Olimaol develop

.github/workflows/test_before_update_master.yml

@@ -16,9 +16,9 @@ jobs:
     runs-on: ubuntu-latest
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
     - name: Set up Python 3.10
-      uses: actions/setup-python@v3
+      uses: actions/setup-python@v5
       with:
         python-version: "3.10"
     - name: Install pytest

.gitignore

@@ -15,3 +15,4 @@ dist/
 !docs/*
 !site/*
 *.pkl
+*json

README.md

@@ -4,6 +4,9 @@ Assisting package for computational neuroscience with ANNarchy ([GitHub](https:/
 The documentation is available online at:
 [https://olimaol.github.io/CompNeuroPy/](https://olimaol.github.io/CompNeuroPy/)
 
+The documentation for olimaol_develop branch is available at:
+[https://compneuropy.github.io/CompNeuroPy/](https://compneuropy.github.io/CompNeuroPy/)
+
 ### Authors
 
 * Oliver Maith (oli_maith@gmx.de)

docs/additional/statistic_functions.md

@@ -0,0 +1,3 @@
+::: CompNeuroPy.statistic_functions
+    options:
+        show_root_heading: false

docs/examples/experiment.md

@@ -36,6 +36,10 @@ class MyExp(CompNeuroExp):
             resets the model and monitors
         results():
             returns a results object
+        store_model_state():
+            stores the state of the model used for reset
+        reset_model_state():
+            resets the stored model state
     """
 
     def __init__(
@@ -90,6 +94,8 @@ class MyExp(CompNeuroExp):
         """
         ### call reset at the beginning of the experiment to ensure that the model
         ### is in the same state at the beginning of each experiment run
+        ### we stored the state that the membrane potential is -90 before (see below)
+        ### this can be seen in the results plots
         self.reset()
 
         ### also always start the monitors, they are stopped automatically at the end
@@ -101,17 +107,18 @@ class MyExp(CompNeuroExp):
         get_population(self.model.populations[0]).E_L = E_L
 
         ### SIMULATION START
-        sim_step.run()
+        sim_ramp.run()
         ### if you want to reset the model, you should use the objects reset()
         ### it's the same as the ANNarchy reset + it resets the CompNeuroMonitors
-        ### creating a new chunk, optionally not changing the parameters
+        ### creating a new chunk, optionally not changing the parameters (but still the
+        ### dynamic variables)
         self.reset(parameters=False)
-        sim_ramp.run()
+        sim_step.run()
         ### SIMULATION END
 
         ### optional: store anything you want in the data dict, for example information
         ### about the simulations
-        self.data["sim"] = [sim_step.simulation_info(), sim_ramp.simulation_info()]
+        self.data["sim"] = [sim_ramp.simulation_info(), sim_step.simulation_info()]
         self.data["population_name"] = self.model.populations[0]
         self.data["time_step"] = dt()
 
@@ -128,16 +135,7 @@ if __name__ == "__main__":
     monitors = CompNeuroMonitors({model.populations[0]: ["v"]})
 
     ### define some simulations e.g. using CompNeuroSim
-    sim_step = CompNeuroSim(
-        simulation_function=current_step,
-        simulation_kwargs={
-            "pop": model.populations[0],
-            "t1": 500,
-            "t2": 500,
-            "a1": 0,
-            "a2": 50,
-        },
-    )
+    ### in the experiment we 1st conduct a ramp simulation
     sim_ramp = CompNeuroSim(
         simulation_function=current_ramp,
         simulation_kwargs={
@@ -148,10 +146,30 @@ if __name__ == "__main__":
             "n": 50,
         },
     )
+    ### and 2nd a step simulation
+    sim_step = CompNeuroSim(
+        simulation_function=current_step,
+        simulation_kwargs={
+            "pop": model.populations[0],
+            "t1": 500,
+            "t2": 500,
+            "a1": 0,
+            "a2": 50,
+        },
+    )
 
     ### init and run the experiment
     my_exp = MyExp(monitors=monitors, model=model, sim_step=sim_step, sim_ramp=sim_ramp)
 
+    ### demonstration of the store_model_state function
+    ### the current state of the model is the compilation state, e.g. v should be -68
+    print(f"Compilation state v = {get_population(model.populations[0]).v}")
+    ### the reset function of CompNeuroExp resets to the compilation state by default
+    ### but you can also store the state of model compartments and reset to this state
+    ### here for example we store that the membrane potential is -90
+    get_population(model.populations[0]).v = -90.0
+    my_exp.store_model_state(compartment_list=model.populations)
+
     ### one use case is to run an experiment multiple times e.g. with different
     ### parameters
     results_run1 = my_exp.run()
@@ -160,7 +178,7 @@ if __name__ == "__main__":
     ### plot of the membrane potential from the first and second chunk using results
     ### experiment run 1
     PlotRecordings(
-        figname="example_experiment_sim_step.png",
+        figname="example_experiment_sim_ramp.png",
         recordings=results_run1.recordings,
         recording_times=results_run1.recording_times,
         chunk=0,
@@ -173,7 +191,7 @@ if __name__ == "__main__":
         },
     )
     PlotRecordings(
-        figname="example_experiment_sim_ramp.png",
+        figname="example_experiment_sim_step.png",
         recordings=results_run1.recordings,
         recording_times=results_run1.recording_times,
         chunk=1,
@@ -187,7 +205,7 @@ if __name__ == "__main__":
     )
     ### experiment run 2
     PlotRecordings(
-        figname="example_experiment2_sim_step.png",
+        figname="example_experiment2_sim_ramp.png",
         recordings=results_run2.recordings,
         recording_times=results_run2.recording_times,
         chunk=0,
@@ -200,7 +218,7 @@ if __name__ == "__main__":
         },
     )
     PlotRecordings(
-        figname="example_experiment2_sim_ramp.png",
+        figname="example_experiment2_sim_step.png",
         recordings=results_run2.recordings,
         recording_times=results_run2.recording_times,
         chunk=1,
@@ -234,29 +252,30 @@ if __name__ == "__main__":
 ```console
 $ python experiment.py 
 ANNarchy 4.7 (4.7.3b) on linux (posix).
-Compiling ...  OK 
-Generate fig example_experiment_sim_step.png... Done
-
+Compilation state v = [-68.]
 Generate fig example_experiment_sim_ramp.png... Done
 
-Generate fig example_experiment2_sim_step.png... Done
+Generate fig example_experiment_sim_step.png... Done
 
 Generate fig example_experiment2_sim_ramp.png... Done
 
+Generate fig example_experiment2_sim_step.png... Done
+
 
 run1:
     data:
-        sim: [<CompNeuroPy.generate_simulation.SimInfo object at 0x7f4798dfb700>, <CompNeuroPy.generate_simulation.SimInfo object at 0x7f4798dfad40>]
+        sim: [<CompNeuroPy.generate_simulation.SimInfo object at 0x7f71f8cd1f00>, <CompNeuroPy.generate_simulation.SimInfo object at 0x7f71f8cd1fc0>]
         population_name: HH_Bischop
         time_step: 0.01
     mon_dict:
         HH_Bischop: ['v']
 
 run2:
     data:
-        sim: [<CompNeuroPy.generate_simulation.SimInfo object at 0x7f4798dfb700>, <CompNeuroPy.generate_simulation.SimInfo object at 0x7f4798dfad40>]
+        sim: [<CompNeuroPy.generate_simulation.SimInfo object at 0x7f71f9181570>, <CompNeuroPy.generate_simulation.SimInfo object at 0x7f71f8c5a8c0>]
         population_name: HH_Bischop
         time_step: 0.01
     mon_dict:
         HH_Bischop: ['v']
+
 ```

docs/examples/opt_neuron.md

@@ -11,15 +11,15 @@ from CompNeuroPy.opt_neuron import OptNeuron
 import numpy as np
 from ANNarchy import Neuron, dt
 
-
 ### in this example we want to fit an ANNarchy neuron model to some data (which ca be
 ### somehow obtained by simulating the neuron and recording variables) for this example,
-### we have the following simple neuron model
+### we have the following simple neuron model, you must not use the :population flag
+### for the parameters!
 my_neuron = Neuron(
     parameters="""
         I_app = 0
-        a = 0 : population
-        b = 0 : population
+        a = 0
+        b = 0
     """,
     equations="""
         r = a*I_app + b
@@ -88,12 +88,14 @@ class my_exp(CompNeuroExp):
 
         For using the CompNeuroExp for OptNeuron, the run function should have
         one argument which is the name of the population which is automatically created
-        by OptNeuron, containing a single neuron of the model which should be optimized.
+        by OptNeuron, containing a single or multiple neurons of the neuron model which
+        should be optimized. Thus, the run function should be able to run the experiment
+        with a single or multiple neurons in the given population!
 
         Args:
             population_name (str):
-                name of the population which contains a single neuron, this will be
-                automatically provided by OptNeuron
+                name of the population with neurons of the tuned neuron model, this will
+                be automatically provided by OptNeuron
 
         Returns:
             results (CompNeuroExp._ResultsCl):
@@ -107,11 +109,6 @@ class my_exp(CompNeuroExp):
                     data (dict):
                         dict with optional data stored during the experiment
         """
-        ### For OptNeuron you have to reset the model and monitors at the beginning of
-        ### the run function! Do not reset the parameters, otherwise the optimization
-        ### will not work!
-        self.reset(parameters=False)
-
         ### you have to start monitors within the run function, otherwise nothing will
         ### be recorded
         self.monitors.start()
@@ -133,9 +130,16 @@ class my_exp(CompNeuroExp):
             monitor_object=self.monitors,
         )
 
-        ### run the simulation, remember setting parameters=False in the reset function!
+        ### run the simulation
         sim_step.run()
-        self.reset(parameters=False)
+        ### Here we reset the model and monitors, creating a new chunk for the next sim
+        ### use the self.reset() function to reset the model and monitors!
+        ### OptNeuron sets the parameters (defined in the variables_bounds dict) of the
+        ### neuron model before each run.
+        ### By using the reset function of the CompNeuroExp class you reset the model to
+        ### this state (all varaiables/parameters not defined in variable bounds are
+        ### reset to compile state)
+        self.reset()
         sim_step.run({"a2": 10})
 
         ### optional: store anything you want in the data dict. For example infomration
@@ -169,6 +173,8 @@ def get_loss(results_ist: CompNeuroExp._ResultsCl, results_soll):
     ### results_ist, we do not use all available information here, but you could
     rec_ist = results_ist.recordings
     pop_ist = results_ist.data["population_name"]
+
+    ### the get_loss function should always calculate the loss for neuron rank 0!
     neuron = 0
 
     ### get the data for calculating the loss from the results_soll
@@ -206,12 +212,12 @@ def main():
         results_soll=experimental_data["results_soll"],
         time_step=experimental_data["time_step"],
         compile_folder_name="annarchy_opt_neuron_example_from_data",
-        method="hyperopt",
+        method="deap",
         record=["r"],
     )
 
     ### run the optimization, define how often the experiment should be repeated
-    fit = opt.run(max_evals=1000, results_file_name="best_from_data")
+    fit = opt.run(max_evals=100, results_file_name="best_from_data")
 
     ### print optimized parameters, we should get around a=0.8 and b=2
     print("a", fit["a"])
@@ -231,10 +237,10 @@ OptNeuron: Initialize OptNeuron... do not create anything with ANNarchy before!
 OptNeuron: WARNING: attributes ['I_app', 'r'] are not used/initialized.
 checking neuron_models, experiment, get_loss...Done
 
-100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:45<00:00, 21.99trial/s, best loss: 0.31922683758789056]
-a 0.7609542202637395
-b 2.171783070482363
-['a', 'b', 'loss', 'all_loss', 'std', 'results', 'results_soll']
+100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:03<00:00, 26.66gen/s, best loss: 0.00000]
+a 0.8000000007960777
+b 1.9999999939091158
+['a', 'b', 'logbook', 'deap_pop', 'loss', 'all_loss', 'std', 'results', 'results_soll']
 ```
 
 ## Optimize neuron model from other neuron model
@@ -262,15 +268,15 @@ from run_opt_neuron_from_data import my_neuron as simple_neuron
 complex_neuron = Neuron(
     parameters="""
         I_app = 0
-        f = 6.0542364610842572e-002 : population
-        e = 3.7144041714209490e+000 : population
-        d = -4.9446336126026436e-001 : population
-        c = 9.0909599124334911e-002 : population
-        b = -4.4497411506061648e-003 : population
-        a = -6.2239117460540167e-005 : population
+        m0 = 1
+        m1 = 2
+        m2 = 3
+        n0 = 1
+        n1 = 0
+        n2 = -1
     """,
     equations="""
-        r = a*I_app**5 + b*I_app**4 + c*I_app**3 + d*I_app**2 + e*I_app**1 + f
+        r = m0*I_app + n0 + m1*I_app + n1 + m2*I_app + n2
     """,
 )
 
@@ -302,6 +308,9 @@ def get_loss(
     pop_ist = results_ist.data["population_name"]
     rec_soll = results_soll.recordings
     pop_soll = results_soll.data["population_name"]
+
+    ### the get_loss function should always calculate the loss for neuron rank 0! For
+    ### both, the target and the optimized neuron model.
     neuron = 0
 
     ### get the data for calculating the loss from the recordings of the
@@ -333,14 +342,14 @@ def main():
         target_neuron_model=complex_neuron,
         time_step=1,
         compile_folder_name="annarchy_opt_neuron_example_from_neuron",
-        method="hyperopt",
+        method="deap",
         record=["r"],
     )
 
     ### run the optimization, define how often the experiment should be repeated
-    fit = opt.run(max_evals=1000, results_file_name="best_from_neuron")
+    fit = opt.run(max_evals=100, results_file_name="best_from_neuron")
 
-    ### print optimized parameters, we should get around a=2.8 and b=0.28
+    ### print optimized parameters, we should get around a=6 and b=0
     print("a", fit["a"])
     print("b", fit["b"])
     print(list(fit.keys()))
@@ -358,8 +367,8 @@ OptNeuron: Initialize OptNeuron... do not create anything with ANNarchy before!
 OptNeuron: WARNING: attributes ['I_app', 'r'] are not used/initialized.
 checking neuron_models, experiment, get_loss...Done
 
-100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1000/1000 [00:47<00:00, 21.10trial/s, best loss: 0.5607444520201438]
-a 2.8009641859311354
-b 0.22697565003968234
-['a', 'b', 'loss', 'all_loss', 'std', 'results', 'results_soll']
+100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:03<00:00, 26.96gen/s, best loss: 0.00000]
+a 5.99999999677215
+b 2.8379565285300652e-08
+['a', 'b', 'logbook', 'deap_pop', 'loss', 'all_loss', 'std', 'results', 'results_soll']
 ```

docs/index.md

@@ -1,4 +1,6 @@
 # Documentation for CompNeuroPy
 
 CompNeuroPy is an assisting Python package for working with ANNarchy ([GitHub](https://github.com/ANNarchy/ANNarchy), [documentation](https://annarchy.github.io/), [DOI](https://doi.org/10.5281/zenodo.6415039)). It is intended to help structure simulations with computational neuroscience models in a modular way and to make them more easily replicable.
-People who want to start working with ANNarchy are strongly recommended to first learn exclusively the functionality of ANNarchy. CompNeuroPy uses very few features of ANNarchy at this time. But also adds various special features.
+People who want to start working with ANNarchy are strongly recommended to first learn exclusively the functionality of ANNarchy. CompNeuroPy uses very few features of ANNarchy at this time. But also adds various special features.
+
+- v1.0.0: [![DOI](https://zenodo.org/badge/422217136.svg)](https://zenodo.org/doi/10.5281/zenodo.10497610)

docs/main/define_experiment.md

@@ -1 +1,3 @@
-::: CompNeuroPy.experiment.CompNeuroExp
+::: CompNeuroPy.experiment.CompNeuroExp
+### Full Example 
+A full example is available in the [Examples](../examples/experiment.md).