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Model fitting toolbox for the Brian 2 simulator

Home Page: https://brian2modelfitting.readthedocs.io

License: Other

Python 99.49% Makefile 0.23% Batchfile 0.28%

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afcarl shivchitinous eslam69 pb663 akapet00 tp030ny

brian2modelfitting's Issues

Online display of best parameters is not readable

When doing an optimization, the program reports the current best value for parameters. However, this is stated as a list of values in random order. To be more useful, it should be shown as a dictionary (ideally with units).

Inestigate additional visualization techniques of parameter space

E.g., visualization of conditional distribution given all other parameters, visualization of conditional correlation matrix of a distribution...

Cannot run another `fit` after `generate`

It should be possible to create an example trace after fitting, and then to continue fitting (at least in runtime mode), but currently it fails like this (after adding a fits = fitter.generate_traces() in between the two fitting calls in hh_nevergrad_multiruns.py):

Traceback (most recent call last):
  File "hh_nevergrad_multiruns.py", line 75, in <module>
    Cm=[0.1*ufarad*cm**-2 * area, 2*ufarad*cm**-2 * area],)
  File "/home/marcel/programming/brian2modelfitting/brian2modelfitting/fitter.py", line 479, in fit
    callback, restart, **params)
  File "/home/marcel/programming/brian2modelfitting/brian2modelfitting/fitter.py", line 303, in fit
    metric)
  File "/home/marcel/programming/brian2modelfitting/brian2modelfitting/fitter.py", line 229, in optimization_iter
    self.simulator.run(self.duration, d_param, self.parameter_names)
  File "/home/marcel/programming/brian2modelfitting/brian2modelfitting/simulator.py", line 100, in run
    self.network[name].set_states(params, units=False)
  File "/mnt/data/anaconda2/envs/brian2modelfitting/lib/python3.6/site-packages/brian2/core/network.py", line 395, in __getitem__
    raise KeyError('No object with name "%s" found' % item)
KeyError: 'No object with name "neurons" found'

Temporal weighting of error

Currently it is possible to include only the part t>t_start in the calculation of error. It would be nice to be able to weight differently different parts of the traces. Typically, with responses to pulses, we might want to weight the onset and offset of the pulse more strongly. A simple way would be to provide a weight trace, of the same size as the other traces.

Issue with Multi-run of Hodgkin-Huxley Example

I'm pretty new to python, so this could be an issue with what I have done. I pip installed brian2 and brian2modelfitting. Now I'm trying to run the Multi-run Hodgkin-Huxley example (https://brian2modelfitting.readthedocs.io/en/stable/examples/hh_multirun.html). I copied and pasted the code from the tutorial but when I run it I get the error:

ValueError                                Traceback (most recent call last)
[c:\Users\mmgee\Documents\DMV\test_brian2modelfitting.py](file:///C:/Users/mmgee/Documents/DMV/test_brian2modelfitting.py) in line 55
     [48](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=47) ## Fitting
     [49](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=48) fitter = TraceFitter(model=eqs, input_var='I', output_var='v',
     [50](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=49)                      input=inp_traces*amp, output=out_traces*mV, dt=dt,
     [51](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=50)                      n_samples=20,
     [52](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=51)                      param_init={'v': -65*mV},
     [53](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=52)                      method='exponential_euler')
---> [55](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=54) res, error = fitter.fit(n_rounds=2,
     [56](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=55)                         optimizer=n_opt, metric=metric,
     [57](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=56)                         callback='progressbar',
     [58](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=57)                         gl = [1e-09 *siemens, 1e-07 *siemens],
     [59](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=58)                         g_na = [2e-06*siemens, 2e-04*siemens],
     [60](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=59)                         g_kd = [6e-07*siemens, 6e-05*siemens],
     [61](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=60)                         Cm=[0.1*ufarad*cm**-2 * area, 2*ufarad*cm**-2 * area])
     [63](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=62) ## Show results
     [64](file:///c%3A/Users/mmgee/Documents/DMV/test_brian2modelfitting.py?line=63) all_output = fitter.results(format='dataframe')

File [~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\brian2modelfitting\fitter.py:866](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/mmgee/Documents/DMV/~/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py:866), in TraceFitter.fit(self, optimizer, metric, n_rounds, callback, restart, start_iteration, penalty, level, **params)
    [861](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=860)         raise ValueError("The 't_weights' argument of the metric has "
    [862](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=861)                          "to be a one-dimensional array of length "
    [863](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=862)                          f"{self.output.shape[1]} but has shape "
    [864](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=863)                          f"{metric.t_weights.shape}")
    [865](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=864) self.bounds = dict(params)
--> [866](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=865) best_params, error = super().fit(optimizer, metric, n_rounds,
    [867](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=866)                                  callback, restart,
    [868](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=867)                                  start_iteration=start_iteration,
    [869](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=868)                                  penalty=penalty,
    [870](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=869)                                  level=level+1,
    [871](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=870)                                  **params)
    [872](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=871) return best_params, error

File [~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\brian2modelfitting\fitter.py:590](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/mmgee/Documents/DMV/~/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py:590), in Fitter.fit(self, optimizer, metric, n_rounds, callback, restart, online_error, start_iteration, penalty, level, **params)
    [586](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=585) # Check whether we can reuse the current simulator or whether we have
    [587](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=586) # to create a new one (only relevant for standalone, but does not hurt
    [588](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=587) # for runtime)
    [589](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=588) if self.simulator is None or self.simulator.current_net != 'fit':
--> [590](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=589)     self.simulator = self.setup_simulator('fit', self.n_neurons,
    [591](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=590)                                           output_var=self.output_var,
    [592](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=591)                                           online_error=online_error,
    [593](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=592)                                           param_init=self.param_init,
    [594](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=593)                                           level=level+1)
    [596](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=595) # Run Optimization Loop
    [597](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=596) for index in range(n_rounds):

File [~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\brian2modelfitting\fitter.py:365](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/mmgee/Documents/DMV/~/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py:365), in Fitter.setup_simulator(self, network_name, n_neurons, output_var, param_init, calc_gradient, optimize, online_error, level)
    [362](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=361) if self.output_var != 'spikes':
    [363](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=362)     namespace['output_var'] = TimedArray(self.output.transpose(),
    [364](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=363)                                          dt=self.dt)
--> [365](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=364) neurons = self.setup_neuron_group(n_neurons, namespace,
    [366](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=365)                                   calc_gradient=calc_gradient,
    [367](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=366)                                   optimize=optimize,
    [368](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=367)                                   online_error=online_error)
    [369](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=368) network = Network(neurons)
    [370](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=369) if isinstance(output_var, str):

File [~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\brian2modelfitting\fitter.py:418](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/mmgee/Documents/DMV/~/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py:418), in Fitter.setup_neuron_group(self, n_neurons, namespace, calc_gradient, optimize, online_error, name)
    [416](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=415)     kwds['method'] = self.method
    [417](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=416) model = self.model + Equations('iteration : integer (constant, shared)')
--> [418](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=417) neurons = NeuronGroup(n_neurons, model,
    [419](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=418)                       threshold=self.threshold, reset=self.reset,
    [420](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=419)                       refractory=self.refractory, name=name,
    [421](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=420)                       namespace=namespace, dt=self.dt, **kwds)
    [422](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=421) if calc_gradient:
    [423](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=422)     sensitivity_eqs = get_sensitivity_equations(neurons,
    [424](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=423)                                                 parameters=self.parameter_names,
    [425](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=424)                                                 optimize=optimize,
    [426](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2modelfitting/fitter.py?line=425)                                                 namespace=namespace)

File [~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\brian2\groups\neurongroup.py:538](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/mmgee/Documents/DMV/~/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py:538), in NeuronGroup.__init__(self, N, model, method, method_options, threshold, reset, refractory, events, namespace, dtype, dt, clock, order, name, codeobj_class)
    [520](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=519) def __init__(
    [521](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=520)     self,
    [522](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=521)     N,
   (...)
    [536](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=535)     codeobj_class=None,
    [537](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=536) ):
--> [538](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=537)     Group.__init__(
    [539](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=538)         self,
    [540](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=539)         dt=dt,
    [541](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=540)         clock=clock,
    [542](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=541)         when="start",
    [543](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=542)         order=order,
    [544](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=543)         namespace=namespace,
    [545](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=544)         name=name,
    [546](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=545)     )
    [547](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=546)     if dtype is None:
    [548](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/groups/neurongroup.py?line=547)         dtype = {}

File [~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\brian2\units\fundamentalunits.py:2780](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/mmgee/Documents/DMV/~/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py:2780), in check_units.<locals>.do_check_units.<locals>.new_f(*args, **kwds)
   [2770](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py?line=2769)             error_message = (
   [2771](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py?line=2770)                 f"Function '{f.__name__}' "
   [2772](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py?line=2771)                 "expected a quantitity with unit "
   [2773](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py?line=2772)                 f"{unit} for argument '{k}' but got "
   [2774](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py?line=2773)                 f"'{value}'"
   [2775](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py?line=2774)             )
   [2776](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py?line=2775)             raise DimensionMismatchError(
   [2777](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py?line=2776)                 error_message, get_dimensions(newkeyset[k])
   [2778](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py?line=2777)             )
-> [2780](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py?line=2779) result = f(*args, **kwds)
   [2781](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py?line=2780) if "result" in au:
   [2782](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/units/fundamentalunits.py?line=2781)     if isinstance(au["result"], Callable) and au["result"] != bool:

File [~\AppData\Local\Packages\PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0\LocalCache\local-packages\Python39\site-packages\brian2\core\base.py:161](https://file+.vscode-resource.vscode-cdn.net/c%3A/Users/mmgee/Documents/DMV/~/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/core/base.py:161), in BrianObject.__init__(self, dt, clock, when, order, namespace, name)
    [159](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/core/base.py?line=158) for key in namespace:
    [160](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/core/base.py?line=159)     if key.startswith("_"):
--> [161](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/core/base.py?line=160)         raise ValueError(
    [162](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/core/base.py?line=161)             "Names starting with underscores are "
    [163](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/core/base.py?line=162)             "reserved for internal use an cannot be "
    [164](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/core/base.py?line=163)             "defined in the namespace argument."
    [165](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/core/base.py?line=164)         )
    [166](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/core/base.py?line=165) #: The group-specific namespace
    [167](file:///c%3A/Users/mmgee/AppData/Local/Packages/PythonSoftwareFoundation.Python.3.9_qbz5n2kfra8p0/LocalCache/local-packages/Python39/site-packages/brian2/core/base.py?line=166) self.namespace = namespace

ValueError: Names starting with underscores are reserved for internal use an cannot be defined in the namespace argument.

Enable GPU support

Enable the use of GPU for training neural density estimator.

The GPU is expected be effective only for large neural networks with operations that are inherently fast on the GPU, e.g., convolutions. No speed ups in training time will be achieved for the default architectures provided in sbi.

Investigate bug with nevergrad 0.3

After updating nevergrad to version 0.3, our test suite fails: https://travis-ci.org/brian-team/brian2modelfitting/jobs/638442404

It fails with an error in the "instrumentation" code:

    def _check_shape(self, x: np.ndarray) -> None:

        if self.shape != (1,) and x.shape != self.shape:

>           raise ValueError(f"Shapes do not match: {self.shape} and {x.shape}")

E           ValueError: Shapes do not match: () and (1,)

../../../virtualenv/python3.6.7/lib/python3.6/site-packages/nevergrad/instrumentation/transforms.py:127: ValueError

I did not look in detail, but I think it expects a scalar where we provide a length-1 array (or the other way around).

Enable all inference methods included in `sbi`

Currently, only SNPE estimation method is supported. SNRE and SNLE should also be included.

Investigate using dimensionality-reduction methods on outputs

Instead of asking the user to provide metrics to extract features, it might be possible to automatically reduce the dimensionality of the output (e.g. voltage trace)?

Improve the simulation-based inference model

Wrap string equations into Equations objects

[Refers to the current state of PR #2]

Again, very minor/trivial: in Brian, we usually allow to give equations as strings or as Equations object. The Fitter currently requires the model to be given as an Equations object, we should simply wrap it if it is a simple string.

Compare model fitting algorithms

Split up simulations if necessary

The first stages of the process detailed in #44 might potentially have to simulate a large number of samples, too big to fit into memory as part of a single simulation. As a first step, we might simply let the user deal with this (e.g. by calling something like inferencer.simulate(n_samples=10_000) repeatedly). A fancier solution would be to ask the user for the maximum memory that the simulation should use, estimate the memory usage of the monitors and split up the simulation accordingly. Even more automatic (but maybe too automatic/too complicated to get right on all platforms?) would be to look at the actual memory available.

Generalize `FeatureMetric`

Currently, users can provide FeatureMetric with a list of feature names known to eFEL. In addition to that functionality, it should be possible to hand over a function instead of a name to support arbitrary feature calculations not supported by eFEL.

Create class to wrap first stages of sbi

This class should take parameters similarly to Fitter, and should wrap the first stages of the sbi process:

Sampling parameter combinations from the priors (simple uniform priors within the bounds as a first step)
Simulating the model on these parameter combinations
Extracting the metrics on the results
Training the inference network with the data

Units of results

It seems that best parameters are unitless. But the parameter ranges, on the other hand, are given with units. It would be more consistent to have units in results too.

Use more specific exception types

[Refers to the current state of PR #2]

Of course a very minor issue, but if possible we should use a more specific exception type than Exception (in most places probably ValueError and TypeError)

brian2modelfitting/brian2modelfitting/modelfitting/modelfitting.py

Line 255 in a4d78d7

raise Exception("You can not change the metric between fits")

Typo in the exception message at the assertion of optimizer's parent class

brian2modelfitting/brian2modelfitting/fitter.py

Line 564 in a6a41e8

raise TypeError("metric has to be a child of class Optimizer")

Should it be "optimizer has to be a child of class Optimizer" instead?

Enable the user to define features for model fitting

Dynamical coupling

There's one optimization strategy with highly nonlinear systems which could be interesting to explore. It consists in adding a coupling term k*(V_recording-V_model), which decays over the time of fitting. This could be implemented quite simply by having k as a parameter, and the error criterion is k**2 + a*sum((V_recording-V_model)**2).

At the end we might want to have a second fitting run with k=0 (but starting from the final population?).

Merging with brian2tools

Shouldn't we close brian2modelfitting and merge with brian2tools? (see wiki)

Interface with sequential optimization algorithms

I think it would be useful to interface with sequential optimization algorithms. For example, lmfit has support for parameter bounds. Our choice of using parallelizable algorithms is for performance reasons, but in practice I found that around 100 000 runs are necessary to fit a model to our data. So, it could very well be that using a more standard sequential algorithm requires fewer runs and ends up being more efficient.

I guess it should be possible to make an ask and tell interface for such algorithms. For example, the algorithm (eg lmfit) runs in a separate thread and calls a function f, which informs an ask method, blocks, and unblocks when tell is called.

Add documentation for sbi

Add tests for sbi

10 times more samples as requested

I'm not sure it's a bug or a documentation issue. When I get results with fitter.results, I obtain 10 times more values than specified with TraceFitter(n_samples=...).

Default values for metric/optimizer

[Refers to the current state of PR #2]

Currently, SpikeFitter and TraceFitter require the specification of a metric and an optimizer, but we could have default values (MSEMetric for TraceFitter, GammaFactor for SpikeFitter, NevergradOptimizer for both?).

Refactor `Inferencer`/`Fitter`

Both classes have a lot in common (everything related to the Brian simulation), and should inherit from a common superclass to share the code. Another possible approach would be to put all the shared code in a separate class that neither Fitter nor Inferencer inherits from, and delegate to an object of that class (e.g. everything related to the simulation could be in a BrianSimulator class, and both Fitter and Inferencer would do something like self.brian_simulator = BrianSimulator(...) in their __init__ and then call self.brian_simulator... for all simulation-related tasks.) Actually, there is already a Simulator class (with implementations for runtime and standalone) – maybe some more code could simply move into this class.

Implement multi-objective optimisation

Nevergrad's recommendation can give wrong result

During testing, I noticed a peculiar problem: the recommendation that we get from Nevergrad is not necessarily the best parameter combination that we have seen so far (at least for the differential evolution algorithm that we use by default, but I think it is more or less the same issue for the other optimizers). The reason is that Nevergrad always assumes that simulations are noisy, so it will not use the observed error as such. Instead, it will try to estimate lower and upper bounds on the error. This is in itself not unreasonable, even though our tests and examples all use deterministic models, so re-evaluating a parameter combination for a second time is a waste of time. But the main problem is that we can run into a numerical issue: Our errors tend to be rather small numerical values, because we use the mean squared error (and not its square root). If we are fitting currents, errors could be on the order of nA, i.e. 1e-9 and the squared error therefore on the order of 1e-18. Now, Nevergrad will not base its recommendation based on the observed error, but on its estimated "lower bound". This lower bound is calculated by reducing the average observed error by something like 10% of the standard error of the observed values. This should be fine if there were indeed several observed values (in a deterministic system, the standard error would be 0 of course), but when running a relatively small number of parameters in a high-dimensional space, almost all parameter combinations have only been tried once. In this case, Nevergrad uses a default variance value that is very inappropriate in our case:

        # TODO May be safer to use a default variance which depends on y for scale invariance?
        self.variance = 1.e6

Using this variance value that is many orders of magnitude higher than typical errors, leads to a dramatic loss in accuracy, e.g. a 10 fold difference between 1e-16 and 1e-15 will be considered the same error and which of the two gets recommended as the "best solution" is completely arbitrary.

I could think of various ways to fix this:

Normalize our values in some way that the absolute values are not that small (but seems to be difficult to generalize)
for differential evolution, there's a parameter that could change the recommendation to use the "average" (i.e., measured) value instead of the calculated "optimistic" value – I did not immediately see how to set these parameters in a "clean" way, though.
Use float128 when calculating our errors, hoping that the type propagates to Nevergrad
Do not use Nevergrad's recommendation at all, but simply return the value with the lowest error (we keep track of all parameters and their errors ourselves already).

The last option is probably the easiest and least prone to break with changes in Nevergrad?

Tools for analyzing results

It would be nice to have some tools to analyze population parameters. Useful ones would be:

mean and variance
groups of correlated parameters (what parameters are independent?)
relations between parameters (PCA?)
do we reach bounds?

Fix string continuation in exceptions

There are a number of exceptions using multiline strings like this:

brian2modelfitting/brian2modelfitting/modelfitting/metric.py

Lines 241 to 242 in a4d78d7

 raise ValueError('you can only use features that return \ 

  one value')

this has the unwanted side effect that all the spaces at the start of the second line are included in the string:

>>> raise ValueError('you can only use features that return \
                      one value') 
[...]
ValueError: you can only use features that return                   one value

A better solution than using the backslash is to use Python's implied continuation:

raise ValueError('you can only use features that return '
                 'one value')

Add option to discard a part of a simulation

[Refers to the current state of PR #2]

I think we already had this option at the point: we need to be able to exclude a certain part of the simulation in the calculation of the error (ignoring a certain time in the beginning should be enough I guess). This could be an (optional) argument to the metric.

Support spikes in sbi

Currently, the sbi Inferencer always works on traces, but it should also work on spikes (cf. SpikeFitter). Internally, this should already mostly work if the name of the output variable is set to spikes. Note that features will no longer receive an array, but instead a list of arrays, since for each parameter combination/input, there can be a different number of spikes.

Store simulation/training results

The first stages of the sbi process (simulation and training of the network) are time-consuming. The results can be reused for several experimental data sets, so it would be wasteful to simulate more often than necessary. Check whether sbi provides mechanisms to store/load these results and wrap them as part of the class created for #44 .

Inconsistent handling between "best result" and error

[Refers to the current state of PR #2]

In this code:

brian2modelfitting/brian2modelfitting/modelfitting/modelfitting.py

Lines 268 to 280 in 4cb7035

 # Run Optimization Loop 

 for k in range(n_rounds): 

 res, parameters, errors = self.optimization_iter(optimizer, metric, 

 param_init) 

 # create output variables 

 self.best_res = make_dic(self.parameter_names, res) 

 error = min(errors) 

 if callback(res, errors, parameters, k) is True: 

 break 

 return self.best_res, error

the result returned by the library is the current best result, i.e. the parameter combination giving the best result over all iterations. This is what is returned in the end, and also what is handed over to the callback. This is inconsistent with the errors that are displayed and returned: they are the errors for the current iteration, the minimal error is therefore only the minimal error for the current iteration and not the overall minimum. The callback will display the (global) best parameters together with the best error of the current iteration, and therefore give misleading information.

Minor issue: the error minimum should probably use nanmin to ignore NaN values.

ERROR      Brian 2 encountered an unexpected error. If you think this is bug in Brian 2, please report this issue either to the mailing list at <http://groups.google.com/group/brian-development/>, or to the issue tracker at <https://github.com/brian-team/brian2/issues>. Please include this file with debug information in your report: /var/folders/m7/w45_jgp92nv46f4mjs9g8_d00000gn/T/brian_debug_zhp0bptb.log  Additionally, you can also include a copy of the script that was run, available at: /var/folders/m7/w45_jgp92nv46f4mjs9g8_d00000gn/T/brian_script_wbk4k1u0.py Thanks! [brian2]
Traceback (most recent call last):
  File "/Users/romainbrette/PycharmProjects/Paramecium-model-fitting/scripts/run_fit.py", line 202, in <module>
    callback='text', **model.bounds)
  File "/Users/romainbrette/PycharmProjects/brian2modelfitting/brian2modelfitting/fitter.py", line 495, in fit
    callback, restart, **params)
  File "/Users/romainbrette/PycharmProjects/brian2modelfitting/brian2modelfitting/fitter.py", line 302, in fit
    **params)
  File "/Users/romainbrette/PycharmProjects/brian2modelfitting/brian2modelfitting/optimizer.py", line 163, in initialize
    [bounds[i][1]])
  File "/anaconda3/envs/Paramecium-model-fitting/lib/python3.7/site-packages/nevergrad/instrumentation/variables.py", line 315, in bounded
    return self.with_transform(Transf(a_min=a_min, a_max=a_max))
  File "/anaconda3/envs/Paramecium-model-fitting/lib/python3.7/site-packages/nevergrad/instrumentation/transforms.py", line 183, in __init__
    self._b = .5 * (self.a_max + self.a_min)
TypeError: can't multiply sequence by non-int of type 'float'

Support multiple output variables for sbi

For more than one output variable, the features argument should not be a list, but instead a dictionary mapping each output variable name to a list of features.

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	raise ValueError('you can only use features that return \
	one value')

	# Run Optimization Loop
	for k in range(n_rounds):
	res, parameters, errors = self.optimization_iter(optimizer, metric,
	param_init)

	# create output variables
	self.best_res = make_dic(self.parameter_names, res)
	error = min(errors)

	if callback(res, errors, parameters, k) is True:
	break

	return self.best_res, error