodes." + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Landslide probability successfully calculated\n" + ] + } + ], + "source": [ + "LS_prob.calculate_landslide_probability()\n", + "print('Landslide probability successfully calculated')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What is the maximum probabilty of failure we found?" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(grid.at_node['landslide__probability_of_failure'][grid.core_nodes])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The outputs of landslide model simulation are:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['landslide__probability_of_failure',\n", + " 'soil__mean_relative_wetness',\n", + " 'soil__probability_of_saturation']" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(LS_prob.output_var_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This simulation generates a probability value for each core node. " + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0., 0., 0., ..., 0., 0., 0.])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.at_node['landslide__probability_of_failure']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This simulation generates a probability of saturation value for each core node as well." + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-9999., -9999., -9999., ..., -9999., -9999., -9999.])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.at_node['soil__probability_of_saturation']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "-9999 means there is no data for that cell, it is a closed node." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.2. Run the Landlab LandslideProbability Component in Fisher Creek for fire conditions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make a 'grid_fire' version of 'grid' for which we will give post-fire cohesion parameter values 30% of the original cohesion. This is a crude estimation of the lowest root cohesion after tree removal based on a combined decay and regroth model (e.g., Sidle, 1992).\n", + "\n", + "Sidle, R. C. (1992), A theoretical model of the effects of timber harvesting\n", + "on slope stability, Water Resour. Res., 28(7), 1897–1910.\n", + "\n", + "#### 3.2.1 Make a Post-fire copy of the grid" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [], + "source": [ + "import copy\n", + "grid_fire=copy.deepcopy(grid)\n", + "grid_fire.at_node['soil__mode_total_cohesion']=grid.at_node['soil__mode_total_cohesion']*0.3\n", + "grid_fire.at_node['soil__minimum_total_cohesion']=grid.at_node['soil__minimum_total_cohesion']*0.3\n", + "grid_fire.at_node['soil__maximum_total_cohesion']=grid.at_node['soil__maximum_total_cohesion']*0.3" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.2.2 Change the Post-fire cohesion values " + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "this is the highest mode value for coehsion before fire across the domain\n", + "10000.0\n", + "this is the lowest mode value for coehsion before fire across the domain\n", + "100.0\n", + "this is the highest mode value for coehsion after fire across the domain\n", + "3000.0\n", + "this is the lowest mode value for coehsion after fire across the domain\n", + "30.0\n" + ] + } + ], + "source": [ + "print(\"this is the highest mode value for coehsion before fire across the domain\")\n", + "print(np.max(grid.at_node['soil__mode_total_cohesion'][grid.core_nodes]))\n", + "print(\"this is the lowest mode value for coehsion before fire across the domain\")\n", + "print(np.min(grid.at_node['soil__mode_total_cohesion'][grid.core_nodes]))\n", + "print(\"this is the highest mode value for coehsion after fire across the domain\")\n", + "print(np.max(grid_fire.at_node['soil__mode_total_cohesion'][grid.core_nodes]))\n", + "print(\"this is the lowest mode value for coehsion after fire across the domain\")\n", + "print(np.min(grid_fire.at_node['soil__mode_total_cohesion'][grid.core_nodes]))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.2.3 Initialize the Post-fire model\n", + "Now we'll run the landslide component with the adjusted cohesion, everything else kept constant." + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Post-fire cohesion successfully instantiated\n" + ] + } + ], + "source": [ + "LS_probFire = LandslideProbability(grid_fire,number_of_iterations=iterations,\n", + " groudwater__recharge_distribution=distribution,\n", + " groudwater__recharge_HSD_inputs=HSD_inputs)\n", + "print('Post-fire cohesion successfully instantiated')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 3.2.4 Run the Post-fire model" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Landslide probability successfully calculated\n" + ] + } + ], + "source": [ + "LS_probFire.calculate_landslide_probability()\n", + "print('Landslide probability successfully calculated')" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.0" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.max(grid_fire.at_node['landslide__probability_of_failure'][grid.core_nodes])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The outputs of landslide model simulation are:" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['landslide__probability_of_failure',\n", + " 'soil__mean_relative_wetness',\n", + " 'soil__probability_of_saturation']" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sorted(LS_probFire.output_var_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Save the arrays as variables by 'attaching the fields to the grid' and view the outputs. **component already does this**\n", + "\n", + "This simulation generates a probability value for each core node. " + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0., 0., 0., ..., 0., 0., 0.])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid_fire.at_node['landslide__probability_of_failure']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 3.3. Display and visualize results of stability analysis" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Set plotting parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [], + "source": [ + "mpl.rcParams['xtick.labelsize'] = 15\n", + "mpl.rcParams['ytick.labelsize'] = 15\n", + "mpl.rcParams['lines.linewidth'] = 1\n", + "mpl.rcParams['axes.labelsize'] = 18\n", + "mpl.rcParams['legend.fontsize'] = 15" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot elevation" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure('Elevations from the DEM [m]')\n", + "imshow_grid_at_node(grid, 'topographic__elevation', cmap='terrain',\n", + " grid_units=('coordinates', 'coordinates'),\n", + " shrink=0.75, var_name='Elevation', var_units='m')\n", + "plt.xlim(36000, 53000)\n", + "plt.ylim(32000, 47000)\n", + "plt.savefig('NOCA_elevation.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Excluded areas from the analysis are shown in black, including outside the park and inside the park areas that are water bodies, snow, glaciers, wetlands, exposed bedrock, and slopes <= 17 degrees. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot slope overlaid with mapped landslide types. Takes about a few minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32000, 47000)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure('Landslides')\n", + "ls_mask1 = grid.at_node['landslides'] != 1.0\n", + "ls_mask2 = grid.at_node['landslides'] != 2.0\n", + "ls_mask3 = grid.at_node['landslides'] != 3.0\n", + "ls_mask4 = grid.at_node['landslides'] != 4.0\n", + "overlay_landslide1 = np.ma.array(grid.at_node['landslides'], mask=ls_mask1)\n", + "overlay_landslide2 = np.ma.array(grid.at_node['landslides'], mask=ls_mask2)\n", + "overlay_landslide3 = np.ma.array(grid.at_node['landslides'], mask=ls_mask3)\n", + "overlay_landslide4 = np.ma.array(grid.at_node['landslides'], mask=ls_mask4)\n", + "imshow_grid_at_node(grid, 'topographic__slope', cmap='pink',\n", + " grid_units=('coordinates', 'coordinates'), vmax=2.,\n", + " shrink=0.75, var_name='Slope', var_units='m/m')\n", + "imshow_grid_at_node(grid, overlay_landslide1, color_for_closed='None',\n", + " allow_colorbar=False, cmap='cool')\n", + "imshow_grid_at_node(grid, overlay_landslide2, color_for_closed='None',\n", + " allow_colorbar=False, cmap='autumn')\n", + "imshow_grid_at_node(grid, overlay_landslide3, color_for_closed='None',\n", + " allow_colorbar=False, cmap='winter')\n", + "imshow_grid_at_node(grid, overlay_landslide4, color_for_closed='None',\n", + " allow_colorbar=False,cmap='summer')\n", + "#plt.savefig('NOCA_Landslides_on_Slope.png')\n", + "plt.xlim(36000, 53000)\n", + "plt.ylim(32000, 47000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Legend to mapped landslides: blue - debris avalanches, cyan - falls/topples, red - debris torrents, and green - slumps/creeps" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot of soil depth (m)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32000, 47000)" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure('Soil Thickness')\n", + "imshow_grid_at_node(grid, 'soil__thickness', cmap='copper_r',\n", + " grid_units=('coordinates', 'coordinates'), shrink=0.75,\n", + " var_name='Soil Thickness', var_units='m')\n", + "plt.xlim(36000, 53000)\n", + "plt.ylim(32000, 47000)\n", + "#plt.savefig('NOCA_SoilDepth.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot probability of saturation " + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32000, 47000)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure('Probability of Saturation')\n", + "xticks = np.arange(-0.1, 0.8, 0.4)\n", + "ax1 = fig.add_subplot(221)\n", + "ax1.xaxis.set_visible(True)\n", + "imshow_grid(grid, 'soil__probability_of_saturation',cmap='YlGnBu',\n", + " limits=((0), (1)),plot_name='Pre-Fire',\n", + " allow_colorbar=False,grid_units=('coordinates', 'coordinates'))\n", + "plt.xlim(36000, 53000)\n", + "plt.ylim(32000, 47000)\n", + "ax2 = fig.add_subplot(222)\n", + "ax2.xaxis.set_visible(True)\n", + "ax2.yaxis.set_visible(False)\n", + "imshow_grid(grid_fire, 'soil__probability_of_saturation',cmap='YlGnBu',\n", + " limits=((0), (1)),plot_name='Post-Fire',grid_units=('coordinates', 'coordinates'))\n", + "plt.xlim(36000, 53000)\n", + "plt.ylim(32000, 47000)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This map shows the probability of saturation as high throughout much of the area because we modeled the annual maximum recharge, which is esssentially the worst case conditions that might lead to instability." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot probability of failure; Compare this with the elevation and slope maps." + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32000, 47000)" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ahZvnlUDbOnQbTcM4QNi3hzACZgDQuZLjugG7gVtsbfdh3L8b2truIrL79gO2PolALhXdt6scqy7cYbUkT7UkT33LP6lxXy3JUy1cZ45b8m5oe1GOauE69ewH7qQH+ravXVn35lbT78SfO0cB9S2eWKP+vj/e0MmxbtWC1epf+ZVOTY5XD5jr1LpenwVd0qP9Pv9/H+hbVf/fTi7RaSkJIRvVuG9TyxAWjOOZD0i1tTW22v5V1TmjyazwuprUD3WCquap6nT7RrnGMUNVl4jIOSLyjoj8w3IP/xcwBViD0dYCPIdxlPhYRE631my8wONquWGr0dYeAkaKyI2WK/oHGCn/dDRj1Rnqx9W6jzF/VOJJpyV5YE947o5ndEIarkNPL2+ztCRvDZ9UHRyiRRq2xb9uOqxezq8dWlR6ve5+7hqj5Tc/igG7fGjBSvT3H+m3eR1nH9IU1M/EhDS+atia5sDEOSv26uc42BCR2pjmahvCEotREgptbYVWW5Upg6L2mrNSex+PkXThgkxVdXS0Y1bBWoxb+JOYdZstwNfASPuXpKr5lmD5DyYuaBsm+Z43bLyHrDnfBzQF5gBnqOqmWoy1R9g96AKv7WY6f/4SJL5pSD8AzZ3LqMB79aM780F3g9+H1xJaWY7DgkMdE7xGE37hqFlT0NItFczK5hrehO5YBbtL2TSkD/7PXsN95RgeSkhjcI/2/NihG1uBeL+SGOvm1OR4yC8ia599sr84QtSmOczSREhMqKquEZFACMsXlRz3ESYe9DERedBqy8RoV+FJVEOnaalP1WI5D3wMnEm5B5nd40zMfPWgzskWTenlwA9Zi3NBXJWuFalN8CBhF9WuIohNAl8p3uQMvAWr8aa0cX7Ye4A6pcKrxJ83n8033kiL92YE28KFkn/zPHTbcnY8/gpxjRvQ4JSjmdA/k5sK14E7nucS0ggkjOyTHM/phdEsLzvYqep67RoXo680C3UyOTEnf65W4VglIruAEar6ZFj7Okw6t5FVHHsUJut2a6tpAzBAVauszBBthdYzMS7Xp2IEz5UYNW4G8AtweBTjHfQEtR2bcAk302mBbX1PXHjDhVWslZrPHR/63sGhHtCSPCQpPUQIAUEh5FvyLr91bkVB5kjYsJInnp1EwgPPw86d3FSSx5OW+fiGohwygBNi3cxwhFC94na7QjYgTUTm2LZIoSeRHlAqDWEBEJFWwIfAXIxcGGC9/kpEDq1qjtGY5v4OfKCqmVYgKsB6Vf2fiEzDCKKrMGYvhxoSrgWFmzpC8JUa81sleEvyYGd+nc7PwSGccLNyCK4Yur16D9LlDNZcPJTMH58Efxly4hD8G2Zx64ZZbPjHQBZMnss6oHCXiS/3gKPF1wMigrviulBeVRoRtQhhsRiBkSl/Vyscxkr7tgzj8FVpbaJoNKJDgO+s14HsBHEAqrobUxLikijGc7BRlcNCkIDWUwnehDS8qR2D4zk41DUhgifsoUdL8pDUDshhJ+J/89+0+fhziIvDv+JrdMX3kLMUzV9GYovG9PvlOdyU39U67bVPcJAh4I51hWw1oLYhLF2AhVoek4mabDULMS7glRKNINpBuQa1A1MKNt22fzvQMorxHGxUGUvkr1kF9tuvt+VudTQjh3pEEtIQ66EnyM582LkN/f4D5JwL8a/7Dtfhf0da9cD1t0uQLmfA7jJSRo1kzc2PM2xgz+ChS/fy/A8WRCSSaa46JgP9RaShrW0IUEK5MhKJ1UA3EYmznb8BJuRmVVUnjEYQ/Yn14KImX9tCjLkOMUniBmO83BxqQaWBrXbX7SoH8IOVysNbkgcNGqPbHddYh/ohogYfmwTuBvw64jXIW4urdW/jhJOcgX/Jx+iWP8DvR1LacujDN7C7pPwBKwOcLAv1gAi4Y9whWw2oUQiLiCwXkZdtx72EUU4+sUJvzgU+BVoBL1R1wmgE0VTgQluah+eBs0TkT4wN8HRMNgSHWlCpRiSuYCaFKtHdPPHsJLwlebxujeWtg7xgDg6VUWE90xWHLvwfx3z6INKpH+orsXb6yT7qOthZyLReN4E7njkXeijcuCV4/Lq9PfmDBpNZwb5Vh6rmA/0wmWi+wCzfPUHFZ4UYbJXIVXUucBbQEHgTeAOTNOCMuvSae4hybzlUdQJmAWo7ZnFrJPBIFOM5hFGTdR37j3/7bZczp2tX8JXyXFI63uJcxiWkMWTUYMZZ/ZynTIf6oDInm6LPfsTVeTC6bQW6/FsTb1S4Bm9xLjue/ZgfSsrwJqQxae0WfLvCE+E712udI4IrNiZkqwmq+oeqnqaqCaraSlVHaVjlAlVtq6pXhbVNU9WTVbWJtZ1iJSuokmgyKxSq6hLLMSHQ9riqHqOqPVX1Ya1pUJJDRGqSc84urJ59fjI9Fi3C9/oDbAS0dDMjinKIG3Qm1/3rbCCy64uDQ30gCWlsXWR0G4lrCGU70bzfkYZtQVykeL14Ns/jnpGDOLVxEsuXbCAQbDC4c6vgOI4wqjtEwB0bE7Ltj0QdcutQ/4S4xtrXiMLWi+61+rmHPYonZyYS3wx2l5J9zA288+wkPPkmt+u/cXCoH0Li3IBD7rgAzZ0LDVJh61b0my9ZO7g//kUfIintQXfT4OIB9BjWjwt+fY5jkuO5qFsGHy/ZsI8+wV8cESTGHbLtj0QliMRwhogMF5FRIpIZto2qr4keTITEadjT9YRnVQAesrQoLVhjAl4btSdzwSsE1NYdQLt6nq/DwYuktAlxXHCfnYU07oKunw2Jicipp9Hk8NZIu1MByGreA3LWM+Opr3B1OpcZhaV8sMDKQweclGDWQx2tqG4QEVyx7pBtf6TGepqIdMR4QHSh8gR2iqm46rCHBISRJKRVyDcXYHRCmsk7p35cbc5ES7eQOWcC60c8w3VjLkLExQNrp5F1SL+o0vc7OERD+PUpKW1MrsS2LvzrZpB43xg233Atr783A8+m2Wy+5U76L/2c9ZcOIh4oBSatNY4LC0tqFqrgUEMEXO79U/jYicZg+DQmKOkeTEK8LVV3d6hLIgkjH4D68SY2x5M3nyyrlLO3ONckQnXFkZXaEU/OTLzJGXgwKVUGRFgkdnDYE+wPTgCuxp3Rkjxch52Hf9100m69kNL3ZvBLjwFG6NhSBKUBRzVLYermArZhNPiVlGtFTsaFPcByVtjficY01xt4UlUfVdVfVXV1pK2+JnqwEZJvzrZmFNjA/EB1xyrzOq073qIcc4C4TCJVi6z03njWTeeEWDc/7fI5Zg+HOkcLVgfXMMOvV3fHv+M6aihDerTn2OwhwWOOxgRh5wHfbi7gBMtstNLaF8C5XmuPQNTu2/uCaGZVhrlGHPYiIRm6bQSEkaS0Nxm3rTgOb0ke3oQ0xjQ7inEJaXhy5xinhQapRgitmw6YMuNrBhy7tz+Ow18USWmDf/nn+P54w1yvYU4MkticznNW4L4km8t7d8GTN59zx19Go0eeoStGuz/jlRsBE5hy5KndQo6f3jiJ4pGD9s6H+StRS/ftvU00gmgKcFJ9TcQhlPBgQUlsHnzSDLHHJ6QhYSWXM5d/gg8YUZKH5v1BVuPOoLvxrJuO/jIFgOKHs3l58lx8Pz6JB9BCJ6TQYc9wHXoKrvRe7B5/GcQmRVzXlIQ0OsxcjCSl4772EXY9cy8X/fEGAKMvf4oHHrmEToC43Yx66frgcd/mF/H22E/wYDSkt/fORzrgkVoKIhE5XESmiUixiOSISLYtmUF1xw4WkV9EpEREtojI1yJSZVmAaATRHUAvEbnTnkvIof6oLBN3oD3grRSSHshfhqt1n2CWbtehp+FZNx1p2JYxGX0p+u53vCV5JBx7GC2BR068jcw5E/D/8WnwR+6YQhxqg6R2RFI74r7uSdQyEwf+QmhaoMCakqtRItmHXwGY9cufxk3iokkeJk/9jdHXPg+YNSQoz75wz8hB/AnO9VoTRHDFuEO26g+RxphMOgqcjyl2dyc1WK4TkWuBiZh8dQOAazGZd6qUgNHoaT9gsq8+AjwkIjmUZ+EOoKpaZZZVh1piFcULz0lXMQ1/XLmtvngjktgSadoN/7JP8QG5c5bTcGc+dDiMa8ZejPvs0yFnPYR51jgLxQ61Iai1z/+KXT/+Stzt49GiHCQpvWI2hqR03MMexfuP+8m/40ZeeuUb7njgPFx9b+S6KW4e659JEQRDEW6+sh8x8XE8OvaTCjce53qtBKE25rgbMGXBB1u55f4rIimAV0QeCSshXn4qkTRMKqCbVfVF265Pqp1mFBVap1NFUaQAqnpqjQb8i1KbipdVEWLe8JUGS0HYtaLAe3tf/3+fIPs8U63XW5yLf95EpENvdMWPzBhwH6fkWDZ8fxm+98fgOvc6KN7E+Db9Ky04crD8yJ0KrXuGbl+B/7uX2T1/KZt/XkVJ3nY6zlpavt5p+wuAr5TXkzNCFqADnnMBhg/pw/PvzaBfy1S+2biNoT3a06x7B8a/8k2l83CuVzi6WSP99oJeIW2NX5pSXYXW74EcVb3E1nYoJrv2eaoasVS4iAwHHgaaWuUfakyNRaWq9o1mYIe6IeQH646voBlVph1pSR7ektvBX4Y3sTnebcvwpnbk/rEXk9QokfUXD+DFL37BW7Ca0VdPwLP2QvS379mGWWOSpJbgcpPV4rjgmJFMIKNeHU7M1RPq6dM7HJDEpaDbdzD/pem0O6U7rtgYfFMfLHewCfurJXlcaTnZBAj3ippguXtP22gek2bNWcHKOSa7fBpQiIlHshN+vaZSXv/oYBFSCEhM1J5yXTAhOkFUdY2IFFv7Igoi4HhgCXCNiNwPtAB+BW5X1R+rOuH+6cvnEELgB+tfb8VelBWA+qtMkhoUSq44vEU5+Fd9C0DsrU/y5arNRggV50JMAp78JUjakbj6Ga8lV+s+xt6f0p5Rb96Ct3AdbiLnrRt99QTHRu8QgiSk4b7wAQ4fchJJhzbipY9n8dNl4/D9+lyl/SUhDc/qKWQunlhh//kdWgBw9elH0tNy8b4iZ2bQA/Sm4lzu/vU5PHnz6W4dE2klpKrSon9dBIlxhWxUXyq8MZG/rnxrX2W0BDoDD2DiTQcCRcDXItKiqlnuN4JIRFqLSKGIqIgk29pFREaKyFrLC+N7ETkqwvHVennU5Vh7G0lIw9W6D1q8EQJTiSCMKnjUJaSBKw5X58F4NszCP+tVI4AAb2Jz2FUIpVvN06jEGFdv9ZtifOpHTjoDLcrhgZyZ7CIUb0keXqvmkbNo7GBHEpuTcOtd+Ip3M2xgT9K7ZrD8ukfwzZkQ4sDg37ow+NrVpj/uLkPJAu7zXkigHvHR83/n7EOa8urU39hpBWNnpffGN/E5zu/QgtGJzU1+xbTuXDDxdgBiI8zJ7rZ1sFyvIiAuCdmwSoXbtki1giKZbKWS9gAuIBm4RlXfVtWvgQswvgQ3VTXPSgWRiKwUkT9FJNZ6v6IG259VnawaxmE07HDuBUZhbI8DrT5TRSRYDTYKL4+6HGufIEnp4HKjO/ODuefCvecixRxJQhquVr1wnfhPk5OuJA9vUQ66cyvsLjZPorsKkfimwawMiAtK89n14jiIiWf4necBkPn9OMCUJvcv+9q83rYMj+2m4uDgSu9N4j2Z/P71r3z67QISmjWGmJhgfS0tzkUSWkTU7OO9H3Ef4Nk8D8RFzzmTuWfkIP7P1uelu9+l0/nH48Nk775kybtkD30CiKwRXXf9gIjtf2ks01yYRlQd+UQ2gDSiasVyq/V3eqDBcmyYCxxe5TQrc1awOSecoaq769NZQUT6AJ8BYzECqaGqFopIPLAJeExVs62+SZiys8+r6gNW233A3UCbgEeHiNyNqSrYUlUL6nKsaj5LvZfCCPnh7ioylTGx2dyLcoynUvKhQWEVqcRESPaGohwkrhG4Y/FatY2CAsnWB38Z45v3CLkavYXrzL7izWS1OyfYvl9I7lrgOCvULVqSx3MJaXRtnMSq/CKu2LoQtq/CZV0rWpLHzEPb0zQ9la4//VplORS1ciu+ntichpikvl0PaUp8Yhwp6U1p88FEtt55O437dyd76BNBLajINoZ9rcjOX/F6PaZlqs64om9IW/K4z2rirLBeVS+1tR0CrKFqZwWaDy7uAAAgAElEQVQvkAkkqWqJrX0asFVVL6rsnJWKR1Xtq6qnBuoP2d5XuVU2XmVYJq+nMZpH+KPRiUAK8L5tXkWYxbIBtn4DgClhQuJdjAviKfUw1r5F/ebH6isFd7kRQkvyTGCqK87UgAHwl6Elm9BtyyoME+LoEJtsBJrEmBikXYUgMeVmOn8Zunw67NzONsBbsDpoOvFv+AktWIM07RaMX4KDw/ThUDOu3zSbpEaJ5AL4fbjancPOMRfhm/og7CripIX/o81Zx1D0wDBexGRSqBRxccWGWZw75iL+fud5TFq7hZVLNlCQs4UfuvamSfbdLHn4I8AIoIATQ2DEgBDqBHS1DesBSr0X1tln3i8QQdyhWw2YDPQXkYa2tiFACfBdFcd9iTHfBeWAiDQCjgXqrEJrfXEDEA88E2FfF4x9MfwuusjaZ++32N5BVdcAxbZ+dTnWPkFL8jCqnPVvc8ejZTsA23qQO94IqLICo9EAktCCVYMG4d88D9/iifjXTguOGcxfFygrvsuyjsalGPNfoA6iKw5SUiHepOvX7cu5Z/M8ALI7/h1X65ODtn9vUY4x1eXNd4TRQU7g/+9qcRzrV20mFtA/THaPuDufZfm9LzOnew/GN+9Bzvd/8OZjnzNgYE++zS/CgwlKedc2nt3MHHfHMyRceBrekjxOu/kcWp9wOCe+dx+b7x5D23PK01cFYo7sWhEYO9KxvToBxpQXD/zb+1EdfwP7mNqZ5p4DdgIfi8jpljODF3jc/oAuIstF5OXAe1Wdg7FsvSwiV4rIOcDnwC4i39+D7FNBJCJNMWUj7lDV8LVwMB4aheElajE2zERbhoeaeHnU5Vj7BAmUfcAWPxTfFC1YQe7QU0ybrxRccWippZmon83XXEjbL79BktJxtTmT4ifH48+bHzl/XVxK+fv4pkYAWeY5V7uzkeQMvCV5+L+aSFazo/AW53I04J/+DFmteuFNSMOblI6qH81bUP9fisN+zZktU/H//DK+ibfTtUd7bvn1Od48eQQewL9yErPmrODYOdPoEutm9qyl9GjbjBe/+IXuwKjnrmFg7y6sAX7r3KqCliSJzXGfeBsAyXfdQpMnngK/n6Zn/Y3SjUV4t6/As3YaF3XL4PRm5rq2V4LNA96atTT4Ptz9+6+AQNQakarmA/0w8vkLjNXyCSoaOWKouBx3GaZc0OPAhxghdJo1ZqVUGkckIiuqnXFFos2s8CDws6pOqmrMCG0SYV9NvDzqcqzyHeaJIdwFsl6wxxX9t0k6x1zch4Ydm9B84nf4/7PJLP6WbjHCqGA1uuE3kts2sYTTFiS+KUljXjGD7SoKrQZr+xsSdBiIXdqxKmjyc1/5MN4rH8b/9WOcX5KHblnAqEeXMvqudwDIatw56J3n4cC1vzvsGSdt3MbWN6bR6IGbmTjnCTjmBpIA7/YVFI+9gytWT2FKq6P5ZZcPH9CtQQxpwAXfj+ORk0dw5eBeDLuyH40vPAEO74HPFYO7/cCQc4THz/m/n8D8T17n91fac9O2ZXR94S6Ii+PEnPUU/Xcew48qCMYlBfBh4pFuKsohKSmdu+v7i9lbiOCqmTkuBFX9Azitmj5tI7QVAv+ythpTlUa0BhNJa998QFugCUZr2G69bmvtW1PTE4vIEcAwIEtEUkUkFUi0djcSkQSMFtIwgut0KlBs06Jq4uVRl2OFoKovBFwhI+2vc3yloH7O2LCQ716fRtwdz5AN6Hdvsf7iAUh8U5NSpeEhSNs+JIx6EXYVIXENy11n/WUmMWWBed6w5wGDUMHkz/nBvA6sO9lwnXWn2de4EzLw/KAdPh6CdZLAWS86mHHFuvG9+ybtMNdFQyD3uit55uFPyWrTn4TkeGKBPsnxFGwtYtDAnnx79igG92jPWx/P4s3Xp7Hzpz/YcPtYHmk/EC3ORYtz2f3mLRXOJQlpuE4ezsl/TGX4T09BXCOkY1/e7TEcDm3DY09/RVJGY24capZ7AzFKYDSk3GFDKAJ+7VBl2MuBhdsVuu2H1MRZIeCEcCfQFLgNaK6qx6jq0UBzTELUJlafmtIR4+4/C3Pzz6fcjrgO48CwGKP6dQg7NnwdZzFh6zeWl0eSrV9djrVPkeQMk41b/QzKm48kNuf+Je/i6n87rf79r2CuOdzxSHwz63WscXKIb2JpOEYZlpT25QOr3/zIw9xp3R0GGeEXicC5AFfGydy54gu8BauDZo6stO5kLnkXz8qvDprYDYdQUp+fTOztJvtGKXB93nzSxtzKnfNeoE9yPMvyizi9bTP6bV5Hmx4daTXiYrYXlnLYrefyt1g3Nz96KQ+P/YSWg3tyT/4S/Es/BaB47gp8iyeGxCUBwYz00qkfvhfvQBo05tINsyCpCaOeu4aEG/9J0QZT1/Oz5ZtCjm161t+C7X+J61XAFSMh2/5INOLxUeB9VX3KnkdIVctU9UmMPXBcFOPNxHhX2LeHrX1nW2P9CBQAQbc/EUnExABNto1VEy+Puhxrn+MBpEFjJLGlyWJ86OkgLlztzzUdAg4N/jK8VmyR/7/PGGcGcZXvtyMu/AvejxgoK8kZFVIK2c/zXUZH/Mu+JLv9QLwpbcicauW5K1yHq3VvpEm5f9Jf4gfuEBXehDSuLFzHfd4L0TU/4sroi6vjufTbuJwLh51Jj3kmA0zGc2NZcvPTZBzSlPlZ77F8lw/38PFkfj8OSU6CXTuQtE6wq5CkW67nkS5D8T17Z8X1zqR0XE2OIObWtyA2CUntiKv1ybgvvR+Ja8hr3y5ghBUXZ+fpqydUMIccyFm+RQRxu0K2/ZFoZnUcMK+K/f9n9akRqpqnqtPtG+UaxwxVXaKqpcBDwEgRuVFE+gEfWPN+2jZctV4edTnW/oC3YHW5QPGXGRPa7+/i/2pciJDxJmeYF7sKcfW7IbIwseHq1B/d8juUFVSaQihkjDLzlZyyfiWuzoNNoGxJHtmn38/NV/bDm5xhskHEpRhPuk2zg+McyD9wh+jIXDwR3PE0uPtZXB0GmPXHrYv486zTaXTZqeiuIjR3LtK4Czl/5nLc0iV8tnwTgwb2RBKbQ0lJMBhWF//AzidGwNzviQdG3/UOM9t0wPfjkxHPHbhe/Rt/Rnes4f2Mvni2LiSh9xFk/vQURwP/HNgTMLb3bRgzoZvyEhQBDsjr9QAwzUWTH7wEk9QucsIo6EX9OJ48hBEW92FMg3MwQbZBnVpV8y3B8h+Ml8c2jJeHtx7H2rdYQay6YxXSoEm50OjmD+nmLcoxXmwrfkaO6VT5eP4y2F2K7lgDMYngjrNikEKdGQIE2xu1NwvEv76J69grzTkT0vDkzefRtO4hx7yU2pFrS/Lw5M5BGrYNSXJpT+NfU+cGLVhdoSigw/7Jzjc+IP7WDvzQ7XR6r1nB04D+8QP/nbmYf3z+A66vZpHQ+wi08TxmFJYyw7o2Mr74BQBXrytZ/4+/02zhcvw7fXw8+mPiMT/OVGDq5gJO9PtY0qM9nb7/yQgvG1qcG2wLOOBot2Nh7TLOWz2FrDb9cVPu6j2j0NzKmmBufA2Bjbbxwq/XwOv9DhEjwPdzoikD8SKWcwFGOyi02pMxa0OZwCuq+s96musBwd6IVA+g21cYgWFl5dbCNcahIBCE6o7H92E2oy9/ygSa+koRS0MK0Xas/lq2Hfw+JNFkPPKvmYokpyOpHcx6U3UR75WwemA/3p/6GyNsGZa9hevwWq7ggTZP7hwkvhneCMLFkzuHrOblviD24+r6BuBkVqgfsq2/9y+eyFNdhrIDGFWSh/9bY5DQnI3835iP+XLVZsAImNttx2txLv6p42FnGU9dNI5S4OzOrZi9ZAPnDelDcW4+X3+7gENcwq9+JbOSeQSvVetBS/OXMuO4K+nzmZdHTh7B3RNv57fMiRx6Ujeeft3E3F0z4FhenjyXkWMuYvwDH1SISQonnsqfyvfm9dqjTZr+fO/5IW0xw1+pMrPCviAaPW0EJmeQF9gqIqtFZBUmLsyDMc2NqOsJOlSOf/3MkNIQrsCNWlzgjsf/5+eMvvwpPJvnmbxwq74xZZwJ03ACQasSYzIs6G7YXYKrzZlI026sv3QQ/h+ex7fglSrnU0FQqR//f5+gzWeTGWE9hXpyZpr5JGeYTMnqx1uSx7ltmyHJh6IFK00g7KbZeDbNDgbHsrsUT+4cwAih/BuHcnnvfRpf7BAlPuDkxkm4Mk7mtsJ15YIiORncbl689vmgEAKj7dht5ogL93kPsuN/C7lwgAlY/WzJBo5q24wti1fTdtJ0rvvpKf7wG9nqAfw5MyvMIxjEnZSOxDXClX4ix9/UHxKNleGnO17ls+WbiE9LInPOBNzAy5PnAjD2gQ84vQYedQHbg2UYx1uwmnY1+I7qHBGIjQndanTYniV+FhGXiMy1klifW13/GgsiVd2GSZNzA/BfTKaBUuv1DUAvq4/DXiL78CtYdXbf4JrQylO7mYBSa8vuNgxvca4JPC3Jw3XYeRSv3FbRVTsp3fKwawqxySaANSbB7PSX0frtDyn97y8UPf8RBTefg3/1lApzCY9DChzrOuN2vEnp+P/8HG9ic3TNr2Q1MwnPL1jwCv7136MFK/hy1WaTDTy+CbpqJpTmg8+YC4lrhCS2QBo0IQ1TDqPR3Vfx5sz9wonRIQq+zS8yji/JGWwa0sdcpyePwHXy8KDpq6dLcGNcXPMA3ySPlSDVXFuNHn+DQz6ewr0ledz/01P0WLSIzg8MMWmskpM5tW0zfBitJCu9N98kxEWcC2BMu644Gtx0LytufpLTDmnKN1bNo1cf+5zsHsNDqsG2Aw49qVvEsQIpr1KBXoN70QnobwnMRX36sNL6bHsXMdWX7Vt1R9RN4udrgdY17RyV8dDKO/eCtTnsY7yWlhEIPG07abpZyPKX4U1KN30su7h/4fs82GM4o0ryzKJwasdgWp8Qs5rdm85fFsyqkJg5oTwBahVrR4E2LckL9vcWrIbYpKA5DkxW5fHNjjJ562znd6V1x7/yK6TZ0SbdUFyKcYiITQLfLoZ/ch+uVsfj3/AzGcC1BashpQ1eZ73ogCMQVJoEIeuFv1gajRuTCeHHK59gy+Ys/PlLcDXuDL5S/Otn4u58CVqSx6ITjqHzM7ey+9UncCXF8eWqzaQCrYALvBfyovcjSkYNJu7Ky00oQgQkpT3t3xhLmw/ep0fb1sy48QUyjjiED2YuDprY3JiCfWU7ioPHZQHFIwfx3bjP+WmXD8/meWQ1O4qY+DiGFuWAbxcZKW1477c1xFufzWN95r0SNFu7NaJalQovP6U0xiQruBd4qSYnrJFGJCLJIuITkVE16e9Q//g3z8O/urxMsiSksftpU/LDn/MjJ1lPgZkfGGtp4MnOm5CGND/W5JIL1CWyaVHlA7qMIFE/+HaVxwv5jef+7qdvwr9hVpWedeU58WJhVxHFo28iDcic5EG3LeeWP97AkzsH3bYM7/YVeLevMOMltzZ57mzphgJZxl1n3Yk3KR1xx5EHeC0hpGXb9+wLdah3Mld8QSfMtQvmZpy54BXunBTZD80HfLxkA73XrOC8DbMY37gzYEIJsjtfggdz7Xb94UdcR19qYpWaNKEr0CXWTT7wkfcjBg3sycrP5/BUh0FB07SdYP66ZkfiPv1kSG/Jlvwilv28jBEvXR8sCukDPPlL+ObjWYCVmw54eOwnLN/lw7NpNtKgMfHAaxO/w5uUzuiUNqyzzhMQaIGx9goCEhMTstWAPU38PBr4AZhWXccANRJElmPCNiC3ur4OewdXs6PI7jIU/9JP8ecvASDm5v8AJgnp6WuMm3T2ReMY9dw1Icc+FEjjU7zR/JgtYTLq1eEsOuGYkCBVdDeoDy21So2oH9RPzM3/wdWql2mqzFHBElq44iA2iUTvS+QBrlNvxtX8GLIPv4Ks5j2QpHS8jdoHBY9uN4Wi/au/AfWz4ep/oLtL0Lzf0I0/4wZ0S3nQ7LiUNuAvO/Dcag8ystsPJBcYb5lmLxvcC/1lDtlnV23x8SakIakd2QH4fnwS35zQ0vS6+Tf8v72P/7eJuM4axpDtK+h6Sjf+cdu5NHIJq39ZRqdhJ3PLglcYe+tbIcX4QohrxIz+97Dzm584654LOOmGsyhdtAEf5ubnxqSuCqSPiQfuLcnjzJap5AFjWhyHN6UNpRCyjpT5wQgybXPehqke5wEmx9ZzhSSR2rhv1zrxs4h0B64G7opmmtE4K3zL/lIGwQGwBMjWLWRZT4qjE9KCKXuCazzA6BteDslMeHLLVLwJaSbrwR9vBDUh19lXcdhFxxs3V0vgmPWieONJF3gfcJCw8H/5cERhJEnpwTgj+5yLHhjGQ43aBwWg/4/PzD5LK3O1Oh6Jb4qrzZlo8UZavfUZmjsPSTsSaXk8o4pzcR1xcfD4IiCreQ+8JXmOMNqPyQB6NEsJ5sna9ud6Rl8dKlQquy17E9LwAdkn3saWx98L6ZvV7hzyn/+CD064xXiFxqVwyOM3E9swjkM6tuSIQSdSvDyfNTc/zt3eC1l86gB8Mx+rcA5JbM7Ji79l8thPSLj5Nho+9CIJ/7qai7plkES5FhP4u82a1wmfj8GTM5NDAc/WhXg2z+Po3xdy9z0X8EDOTKSXqbvk2TArOOfAr+WnXfWsGwVMc/atevYk8fPTwDOqujyaaUbrNddbRLIse6HDfoCr1z/xFhrlf1RJHpLSnswp2SbpaFFOcJ8P8Kz8CiC4GOtZ+ZXRSqyxslocx9gHPmBdIIjQwpuUDuIy600BDzsIpv1586JxfGxLxhpAS/KMCbDUpFPRjT8DkDTmFe6e5MGbkEbmglfIPqE8Z1hLKDcJYtaMJLE57sOvKF+PClSmLd1CKka4eVZ+hTchjaNr/U061Df/BGZuLn8wOfy3NXiLcxly5KHBG7T9ttySyDS99/IKfRNaNmSIlfDUv/B9+PNP4i67GJ/PT0xiDDEN42h1SmdKNxTT7rwePNL7zojXq6S0Z+DYiyG+Cb53slh0wc10fX8sVw09BTcEc9TZWT78cSSxBZdPfRBXkyNwNTsKSUgj6eFPcaX3NnkYm7ahIHs0PuD+sRcHP+9e8fys6KyQJiJzbFukhM1RlwoXkUuAzsCYaKcYTRzRCoxG2dRq2oxR1exEm337L8fejMsAeBsYanMc0JI8NO83/FM/gp07efaGl4NPX27gvkcuYczd74aM4Vk3nayMviEBfQHhpPbYn5I8fK/fg/vy0cabLejqXRo0q0UKfA06L/jLTI67SOmFIlCjuKWdJru8lm1Hl8/kx/Mz6bN5zxJgOHFE9YcH8KyewvNt+gfTM2djrrsswLd4ItldhgJErK4awB5IGngPZiFjUVjfy3p14utZS2kCnDH0FFbOWEDTNs1oN3IIbptZUItyjBaPidHzL/oSnTkL93UPgfrwb1mIxCaia39HOp2G/8PxuAZdD7FJ+N9/nJgbXqYytCgHLdvBmosu5tWpv4UI3j2NK6oyjqhjus4eHxra6T4nu7oKrbkYrSYrrL0QyFLVCqncRCQWWIEp//Cq1XwopiDeJcAkVd1R2Tmj0YjWAAuB761tERWzc9c4+7ZD3RCopuK1bvbehDRwN8B9STYxN7zMTbaEkD5gzN3vkoERSoEfelZGX7wFqyMGAAa0mcA53Fc+bAkTN+y2qgHHJpevBwWOs7mIB4QRrriIQigY1xG22cfx/fhkSHLLYJ8Gjc2mfqRVR05aMqtC3jGH/QtJ7chGzP/WA4wqXMetw84ECAohMAKoCCLG32wa0ifkBh4Ilr2E0LQ8biCpVVPSgZNOP5L8pWvZunEby35eBomJhGB5eWpJHtKoPa4jh+IePh7/lP9ATDyuVr2QJl1xn3gbrrTuxNzwMq4Wx5mcdpYQ8mACb58AXsSWgSQpHYlvSpvPJuMtXEcnyh/61g3sWWVA+B5RO9NcbRI/J2Gsr49TnsQ6UJX1XUycaaXU2K9PVfvWtK/D3sdr3ez/DWjBGrLSe/MiBJ/wAngg6MVTBEEtyD/r9aAg8+TNBys9j1gVWZOAOz8YwWcJaZxfkgcxCXgTm5M5JRs5/CSk6d/4v78dUX4im+t3SElyS2BKBFOeHS3JK1/vUj+u7n8PttvHDI6X0h5S2qPbluGf9lq58HPY7/A2ao9n9RQ+s/4/o5MzGFWUwyOvVKwD5MG4TIfz7Xsz2P3S9Xx93Qv84ldjesZoF/arygcsmDyX+cD8qeXVqlOBM5PL8xrr9hV8ndLCaEVV1eaSyLfMwPWqm+bzcWJz2gHnvjo8mHBYEpuHjDekOBfdsYqsFsfx4he/4ClYwYsY82Wd44raIWIyMEJEGtq0mOoSPxdiKxFu0RJ4BxgJ/K+qE9bYNOdQM/aFqSNAIO1NQChNjnUzIMJiqG5bhje1Y7Vjey1ThRasDgqeAC0Jzb3ltd34q3LprkpAVBBQ6gfdje7MR+IamTafpXmFmQLt59Qdq8hq3gPPuum4MvpW+zkrzMMxzdUr9mu2HXClZbYNf2iK1L8qAtd/FuDPm0+WLdfhuW2bhWRtGHHneSRc83fch18BgBauQ7csRJodbcxoO9ZWKMAXjv161R2r2DriHpZMmcs3G7cF59LTSjc0KuwBCozmpIVrQlJXdQcurOHnDY5TlWmuc4bOfu6mkDb3afdVZ5prDPwBLMBURGiP0XSeVNUHbP2WA9+p6jWVjNMW8xwxUFW/rOozRJ2KVUQOE5E7ROQ/1naHiBzU60L7Ert5IriWk5CGB+OR4wH8G2aFHDOuBkIIwP/7h4AVfa67g5m1Af750vWMevRSRr15SzCivPzAsvAWUw22cF3Fdiqa8YIeebsKwRWH/81/m7x0VjCsP/dXKCsImiMhNCN4oIBfVkZfE23vsN+yEmBnPt6k9GBm6/BMCDVdQwlc/7+0bcasbicTjwlJAJhqE0Lntm3G9Ce+CAoh38Tb0S0L0fWL8M+biP72Fa4mXZjeOAnfJ/dVqHcUQBLS8M2ZwJYbhrD4jMF8+Xp52ExwLlZwbsAjNCSjSWJzpGFbMr8vX3IZXLC6hp82CgJZ+isr/xLGHpYKr90Uo9GIRGQ0Jlo2/OR+YKyqVpZn8KBhXzxhgrlCumIMszcUrEZLNpHVwlTlOLVxEn3zQ5d81crKXel4uXPQpd+xZcLnFKzZxGFfTwV3PFqwAv+bTwRLgmd+Pw6at8Zt2fa1KMeY5Hyl4PcZN/LdJeCOZeHxx5HarhWt3y8v/xRe5tm88Ad/MP71M3C17gO7iiokQ/UWrjOu5BHG8f82kewTbsG7fUUwg0RNcTSi+seesXpGsxSmhjmY2IXP5Fg3xbt8zI9i/IAzQxqhpjqAkxLiOLPEPCz5Fk9Emh+Jbv6dRzpfQhqwG7h+9RTGtOkPwE3DziR1TCaI22QkCdPofYsnsv72x5gxeW5wzdazYRZZVpxdgDTgxg2z0O0rg78XAH/uHJ5v3oONmCwk3pQ2UTkwVKkRdTlEZ794a0ib++QRB27SUxEZBtwP/AwMwlRY7QhcgKmyer+IXF0fk3SoGYuwCsXHJpHV4jgyrViJb/OLeNHWT0vy8Calc42VBwsIeQ0mLie79500n/gdb85cXK55pLTH/a/H8JbkEQ+4el5dLoQK1xkhtDO/3IQmLnTLAnDF0eyIdrR+5xNrf2lwLhW0GnGVxza17mPGiU0inICG5E1IC02OCUhncxPxr50OHIA1ZA4iTvptUoU2D+BbYrw7B1hCKJLrdDvKnW4CdLKOHznmIoZPyaYTxmUa4O57LggKId22DP3lJ95pcgQTOl/CJad24/CWqbQCstr0Z1RRDvf/+hyNn3qF8em9ybnuFvCVVizC17gDqZ1bsRTwbl9BV0AaVTQS5QGzjh5AdpehIdejy55VPqUNmfNeoOieCyr/wqLF5Qrd9kOicd+eC5QBfaycc/Z9McAMIE5Vj410/MHCvn7C9Gyajf75I4+deFtEt9eWwHV/vMG3x10XrLkSTlcgLTmeUz8aibt/ZrnrtcvUKMq/+SpSHxqLNGxbvqZTVgDqK89HZxcc/jL8f07C1fECdNsyY+qTmKCmFO7UEDTTFaxA4tPMepB1/oCAyvz8fnRLPqOvnmA0I8sjLzhGUQ7+hR9DcgrsLiO7+7VADescORrRXsGDERr/oPIHBTfQr2Uqf2zcRmTDrhFEgXpBPV3COfl/4t/wE9ldhgbXhka9eQuufhfjSu9tri9fKROTM+jUthlLV23mnH+dTcr9d6OLf4DcPDjuZB608tLdNXIQDU44nDfPe5ABQ/qQ9uAduFr3qbDes/32KynbUUyz595gTvce9Jg/J2JJk66YrAzn29zFd795C2MvfwofMGr8ZUi/08juNmyPr9ceXQ/R2a/cGfqdnnj7fqcRRSOIioH7VHV8JftvBf6tqomR9h8s7OsfNpgfZlX1UjpR7vYdzgmxbn7a5cNbksfohDRTL2bJx7janY5/4edMOPE2borkmOArRXeshd3FSKMOVpLS0vJaScUbkaR0dj58PfHej4z5bMMsXC2PDyY3tTsqaMkmJMGkSXkoIY17Ns1Gt62AXcVkdxsGwKiXrocGDRh9+VOMeul63P94sMK8/F8+jOvce6KqXeQIor1DfWip7YDDmqXw7eaCoHu0J5As1aLUeyGxnQ4le+gTwTZvSR5augWd+hK6vYDR1z5vgq27DQum8tHcuWS16U88cM/mebiaHWVCC6yaX4GAcWl+NNNatK/0Qc9OS4wVQ0vy8C/7lOzu14bUMtpzQXSozn4ttDqP+4Rb9jtBFI2eVoYJaK2MhlYfh31MdUW7lgJXnRo5lX0w5cjOfOPpszMfaXUUs7v1hB0FIUIoSFmBVVCvBZJ2pNFyILh+g7jQ/KWMTkgj9pgu5fFOC34ya0GBHHOB9l1FPNrEuIJLQhr3Aa4Wx5Hd+RIoLo+hHn3t8+YGsGEW0u0I4yJ1SlcAACAASURBVBAR7rF3mONHc6CQUX2XSvGs/IqjgZN6daLXPefgw6yNZs5/KRiCAFB453k84f2Ix4Y+QQbgyZlJ5vfj2H7HFbgad0ZOvRxJb8nt/zqb7G7DGD6kj6l10ySdrDb98WxdyIjnrgkWmASYO/JtAKTl8fhnTkGS0jm9CiGUAabGFnDd/PLk1JLRi2sGHMu9Bavxbl9RN14AAC4J3fZDohFEvwDXi0iFqlAi0hy4DrN+5LCPCDw9XX36kYBxv66M175dUOk+b0ke/jXf4XvhNvwrpqEFazh+7Rbc/Y0vipbk4Vv+iakQa2U2AEDcJgDWMuGZzn60dAuujL4mBdHRp+FNSCMeyD7vwWB12BDhFptEEeXefwD+rQvx5M0n+7ibKvxAs1r1gqbpFD94a2gGcSD7mBvAV4onf8n+Wcr5ICbw/8hc/gkA5wzuVXnnalg3PJP/A96atZSxd72DZ9NsTv79K1yHnY3//97i38BHwITHPqcr0BwT+DIpow/SpjtPPTsJDyallKvvjTS8Yxi3DjszWKrimIvNWuXGa6/lLSt4NfDQc9xSk3TYm5BmHoysOQWCxkc9emnoXIGHLM9V16HWupevFGnQmJcnz+XJlDYQl1I3GbpFTByRfdsPiUYQjcaU+FgkIuNE5GprexSzTt6SWuQYcqh72k79rdKnS28Nsg54E9JwdbyAZ299C1eXwRFjcVzpJ5ky5Q0amywLsUnGSaJxZ3TLAnRnvnFZTWxunjQT0tCSTWj+Sq46tZtJvFqSZ+KEynYEz+vdvqLCuTzAO02OICutO/eMHERjCHEZ95bk4croS/yQs4LCVyx32cw5E8AdH0wM67B/kQU8Yq3FbF26tsL+zHk1K32Wfr7J6O3JncOo7StMxoOMvuahKLUJ/Tu04PiBPblscC8WAUd3bsU2jHt12asvcKg1jgdAXLgy+tL4qfKKxE+/Pg3Pptms/WUpl710ffD6Cn/wwTbOyFeHUwTs3GwykKRa+06IdXNvoARLakcTj5ScgTcpnXbAbdtXBOOh6oS/kkakqt8Dg4EdmGp9L1vbHVbbYFWdUR+TdKg5gYv3WttCaKA9c94LPGkFpXptMT1pUKFUBMC/3rylQtxBSOJRcRmzXMAU5y8jc/5LZGX0RRJaBGOOAp5x4oqDDTm89u0CTly5HC3ZhH/J50HTiddKkhrpxx1Y03p47P+3d+ZhUlTXAv+dGSQMyBZHRGOMIgZQIhiXJy4BA4lP8tSIGpdowEQgxiVG44Igs0QkaAwmRgX0qdFoMMYtuKDyIhIUVDSgaDKRTUWWYWQTZhToOe+Pc7unuqfXmWa6e+b+vu9+3V1169St6lN17nrOk1y2bTXjAvsiM+z6nRU1Jb28rgaq10fdF0/+MQ4bwO+/tEEnw2HhKweaP87zj+rVaHZckOLzxnMEsPi470XHsQKKDhrO4ePPpOfg3vQ49Qiur6uh/52XRRyOTi5/nAsXz4isk3ukpNQc9BZ3iLS+y1bPZcfdU3ju4095fYJ5/+6AVewSGaOwZ/FbpjzFxIW/j4z7LNwZ4umOPSLd4/rZKsC661ZinieyRitsEaGqs7DxwP/C3DqdBxwD9Eq1cjYWETlLRF4TkU9F5HMRqRKRCSLSPpBnlYt5Hkzr4shKGV9djBtE5GMRqROReSIysCmyCoF4q9QrB47hslvOtR/FHSj72Bbg1QA3xzhsLC8ppfgs64qL9fsWPId07dUwU66ofWQM59bAAtNw/CO+1J3Kkycy8e1p8MUmKr58GEX9zmpUzvLYcZ7Y/XvuT92NIyjf+mGk5ResndbXvIPW1fBoSSlFJ/wYrX7LT9/Oc86eOyV6g9OpsPF5ZNGKpGOf5V17cdraBRyx+K2I3tVXL6K2/GJCD49H+vWl3S//TDtnHIoGXcRB5RdGjq8cOIb/nHgM5SWl5kR4j05ISSk3uAra9P2HMLn8cSYunsFxy98H4LqVz3J7xx4p9RVg0rFXMA443cUp+ifwmOse/52bvv2vwUM5vqQ9xTRvvKwRTZi+3ZT3oIgcLSL3i8gyd1yViJSJSKM1742KmOalRFDVelV9U1X/oqqPquoi1WAktbTZC4txdDEWEfA+bJ3Sb2PyPQIMCqThwZ0ZxFe/HrgRc1lxKtZFPEdEejZBVsFQTHQ31k3XzuTa674Poc8p+urQSK0wtj+6bM18+xLwkqA1Db66YsOFhx/+om+cxcSFv+eauhoqsZrt50D97Nv45AenWJ5+Z1H/0Ss2FlX1BPVVTzQYuPod/DHsIWL7moQP+atTn+O1rw+IO6W3ovRw7i0p5Rz3QqFDqhAqnlxTPOS6qN9Fzj1P0PhU0PAij/dG3Hj9BP6vZ+/IOh995wU6TXkK3biFpSMn25gmTl877UfR8WMp21RFB+DykUN5ZJFNECgvKaVi/yFUYhW0EX32ZR3Wo1A5cEyDrnbrzQ/PHxzxsdgNW5AajxAWqOfpZesj28LXthl70fRb+Dav1u1gX2wcKWuVpwwNUTPeg+cAB2Pv2OHAnViP2cMpz5mprznnzud0zP8QmOvvp1V1eUaC4sueBFwKdFdVFZFVwF9VNWG0PxEZh4V//1o4tK2IXAuUAz1VdauzyOuB21S10uXpBKwCpof9J6UjK41ryCvnfWW4tUOPXUPl2eZKpBiYUL0I6Xwgun0Ny089iz8t+E/UtNEwYUMQmnElxaMqo9b0hIkX+kE3f4BueI+iAwZza7dD2I750RoRmO5NcQfqX5rKylsep3dg0WzYs0J5SSndsAc1/ASkejgn/m08ladNivi+i8jcsTVtDwt++nZuCTv/DP7XxRDxDj8V04mgvpbXVlMeaJ2EZk5EjvsWRV85Aa1dh25axkN9zmUljd+moblTuGvIdZxz/mD2KrvEZmc6Jv5tPKGlH/DrG/7CHsBPzh/MtEdeYSIQWvYkC0+4iKOv+C6TbvhLo+tIV2eBiF9E3fohs/fqxZs7Q+wBUV3QiUg6fbv/QfrGY5VR24oP/VEqX3NNeg+KyN6quiFm2xhgOnCgqib2X6SqaSdswsJOzKVPMO0CKjORlUD+VViMo7CBXAX8JsUx84CZMdsOwKz5qe73t93vvjH57gPeykRWGteghZJCVTNVa6s1NGeSloFWHdVLa0YO1TKISlpXo6F5t2rosWsU91vralS3r9H6je9Ffgdla12NhqpmahloJajWVlve2mrbt3iG1lcvijo2/L0MtH79G1rpzp+o/PH26fY1kXJ/dMqRkXxlKWRFyWimHhdSyrUOZpLKQNeMGKS77hwV0avNY0/RMtAvbjo78h/Xr56rL5a0V922WgFdNWyA1t04QnfdO1YrQUMvVKpu/TBKdv2a+fpAjK6UgT4OGnpynD7gjlt/zok6Z88OdkzNO/pqz266pM+++ngaepZoXxmoblsd2Rf6++Ss6euRhx2koX/9KSoBi5r7Ts1Av452xx2dNF8GAn+MGZ35WIvoYJdOw7wqhICLmvAgFAMdgRMww3NbYN8qYCO2PmkL8FfMSgePrwbK48jdDlzjvv8MM5bFMXmuAbZnIqs1PdgRRa6r0aqjemn96rm68qT+ZnRWzNLn9yiOMkYRg7F9TYMxqq1W3fphI2MU3le/doEZom2rVTd/oHeAam11lJyoYwLGKN0HMZjKQKcGHvrVpx4dKX82HuzWlnKte01Joddu18mYEXqxpL3O7d4p8t+WBVL1+YO16qheuv6cE3XXtJ/ort9doK9/dS9dNWyA1q9/I6KHYd3b9ZvzdNvVp+lk4huV0Dv3av2GxREj9nn5mVFGpCn6CuiSPvtGne9u92xkQ1+PPKyXhqpmRiVSG6JmvwcDx/wcsw1dkuXLZIzoUmyd0BBVfVpVl7v0NywOxRvAZUklxCcc/+ofWKyL4DLgp915h7rtg4B/iEjXQJ504qt3B7apauxQyCagY2CCRHNitRcsUlJKn0UrKNp/CHsNOJBZ37qGhcddyLtucWsxUF/1BIB5sy5qH/HoXb/iGcq7fM0mCmi9ubYPL3qVIqTbIUzYVMV7xx5LebdDqIGGcBLOOWpo6X0NnhDWLjDP2ttWpzUIHEsF8IvA73tmvQlA9agzmiTPk38UH3cl44Bu05/n1bodvLxpO8VEd4HdeP/PmPbIK+w/9HDeffZNpO/Xmf2Lh3nu40+5f84SKvY5htld92XzJcMj+lp86R3Urt3CtW9PsxlxbrynDAjNu5W7Dr8Ydm5j+XDzY/il66YDNnmmAzYtuykMqFob9XsdcHsSh8QZIUBRu+iUmqy8B934+3jgIU01rJGBZasFfp7C8tU2oUb2Taw1dJW7+LuS5O2PtWyuDGzbGa9cwCfAJPd9PLApTp7RWK1ij3RlJSjXGGCRSzmvMTY1hZ4r0/XnnKgrT+of6aorA63f+F6kthauLQZbL+HWD4Htic5RFjx+64daBhp6e1qUrPrqRVG11eakMhpaReke09KtklymXOtcc9JM0JUn9de73f+8atgALaOhuy6Ywnp1N2j9pirTuyfHReluMn0tA31/wAFaBnpPWN6GxRp64w/6/oADVGur9YEs6Ws4ZUNfj+x/sIaWPRmVsJ6mRYE0JkYnmvQejMnbHuviW4GN+SfNn3aEVnaTix9Vfdt9nS8iNcAfReQ2jTP5QVWXikgVZrzCbKJhrViQrjRY9U1AZxEp1uhWUTfMeO7MQFa8a5gBzID8HPxNl+LhFRYTZlMVdO/D5vVbuHzkUH7n3O0cBAw+qT8PvLw0au1EeFBWt6xIGRW1AiwERSBf7BRu6Xwg+tmqSMTN5hJuFXlaF+cCZS8v5bs9u1GybjP3z1nC2BGDmDr9+UZ5o/QtvLj5jMlUMDnpOSoAdRMhHl3yEWAz2spLSpm4YhZF3ziXR5dcRs+OPWi0rqQJhMNXZA+J1wqq0eS+5pr0HoycUUSAB4HDgOPV4hslJd9c/ISNUrww9UGCL/t04qv/G+th6h0jpy/RMdibEqu91VHRvQ8VwDcv/w6fb9zK6KtPAyyIWdg10Il7Nl4akO6stOAap68Ht7tp4OUlpZHgds2lIubT07qoAF5ctzny5pz+xIK4b9BEx6aDBCITh7W+vK6Gon0HRSpe2TBCwXJlTV8FRIqiUho09z04FZtHcLqqpvXezDcXP8e7z5XxdopIf6AP8FZg8/PAySLSObAtNr76a8BW4OyArI7YeqJg9SkdWa2a8ENQBrzx29nsqtvB3KmzGuUbcsePm32e8u1r+Mj9fu/ogQQnmSZard7Uc3laLxVYNf2I7p3oSfxBjGF7d4mzNbNzVNAwXfy9owfG1dHstmaygTRljKjJ70E39fty4AJVnZ9uKdPumlPVeSIyAvgDtrgpyEfASM3AxY+IzMYWTb2Hzao43sl9VFWXi8j3gAuAZ4A1mIWe4M71QEDUNOAK4AkRCcdXLwd+q26ATFU/F5FfAzeKyCbMql+FGeI7MpHVVqgAWLfZUhzuueiuxiHCM0Q67Udo3q0Rv1r11YsI9UjWY+DxxKcCYFNi3wtzNmylmMYLtzOlJ9b66Vd2HhVn30oZbuG1a+XnX6UnbtdcKtJ6D4rIMuAVVf2J+30+cDP2fv5ERI4NyFyuMWuMgmRUQlWdJSLPAkdi3WcCLAfe1sy9K7wJjAIOxCYgrMDWb01z+z/GnOTejrW6PwVmAzcEb4aqbhKRoZiBnIVVjqZiNy7IrzHDMw7z6rAI+I6qrm+CrDZB7IK84ALCrHVFtNsjco6KgBHKvwfak++kWkCaDW/WYb2vdEYIoLzTfvmrr4I5Jc6ADN6D7Yh2cvFd9znKpSAXEd2AiC6mm+HgyRKFPFkhHYIPeTFwY211VB96c2VOfP9Big/9UbPkNRf1nhVaFVH6tfD3FB97RdbkhcmlIUqmr0cN6Ktvzo72I1m03wl5Fxgv7RaRs5DDVDWu1wkRmQy8qKovZ6twnvwj+MCVQbONUFBmGcDGhK13j6dJBPWruUYoVl7wd34iGbeIckEmocJfBLao6tkJ9s8Euqnqf2exfAVHW6hhtnZ8i8hTSCRtEQ3sp2+++GDUtqJ9jsm7FlEms+YGAAuT7H/d5fF4PB5PXiAg7aJTHpJJqbpC0pAgdbRiNzgej8dTeEij4Jb5SCaG6BNstlwijiS767o8Ho/H01zyNCprkExM5bPASBEZFrvDTWQYCTyXrYJ5PB6Pp5mIaxEFUx6SSYtoEnAm8IKIPA8sxlztHIFFWF2HeV/weDweT76Qp+NCQdI2j27h53HAC5jhuQHzan0K5hLieFVdm1iCx+PxeFoWaYqvOUTkUBH5PxGpFZE1IlIpknoeuIh0FZH7RWSTiGwRkYdFZK9Ux2XqWeFDYLiLad4bW7f7QTreVT0ej8eTAzJcR+Te73OA92kIgnob1nCZkOLwRzF/oBdjgVSnAE8BJyY7qEltNmd4vG99j8fjyWekSb7mfgqUACOcO7WXRKQLUC4ityTyuykig4CTgcGqOs9t+wR4XUSGqeqcRCdM2E5zVrFJNOdYj8fj8WSLJk1WOAV4IcbgzMSM0+AUx60PGyEAVX0Di6ZwSrITJivVKhGZmE7/XhgR2VtEfkWCMA4ej8fjaWGkODqlJjZOG6r6ERalu2/cIxIc5/hXiuOSGqLrgUsxd95PishoERkgIpEorSLSWUS+KSI/E5FnsLVGo4Hrkp3U4/F4PC2BNMUQdSd+JNZNJHda0NTjEo8RqerdIvIwZoxGY4NWCiAiu7CJCuGrEiyMw3hgmqp+luykrZwa4MMWOE+pO1dbJ9v34WtZlFUIeH1tWVpUX9/655IXpGOP2Ah+HURkUeD3DFWdEZMnng9CSbC92cclHcVyfYSTXVC5Y7D+wUOBvZ3gDcBSYK6qvpVQUBtCVfduifOIyKJ8c1yYC/x9aB5eX1uWlr4PTXRCHY68HktX4rd4gsfF06duKY5Lb9acmovu113yeDweT+vl38SM6YjIV4FOxB8DCh4Xb5p2X2wKd0Ly09+Dx+PxeHLF88DJItI5sO0czLH1KymO6ykiJ4Q3iMhRWKjx55Od0BuiwiW2T7et4u9DYeD/J6MQ7sM04AvgCREZJiJjsDDhvw1O6RaRZSISCf+qqgswzzsPisgIEfk+8DAwP9kaIvChwj0ej8cTg4gcCvwBGISN79wLlKtqKJBnFTY/YFRgWzdgKnAG1tB5BrhCVZNO0PCGyOPxeDw5xXfNtSAi8hUR2SYiGrMeS0TkBhH5WETqRGSeiAyMc3xKR4TZlJXF6x7lrjk2/bSt3INCpK3qqzuf19mWRFUTJuAvQPdkeXxKPwGPYOEyFNgzsH0cNhB4GTAMi+tUA/QM5OkOrMGcEX4H8we1Hbgp5hxZk5XF6x7lrvkk4NhA6tFW7kEhpraqr15nc6BrKf6MXe7CT811QQs9YdMaNwK/DD7YQAdgCzAxkLcTtkbrpsC2cdg8/S6Bbddibje6ZFtWlq89/FDvmWB/q78HhZbasr56nW35lKprbghmcZ8SkfskejqfJ01c0/kOoJLGq6qPA7pgrU8AVHU7MItoR4HpOCLMpqyWxN+DPMLra1r4+5BFkhoiVZ0PHA7chYUCf1csLLgnM36K1XrujLOvLxACPojZHusoMB1HhNmUtTtYLiK7RKRKRMbGlKet3INCwOtrA15nW4CUnhVUtQ64XEQeB+4DXhSR6cDCBPkfzG4RCxsx7+W/Ai5Q1Z0iEpulO7BNA9MiHZuAjiLSXlV3kJ5DwWzKyiZrgRuBNzD/hOcB00Sko6pOzXK58/UeFAReXyN4nW1B0o6YpKpzxVbMLgbGuhQk7NjOG6JoJgGvq+pzSfIkchQYuy8dh4LZlJUVVPUFbKFbmOdF5EvABBH5XYryxO4ryHtQQLR5fQWvsy1N2oZIRL6NtYhKsdXBC3ZXoVoLInIY8GPgW2ILvQA6us+uIhLCajOdRaQ4pkbUDahV1Z3udzqOCLMpa3fzV+AHwIG03XuQV3h9TYnX2d1ESkMkIiXALcAl2Ay6k1X1pd1dsFbCIcAexDfaq4H/xabIFgO9garA/tj+4HQcEf47i7JaCiW75S7Ee5AveH1ND6+zWSbpZAUROQ5YgsUkehj4hjdCGTEfW4cQTFPcvuHArcBrwFbg7PBBItIROJVoR4HpOCLMpqzdzZk0xMJpq/cg3/D6mhyvs7uLZHO7sXVE64DTcz3PvLUk4qxPwNYH1GIGfyjwLKbw+wTydMcGUF/CFryNAbYRf2FcVmRl8Zofx6L2ngL8D/CQuweXt5V7UKipLeqr19kc6FmKP+MxoDTXhWxNKcGDLVh029VY7eYfwBFxjj0U+LvLsxab3VQckydrsrJ4zTdjXQ617nxvARfurnLn4z0o1NQW9dXrbMsn7/TU4/F4PDnFOz31eDweT07xhsjj8Xg8OcUbIo/H4/HkFG+IPB6Px5NTvCHyeDweT07xhsjj8Xg8OcUbohZGRHqIyFYRGZ3rsjSXQDjlIcm2tRZE5PsiskNEDsl1WVoKr6+FSyHpqzdEKRCRm52iXhRnn4jIKyLyhYj0T1PkTUA1cH9WC9pGEZErRWRUS5xLVZ8C3qXB7U3e4fU1v/H6moBcr6jN9wS0x/7MzcBXY/b9Alt1fn2asvbH3Cb9PNfXlaV7M8pd/5DAtmIsqFpRC5VhFTC3Ba/5R+6aD8v1/U9QPq+via/H62ueJt8iSoFaQKqRmIfbe8PbRaQPLnYL5gwyHcZiSvHnLBcz64hIsXO8mBGqGlLVz1W1fneUKw94AnP78tNcFyQeXl8zw+trnpBrS1goCajEHsoxWC3qdczfU58MZPwbWJBgnwCjndxtLr0LVMbkK8VCOH8M7HCfdwJ7xZGZVl4aaorDsKiUy4GdwKhAnotd+b8AlgE/By6icQ1zVJJt3wZ+6eR/AfwHGBmn3OcAfwM+cvlqgKeAw2PyaYJ0YCDPUcCTTsYXmP+w8UC7GFmHYb4VP3H51gEvA9+LU77ZwLpc66TXV6+vrUVf0w6M5+FXmFv23wADgWOAq1S1KulRDhHZB+gD/D5BloeAH2IP9iSsa6UvcBYw0cnoirmM740FKXwbOAKLFfVtETlGVT/LNG+A32DxaO7B3NJXOVlXAlOxkCA3YMHSrsHGDjLhZqAEmI49PJcAD4jIMlV9NZDvMmAjFoBxHXAw9kJ9VUS+qaofuHwXunLVuHsWZoMr93DsoV4G3OZkDsJe0gNxbvddeOy/u2OnYW7+S7GXwn9hnpCDLMDc8fdV1XyNA+P11etrmPzX11xbwkJKwOFYTU0x77hp9ytjsV0UuCLOvh+4fQ/Fygz+xpRXgZ/F5LnUbf9VE/OOctuqgI4x+bsB24H3g/uw8YNtZFbD/CfQPrD9K9gD/ueYc3aKc4/6ubx3xWxfRZw+d6zffx0wj8a1yV8Eywic5n7/IM3/8gKX/8xc66TXV6+vrUFf/RhRZmzFlAvgOc2sX3lv97kxzr4fus9fxsqM+X0GVnuaEXP8dKyWdUYT84a5W1VrY7Z9F6tR3hncp6qrsWCJmXCX2hhGWMYnWHdH1PRSVd0OkVleXUSk1F1LFVbjS4fvAPtgs726iUhpOAHPBa4NYIv7PEVEuqQh+1P32SPNsuQKr68N5fL6msf66g1RmoiIYErSHvgXMEFEDs5AhIZFxdl3CLBWVdenkHEQUKWqu6IE2+8qoFcT84b5T5xt4XzxmvTvpyhvLCvibPsU2Cu4QUSOEJFngM+wh26DS9/AgoOlQz/3eV/g+HAKX8s+AKr6CvAgVhOuEZFXRaRCRA5NIDv8H2qC/TnH66vX12AR3Wfe6qsfI0qfy4Eh2MDh01gf9n0iMkRd+zcFG9znl+PsE/JDSWJrl5BcieO9pJIRSrA9IkdEDsC6J7Zi4xxVWFeLArcDe6Z5rrDMa4DFCfKsCX9R1ZEicisWEvsE4GpgvIhcqap/iDku/B9uIH/x+pp4X7p4fW0hvCFKA7cyeTLwJjBFVUMiUoH1a19O4gHdIO9hyhlvlXMVcLqI7JOilrkC6CMi7YI1RxFpB3yd6BpcJnmTsdx99qNhgJTAtmxzBvbwnqaqLwd3uEHaL2LyJ3ohhgeIt6vqnHROrKpLgaXALSLSDRuI/7WI3Bnz8u7tPpemI7el8foKeH0tGH0F3zWXEhEpAh7ApsCOVNVwLWkKsAiYnE6Xh6puwLoGjo2zO9x3fYs7X/D8wVrcU1jf/cUxx492259sYt5kvIRN+700uE5DRPYHzk9TRiaE729U7VXMxUzPOPm3Eb/W/gI2S+p6EWm0X0RKRKSz+/7l2PuuqpuBldh4Q4eYw48F1muaM9BaEq+vXl8pIH2NkOvZEvmesKayAtfG2XcYVuN5BSzsegpZZdhK9X3j7JvpzvMacD22mPA2YGkgT1esXzyEDepe4j5DWD9y5ybmHUXMzKGYsl3t9i8GrgImAKux7p5MZiE1kg/MBVYFfh+MdW18AozDFuI9hPXNLwvmdfnvAeqxbpHzgXNxs5iAk7GX0gbsRTza/Z//C2yiYRbSldgU2NuxqbhjgUdcmR+NOd+ernx35Fo3vb56fS10fY2UM9cFyOeENeXrsHn4xQnyjCPBNNc4effDFt5dHWdfETZV9W2s7/sz4B2gLCbf3sBd7sHa6T7vBErjyEwrb6oH2+UZi3XJhBcIXknmCwQbyY99sN22bwHz3T3YjK2L6J8gbw/gcWx2Vz2NFwj2B/7kXhQ7gPXYy/NG4Msuz0Dgj+66tmP9/UuwF9qXYs430p2jf6710+ur19dC1tdgEldYTwshItOwaZh9VHVnrsvjyQwReQv4UFVH5LosLYHX18KmUPTVjxG1PBOx6Z+NvCN78hsR+T42Jfe6XJelBfH6WqAUkr76FpHH4/F4copvEXk8Ho8np3hD5PF4PJ6c4g2Rx+PxeHKKN0Qej8fjySneEHk8Ho8np3hD5PF4lCpvAQAAABpJREFUPJ6c4g2Rx+PxeHKKN0Qej8fjySn/D/utVWXGReEtAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure('Probability of Failure')\n", + "xticks = np.arange(-0.1, 0.8, 0.4)\n", + "ax1 = fig.add_subplot(221)\n", + "#ax1.xaxis.set_visible(False)\n", + "imshow_grid(grid, 'landslide__probability_of_failure',cmap='OrRd',\n", + " limits=((0), (1)),plot_name='NOCA cohesion',\n", + " allow_colorbar=False,grid_units=('coordinates', 'coordinates'))\n", + "plt.xlim(36000, 53000)\n", + "plt.ylim(32000, 47000)\n", + "ax2 = fig.add_subplot(222)\n", + "ax2.yaxis.set_visible(False)\n", + "imshow_grid(grid_fire, 'landslide__probability_of_failure',cmap='OrRd',\n", + " limits=((0), (1)),plot_name='Post-Fire',grid_units=('coordinates', 'coordinates'))\n", + "plt.xlim(36000, 53000)\n", + "plt.ylim(32000, 47000)\n", + "\n", + "#plt.savefig('Probability_of_Failure_Original_Fire.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The map of probability of failure shows higher probabilities in a post-fire scenario with reduced cohesion." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To review the fields assigned to the grid, simply execute the following command." + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'topographic__elevation': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n", + " 'topographic__slope': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n", + " 'topographic__specific_contributing_area': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n", + " 'soil__transmissivity': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n", + " 'soil__mode_total_cohesion': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n", + " 'soil__minimum_total_cohesion': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n", + " 'soil__maximum_total_cohesion': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n", + " 'soil__internal_friction_angle': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n", + " 'soil__density': array([ 2000., 2000., 2000., ..., 2000., 2000., 2000.]),\n", + " 'soil__thickness': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n", + " 'landslides': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.]),\n", + " 'soil__saturated_hydraulic_conductivity': array([ 0., 0., 0., ..., 0., 0., 0.]),\n", + " 'soil__mean_relative_wetness': array([ 0., 0., 0., ..., 0., 0., 0.]),\n", + " 'landslide__probability_of_failure': array([ 0., 0., 0., ..., 0., 0., 0.]),\n", + " 'soil__probability_of_saturation': array([-9999., -9999., -9999., ..., -9999., -9999., -9999.])}" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid.at_node" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Export data from model run: FS probability, mean Reletive wetness, probability of saturation" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "core_nodes = grid.core_nodes\n", + "data_extracted = {'Prob_fail_std': np.array(\n", + " grid.at_node['landslide__probability_of_failure'][grid.core_nodes]),\n", + " 'mean_RW_std': np.array(grid.at_node['soil__mean_relative_wetness']\n", + " [grid.core_nodes]),'prob_sat_std': np.array(\n", + " grid.at_node['soil__probability_of_saturation'][grid.core_nodes]),\n", + " 'Prob_fail_fire': np.array(\n", + " grid_fire.at_node['landslide__probability_of_failure'][grid_fire.core_nodes]),\n", + " 'mean_RW_fire': np.array(grid_fire.at_node['soil__mean_relative_wetness']\n", + " [grid_fire.core_nodes]),'prob_sat_fire': np.array(\n", + " grid_fire.at_node['soil__probability_of_saturation'][grid_fire.core_nodes])}\n", + "headers = ['Prob_fail_std','mean_RW_std','prob_sat_std','Prob_fail_fire','mean_RW_fire','prob_sat_fire']\n", + "df = pd.DataFrame(data_extracted, index=core_nodes, columns=(headers))\n", + "df.to_csv('Landslide_std_fire.csv')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Make ascii files for raster creation in GIS" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['prob_sat_fire.txt']" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "write_esri_ascii('prob_fail_std.txt',grid,names='landslide__probability_of_failure')\n", + "write_esri_ascii('mean_RW_std.txt',grid,names='soil__mean_relative_wetness')\n", + "write_esri_ascii('prob_sat_std.txt',grid,names='soil__probability_of_saturation')\n", + "write_esri_ascii('prob_fail_fire.txt',grid_fire,names='landslide__probability_of_failure')\n", + "write_esri_ascii('mean_RW_fire.txt',grid_fire,names='soil__mean_relative_wetness')\n", + "write_esri_ascii('prob_sat_fire.txt',grid_fire,names='soil__probability_of_saturation')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "How long did the code above take to run?" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time is 3703.20 seconds\n" + ] + } + ], + "source": [ + "print('Elapsed time is %3.2f seconds' % (time.time() - st))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4.0. Save the results back into HydroShare\n", + "\n", + "\n", + "Using the `hs_utils` library, the results of the Geoprocessing steps above can be saved back into HydroShare. First, define all of the required metadata for resource creation, i.e. *title*, *abstract*, *keywords*, *content files*. In addition, we must define the type of resource that will be created, in this case *genericresource*. \n", + "\n", + "***Note:*** Make sure you save the notebook at this point, so that all notebook changes will be saved into the new HydroShare resource.\n", + "\n", + "\n", + "***Option A*** : define the resource from which this \"NEW\" content has been derived. This is one method for tracking resource provenance." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create list of files to save to HydroShare. Verify location and names." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/Replicate_Landslide_model_for_fire_Fisher_Creek.ipynb', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/Landslide_std_fire.csv', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/prob_fail_std.txt', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/mean_RW_std.txt', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/prob_sat_std.txt', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/prob_fail_fire.txt', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/mean_RW_fire.txt', '/home/jovyan/work/notebooks/data/70b977e22af544f8a7e5a803935c329c/70b977e22af544f8a7e5a803935c329c/data/contents/prob_sat_fire.txt']\n" + ] + } + ], + "source": [ + "ThisNotebook='Replicate_Landslide_model_for_fire_Fisher_Creek.ipynb' #check name for consistency\n", + "files=[homedir+ ThisNotebook,\n", + " homedir+'Landslide_std_fire.csv',\n", + " homedir+ 'prob_fail_std.txt',\n", + " homedir+ 'mean_RW_std.txt',\n", + " homedir+ 'prob_sat_std.txt',\n", + " homedir+ 'prob_fail_fire.txt',\n", + " homedir+ 'mean_RW_fire.txt',\n", + " homedir+ 'prob_sat_fire.txt']\n", + "print(files) #print location and names of files to save" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Adding the following system variables:\n", + " HS_USR_NAME = swalt826\n", + " HS_RES_ID = 70b977e22af544f8a7e5a803935c329c\n", + " HS_RES_TYPE = genericresource\n", + " JUPYTER_HUB_IP = jupyter.cuahsi.org\n", + "\n", + "These can be accessed using the following command: \n", + " os.environ[key]\n", + "\n", + " (e.g.)\n", + " os.environ[\"HS_USR_NAME\"] => swalt826\n", + "Successfully established a connection with HydroShare\n", + "Help on hydroshare in module utilities.hydroshare.hydroshare object:\n", + "\n", + "class hydroshare(builtins.object)\n", + " | Methods defined here:\n", + " | \n", + " | __init__(self, username=None, password=None, cache=True)\n", + " | Initialize self. See help(type(self)) for accurate signature.\n", + " | \n", + " | addContentToExistingResource(self, resid, content)\n", + " | Adds content files to an existing hydroshare resource.\n", + " | \n", + " | args:\n", + " | -- resid: id of an existing hydroshare resource (str)\n", + " | -- content: files paths to be added to resource (list)\n", + " | \n", + " | returns:\n", + " | -- None\n", + " | \n", + " | createHydroShareResource(self, abstract, title, derivedFromId=None, keywords=[], resource_type='GenericResource', content_files=[], public=False)\n", + " | Creates a hydroshare resource.\n", + " | \n", + " | args:\n", + " | -- abstract: abstract for resource (str, required)\n", + " | -- title: title of resource (str, required)\n", + " | -- derivedFromId: id of parent hydroshare resource (str, default=>None)\n", + " | -- keywords: list of subject keywords (list, default=>[])\n", + " | -- resource_type: type of resource to create (str, default=>\n", + " | 'GenericResource')\n", + " | -- content_files: data to save as resource content (list, default=>[])\n", + " | -- public: resource sharing status (bool, default=>False)\n", + " | \n", + " | returns:\n", + " | -- None\n", + " | \n", + " | getContentFiles(self, resourceid)\n", + " | Gets the content files for a resource that exists on the\n", + " | Jupyter Server\n", + " | \n", + " | args:\n", + " | -- resourceid: the id of the hydroshare resource\n", + " | \n", + " | returns:\n", + " | -- {content file name: path}\n", + " | \n", + " | getContentPath(self, resourceid)\n", + " | Gets the server path of a resources content files.\n", + " | \n", + " | args:\n", + " | -- resourceid: the id of the hydroshare resource\n", + " | \n", + " | returns:\n", + " | -- server path the the resource content files\n", + " | \n", + " | getResourceFromHydroShare(self, resourceid, destination='.')\n", + " | Downloads content of a hydroshare resource.\n", + " | \n", + " | args:\n", + " | -- resourceid: id of the hydroshare resource (str)\n", + " | -- destination: path to save resource, default\n", + " | /user/[username]/notebooks/data (str)\n", + " | \n", + " | returns:\n", + " | -- None\n", + " | \n", + " | getResourceMetadata(self, resid)\n", + " | Gets metadata for a specified resource.\n", + " | \n", + " | args:\n", + " | -- resid: hydroshare resource id\n", + " | \n", + " | returns:\n", + " | -- resource metadata object\n", + " | \n", + " | getSecureConnection(self, username=None)\n", + " | Establishes a secure connection with hydroshare.\n", + " | \n", + " | args:\n", + " | -- email: email address associated with hydroshare\n", + " | \n", + " | returns:\n", + " | -- hydroshare api connection\n", + " | \n", + " | loadResource(self, resourceid)\n", + " | Loads the contents of a previously downloaded resource.\n", + " | \n", + " | args:\n", + " | -- resourceid: the id of the resource that has been downloaded (str)\n", + " | \n", + " | returns:\n", + " | -- {content file name: path}\n", + " | \n", + " | ----------------------------------------------------------------------\n", + " | Data descriptors defined here:\n", + " | \n", + " | __dict__\n", + " | dictionary for instance variables (if defined)\n", + " | \n", + " | __weakref__\n", + " | list of weak references to the object (if defined)\n", + "\n" + ] + } + ], + "source": [ + "hs=hydroshare.hydroshare()\n", + "help(hs)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Resource Created Successfully \n", + "Successfully Added Content Files \n" + ] + }, + { + "data": { + "text/html": [ + "Resource id: 1c4f83e308f340f4ae16997b90899cca" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "Open Resource in HydroShare" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# for each file downloaded onto the server folder, move to a new HydroShare Generic Resource\n", + "title = 'Landslide Model run with Monte Carlo n=100 for eSurf paper from NOCA Observatory - Fisher Creek with Fire' # title for the new resource\n", + "abstract = 'This a reproducible demonstration of the landslide modeling results from eSurf paper: Strauch et al. (2018) ' # abstract for the new resource\n", + "keywords = ['landslide', 'climate', 'VIC','saturation','relative wetness','fire','Geohackweek'] # keywords for the new resource\n", + "rtype = 'genericresource' # Hydroshare resource type\n", + "\n", + "# create the new resource\n", + "resource_id = hs.createHydroShareResource(abstract, \n", + " title,\n", + " keywords=keywords, \n", + " resource_type=rtype, \n", + " content_files=files, \n", + " public=False)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +}