From bb63b997372f5ba0fe93e871cb669e31f5c2eb72 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Wed, 16 Jun 2021 13:20:57 +0200
Subject: [PATCH 001/121] fix tqdm import and add some stylistic improvements

---
 brian2modelfitting/utils.py | 18 +++++++-----------
 1 file changed, 7 insertions(+), 11 deletions(-)

diff --git a/brian2modelfitting/utils.py b/brian2modelfitting/utils.py
index 984e0a6..4b46a31 100644
--- a/brian2modelfitting/utils.py
+++ b/brian2modelfitting/utils.py
@@ -2,7 +2,7 @@
 
 from brian2 import have_same_dimensions
 from brian2.units.fundamentalunits import Quantity
-from tqdm.autonotebook import tqdm
+from tqdm import tqdm
 
 
 def _format_quantity(v, precision=3):
@@ -12,7 +12,8 @@ def _format_quantity(v, precision=3):
         return f'{v:.{precision}g}'
 
 
-def callback_text(params, errors, best_params, best_error, index, additional_info):
+def callback_text(params, errors, best_params, best_error, index,
+                  additional_info):
     """Default callback print-out for Fitters"""
     params = []
     for p, v in sorted(best_params.items()):
@@ -25,14 +26,11 @@ def callback_text(params, errors, best_params, best_error, index, additional_inf
         for error, normed_error, varname in zip(additional_info['objective_errors'],
                                                 additional_info['objective_errors_normalized'],
                                                 additional_info['output_var']):
-
             if not have_same_dimensions(error, normed_error) or error != normed_error:
                 raw_error_str = f', unnormalized error: {_format_quantity(error)}'
             else:
                 raw_error_str = ''
-
             errors.append(f'{_format_quantity(normed_error)} ({varname}{raw_error_str})')
-
         error_sum = ' + '.join(errors)
         print(f"{round}Best parameters {param_str}\n"
               f"{' '*len(round)}Best error: {best_error_str} = {error_sum}")
@@ -53,8 +51,8 @@ def callback_text(params, errors, best_params, best_error, index, additional_inf
             print(f"{' ' * len(round)}Best error: {best_error_str} ({additional_info['output_var'][0]})")
 
 
-
-def callback_none(params, errors, best_params, best_error, index, additional_info):
+def callback_none(params, errors, best_params, best_error, index,
+                  additional_info):
     """Non-verbose callback"""
     pass
 
@@ -83,14 +81,12 @@ def callback_setup(set_type, n_rounds):
     elif type(set_type) is FunctionType:
         callback = set_type
     else:
-        raise TypeError("callback has to be a str ('text' or 'progressbar'), "
-                        "callable or None")
-
+        raise TypeError('callback has to be a str (`text` or `progressbar`), '
+                        'allable or None')
     return callback
 
 
 def make_dic(names, values):
     """Create dictionary based on list of strings and 2D array"""
     result_dict = {name: value for name, value in zip(names, values)}
-
     return result_dict

From 76ea512e296bcf886f6349eafaf5b937bca6917d Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Wed, 16 Jun 2021 13:21:41 +0200
Subject: [PATCH 002/121] add Inferencer class to __all__

---
 brian2modelfitting/__init__.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/brian2modelfitting/__init__.py b/brian2modelfitting/__init__.py
index c3c8bbc..e1994f6 100644
--- a/brian2modelfitting/__init__.py
+++ b/brian2modelfitting/__init__.py
@@ -6,6 +6,7 @@
 
 from .tests import run as run_test
 from .fitter import *
+from .inferencer import Inferencer
 from .optimizer import *
 from .metric import *
 from .simulator import *
@@ -16,4 +17,5 @@
            'Simulator', 'RuntimeSimulator', 'CPPStandaloneSimulator',
            'MSEMetric', 'Metric', 'GammaFactor', 'FeatureMetric',
            'SpikeMetric', 'TraceMetric', 'get_gamma_factor', 'firing_rate',
-           'Fitter', 'SpikeFitter', 'TraceFitter', 'OnlineTraceFitter']
+           'Fitter', 'SpikeFitter', 'TraceFitter', 'OnlineTraceFitter',
+           'Inferencer']

From b05f789f6e3460ada465192cb135d4108e55299b Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Wed, 16 Jun 2021 13:22:07 +0200
Subject: [PATCH 003/121] add sbi in requirements

---
 requirements.txt | 1 +
 1 file changed, 1 insertion(+)

diff --git a/requirements.txt b/requirements.txt
index 83b5aee..2d05504 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,3 +5,4 @@ scikit-learn!=0.23.0
 lmfit
 tqdm
 efel
+sbi

From bf739ae64b9a1c32301a95f3a213f2c5b25942f9 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Wed, 16 Jun 2021 13:22:54 +0200
Subject: [PATCH 004/121] add sbi as optional dependency

---
 setup.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index bd9d745..d3fc2e9 100644
--- a/setup.py
+++ b/setup.py
@@ -32,8 +32,7 @@ def readme():
       provides=['brian2modelfitting'],
       extras_require={'test': ['pytest'],
                       'docs': ['sphinx>=1.8'],
-                      'full': ['efel',
-                               'lmfit']},
+                      'full': ['efel', 'lmfit', 'sbi']},
       python_requires='>=3.6',
       use_2to3=False,
       zip_safe=False,

From ad3e8caf0fc9fbbf99c2a98a3b4efb24657c0235 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Wed, 16 Jun 2021 13:29:18 +0200
Subject: [PATCH 005/121] add minor stylistic changes

---
 setup.py | 28 ++++++++++++----------------
 1 file changed, 12 insertions(+), 16 deletions(-)

diff --git a/setup.py b/setup.py
index d3fc2e9..98ac0bb 100644
--- a/setup.py
+++ b/setup.py
@@ -1,27 +1,22 @@
 #! /usr/bin/env python
-'''
-brian2modelfitting setup script
-'''
+"""brian2modelfitting setup script"""
+
 import os
-import sys
 
 from setuptools import setup, find_packages
 
-def readme():
-    with open('README.md') as f:
-        return f.read()
 
 version = {}
 with open(os.path.join('brian2modelfitting', 'version.py')) as fp:
     exec(fp.read(), version)
 
-# Note that this does not set a version number explicitly, but automatically
-# figures out a version based on git tags
+with open('README.md') as f:
+    long_description = f.read()
+
 setup(name='brian2modelfitting',
       url='https://github.com/brian-team/brian2modelfitting',
       version=version['version'],
       packages=find_packages(),
-      # package_data={},
       install_requires=['numpy',
                         'brian2>=2.2',
                         'setuptools',
@@ -32,24 +27,25 @@ def readme():
       provides=['brian2modelfitting'],
       extras_require={'test': ['pytest'],
                       'docs': ['sphinx>=1.8'],
-                      'full': ['efel', 'lmfit', 'sbi']},
+                      'full': ['efel', 'lmfit', 'sbi'],
+                      },
       python_requires='>=3.6',
       use_2to3=False,
       zip_safe=False,
       description='Modelfitting Toolbox for the Brian 2 simulator',
-      long_description=readme(),
+      long_description=long_description,
       author='Aleksandra Teska, Marcel Stimberg, Romain Brette, Dan Goodman',
       author_email='team@briansimulator.org',
       license='CeCILL-2.1',
       classifiers=[
           'Development Status :: 4 - Beta',
           'Intended Audience :: Science/Research',
-          'License :: OSI Approved :: CEA CNRS Inria Logiciel Libre License, version 2.1 (CeCILL-2.1)',
+          'License :: OSI Approved :: CEA CNRS Inria Logiciel Libre License, '
+          'version 2.1 (CeCILL-2.1)',
           'Natural Language :: English',
           'Operating System :: OS Independent',
           'Programming Language :: Python',
           'Programming Language :: Python :: 3',
-          'Topic :: Scientific/Engineering :: Bio-Informatics'
-      ],
-      keywords='model fitting computational neuroscience'
+          'Topic :: Scientific/Engineering :: Bio-Informatics'],
+      keywords='model fitting computational neuroscience',
       )

From 0e9de9e744be7fbdbdb8cdb6b8f685f76ca1e9fa Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Wed, 16 Jun 2021 13:48:36 +0200
Subject: [PATCH 006/121] first steps into the development of sbi wrapper class

---
 brian2modelfitting/inferencer.py | 297 +++++++++++++++++++++++++++++++
 1 file changed, 297 insertions(+)
 create mode 100644 brian2modelfitting/inferencer.py

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
new file mode 100644
index 0000000..bf46c4c
--- /dev/null
+++ b/brian2modelfitting/inferencer.py
@@ -0,0 +1,297 @@
+from numbers import Number
+from typing import Mapping
+
+from brian2.core.functions import Function
+from brian2.core.namespace import get_local_namespace
+from brian2.core.network import Network
+from brian2.devices.cpp_standalone.device import CPPStandaloneDevice
+from brian2.devices.device import get_device, device
+from brian2.equations.equations import Equations
+from brian2.groups.neurongroup import NeuronGroup
+from brian2.input.timedarray import TimedArray
+from brian2.monitors.spikemonitor import SpikeMonitor
+from brian2.monitors.statemonitor import StateMonitor
+from brian2.units.allunits import *  # all physical units
+from brian2.units.fundamentalunits import (DIMENSIONLESS, get_dimensions,
+                                           Quantity)
+import matplotlib.pyplot as plt
+import numpy as np
+from sbi.utils.get_nn_models import posterior_nn
+from sbi.utils.torchutils import BoxUniform
+import sbi.analysis
+import sbi.inference
+import torch
+
+from .simulator import RuntimeSimulator, CPPStandaloneSimulator
+
+
+def setup_simulator():
+    """Return the configured simulator, which can be either
+    `RuntimeSimulator`, object for use with `RuntimeDevice`, or
+    `CPPStandaloneSimulator`, object for use with `CPPStandaloneDevice.
+
+    Parameters
+    ----------
+    None
+
+    Returns
+    -------
+    brian2modelfitting.simulator.Simulator
+        Either `RuntimeSimulator` or `CPPStandaloneSimulator` depending
+        on the currently active `Device` object describing the
+        available computational engine.
+    """
+    simulators = {'CPPStandaloneDevice': CPPStandaloneSimulator(),
+                  'RuntimeDevice': RuntimeSimulator()}
+    if isinstance(get_device(), CPPStandaloneDevice):
+        if device.has_been_run is True:
+            build_options = dict(device.build_options)
+            get_device().reinit()
+            get_device().activate(**build_options)
+    return simulators[get_device().__class__.__name__]
+
+
+def get_full_namespace(additional_namespace, level=0):
+    """Return the namespace with added `additional_namespace`, in which
+    references to external parameters or functions are stored.
+
+    Parameters
+    ----------
+    additional_namespace : dict
+        References to external parameters or functions, where key is
+        the name and value is the value of the external param/func.
+    level : int, optional
+        How far to go back to get the locals/globals.
+
+    Returns
+    -------
+    dict
+        Namespace with additional references to external parameters or
+        functions.
+    """
+    namespace = {key: value
+                 for key, value in get_local_namespace(level=level + 1).items()
+                 if isinstance(value, (Number, Quantity, Function))}
+    namespace.update(additional_namespace)
+    return namespace
+
+
+def get_param_dict(param_values, param_names, n_samples):
+    """Return a dictionary compiled of parameter names and values.
+
+    Parameters
+    ----------
+    param_values : iterable
+        Iterable of size (`n_samples`, `len(param_names)` containing
+        parameter values.
+    param_names : iterable
+        Iterable containing parameter names
+    n_samples : int
+        Total number of given values for a single parameter.
+
+    Returns
+    -------
+    dict
+        Dictionary containing key-value pairs thet correspond to a
+        parameter name and value(s)
+    """
+    param_values = np.array(param_values)
+    param_dict = dict()
+    for name, value in zip(param_names, param_values.T):
+        param_dict[name] = (np.ones((n_samples, )) * value)
+    return param_dict
+
+
+def calc_prior(param_names, **params):
+    """Return prior distribution over given parameters. Note that the
+    only available prior distribution currently supported is
+    multidimensional uniform distribution defined on a box.
+
+    Parameters
+    ----------
+    param_names : iterable
+        Iterable containing parameter names.
+    params : dict
+        Dictionary with keys that correspond to parameter names, and
+        values should be a single dimensional lists or arrays
+
+    Return
+    ------
+    sbi.utils.torchutils.BoxUniform
+        `sbi` compatible object that contains a uniform prior
+        distribution over a given set of parameter
+    """
+    for param_name in param_names:
+        if param_name not in params:
+            raise TypeError(f'"Bounds must be set for parameter {param_name}')
+    prior_min = []
+    prior_max = []
+    for param_name in param_names:
+        prior_min.append(min(params[param_name]).item())
+        prior_max.append(max(params[param_name]).item())
+    prior = BoxUniform(low=torch.as_tensor(prior_min),
+                       high=torch.as_tensor(prior_max))
+    return prior
+
+
+class Inferencer(object):
+    """Class for simulation-based inference.
+
+    It offers an interface similar to that of `Fitter` class but
+    instead of fitting, neural density estimator is trained using a
+    generative model. This class serves as a wrapper for `sbi` library
+    for inferencing posterior over unknown parameters of a given model.
+
+    Parameters
+    ----------
+    dt : brian2.units.fundamentalunits.Quantity
+        Integration time step.
+    model : str or brian2.equations.equations.Equations
+        Single cell model equations.
+    input : dict
+        Input traces in dictionary format, where key corresponds to the
+        name of the input variable as defined in `model` and value
+        corresponds to a single dimensional array of data traces.
+    output : dict
+        Dictionary of recorded (or simulated) output data traces, where
+        key corresponds to the name of the output variable as defined
+        in `model` and value corresponds to a single dimensional array
+        of recorded data traces.
+    method : str, optional
+        Integration method.
+    threshold : str, optional
+        The condition which produces spikes. Should be a single line
+        boolean expression.
+    reset : str, optional
+        The (possibly multi-line) string with the code to execute on
+        reset.
+    refractory : str, optional
+        Either the length of the refractory period (e.g., `2*ms`), a
+        string expression that evaluates to the length of the
+        refractory period after each spike (e.g., `'(1 + rand())*ms'`),
+        or a string expression evaluating to a boolean value, given the
+        condition under which the neuron stays refractory after a spike
+        (e.g., `'v > -20*mV'`).
+    param_init : dict, optional
+        Dictionary of state variables to be initialized with respective
+        values.
+    """
+    def __init__(self, dt, model, input, output, method=None, threshold=None,
+                 reset=None, refractory=None, param_init=None):
+        self.dt = dt
+        if isinstance(model, str):
+            self.model = Equations(model)
+        else:
+            raise TypeError('Equations must be appropriately formatted.')
+        if not isinstance(input, Mapping):
+            raise TypeError('`input` argument must be a dictionary mapping'
+                            ' the name of the input variable and `input`.')
+        if len(input) > 1:
+            raise NotImplementedError('Only a single input is supported.')
+        self.input_var = list(input.keys())[0]
+        self.input = input[self.input_var]
+        if not isinstance(output, Mapping):
+            raise TypeError('`output` argument must be a dictionary mapping'
+                            ' the name of the output variable and `output`')
+        if len(output) > 1:
+            raise NotImplementedError('Only a single output is supported')
+        self.output_var = list(output.keys())[0]
+        self.output = output[self.output_var]
+        input_dim = get_dimensions(self.input)
+        input_dim = '1' if input_dim is DIMENSIONLESS else repr(input_dim)
+        input_eqs = f'{self.input_var} = input_var(t) : {input_dim}'
+        self.model += input_eqs
+        self.input_traces = TimedArray(self.input.transpose(), dt=self.dt)
+        n_steps = self.input.size
+        self.sim_time = self.dt * n_steps
+        if not param_init:
+            param_init = {}
+        for param, val in param_init.items():
+            if not (param in self.model.diff_eq_names or
+                    param in self.model.parameter_names):
+                raise ValueError(f'{param} is not a model variable or a'
+                                 ' parameter in the model')
+        self.param_init = param_init
+        self.param_names = self.model.parameter_names
+        self.method = method
+        self.threshold = threshold
+        self.reset = reset
+        self.refractory = refractory
+
+    def setup_simulator(self, n_samples, output_var, param_init, level=1):
+        simulator = setup_simulator()
+        namespace = get_full_namespace({'input_var': self.input_traces},
+                                       level=level+1)
+        namespace['output_var'] = TimedArray(self.output.transpose(),
+                                             dt=self.dt)
+        kwargs = {}
+        if self.method is not None:
+            kwargs['method'] = self.method
+        model = (self.model
+                 + Equations('iteration : integer (constant, shared)'))
+        neurons = NeuronGroup(N=n_samples,
+                              model=model,
+                              threshold=self.threshold,
+                              reset=self.reset,
+                              refractory=self.refractory,
+                              dt=self.dt,
+                              namespace=namespace,
+                              name='neurons',
+                              **kwargs)
+        network = Network(neurons)
+        network.add(StateMonitor(source=neurons, variables=output_var,
+                                 record=True, dt=self.dt, name='statemonitor'))
+        simulator.initialize(network, param_init)
+        return simulator
+
+    def generate(self, n_samples, level=0, **params):
+        try:
+            n_samples = int(n_samples)
+        except ValueError as e:
+            print(e)
+        for param in params:
+            if param not in self.param_names:
+                raise ValueError(f'Parameter {param} must be defined as a'
+                                 ' model\'s parameter')
+        self.prior = calc_prior(self.param_names, **params)
+        self.theta = self.prior.sample((n_samples, ))
+        theta = np.atleast_2d(self.theta.numpy())
+        self.simulator = self.setup_simulator(n_samples=n_samples,
+                                              output_var=self.output_var,
+                                              param_init=self.param_init,
+                                              level=1)
+        d_param = get_param_dict(theta, self.param_names, n_samples)
+        self.simulator.run(self.sim_time, d_param, self.param_names, 0)
+
+    def _create_sum_stats(self, obs, obs_dim):
+        obs_mean = obs.mean(axis=0)
+        obs_std = obs.std(axis=0)
+        obs_ptp = obs.ptp(axis=0)
+        obs_mean = obs_mean / Quantity(np.ones_like(obs_mean), obs_dim)
+        obs_std = obs_std / Quantity(np.ones_like(obs_std), obs_dim)
+        obs_ptp = obs_ptp / Quantity(np.ones_like(obs_ptp), obs_dim)
+        return np.hstack((np.asarray(obs_mean).reshape(-1, 1),
+                          np.asarray(obs_std).reshape(-1, 1),
+                          np.asarray(obs_ptp).reshape(-1, 1)))
+
+    def train(self):
+        obs_val = self.simulator.statemonitor.recorded_variables
+        obs = obs_val[self.output_var].get_value_with_unit()
+        obs_dim = self.simulator.statemonitor.recorded_variables
+        dim = get_dimensions(obs_dim[self.output_var])
+        x = torch.tensor(self._create_sum_stats(obs, dim), dtype=torch.float32)
+        density_esimator = posterior_nn(model='maf')
+        self.inference = sbi.inference.SNPE(self.prior, density_esimator)
+        self.inference.append_simulations(self.theta, x).train()
+        self.posterior = self.inference.build_posterior()
+
+    def sample(self, size, viz=False, **kwargs):
+        dim = get_dimensions(self.output)
+        x_o = torch.tensor(self._create_sum_stats(self.output.ravel(), dim),
+                           dtype=torch.float32)
+        samples = self.posterior.sample((size, ), x_o)
+        if viz:
+            sbi.analysis.pairplot(samples, **kwargs)
+            plt.tight_layout()
+            plt.show()
+        return samples

From e8f91fd251765c56964dd1853692070bab3e56b7 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 17 Jun 2021 16:46:08 +0200
Subject: [PATCH 007/121] update gitignore with sbi-logs directory

---
 .gitignore | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/.gitignore b/.gitignore
index fe292b6..9b35b69 100644
--- a/.gitignore
+++ b/.gitignore
@@ -80,3 +80,6 @@ target/
 
 # Installation and auto-generated files
 brian2modelfitting.egg-info
+
+# sbi logs
+brian2modelfitting/examples/sbi-logs/

From b7afcddee5efc39a3f2c271194989d2ea142178e Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 18 Jun 2021 16:27:07 +0200
Subject: [PATCH 008/121] Fix the location of sbi_logs in gitignore

---
 .gitignore | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 9b35b69..6b47837 100644
--- a/.gitignore
+++ b/.gitignore
@@ -82,4 +82,4 @@ target/
 brian2modelfitting.egg-info
 
 # sbi logs
-brian2modelfitting/examples/sbi-logs/
+examples/sbi-logs/

From fc9a87ff3f1456e3d0f0d98f51209aaf55f1d937 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 18 Jun 2021 16:27:29 +0200
Subject: [PATCH 009/121] Add example for simulation-based inference

---
 examples/hh_sbi.py | 55 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 55 insertions(+)
 create mode 100644 examples/hh_sbi.py

diff --git a/examples/hh_sbi.py b/examples/hh_sbi.py
new file mode 100644
index 0000000..e070725
--- /dev/null
+++ b/examples/hh_sbi.py
@@ -0,0 +1,55 @@
+from brian2 import um, cm, ms, mV, amp, siemens, uF
+from brian2modelfitting import Inferencer
+import pandas as pd
+
+
+# Load input and output data traces
+df_inp_traces = pd.read_csv('input_traces_hh.csv')
+df_out_traces = pd.read_csv('output_traces_hh.csv')
+inp_traces = df_inp_traces.to_numpy()
+inp_traces = inp_traces[:3, 1:]
+out_traces = df_out_traces.to_numpy()
+out_traces = out_traces[:3, 1:]
+
+# Model and its parameters
+area = 20_000 * um ** 2
+El = -65 * mV
+EK = -90 * mV
+ENa = 50 * mV
+VT = -63 * mV
+dt = 0.01 * ms
+eqs = '''
+    dv/dt = (gl*(El-v) - g_na*(m*m*m)*h*(v-ENa) - g_kd*(n*n*n*n)*(v-EK) + I)/Cm : volt
+    dm/dt = 0.32*(mV**-1)*(13.*mV-v+VT)/
+        (exp((13.*mV-v+VT)/(4.*mV))-1.)/ms*(1-m)-0.28*(mV**-1)*(v-VT-40.*mV)/
+        (exp((v-VT-40.*mV)/(5.*mV))-1.)/ms*m : 1
+    dn/dt = 0.032*(mV**-1)*(15.*mV-v+VT)/
+        (exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1
+    dh/dt = 0.128*exp((17.*mV-v+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-v+VT)/(5.*mV)))/ms*h : 1
+    g_na : siemens (constant)
+    g_kd : siemens (constant)
+    gl   : siemens (constant)
+    Cm   : farad (constant)
+'''
+
+# Simulation-based inference object instantiation
+inferencer = Inferencer(dt=dt, model=eqs,
+                        input={'I': inp_traces * amp},
+                        output={'v': out_traces * mV},
+                        method='exponential_euler',
+                        threshold='m > 0.5',
+                        refractory='m > 0.5',
+                        param_init={'v': 'VT'})
+
+# Generate prior and train the neural density estimator
+inferencer.train(n_samples=100,
+                 gl=[1e-09 * siemens, 1e-07 * siemens],
+                 g_na=[2e-06 * siemens, 2e-04 * siemens],
+                 g_kd=[6e-07 * siemens, 6e-05 * siemens],
+                 Cm=[0.1 * uF * cm ** -2 * area, 2 * uF * cm ** -2 * area])
+
+# # Draw samples from posterior and visualize the results
+# labels_params = [r'$\overline{g}_{l}$', r'$\overline{g}_{Na}$',
+#                  r'$\overline{g}_{K}$', r'$\overline{C}_{m}$']
+# samples = inferencer.sample(1000, viz=True,
+#                             labels=labels_params, figsize=(10, 10))

From c72c0d2995f464c27de293f7fa182a69e7f7275e Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 18 Jun 2021 16:28:01 +0200
Subject: [PATCH 010/121] [WIP] Add multi-trace support

---
 brian2modelfitting/inferencer.py | 277 +++++++++++++++++++++++++------
 1 file changed, 228 insertions(+), 49 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index bf46c4c..5860ab4 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -9,10 +9,11 @@
 from brian2.equations.equations import Equations
 from brian2.groups.neurongroup import NeuronGroup
 from brian2.input.timedarray import TimedArray
-from brian2.monitors.spikemonitor import SpikeMonitor
 from brian2.monitors.statemonitor import StateMonitor
 from brian2.units.allunits import *  # all physical units
-from brian2.units.fundamentalunits import (DIMENSIONLESS, get_dimensions,
+from brian2.units.fundamentalunits import (DIMENSIONLESS,
+                                           fail_for_dimension_mismatch,
+                                           get_dimensions,
                                            Quantity)
 import matplotlib.pyplot as plt
 import numpy as np
@@ -25,7 +26,7 @@
 from .simulator import RuntimeSimulator, CPPStandaloneSimulator
 
 
-def setup_simulator():
+def configure_simulator():
     """Return the configured simulator, which can be either
     `RuntimeSimulator`, object for use with `RuntimeDevice`, or
     `CPPStandaloneSimulator`, object for use with `CPPStandaloneDevice.
@@ -76,7 +77,7 @@ def get_full_namespace(additional_namespace, level=0):
     return namespace
 
 
-def get_param_dict(param_values, param_names, n_samples):
+def get_param_dict(param_values, param_names, n_values):
     """Return a dictionary compiled of parameter names and values.
 
     Parameters
@@ -86,7 +87,7 @@ def get_param_dict(param_values, param_names, n_samples):
         parameter values.
     param_names : iterable
         Iterable containing parameter names
-    n_samples : int
+    n_values : int
         Total number of given values for a single parameter.
 
     Returns
@@ -98,12 +99,12 @@ def get_param_dict(param_values, param_names, n_samples):
     param_values = np.array(param_values)
     param_dict = dict()
     for name, value in zip(param_names, param_values.T):
-        param_dict[name] = (np.ones((n_samples, )) * value)
+        param_dict[name] = (np.ones((n_values, )) * value)
     return param_dict
 
 
 def calc_prior(param_names, **params):
-    """Return prior distribution over given parameters. Note that the
+    """Return prior distributparion over given parameters. Note that the
     only available prior distribution currently supported is
     multidimensional uniform distribution defined on a box.
 
@@ -178,58 +179,160 @@ class Inferencer(object):
     """
     def __init__(self, dt, model, input, output, method=None, threshold=None,
                  reset=None, refractory=None, param_init=None):
+        # time scale
         self.dt = dt
+
+        # model equations
         if isinstance(model, str):
-            self.model = Equations(model)
+            model = Equations(model)
         else:
             raise TypeError('Equations must be appropriately formatted.')
+
+        # input data traces
         if not isinstance(input, Mapping):
             raise TypeError('`input` argument must be a dictionary mapping'
                             ' the name of the input variable and `input`.')
         if len(input) > 1:
             raise NotImplementedError('Only a single input is supported.')
-        self.input_var = list(input.keys())[0]
-        self.input = input[self.input_var]
+        input_var = list(input.keys())[0]
+        input = input[input_var]
+        if input_var not in model.identifiers:
+            raise NameError(f'{input_var} is not an identifier in the model.')
+
+        # output data traces
         if not isinstance(output, Mapping):
             raise TypeError('`output` argument must be a dictionary mapping'
                             ' the name of the output variable and `output`')
-        if len(output) > 1:
-            raise NotImplementedError('Only a single output is supported')
-        self.output_var = list(output.keys())[0]
-        self.output = output[self.output_var]
-        input_dim = get_dimensions(self.input)
+        output_var = list(output.keys())
+        output = list(output.values())
+        for o_var in output_var:
+            if o_var not in model.names:
+                raise NameError(f'{o_var} is not a model variable')
+        self.output_var = output_var
+        self.output = output
+
+        # create variable for parameter names
+        self.param_names = model.parameter_names
+
+        # set the simulation time for a given time scale
+        self.n_traces, n_steps = input.shape
+        self.sim_time = dt * n_steps
+
+        # handle multiple output variables
+        self.output_dim = []
+        for o_var, out in zip(self.output_var, self.output):
+            self.output_dim.append(model[o_var].dim)
+            fail_for_dimension_mismatch(out, self.output_dim[-1],
+                                        'The provided target values must have'
+                                        ' the same units as the variable'
+                                        f' {o_var}')
+
+        # add input to equations
+        self.model = model
+        input_dim = get_dimensions(input)
         input_dim = '1' if input_dim is DIMENSIONLESS else repr(input_dim)
-        input_eqs = f'{self.input_var} = input_var(t) : {input_dim}'
+        input_eqs = f'{input_var} = input_var(t, i % n_traces) : {input_dim}'
         self.model += input_eqs
-        self.input_traces = TimedArray(self.input.transpose(), dt=self.dt)
-        n_steps = self.input.size
-        self.sim_time = self.dt * n_steps
+
+        # add output to equations
+        counter = 0
+        for o_var, o_dim in zip(self.output_var, self.output_dim):
+            counter += 1
+            output_expr = f'output_var_{counter}(t, i % n_traces)'
+            output_dim = ('1' if o_dim is DIMENSIONLESS else repr(o_dim))
+            output_eqs = f'{o_var}_target = {output_expr} : {output_dim}'
+            self.model += output_eqs
+
+        # create the `TimedArray` object for input w.r.t. a given time scale
+        self.input_traces = TimedArray(input.transpose(), dt=self.dt)
+
+        # handle initial values for the ODE system
         if not param_init:
             param_init = {}
-        for param, val in param_init.items():
+        for param in param_init.keys():
             if not (param in self.model.diff_eq_names or
                     param in self.model.parameter_names):
                 raise ValueError(f'{param} is not a model variable or a'
                                  ' parameter in the model')
         self.param_init = param_init
-        self.param_names = self.model.parameter_names
+
+        # handle the rest of optional parameters for the `NeuronGroup` class
         self.method = method
         self.threshold = threshold
         self.reset = reset
         self.refractory = refractory
 
-    def setup_simulator(self, n_samples, output_var, param_init, level=1):
-        simulator = setup_simulator()
-        namespace = get_full_namespace({'input_var': self.input_traces},
+        # placeholder for the number of samples
+        self.n_samples = None
+
+    @property
+    def n_neurons(self):
+        """Return the number of neurons that are used in `NeuronGroup`
+        class while generating data for training the neural density
+        estimator.
+
+        Unlike the `Fitter` class, `Inferencer` does not take the total
+        number of samples in the constructor. Thus, this property
+        becomes available only after the simulation is performed.
+
+        Parameters
+        ----------
+        None
+
+        Returns
+        -------
+        int
+            The total number of neurons.
+        """
+        if self.n_samples is None:
+            raise ValueError('Number of samples is not yet defined.'
+                             'Call `initialize_prior` method first.')
+        return self.n_traces * self.n_samples
+
+    def setup_simulator(self, network_name, n_neurons, output_var, param_init,
+                        level=1):
+        """Return configured simulator.
+
+        Parameters
+        ----------
+        network_name : str
+            Network name.
+        n_neurons : int
+            Number of neurons which equals to the number of samples
+            times the number of input/output traces.
+        output_var : str
+            Name of the output variable.
+        param_init : dict
+            Dictionary of state variables to be initialized with
+            respective values.
+        level : int, optional
+            How far to go back to get the locals/globals.
+
+        Returns
+        -------
+        brian2modelfitting.simulator.Simulator
+            Configured simulator w.r.t. to the available device.
+        """
+        # configure the simulator
+        simulator = configure_simulator()
+
+        # update the local namespace
+        namespace = get_full_namespace({'input_var': self.input_traces,
+                                        'n_traces': self.n_traces},
                                        level=level+1)
-        namespace['output_var'] = TimedArray(self.output.transpose(),
-                                             dt=self.dt)
-        kwargs = {}
+        counter = 0
+        for out in self.output:
+            counter += 1
+            namespace[f'output_var_{counter}'] = TimedArray(out.transpose(),
+                                                            dt=self.dt)
+
+        # setup neuron group
+        kwds = {}
         if self.method is not None:
-            kwargs['method'] = self.method
+            kwds['method'] = self.method
         model = (self.model
                  + Equations('iteration : integer (constant, shared)'))
-        neurons = NeuronGroup(N=n_samples,
+        neurons = NeuronGroup(N=n_neurons,
                               model=model,
                               threshold=self.threshold,
                               reset=self.reset,
@@ -237,16 +340,35 @@ def setup_simulator(self, n_samples, output_var, param_init, level=1):
                               dt=self.dt,
                               namespace=namespace,
                               name='neurons',
-                              **kwargs)
+                              **kwds)
         network = Network(neurons)
         network.add(StateMonitor(source=neurons, variables=output_var,
                                  record=True, dt=self.dt, name='statemonitor'))
-        simulator.initialize(network, param_init)
+        
+        # initialize the simulator
+        simulator.initialize(network, param_init, name=network_name)
         return simulator
 
-    def generate(self, n_samples, level=0, **params):
+    def initialize_prior(self, n_samples, **params):
+        """Return the dictionary in which the keys correspond to the
+        unknown parameter names and values to their respective
+        uniformly sampled values.
+
+        Parameters
+        ----------
+        n_samples : int
+            The number of samples.
+        params : dict
+            Bounds for each parameter.
+
+        Returns
+        -------
+        dict
+            Dictionary with the parameter names and uniformly
+            distributed prior distribution.
+        """
         try:
-            n_samples = int(n_samples)
+            self.n_samples = int(n_samples)
         except ValueError as e:
             print(e)
         for param in params:
@@ -256,17 +378,43 @@ def generate(self, n_samples, level=0, **params):
         self.prior = calc_prior(self.param_names, **params)
         self.theta = self.prior.sample((n_samples, ))
         theta = np.atleast_2d(self.theta.numpy())
-        self.simulator = self.setup_simulator(n_samples=n_samples,
-                                              output_var=self.output_var,
-                                              param_init=self.param_init,
-                                              level=1)
-        d_param = get_param_dict(theta, self.param_names, n_samples)
-        self.simulator.run(self.sim_time, d_param, self.param_names, 0)
+        # theta = np.vstack(tuple([theta for _ in range(self.n_traces)]))
+        theta = np.repeat(theta, repeats=self.n_traces, axis=0)
+        return get_param_dict(theta, self.param_names, self.n_neurons)
+
+    def generate_training_data(self, n_samples, level=0, **params):
+        """Return executed simulator containing recorded variables to
+        be used for training the neural density estimator.
+
+        Parameter
+        ---------
+        n_samples : int
+            The number of samples.
+        level : int, optional
+            How far to go back to get the locals/globals.
+        params : dict
+            Bounds for each parameter.
+
+        Returns
+        -------
+        brian2modelfitting.simulator.Simulator
+            Executed simulator.
+        """
+        network_name = 'infere'
+        d_param = self.initialize_prior(n_samples, **params)
+        simulator = self.setup_simulator(network_name=network_name,
+                                         n_neurons=self.n_neurons,
+                                         output_var=self.output_var,
+                                         param_init=self.param_init,
+                                         level=level+1)
+        simulator.run(self.sim_time, d_param, self.param_names, iteration=0,
+                      name=network_name)
+        return simulator
 
     def _create_sum_stats(self, obs, obs_dim):
-        obs_mean = obs.mean(axis=0)
-        obs_std = obs.std(axis=0)
-        obs_ptp = obs.ptp(axis=0)
+        obs_mean = obs.mean(axis=1)
+        obs_std = obs.std(axis=1)
+        obs_ptp = obs.ptp(axis=1)
         obs_mean = obs_mean / Quantity(np.ones_like(obs_mean), obs_dim)
         obs_std = obs_std / Quantity(np.ones_like(obs_std), obs_dim)
         obs_ptp = obs_ptp / Quantity(np.ones_like(obs_ptp), obs_dim)
@@ -274,16 +422,47 @@ def _create_sum_stats(self, obs, obs_dim):
                           np.asarray(obs_std).reshape(-1, 1),
                           np.asarray(obs_ptp).reshape(-1, 1)))
 
-    def train(self):
-        obs_val = self.simulator.statemonitor.recorded_variables
+    def train(self, n_samples, n_rounds=1, **params):
+        """Return the trained neural density estimator.
+
+        Currently only sequential neural posterior estimator is
+        supported.
+
+        Parameter
+        ---------
+        n_samples : int
+            The number of samples.
+        n_round : int or str, optional
+            If `n_rounds`is set to 1, amortized inference will be
+            performed. Otherwise, if `n_rounds` is integer larger than
+            1, multi-round inference will be performed.
+        params : dict
+            Bounds for each parameter.
+
+        Returns
+        -------
+        sbi.inference.posteriors.direct_posterior.DirectPosterior
+            Trained posterior.
+        """
+        if not isinstance(n_rounds, int):
+            raise ValueError('Number of rounds must be a positive integer.')
+        if n_rounds != 1:
+            raise NotImplementedError('Only amortized inference is supported.')
+        simulator = self.generate_training_data(n_samples, **params)
+        obs_val = simulator.statemonitor.recorded_variables
         obs = obs_val[self.output_var].get_value_with_unit()
-        obs_dim = self.simulator.statemonitor.recorded_variables
+        obs_dim = simulator.statemonitor.recorded_variables
         dim = get_dimensions(obs_dim[self.output_var])
-        x = torch.tensor(self._create_sum_stats(obs, dim), dtype=torch.float32)
+        x = torch.tensor(self._create_sum_stats(obs, dim),
+                            dtype=torch.float32)
+        x = x.reshape(self.n_samples, self.n_traces)
         density_esimator = posterior_nn(model='maf')
-        self.inference = sbi.inference.SNPE(self.prior, density_esimator)
-        self.inference.append_simulations(self.theta, x).train()
-        self.posterior = self.inference.build_posterior()
+        inference = sbi.inference.SNPE(proposal, density_esimator)
+        inference.append_simulations(self.theta, x, proposal).train()
+        posterior = self.inference.build_posterior()
+        posteriors.append(posterior)
+        self.posterior = posterior
+        return posterior
 
     def sample(self, size, viz=False, **kwargs):
         dim = get_dimensions(self.output)

From 2346e0124ebe65eb6df37f3856e5f973c675a041 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 19 Jun 2021 11:49:58 +0200
Subject: [PATCH 011/121] Further progress of train method

---
 examples/hh_sbi.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/examples/hh_sbi.py b/examples/hh_sbi.py
index e070725..33539c5 100644
--- a/examples/hh_sbi.py
+++ b/examples/hh_sbi.py
@@ -43,13 +43,15 @@
 
 # Generate prior and train the neural density estimator
 inferencer.train(n_samples=100,
+                 n_rounds=2,
+                 density_estimator_model='made',
                  gl=[1e-09 * siemens, 1e-07 * siemens],
                  g_na=[2e-06 * siemens, 2e-04 * siemens],
                  g_kd=[6e-07 * siemens, 6e-05 * siemens],
                  Cm=[0.1 * uF * cm ** -2 * area, 2 * uF * cm ** -2 * area])
 
-# # Draw samples from posterior and visualize the results
-# labels_params = [r'$\overline{g}_{l}$', r'$\overline{g}_{Na}$',
-#                  r'$\overline{g}_{K}$', r'$\overline{C}_{m}$']
-# samples = inferencer.sample(1000, viz=True,
-#                             labels=labels_params, figsize=(10, 10))
+# Draw samples from posterior and visualize the results
+labels_params = [r'$\overline{g}_{l}$', r'$\overline{g}_{Na}$',
+                 r'$\overline{g}_{K}$', r'$\overline{C}_{m}$']
+samples = inferencer.sample(1000, viz=True,
+                            labels=labels_params, figsize=(10, 10))

From 5d0917d1b7db664f45117b55994160e099c184d8 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 19 Jun 2021 11:50:38 +0200
Subject: [PATCH 012/121] add multi-round inference support

---
 brian2modelfitting/inferencer.py | 156 +++++++++++++++++++------------
 1 file changed, 95 insertions(+), 61 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 5860ab4..0294135 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -282,11 +282,11 @@ class while generating data for training the neural density
         Returns
         -------
         int
-            The total number of neurons.
+            Total number of neurons.
         """
         if self.n_samples is None:
             raise ValueError('Number of samples is not yet defined.'
-                             'Call `initialize_prior` method first.')
+                             'Call `generate_training_data` method first.')
         return self.n_traces * self.n_samples
 
     def setup_simulator(self, network_name, n_neurons, output_var, param_init,
@@ -344,64 +344,67 @@ def setup_simulator(self, network_name, n_neurons, output_var, param_init,
         network = Network(neurons)
         network.add(StateMonitor(source=neurons, variables=output_var,
                                  record=True, dt=self.dt, name='statemonitor'))
-        
+
         # initialize the simulator
         simulator.initialize(network, param_init, name=network_name)
         return simulator
 
-    def initialize_prior(self, n_samples, **params):
-        """Return the dictionary in which the keys correspond to the
-        unknown parameter names and values to their respective
-        uniformly sampled values.
+    def initialize_prior(self, **params):
+        """Return the prior uniform distribution over parameters.
 
         Parameters
         ----------
-        n_samples : int
-            The number of samples.
         params : dict
             Bounds for each parameter.
 
         Returns
         -------
-        dict
-            Dictionary with the parameter names and uniformly
-            distributed prior distribution.
+        sbi.utils.BoxUniform
+            Uniformly distributed prior over given parameters.
         """
-        try:
-            self.n_samples = int(n_samples)
-        except ValueError as e:
-            print(e)
         for param in params:
             if param not in self.param_names:
                 raise ValueError(f'Parameter {param} must be defined as a'
                                  ' model\'s parameter')
-        self.prior = calc_prior(self.param_names, **params)
-        self.theta = self.prior.sample((n_samples, ))
-        theta = np.atleast_2d(self.theta.numpy())
-        # theta = np.vstack(tuple([theta for _ in range(self.n_traces)]))
-        theta = np.repeat(theta, repeats=self.n_traces, axis=0)
-        return get_param_dict(theta, self.param_names, self.n_neurons)
+        prior = calc_prior(self.param_names, **params)
+        return prior
 
-    def generate_training_data(self, n_samples, level=0, **params):
-        """Return executed simulator containing recorded variables to
-        be used for training the neural density estimator.
+    def generate_training_data(self, n_samples, prior, level=1):
+        """Return sampled prior and executed simulator containing
+        recorded variables to be used for training the neural density
+        estimator.
 
         Parameter
         ---------
         n_samples : int
             The number of samples.
+        prior : sbi.utils.BoxUniform
+            Uniformly distributed prior over given parameters.
         level : int, optional
             How far to go back to get the locals/globals.
-        params : dict
-            Bounds for each parameter.
 
         Returns
         -------
+        numpy.ndarray
+            Sampled prior of shape (`n_samples`, -1)
         brian2modelfitting.simulator.Simulator
             Executed simulator.
         """
+        # set n_samples to class variable to be able to call self.n_neurons
+        self.n_samples = n_samples
+
+        # sample from prior
+        theta = prior.sample((n_samples, ))
+        theta = np.atleast_2d(theta.numpy())
+
+        # repeat each row for how many input/output different trace are there
+        _theta = np.repeat(theta, repeats=self.n_traces, axis=0)
+
+        # create a dictionary with repeated sampled prior
+        d_param = get_param_dict(_theta, self.param_names, self.n_neurons)
+
+        # setup and run the simulator
         network_name = 'infere'
-        d_param = self.initialize_prior(n_samples, **params)
         simulator = self.setup_simulator(network_name=network_name,
                                          n_neurons=self.n_neurons,
                                          output_var=self.output_var,
@@ -409,20 +412,10 @@ def generate_training_data(self, n_samples, level=0, **params):
                                          level=level+1)
         simulator.run(self.sim_time, d_param, self.param_names, iteration=0,
                       name=network_name)
-        return simulator
-
-    def _create_sum_stats(self, obs, obs_dim):
-        obs_mean = obs.mean(axis=1)
-        obs_std = obs.std(axis=1)
-        obs_ptp = obs.ptp(axis=1)
-        obs_mean = obs_mean / Quantity(np.ones_like(obs_mean), obs_dim)
-        obs_std = obs_std / Quantity(np.ones_like(obs_std), obs_dim)
-        obs_ptp = obs_ptp / Quantity(np.ones_like(obs_ptp), obs_dim)
-        return np.hstack((np.asarray(obs_mean).reshape(-1, 1),
-                          np.asarray(obs_std).reshape(-1, 1),
-                          np.asarray(obs_ptp).reshape(-1, 1)))
+        return (theta, simulator)
 
-    def train(self, n_samples, n_rounds=1, **params):
+    def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
+              density_estimator_model='maf', **params):
         """Return the trained neural density estimator.
 
         Currently only sequential neural posterior estimator is
@@ -432,10 +425,16 @@ def train(self, n_samples, n_rounds=1, **params):
         ---------
         n_samples : int
             The number of samples.
-        n_round : int or str, optional
+        n_rounds : int or str, optional
             If `n_rounds`is set to 1, amortized inference will be
             performed. Otherwise, if `n_rounds` is integer larger than
             1, multi-round inference will be performed.
+        estimation_method : str
+            Inference method. Either of SNPE, SNLE or SNRE. Currently,
+            only SNPE is supported.
+        density_estimator_model : str
+            The type of density estimator to be created. Either `mdn`,
+            `made`, `maf` or `nsf`.
         params : dict
             Bounds for each parameter.
 
@@ -446,29 +445,64 @@ def train(self, n_samples, n_rounds=1, **params):
         """
         if not isinstance(n_rounds, int):
             raise ValueError('Number of rounds must be a positive integer.')
-        if n_rounds != 1:
-            raise NotImplementedError('Only amortized inference is supported.')
-        simulator = self.generate_training_data(n_samples, **params)
-        obs_val = simulator.statemonitor.recorded_variables
-        obs = obs_val[self.output_var].get_value_with_unit()
-        obs_dim = simulator.statemonitor.recorded_variables
-        dim = get_dimensions(obs_dim[self.output_var])
-        x = torch.tensor(self._create_sum_stats(obs, dim),
-                            dtype=torch.float32)
-        x = x.reshape(self.n_samples, self.n_traces)
-        density_esimator = posterior_nn(model='maf')
-        inference = sbi.inference.SNPE(proposal, density_esimator)
-        inference.append_simulations(self.theta, x, proposal).train()
-        posterior = self.inference.build_posterior()
-        posteriors.append(posterior)
+        if str.upper(estimation_method) != 'SNPE':
+            raise NotImplementedError('Only SNPE estimator is supported.')
+
+        # observation the focus is on
+        x_o = []
+        for o in self.output:
+            o_dim = get_dimensions(o)
+            o_obs = self.extract_features(o.transpose(), o_dim)
+            x_o.append(o_obs.flatten())
+        x_o = torch.tensor(x_o, dtype=torch.float32)
+        self.x_o = x_o
+
+        # initialize prior, density estimator and inference method
+        prior = self.initialize_prior(**params)
+        density_esimator = posterior_nn(density_estimator_model)
+        inference = sbi.inference.SNPE(prior, density_esimator)
+
+        # allocate empty list of posteriors, for multi-round inference only
+        posteriors = []
+        proposal = prior
+        for _ in range(n_rounds):
+            # extract the data and make adjustments for `sbi`
+            theta, simulator = self.generate_training_data(n_samples, proposal)
+            theta = torch.tensor(theta, dtype=torch.float32)
+            obs = simulator.statemonitor.recorded_variables
+            x_val = obs[self.output_var[0]].get_value_with_unit()
+            x_dim = get_dimensions(obs[self.output_var[0]])
+            x = torch.tensor(self.extract_features(x_val, x_dim),
+                             dtype=torch.float32)
+            x = x.reshape(self.n_samples, -1)
+
+            # pass the simulated data to the inference object and train it
+            de = inference.append_simulations(theta, x, proposal).train()
+
+            # use the density estimator to build the posterior
+            posterior = inference.build_posterior(de)
+
+            # append the current posterior to the list of posteriors
+            posteriors.append(posterior)
+
+            # update the proposal given the observation
+            proposal = posterior.set_default_x(x_o)
         self.posterior = posterior
         return posterior
 
+    def extract_features(self, obs, obs_dim):
+        obs_mean = obs.mean(axis=0)
+        obs_std = obs.std(axis=0)
+        obs_ptp = obs.ptp(axis=0)
+        obs_mean = obs_mean / Quantity(np.ones_like(obs_mean), obs_dim)
+        obs_std = obs_std / Quantity(np.ones_like(obs_std), obs_dim)
+        obs_ptp = obs_ptp / Quantity(np.ones_like(obs_ptp), obs_dim)
+        return np.hstack((np.array(obs_mean).reshape(-1, 1),
+                          np.array(obs_std).reshape(-1, 1),
+                          np.array(obs_ptp).reshape(-1, 1)))
+
     def sample(self, size, viz=False, **kwargs):
-        dim = get_dimensions(self.output)
-        x_o = torch.tensor(self._create_sum_stats(self.output.ravel(), dim),
-                           dtype=torch.float32)
-        samples = self.posterior.sample((size, ), x_o)
+        samples = self.posterior.sample((size, ))
         if viz:
             sbi.analysis.pairplot(samples, **kwargs)
             plt.tight_layout()

From b57b0e235f069a747efd7ac33e9eff2a261f9942 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 19 Jun 2021 12:53:08 +0200
Subject: [PATCH 013/121] add support for multiple output variables

---
 brian2modelfitting/inferencer.py | 11 +++++++----
 1 file changed, 7 insertions(+), 4 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 0294135..38ddb36 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -470,10 +470,13 @@ def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
             theta, simulator = self.generate_training_data(n_samples, proposal)
             theta = torch.tensor(theta, dtype=torch.float32)
             obs = simulator.statemonitor.recorded_variables
-            x_val = obs[self.output_var[0]].get_value_with_unit()
-            x_dim = get_dimensions(obs[self.output_var[0]])
-            x = torch.tensor(self.extract_features(x_val, x_dim),
-                             dtype=torch.float32)
+            x = []
+            for ov in self.output_var:
+                x_val = obs[ov].get_value_with_unit()
+                x_dim = get_dimensions(obs[ov])
+                features = self.extract_features(x_val, x_dim)
+                x.append(features)
+            x = torch.tensor(x, dtype=torch.float32)
             x = x.reshape(self.n_samples, -1)
 
             # pass the simulated data to the inference object and train it

From 6550855e06b5776776c466432dd5faa7655a8559 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 24 Jun 2021 09:05:46 +0200
Subject: [PATCH 014/121] use tqdm.auto.trange instead of tqdm.trange

---
 brian2modelfitting/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/brian2modelfitting/utils.py b/brian2modelfitting/utils.py
index 4b46a31..534c696 100644
--- a/brian2modelfitting/utils.py
+++ b/brian2modelfitting/utils.py
@@ -2,7 +2,7 @@
 
 from brian2 import have_same_dimensions
 from brian2.units.fundamentalunits import Quantity
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 
 def _format_quantity(v, precision=3):

From 620aa2a2390b45f9784488ad458376c2a5f4d5de Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 24 Jun 2021 09:17:36 +0200
Subject: [PATCH 015/121] add double backticks instead of single backticks in
 docstrings

---
 brian2modelfitting/inferencer.py | 74 ++++++++++++++++----------------
 1 file changed, 38 insertions(+), 36 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 38ddb36..7344523 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -28,8 +28,9 @@
 
 def configure_simulator():
     """Return the configured simulator, which can be either
-    `RuntimeSimulator`, object for use with `RuntimeDevice`, or
-    `CPPStandaloneSimulator`, object for use with `CPPStandaloneDevice.
+    ``RuntimeSimulator``, object for use with ``RuntimeDevice``, or
+    ``CPPStandaloneSimulator``, object for use with
+    ``CPPStandaloneDevice``.
 
     Parameters
     ----------
@@ -38,9 +39,9 @@ def configure_simulator():
     Returns
     -------
     brian2modelfitting.simulator.Simulator
-        Either `RuntimeSimulator` or `CPPStandaloneSimulator` depending
-        on the currently active `Device` object describing the
-        available computational engine.
+        Either ``RuntimeSimulator`` or ``CPPStandaloneSimulator``
+        depending on the currently active ``Device`` object describing
+        the available computational engine.
     """
     simulators = {'CPPStandaloneDevice': CPPStandaloneSimulator(),
                   'RuntimeDevice': RuntimeSimulator()}
@@ -53,8 +54,8 @@ def configure_simulator():
 
 
 def get_full_namespace(additional_namespace, level=0):
-    """Return the namespace with added `additional_namespace`, in which
-    references to external parameters or functions are stored.
+    """Return the namespace with added ``additional_namespace``, in
+    which references to external parameters or functions are stored.
 
     Parameters
     ----------
@@ -83,8 +84,8 @@ def get_param_dict(param_values, param_names, n_values):
     Parameters
     ----------
     param_values : iterable
-        Iterable of size (`n_samples`, `len(param_names)` containing
-        parameter values.
+        Iterable of size (``n_samples``, ``len(param_names)``
+        containing parameter values.
     param_names : iterable
         Iterable containing parameter names
     n_values : int
@@ -119,7 +120,7 @@ def calc_prior(param_names, **params):
     Return
     ------
     sbi.utils.torchutils.BoxUniform
-        `sbi` compatible object that contains a uniform prior
+        ``sbi`` compatible object that contains a uniform prior
         distribution over a given set of parameter
     """
     for param_name in param_names:
@@ -138,10 +139,11 @@ def calc_prior(param_names, **params):
 class Inferencer(object):
     """Class for simulation-based inference.
 
-    It offers an interface similar to that of `Fitter` class but
+    It offers an interface similar to that of ``Fitter`` class but
     instead of fitting, neural density estimator is trained using a
-    generative model. This class serves as a wrapper for `sbi` library
-    for inferencing posterior over unknown parameters of a given model.
+    generative model. This class serves as a wrapper for ``sbi``
+    library for inferencing posterior over unknown parameters of a
+    given model.
 
     Parameters
     ----------
@@ -151,13 +153,13 @@ class Inferencer(object):
         Single cell model equations.
     input : dict
         Input traces in dictionary format, where key corresponds to the
-        name of the input variable as defined in `model` and value
+        name of the input variable as defined in ``model`` and value
         corresponds to a single dimensional array of data traces.
     output : dict
         Dictionary of recorded (or simulated) output data traces, where
         key corresponds to the name of the output variable as defined
-        in `model` and value corresponds to a single dimensional array
-        of recorded data traces.
+        in ``model`` and value corresponds to a single dimensional
+        array of recorded data traces.
     method : str, optional
         Integration method.
     threshold : str, optional
@@ -167,12 +169,12 @@ class Inferencer(object):
         The (possibly multi-line) string with the code to execute on
         reset.
     refractory : str, optional
-        Either the length of the refractory period (e.g., `2*ms`), a
+        Either the length of the refractory period (e.g., ``2*ms``), a
         string expression that evaluates to the length of the
-        refractory period after each spike (e.g., `'(1 + rand())*ms'`),
+        refractory period after each spike, e.g., ``'(1 + rand())*ms'``,
         or a string expression evaluating to a boolean value, given the
-        condition under which the neuron stays refractory after a spike
-        (e.g., `'v > -20*mV'`).
+        condition under which the neuron stays refractory after a spike,
+        e.g., ``'v > -20*mV'``.
     param_init : dict, optional
         Dictionary of state variables to be initialized with respective
         values.
@@ -190,8 +192,8 @@ def __init__(self, dt, model, input, output, method=None, threshold=None,
 
         # input data traces
         if not isinstance(input, Mapping):
-            raise TypeError('`input` argument must be a dictionary mapping'
-                            ' the name of the input variable and `input`.')
+            raise TypeError('``input`` argument must be a dictionary mapping'
+                            ' the name of the input variable and ``input``.')
         if len(input) > 1:
             raise NotImplementedError('Only a single input is supported.')
         input_var = list(input.keys())[0]
@@ -201,8 +203,8 @@ def __init__(self, dt, model, input, output, method=None, threshold=None,
 
         # output data traces
         if not isinstance(output, Mapping):
-            raise TypeError('`output` argument must be a dictionary mapping'
-                            ' the name of the output variable and `output`')
+            raise TypeError('``output`` argument must be a dictionary mapping'
+                            ' the name of the output variable and ``output``')
         output_var = list(output.keys())
         output = list(output.values())
         for o_var in output_var:
@@ -243,7 +245,7 @@ def __init__(self, dt, model, input, output, method=None, threshold=None,
             output_eqs = f'{o_var}_target = {output_expr} : {output_dim}'
             self.model += output_eqs
 
-        # create the `TimedArray` object for input w.r.t. a given time scale
+        # create ``TimedArray`` object for input w.r.t. a given time scale
         self.input_traces = TimedArray(input.transpose(), dt=self.dt)
 
         # handle initial values for the ODE system
@@ -256,7 +258,7 @@ def __init__(self, dt, model, input, output, method=None, threshold=None,
                                  ' parameter in the model')
         self.param_init = param_init
 
-        # handle the rest of optional parameters for the `NeuronGroup` class
+        # handle the rest of optional parameters for the ``NeuronGroup`` class
         self.method = method
         self.threshold = threshold
         self.reset = reset
@@ -267,12 +269,12 @@ def __init__(self, dt, model, input, output, method=None, threshold=None,
 
     @property
     def n_neurons(self):
-        """Return the number of neurons that are used in `NeuronGroup`
+        """Return the number of neurons that are used in ``NeuronGroup``
         class while generating data for training the neural density
         estimator.
 
-        Unlike the `Fitter` class, `Inferencer` does not take the total
-        number of samples in the constructor. Thus, this property
+        Unlike the ``Fitter`` class, ``Inferencer`` does not take the
+        total number of samples in the constructor. Thus, this property
         becomes available only after the simulation is performed.
 
         Parameters
@@ -286,7 +288,7 @@ class while generating data for training the neural density
         """
         if self.n_samples is None:
             raise ValueError('Number of samples is not yet defined.'
-                             'Call `generate_training_data` method first.')
+                             'Call ``generate_training_data`` method first.')
         return self.n_traces * self.n_samples
 
     def setup_simulator(self, network_name, n_neurons, output_var, param_init,
@@ -386,7 +388,7 @@ def generate_training_data(self, n_samples, prior, level=1):
         Returns
         -------
         numpy.ndarray
-            Sampled prior of shape (`n_samples`, -1)
+            Sampled prior of shape (``n_samples``, -1)
         brian2modelfitting.simulator.Simulator
             Executed simulator.
         """
@@ -426,15 +428,15 @@ def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
         n_samples : int
             The number of samples.
         n_rounds : int or str, optional
-            If `n_rounds`is set to 1, amortized inference will be
-            performed. Otherwise, if `n_rounds` is integer larger than
-            1, multi-round inference will be performed.
+            If ``n_rounds`` is set to 1, amortized inference will be
+            performed. Otherwise, if ``n_rounds`` is integer larger
+            than 1, multi-round inference will be performed.
         estimation_method : str
             Inference method. Either of SNPE, SNLE or SNRE. Currently,
             only SNPE is supported.
         density_estimator_model : str
-            The type of density estimator to be created. Either `mdn`,
-            `made`, `maf` or `nsf`.
+            The type of density estimator to be created. Either
+            ``mdn``, ``made``, ``maf`` or ``nsf``.
         params : dict
             Bounds for each parameter.
 

From 948088f27b8e784aa8607412fcb5ec430f1525e1 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 24 Jun 2021 09:34:14 +0200
Subject: [PATCH 016/121] add cleaner extraction of recorded values

---
 brian2modelfitting/inferencer.py | 38 +++++++++++++++-----------------
 1 file changed, 18 insertions(+), 20 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 7344523..7c4a047 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -453,10 +453,11 @@ def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
         # observation the focus is on
         x_o = []
         for o in self.output:
-            o_dim = get_dimensions(o)
-            o_obs = self.extract_features(o.transpose(), o_dim)
-            x_o.append(o_obs.flatten())
+            o = np.array(o)
+            obs = extract_features(o.transpose())
+            x_o.append(obs.flatten())
         x_o = torch.tensor(x_o, dtype=torch.float32)
+        print(x_o.shape)
         self.x_o = x_o
 
         # initialize prior, density estimator and inference method
@@ -468,18 +469,17 @@ def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
         posteriors = []
         proposal = prior
         for _ in range(n_rounds):
-            # extract the data and make adjustments for `sbi`
+            # extract the data and make adjustments for ``sbi``
             theta, simulator = self.generate_training_data(n_samples, proposal)
             theta = torch.tensor(theta, dtype=torch.float32)
             obs = simulator.statemonitor.recorded_variables
             x = []
             for ov in self.output_var:
-                x_val = obs[ov].get_value_with_unit()
-                x_dim = get_dimensions(obs[ov])
-                features = self.extract_features(x_val, x_dim)
+                x_val = obs[ov].get_value()
+                features = extract_features(x_val)
                 x.append(features)
-            x = torch.tensor(x, dtype=torch.float32)
-            x = x.reshape(self.n_samples, -1)
+            x = torch.tensor(x, dtype=torch.float32).reshape(self.n_samples, -1)
+            print(x.shape)
 
             # pass the simulated data to the inference object and train it
             de = inference.append_simulations(theta, x, proposal).train()
@@ -495,17 +495,6 @@ def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
         self.posterior = posterior
         return posterior
 
-    def extract_features(self, obs, obs_dim):
-        obs_mean = obs.mean(axis=0)
-        obs_std = obs.std(axis=0)
-        obs_ptp = obs.ptp(axis=0)
-        obs_mean = obs_mean / Quantity(np.ones_like(obs_mean), obs_dim)
-        obs_std = obs_std / Quantity(np.ones_like(obs_std), obs_dim)
-        obs_ptp = obs_ptp / Quantity(np.ones_like(obs_ptp), obs_dim)
-        return np.hstack((np.array(obs_mean).reshape(-1, 1),
-                          np.array(obs_std).reshape(-1, 1),
-                          np.array(obs_ptp).reshape(-1, 1)))
-
     def sample(self, size, viz=False, **kwargs):
         samples = self.posterior.sample((size, ))
         if viz:
@@ -513,3 +502,12 @@ def sample(self, size, viz=False, **kwargs):
             plt.tight_layout()
             plt.show()
         return samples
+
+
+def extract_features(obs):
+    obs_mean = obs.mean(axis=0)
+    obs_std = obs.std(axis=0)
+    obs_ptp = obs.ptp(axis=0)
+    return np.hstack((np.array(obs_mean).reshape(-1, 1),
+                      np.array(obs_std).reshape(-1, 1),
+                      np.array(obs_ptp).reshape(-1, 1)))
\ No newline at end of file

From 8580e42d257747d5d765d13e567cec8cd7c6a252 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 24 Jun 2021 11:47:20 +0200
Subject: [PATCH 017/121] add seperate method for visualization and add method
 for generating fitted traces

---
 brian2modelfitting/inferencer.py | 136 ++++++++++++++++++++++++++++---
 1 file changed, 126 insertions(+), 10 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 7c4a047..0f07f59 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -442,7 +442,7 @@ def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
 
         Returns
         -------
-        sbi.inference.posteriors.direct_posterior.DirectPosterior
+        sbi.inference.posteriors.DirectPosterior
             Trained posterior.
         """
         if not isinstance(n_rounds, int):
@@ -457,7 +457,6 @@ def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
             obs = extract_features(o.transpose())
             x_o.append(obs.flatten())
         x_o = torch.tensor(x_o, dtype=torch.float32)
-        print(x_o.shape)
         self.x_o = x_o
 
         # initialize prior, density estimator and inference method
@@ -478,8 +477,8 @@ def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
                 x_val = obs[ov].get_value()
                 features = extract_features(x_val)
                 x.append(features)
-            x = torch.tensor(x, dtype=torch.float32).reshape(self.n_samples, -1)
-            print(x.shape)
+            x = torch.tensor(x, dtype=torch.float32)
+            x = x.view((self.n_samples, -1))
 
             # pass the simulated data to the inference object and train it
             de = inference.append_simulations(theta, x, proposal).train()
@@ -495,14 +494,131 @@ def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
         self.posterior = posterior
         return posterior
 
-    def sample(self, size, viz=False, **kwargs):
-        samples = self.posterior.sample((size, ))
-        if viz:
-            sbi.analysis.pairplot(samples, **kwargs)
-            plt.tight_layout()
-            plt.show()
+    def sample(self, shape, posterior=None, **kwargs):
+        """Return samples from posterior distribution.
+
+        Parameters
+        ----------
+        shape : tuple
+            Desired shape of samples that are drawn from posterior.
+        posterior : sbi.inference.posteriors.DirectPosterior, optional
+            Posterior distribution.
+        **kwargs : dict, optional
+            Additional keyword arguments for ``sample`` method in
+            ``sbi.inference.posteriors.DirectPosterior`` class
+        Returns
+        -------
+        torch.tensor
+            Samples from posterior of the shape as given in ``shape``.
+        """
+        if posterior:
+            p = posterior
+        else:
+            try:
+                p = self.posterior
+            except NameError as e:
+                print(e, 'Posterior object is not found.')
+        samples = p.sample(shape)
+        self.samples = samples
         return samples
 
+    def pairplot(self, samples=None, **kwargs):
+        """Plot samples in a 2-D grid with marginals and pairwise
+        marginals.
+
+        Check ``sbi.analysis.plot.pairplot`` for more details.
+
+        Parameters
+        ----------
+        samples : iterable, optional
+            Samples used to build the pairplot.
+        **kwargs : dict, optional
+            Additional keyword arguments for the
+            ``sbi.analysis.plot.pairplot`` function.
+
+        Returns
+        -------
+        tuple
+            Figure and axis of posterior distribution plot.
+        """
+        if samples is not None:
+            s = samples
+        else:
+            try:
+                s = self.samples
+            except AttributeError as e:
+                print(e, '\nProvide samples or call ``sample`` method first.')
+                raise
+        fig, axes = sbi.analysis.pairplot(s, **kwargs)
+        plt.show()
+        return (fig, axes)
+
+    def generate_traces(self, posterior=None, output_var=None, param_init=None,
+                        level=0):
+        """Generates traces for a single drawn sample from the trained
+        posterior and all inputs.
+
+        Parameters
+        ----------
+        posterior : sbi.inference.posteriors.DirectPosterior, optional
+            Posterior distribution.
+        output_var: str or sequence of str
+            Name of the output variable to be monitored, it can also be
+            a sequence of names to record multiple variables.
+        param_init : dict
+            Dictionary of initial values for the model.
+        level : int, optional
+            How far to go back to get the locals/globals.
+
+        Returns
+        -------
+        brian2.units.fundamentalunits.Quantity or dict
+            If a single output variable is observed, 2-D array of
+            traces generated by using a set of parameters sampled from
+            the trained posterior distribution of shape
+            (``n.traces``, number of time steps). Otherwise, a
+            dictionary with keys set to names of output variables, and
+            values to generated traces of respective output variables.
+        """
+        # sample a single set of parameters from posterior distribution
+        if posterior:
+            p = posterior
+        else:
+            try:
+                p = self.posterior
+            except NameError as e:
+                print(e, 'Posterior object is not found.')
+        params = p.sample((1, ))
+
+        # set up initial values
+        if param_init is None:
+            param_init = self.param_init
+        else:
+            param_init = dict(self.param_init)
+            self.param_init.update(param_init)
+
+        # create a dictionary with repeated sampled prior
+        d_param = get_param_dict(params, self.param_names, self.n_traces)
+
+        # set up and run the simulator
+        network_name = 'generate_traces'
+        simulator = self.setup_simulator('generate_traces',
+                                         self.n_traces,
+                                         output_var=self.output_var,
+                                         param_init=param_init,
+                                         level=level+1)
+        simulator.run(self.sim_time, d_param, self.param_names, iteration=0,
+                      name=network_name)
+
+        # create dictionary of traces for multiple observed output variables
+        if self.output_var > 1:
+            for ov in self.output_var:
+                trace = getattr(simulator.statemonitor, ov)[:]
+                traces = {ov: trace}
+        else:
+            traces = getattr(simulator.statemonitor, self.output_var[0])[:]
+        return traces
+
 
 def extract_features(obs):
     obs_mean = obs.mean(axis=0)

From d7195439df91bfa3e20419b3c6ac2e9253ff388f Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 24 Jun 2021 11:59:07 +0200
Subject: [PATCH 018/121] enable train to take a list of functions for summary
 feature extraction

---
 brian2modelfitting/inferencer.py | 28 +++++++++++++---------------
 1 file changed, 13 insertions(+), 15 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 0f07f59..b2d8ffa 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -416,7 +416,7 @@ def generate_training_data(self, n_samples, prior, level=1):
                       name=network_name)
         return (theta, simulator)
 
-    def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
+    def train(self, n_samples, features, n_rounds=1, estimation_method='SNPE',
               density_estimator_model='maf', **params):
         """Return the trained neural density estimator.
 
@@ -427,6 +427,9 @@ def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
         ---------
         n_samples : int
             The number of samples.
+        features : list
+            List of callables that take the voltage trace and output
+            summary statistics stored in `numpy.array`.
         n_rounds : int or str, optional
             If ``n_rounds`` is set to 1, amortized inference will be
             performed. Otherwise, if ``n_rounds`` is integer larger
@@ -454,8 +457,10 @@ def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
         x_o = []
         for o in self.output:
             o = np.array(o)
-            obs = extract_features(o.transpose())
-            x_o.append(obs.flatten())
+            obs = []
+            for feature in features:
+                obs.extend(feature(o.transpose()))
+            x_o.append(obs)
         x_o = torch.tensor(x_o, dtype=torch.float32)
         self.x_o = x_o
 
@@ -475,8 +480,10 @@ def train(self, n_samples, n_rounds=1, estimation_method='SNPE',
             x = []
             for ov in self.output_var:
                 x_val = obs[ov].get_value()
-                features = extract_features(x_val)
-                x.append(features)
+                summary_statistics = []
+                for feature in features:
+                    summary_statistics.append(feature(x_val))
+                x.append(summary_statistics)
             x = torch.tensor(x, dtype=torch.float32)
             x = x.view((self.n_samples, -1))
 
@@ -611,19 +618,10 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
                       name=network_name)
 
         # create dictionary of traces for multiple observed output variables
-        if self.output_var > 1:
+        if len(self.output_var) > 1:
             for ov in self.output_var:
                 trace = getattr(simulator.statemonitor, ov)[:]
                 traces = {ov: trace}
         else:
             traces = getattr(simulator.statemonitor, self.output_var[0])[:]
         return traces
-
-
-def extract_features(obs):
-    obs_mean = obs.mean(axis=0)
-    obs_std = obs.std(axis=0)
-    obs_ptp = obs.ptp(axis=0)
-    return np.hstack((np.array(obs_mean).reshape(-1, 1),
-                      np.array(obs_std).reshape(-1, 1),
-                      np.array(obs_ptp).reshape(-1, 1)))
\ No newline at end of file

From eb1e5124e73b23a057b661ed41e152eb01f95c25 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 24 Jun 2021 11:59:32 +0200
Subject: [PATCH 019/121] update according to changes in Inferencer class

---
 examples/hh_sbi.py | 17 +++++++++++++----
 1 file changed, 13 insertions(+), 4 deletions(-)

diff --git a/examples/hh_sbi.py b/examples/hh_sbi.py
index 33539c5..939e2c8 100644
--- a/examples/hh_sbi.py
+++ b/examples/hh_sbi.py
@@ -42,8 +42,12 @@
                         param_init={'v': 'VT'})
 
 # Generate prior and train the neural density estimator
-inferencer.train(n_samples=100,
-                 n_rounds=2,
+inferencer.train(n_samples=15,
+                 features=[
+                    lambda x: x.mean(axis=0),
+                    lambda x: x.std(axis=0),
+                    lambda x: x.ptp(axis=0)],
+                 n_rounds=1,
                  density_estimator_model='made',
                  gl=[1e-09 * siemens, 1e-07 * siemens],
                  g_na=[2e-06 * siemens, 2e-04 * siemens],
@@ -53,5 +57,10 @@
 # Draw samples from posterior and visualize the results
 labels_params = [r'$\overline{g}_{l}$', r'$\overline{g}_{Na}$',
                  r'$\overline{g}_{K}$', r'$\overline{C}_{m}$']
-samples = inferencer.sample(1000, viz=True,
-                            labels=labels_params, figsize=(10, 10))
+inferencer.sample((1000,))
+
+# Create pairplot from samples
+inferencer.pairplot()
+
+# Generate traces by using a single sample from the trained posterior
+fits = inferencer.generate_traces()

From ba37935801e895409d51b134ab9ed241026092a6 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 24 Jun 2021 17:13:59 +0200
Subject: [PATCH 020/121] add visualization of fitted traces

---
 examples/hh_sbi.py | 37 +++++++++++++++++++++++++++++--------
 1 file changed, 29 insertions(+), 8 deletions(-)

diff --git a/examples/hh_sbi.py b/examples/hh_sbi.py
index 939e2c8..a50255e 100644
--- a/examples/hh_sbi.py
+++ b/examples/hh_sbi.py
@@ -1,5 +1,5 @@
-from brian2 import um, cm, ms, mV, amp, siemens, uF
-from brian2modelfitting import Inferencer
+from brian2 import *
+from brian2modelfitting import *
 import pandas as pd
 
 
@@ -42,7 +42,7 @@
                         param_init={'v': 'VT'})
 
 # Generate prior and train the neural density estimator
-inferencer.train(n_samples=15,
+inferencer.train(n_samples=1000,
                  features=[
                     lambda x: x.mean(axis=0),
                     lambda x: x.std(axis=0),
@@ -54,13 +54,34 @@
                  g_kd=[6e-07 * siemens, 6e-05 * siemens],
                  Cm=[0.1 * uF * cm ** -2 * area, 2 * uF * cm ** -2 * area])
 
-# Draw samples from posterior and visualize the results
-labels_params = [r'$\overline{g}_{l}$', r'$\overline{g}_{Na}$',
-                 r'$\overline{g}_{K}$', r'$\overline{C}_{m}$']
+# Draw samples from posterior
 inferencer.sample((1000,))
 
 # Create pairplot from samples
-inferencer.pairplot()
+labels_params = [r'$\overline{g}_{l}$', r'$\overline{g}_{Na}$',
+                 r'$\overline{g}_{K}$', r'$\overline{C}_{m}$']
+inferencer.pairplot(labels=labels_params)
 
 # Generate traces by using a single sample from the trained posterior
-fits = inferencer.generate_traces()
+inf_traces = inferencer.generate_traces()
+
+# Visualize traces
+t = arange(0, out_traces.shape[1] * dt / ms, dt / ms)
+nrows = 2
+ncols = out_traces.shape[0]
+fig, axs = subplots(nrows, ncols, sharex=True,
+                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(15, 4))
+for idx in range(ncols):
+    axs[0, idx].plot(out_traces[idx, :].T, label='measurements')
+    axs[0, idx].plot(inf_traces[idx, :].T / mV, label='fits')
+    axs[1, idx].plot(inp_traces[idx, :].T / amp, 'k-', label='stimulus')
+    axs[1, idx].set_xlabel('t, ms')
+    if idx == 0:
+        axs[0, idx].set_ylabel('$v$, mV')
+        axs[1, idx].set_ylabel('$I$, nA')
+handles, labels = [(h + l) for h, l
+                   in zip(axs[0, idx].get_legend_handles_labels(),
+                   axs[1, idx].get_legend_handles_labels())]
+fig.legend(handles, labels)
+tight_layout()
+show()
\ No newline at end of file

From 32085fe112d018167aff487c3cc88e5d57abc3d1 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 24 Jun 2021 17:15:00 +0200
Subject: [PATCH 021/121] fix docstrings to work well with sphinx

---
 brian2modelfitting/inferencer.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index b2d8ffa..f401993 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -28,9 +28,9 @@
 
 def configure_simulator():
     """Return the configured simulator, which can be either
-    ``RuntimeSimulator``, object for use with ``RuntimeDevice``, or
-    ``CPPStandaloneSimulator``, object for use with
-    ``CPPStandaloneDevice``.
+    `.RuntimeSimulator`, object for use with `.RuntimeDevice`, or
+    `.CPPStandaloneSimulator`, object for use with
+    `.CPPStandaloneDevice`.
 
     Parameters
     ----------
@@ -39,7 +39,7 @@ def configure_simulator():
     Returns
     -------
     brian2modelfitting.simulator.Simulator
-        Either ``RuntimeSimulator`` or ``CPPStandaloneSimulator``
+        Either `.RuntimeSimulator` or `.CPPStandaloneSimulator`
         depending on the currently active ``Device`` object describing
         the available computational engine.
     """
@@ -84,7 +84,7 @@ def get_param_dict(param_values, param_names, n_values):
     Parameters
     ----------
     param_values : iterable
-        Iterable of size (``n_samples``, ``len(param_names)``
+        Iterable of size (``n_samples``, ``len(param_names)``)
         containing parameter values.
     param_names : iterable
         Iterable containing parameter names

From 5763c475e959cb17b7903e7ebf113ddb840af645 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 25 Jun 2021 11:39:28 +0200
Subject: [PATCH 022/121] rename train to infere and additional refactoring

---
 brian2modelfitting/inferencer.py | 240 +++++++++++++++++++++++++------
 1 file changed, 200 insertions(+), 40 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index f401993..01ef5d0 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -17,7 +17,9 @@
                                            Quantity)
 import matplotlib.pyplot as plt
 import numpy as np
-from sbi.utils.get_nn_models import posterior_nn
+from sbi.utils.get_nn_models import (posterior_nn,
+                                     likelihood_nn,
+                                     classifier_nn)
 from sbi.utils.torchutils import BoxUniform
 import sbi.analysis
 import sbi.inference
@@ -139,7 +141,7 @@ def calc_prior(param_names, **params):
 class Inferencer(object):
     """Class for simulation-based inference.
 
-    It offers an interface similar to that of ``Fitter`` class but
+    It offers an interface similar to that of `.Fitter` class but
     instead of fitting, neural density estimator is trained using a
     generative model. This class serves as a wrapper for ``sbi``
     library for inferencing posterior over unknown parameters of a
@@ -269,11 +271,11 @@ def __init__(self, dt, model, input, output, method=None, threshold=None,
 
     @property
     def n_neurons(self):
-        """Return the number of neurons that are used in ``NeuronGroup``
+        """Return the number of neurons that are used in `.NeuronGroup`
         class while generating data for training the neural density
         estimator.
 
-        Unlike the ``Fitter`` class, ``Inferencer`` does not take the
+        Unlike the `.Fitter` class, `.Inferencer` does not take the
         total number of samples in the constructor. Thus, this property
         becomes available only after the simulation is performed.
 
@@ -371,7 +373,7 @@ def initialize_prior(self, **params):
         prior = calc_prior(self.param_names, **params)
         return prior
 
-    def generate_training_data(self, n_samples, prior, level=1):
+    def generate_training_data(self, n_samples, prior):
         """Return sampled prior and executed simulator containing
         recorded variables to be used for training the neural density
         estimator.
@@ -382,15 +384,11 @@ def generate_training_data(self, n_samples, prior, level=1):
             The number of samples.
         prior : sbi.utils.BoxUniform
             Uniformly distributed prior over given parameters.
-        level : int, optional
-            How far to go back to get the locals/globals.
 
         Returns
         -------
         numpy.ndarray
-            Sampled prior of shape (``n_samples``, -1)
-        brian2modelfitting.simulator.Simulator
-            Executed simulator.
+            Sampled prior of shape (``n_samples``, -1).
         """
         # set n_samples to class variable to be able to call self.n_neurons
         self.n_samples = n_samples
@@ -398,14 +396,34 @@ def generate_training_data(self, n_samples, prior, level=1):
         # sample from prior
         theta = prior.sample((n_samples, ))
         theta = np.atleast_2d(theta.numpy())
+        return theta
+
+    def extract_summary_statistics(self, theta, features, level=1):
+        """Return summary statistics to be used for training the neural
+        density estimator.
+
+        Parameters
+        ----------
+        theta : numpy.ndarray
+            Sampled prior of shape (``n_samples``, -1).
+        features : list
+            List of callables that take the voltage trace and output
+            summary statistics stored in `numpy.array`.
+        level : int, optional
+            How far to go back to get the locals/globals.
 
+        Returns
+        -------
+        numpy.ndarray
+            Summary statistics.
+        """
         # repeat each row for how many input/output different trace are there
         _theta = np.repeat(theta, repeats=self.n_traces, axis=0)
 
         # create a dictionary with repeated sampled prior
         d_param = get_param_dict(_theta, self.param_names, self.n_neurons)
 
-        # setup and run the simulator
+        # set up and run the simulator
         network_name = 'infere'
         simulator = self.setup_simulator(network_name=network_name,
                                          n_neurons=self.n_neurons,
@@ -414,10 +432,131 @@ def generate_training_data(self, n_samples, prior, level=1):
                                          level=level+1)
         simulator.run(self.sim_time, d_param, self.param_names, iteration=0,
                       name=network_name)
-        return (theta, simulator)
 
-    def train(self, n_samples, features, n_rounds=1, estimation_method='SNPE',
-              density_estimator_model='maf', **params):
+        # extract features
+        obs = simulator.statemonitor.recorded_variables
+        x = []
+        for ov in self.output_var:
+            x_val = obs[ov].get_value()
+            summary_statistics = []
+            for feature in features:
+                summary_statistics.append(feature(x_val))
+            x.append(summary_statistics)
+        x = np.array(x, dtype=np.float32)
+        x = x.reshape((self.n_samples, -1))
+        return x
+
+    def init_inference(self, inference_method, density_estimator_model, prior,
+                       **inference_kwargs):
+        """Return instantiated inference object.
+
+        Parameters
+        ----------
+        inference_method : str
+            Inference method. Either of SNPE, SNLE or SNRE.
+        density_estimator_model : str
+            The type of density estimator to be created. Either
+            ``mdn``, ``made``, ``maf``, ``nsf`` for SNPE and SNLE, or
+            ``linear``, ``mlp``, ``resnet`` for SNRE.
+        prior : sbi.utils.BoxUniform
+            Uniformly distributed prior over given parameters.
+        inference_kwargs : dict, optional
+            Additional keyword arguments for
+            ``sbi.utils.get_nn_models.posterior_nn`` method.
+
+        Returns
+        -------
+        sbi.inference.NeuralInference
+            Instantiated inference object.
+        """
+        try:
+            inference_method = str.upper(inference_method)
+            inference_method_fun = getattr(sbi.inference, inference_method)
+        except AttributeError:
+            raise NameError(f'Inference method {inference_method} is not '
+                            'supported. Choose between SNPE, SNLE or SNRE.')
+        finally:
+            if inference_method == 'SNPE':
+                density_estimator_builder = posterior_nn(
+                    model=density_estimator_model, **inference_kwargs)
+            elif inference_method == 'SNLE':
+                density_estimator_builder = likelihood_nn(
+                    model=density_estimator_model, **inference_kwargs)
+            else:
+                density_estimator_builder = classifier_nn(
+                    model=density_estimator_model, **inference_kwargs)
+        inference = inference_method_fun(prior, density_estimator_builder,
+                                         device='cpu',
+                                         show_progress_bars=True)
+        return inference
+
+    def train(self, inference, theta, x, *args, **train_kwargs):
+        """Return inference object with stored training data and
+        trained density estimator.
+
+        Parameters
+        ----------
+        inference : sbi.inference.NeuralInference
+            Instantiated inference object with stored paramaters and
+            simulation outputs prepared for training.
+        theta : torch.tensor
+            Sampled prior.
+        x : torch.tensor
+            Summary statistics.
+        args : list, optional
+            Contains a uniformly distributed sbi.utils.BoxUniform
+            prior/proposal. Used only for SNPE, for SNLE and SNRE,
+            ``proposal`` should not be passed to ``append_simulations``
+            method, thus ``args`` should not be passed.
+        train_kwargs : dict, optional
+            Additional keyword arguments for ``train`` method of
+            ``sbi.inference.NeuralInference`` object.
+
+        Returns
+        -------
+        tuple
+            ``sbi.inference.NeuralInference`` object with stored
+            paramaters and simulation outputs prepared for training and
+            trained neural density estimator object.
+        """
+        inference = inference.append_simulations(theta, x, *args)
+        density_estimator = inference.train(**train_kwargs)
+        return (inference, density_estimator)
+
+    def build_posterior(self, inference, density_estimator):
+        """Return instantiated inference object.
+
+        Parameters
+        ----------
+        inference : sbi.inference.NeuralInference
+            Instantiated inference object with stored paramaters and
+            simulation outputs prepared for training.
+        theta : torch.tensor
+            Sampled prior.
+        x : torch.tensor
+            Summary statistics.
+        args : list, optional
+            Contains a uniformly distributed sbi.utils.BoxUniform
+            prior/proposal. Used only for SNPE, for SNLE and SNRE,
+            ``proposal`` should not be passed to ``append_simulations``
+            method, thus ``args`` should not be passed.
+        train_kwargs : dict, optional
+            Additional keyword arguments for ``train`` method of
+            ``sbi.inference.NeuralInference`` object.
+
+        Returns
+        -------
+        tuple
+            ``sbi.inference.NeuralInference`` object with stored
+            paramaters and simulation outputs prepared for training and
+            ``sbi.inference.NeuralInference`` object from which.
+        """
+        posterior = inference.build_posterior(density_estimator)
+        return (inference, posterior)
+
+    def infere(self, n_samples, features, n_rounds=1, inference_method='SNPE',
+               density_estimator_model='maf', inference_kwargs={},
+               train_kwargs={}, posterior_kwargs={}, **params):
         """Return the trained neural density estimator.
 
         Currently only sequential neural posterior estimator is
@@ -434,12 +573,14 @@ def train(self, n_samples, features, n_rounds=1, estimation_method='SNPE',
             If ``n_rounds`` is set to 1, amortized inference will be
             performed. Otherwise, if ``n_rounds`` is integer larger
             than 1, multi-round inference will be performed.
-        estimation_method : str
+        inference_method : str
             Inference method. Either of SNPE, SNLE or SNRE. Currently,
             only SNPE is supported.
         density_estimator_model : str
             The type of density estimator to be created. Either
             ``mdn``, ``made``, ``maf`` or ``nsf``.
+        train_kwargs : dict, optional
+            Dictionary of arguments for training the posterior estimator.
         params : dict
             Bounds for each parameter.
 
@@ -450,8 +591,13 @@ def train(self, n_samples, features, n_rounds=1, estimation_method='SNPE',
         """
         if not isinstance(n_rounds, int):
             raise ValueError('Number of rounds must be a positive integer.')
-        if str.upper(estimation_method) != 'SNPE':
-            raise NotImplementedError('Only SNPE estimator is supported.')
+        try:
+            inference_method = str.upper(inference_method)
+        except ValueError as e:
+            print(e, '\nInvalid inference method.')
+        if inference_method not in ['SNPE', 'SNLE', 'SNRE']:
+            raise ValueError(f'Inference method {inference_method} is not '
+                             'supported. Choose between SNPE, SNLE or SNRE.')
 
         # observation the focus is on
         x_o = []
@@ -464,34 +610,44 @@ def train(self, n_samples, features, n_rounds=1, estimation_method='SNPE',
         x_o = torch.tensor(x_o, dtype=torch.float32)
         self.x_o = x_o
 
-        # initialize prior, density estimator and inference method
+        # initialize prior
         prior = self.initialize_prior(**params)
-        density_esimator = posterior_nn(density_estimator_model)
-        inference = sbi.inference.SNPE(prior, density_esimator)
 
-        # allocate empty list of posteriors, for multi-round inference only
+        # initialize inference object
+        inference = self.init_inference(inference_method,
+                                        density_estimator_model,
+                                        prior,
+                                        **inference_kwargs)
+
+        # allocate empty list of posteriors
         posteriors = []
         proposal = prior
-        for _ in range(n_rounds):
-            # extract the data and make adjustments for ``sbi``
-            theta, simulator = self.generate_training_data(n_samples, proposal)
+        if inference_method == 'SNPE':
+            args = [proposal]
+        else:
+            args = []
+        for round in range(n_rounds):
+            print(f'Round {round + 1} of inference.')
+
+            # extract the training data and make adjustments for ``sbi``
+            print('Generating training data...')
+            theta = self.generate_training_data(n_samples, proposal)
             theta = torch.tensor(theta, dtype=torch.float32)
-            obs = simulator.statemonitor.recorded_variables
-            x = []
-            for ov in self.output_var:
-                x_val = obs[ov].get_value()
-                summary_statistics = []
-                for feature in features:
-                    summary_statistics.append(feature(x_val))
-                x.append(summary_statistics)
-            x = torch.tensor(x, dtype=torch.float32)
-            x = x.view((self.n_samples, -1))
+
+            # extract the summary statistics and make adjustments for ``sbi``
+            x = self.extract_summary_statistics(theta, features)
+            x = torch.tensor(x)
 
             # pass the simulated data to the inference object and train it
-            de = inference.append_simulations(theta, x, proposal).train()
+            print('Training the neural density estimator...')
+            inference, density_estimator = self.train(inference,
+                                                      theta, x,
+                                                      *args, **train_kwargs)
 
             # use the density estimator to build the posterior
-            posterior = inference.build_posterior(de)
+            inference, posterior = self.build_posterior(inference,
+                                                        density_estimator,
+                                                        **posterior_kwargs)
 
             # append the current posterior to the list of posteriors
             posteriors.append(posterior)
@@ -597,6 +753,10 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
                 print(e, 'Posterior object is not found.')
         params = p.sample((1, ))
 
+        # set output variable that is monitored
+        if output_var is None:
+            output_var = self.output_var
+
         # set up initial values
         if param_init is None:
             param_init = self.param_init
@@ -611,17 +771,17 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
         network_name = 'generate_traces'
         simulator = self.setup_simulator('generate_traces',
                                          self.n_traces,
-                                         output_var=self.output_var,
+                                         output_var=output_var,
                                          param_init=param_init,
                                          level=level+1)
         simulator.run(self.sim_time, d_param, self.param_names, iteration=0,
                       name=network_name)
 
         # create dictionary of traces for multiple observed output variables
-        if len(self.output_var) > 1:
-            for ov in self.output_var:
+        if len(output_var) > 1:
+            for ov in output_var:
                 trace = getattr(simulator.statemonitor, ov)[:]
                 traces = {ov: trace}
         else:
-            traces = getattr(simulator.statemonitor, self.output_var[0])[:]
+            traces = getattr(simulator.statemonitor, output_var[0])[:]
         return traces

From 49f673e67341e0f7e092b9febcc21ca85af883a2 Mon Sep 17 00:00:00 2001
From: Marcel Stimberg <marcel.stimberg@inserm.fr>
Date: Fri, 25 Jun 2021 17:31:43 +0200
Subject: [PATCH 023/121] Remove plt.show from pairplot

---
 brian2modelfitting/inferencer.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 01ef5d0..bb00878 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -713,8 +713,7 @@ def pairplot(self, samples=None, **kwargs):
                 print(e, '\nProvide samples or call ``sample`` method first.')
                 raise
         fig, axes = sbi.analysis.pairplot(s, **kwargs)
-        plt.show()
-        return (fig, axes)
+        return fig, axes
 
     def generate_traces(self, posterior=None, output_var=None, param_init=None,
                         level=0):

From 1d79aaf602c22d6db8ce8619f87058fcb1a97734 Mon Sep 17 00:00:00 2001
From: Marcel Stimberg <marcel.stimberg@inserm.fr>
Date: Fri, 25 Jun 2021 17:31:57 +0200
Subject: [PATCH 024/121] =?UTF-8?q?Adapt=20example=20to=20train=E2=86=92in?=
 =?UTF-8?q?fere=20rename?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 examples/hh_sbi.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/examples/hh_sbi.py b/examples/hh_sbi.py
index a50255e..4ca1c00 100644
--- a/examples/hh_sbi.py
+++ b/examples/hh_sbi.py
@@ -42,17 +42,17 @@
                         param_init={'v': 'VT'})
 
 # Generate prior and train the neural density estimator
-inferencer.train(n_samples=1000,
-                 features=[
-                    lambda x: x.mean(axis=0),
-                    lambda x: x.std(axis=0),
-                    lambda x: x.ptp(axis=0)],
-                 n_rounds=1,
-                 density_estimator_model='made',
-                 gl=[1e-09 * siemens, 1e-07 * siemens],
-                 g_na=[2e-06 * siemens, 2e-04 * siemens],
-                 g_kd=[6e-07 * siemens, 6e-05 * siemens],
-                 Cm=[0.1 * uF * cm ** -2 * area, 2 * uF * cm ** -2 * area])
+inferencer.infere(n_samples=1000,
+                  features=[
+                      lambda x: x.mean(axis=0),
+                      lambda x: x.std(axis=0),
+                      lambda x: x.ptp(axis=0)],
+                  n_rounds=2,
+                  density_estimator_model='made',
+                  gl=[1e-09 * siemens, 1e-07 * siemens],
+                  g_na=[2e-06 * siemens, 2e-04 * siemens],
+                  g_kd=[6e-07 * siemens, 6e-05 * siemens],
+                  Cm=[0.1 * uF * cm ** -2 * area, 2 * uF * cm ** -2 * area])
 
 # Draw samples from posterior
 inferencer.sample((1000,))
@@ -84,4 +84,4 @@
                    axs[1, idx].get_legend_handles_labels())]
 fig.legend(handles, labels)
 tight_layout()
-show()
\ No newline at end of file
+show()

From ceb7348accbb82359cb3db98678b061c18d6acb7 Mon Sep 17 00:00:00 2001
From: Marcel Stimberg <marcel.stimberg@inserm.fr>
Date: Fri, 25 Jun 2021 17:40:09 +0200
Subject: [PATCH 025/121] Moving attributes into Inferencer.__init__

---
 brian2modelfitting/inferencer.py | 14 +++++++++-----
 1 file changed, 9 insertions(+), 5 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index bb00878..02601ff 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -266,8 +266,11 @@ def __init__(self, dt, model, input, output, method=None, threshold=None,
         self.reset = reset
         self.refractory = refractory
 
-        # placeholder for the number of samples
+        # placeholder for samples
         self.n_samples = None
+        self.samples = None
+        # placeholder for the posterior
+        self.posterior = None
 
     @property
     def n_neurons(self):
@@ -677,10 +680,11 @@ def sample(self, shape, posterior=None, **kwargs):
         if posterior:
             p = posterior
         else:
-            try:
-                p = self.posterior
-            except NameError as e:
-                print(e, 'Posterior object is not found.')
+            p = self.posterior
+        if not p:
+            raise ValueError("Need to provide posterior argument if no "
+                             "posterior has been calculated by the 'infere' "
+                             "method.")
         samples = p.sample(shape)
         self.samples = samples
         return samples

From f6916c4ab0885b2396885d4c15f363e31df85ce5 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sun, 27 Jun 2021 13:56:39 +0200
Subject: [PATCH 026/121] add pt and pth files

---
 .gitignore | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.gitignore b/.gitignore
index 6b47837..0ad66b4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -83,3 +83,7 @@ brian2modelfitting.egg-info
 
 # sbi logs
 examples/sbi-logs/
+
+# other sbi and torch stuff
+examples/*.pt
+examples/*.pth

From ae627e11e0adba0d08d6c4262cae4f86e8040447 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sun, 27 Jun 2021 13:57:20 +0200
Subject: [PATCH 027/121] add slight changes in API and load/store
 functionality

---
 brian2modelfitting/inferencer.py | 105 +++++++++++++++++++++++--------
 1 file changed, 78 insertions(+), 27 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 02601ff..c67605d 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -15,7 +15,6 @@
                                            fail_for_dimension_mismatch,
                                            get_dimensions,
                                            Quantity)
-import matplotlib.pyplot as plt
 import numpy as np
 from sbi.utils.get_nn_models import (posterior_nn,
                                      likelihood_nn,
@@ -162,6 +161,9 @@ class Inferencer(object):
         key corresponds to the name of the output variable as defined
         in ``model`` and value corresponds to a single dimensional
         array of recorded data traces.
+    features : list
+            List of callables that take the voltage trace and output
+            summary statistics stored in `numpy.array`.
     method : str, optional
         Integration method.
     threshold : str, optional
@@ -181,8 +183,8 @@ class Inferencer(object):
         Dictionary of state variables to be initialized with respective
         values.
     """
-    def __init__(self, dt, model, input, output, method=None, threshold=None,
-                 reset=None, refractory=None, param_init=None):
+    def __init__(self, dt, model, input, output, features, method=None,
+                 threshold=None, reset=None, refractory=None, param_init=None):
         # time scale
         self.dt = dt
 
@@ -271,6 +273,17 @@ def __init__(self, dt, model, input, output, method=None, threshold=None,
         self.samples = None
         # placeholder for the posterior
         self.posterior = None
+        # observation the focus is on
+        x_o = []
+        for o in self.output:
+            o = np.array(o)
+            obs = []
+            for feature in features:
+                obs.extend(feature(o.transpose()))
+            x_o.append(obs)
+        x_o = torch.tensor(x_o, dtype=torch.float32)
+        self.x_o = x_o
+        self.features = features
 
     @property
     def n_neurons(self):
@@ -526,7 +539,8 @@ def train(self, inference, theta, x, *args, **train_kwargs):
         density_estimator = inference.train(**train_kwargs)
         return (inference, density_estimator)
 
-    def build_posterior(self, inference, density_estimator):
+    def build_posterior(self, inference, density_estimator,
+                        **posterior_kwargs):
         """Return instantiated inference object.
 
         Parameters
@@ -543,9 +557,9 @@ def build_posterior(self, inference, density_estimator):
             prior/proposal. Used only for SNPE, for SNLE and SNRE,
             ``proposal`` should not be passed to ``append_simulations``
             method, thus ``args`` should not be passed.
-        train_kwargs : dict, optional
-            Additional keyword arguments for ``train`` method of
-            ``sbi.inference.NeuralInference`` object.
+        posterior_kwargs : dict, optional
+            Additional keyword arguments for ``build_posterior`` method
+            of ``sbi.inference.NeuralInference`` object.
 
         Returns
         -------
@@ -554,10 +568,11 @@ def build_posterior(self, inference, density_estimator):
             paramaters and simulation outputs prepared for training and
             ``sbi.inference.NeuralInference`` object from which.
         """
-        posterior = inference.build_posterior(density_estimator)
+        posterior = inference.build_posterior(density_estimator,
+                                              **posterior_kwargs)
         return (inference, posterior)
 
-    def infere(self, n_samples, features, n_rounds=1, inference_method='SNPE',
+    def infere(self, n_samples, n_rounds=1, inference_method='SNPE',
                density_estimator_model='maf', inference_kwargs={},
                train_kwargs={}, posterior_kwargs={}, **params):
         """Return the trained neural density estimator.
@@ -569,9 +584,6 @@ def infere(self, n_samples, features, n_rounds=1, inference_method='SNPE',
         ---------
         n_samples : int
             The number of samples.
-        features : list
-            List of callables that take the voltage trace and output
-            summary statistics stored in `numpy.array`.
         n_rounds : int or str, optional
             If ``n_rounds`` is set to 1, amortized inference will be
             performed. Otherwise, if ``n_rounds`` is integer larger
@@ -582,14 +594,19 @@ def infere(self, n_samples, features, n_rounds=1, inference_method='SNPE',
         density_estimator_model : str
             The type of density estimator to be created. Either
             ``mdn``, ``made``, ``maf`` or ``nsf``.
+        inference_kwargs : dict, optional
+            Additional keyword arguments for
+            ``sbi.utils.get_nn_models.posterior_nn`` method.
         train_kwargs : dict, optional
             Dictionary of arguments for training the posterior estimator.
+        posterior_kwargs : dict, optional
+            Dictionary of arguments for `.build_posterior` method.
         params : dict
             Bounds for each parameter.
 
         Returns
         -------
-        sbi.inference.posteriors.DirectPosterior
+        sbi.inference.NeuralPosterior
             Trained posterior.
         """
         if not isinstance(n_rounds, int):
@@ -602,17 +619,6 @@ def infere(self, n_samples, features, n_rounds=1, inference_method='SNPE',
             raise ValueError(f'Inference method {inference_method} is not '
                              'supported. Choose between SNPE, SNLE or SNRE.')
 
-        # observation the focus is on
-        x_o = []
-        for o in self.output:
-            o = np.array(o)
-            obs = []
-            for feature in features:
-                obs.extend(feature(o.transpose()))
-            x_o.append(obs)
-        x_o = torch.tensor(x_o, dtype=torch.float32)
-        self.x_o = x_o
-
         # initialize prior
         prior = self.initialize_prior(**params)
 
@@ -638,7 +644,7 @@ def infere(self, n_samples, features, n_rounds=1, inference_method='SNPE',
             theta = torch.tensor(theta, dtype=torch.float32)
 
             # extract the summary statistics and make adjustments for ``sbi``
-            x = self.extract_summary_statistics(theta, features)
+            x = self.extract_summary_statistics(theta, self.features)
             x = torch.tensor(x)
 
             # pass the simulated data to the inference object and train it
@@ -656,7 +662,7 @@ def infere(self, n_samples, features, n_rounds=1, inference_method='SNPE',
             posteriors.append(posterior)
 
             # update the proposal given the observation
-            proposal = posterior.set_default_x(x_o)
+            proposal = posterior.set_default_x(self.x_o)
         self.posterior = posterior
         return posterior
 
@@ -685,7 +691,7 @@ def sample(self, shape, posterior=None, **kwargs):
             raise ValueError("Need to provide posterior argument if no "
                              "posterior has been calculated by the 'infere' "
                              "method.")
-        samples = p.sample(shape)
+        samples = p.sample(shape, **kwargs)
         self.samples = samples
         return samples
 
@@ -788,3 +794,48 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
         else:
             traces = getattr(simulator.statemonitor, output_var[0])[:]
         return traces
+
+    def save(self, f, posterior=None):
+        """Saves the density estimator state dictionary to a disk file.
+
+        Parameters
+        ----------
+        posterior : sbi.inference.posteriors.DirectPosterior, optional
+            Posterior distribution.
+        f : str or os.PathLike
+            Path to file either as string or ``os.PathLike`` object
+            that contains file name.
+
+        Returns
+        -------
+        None
+        """
+        if posterior:
+            p = posterior
+        else:
+            p = self.posterior
+        if not p:
+            raise ValueError("Need to provide posterior argument if no "
+                             "posterior has been calculated by the 'infere' "
+                             "method.")
+        torch.save(p, f)
+
+    def load_posterior(self, f):
+        """Loads the density estimator state dictionary from a disk file.
+
+        Parameters
+        ----------
+        f : str or os.PathLike
+            Path to file either as string or ``os.PathLike`` object
+            that contains file name.
+
+        Returns
+        -------
+        sbi.inference.NeuralPosterior
+            Loaded neural posterior with defined method family, density
+            estimator state dictionary, prior over parameters and
+            output shape of the simulator.
+        """
+        p = torch.load(f)
+        self.posterior = p
+        return p

From f843194cbec57d176a96e59ce51c8cd13c7f2c47 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sun, 27 Jun 2021 13:58:33 +0200
Subject: [PATCH 028/121] add changes w.r.t. changes in Inferencer API

---
 examples/hh_sbi.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/examples/hh_sbi.py b/examples/hh_sbi.py
index 4ca1c00..a500990 100644
--- a/examples/hh_sbi.py
+++ b/examples/hh_sbi.py
@@ -36,6 +36,10 @@
 inferencer = Inferencer(dt=dt, model=eqs,
                         input={'I': inp_traces * amp},
                         output={'v': out_traces * mV},
+                        features=[
+                            lambda x: x.mean(axis=0),
+                            lambda x: x.std(axis=0),
+                            lambda x: x.ptp(axis=0)],
                         method='exponential_euler',
                         threshold='m > 0.5',
                         refractory='m > 0.5',
@@ -43,12 +47,7 @@
 
 # Generate prior and train the neural density estimator
 inferencer.infere(n_samples=1000,
-                  features=[
-                      lambda x: x.mean(axis=0),
-                      lambda x: x.std(axis=0),
-                      lambda x: x.ptp(axis=0)],
-                  n_rounds=2,
-                  density_estimator_model='made',
+                  n_rounds=1,
                   gl=[1e-09 * siemens, 1e-07 * siemens],
                   g_na=[2e-06 * siemens, 2e-04 * siemens],
                   g_kd=[6e-07 * siemens, 6e-05 * siemens],

From f6913c45ef0aee6a2a6f4654cfa15a0d88cd822e Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Mon, 28 Jun 2021 22:53:33 +0200
Subject: [PATCH 029/121] add saved numpy arrays

---
 .gitignore | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.gitignore b/.gitignore
index 0ad66b4..09ffcc7 100644
--- a/.gitignore
+++ b/.gitignore
@@ -87,3 +87,7 @@ examples/sbi-logs/
 # other sbi and torch stuff
 examples/*.pt
 examples/*.pth
+
+# numpy files
+examples/*.npy
+examples/*.npz
\ No newline at end of file

From 6fa9eeddb39d89a25572492f4ccb5413e96583cb Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Mon, 28 Jun 2021 22:54:15 +0200
Subject: [PATCH 030/121] add storing functionality for theta and x

---
 brian2modelfitting/inferencer.py | 27 +++++++++++++++++++++++++--
 1 file changed, 25 insertions(+), 2 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index c67605d..b0ecf30 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -389,7 +389,7 @@ def initialize_prior(self, **params):
         prior = calc_prior(self.param_names, **params)
         return prior
 
-    def generate_training_data(self, n_samples, prior):
+    def generate_training_data(self, n_samples, prior, save=False):
         """Return sampled prior and executed simulator containing
         recorded variables to be used for training the neural density
         estimator.
@@ -400,6 +400,11 @@ def generate_training_data(self, n_samples, prior):
             The number of samples.
         prior : sbi.utils.BoxUniform
             Uniformly distributed prior over given parameters.
+        save : bool or str, optional
+            Save training data for later use into ``.npz`` file. If set
+            to True, sampled prior over parameters will be stored to a
+            ``theta.npz`` file. Otherwise, if set to a string the name
+            of the file will be forwarded string value.
 
         Returns
         -------
@@ -412,9 +417,16 @@ def generate_training_data(self, n_samples, prior):
         # sample from prior
         theta = prior.sample((n_samples, ))
         theta = np.atleast_2d(theta.numpy())
+
+        if save:
+            if isinstance(save, (str, )):
+                fn = save + '.npz'
+            else:
+                fn = 'theta.npz'
+            np.savez(fn, theta)
         return theta
 
-    def extract_summary_statistics(self, theta, features, level=1):
+    def extract_summary_statistics(self, theta, features, save=False, level=1):
         """Return summary statistics to be used for training the neural
         density estimator.
 
@@ -425,6 +437,11 @@ def extract_summary_statistics(self, theta, features, level=1):
         features : list
             List of callables that take the voltage trace and output
             summary statistics stored in `numpy.array`.
+         save : bool or str, optional
+            Save training data for later use into ``.npz`` file. If set
+            to True, extracted features will be stored to a ``x.npz``
+            file. Otherwise, if set to a string the name of the file
+            will be forwarded string value.
         level : int, optional
             How far to go back to get the locals/globals.
 
@@ -460,6 +477,12 @@ def extract_summary_statistics(self, theta, features, level=1):
             x.append(summary_statistics)
         x = np.array(x, dtype=np.float32)
         x = x.reshape((self.n_samples, -1))
+        if save:
+            if isinstance(save, (str, )):
+                fn = save + '.npz'
+            else:
+                fn = 'x.npz'
+            np.savez(fn, theta)
         return x
 
     def init_inference(self, inference_method, density_estimator_model, prior,

From 97f5307593c0782648b61b13648a5b00e8569c07 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 29 Jun 2021 13:44:32 +0200
Subject: [PATCH 031/121] fix minor issues

---
 brian2modelfitting/inferencer.py |  6 +++---
 examples/hh_sbi.py               | 35 ++++++++++++++++++++++----------
 2 files changed, 27 insertions(+), 14 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index b0ecf30..25e6942 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -162,8 +162,8 @@ class Inferencer(object):
         in ``model`` and value corresponds to a single dimensional
         array of recorded data traces.
     features : list
-            List of callables that take the voltage trace and output
-            summary statistics stored in `numpy.array`.
+        List of callables that take the voltage trace and output
+        summary statistics.
     method : str, optional
         Integration method.
     threshold : str, optional
@@ -659,7 +659,7 @@ def infere(self, n_samples, n_rounds=1, inference_method='SNPE',
         else:
             args = []
         for round in range(n_rounds):
-            print(f'Round {round + 1} of inference.')
+            print(f'Round {round + 1}/{n_rounds}.')
 
             # extract the training data and make adjustments for ``sbi``
             print('Generating training data...')
diff --git a/examples/hh_sbi.py b/examples/hh_sbi.py
index a500990..f25a266 100644
--- a/examples/hh_sbi.py
+++ b/examples/hh_sbi.py
@@ -1,15 +1,17 @@
 from brian2 import *
 from brian2modelfitting import *
 import pandas as pd
+import numpy as np
+from scipy.signal import find_peaks
 
 
 # Load input and output data traces
 df_inp_traces = pd.read_csv('input_traces_hh.csv')
 df_out_traces = pd.read_csv('output_traces_hh.csv')
 inp_traces = df_inp_traces.to_numpy()
-inp_traces = inp_traces[:3, 1:]
+inp_traces = inp_traces[:2, 1:]
 out_traces = df_out_traces.to_numpy()
-out_traces = out_traces[:3, 1:]
+out_traces = out_traces[:2, 1:]
 
 # Model and its parameters
 area = 20_000 * um ** 2
@@ -32,14 +34,26 @@
     Cm   : farad (constant)
 '''
 
+# Time domain
+t = arange(0, out_traces.shape[1] * dt / ms, dt / ms)
+
+
+def n_peaks(x):
+    n_p = []
+    for _x in x.transpose():
+        p_i = find_peaks(_x, height=0)[0]
+        n_p.append(p_i.size - sum(np.diff(t[p_i]) < 4))
+    return n_p
+
+
 # Simulation-based inference object instantiation
 inferencer = Inferencer(dt=dt, model=eqs,
                         input={'I': inp_traces * amp},
                         output={'v': out_traces * mV},
-                        features=[
-                            lambda x: x.mean(axis=0),
-                            lambda x: x.std(axis=0),
-                            lambda x: x.ptp(axis=0)],
+                        features=[n_peaks,
+                                  lambda x: x[(t > 5) & (t < 10), :].mean(axis=0),
+                                  lambda x: x[(t > 5) & (t < 10), :].std(axis=0),
+                                  lambda x: x.ptp(axis=0)],
                         method='exponential_euler',
                         threshold='m > 0.5',
                         refractory='m > 0.5',
@@ -54,7 +68,7 @@
                   Cm=[0.1 * uF * cm ** -2 * area, 2 * uF * cm ** -2 * area])
 
 # Draw samples from posterior
-inferencer.sample((1000,))
+inferencer.sample((10000,))
 
 # Create pairplot from samples
 labels_params = [r'$\overline{g}_{l}$', r'$\overline{g}_{Na}$',
@@ -65,15 +79,14 @@
 inf_traces = inferencer.generate_traces()
 
 # Visualize traces
-t = arange(0, out_traces.shape[1] * dt / ms, dt / ms)
 nrows = 2
 ncols = out_traces.shape[0]
 fig, axs = subplots(nrows, ncols, sharex=True,
                     gridspec_kw={'height_ratios': [3, 1]}, figsize=(15, 4))
 for idx in range(ncols):
-    axs[0, idx].plot(out_traces[idx, :].T, label='measurements')
-    axs[0, idx].plot(inf_traces[idx, :].T / mV, label='fits')
-    axs[1, idx].plot(inp_traces[idx, :].T / amp, 'k-', label='stimulus')
+    axs[0, idx].plot(t, out_traces[idx, :].T, label='measurements')
+    axs[0, idx].plot(t, inf_traces[idx, :].T / mV, label='fits')
+    axs[1, idx].plot(t, inp_traces[idx, :].T / amp, 'k-', label='stimulus')
     axs[1, idx].set_xlabel('t, ms')
     if idx == 0:
         axs[0, idx].set_ylabel('$v$, mV')

From b6a394d820c320bc89d7567c05d15fe81502247e Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Wed, 7 Jul 2021 10:34:40 +0200
Subject: [PATCH 032/121] fix refactory variable default value

---
 brian2modelfitting/inferencer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 25e6942..8f2a0ed 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -172,7 +172,7 @@ class Inferencer(object):
     reset : str, optional
         The (possibly multi-line) string with the code to execute on
         reset.
-    refractory : str, optional
+    refractory : bool or str, optional
         Either the length of the refractory period (e.g., ``2*ms``), a
         string expression that evaluates to the length of the
         refractory period after each spike, e.g., ``'(1 + rand())*ms'``,
@@ -184,7 +184,7 @@ class Inferencer(object):
         values.
     """
     def __init__(self, dt, model, input, output, features, method=None,
-                 threshold=None, reset=None, refractory=None, param_init=None):
+                 threshold=None, reset=None, refractory=False, param_init=None):
         # time scale
         self.dt = dt
 

From c250909073856849d90a2fcbdfe971746483ec74 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 8 Jul 2021 17:27:48 +0200
Subject: [PATCH 033/121] added separate methods for storing and loading data +
 additional refactoring

---
 brian2modelfitting/inferencer.py | 337 ++++++++++++++++++++-----------
 1 file changed, 220 insertions(+), 117 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 8f2a0ed..237d2a3 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -126,7 +126,7 @@ def calc_prior(param_names, **params):
     """
     for param_name in param_names:
         if param_name not in params:
-            raise TypeError(f'"Bounds must be set for parameter {param_name}')
+            raise TypeError(f'Bounds must be set for parameter {param_name}')
     prior_min = []
     prior_max = []
     for param_name in param_names:
@@ -184,7 +184,8 @@ class Inferencer(object):
         values.
     """
     def __init__(self, dt, model, input, output, features, method=None,
-                 threshold=None, reset=None, refractory=False, param_init=None):
+                 threshold=None, reset=None, refractory=False,
+                 param_init=None):
         # time scale
         self.dt = dt
 
@@ -284,6 +285,8 @@ def __init__(self, dt, model, input, output, features, method=None,
         x_o = torch.tensor(x_o, dtype=torch.float32)
         self.x_o = x_o
         self.features = features
+        self.theta = None
+        self.x = None
 
     @property
     def n_neurons(self):
@@ -369,7 +372,7 @@ def setup_simulator(self, network_name, n_neurons, output_var, param_init,
         simulator.initialize(network, param_init, name=network_name)
         return simulator
 
-    def initialize_prior(self, **params):
+    def init_prior(self, **params):
         """Return the prior uniform distribution over parameters.
 
         Parameters
@@ -389,7 +392,7 @@ def initialize_prior(self, **params):
         prior = calc_prior(self.param_names, **params)
         return prior
 
-    def generate_training_data(self, n_samples, prior, save=False):
+    def generate_training_data(self, n_samples, prior):
         """Return sampled prior and executed simulator containing
         recorded variables to be used for training the neural density
         estimator.
@@ -400,11 +403,6 @@ def generate_training_data(self, n_samples, prior, save=False):
             The number of samples.
         prior : sbi.utils.BoxUniform
             Uniformly distributed prior over given parameters.
-        save : bool or str, optional
-            Save training data for later use into ``.npz`` file. If set
-            to True, sampled prior over parameters will be stored to a
-            ``theta.npz`` file. Otherwise, if set to a string the name
-            of the file will be forwarded string value.
 
         Returns
         -------
@@ -418,15 +416,9 @@ def generate_training_data(self, n_samples, prior, save=False):
         theta = prior.sample((n_samples, ))
         theta = np.atleast_2d(theta.numpy())
 
-        if save:
-            if isinstance(save, (str, )):
-                fn = save + '.npz'
-            else:
-                fn = 'theta.npz'
-            np.savez(fn, theta)
         return theta
 
-    def extract_summary_statistics(self, theta, features, save=False, level=1):
+    def extract_summary_statistics(self, theta, level=1):
         """Return summary statistics to be used for training the neural
         density estimator.
 
@@ -434,14 +426,6 @@ def extract_summary_statistics(self, theta, features, save=False, level=1):
         ----------
         theta : numpy.ndarray
             Sampled prior of shape (``n_samples``, -1).
-        features : list
-            List of callables that take the voltage trace and output
-            summary statistics stored in `numpy.array`.
-         save : bool or str, optional
-            Save training data for later use into ``.npz`` file. If set
-            to True, extracted features will be stored to a ``x.npz``
-            file. Otherwise, if set to a string the name of the file
-            will be forwarded string value.
         level : int, optional
             How far to go back to get the locals/globals.
 
@@ -472,19 +456,77 @@ def extract_summary_statistics(self, theta, features, save=False, level=1):
         for ov in self.output_var:
             x_val = obs[ov].get_value()
             summary_statistics = []
-            for feature in features:
+            for feature in self.features:
                 summary_statistics.append(feature(x_val))
             x.append(summary_statistics)
         x = np.array(x, dtype=np.float32)
         x = x.reshape((self.n_samples, -1))
-        if save:
-            if isinstance(save, (str, )):
-                fn = save + '.npz'
-            else:
-                fn = 'x.npz'
-            np.savez(fn, theta)
         return x
 
+    def save_summary_statistics(self, f, theta=None, x=None):
+        """Save sampled prior data, theta, and extracted summary
+        statistics, x, into a single file in compressed ``.pz`` format.
+
+        Parameters
+        ----------
+        f : str or os.PathLike
+            Path to file either as string or ``os.PathLike`` object
+            that contains file name.
+        theta : numpy.ndarray, optional
+            Sampled prior.
+        x : numpy.ndarray, optional
+            Summary statistics.
+
+        Returns
+        -------
+        None
+        """
+        if theta is not None:
+            t = theta
+        elif self.theta is not None:
+            t = self.theta
+        else:
+            raise AttributeError('Provide sampled prior or call '
+                                 '``infere_step``method first.')
+        if x is not None:
+            pass
+        elif self.x is not None:
+            x = self.x
+        else:
+            raise AttributeError('Provide summary feautures or call '
+                                 '``infere_step``method first.')
+        np.savez_compressed(f, theta=t, x=x)
+
+    def load_summary_statistics(self, f, **kwargs):
+        """Load sampled prior data, theta, and extracted summary
+        statistics, x, from a compressed ``.npz`` format. Arrays should
+        be named either `arr_0` and `arr_1` or `theta` and `x` for
+        sampled priors and extracted features, respectively.
+
+        Parameters
+        ----------
+        f : str or os.PathLike
+            Path to file either as string or ``os.PathLike`` object
+            that contains file name.
+        kwargs : dict, optional
+            Additional keyword arguments for ``numpy.load`` method.
+
+        Returns
+        -------
+        tuple
+            Consisting of sampled prior and summary statistics arrays.
+        """
+        loaded = np.load(f, allow_pickle=True)
+        if loaded.files == ['theta', 'x'] or loaded.files == ['x', 'theta']:
+            theta = loaded['theta']
+            x = loaded['x']
+        elif loaded.files == ['arr_0', 'arr_1']:
+            theta = loaded['arr_0']
+            x = loaded['arr_1']
+        self.theta = theta
+        self.x = x
+        return (theta, x)
+
     def init_inference(self, inference_method, density_estimator_model, prior,
                        **inference_kwargs):
         """Return instantiated inference object.
@@ -595,25 +637,89 @@ def build_posterior(self, inference, density_estimator,
                                               **posterior_kwargs)
         return (inference, posterior)
 
-    def infere(self, n_samples, n_rounds=1, inference_method='SNPE',
-               density_estimator_model='maf', inference_kwargs={},
-               train_kwargs={}, posterior_kwargs={}, **params):
+    def infere_step(self, proposal, inference,
+                    n_samples=None, theta=None, x=None, level=1,
+                    train_kwargs={}, posterior_kwargs={}, *args):
         """Return the trained neural density estimator.
 
-        Currently only sequential neural posterior estimator is
-        supported.
-
         Parameter
         ---------
-        n_samples : int
+        proposal : ``sbi.utils.torchutils.BoxUniform``
+            Prior over parameters for current inference round.
+        inference : ``sbi.inference.NeuralInference``
+            Inference object obtained via `.init_inference` method.
+        n_samples : int, optional
             The number of samples.
+        theta : torch.tensor, optional
+            Sampled prior.
+        x : torch.tensor, optional
+            Summary statistics.
+        level : int, optional
+        train_kwargs : dict, optional
+            Dictionary of arguments for training a posterior estimator.
+        posterior_kwargs : dict, optional
+            Dictionary of arguments for `.build_posterior` method.
+        args : list, optional
+            Additional arguments for train method if SNPE is used as
+            an inference method.
+
+        Returns
+        -------
+        sbi.inference.NeuralPosterior
+            Trained posterior.
+        """
+        # extract the training data and make adjustments for ``sbi``
+        if theta is None:
+            if n_samples is None:
+                raise ValueError('Either provide ``theta`` or ``n_samples``.')
+            else:
+                theta = self.generate_training_data(n_samples, proposal)
+        self.theta = theta
+        theta = torch.tensor(theta, dtype=torch.float32)
+
+        # extract the summary statistics and make adjustments for ``sbi``
+        if x is None:
+            if n_samples is None:
+                raise ValueError('Either provide ``x`` or ``n_samples``.')
+            else:
+                x = self.extract_summary_statistics(theta, level=level+1)
+        self.x = x
+        x = torch.tensor(x)
+
+        # pass the simulated data to the inference object and train it
+        print('Training the neural density estimator...')
+        inference, density_estimator = self.train(inference,
+                                                  theta, x,
+                                                  *args, **train_kwargs)
+
+        # use the density estimator to build the posterior
+        inference, posterior = self.build_posterior(inference,
+                                                    density_estimator,
+                                                    **posterior_kwargs)
+        self.posterior = posterior
+        return posterior
+
+    def infere(self, n_rounds=1,
+               n_samples=None, theta=None, x=None,
+               inference_method='SNPE', density_estimator_model='maf',
+               inference_kwargs={}, train_kwargs={}, posterior_kwargs={},
+               **params):
+        """Return the trained neural density estimator.
+
+        Parameter
+        ---------
         n_rounds : int or str, optional
             If ``n_rounds`` is set to 1, amortized inference will be
             performed. Otherwise, if ``n_rounds`` is integer larger
             than 1, multi-round inference will be performed.
+        n_samples : int
+            The number of samples.
+        theta : torch.tensor
+            Sampled prior.
+        x : torch.tensor
+            Summary statistics.
         inference_method : str
-            Inference method. Either of SNPE, SNLE or SNRE. Currently,
-            only SNPE is supported.
+            Inference method. Either of SNPE, SNLE or SNRE.
         density_estimator_model : str
             The type of density estimator to be created. Either
             ``mdn``, ``made``, ``maf`` or ``nsf``.
@@ -643,7 +749,26 @@ def infere(self, n_samples, n_rounds=1, inference_method='SNPE',
                              'supported. Choose between SNPE, SNLE or SNRE.')
 
         # initialize prior
-        prior = self.initialize_prior(**params)
+        if self.posterior is None:
+            prior = self.init_prior(**params)
+        else:
+            prior = self.posterior.set_default_x(self.x_o)
+
+        # extract the training data and make adjustments for ``sbi``
+        if theta is None:
+            if n_samples is None:
+                raise ValueError('Either provide ``theta`` or ``n_samples``.')
+            else:
+                theta = self.generate_training_data(n_samples, prior)
+        self.theta = theta
+
+        # extract the summary statistics and make adjustments for ``sbi``
+        if x is None:
+            if n_samples is None:
+                raise ValueError('Either provide ``x`` or ``n_samples``.')
+            else:
+                x = self.extract_summary_statistics(theta)
+        self.x = x
 
         # initialize inference object
         inference = self.init_inference(inference_method,
@@ -661,34 +786,56 @@ def infere(self, n_samples, n_rounds=1, inference_method='SNPE',
         for round in range(n_rounds):
             print(f'Round {round + 1}/{n_rounds}.')
 
-            # extract the training data and make adjustments for ``sbi``
-            print('Generating training data...')
-            theta = self.generate_training_data(n_samples, proposal)
-            theta = torch.tensor(theta, dtype=torch.float32)
-
-            # extract the summary statistics and make adjustments for ``sbi``
-            x = self.extract_summary_statistics(theta, self.features)
-            x = torch.tensor(x)
-
-            # pass the simulated data to the inference object and train it
-            print('Training the neural density estimator...')
-            inference, density_estimator = self.train(inference,
-                                                      theta, x,
-                                                      *args, **train_kwargs)
-
-            # use the density estimator to build the posterior
-            inference, posterior = self.build_posterior(inference,
-                                                        density_estimator,
-                                                        **posterior_kwargs)
+            posterior = self.infere_step(proposal, inference,
+                                         n_samples, self.theta, self.x, 1,
+                                         train_kwargs, posterior_kwargs, *args)
 
             # append the current posterior to the list of posteriors
             posteriors.append(posterior)
 
             # update the proposal given the observation
-            proposal = posterior.set_default_x(self.x_o)
+            if n_rounds > 1:
+                proposal = posterior.set_default_x(self.x_o)
         self.posterior = posterior
         return posterior
 
+    def save_posterior(self, f):
+        """Saves the density estimator state dictionary to a disk file.
+
+        Parameters
+        ----------
+        posterior : sbi.inference.posteriors.DirectPosterior, optional
+            Posterior distribution.
+        f : str or os.PathLike
+            Path to file either as string or ``os.PathLike`` object
+            that contains file name.
+
+        Returns
+        -------
+        None
+        """
+        torch.save(self.posterior, f)
+
+    def load_posterior(self, f):
+        """Loads the density estimator state dictionary from a disk file.
+
+        Parameters
+        ----------
+        f : str or os.PathLike
+            Path to file either as string or ``os.PathLike`` object
+            that contains file name.
+
+        Returns
+        -------
+        sbi.inference.NeuralPosterior
+            Loaded neural posterior with defined method family, density
+            estimator state dictionary, prior over parameters and
+            output shape of the simulator.
+        """
+        p = torch.load(f)
+        self.posterior = p
+        return p
+
     def sample(self, shape, posterior=None, **kwargs):
         """Return samples from posterior distribution.
 
@@ -708,13 +855,13 @@ def sample(self, shape, posterior=None, **kwargs):
         """
         if posterior:
             p = posterior
-        else:
+        elif posterior is None and self.posterior:
             p = self.posterior
-        if not p:
-            raise ValueError("Need to provide posterior argument if no "
-                             "posterior has been calculated by the 'infere' "
-                             "method.")
-        samples = p.sample(shape, **kwargs)
+        else:
+            raise ValueError('Need to provide posterior argument if no '
+                             'posterior has been calculated by the ``infere`` '
+                             'method.')
+        samples = p.sample(shape, x=self.x_o, **kwargs)
         self.samples = samples
         return samples
 
@@ -778,12 +925,13 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
         # sample a single set of parameters from posterior distribution
         if posterior:
             p = posterior
+        elif posterior is None and self.posterior:
+            p = self.posterior
         else:
-            try:
-                p = self.posterior
-            except NameError as e:
-                print(e, 'Posterior object is not found.')
-        params = p.sample((1, ))
+            raise ValueError('Need to provide posterior argument if no '
+                             'posterior has been calculated by the ``infere`` '
+                             'method.')
+        params = p.sample((1, ), x=self.x_o)
 
         # set output variable that is monitored
         if output_var is None:
@@ -817,48 +965,3 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
         else:
             traces = getattr(simulator.statemonitor, output_var[0])[:]
         return traces
-
-    def save(self, f, posterior=None):
-        """Saves the density estimator state dictionary to a disk file.
-
-        Parameters
-        ----------
-        posterior : sbi.inference.posteriors.DirectPosterior, optional
-            Posterior distribution.
-        f : str or os.PathLike
-            Path to file either as string or ``os.PathLike`` object
-            that contains file name.
-
-        Returns
-        -------
-        None
-        """
-        if posterior:
-            p = posterior
-        else:
-            p = self.posterior
-        if not p:
-            raise ValueError("Need to provide posterior argument if no "
-                             "posterior has been calculated by the 'infere' "
-                             "method.")
-        torch.save(p, f)
-
-    def load_posterior(self, f):
-        """Loads the density estimator state dictionary from a disk file.
-
-        Parameters
-        ----------
-        f : str or os.PathLike
-            Path to file either as string or ``os.PathLike`` object
-            that contains file name.
-
-        Returns
-        -------
-        sbi.inference.NeuralPosterior
-            Loaded neural posterior with defined method family, density
-            estimator state dictionary, prior over parameters and
-            output shape of the simulator.
-        """
-        p = torch.load(f)
-        self.posterior = p
-        return p

From 88a1a39164d25c63a0668956ad41d35dd952c5a3 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 8 Jul 2021 17:28:30 +0200
Subject: [PATCH 034/121] add small changes due to changes in Inferencer class

---
 examples/hh_sbi.py | 40 ++++++++++++++++++++--------------------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/examples/hh_sbi.py b/examples/hh_sbi.py
index f25a266..280808c 100644
--- a/examples/hh_sbi.py
+++ b/examples/hh_sbi.py
@@ -14,19 +14,19 @@
 out_traces = out_traces[:2, 1:]
 
 # Model and its parameters
-area = 20_000 * um ** 2
-El = -65 * mV
-EK = -90 * mV
-ENa = 50 * mV
-VT = -63 * mV
-dt = 0.01 * ms
+area = 20_000*um**2
+El = -65*mV
+EK = -90*mV
+ENa = 50*mV
+VT = -63*mV
+dt = 0.01*ms
 eqs = '''
     dv/dt = (gl*(El-v) - g_na*(m*m*m)*h*(v-ENa) - g_kd*(n*n*n*n)*(v-EK) + I)/Cm : volt
     dm/dt = 0.32*(mV**-1)*(13.*mV-v+VT)/
-        (exp((13.*mV-v+VT)/(4.*mV))-1.)/ms*(1-m)-0.28*(mV**-1)*(v-VT-40.*mV)/
-        (exp((v-VT-40.*mV)/(5.*mV))-1.)/ms*m : 1
+            (exp((13.*mV-v+VT)/(4.*mV))-1.)/ms*(1-m)-0.28*(mV**-1)*(v-VT-40.*mV)/
+            (exp((v-VT-40.*mV)/(5.*mV))-1.)/ms*m : 1
     dn/dt = 0.032*(mV**-1)*(15.*mV-v+VT)/
-        (exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1
+            (exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1
     dh/dt = 0.128*exp((17.*mV-v+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-v+VT)/(5.*mV)))/ms*h : 1
     g_na : siemens (constant)
     g_kd : siemens (constant)
@@ -35,7 +35,7 @@
 '''
 
 # Time domain
-t = arange(0, out_traces.shape[1] * dt / ms, dt / ms)
+t = arange(0, out_traces.shape[1]*dt/ms, dt/ms)
 
 
 def n_peaks(x):
@@ -48,8 +48,8 @@ def n_peaks(x):
 
 # Simulation-based inference object instantiation
 inferencer = Inferencer(dt=dt, model=eqs,
-                        input={'I': inp_traces * amp},
-                        output={'v': out_traces * mV},
+                        input={'I': inp_traces*amp},
+                        output={'v': out_traces*mV},
                         features=[n_peaks,
                                   lambda x: x[(t > 5) & (t < 10), :].mean(axis=0),
                                   lambda x: x[(t > 5) & (t < 10), :].std(axis=0),
@@ -60,12 +60,12 @@ def n_peaks(x):
                         param_init={'v': 'VT'})
 
 # Generate prior and train the neural density estimator
-inferencer.infere(n_samples=1000,
-                  n_rounds=1,
-                  gl=[1e-09 * siemens, 1e-07 * siemens],
-                  g_na=[2e-06 * siemens, 2e-04 * siemens],
-                  g_kd=[6e-07 * siemens, 6e-05 * siemens],
-                  Cm=[0.1 * uF * cm ** -2 * area, 2 * uF * cm ** -2 * area])
+inferencer.infere(n_rounds=2,
+                  n_samples=1000,
+                  gl=[1e-09*siemens, 1e-07*siemens],
+                  g_na=[2e-06*siemens, 2e-04*siemens],
+                  g_kd=[6e-07*siemens, 6e-05*siemens],
+                  Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])
 
 # Draw samples from posterior
 inferencer.sample((10000,))
@@ -85,8 +85,8 @@ def n_peaks(x):
                     gridspec_kw={'height_ratios': [3, 1]}, figsize=(15, 4))
 for idx in range(ncols):
     axs[0, idx].plot(t, out_traces[idx, :].T, label='measurements')
-    axs[0, idx].plot(t, inf_traces[idx, :].T / mV, label='fits')
-    axs[1, idx].plot(t, inp_traces[idx, :].T / amp, 'k-', label='stimulus')
+    axs[0, idx].plot(t, inf_traces[idx, :].T/mV, label='fits')
+    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, 'k-', label='stimulus')
     axs[1, idx].set_xlabel('t, ms')
     if idx == 0:
         axs[0, idx].set_ylabel('$v$, mV')

From a1eb97adc1888e3fbc7592b7ae32d4a8b4eb2beb Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 8 Jul 2021 17:28:47 +0200
Subject: [PATCH 035/121] add integrate-and-fire neuron example

---
 examples/IF_sbi.py | 77 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 77 insertions(+)
 create mode 100644 examples/IF_sbi.py

diff --git a/examples/IF_sbi.py b/examples/IF_sbi.py
new file mode 100644
index 0000000..17ecbdb
--- /dev/null
+++ b/examples/IF_sbi.py
@@ -0,0 +1,77 @@
+from brian2 import *
+from brian2modelfitting import *
+from scipy.signal import find_peaks
+from scipy.stats import kurtosis, skew
+
+
+# Generate input and output data traces
+dt = 0.1 * ms
+defaultclock.dt = dt
+inp_trace = hstack([zeros(int(round(5*ms/dt))),
+                    ones(int(round(25*ms/dt))),
+                    zeros(int(round(30*ms/dt)))]) * 10 * nA
+I = TimedArray(inp_trace, dt=dt)
+El = -70 * mV
+VT = -50 * mV
+DeltaT = 2 * mV
+eqs = '''
+    dv/dt = (gl*(El - v) + gl*DeltaT*exp((v - VT) / DeltaT) + I(t)) / C : volt
+    gl    : siemens (constant)
+    C     : farad (constant)
+'''
+
+neurons = NeuronGroup(1, eqs,
+                      threshold='v > -50 * mV',
+                      reset='v = -70 * mV',
+                      method='exponential_euler')
+neurons.v = -70 * mV
+neurons.set_states({'gl': 30 * nS,
+                    'C': 1 * nF})
+monitor = StateMonitor(neurons, 'v', record=True)
+run(60 * ms)
+out_trace = monitor.v
+
+# Inference
+start_scope()
+El = -70 * mV
+VT = -50 * mV
+DeltaT = 2 * mV
+eqs_inf = '''
+    dv/dt = (gl*(El - v) + gl*DeltaT*exp((v - VT) / DeltaT) + I_syn) / C : volt
+    gl    : siemens (constant)
+    C     : farad (constant)
+'''
+
+t = arange(0, out_trace.shape[1] * dt / ms, dt / ms)
+start_syn = t[min(where(inp_trace != 0)[0])]
+end_syn = t[max(where(inp_trace != 0)[0])]
+
+def n_peaks(x):
+    n_p = []
+    for _x in x.transpose():
+        p_i = find_peaks(_x, height=-60)[0]
+        n_p.append(p_i.size)
+    return n_p
+
+
+inferencer = Inferencer(dt=dt, model=eqs_inf,
+                        input={'I_syn': inp_trace.reshape(1, -1)},
+                        output={'v': out_trace},
+                        features=[lambda x: x[(t > start_syn) & (t < end_syn), :].mean(axis=0),
+                                  lambda x: x[(t > start_syn) & (t < end_syn), :].std(axis=0),
+                                  lambda x: x[(t > start_syn) & (t < end_syn), :].ptp(axis=0),
+                                  n_peaks],
+                        method='exponential_euler',
+                        threshold='v > -50 * mV',
+                        reset='v = -70 * mV',
+                        param_init={'v': -70 * mV})
+
+inferencer.infere(n_samples=10_000,
+                  inference_method='SNPE',
+                  density_estimator_model='mdn',
+                  gl=[10*nS, 100*nS],
+                  C=[0.1*nF, 10*nF])
+
+samples = inferencer.sample((10_000, ))
+inferencer.pairplot(samples)
+show()

From 7ea20988a514cdb9ec68409fd5040900a52dd5af Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 8 Jul 2021 17:29:01 +0200
Subject: [PATCH 036/121] add advanced example

---
 examples/hh_sbi_advanced.py | 134 ++++++++++++++++++++++++++++++++++++
 1 file changed, 134 insertions(+)
 create mode 100644 examples/hh_sbi_advanced.py

diff --git a/examples/hh_sbi_advanced.py b/examples/hh_sbi_advanced.py
new file mode 100644
index 0000000..5de50a6
--- /dev/null
+++ b/examples/hh_sbi_advanced.py
@@ -0,0 +1,134 @@
+from brian2 import *
+from brian2modelfitting import *
+import pandas as pd
+import numpy as np
+from scipy.signal import find_peaks
+
+
+# Load input and output data traces
+df_inp_traces = pd.read_csv('input_traces_hh.csv')
+df_out_traces = pd.read_csv('output_traces_hh.csv')
+inp_traces = df_inp_traces.to_numpy()
+inp_traces = inp_traces[:2, 1:]
+out_traces = df_out_traces.to_numpy()
+out_traces = out_traces[:2, 1:]
+
+# Model and its parameters
+area = 20_000*um**2
+El = -65*mV
+EK = -90*mV
+ENa = 50*mV
+VT = -63*mV
+dt = 0.01*ms
+eqs = '''
+    dv/dt = (gl*(El-v) - g_na*(m*m*m)*h*(v-ENa) - g_kd*(n*n*n*n)*(v-EK) + I)/Cm : volt
+    dm/dt = 0.32*(mV**-1)*(13.*mV-v+VT)/
+            (exp((13.*mV-v+VT)/(4.*mV))-1.)/ms*(1-m)-0.28*(mV**-1)*(v-VT-40.*mV)/
+            (exp((v-VT-40.*mV)/(5.*mV))-1.)/ms*m : 1
+    dn/dt = 0.032*(mV**-1)*(15.*mV-v+VT)/
+            (exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1
+    dh/dt = 0.128*exp((17.*mV-v+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-v+VT)/(5.*mV)))/ms*h : 1
+    g_na : siemens (constant)
+    g_kd : siemens (constant)
+    gl   : siemens (constant)
+    Cm   : farad (constant)
+'''
+
+# Time domain
+t = arange(0, out_traces.shape[1]*dt/ms, dt/ms)
+
+
+def n_peaks(x):
+    n_p = []
+    for _x in x.transpose():
+        p_i = find_peaks(_x, height=0)[0]
+        n_p.append(p_i.size - sum(np.diff(t[p_i]) < 4))
+    return n_p
+
+
+# Step-by-step inference
+# Start with the regular instatiation of the class
+inferencer = Inferencer(dt=dt, model=eqs,
+                        input={'I': inp_traces*amp},
+                        output={'v': out_traces*mV},
+                        features=[n_peaks,
+                                  lambda x: x[(t > 5) & (t < 10), :].mean(axis=0),
+                                  lambda x: x[(t > 5) & (t < 10), :].std(axis=0),
+                                  lambda x: x.ptp(axis=0)],
+                        method='exponential_euler',
+                        threshold='m > 0.5',
+                        refractory='m > 0.5',
+                        param_init={'v': 'VT'})
+
+# Data generation
+# Initializing of the prior
+prior = inferencer.init_prior(gl=[1e-09*siemens, 1e-07*siemens],
+                              g_na=[2e-06*siemens, 2e-04*siemens],
+                              g_kd=[6e-07*siemens, 6e-05*siemens],
+                              Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])
+# Generate training data
+theta = inferencer.generate_training_data(n_samples=1000,
+                                          prior=prior)
+# Extract summary stats
+x = inferencer.extract_summary_statistics(theta=theta, level=0)
+
+# Loading and storing of the data
+# For large training data, you can store it into .npz and reuse it later
+path_to_data = __file__[:-3] + '_data.npz'
+inferencer.save_summary_statistics(path_to_data, theta, x)
+# Training data will be directly set to class variables once they are loaded
+theta_loaded, x_loaded = inferencer.load_summary_statistics(path_to_data)
+
+# Amortized inference
+# Training the neural density estimator
+inference = inferencer.init_inference(inference_method='SNPE',
+                                      density_estimator_model='maf',
+                                      prior=prior,
+                                      hidden_features=50,
+                                      num_transforms=10,
+                                      num_bins=20,
+                                      num_components=10)
+# First round of inference where no observation data is set to posterior
+posterior = inferencer.infere_step(proposal=prior,
+                                   inference=inference,
+                                   theta=theta_loaded, x=x_loaded,
+                                   train_kwargs={'learning_rate': 1e-4})
+# Storing the trained posterior without a default observation
+path_to_posterior = __file__[:-3] + '_posterior.pth'
+inferencer.save_posterior(path_to_posterior)
+# Loading the trained posterior directly to Inferencer class
+posterior_loaded = inferencer.load_posterior(path_to_posterior)
+
+# Sampling from the posterior given observations, ...
+inferencer.sample((10000, ), posterior=posterior_loaded)
+# ...visualize the samples, ...
+inferencer.pairplot(labels=[r'$\overline{g}_{l}$',
+                            r'$\overline{g}_{Na}$',
+                            r'$\overline{g}_{K}$',
+                            r'$\overline{C}_{m}$'])
+# ... and generate traces from a single sample of parameters
+inf_traces = inferencer.generate_traces(posterior=posterior_loaded)
+nrows = 2
+ncols = out_traces.shape[0]
+fig, axs = subplots(nrows, ncols, sharex=True,
+                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(15, 4))
+for idx in range(ncols):
+    axs[0, idx].plot(t, out_traces[idx, :].T, label='measurements')
+    axs[0, idx].plot(t, inf_traces[idx, :].T/mV, label='fits')
+    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, 'k-', label='stimulus')
+    axs[1, idx].set_xlabel('t, ms')
+    if idx == 0:
+        axs[0, idx].set_ylabel('$v$, mV')
+        axs[1, idx].set_ylabel('$I$, nA')
+handles, labels = [(h + l) for h, l
+                   in zip(axs[0, idx].get_legend_handles_labels(),
+                   axs[1, idx].get_legend_handles_labels())]
+fig.legend(handles, labels)
+tight_layout()
+show()
+
+# Optionally, continue the multiround inference using the ``infere`` method
+posterior_multi_round = inferencer.infere(n_rounds=2,
+                                          theta=theta_loaded, x=x_loaded,
+                                          inference_method='SNPE',
+                                          density_estimator_model='maf')

From 19a3cba9e88de235753d3592b60192cb14d85fbb Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 9 Jul 2021 11:57:51 +0200
Subject: [PATCH 037/121] clean up

---
 brian2modelfitting/inferencer.py | 60 ++++++++++++++++++++------------
 1 file changed, 37 insertions(+), 23 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 237d2a3..e7e8a4f 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -638,7 +638,7 @@ def build_posterior(self, inference, density_estimator,
         return (inference, posterior)
 
     def infere_step(self, proposal, inference,
-                    n_samples=None, theta=None, x=None, level=1,
+                    n_samples=None, theta=None, x=None,
                     train_kwargs={}, posterior_kwargs={}, *args):
         """Return the trained neural density estimator.
 
@@ -650,17 +650,17 @@ def infere_step(self, proposal, inference,
             Inference object obtained via `.init_inference` method.
         n_samples : int, optional
             The number of samples.
-        theta : torch.tensor, optional
+        theta : numpy.ndarray, optional
             Sampled prior.
-        x : torch.tensor, optional
+        x : numpy.ndarray, optional
             Summary statistics.
-        level : int, optional
         train_kwargs : dict, optional
-            Dictionary of arguments for training a posterior estimator.
+            Additional keyword arguments for training the posterior
+            estimator.
         posterior_kwargs : dict, optional
             Dictionary of arguments for `.build_posterior` method.
         args : list, optional
-            Additional arguments for train method if SNPE is used as
+            Additional arguments for `.train` method if SNPE is used as
             an inference method.
 
         Returns
@@ -682,12 +682,11 @@ def infere_step(self, proposal, inference,
             if n_samples is None:
                 raise ValueError('Either provide ``x`` or ``n_samples``.')
             else:
-                x = self.extract_summary_statistics(theta, level=level+1)
+                x = self.extract_summary_statistics(theta, level=2)
         self.x = x
         x = torch.tensor(x)
 
         # pass the simulated data to the inference object and train it
-        print('Training the neural density estimator...')
         inference, density_estimator = self.train(inference,
                                                   theta, x,
                                                   *args, **train_kwargs)
@@ -699,35 +698,40 @@ def infere_step(self, proposal, inference,
         self.posterior = posterior
         return posterior
 
-    def infere(self, n_rounds=1,
-               n_samples=None, theta=None, x=None,
+    def infere(self, n_samples=None, theta=None, x=None, n_rounds=1,
                inference_method='SNPE', density_estimator_model='maf',
                inference_kwargs={}, train_kwargs={}, posterior_kwargs={},
                **params):
-        """Return the trained neural density estimator.
+        """Return the trained posterior.
+
+        If ``theta`` and ``x`` are not provided, ``n_samples`` has to
+        be defined. Otherwise, if ``n_samples`` is provided, neither
+        ``theta`` nor ``x`` needs to be provided.
 
         Parameter
         ---------
-        n_rounds : int or str, optional
-            If ``n_rounds`` is set to 1, amortized inference will be
-            performed. Otherwise, if ``n_rounds`` is integer larger
-            than 1, multi-round inference will be performed.
-        n_samples : int
+        n_samples : int, optional
             The number of samples.
-        theta : torch.tensor
+        theta : numpy.ndarray, optional
             Sampled prior.
-        x : torch.tensor
+        x : numpy.ndarray, optional
             Summary statistics.
-        inference_method : str
+        n_rounds : int, optional
+            If ``n_rounds`` is set to 1, amortized inference will be
+            performed. Otherwise, if ``n_rounds`` is integer larger
+            than 1, multi-round inference will be performed. This is
+            only valid if posterior has not been defined manually.
+        inference_method : str, optional
             Inference method. Either of SNPE, SNLE or SNRE.
-        density_estimator_model : str
+        density_estimator_model : str, optional
             The type of density estimator to be created. Either
             ``mdn``, ``made``, ``maf`` or ``nsf``.
         inference_kwargs : dict, optional
             Additional keyword arguments for
             ``sbi.utils.get_nn_models.posterior_nn`` method.
         train_kwargs : dict, optional
-            Dictionary of arguments for training the posterior estimator.
+            Additional keyword arguments for training the posterior
+            estimator.
         posterior_kwargs : dict, optional
             Dictionary of arguments for `.build_posterior` method.
         params : dict
@@ -738,8 +742,11 @@ def infere(self, n_rounds=1,
         sbi.inference.NeuralPosterior
             Trained posterior.
         """
+        # handle the number of rounds
         if not isinstance(n_rounds, int):
             raise ValueError('Number of rounds must be a positive integer.')
+
+        # handle inference methods
         try:
             inference_method = str.upper(inference_method)
         except ValueError as e:
@@ -776,18 +783,25 @@ def infere(self, n_rounds=1,
                                         prior,
                                         **inference_kwargs)
 
-        # allocate empty list of posteriors
+        # allocate empty list of posterior
         posteriors = []
+
+        # set a proposal
         proposal = prior
+
+        # additional arguments for `.train` method are needed only for SNPE
         if inference_method == 'SNPE':
             args = [proposal]
         else:
             args = []
+
+        # main inference loop
         for round in range(n_rounds):
             print(f'Round {round + 1}/{n_rounds}.')
 
+            # inference step
             posterior = self.infere_step(proposal, inference,
-                                         n_samples, self.theta, self.x, 1,
+                                         n_samples, self.theta, self.x,
                                          train_kwargs, posterior_kwargs, *args)
 
             # append the current posterior to the list of posteriors

From be02ef3bfab8b4cf4996c4fd68e17885f448df86 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 9 Jul 2021 11:58:18 +0200
Subject: [PATCH 038/121] add better pairplotting

---
 examples/IF_sbi.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/examples/IF_sbi.py b/examples/IF_sbi.py
index 17ecbdb..199fad4 100644
--- a/examples/IF_sbi.py
+++ b/examples/IF_sbi.py
@@ -1,7 +1,6 @@
 from brian2 import *
 from brian2modelfitting import *
 from scipy.signal import find_peaks
-from scipy.stats import kurtosis, skew
 
 
 # Generate input and output data traces
@@ -46,6 +45,7 @@
 start_syn = t[min(where(inp_trace != 0)[0])]
 end_syn = t[max(where(inp_trace != 0)[0])]
 
+
 def n_peaks(x):
     n_p = []
     for _x in x.transpose():
@@ -59,19 +59,21 @@ def n_peaks(x):
                         output={'v': out_trace},
                         features=[lambda x: x[(t > start_syn) & (t < end_syn), :].mean(axis=0),
                                   lambda x: x[(t > start_syn) & (t < end_syn), :].std(axis=0),
-                                  lambda x: x[(t > start_syn) & (t < end_syn), :].ptp(axis=0),
+                                  lambda x: x[(t > start_syn) & (t < end_syn), :].max(axis=0),
                                   n_peaks],
                         method='exponential_euler',
                         threshold='v > -50 * mV',
                         reset='v = -70 * mV',
                         param_init={'v': -70 * mV})
 
-inferencer.infere(n_samples=10_000,
-                  inference_method='SNPE',
-                  density_estimator_model='mdn',
+inferencer.infere(n_samples=1000,
+                  inference_method='SNLE',
                   gl=[10*nS, 100*nS],
                   C=[0.1*nF, 10*nF])
 
-samples = inferencer.sample((10_000, ))
-inferencer.pairplot(samples)
+inferencer.sample((10000, ))
+inferencer.pairplot(labels=[r'$\overline{g}_{l}$', r'$C$'],
+                    points=[[1e-9, 30e-9]],
+                    points_offdiag={'markersize': 9},
+                    points_colors=['r'])
 show()

From 728ad0e9581d854fac4146727611325c80ce588a Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 9 Jul 2021 11:58:43 +0200
Subject: [PATCH 039/121] clean up

---
 examples/hh_sbi.py | 15 ++++++++-------
 1 file changed, 8 insertions(+), 7 deletions(-)

diff --git a/examples/hh_sbi.py b/examples/hh_sbi.py
index 280808c..6c3a67f 100644
--- a/examples/hh_sbi.py
+++ b/examples/hh_sbi.py
@@ -53,27 +53,28 @@ def n_peaks(x):
                         features=[n_peaks,
                                   lambda x: x[(t > 5) & (t < 10), :].mean(axis=0),
                                   lambda x: x[(t > 5) & (t < 10), :].std(axis=0),
-                                  lambda x: x.ptp(axis=0)],
+                                  lambda x: x.max(axis=0)],
                         method='exponential_euler',
                         threshold='m > 0.5',
                         refractory='m > 0.5',
                         param_init={'v': 'VT'})
 
 # Generate prior and train the neural density estimator
-inferencer.infere(n_rounds=2,
-                  n_samples=1000,
+inferencer.infere(n_samples=1000,
+                  n_rounds=2,
                   gl=[1e-09*siemens, 1e-07*siemens],
                   g_na=[2e-06*siemens, 2e-04*siemens],
                   g_kd=[6e-07*siemens, 6e-05*siemens],
                   Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])
 
 # Draw samples from posterior
-inferencer.sample((10000,))
+inferencer.sample((10000, ))
 
 # Create pairplot from samples
-labels_params = [r'$\overline{g}_{l}$', r'$\overline{g}_{Na}$',
-                 r'$\overline{g}_{K}$', r'$\overline{C}_{m}$']
-inferencer.pairplot(labels=labels_params)
+inferencer.pairplot(labels=[r'$\overline{g}_{l}$',
+                            r'$\overline{g}_{Na}$',
+                            r'$\overline{g}_{K}$',
+                            r'$\overline{C}_{m}$'])
 
 # Generate traces by using a single sample from the trained posterior
 inf_traces = inferencer.generate_traces()

From 88fc065ced5272cde613c6b062d36db0e8518beb Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 9 Jul 2021 11:59:35 +0200
Subject: [PATCH 040/121] add better handling of keyword arguments

---
 examples/hh_sbi_advanced.py | 29 +++++++++++++++++------------
 1 file changed, 17 insertions(+), 12 deletions(-)

diff --git a/examples/hh_sbi_advanced.py b/examples/hh_sbi_advanced.py
index 5de50a6..ada4e35 100644
--- a/examples/hh_sbi_advanced.py
+++ b/examples/hh_sbi_advanced.py
@@ -85,14 +85,14 @@ def n_peaks(x):
                                       density_estimator_model='maf',
                                       prior=prior,
                                       hidden_features=50,
-                                      num_transforms=10,
-                                      num_bins=20,
                                       num_components=10)
 # First round of inference where no observation data is set to posterior
 posterior = inferencer.infere_step(proposal=prior,
                                    inference=inference,
                                    theta=theta_loaded, x=x_loaded,
-                                   train_kwargs={'learning_rate': 1e-4})
+                                   train_kwargs={'num_atoms': 10,
+                                                 'learning_rate': 0.0005,
+                                                 'show_train_summary': True})
 # Storing the trained posterior without a default observation
 path_to_posterior = __file__[:-3] + '_posterior.pth'
 inferencer.save_posterior(path_to_posterior)
@@ -106,8 +106,19 @@ def n_peaks(x):
                             r'$\overline{g}_{Na}$',
                             r'$\overline{g}_{K}$',
                             r'$\overline{C}_{m}$'])
-# ... and generate traces from a single sample of parameters
-inf_traces = inferencer.generate_traces(posterior=posterior_loaded)
+# ...and optionally, continue the multiround inference via ``infere`` method
+posterior_multi_round = inferencer.infere(n_rounds=2,
+                                          theta=theta_loaded, x=x_loaded,
+                                          inference_method='SNPE',
+                                          density_estimator_model='maf')
+inferencer.sample((10000, ))
+inferencer.pairplot(labels=[r'$\overline{g}_{l}$',
+                            r'$\overline{g}_{Na}$',
+                            r'$\overline{g}_{K}$',
+                            r'$\overline{C}_{m}$'])
+
+# Generate traces from a single sample of parameters
+inf_traces = inferencer.generate_traces()
 nrows = 2
 ncols = out_traces.shape[0]
 fig, axs = subplots(nrows, ncols, sharex=True,
@@ -125,10 +136,4 @@ def n_peaks(x):
                    axs[1, idx].get_legend_handles_labels())]
 fig.legend(handles, labels)
 tight_layout()
-show()
-
-# Optionally, continue the multiround inference using the ``infere`` method
-posterior_multi_round = inferencer.infere(n_rounds=2,
-                                          theta=theta_loaded, x=x_loaded,
-                                          inference_method='SNPE',
-                                          density_estimator_model='maf')
+show()
\ No newline at end of file

From 7461b39798e61d9dd08b7c1a0898962823603a6b Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 10 Jul 2021 18:00:59 +0200
Subject: [PATCH 041/121] minor updates

---
 brian2modelfitting/inferencer.py | 132 ++++++++++++++++---------------
 examples/IF_sbi.py               |  10 +--
 examples/hh_sbi_advanced.py      |  31 ++++----
 3 files changed, 87 insertions(+), 86 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index e7e8a4f..e4e42fb 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -197,8 +197,8 @@ def __init__(self, dt, model, input, output, features, method=None,
 
         # input data traces
         if not isinstance(input, Mapping):
-            raise TypeError('``input`` argument must be a dictionary mapping'
-                            ' the name of the input variable and ``input``.')
+            raise TypeError('`input` argument must be a dictionary mapping'
+                            ' the name of the input variable and `input`.')
         if len(input) > 1:
             raise NotImplementedError('Only a single input is supported.')
         input_var = list(input.keys())[0]
@@ -208,8 +208,8 @@ def __init__(self, dt, model, input, output, features, method=None,
 
         # output data traces
         if not isinstance(output, Mapping):
-            raise TypeError('``output`` argument must be a dictionary mapping'
-                            ' the name of the output variable and ``output``')
+            raise TypeError('`output` argument must be a dictionary mapping'
+                            ' the name of the output variable and `output`.')
         output_var = list(output.keys())
         output = list(output.values())
         for o_var in output_var:
@@ -309,7 +309,7 @@ class while generating data for training the neural density
         """
         if self.n_samples is None:
             raise ValueError('Number of samples is not yet defined.'
-                             'Call ``generate_training_data`` method first.')
+                             'Call `generate_training_data` method first.')
         return self.n_traces * self.n_samples
 
     def setup_simulator(self, network_name, n_neurons, output_var, param_init,
@@ -486,15 +486,15 @@ def save_summary_statistics(self, f, theta=None, x=None):
         elif self.theta is not None:
             t = self.theta
         else:
-            raise AttributeError('Provide sampled prior or call '
-                                 '``infere_step``method first.')
+            raise AttributeError('Provide sampled prior or call'
+                                 ' `infere_step` method first.')
         if x is not None:
             pass
         elif self.x is not None:
             x = self.x
         else:
             raise AttributeError('Provide summary feautures or call '
-                                 '``infere_step``method first.')
+                                 ' `infere_step` method first.')
         np.savez_compressed(f, theta=t, x=x)
 
     def load_summary_statistics(self, f, **kwargs):
@@ -517,12 +517,9 @@ def load_summary_statistics(self, f, **kwargs):
             Consisting of sampled prior and summary statistics arrays.
         """
         loaded = np.load(f, allow_pickle=True)
-        if loaded.files == ['theta', 'x'] or loaded.files == ['x', 'theta']:
+        if set(loaded.files) == {'theta', 'x'}:
             theta = loaded['theta']
             x = loaded['x']
-        elif loaded.files == ['arr_0', 'arr_1']:
-            theta = loaded['arr_0']
-            x = loaded['arr_1']
         self.theta = theta
         self.x = x
         return (theta, x)
@@ -554,8 +551,8 @@ def init_inference(self, inference_method, density_estimator_model, prior,
             inference_method = str.upper(inference_method)
             inference_method_fun = getattr(sbi.inference, inference_method)
         except AttributeError:
-            raise NameError(f'Inference method {inference_method} is not '
-                            'supported. Choose between SNPE, SNLE or SNRE.')
+            raise NameError(f'Inference method {inference_method} is not'
+                            ' supported. Choose between SNPE, SNLE or SNRE.')
         finally:
             if inference_method == 'SNPE':
                 density_estimator_builder = posterior_nn(
@@ -671,7 +668,7 @@ def infere_step(self, proposal, inference,
         # extract the training data and make adjustments for ``sbi``
         if theta is None:
             if n_samples is None:
-                raise ValueError('Either provide ``theta`` or ``n_samples``.')
+                raise ValueError('Either provide `theta` or `n_samples`.')
             else:
                 theta = self.generate_training_data(n_samples, proposal)
         self.theta = theta
@@ -680,7 +677,7 @@ def infere_step(self, proposal, inference,
         # extract the summary statistics and make adjustments for ``sbi``
         if x is None:
             if n_samples is None:
-                raise ValueError('Either provide ``x`` or ``n_samples``.')
+                raise ValueError('Either provide `x` or `n_samples`.')
             else:
                 x = self.extract_summary_statistics(theta, level=2)
         self.x = x
@@ -695,6 +692,7 @@ def infere_step(self, proposal, inference,
         inference, posterior = self.build_posterior(inference,
                                                     density_estimator,
                                                     **posterior_kwargs)
+        self.inference = inference
         self.posterior = posterior
         return posterior
 
@@ -742,46 +740,56 @@ def infere(self, n_samples=None, theta=None, x=None, n_rounds=1,
         sbi.inference.NeuralPosterior
             Trained posterior.
         """
-        # handle the number of rounds
-        if not isinstance(n_rounds, int):
-            raise ValueError('Number of rounds must be a positive integer.')
+        if self.posterior is None:  # `.infere_step` has not been called
+            # handle the number of rounds
+            if not isinstance(n_rounds, int):
+                raise ValueError('`n_rounds` has to be a positive integer.')
 
-        # handle inference methods
-        try:
-            inference_method = str.upper(inference_method)
-        except ValueError as e:
-            print(e, '\nInvalid inference method.')
-        if inference_method not in ['SNPE', 'SNLE', 'SNRE']:
-            raise ValueError(f'Inference method {inference_method} is not '
-                             'supported. Choose between SNPE, SNLE or SNRE.')
-
-        # initialize prior
-        if self.posterior is None:
+            # handle inference methods
+            try:
+                inference_method = str.upper(inference_method)
+            except ValueError as e:
+                print(e, '\nInvalid inference method.')
+            if inference_method not in ['SNPE', 'SNLE', 'SNRE']:
+                raise ValueError(f'Inference method {inference_method} is not'
+                                 ' supported.')
+
+            # initialize prior
             prior = self.init_prior(**params)
-        else:
-            prior = self.posterior.set_default_x(self.x_o)
 
-        # extract the training data and make adjustments for ``sbi``
-        if theta is None:
-            if n_samples is None:
-                raise ValueError('Either provide ``theta`` or ``n_samples``.')
+            # extract the training data and make adjustments for ``sbi``
+            if theta is None:
+                if n_samples is None:
+                    raise ValueError('Either provide `theta` or `n_samples`.')
+                else:
+                    theta = self.generate_training_data(n_samples, prior)
+            self.theta = theta
+
+            # extract the summary statistics and make adjustments for ``sbi``
+            if x is None:
+                if n_samples is None:
+                    raise ValueError('Either provide `x` or `n_samples`.')
+                else:
+                    x = self.extract_summary_statistics(theta)
+            self.x = x
+
+            # initialize inference object
+            self.inference = self.init_inference(inference_method,
+                                                 density_estimator_model,
+                                                 prior,
+                                                 **inference_kwargs)
+
+            # additional args for `.train` method are needed only for SNPE
+            if inference_method == 'SNPE':
+                args = [prior]
             else:
-                theta = self.generate_training_data(n_samples, prior)
-        self.theta = theta
-
-        # extract the summary statistics and make adjustments for ``sbi``
-        if x is None:
-            if n_samples is None:
-                raise ValueError('Either provide ``x`` or ``n_samples``.')
+                args = []
+        else:  # `.infere_step` has been called manually
+            prior = self.posterior.set_default_x(self.x_o)
+            if self.posterior._method_family == 'snpe':
+                args = [prior]
             else:
-                x = self.extract_summary_statistics(theta)
-        self.x = x
-
-        # initialize inference object
-        inference = self.init_inference(inference_method,
-                                        density_estimator_model,
-                                        prior,
-                                        **inference_kwargs)
+                args = []
 
         # allocate empty list of posterior
         posteriors = []
@@ -789,18 +797,12 @@ def infere(self, n_samples=None, theta=None, x=None, n_rounds=1,
         # set a proposal
         proposal = prior
 
-        # additional arguments for `.train` method are needed only for SNPE
-        if inference_method == 'SNPE':
-            args = [proposal]
-        else:
-            args = []
-
         # main inference loop
         for round in range(n_rounds):
             print(f'Round {round + 1}/{n_rounds}.')
 
             # inference step
-            posterior = self.infere_step(proposal, inference,
+            posterior = self.infere_step(proposal, self.inference,
                                          n_samples, self.theta, self.x,
                                          train_kwargs, posterior_kwargs, *args)
 
@@ -872,9 +874,9 @@ def sample(self, shape, posterior=None, **kwargs):
         elif posterior is None and self.posterior:
             p = self.posterior
         else:
-            raise ValueError('Need to provide posterior argument if no '
-                             'posterior has been calculated by the ``infere`` '
-                             'method.')
+            raise ValueError('Need to provide posterior argument if no'
+                             ' posterior has been calculated by the `infere`'
+                             ' method.')
         samples = p.sample(shape, x=self.x_o, **kwargs)
         self.samples = samples
         return samples
@@ -904,7 +906,7 @@ def pairplot(self, samples=None, **kwargs):
             try:
                 s = self.samples
             except AttributeError as e:
-                print(e, '\nProvide samples or call ``sample`` method first.')
+                print(e, '\nProvide samples or call `sample` method first.')
                 raise
         fig, axes = sbi.analysis.pairplot(s, **kwargs)
         return fig, axes
@@ -942,9 +944,9 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
         elif posterior is None and self.posterior:
             p = self.posterior
         else:
-            raise ValueError('Need to provide posterior argument if no '
-                             'posterior has been calculated by the ``infere`` '
-                             'method.')
+            raise ValueError('Need to provide posterior argument if no'
+                             ' posterior has been calculated by the `infere`'
+                             ' method.')
         params = p.sample((1, ), x=self.x_o)
 
         # set output variable that is monitored
diff --git a/examples/IF_sbi.py b/examples/IF_sbi.py
index 199fad4..b7b8ec0 100644
--- a/examples/IF_sbi.py
+++ b/examples/IF_sbi.py
@@ -49,7 +49,7 @@
 def n_peaks(x):
     n_p = []
     for _x in x.transpose():
-        p_i = find_peaks(_x, height=-60)[0]
+        p_i = find_peaks(_x)[0]
         n_p.append(p_i.size)
     return n_p
 
@@ -57,9 +57,9 @@ def n_peaks(x):
 inferencer = Inferencer(dt=dt, model=eqs_inf,
                         input={'I_syn': inp_trace.reshape(1, -1)},
                         output={'v': out_trace},
-                        features=[lambda x: x[(t > start_syn) & (t < end_syn), :].mean(axis=0),
-                                  lambda x: x[(t > start_syn) & (t < end_syn), :].std(axis=0),
-                                  lambda x: x[(t > start_syn) & (t < end_syn), :].max(axis=0),
+                        features=[lambda x: x[(t > start_syn) & (t < end_syn), :].mean(axis=1),
+                                  lambda x: x[(t > start_syn) & (t < end_syn), :].std(axis=1),
+                                  lambda x: x[(t > start_syn) & (t < end_syn), :].max(axis=1),
                                   n_peaks],
                         method='exponential_euler',
                         threshold='v > -50 * mV',
@@ -67,7 +67,7 @@ def n_peaks(x):
                         param_init={'v': -70 * mV})
 
 inferencer.infere(n_samples=1000,
-                  inference_method='SNLE',
+                  inference_method='SNPE',
                   gl=[10*nS, 100*nS],
                   C=[0.1*nF, 10*nF])
 
diff --git a/examples/hh_sbi_advanced.py b/examples/hh_sbi_advanced.py
index ada4e35..67bba5e 100644
--- a/examples/hh_sbi_advanced.py
+++ b/examples/hh_sbi_advanced.py
@@ -1,3 +1,5 @@
+import os
+
 from brian2 import *
 from brian2modelfitting import *
 import pandas as pd
@@ -66,18 +68,18 @@ def n_peaks(x):
                               g_na=[2e-06*siemens, 2e-04*siemens],
                               g_kd=[6e-07*siemens, 6e-05*siemens],
                               Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])
-# Generate training data
-theta = inferencer.generate_training_data(n_samples=1000,
-                                          prior=prior)
-# Extract summary stats
-x = inferencer.extract_summary_statistics(theta=theta, level=0)
-
-# Loading and storing of the data
-# For large training data, you can store it into .npz and reuse it later
+# Prepare training data
 path_to_data = __file__[:-3] + '_data.npz'
-inferencer.save_summary_statistics(path_to_data, theta, x)
-# Training data will be directly set to class variables once they are loaded
-theta_loaded, x_loaded = inferencer.load_summary_statistics(path_to_data)
+if os.path.exists(path_to_data):
+    theta, x = inferencer.load_summary_statistics(path_to_data)
+else:
+    # Generate training data
+    theta = inferencer.generate_training_data(n_samples=1000,
+                                              prior=prior)
+    # Extract summary stats
+    x = inferencer.extract_summary_statistics(theta=theta, level=0)
+    # Save the data for later use
+    inferencer.save_summary_statistics(path_to_data, theta, x)
 
 # Amortized inference
 # Training the neural density estimator
@@ -89,7 +91,7 @@ def n_peaks(x):
 # First round of inference where no observation data is set to posterior
 posterior = inferencer.infere_step(proposal=prior,
                                    inference=inference,
-                                   theta=theta_loaded, x=x_loaded,
+                                   theta=theta, x=x,
                                    train_kwargs={'num_atoms': 10,
                                                  'learning_rate': 0.0005,
                                                  'show_train_summary': True})
@@ -107,10 +109,7 @@ def n_peaks(x):
                             r'$\overline{g}_{K}$',
                             r'$\overline{C}_{m}$'])
 # ...and optionally, continue the multiround inference via ``infere`` method
-posterior_multi_round = inferencer.infere(n_rounds=2,
-                                          theta=theta_loaded, x=x_loaded,
-                                          inference_method='SNPE',
-                                          density_estimator_model='maf')
+posterior_multi_round = inferencer.infere(n_rounds=2)
 inferencer.sample((10000, ))
 inferencer.pairplot(labels=[r'$\overline{g}_{l}$',
                             r'$\overline{g}_{Na}$',

From 9545887a882183e3283260a3ee0c51748a0a7b95 Mon Sep 17 00:00:00 2001
From: Marcel Stimberg <marcel.stimberg@inserm.fr>
Date: Tue, 13 Jul 2021 15:59:15 +0200
Subject: [PATCH 042/121] Revert testing change that broke example

---
 examples/IF_sbi.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/IF_sbi.py b/examples/IF_sbi.py
index b7b8ec0..8de5adc 100644
--- a/examples/IF_sbi.py
+++ b/examples/IF_sbi.py
@@ -57,9 +57,9 @@ def n_peaks(x):
 inferencer = Inferencer(dt=dt, model=eqs_inf,
                         input={'I_syn': inp_trace.reshape(1, -1)},
                         output={'v': out_trace},
-                        features=[lambda x: x[(t > start_syn) & (t < end_syn), :].mean(axis=1),
-                                  lambda x: x[(t > start_syn) & (t < end_syn), :].std(axis=1),
-                                  lambda x: x[(t > start_syn) & (t < end_syn), :].max(axis=1),
+                        features=[lambda x: x[(t > start_syn) & (t < end_syn), :].mean(axis=0),
+                                  lambda x: x[(t > start_syn) & (t < end_syn), :].std(axis=0),
+                                  lambda x: x[(t > start_syn) & (t < end_syn), :].max(axis=0),
                                   n_peaks],
                         method='exponential_euler',
                         threshold='v > -50 * mV',

From c85ce9786400f92070aec6ad29f88164a20379d6 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 17 Jul 2021 17:28:59 +0200
Subject: [PATCH 043/121] implement wrapper funcs for sbi's
 conditional_pairplot and conditional_corrcoeff

---
 brian2modelfitting/inferencer.py | 76 ++++++++++++++++++++++++++++++++
 1 file changed, 76 insertions(+)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index e4e42fb..c4ec83a 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -911,6 +911,82 @@ def pairplot(self, samples=None, **kwargs):
         fig, axes = sbi.analysis.pairplot(s, **kwargs)
         return fig, axes
 
+    def conditional_pairplot(self, condition, limits, density=None, **kwargs):
+        """Plot conditional distribution given all other parameters.
+
+        Check ``sbi.analysis.plot.conditional_pairplot`` for more
+        details.
+
+        Parameters
+        ----------
+        condition : torch.tensor
+            Condition that all but the one/two regarded parameters are
+            fixed to.
+        limits : list or torch.tensor
+            Limits in between which each parameter will be evaulated.
+        density : sbi.inference.NeuralPosterior, optional
+            Posterior probability density.
+        **kwargs : dict, optional
+            Additional keyword arguments for the
+            ``sbi.analysis.plot.pairplot`` function.
+
+        Returns
+        -------
+        tuple
+            Figure and axis of posterior distribution plot.
+        """
+        if density is not None:
+            d = density
+        else:
+            try:
+                d = self.posterior
+            except AttributeError as e:
+                print(e, '\nDensity is not available.')
+                raise
+        fig, axes = sbi.analysis.conditional_pairplot(density=d,
+                                                      condition=condition,
+                                                      limits=limits,
+                                                      *kwargs)
+        return fig, axes
+
+    def conditional_corrcoeff(self, condition, limits, density=None, **kwargs):
+        """Plot conditional distribution given all other parameters.
+
+        Check ``sbi.analysis.conditional_density.conditional_corrcoeff``
+        for more details.
+
+        Parameters
+        ----------
+        condition : torch.tensor
+            Condition that all but the one/two regarded parameters are
+            fixed to.
+        limits : list or torch.tensor
+            Limits in between which each parameter will be evaulated.
+        density : sbi.inference.NeuralPosterior, optional
+            Posterior probability density.
+        **kwargs : dict, optional
+            Additional keyword arguments for the
+            ``sbi.analysis.plot.pairplot`` function.
+
+        Returns
+        -------
+        tuple
+            Figure and axis of posterior distribution plot.
+        """
+        if density is not None:
+            d = density
+        else:
+            try:
+                d = self.posterior
+            except AttributeError as e:
+                print(e, '\nDensity is not available.')
+                raise
+        fig, axes = sbi.analysis.conditional_corrcoeff(density=d,
+                                                       condition=condition,
+                                                       limits=limits,
+                                                       *kwargs)
+        return fig, axes
+
     def generate_traces(self, posterior=None, output_var=None, param_init=None,
                         level=0):
         """Generates traces for a single drawn sample from the trained

From 6ab9a4bcd558891bd19316494c0725f47b0adaa3 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 17 Jul 2021 17:36:44 +0200
Subject: [PATCH 044/121] fix minor issues in cond_corrcoeff

---
 brian2modelfitting/inferencer.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index c4ec83a..048df89 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -933,7 +933,7 @@ def conditional_pairplot(self, condition, limits, density=None, **kwargs):
         Returns
         -------
         tuple
-            Figure and axis of posterior distribution plot.
+            Figure and axis of conditional pairplot.
         """
         if density is not None:
             d = density
@@ -970,8 +970,8 @@ def conditional_corrcoeff(self, condition, limits, density=None, **kwargs):
 
         Returns
         -------
-        tuple
-            Figure and axis of posterior distribution plot.
+        torch.tensor
+            Average conditional correlation matrix.
         """
         if density is not None:
             d = density
@@ -981,11 +981,11 @@ def conditional_corrcoeff(self, condition, limits, density=None, **kwargs):
             except AttributeError as e:
                 print(e, '\nDensity is not available.')
                 raise
-        fig, axes = sbi.analysis.conditional_corrcoeff(density=d,
-                                                       condition=condition,
-                                                       limits=limits,
-                                                       *kwargs)
-        return fig, axes
+        cond_coeff = sbi.analysis.conditional_corrcoeff(density=d,
+                                                        condition=condition,
+                                                        limits=limits,
+                                                        *kwargs)
+        return cond_coeff
 
     def generate_traces(self, posterior=None, output_var=None, param_init=None,
                         level=0):

From dd278dc3047c69cd02850fa4f045539fbbc576de Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 20 Jul 2021 17:03:13 +0200
Subject: [PATCH 045/121] fix docstrings + change features argument to be
 optional

---
 brian2modelfitting/inferencer.py | 41 +++++++++++++++++++-------------
 1 file changed, 24 insertions(+), 17 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 048df89..f30e054 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -161,7 +161,7 @@ class Inferencer(object):
         key corresponds to the name of the output variable as defined
         in ``model`` and value corresponds to a single dimensional
         array of recorded data traces.
-    features : list
+    features : list, optional
         List of callables that take the voltage trace and output
         summary statistics.
     method : str, optional
@@ -183,7 +183,7 @@ class Inferencer(object):
         Dictionary of state variables to be initialized with respective
         values.
     """
-    def __init__(self, dt, model, input, output, features, method=None,
+    def __init__(self, dt, model, input, output, features=None, method=None,
                  threshold=None, reset=None, refractory=False,
                  param_init=None):
         # time scale
@@ -275,13 +275,16 @@ def __init__(self, dt, model, input, output, features, method=None,
         # placeholder for the posterior
         self.posterior = None
         # observation the focus is on
-        x_o = []
-        for o in self.output:
-            o = np.array(o)
-            obs = []
-            for feature in features:
-                obs.extend(feature(o.transpose()))
-            x_o.append(obs)
+        if features:
+            x_o = []
+            for o in self.output:
+                o = np.array(o)
+                obs = []
+                for feature in features:
+                    obs.extend(feature(o.transpose()))
+                x_o.append(obs)
+        else:
+            x_o = np.vstack(self.output)
         x_o = torch.tensor(x_o, dtype=torch.float32)
         self.x_o = x_o
         self.features = features
@@ -455,11 +458,15 @@ def extract_summary_statistics(self, theta, level=1):
         x = []
         for ov in self.output_var:
             x_val = obs[ov].get_value()
-            summary_statistics = []
-            for feature in self.features:
-                summary_statistics.append(feature(x_val))
-            x.append(summary_statistics)
-        x = np.array(x, dtype=np.float32)
+            if self.features:
+                summary_statistics = []
+                for feature in self.features:
+                    summary_statistics.append(feature(x_val))
+                x.append(summary_statistics)
+                x = np.array(x, dtype=np.float32)
+            else:
+                x.append(x_val)
+                x = np.vstack(x).astype(np.float32)
         x = x.reshape((self.n_samples, -1))
         return x
 
@@ -893,7 +900,7 @@ def pairplot(self, samples=None, **kwargs):
             Samples used to build the pairplot.
         **kwargs : dict, optional
             Additional keyword arguments for the
-            ``sbi.analysis.plot.pairplot`` function.
+            ``sbi.analysis.pairplot`` function.
 
         Returns
         -------
@@ -928,7 +935,7 @@ def conditional_pairplot(self, condition, limits, density=None, **kwargs):
             Posterior probability density.
         **kwargs : dict, optional
             Additional keyword arguments for the
-            ``sbi.analysis.plot.pairplot`` function.
+            ``sbi.analysis.conditional_pairplot`` function.
 
         Returns
         -------
@@ -966,7 +973,7 @@ def conditional_corrcoeff(self, condition, limits, density=None, **kwargs):
             Posterior probability density.
         **kwargs : dict, optional
             Additional keyword arguments for the
-            ``sbi.analysis.plot.pairplot`` function.
+            ``sbi.analysis.conditional_corrcoeff`` function.
 
         Returns
         -------

From c5eb245ff7c8e7c862b69f9009376f7c5cdf4099 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Mon, 2 Aug 2021 23:37:04 +0200
Subject: [PATCH 046/121] better handling of plotting parameters + additional
 fixes

---
 brian2modelfitting/inferencer.py | 228 +++++++++++++++++++++++++------
 1 file changed, 189 insertions(+), 39 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index f30e054..9e89cfa 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -219,7 +219,7 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         self.output = output
 
         # create variable for parameter names
-        self.param_names = model.parameter_names
+        self.param_names = sorted(model.parameter_names)
 
         # set the simulation time for a given time scale
         self.n_traces, n_steps = input.shape
@@ -269,11 +269,13 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         self.reset = reset
         self.refractory = refractory
 
-        # placeholder for samples
+        self.params = None
         self.n_samples = None
         self.samples = None
-        # placeholder for the posterior
         self.posterior = None
+        self.theta = None
+        self.x = None
+
         # observation the focus is on
         if features:
             x_o = []
@@ -284,12 +286,10 @@ def __init__(self, dt, model, input, output, features=None, method=None,
                     obs.extend(feature(o.transpose()))
                 x_o.append(obs)
         else:
-            x_o = np.vstack(self.output)
+            x_o = np.vstack(self.output).reshape(self.n_traces, -1)
         x_o = torch.tensor(x_o, dtype=torch.float32)
         self.x_o = x_o
         self.features = features
-        self.theta = None
-        self.x = None
 
     @property
     def n_neurons(self):
@@ -337,7 +337,7 @@ def setup_simulator(self, network_name, n_neurons, output_var, param_init,
         Returns
         -------
         brian2modelfitting.simulator.Simulator
-            Configured simulator w.r.t. to the available device.
+            Configured simulator w.r.t. the available device.
         """
         # configure the simulator
         simulator = configure_simulator()
@@ -393,6 +393,7 @@ def init_prior(self, **params):
                 raise ValueError(f'Parameter {param} must be defined as a'
                                  ' model\'s parameter')
         prior = calc_prior(self.param_names, **params)
+        self.params = params
         return prior
 
     def generate_training_data(self, n_samples, prior):
@@ -504,7 +505,7 @@ def save_summary_statistics(self, f, theta=None, x=None):
                                  ' `infere_step` method first.')
         np.savez_compressed(f, theta=t, x=x)
 
-    def load_summary_statistics(self, f, **kwargs):
+    def load_summary_statistics(self, f):
         """Load sampled prior data, theta, and extracted summary
         statistics, x, from a compressed ``.npz`` format. Arrays should
         be named either `arr_0` and `arr_1` or `theta` and `x` for
@@ -515,8 +516,6 @@ def load_summary_statistics(self, f, **kwargs):
         f : str or os.PathLike
             Path to file either as string or ``os.PathLike`` object
             that contains file name.
-        kwargs : dict, optional
-            Additional keyword arguments for ``numpy.load`` method.
 
         Returns
         -------
@@ -873,7 +872,7 @@ def sample(self, shape, posterior=None, **kwargs):
             ``sbi.inference.posteriors.DirectPosterior`` class
         Returns
         -------
-        torch.tensor
+        numpy.ndarray
             Samples from posterior of the shape as given in ``shape``.
         """
         if posterior:
@@ -886,9 +885,10 @@ def sample(self, shape, posterior=None, **kwargs):
                              ' method.')
         samples = p.sample(shape, x=self.x_o, **kwargs)
         self.samples = samples
-        return samples
+        return samples.numpy()
 
-    def pairplot(self, samples=None, **kwargs):
+    def pairplot(self, samples=None, points=None, limits=None, subset=None,
+                 labels=None, ticks=None, **kwargs):
         """Plot samples in a 2-D grid with marginals and pairwise
         marginals.
 
@@ -898,6 +898,19 @@ def pairplot(self, samples=None, **kwargs):
         ----------
         samples : iterable, optional
             Samples used to build the pairplot.
+        points : dict, optional
+            Additional points to scatter, e.g., true parameter values,
+            if known.
+        limits : dict, optional
+            Limits for each parameter. If None, min and max of the
+            given samples will be used.
+        subset : list, optional
+            Which parameters to plot.
+        labels : dict, optional
+            Names for each parameter.
+        ticks : dict, optional
+            Position of the ticks. If None, default ticks positions
+            will be used.
         **kwargs : dict, optional
             Additional keyword arguments for the
             ``sbi.analysis.pairplot`` function.
@@ -910,15 +923,61 @@ def pairplot(self, samples=None, **kwargs):
         if samples is not None:
             s = samples
         else:
-            try:
+            if self.samples is not None:
                 s = self.samples
-            except AttributeError as e:
-                print(e, '\nProvide samples or call `sample` method first.')
-                raise
-        fig, axes = sbi.analysis.pairplot(s, **kwargs)
+            else:
+                raise ValueError('Samples are not available.')
+        if points:
+            for param_name in points.keys():
+                if param_name not in self.param_names:
+                    raise AttributeError(f'Invalid parameter: {param_name}')
+            points = np.array([[points[param_name].item()
+                               for param_name in self.param_names]])
+        if limits:
+            for param_name, lim_vals in limits.items():
+                if param_name not in self.param_names:
+                    raise AttributeError(f'Invalid parameter: {param_name}')
+                if len(lim_vals) != 2:
+                    raise ValueError('Invalid limits for parameter: '
+                                     f'{param_name}')
+            limits = [[limits[param_name][0].item(),
+                       limits[param_name][1].item()]
+                      for param_name in self.param_names]
+        if subset:
+            for param_name in subset:
+                if param_name not in self.param_names:
+                    raise AttributeError(f'Invalid parameter: {param_name}')
+            subset = [self.param_names.index(param_name)
+                      for param_name in subset]
+        if ticks:
+            for param_name, lim_vals in ticks.items():
+                if param_name not in self.param_names:
+                    raise AttributeError(f'Invalid parameter: {param_name}')
+                if len(lim_vals) != 2:
+                    raise ValueError('Invalid limits for parameter: '
+                                     f'{param_name}')
+            ticks = [[ticks[param_name][0].item(),
+                      ticks[param_name][1].item()]
+                     for param_name in self.param_names]
+        else:
+            ticks = []
+        if labels:
+            for param_name in labels.keys():
+                if param_name not in self.param_names:
+                    raise AttributeError(f'Invalid parameter: {param_name}')
+            labels = [labels[param_name] for param_name in self.param_names]
+        fig, axes = sbi.analysis.pairplot(samples=s,
+                                          points=points,
+                                          limits=limits,
+                                          subset=subset,
+                                          labels=labels,
+                                          ticks=ticks,
+                                          **kwargs)
         return fig, axes
 
-    def conditional_pairplot(self, condition, limits, density=None, **kwargs):
+    def conditional_pairplot(self, condition, density=None, points=None,
+                             limits=None, subset=None, labels=None,
+                             ticks=None, **kwargs):
         """Plot conditional distribution given all other parameters.
 
         Check ``sbi.analysis.plot.conditional_pairplot`` for more
@@ -926,13 +985,24 @@ def conditional_pairplot(self, condition, limits, density=None, **kwargs):
 
         Parameters
         ----------
-        condition : torch.tensor
+        condition : numpy.ndarray
             Condition that all but the one/two regarded parameters are
             fixed to.
-        limits : list or torch.tensor
-            Limits in between which each parameter will be evaulated.
         density : sbi.inference.NeuralPosterior, optional
             Posterior probability density.
+        points : dict, optional
+            Additional points to scatter, e.g., true parameter values,
+            if known.
+        limits : dict, optional
+            Limits for each parameter. If None, min and max of the
+            given samples will be used.
+        subset : list, optional
+            Which parameters to plot.
+        labels : dict, optional
+            Names for each parameter.
+        ticks : dict, optional
+            Position of the ticks. If None, default ticks positions
+            will be used.
         **kwargs : dict, optional
             Additional keyword arguments for the
             ``sbi.analysis.conditional_pairplot`` function.
@@ -942,21 +1012,72 @@ def conditional_pairplot(self, condition, limits, density=None, **kwargs):
         tuple
             Figure and axis of conditional pairplot.
         """
+        condition = torch.from_numpy(condition)
         if density is not None:
             d = density
         else:
-            try:
-                d = self.posterior
-            except AttributeError as e:
-                print(e, '\nDensity is not available.')
-                raise
+            if self.posterior is not None:
+                if self.posterior.default_x is None:
+                    d = self.posterior.set_default_x(self.x_o)
+                else:
+                    d = self.posterior
+            else:
+                raise ValueError('Density is not available.')
+        if points:
+            for param_name in points.keys():
+                if param_name not in self.param_names:
+                    raise AttributeError(f'Invalid parameter: {param_name}')
+            points = np.array([[points[param_name].item()
+                               for param_name in self.param_names]])
+        if limits:
+            for param_name, lim_vals in limits.items():
+                if param_name not in self.param_names:
+                    raise AttributeError(f'Invalid parameter: {param_name}')
+                if len(lim_vals) != 2:
+                    raise ValueError('Invalid limits for parameter: '
+                                     f'{param_name}')
+            limits = [[limits[param_name][0].item(),
+                       limits[param_name][1].item()]
+                      for param_name in self.param_names]
+        else:
+            limits = [[self.params[param_name][0].item(),
+                       self.params[param_name][1].item()]
+                      for param_name in self.param_names]
+        if subset:
+            for param_name in subset:
+                if param_name not in self.param_names:
+                    raise AttributeError(f'Invalid parameter: {param_name}')
+            subset = [self.param_names.index(param_name)
+                      for param_name in subset]
+        if ticks:
+            for param_name, lim_vals in ticks.items():
+                if param_name not in self.param_names:
+                    raise AttributeError(f'Invalid parameter: {param_name}')
+                if len(lim_vals) != 2:
+                    raise ValueError('Invalid limits for parameter: '
+                                     f'{param_name}')
+            ticks = [[ticks[param_name][0].item(),
+                      ticks[param_name][1].item()]
+                     for param_name in self.param_names]
+        else:
+            ticks = []
+        if labels:
+            for param_name in labels.keys():
+                if param_name not in self.param_names:
+                    raise AttributeError(f'Invalid parameter: {param_name}')
+            labels = [labels[param_name] for param_name in self.param_names]
         fig, axes = sbi.analysis.conditional_pairplot(density=d,
                                                       condition=condition,
                                                       limits=limits,
+                                                      points=points,
+                                                      subset=subset,
+                                                      labels=labels,
+                                                      ticks=ticks,
                                                       *kwargs)
         return fig, axes
 
-    def conditional_corrcoeff(self, condition, limits, density=None, **kwargs):
+    def conditional_corrcoeff(self, condition, density=None, limits=None,
+                              subset=None, **kwargs):
         """Plot conditional distribution given all other parameters.
 
         Check ``sbi.analysis.conditional_density.conditional_corrcoeff``
@@ -964,35 +1085,64 @@ def conditional_corrcoeff(self, condition, limits, density=None, **kwargs):
 
         Parameters
         ----------
-        condition : torch.tensor
+        condition : numpy.ndarray
             Condition that all but the one/two regarded parameters are
             fixed to.
-        limits : list or torch.tensor
-            Limits in between which each parameter will be evaulated.
         density : sbi.inference.NeuralPosterior, optional
             Posterior probability density.
+        limits : dict, optional
+            Limits for each parameter. If None, min and max of the
+            given samples will be used.
+        subset : list, optional
+            Parameters that are taken for conditional distribution, if
+            None all parameters are considered.
         **kwargs : dict, optional
             Additional keyword arguments for the
             ``sbi.analysis.conditional_corrcoeff`` function.
 
         Returns
         -------
-        torch.tensor
+        numpy.ndarray
             Average conditional correlation matrix.
         """
+        condition = torch.from_numpy(condition)
         if density is not None:
             d = density
         else:
-            try:
-                d = self.posterior
-            except AttributeError as e:
-                print(e, '\nDensity is not available.')
-                raise
+            if self.posterior is not None:
+                if self.posterior.default_x is None:
+                    d = self.posterior.set_default_x(self.x_o)
+                else:
+                    d = self.posterior
+            else:
+                raise ValueError('Density is not available.')
+        if limits:
+            for param_name, lim_vals in limits.items():
+                if param_name not in self.param_names:
+                    raise AttributeError(f'Invalid parameter: {param_name}')
+                if len(lim_vals) != 2:
+                    raise ValueError('Invalid limits for parameter: '
+                                     f'{param_name}')
+            limits = [[limits[param_name][0].item(),
+                       limits[param_name][1].item()]
+                      for param_name in self.param_names]
+        else:
+            limits = [[self.params[param_name][0].item(),
+                       self.params[param_name][1].item()]
+                      for param_name in self.param_names]
+        limits = torch.tensor(limits)
+        if subset:
+            for param_name in subset:
+                if param_name not in self.param_names:
+                    raise AttributeError(f'Invalid parameter: {param_name}')
+            subset = [self.param_names.index(param_name)
+                      for param_name in subset]
         cond_coeff = sbi.analysis.conditional_corrcoeff(density=d,
-                                                        condition=condition,
                                                         limits=limits,
+                                                        condition=condition,
+                                                        subset=subset,
                                                         *kwargs)
-        return cond_coeff
+        return cond_coeff.numpy()
 
     def generate_traces(self, posterior=None, output_var=None, param_init=None,
                         level=0):

From 70279b606afc6a1e0aaf19b1031ac95d24819378 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 3 Aug 2021 11:22:26 +0200
Subject: [PATCH 047/121] x and x_0 shapes fixed

---
 brian2modelfitting/inferencer.py | 61 +++++++++++++-------------------
 1 file changed, 25 insertions(+), 36 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 9e89cfa..838de1d 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -287,7 +287,6 @@ def __init__(self, dt, model, input, output, features=None, method=None,
                 x_o.append(obs)
         else:
             x_o = np.vstack(self.output).reshape(self.n_traces, -1)
-        x_o = torch.tensor(x_o, dtype=torch.float32)
         self.x_o = x_o
         self.features = features
 
@@ -419,7 +418,6 @@ def generate_training_data(self, n_samples, prior):
         # sample from prior
         theta = prior.sample((n_samples, ))
         theta = np.atleast_2d(theta.numpy())
-
         return theta
 
     def extract_summary_statistics(self, theta, level=1):
@@ -466,9 +464,9 @@ def extract_summary_statistics(self, theta, level=1):
                 x.append(summary_statistics)
                 x = np.array(x, dtype=np.float32)
             else:
-                x.append(x_val)
-                x = np.vstack(x).astype(np.float32)
-        x = x.reshape((self.n_samples, -1))
+                x.append(x_val.T)
+        x = np.hstack(x).astype(np.float32)
+        # x = x.reshape((self.n_samples, -1))
         return x
 
     def save_summary_statistics(self, f, theta=None, x=None):
@@ -598,17 +596,15 @@ def train(self, inference, theta, x, *args, **train_kwargs):
 
         Returns
         -------
-        tuple
-            ``sbi.inference.NeuralInference`` object with stored
-            paramaters and simulation outputs prepared for training and
-            trained neural density estimator object.
+        ``sbi.inference.NeuralInference``
+            trained inference object.
         """
-        inference = inference.append_simulations(theta, x, *args)
-        density_estimator = inference.train(**train_kwargs)
-        return (inference, density_estimator)
+        # inference = inference.append_simulations(theta, x, *args)
+        inference = inference.append_simulations(theta, x)
+        _ = inference.train(**train_kwargs)
+        return inference
 
-    def build_posterior(self, inference, density_estimator,
-                        **posterior_kwargs):
+    def build_posterior(self, inference, **posterior_kwargs):
         """Return instantiated inference object.
 
         Parameters
@@ -616,15 +612,6 @@ def build_posterior(self, inference, density_estimator,
         inference : sbi.inference.NeuralInference
             Instantiated inference object with stored paramaters and
             simulation outputs prepared for training.
-        theta : torch.tensor
-            Sampled prior.
-        x : torch.tensor
-            Summary statistics.
-        args : list, optional
-            Contains a uniformly distributed sbi.utils.BoxUniform
-            prior/proposal. Used only for SNPE, for SNLE and SNRE,
-            ``proposal`` should not be passed to ``append_simulations``
-            method, thus ``args`` should not be passed.
         posterior_kwargs : dict, optional
             Additional keyword arguments for ``build_posterior`` method
             of ``sbi.inference.NeuralInference`` object.
@@ -634,10 +621,9 @@ def build_posterior(self, inference, density_estimator,
         tuple
             ``sbi.inference.NeuralInference`` object with stored
             paramaters and simulation outputs prepared for training and
-            ``sbi.inference.NeuralInference`` object from which.
+            ``sbi.inference.NeuralPosterior`` object.
         """
-        posterior = inference.build_posterior(density_estimator,
-                                              **posterior_kwargs)
+        posterior = inference.build_posterior(**posterior_kwargs)
         return (inference, posterior)
 
     def infere_step(self, proposal, inference,
@@ -690,13 +676,10 @@ def infere_step(self, proposal, inference,
         x = torch.tensor(x)
 
         # pass the simulated data to the inference object and train it
-        inference, density_estimator = self.train(inference,
-                                                  theta, x,
-                                                  *args, **train_kwargs)
+        inference = self.train(inference, theta, x, *args, **train_kwargs)
 
         # use the density estimator to build the posterior
         inference, posterior = self.build_posterior(inference,
-                                                    density_estimator,
                                                     **posterior_kwargs)
         self.inference = inference
         self.posterior = posterior
@@ -791,7 +774,8 @@ def infere(self, n_samples=None, theta=None, x=None, n_rounds=1,
             else:
                 args = []
         else:  # `.infere_step` has been called manually
-            prior = self.posterior.set_default_x(self.x_o)
+            x_o = torch.tensor(self.x_o, dtype=torch.float32)
+            prior = self.posterior.set_default_x(x_o)
             if self.posterior._method_family == 'snpe':
                 args = [prior]
             else:
@@ -817,7 +801,8 @@ def infere(self, n_samples=None, theta=None, x=None, n_rounds=1,
 
             # update the proposal given the observation
             if n_rounds > 1:
-                proposal = posterior.set_default_x(self.x_o)
+                x_o = torch.tensor(self.x_o, dtype=torch.float32)
+                proposal = posterior.set_default_x(x_o)
         self.posterior = posterior
         return posterior
 
@@ -883,7 +868,8 @@ def sample(self, shape, posterior=None, **kwargs):
             raise ValueError('Need to provide posterior argument if no'
                              ' posterior has been calculated by the `infere`'
                              ' method.')
-        samples = p.sample(shape, x=self.x_o, **kwargs)
+        x_o = torch.tensor(self.x_o, dtype=torch.float32)
+        samples = p.sample(shape, x=x_o, **kwargs)
         self.samples = samples
         return samples.numpy()
 
@@ -1018,7 +1004,8 @@ def conditional_pairplot(self, condition, density=None, points=None,
         else:
             if self.posterior is not None:
                 if self.posterior.default_x is None:
-                    d = self.posterior.set_default_x(self.x_o)
+                    x_o = torch.tensor(self.x_o, dtype=torch.float32)
+                    d = self.posterior.set_default_x(x_o)
                 else:
                     d = self.posterior
             else:
@@ -1111,7 +1098,8 @@ def conditional_corrcoeff(self, condition, density=None, limits=None,
         else:
             if self.posterior is not None:
                 if self.posterior.default_x is None:
-                    d = self.posterior.set_default_x(self.x_o)
+                    x_o = torch.tensor(self.x_o, dtype=torch.float32)
+                    d = self.posterior.set_default_x(x_o)
                 else:
                     d = self.posterior
             else:
@@ -1180,7 +1168,8 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
             raise ValueError('Need to provide posterior argument if no'
                              ' posterior has been calculated by the `infere`'
                              ' method.')
-        params = p.sample((1, ), x=self.x_o)
+        x_o = torch.tensor(self.x_o, dtype=torch.float32)
+        params = p.sample((1, ), x=x_o)
 
         # set output variable that is monitored
         if output_var is None:

From 0d89a8e3f6cb530c346a4c25c5f8323c15d93d4b Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 3 Aug 2021 18:19:55 +0200
Subject: [PATCH 048/121] fix kwargs forwarding

---
 brian2modelfitting/inferencer.py | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 838de1d..3ee89b4 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -599,8 +599,8 @@ def train(self, inference, theta, x, *args, **train_kwargs):
         ``sbi.inference.NeuralInference``
             trained inference object.
         """
-        # inference = inference.append_simulations(theta, x, *args)
-        inference = inference.append_simulations(theta, x)
+        inference = inference.append_simulations(theta, x, *args)
+        #inference = inference.append_simulations(theta, x)
         _ = inference.train(**train_kwargs)
         return inference
 
@@ -852,7 +852,7 @@ def sample(self, shape, posterior=None, **kwargs):
             Desired shape of samples that are drawn from posterior.
         posterior : sbi.inference.posteriors.DirectPosterior, optional
             Posterior distribution.
-        **kwargs : dict, optional
+        kwargs : dict, optional
             Additional keyword arguments for ``sample`` method in
             ``sbi.inference.posteriors.DirectPosterior`` class
         Returns
@@ -897,7 +897,7 @@ def pairplot(self, samples=None, points=None, limits=None, subset=None,
         ticks : dict, optional
             Position of the ticks. If None, default ticks positions
             will be used.
-        **kwargs : dict, optional
+        kwargs : dict, optional
             Additional keyword arguments for the
             ``sbi.analysis.pairplot`` function.
 
@@ -989,7 +989,7 @@ def conditional_pairplot(self, condition, density=None, points=None,
         ticks : dict, optional
             Position of the ticks. If None, default ticks positions
             will be used.
-        **kwargs : dict, optional
+        kwargs : dict, optional
             Additional keyword arguments for the
             ``sbi.analysis.conditional_pairplot`` function.
 
@@ -1060,7 +1060,7 @@ def conditional_pairplot(self, condition, density=None, points=None,
                                                       subset=subset,
                                                       labels=labels,
                                                       ticks=ticks,
-                                                      *kwargs)
+                                                      **kwargs)
         return fig, axes
 
     def conditional_corrcoeff(self, condition, density=None, limits=None,
@@ -1083,7 +1083,7 @@ def conditional_corrcoeff(self, condition, density=None, limits=None,
         subset : list, optional
             Parameters that are taken for conditional distribution, if
             None all parameters are considered.
-        **kwargs : dict, optional
+        kwargs : dict, optional
             Additional keyword arguments for the
             ``sbi.analysis.conditional_corrcoeff`` function.
 
@@ -1129,7 +1129,7 @@ def conditional_corrcoeff(self, condition, density=None, limits=None,
                                                         limits=limits,
                                                         condition=condition,
                                                         subset=subset,
-                                                        *kwargs)
+                                                        **kwargs)
         return cond_coeff.numpy()
 
     def generate_traces(self, posterior=None, output_var=None, param_init=None,

From 4fb82bec15469f2b6221a477c7c28a63a957573e Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 3 Aug 2021 20:23:03 +0200
Subject: [PATCH 049/121] adjust for the changes in api

---
 examples/IF_sbi.py | 55 +++++++++++++++++++---------------------------
 1 file changed, 22 insertions(+), 33 deletions(-)

diff --git a/examples/IF_sbi.py b/examples/IF_sbi.py
index 8de5adc..bbb4e5e 100644
--- a/examples/IF_sbi.py
+++ b/examples/IF_sbi.py
@@ -4,76 +4,65 @@
 
 
 # Generate input and output data traces
-dt = 0.1 * ms
+dt = 0.1*ms
 defaultclock.dt = dt
 inp_trace = hstack([zeros(int(round(5*ms/dt))),
                     ones(int(round(25*ms/dt))),
-                    zeros(int(round(30*ms/dt)))]) * 10 * nA
+                    zeros(int(round(30*ms/dt)))]) * 10*nA
 I = TimedArray(inp_trace, dt=dt)
-El = -70 * mV
-VT = -50 * mV
-DeltaT = 2 * mV
+El = -70*mV
+VT = -50*mV
+DeltaT = 2*mV
 eqs = '''
     dv/dt = (gl*(El - v) + gl*DeltaT*exp((v - VT) / DeltaT) + I(t)) / C : volt
     gl    : siemens (constant)
     C     : farad (constant)
-'''
+    '''
 
 neurons = NeuronGroup(1, eqs,
                       threshold='v > -50 * mV',
                       reset='v = -70 * mV',
                       method='exponential_euler')
 neurons.v = -70 * mV
-neurons.set_states({'gl': 30 * nS,
-                    'C': 1 * nF})
+neurons.set_states({'gl': 30*nS,
+                    'C': 1*nF})
 monitor = StateMonitor(neurons, 'v', record=True)
-run(60 * ms)
+run(60*ms)
 out_trace = monitor.v
 
 # Inference
 start_scope()
-El = -70 * mV
-VT = -50 * mV
-DeltaT = 2 * mV
+El = -70*mV
+VT = -50*mV
+DeltaT = 2*mV
 eqs_inf = '''
     dv/dt = (gl*(El - v) + gl*DeltaT*exp((v - VT) / DeltaT) + I_syn) / C : volt
     gl    : siemens (constant)
     C     : farad (constant)
-'''
-
-t = arange(0, out_trace.shape[1] * dt / ms, dt / ms)
-start_syn = t[min(where(inp_trace != 0)[0])]
-end_syn = t[max(where(inp_trace != 0)[0])]
-
-
-def n_peaks(x):
-    n_p = []
-    for _x in x.transpose():
-        p_i = find_peaks(_x)[0]
-        n_p.append(p_i.size)
-    return n_p
-
+    '''
 
 inferencer = Inferencer(dt=dt, model=eqs_inf,
                         input={'I_syn': inp_trace.reshape(1, -1)},
                         output={'v': out_trace},
-                        features=[lambda x: x[(t > start_syn) & (t < end_syn), :].mean(axis=0),
-                                  lambda x: x[(t > start_syn) & (t < end_syn), :].std(axis=0),
-                                  lambda x: x[(t > start_syn) & (t < end_syn), :].max(axis=0),
-                                  n_peaks],
                         method='exponential_euler',
                         threshold='v > -50 * mV',
                         reset='v = -70 * mV',
                         param_init={'v': -70 * mV})
 
-inferencer.infere(n_samples=1000,
+inferencer.infere(n_samples=10000,
                   inference_method='SNPE',
                   gl=[10*nS, 100*nS],
                   C=[0.1*nF, 10*nF])
 
 inferencer.sample((10000, ))
-inferencer.pairplot(labels=[r'$\overline{g}_{l}$', r'$C$'],
-                    points=[[1e-9, 30e-9]],
+inferencer.pairplot(labels={'gl': r'$\overline{g}_{l}$',
+                            'C': r'$C$'},
+                    points={'gl': 30*nS,
+                            'C': 1*nF},
+                    limits={'gl': [10*nS, 100*nS],
+                            'C': [0.1*nF, 10*nF]},
+                    ticks={'gl': [10*nS, 100*nS],
+                           'C': [0.1*nF, 10*nF]},
                     points_offdiag={'markersize': 9},
                     points_colors=['r'])
 show()

From 3031d73cf9f5d080e2b898e581fca6adc2804ec8 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 3 Aug 2021 20:52:47 +0200
Subject: [PATCH 050/121] adjust to changes made in api

---
 examples/hh_sim_sbi.py | 107 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 107 insertions(+)
 create mode 100644 examples/hh_sim_sbi.py

diff --git a/examples/hh_sim_sbi.py b/examples/hh_sim_sbi.py
new file mode 100644
index 0000000..64dea1f
--- /dev/null
+++ b/examples/hh_sim_sbi.py
@@ -0,0 +1,107 @@
+from brian2modelfitting import *
+from brian2 import *
+
+
+# Input and output data traces simulation
+dt = 0.05*ms
+defaultclock.dt = dt
+t_on = 50*ms
+t_total = 350*ms
+t_off = t_total - t_on
+I = 1*nA
+gleak = 10*nS
+Eleak = -70*mV
+VT = -60*mV
+C = 200*pF
+gNa = 32*uS
+ENa = 53*mV
+gK = 1*uS
+EK = -107*mV
+eqs = '''
+    dVm/dt = -(gNa*m**3*h*(Vm - ENa) + gK*n**4*(Vm - EK) + gleak*(Vm - Eleak) - I_inj) / C : volt
+    I_inj = int(t >= t_on and t < t_off)*I : amp (shared)
+    dm/dt = alpham*(1-m) - betam*m : 1
+    dn/dt = alphan*(1-n) - betan*n : 1
+    dh/dt = alphah*(1-h) - betah*h : 1
+    alpham = (-0.32/mV) * (Vm - VT - 13.*mV) / (exp((-(Vm - VT - 13.*mV))/(4.*mV)) - 1)/ms : Hz
+    betam = (0.28/mV) * (Vm - VT - 40.*mV) / (exp((Vm - VT - 40.*mV)/(5.*mV)) - 1)/ms : Hz
+    alphah = 0.128 * exp(-(Vm - VT - 17.*mV) / (18.*mV))/ms : Hz
+    betah = 4/(1 + exp((-(Vm - VT - 40.*mV)) / (5.*mV)))/ms : Hz
+    alphan = (-0.032/mV) * (Vm - VT - 15.*mV) / (exp((-(Vm - VT - 15.*mV)) / (5.*mV)) - 1)/ms : Hz
+    betan = 0.5*exp(-(Vm - VT - 10.*mV) / (40.*mV))/ms : Hz
+    '''
+neurons = NeuronGroup(1, eqs, dt=dt,
+                      threshold='m>0.5', refractory='m>0.5',
+                      method='exponential_euler')
+state_monitor = StateMonitor(neurons, 'Vm', record=True)
+spike_monitor = SpikeMonitor(neurons, record=False)
+neurons.Vm = 'Eleak'
+neurons.m = '1/(1 + betam/alpham)'
+neurons.h = '1/(1 + betah/alphah)'
+neurons.n = '1/(1 + betan/alphan)'
+run(t_total)
+I_inj = ((state_monitor.t >= t_on) & (state_monitor.t < t_off)) * I
+t = state_monitor.t
+out_trace = state_monitor.Vm
+
+# Inference
+start_scope()
+eqs = '''
+    dVm/dt = -(gNa*m**3*h*(Vm - ENa) + gK*n**4*(Vm - EK) + gleak*(Vm - Eleak) - I_inj) / C : volt
+    dm/dt = alpham*(1-m) - betam*m : 1
+    dn/dt = alphan*(1-n) - betan*n : 1
+    dh/dt = alphah*(1-h) - betah*h : 1
+    alpham = (-0.32/mV) * (Vm - VT - 13.*mV) / (exp((-(Vm - VT - 13.*mV))/(4.*mV)) - 1)/ms : Hz
+    betam = (0.28/mV) * (Vm - VT - 40.*mV) / (exp((Vm - VT - 40.*mV)/(5.*mV)) - 1)/ms : Hz
+    alphah = 0.128 * exp(-(Vm - VT - 17.*mV) / (18.*mV))/ms : Hz
+    betah = 4/(1 + exp((-(Vm - VT - 40.*mV)) / (5.*mV)))/ms : Hz
+    alphan = (-0.032/mV) * (Vm - VT - 15.*mV) / (exp((-(Vm - VT - 15.*mV)) / (5.*mV)) - 1)/ms : Hz
+    betan = 0.5*exp(-(Vm - VT - 10.*mV) / (40.*mV))/ms : Hz
+
+    # The parameters to fit
+    gNa : siemens (constant)
+    gK : siemens (constant)
+    '''
+inferencer = Inferencer(dt=dt, model=eqs,
+                        input={'I_inj': I_inj.reshape(1, -1)},
+                        output={'Vm': out_trace},
+                        method='exponential_euler',
+                        threshold='m>0.5', refractory='m>0.5',
+                        param_init={'Vm': 'Eleak',
+                                    'm': '1/(1 + betam/alpham)',
+                                    'h': '1/(1 + betah/alphah)',
+                                    'n': '1/(1 + betan/alphan)'})
+inferencer.infere(n_samples=10_000,
+                  inference_method='SNPE',
+                  density_estimator_model='mdn',
+                  gNa=[.5*uS, 80.*uS],
+                  gK=[1e-4*uS, 15.*uS])
+inferencer.sample((10_000, ))
+
+# Visualize estimated posterior distribution
+inferencer.pairplot(limits={'gNa': [.5*uS, 80.*uS],
+                            'gK': [1e-4*uS, 15.*uS]},
+                    ticks={'gNa': [.5*uS, 80.*uS],
+                           'gK': [1e-4*uS, 15.*uS]},
+                    labels={'gNa': r'$\overline{g}_{Na}$',
+                            'gK': r'$\overline{g}_{K}$'},
+                    points={'gNa': 32*uS,
+                            'gK': 1*uS},
+                    points_offdiag={'markersize': 6},
+                    points_colors=['r'],
+                    figsize=(6, 6))
+
+# Visualize traces for a single sample from posterior
+inf_trace = inferencer.generate_traces()
+fig, axs = subplots(2, 1, sharex=True,
+                    gridspec_kw={'height_ratios': [3, 1]},
+                    figsize=(6, 4))
+axs[0].plot(t, out_trace.ravel()/mV, label='simulated recordings')
+axs[0].plot(t, inf_trace.ravel()/mV, '--', label='posterior sample')
+axs[0].set(ylabel='Vm, mV')
+axs[0].legend()
+axs[1].plot(t, I_inj.ravel()/nA, 'k-', label='stimulus')
+axs[1].set(xlabel='t, ms', ylabel='I, nA')
+axs[1].legend()
+
+show()

From 03140698729567d5acb14bd2a611502ae47a1f7b Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 5 Aug 2021 22:11:34 +0200
Subject: [PATCH 051/121] enable multitrace support with or without given list
 of features

---
 brian2modelfitting/inferencer.py | 38 ++++++++++++++++----------------
 1 file changed, 19 insertions(+), 19 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 3ee89b4..595e1c7 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -277,16 +277,16 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         self.x = None
 
         # observation the focus is on
-        if features:
-            x_o = []
-            for o in self.output:
-                o = np.array(o)
+        for o in self.output:
+            o = np.array(o)
+            if features:
                 obs = []
-                for feature in features:
-                    obs.extend(feature(o.transpose()))
-                x_o.append(obs)
-        else:
-            x_o = np.vstack(self.output).reshape(self.n_traces, -1)
+                for _o in o:
+                    for feature in features:
+                        obs.append(feature(_o))
+                x_o = np.array(obs, dtype=np.float32)
+            else:
+                x_o = o.ravel().astype(np.float32)
         self.x_o = x_o
         self.features = features
 
@@ -454,19 +454,19 @@ def extract_summary_statistics(self, theta, level=1):
 
         # extract features
         obs = simulator.statemonitor.recorded_variables
-        x = []
         for ov in self.output_var:
-            x_val = obs[ov].get_value()
+            o = obs[ov].get_value()
+            o = o.T
             if self.features:
                 summary_statistics = []
-                for feature in self.features:
-                    summary_statistics.append(feature(x_val))
-                x.append(summary_statistics)
-                x = np.array(x, dtype=np.float32)
+                # TODO: should be vectorized
+                for _o in o:
+                    for feature in self.features:
+                        summary_statistics.append(feature(_o))
+                x = np.array(summary_statistics, dtype=np.float32)
+                x = x.reshape(self.n_samples, -1)
             else:
-                x.append(x_val.T)
-        x = np.hstack(x).astype(np.float32)
-        # x = x.reshape((self.n_samples, -1))
+                x = o.reshape(self.n_samples, -1).astype(np.float32)
         return x
 
     def save_summary_statistics(self, f, theta=None, x=None):
@@ -600,7 +600,7 @@ def train(self, inference, theta, x, *args, **train_kwargs):
             trained inference object.
         """
         inference = inference.append_simulations(theta, x, *args)
-        #inference = inference.append_simulations(theta, x)
+        # inference = inference.append_simulations(theta, x)
         _ = inference.train(**train_kwargs)
         return inference
 

From e09f17535f7357651519496b79509783712966d2 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 5 Aug 2021 22:35:56 +0200
Subject: [PATCH 052/121] adjust to changes in api

---
 examples/hh_sbi.py | 44 ++++++++++++++++++--------------------------
 1 file changed, 18 insertions(+), 26 deletions(-)

diff --git a/examples/hh_sbi.py b/examples/hh_sbi.py
index 6c3a67f..47f718e 100644
--- a/examples/hh_sbi.py
+++ b/examples/hh_sbi.py
@@ -1,17 +1,15 @@
 from brian2 import *
 from brian2modelfitting import *
 import pandas as pd
-import numpy as np
-from scipy.signal import find_peaks
 
 
 # Load input and output data traces
 df_inp_traces = pd.read_csv('input_traces_hh.csv')
 df_out_traces = pd.read_csv('output_traces_hh.csv')
 inp_traces = df_inp_traces.to_numpy()
-inp_traces = inp_traces[:2, 1:]
+inp_traces = inp_traces[:3, 1:]
 out_traces = df_out_traces.to_numpy()
-out_traces = out_traces[:2, 1:]
+out_traces = out_traces[:3, 1:]
 
 # Model and its parameters
 area = 20_000*um**2
@@ -37,31 +35,21 @@
 # Time domain
 t = arange(0, out_traces.shape[1]*dt/ms, dt/ms)
 
-
-def n_peaks(x):
-    n_p = []
-    for _x in x.transpose():
-        p_i = find_peaks(_x, height=0)[0]
-        n_p.append(p_i.size - sum(np.diff(t[p_i]) < 4))
-    return n_p
-
-
 # Simulation-based inference object instantiation
 inferencer = Inferencer(dt=dt, model=eqs,
                         input={'I': inp_traces*amp},
                         output={'v': out_traces*mV},
-                        features=[n_peaks,
-                                  lambda x: x[(t > 5) & (t < 10), :].mean(axis=0),
-                                  lambda x: x[(t > 5) & (t < 10), :].std(axis=0),
-                                  lambda x: x.max(axis=0)],
+                        features=[lambda x: x[(t > 5) & (t < 10)].mean(),
+                                  lambda x: x[(t > 5) & (t < 10)].std(),
+                                  lambda x: x.max()],
                         method='exponential_euler',
                         threshold='m > 0.5',
                         refractory='m > 0.5',
                         param_init={'v': 'VT'})
 
 # Generate prior and train the neural density estimator
-inferencer.infere(n_samples=1000,
-                  n_rounds=2,
+inferencer.infere(n_samples=10000,
+                  n_rounds=1,
                   gl=[1e-09*siemens, 1e-07*siemens],
                   g_na=[2e-06*siemens, 2e-04*siemens],
                   g_kd=[6e-07*siemens, 6e-05*siemens],
@@ -71,10 +59,14 @@ def n_peaks(x):
 inferencer.sample((10000, ))
 
 # Create pairplot from samples
-inferencer.pairplot(labels=[r'$\overline{g}_{l}$',
-                            r'$\overline{g}_{Na}$',
-                            r'$\overline{g}_{K}$',
-                            r'$\overline{C}_{m}$'])
+inferencer.pairplot(limits={'gl': [1e-09*siemens, 1e-07*siemens],
+                            'g_na': [2e-06*siemens, 2e-04*siemens],
+                            'g_kd': [6e-07*siemens, 6e-05*siemens],
+                            'Cm': [0.1*uF*cm**-2*area, 2*uF*cm**-2*area]},
+                    labels={'gl': r'$\overline{g}_{l}$',
+                            'g_na': r'$\overline{g}_{Na}$',
+                            'g_kd': r'$\overline{g}_{K}$',
+                            'Cm': r'$\overline{C}_{m}$'})
 
 # Generate traces by using a single sample from the trained posterior
 inf_traces = inferencer.generate_traces()
@@ -85,9 +77,9 @@ def n_peaks(x):
 fig, axs = subplots(nrows, ncols, sharex=True,
                     gridspec_kw={'height_ratios': [3, 1]}, figsize=(15, 4))
 for idx in range(ncols):
-    axs[0, idx].plot(t, out_traces[idx, :].T, label='measurements')
-    axs[0, idx].plot(t, inf_traces[idx, :].T/mV, label='fits')
-    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, 'k-', label='stimulus')
+    axs[0, idx].plot(t, out_traces[idx, :], lw=3, label='measurements')
+    axs[0, idx].plot(t, inf_traces[idx, :]/mV, '--', lw=3,label='fits')
+    axs[1, idx].plot(t, inp_traces[idx, :]/nA, 'k-', label='stimulus')
     axs[1, idx].set_xlabel('t, ms')
     if idx == 0:
         axs[0, idx].set_ylabel('$v$, mV')

From 516714396a245c0e02b086a9262554d8d1ec0d0b Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 6 Aug 2021 11:29:16 +0200
Subject: [PATCH 053/121] change features type to dict instead of list to
 enable multi output inference support

---
 brian2modelfitting/inferencer.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 595e1c7..816be2b 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -277,12 +277,12 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         self.x = None
 
         # observation the focus is on
-        for o in self.output:
+        for ov, o in zip(self.output_var, self.output):
             o = np.array(o)
             if features:
                 obs = []
                 for _o in o:
-                    for feature in features:
+                    for feature in features[ov]:
                         obs.append(feature(_o))
                 x_o = np.array(obs, dtype=np.float32)
             else:
@@ -461,7 +461,7 @@ def extract_summary_statistics(self, theta, level=1):
                 summary_statistics = []
                 # TODO: should be vectorized
                 for _o in o:
-                    for feature in self.features:
+                    for feature in self.features[ov]:
                         summary_statistics.append(feature(_o))
                 x = np.array(summary_statistics, dtype=np.float32)
                 x = x.reshape(self.n_samples, -1)

From 487612e6e8c413f34a22f81d86e295a04ed66c21 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 6 Aug 2021 14:06:17 +0200
Subject: [PATCH 054/121] fix docstrings + small updates

---
 brian2modelfitting/inferencer.py | 193 +++++++++++++++++--------------
 1 file changed, 106 insertions(+), 87 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 816be2b..e0185ed 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -10,7 +10,6 @@
 from brian2.groups.neurongroup import NeuronGroup
 from brian2.input.timedarray import TimedArray
 from brian2.monitors.statemonitor import StateMonitor
-from brian2.units.allunits import *  # all physical units
 from brian2.units.fundamentalunits import (DIMENSIONLESS,
                                            fail_for_dimension_mismatch,
                                            get_dimensions,
@@ -29,8 +28,8 @@
 
 def configure_simulator():
     """Return the configured simulator, which can be either
-    `.RuntimeSimulator`, object for use with `.RuntimeDevice`, or
-    `.CPPStandaloneSimulator`, object for use with
+    `.RuntimeSimulator`, object for the use with `.RuntimeDevice`, or
+    `.CPPStandaloneSimulator`, object for the use with
     `.CPPStandaloneDevice`.
 
     Parameters
@@ -62,15 +61,16 @@ def get_full_namespace(additional_namespace, level=0):
     ----------
     additional_namespace : dict
         References to external parameters or functions, where key is
-        the name and value is the value of the external param/func.
+        the name and value is the value of the external parameter or
+        function.
     level : int, optional
         How far to go back to get the locals/globals.
 
     Returns
     -------
     dict
-        Namespace with additional references to external parameters or
-        functions.
+        Namespace with additional references to the external parameters
+        or functions.
     """
     namespace = {key: value
                  for key, value in get_local_namespace(level=level + 1).items()
@@ -95,8 +95,8 @@ def get_param_dict(param_values, param_names, n_values):
     Returns
     -------
     dict
-        Dictionary containing key-value pairs thet correspond to a
-        parameter name and value(s)
+        Dictionary containing key-value pairs that correspond to a
+        parameter name and value(s).
     """
     param_values = np.array(param_values)
     param_dict = dict()
@@ -106,23 +106,24 @@ def get_param_dict(param_values, param_names, n_values):
 
 
 def calc_prior(param_names, **params):
-    """Return prior distributparion over given parameters. Note that the
-    only available prior distribution currently supported is
-    multidimensional uniform distribution defined on a box.
+    """Return the prior distribution over given parameters.
+
+    N.B. The only currently supported prior distribution is
+    the multi-dimensional uniform distribution defined on a box.
 
     Parameters
     ----------
     param_names : iterable
         Iterable containing parameter names.
     params : dict
-        Dictionary with keys that correspond to parameter names, and
-        values should be a single dimensional lists or arrays
+        Dictionary with keys that correspond to parameter names, values
+        are a single dimensional lists or arrays.
 
     Return
     ------
     sbi.utils.torchutils.BoxUniform
         ``sbi`` compatible object that contains a uniform prior
-        distribution over a given set of parameter
+        distribution over a given set of parameters.
     """
     for param_name in param_names:
         if param_name not in params:
@@ -140,11 +141,12 @@ def calc_prior(param_names, **params):
 class Inferencer(object):
     """Class for simulation-based inference.
 
-    It offers an interface similar to that of `.Fitter` class but
+    It offers an interface similar to that of the `.Fitter` class but
     instead of fitting, neural density estimator is trained using a
-    generative model. This class serves as a wrapper for ``sbi``
-    library for inferencing posterior over unknown parameters of a
-    given model.
+    generative model which ultimately provides the posterior
+    distribution over free parameters.
+    This class serves as a wrapper for ``sbi`` library for inferencing
+    posterior over unknown parameters of a given model.
 
     Parameters
     ----------
@@ -153,17 +155,21 @@ class Inferencer(object):
     model : str or brian2.equations.equations.Equations
         Single cell model equations.
     input : dict
-        Input traces in dictionary format, where key corresponds to the
+        Input traces in dictionary format where key corresponds to the
         name of the input variable as defined in ``model`` and value
-        corresponds to a single dimensional array of data traces.
+        corresponds to an array of input traces.
     output : dict
         Dictionary of recorded (or simulated) output data traces, where
         key corresponds to the name of the output variable as defined
-        in ``model`` and value corresponds to a single dimensional
-        array of recorded data traces.
-    features : list, optional
-        List of callables that take the voltage trace and output
-        summary statistics.
+        in ``model`` and value corresponds to a an array of recorded
+        traces.
+    features : dict, optional
+        Dictionary of callables that take the 1-dimensional voltage
+        trace and output summary statistics. Keys correspond to output
+        variable names, while values are lists of callables.
+        If features are not provided, automatic feature extraction will
+        be performed either by using the default multi-layer perceptron
+        or by using the user-provided embedding network.
     method : str, optional
         Integration method.
     threshold : str, optional
@@ -269,13 +275,6 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         self.reset = reset
         self.refractory = refractory
 
-        self.params = None
-        self.n_samples = None
-        self.samples = None
-        self.posterior = None
-        self.theta = None
-        self.x = None
-
         # observation the focus is on
         for ov, o in zip(self.output_var, self.output):
             o = np.array(o)
@@ -290,6 +289,14 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         self.x_o = x_o
         self.features = features
 
+        # additional placeholders
+        self.params = None
+        self.n_samples = None
+        self.samples = None
+        self.posterior = None
+        self.theta = None
+        self.x = None
+
     @property
     def n_neurons(self):
         """Return the number of neurons that are used in `.NeuronGroup`
@@ -310,7 +317,7 @@ class while generating data for training the neural density
             Total number of neurons.
         """
         if self.n_samples is None:
-            raise ValueError('Number of samples is not yet defined.'
+            raise ValueError('Number of samples have not been yet defined.'
                              'Call `generate_training_data` method first.')
         return self.n_traces * self.n_samples
 
@@ -396,9 +403,7 @@ def init_prior(self, **params):
         return prior
 
     def generate_training_data(self, n_samples, prior):
-        """Return sampled prior and executed simulator containing
-        recorded variables to be used for training the neural density
-        estimator.
+        """Return sampled prior given the total number of samples.
 
         Parameter
         ---------
@@ -415,19 +420,20 @@ def generate_training_data(self, n_samples, prior):
         # set n_samples to class variable to be able to call self.n_neurons
         self.n_samples = n_samples
 
-        # sample from prior
+        # sample from the prior distribution
         theta = prior.sample((n_samples, ))
         theta = np.atleast_2d(theta.numpy())
         return theta
 
     def extract_summary_statistics(self, theta, level=1):
-        """Return summary statistics to be used for training the neural
-        density estimator.
+        """Return summary statistics for training the neural density
+        estimator.
 
         Parameters
         ----------
         theta : numpy.ndarray
-            Sampled prior of shape (``n_samples``, -1).
+            Sampled prior with ``n_samples`` rows, while then umber of
+            columns is equal to the number of free parameters.
         level : int, optional
             How far to go back to get the locals/globals.
 
@@ -452,7 +458,7 @@ def extract_summary_statistics(self, theta, level=1):
         simulator.run(self.sim_time, d_param, self.param_names, iteration=0,
                       name=network_name)
 
-        # extract features
+        # extract features for each output variable and each trace
         obs = simulator.statemonitor.recorded_variables
         for ov in self.output_var:
             o = obs[ov].get_value()
@@ -470,13 +476,13 @@ def extract_summary_statistics(self, theta, level=1):
         return x
 
     def save_summary_statistics(self, f, theta=None, x=None):
-        """Save sampled prior data, theta, and extracted summary
-        statistics, x, into a single file in compressed ``.pz`` format.
+        """Save sampled prior, and extracted summary statistics into a
+        single file in compressed ``.npz`` format.
 
         Parameters
         ----------
         f : str or os.PathLike
-            Path to file either as string or ``os.PathLike`` object
+            Path to a file either as string or ``os.PathLike`` object
             that contains file name.
         theta : numpy.ndarray, optional
             Sampled prior.
@@ -493,21 +499,19 @@ def save_summary_statistics(self, f, theta=None, x=None):
             t = self.theta
         else:
             raise AttributeError('Provide sampled prior or call'
-                                 ' `infere_step` method first.')
+                                 ' `infer_step` method first.')
         if x is not None:
             pass
         elif self.x is not None:
             x = self.x
         else:
             raise AttributeError('Provide summary feautures or call '
-                                 ' `infere_step` method first.')
+                                 ' `infer_step` method first.')
         np.savez_compressed(f, theta=t, x=x)
 
     def load_summary_statistics(self, f):
-        """Load sampled prior data, theta, and extracted summary
-        statistics, x, from a compressed ``.npz`` format. Arrays should
-        be named either `arr_0` and `arr_1` or `theta` and `x` for
-        sampled priors and extracted features, respectively.
+        """Load samples from a prior and extracted summary statistics
+        from a compressed ``.npz`` file.
 
         Parameters
         ----------
@@ -518,7 +522,7 @@ def load_summary_statistics(self, f):
         Returns
         -------
         tuple
-            Consisting of sampled prior and summary statistics arrays.
+            Sampled prior and summary statistics arrays.
         """
         loaded = np.load(f, allow_pickle=True)
         if set(loaded.files) == {'theta', 'x'}:
@@ -544,7 +548,11 @@ def init_inference(self, inference_method, density_estimator_model, prior,
             Uniformly distributed prior over given parameters.
         inference_kwargs : dict, optional
             Additional keyword arguments for
-            ``sbi.utils.get_nn_models.posterior_nn`` method.
+            ``sbi.utils.get_nn_models.posterior_nn`` method. The user
+            is free to provide own embedding network to learn features
+            from potentially high-dimensional simulation outputs. By
+            default multi-layer perceptron is used if no user defined
+            embedding network is provided.
 
         Returns
         -------
@@ -573,8 +581,7 @@ def init_inference(self, inference_method, density_estimator_model, prior,
         return inference
 
     def train(self, inference, theta, x, *args, **train_kwargs):
-        """Return inference object with stored training data and
-        trained density estimator.
+        """Return trained neural inference object.
 
         Parameters
         ----------
@@ -587,25 +594,30 @@ def train(self, inference, theta, x, *args, **train_kwargs):
             Summary statistics.
         args : list, optional
             Contains a uniformly distributed sbi.utils.BoxUniform
-            prior/proposal. Used only for SNPE, for SNLE and SNRE,
+            proposal. Used only for SNPE, for SNLE and SNRE,
             ``proposal`` should not be passed to ``append_simulations``
-            method, thus ``args`` should not be passed.
+            method, thus ``args`` should not be passed. The args should
+            be provided only if the parameters were not sampled from
+            the prior, e.g., during the multi-round inference.
+            For SNLE and SNRE, args can hold the number of round from
+            which the data is stemmed from. Round 0 means from prior.
+            This is used only if the `discard_prior_samples` are set to
+            True inside the `train_kwargs`.
         train_kwargs : dict, optional
             Additional keyword arguments for ``train`` method of
             ``sbi.inference.NeuralInference`` object.
 
         Returns
         -------
-        ``sbi.inference.NeuralInference``
-            trained inference object.
+        sbi.inference.NeuralInference
+            Trained inference object.
         """
         inference = inference.append_simulations(theta, x, *args)
-        # inference = inference.append_simulations(theta, x)
         _ = inference.train(**train_kwargs)
         return inference
 
     def build_posterior(self, inference, **posterior_kwargs):
-        """Return instantiated inference object.
+        """Return inference and neural posterior objects.
 
         Parameters
         ----------
@@ -626,9 +638,9 @@ def build_posterior(self, inference, **posterior_kwargs):
         posterior = inference.build_posterior(**posterior_kwargs)
         return (inference, posterior)
 
-    def infere_step(self, proposal, inference,
-                    n_samples=None, theta=None, x=None,
-                    train_kwargs={}, posterior_kwargs={}, *args):
+    def infer_step(self, proposal, inference,
+                   n_samples=None, theta=None, x=None,
+                   train_kwargs={}, posterior_kwargs={}, *args):
         """Return the trained neural density estimator.
 
         Parameter
@@ -685,10 +697,10 @@ def infere_step(self, proposal, inference,
         self.posterior = posterior
         return posterior
 
-    def infere(self, n_samples=None, theta=None, x=None, n_rounds=1,
-               inference_method='SNPE', density_estimator_model='maf',
-               inference_kwargs={}, train_kwargs={}, posterior_kwargs={},
-               **params):
+    def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
+              inference_method='SNPE', density_estimator_model='maf',
+              inference_kwargs={}, train_kwargs={}, posterior_kwargs={},
+              **params):
         """Return the trained posterior.
 
         If ``theta`` and ``x`` are not provided, ``n_samples`` has to
@@ -708,28 +720,33 @@ def infere(self, n_samples=None, theta=None, x=None, n_rounds=1,
             performed. Otherwise, if ``n_rounds`` is integer larger
             than 1, multi-round inference will be performed. This is
             only valid if posterior has not been defined manually.
+            Otherwise, if this method is called after posterior has
+            already been built, multi-round inference is performed.
         inference_method : str, optional
             Inference method. Either of SNPE, SNLE or SNRE.
         density_estimator_model : str, optional
             The type of density estimator to be created. Either
             ``mdn``, ``made``, ``maf`` or ``nsf``.
         inference_kwargs : dict, optional
-            Additional keyword arguments for
-            ``sbi.utils.get_nn_models.posterior_nn`` method.
+            Additional keyword arguments for the inferencer method
+            definition.
         train_kwargs : dict, optional
             Additional keyword arguments for training the posterior
             estimator.
         posterior_kwargs : dict, optional
-            Dictionary of arguments for `.build_posterior` method.
+            Additional keyword arguments for builing the posterior.
         params : dict
-            Bounds for each parameter.
+            Bounds for each parameter. Keys should correspond to names
+            of parameters as defined in the model equaions, values
+            are lists with lower and upper bounds with quantities of
+            respective parameter.
 
         Returns
         -------
         sbi.inference.NeuralPosterior
-            Trained posterior.
+            Approximated posterior distribution over parameters.
         """
-        if self.posterior is None:  # `.infere_step` has not been called
+        if self.posterior is None:
             # handle the number of rounds
             if not isinstance(n_rounds, int):
                 raise ValueError('`n_rounds` has to be a positive integer.')
@@ -738,7 +755,7 @@ def infere(self, n_samples=None, theta=None, x=None, n_rounds=1,
             try:
                 inference_method = str.upper(inference_method)
             except ValueError as e:
-                print(e, '\nInvalid inference method.')
+                print(e, '\nInference method should be defined as string.')
             if inference_method not in ['SNPE', 'SNLE', 'SNRE']:
                 raise ValueError(f'Inference method {inference_method} is not'
                                  ' supported.')
@@ -768,12 +785,15 @@ def infere(self, n_samples=None, theta=None, x=None, n_rounds=1,
                                                  prior,
                                                  **inference_kwargs)
 
-            # additional args for `.train` method are needed only for SNPE
+            # args for SNPE in `.train`
+            # args for SNRE and SNLE are not supported here, if needed the user
+            # could provide them by using more flexible inferface via
+            # `.infer_step` method
             if inference_method == 'SNPE':
                 args = [prior]
             else:
                 args = []
-        else:  # `.infere_step` has been called manually
+        else:  # `.infer_step` has been called manually
             x_o = torch.tensor(self.x_o, dtype=torch.float32)
             prior = self.posterior.set_default_x(x_o)
             if self.posterior._method_family == 'snpe':
@@ -781,7 +801,7 @@ def infere(self, n_samples=None, theta=None, x=None, n_rounds=1,
             else:
                 args = []
 
-        # allocate empty list of posterior
+        # allocate empty list of posteriors
         posteriors = []
 
         # set a proposal
@@ -792,9 +812,9 @@ def infere(self, n_samples=None, theta=None, x=None, n_rounds=1,
             print(f'Round {round + 1}/{n_rounds}.')
 
             # inference step
-            posterior = self.infere_step(proposal, self.inference,
-                                         n_samples, self.theta, self.x,
-                                         train_kwargs, posterior_kwargs, *args)
+            posterior = self.infer_step(proposal, self.inference,
+                                        n_samples, self.theta, self.x,
+                                        train_kwargs, posterior_kwargs, *args)
 
             # append the current posterior to the list of posteriors
             posteriors.append(posterior)
@@ -811,7 +831,7 @@ def save_posterior(self, f):
 
         Parameters
         ----------
-        posterior : sbi.inference.posteriors.DirectPosterior, optional
+        posterior : sbi.inference.NeuralPosterior, optional
             Posterior distribution.
         f : str or os.PathLike
             Path to file either as string or ``os.PathLike`` object
@@ -850,11 +870,11 @@ def sample(self, shape, posterior=None, **kwargs):
         ----------
         shape : tuple
             Desired shape of samples that are drawn from posterior.
-        posterior : sbi.inference.posteriors.DirectPosterior, optional
+        posterior : sbi.inference.NeuralPosterior, optional
             Posterior distribution.
         kwargs : dict, optional
-            Additional keyword arguments for ``sample`` method in
-            ``sbi.inference.posteriors.DirectPosterior`` class
+            Additional keyword arguments for ``sample`` method of
+            ``sbi.inference.NeuralPosterior`` object.
         Returns
         -------
         numpy.ndarray
@@ -866,8 +886,7 @@ def sample(self, shape, posterior=None, **kwargs):
             p = self.posterior
         else:
             raise ValueError('Need to provide posterior argument if no'
-                             ' posterior has been calculated by the `infere`'
-                             ' method.')
+                             ' posterior has computed built yet.')
         x_o = torch.tensor(self.x_o, dtype=torch.float32)
         samples = p.sample(shape, x=x_o, **kwargs)
         self.samples = samples
@@ -1139,7 +1158,7 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
 
         Parameters
         ----------
-        posterior : sbi.inference.posteriors.DirectPosterior, optional
+        posterior : sbi.inference.NeuralPosterior, optional
             Posterior distribution.
         output_var: str or sequence of str
             Name of the output variable to be monitored, it can also be

From 040407609cd408e03cac261b139eac5f06430522 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 6 Aug 2021 17:52:26 +0200
Subject: [PATCH 055/121] adjust to changes in inferencer class

---
 examples/IF_sbi.py | 10 ++++------
 1 file changed, 4 insertions(+), 6 deletions(-)

diff --git a/examples/IF_sbi.py b/examples/IF_sbi.py
index bbb4e5e..a6284f7 100644
--- a/examples/IF_sbi.py
+++ b/examples/IF_sbi.py
@@ -1,6 +1,5 @@
 from brian2 import *
 from brian2modelfitting import *
-from scipy.signal import find_peaks
 
 
 # Generate input and output data traces
@@ -23,7 +22,7 @@
                       threshold='v > -50 * mV',
                       reset='v = -70 * mV',
                       method='exponential_euler')
-neurons.v = -70 * mV
+neurons.v = -70*mV
 neurons.set_states({'gl': 30*nS,
                     'C': 1*nF})
 monitor = StateMonitor(neurons, 'v', record=True)
@@ -49,10 +48,9 @@
                         reset='v = -70 * mV',
                         param_init={'v': -70 * mV})
 
-inferencer.infere(n_samples=10000,
-                  inference_method='SNPE',
-                  gl=[10*nS, 100*nS],
-                  C=[0.1*nF, 10*nF])
+inferencer.infer(n_samples=10_000,
+                 gl=[10*nS, 100*nS],
+                 C=[0.1*nF, 10*nF])
 
 inferencer.sample((10000, ))
 inferencer.pairplot(labels={'gl': r'$\overline{g}_{l}$',

From 6de7ada2ab55d3865271a4ab57c9429e01c83cea Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 6 Aug 2021 17:53:03 +0200
Subject: [PATCH 056/121] rename file and adjust due to changes in inferencer
 class

---
 .../{hh_sbi.py => hh_sbi_simple_interface.py} | 63 ++++++++++++-------
 1 file changed, 40 insertions(+), 23 deletions(-)
 rename examples/{hh_sbi.py => hh_sbi_simple_interface.py} (56%)

diff --git a/examples/hh_sbi.py b/examples/hh_sbi_simple_interface.py
similarity index 56%
rename from examples/hh_sbi.py
rename to examples/hh_sbi_simple_interface.py
index 47f718e..4bb4b62 100644
--- a/examples/hh_sbi.py
+++ b/examples/hh_sbi_simple_interface.py
@@ -7,11 +7,11 @@
 df_inp_traces = pd.read_csv('input_traces_hh.csv')
 df_out_traces = pd.read_csv('output_traces_hh.csv')
 inp_traces = df_inp_traces.to_numpy()
-inp_traces = inp_traces[:3, 1:]
+inp_traces = inp_traces[[0, 1, 3], 1:]
 out_traces = df_out_traces.to_numpy()
-out_traces = out_traces[:3, 1:]
+out_traces = out_traces[[0, 1, 3], 1:]
 
-# Model and its parameters
+# Define model and its parameters
 area = 20_000*um**2
 El = -65*mV
 EK = -90*mV
@@ -39,36 +39,53 @@
 inferencer = Inferencer(dt=dt, model=eqs,
                         input={'I': inp_traces*amp},
                         output={'v': out_traces*mV},
-                        features=[lambda x: x[(t > 5) & (t < 10)].mean(),
-                                  lambda x: x[(t > 5) & (t < 10)].std(),
-                                  lambda x: x.max()],
+                        features={'v': [lambda x: x[(t > 5) & (t < 10)].mean(),
+                                        lambda x: x[(t > 5) & (t < 10)].std(),
+                                        lambda x: x.max()]},
                         method='exponential_euler',
                         threshold='m > 0.5',
                         refractory='m > 0.5',
                         param_init={'v': 'VT'})
 
 # Generate prior and train the neural density estimator
-inferencer.infere(n_samples=10000,
-                  n_rounds=1,
-                  gl=[1e-09*siemens, 1e-07*siemens],
-                  g_na=[2e-06*siemens, 2e-04*siemens],
-                  g_kd=[6e-07*siemens, 6e-05*siemens],
-                  Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])
+inferencer.infer(n_samples=10_000,
+                 n_rounds=1,
+                 gl=[1e-09*siemens, 1e-07*siemens],
+                 g_na=[2e-06*siemens, 2e-04*siemens],
+                 g_kd=[6e-07*siemens, 6e-05*siemens],
+                 Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])
 
 # Draw samples from posterior
-inferencer.sample((10000, ))
+inferencer.sample((10_000, ))
 
-# Create pairplot from samples
-inferencer.pairplot(limits={'gl': [1e-09*siemens, 1e-07*siemens],
-                            'g_na': [2e-06*siemens, 2e-04*siemens],
-                            'g_kd': [6e-07*siemens, 6e-05*siemens],
-                            'Cm': [0.1*uF*cm**-2*area, 2*uF*cm**-2*area]},
-                    labels={'gl': r'$\overline{g}_{l}$',
-                            'g_na': r'$\overline{g}_{Na}$',
-                            'g_kd': r'$\overline{g}_{K}$',
-                            'Cm': r'$\overline{C}_{m}$'})
+# Visualize pairplot and conditional pairplot
+limits = {'gl': [1e-09*siemens, 1e-07*siemens],
+          'g_na': [2e-06*siemens, 2e-04*siemens],
+          'g_kd': [6e-07*siemens, 6e-05*siemens],
+          'Cm': [0.1*uF*cm**-2*area, 2*uF*cm**-2*area]}
+labels = {'gl': r'$\overline{g}_\mathrm{l}$',
+          'g_na': r'$\overline{g}_\mathrm{Na}$',
+          'g_kd': r'$\overline{g}_\mathrm{K}$',
+          'Cm': r'$\overline{C}_{m}$'}
+inferencer.pairplot(limits=limits,
+                    ticks=limits,
+                    labels=labels,
+                    figsize=(6, 6))
+condition = inferencer.sample((1, ))
+inferencer.conditional_pairplot(condition=condition,
+                                limits=limits,
+                                labels=labels,
+                                ticks=limits,
+                                figsize=(6, 6))
 
-# Generate traces by using a single sample from the trained posterior
+# Construct and visualize conditional coefficient matrix
+cond_coeff_mat = inferencer.conditional_corrcoeff(condition=condition,
+                                                  limits=limits)
+fig, ax = subplots(1,1, figsize=(6, 6))
+im = imshow(cond_coeff_mat, clim=[-1, 1])
+_ = fig.colorbar(im)
+
+# Generate and visualize traces by sampling posterior
 inf_traces = inferencer.generate_traces()
 
 # Visualize traces

From a4be2b1d72f0b6125993b1b2b43bdab9c82206a0 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 6 Aug 2021 17:54:19 +0200
Subject: [PATCH 057/121] rename and adjust to new changes in inferencer class

---
 ..._sim_sbi.py => hh_sbi_synthetic_traces.py} | 23 +++++++++++--------
 1 file changed, 13 insertions(+), 10 deletions(-)
 rename examples/{hh_sim_sbi.py => hh_sbi_synthetic_traces.py} (89%)

diff --git a/examples/hh_sim_sbi.py b/examples/hh_sbi_synthetic_traces.py
similarity index 89%
rename from examples/hh_sim_sbi.py
rename to examples/hh_sbi_synthetic_traces.py
index 64dea1f..94116b5 100644
--- a/examples/hh_sim_sbi.py
+++ b/examples/hh_sbi_synthetic_traces.py
@@ -51,8 +51,8 @@
     dm/dt = alpham*(1-m) - betam*m : 1
     dn/dt = alphan*(1-n) - betan*n : 1
     dh/dt = alphah*(1-h) - betah*h : 1
-    alpham = (-0.32/mV) * (Vm - VT - 13.*mV) / (exp((-(Vm - VT - 13.*mV))/(4.*mV)) - 1)/ms : Hz
-    betam = (0.28/mV) * (Vm - VT - 40.*mV) / (exp((Vm - VT - 40.*mV)/(5.*mV)) - 1)/ms : Hz
+    alpham = (-0.32/mV) * (Vm - VT - 13.*mV) / (exp((-(Vm - VT - 13.*mV)) / (4.*mV)) - 1)/ms : Hz
+    betam = (0.28/mV) * (Vm - VT - 40.*mV) / (exp((Vm - VT - 40.*mV) / (5.*mV)) - 1)/ms : Hz
     alphah = 0.128 * exp(-(Vm - VT - 17.*mV) / (18.*mV))/ms : Hz
     betah = 4/(1 + exp((-(Vm - VT - 40.*mV)) / (5.*mV)))/ms : Hz
     alphan = (-0.032/mV) * (Vm - VT - 15.*mV) / (exp((-(Vm - VT - 15.*mV)) / (5.*mV)) - 1)/ms : Hz
@@ -71,11 +71,15 @@
                                     'm': '1/(1 + betam/alpham)',
                                     'h': '1/(1 + betah/alphah)',
                                     'n': '1/(1 + betan/alphan)'})
-inferencer.infere(n_samples=10_000,
-                  inference_method='SNPE',
-                  density_estimator_model='mdn',
-                  gNa=[.5*uS, 80.*uS],
-                  gK=[1e-4*uS, 15.*uS])
+
+# Amortized inference
+inferencer.infer(n_samples=10_000,
+                 inference_method='SNPE',
+                 density_estimator_model='mdn',
+                 gNa=[.5*uS, 80.*uS],
+                 gK=[1e-4*uS, 15.*uS])
+
+# Sample from posterior
 inferencer.sample((10_000, ))
 
 # Visualize estimated posterior distribution
@@ -83,8 +87,8 @@
                             'gK': [1e-4*uS, 15.*uS]},
                     ticks={'gNa': [.5*uS, 80.*uS],
                            'gK': [1e-4*uS, 15.*uS]},
-                    labels={'gNa': r'$\overline{g}_{Na}$',
-                            'gK': r'$\overline{g}_{K}$'},
+                    labels={'gNa': r'$\overline{g}_\mathrm{Na}$',
+                            'gK': r'$\overline{g}_\mathrm{K}$'},
                     points={'gNa': 32*uS,
                             'gK': 1*uS},
                     points_offdiag={'markersize': 6},
@@ -103,5 +107,4 @@
 axs[1].plot(t, I_inj.ravel()/nA, 'k-', label='stimulus')
 axs[1].set(xlabel='t, ms', ylabel='I, nA')
 axs[1].legend()
-
 show()

From 291edbdbe5c8aca1d8d9eb1091d76347a1d55441 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 6 Aug 2021 17:54:59 +0200
Subject: [PATCH 058/121] fix docstrings

---
 brian2modelfitting/inferencer.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index e0185ed..65e9a37 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -726,7 +726,8 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
             Inference method. Either of SNPE, SNLE or SNRE.
         density_estimator_model : str, optional
             The type of density estimator to be created. Either
-            ``mdn``, ``made``, ``maf`` or ``nsf``.
+            ``mdn``, ``made``, ``maf``, ``nsf`` for SNPE and SNLE, or
+            ``linear``, ``mlp``, ``resnet`` for SNRE.
         inference_kwargs : dict, optional
             Additional keyword arguments for the inferencer method
             definition.

From a00c63f5b9d27c7d65443b952c76bb4f98a6c13c Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 6 Aug 2021 21:27:48 +0200
Subject: [PATCH 059/121] rename file and adjust to changes in backend

---
 ...vanced.py => hh_sbi_flexible_interface.py} | 71 +++++++------------
 1 file changed, 27 insertions(+), 44 deletions(-)
 rename examples/{hh_sbi_advanced.py => hh_sbi_flexible_interface.py} (65%)

diff --git a/examples/hh_sbi_advanced.py b/examples/hh_sbi_flexible_interface.py
similarity index 65%
rename from examples/hh_sbi_advanced.py
rename to examples/hh_sbi_flexible_interface.py
index 67bba5e..20cd339 100644
--- a/examples/hh_sbi_advanced.py
+++ b/examples/hh_sbi_flexible_interface.py
@@ -3,17 +3,15 @@
 from brian2 import *
 from brian2modelfitting import *
 import pandas as pd
-import numpy as np
-from scipy.signal import find_peaks
 
 
 # Load input and output data traces
 df_inp_traces = pd.read_csv('input_traces_hh.csv')
 df_out_traces = pd.read_csv('output_traces_hh.csv')
 inp_traces = df_inp_traces.to_numpy()
-inp_traces = inp_traces[:2, 1:]
+inp_traces = inp_traces[[0, 1, 3], 1:]
 out_traces = df_out_traces.to_numpy()
-out_traces = out_traces[:2, 1:]
+out_traces = out_traces[[0, 1, 3], 1:]
 
 # Model and its parameters
 area = 20_000*um**2
@@ -39,31 +37,22 @@
 # Time domain
 t = arange(0, out_traces.shape[1]*dt/ms, dt/ms)
 
-
-def n_peaks(x):
-    n_p = []
-    for _x in x.transpose():
-        p_i = find_peaks(_x, height=0)[0]
-        n_p.append(p_i.size - sum(np.diff(t[p_i]) < 4))
-    return n_p
-
-
 # Step-by-step inference
 # Start with the regular instatiation of the class
 inferencer = Inferencer(dt=dt, model=eqs,
                         input={'I': inp_traces*amp},
                         output={'v': out_traces*mV},
-                        features=[n_peaks,
-                                  lambda x: x[(t > 5) & (t < 10), :].mean(axis=0),
-                                  lambda x: x[(t > 5) & (t < 10), :].std(axis=0),
-                                  lambda x: x.ptp(axis=0)],
+                        features={'v': [lambda x: x.mean(),
+                                        lambda x: x[(t > 5) & (t < 10)].mean(),
+                                        lambda x: x[(t > 5) & (t < 10)].std(),
+                                        lambda x: x.ptp()]},
                         method='exponential_euler',
                         threshold='m > 0.5',
                         refractory='m > 0.5',
                         param_init={'v': 'VT'})
 
 # Data generation
-# Initializing of the prior
+# Initializing the prior
 prior = inferencer.init_prior(gl=[1e-09*siemens, 1e-07*siemens],
                               g_na=[2e-06*siemens, 2e-04*siemens],
                               g_kd=[6e-07*siemens, 6e-05*siemens],
@@ -74,47 +63,41 @@ def n_peaks(x):
     theta, x = inferencer.load_summary_statistics(path_to_data)
 else:
     # Generate training data
-    theta = inferencer.generate_training_data(n_samples=1000,
+    theta = inferencer.generate_training_data(n_samples=10_000,
                                               prior=prior)
     # Extract summary stats
-    x = inferencer.extract_summary_statistics(theta=theta, level=0)
+    x = inferencer.extract_summary_statistics(theta)
     # Save the data for later use
     inferencer.save_summary_statistics(path_to_data, theta, x)
 
 # Amortized inference
 # Training the neural density estimator
 inference = inferencer.init_inference(inference_method='SNPE',
-                                      density_estimator_model='maf',
+                                      density_estimator_model='mdn',
                                       prior=prior,
                                       hidden_features=50,
                                       num_components=10)
 # First round of inference where no observation data is set to posterior
-posterior = inferencer.infere_step(proposal=prior,
-                                   inference=inference,
-                                   theta=theta, x=x,
-                                   train_kwargs={'num_atoms': 10,
-                                                 'learning_rate': 0.0005,
-                                                 'show_train_summary': True})
+posterior_amortized = inferencer.infer_step(proposal=prior,
+                                            inference=inference,
+                                            theta=theta, x=x,
+                                            train_kwargs={'num_atoms': 10})
 # Storing the trained posterior without a default observation
 path_to_posterior = __file__[:-3] + '_posterior.pth'
 inferencer.save_posterior(path_to_posterior)
-# Loading the trained posterior directly to Inferencer class
-posterior_loaded = inferencer.load_posterior(path_to_posterior)
 
 # Sampling from the posterior given observations, ...
-inferencer.sample((10000, ), posterior=posterior_loaded)
+inferencer.sample((10_000, ))
 # ...visualize the samples, ...
-inferencer.pairplot(labels=[r'$\overline{g}_{l}$',
-                            r'$\overline{g}_{Na}$',
-                            r'$\overline{g}_{K}$',
-                            r'$\overline{C}_{m}$'])
-# ...and optionally, continue the multiround inference via ``infere`` method
-posterior_multi_round = inferencer.infere(n_rounds=2)
-inferencer.sample((10000, ))
-inferencer.pairplot(labels=[r'$\overline{g}_{l}$',
-                            r'$\overline{g}_{Na}$',
-                            r'$\overline{g}_{K}$',
-                            r'$\overline{C}_{m}$'])
+labels = {'gl': r'$\overline{g}_\mathrm{l}$',
+          'g_na': r'$\overline{g}_\mathrm{Na}$',
+          'g_kd': r'$\overline{g}_\mathrm{K}$',
+          'Cm': r'$\overline{C}_{m}$'}
+inferencer.pairplot(labels=labels)
+# ...and optionally, continue the multiround inference using ``infer`` method
+posterior_multiround = inferencer.infer(n_rounds=2)
+inferencer.sample((10_000, ))
+inferencer.pairplot(labels=labels)
 
 # Generate traces from a single sample of parameters
 inf_traces = inferencer.generate_traces()
@@ -126,13 +109,13 @@ def n_peaks(x):
     axs[0, idx].plot(t, out_traces[idx, :].T, label='measurements')
     axs[0, idx].plot(t, inf_traces[idx, :].T/mV, label='fits')
     axs[1, idx].plot(t, inp_traces[idx, :].T/nA, 'k-', label='stimulus')
-    axs[1, idx].set_xlabel('t, ms')
+    axs[1, idx].set_xlabel('$t$, ms')
     if idx == 0:
-        axs[0, idx].set_ylabel('$v$, mV')
+        axs[0, idx].set_ylabel('$V$, mV')
         axs[1, idx].set_ylabel('$I$, nA')
 handles, labels = [(h + l) for h, l
                    in zip(axs[0, idx].get_legend_handles_labels(),
                    axs[1, idx].get_legend_handles_labels())]
 fig.legend(handles, labels)
 tight_layout()
-show()
\ No newline at end of file
+show()

From 7cd1c0a9c8b94752886a32bb6a00edac43b8ab09 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 6 Aug 2021 21:28:23 +0200
Subject: [PATCH 060/121] small updates in visualization

---
 examples/hh_sbi_simple_interface.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/hh_sbi_simple_interface.py b/examples/hh_sbi_simple_interface.py
index 4bb4b62..42b2a5c 100644
--- a/examples/hh_sbi_simple_interface.py
+++ b/examples/hh_sbi_simple_interface.py
@@ -81,7 +81,7 @@
 # Construct and visualize conditional coefficient matrix
 cond_coeff_mat = inferencer.conditional_corrcoeff(condition=condition,
                                                   limits=limits)
-fig, ax = subplots(1,1, figsize=(6, 6))
+fig, ax = subplots(1, 1, figsize=(6, 6))
 im = imshow(cond_coeff_mat, clim=[-1, 1])
 _ = fig.colorbar(im)
 
@@ -97,7 +97,7 @@
     axs[0, idx].plot(t, out_traces[idx, :], lw=3, label='measurements')
     axs[0, idx].plot(t, inf_traces[idx, :]/mV, '--', lw=3,label='fits')
     axs[1, idx].plot(t, inp_traces[idx, :]/nA, 'k-', label='stimulus')
-    axs[1, idx].set_xlabel('t, ms')
+    axs[1, idx].set_xlabel('$t$, ms')
     if idx == 0:
         axs[0, idx].set_ylabel('$v$, mV')
         axs[1, idx].set_ylabel('$I$, nA')

From a9d7047190c636b25f22ca5e73f4f7fceacbafee Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 6 Aug 2021 21:28:50 +0200
Subject: [PATCH 061/121] adjust level values for simulator

---
 brian2modelfitting/inferencer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 65e9a37..77ae541 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -425,7 +425,7 @@ def generate_training_data(self, n_samples, prior):
         theta = np.atleast_2d(theta.numpy())
         return theta
 
-    def extract_summary_statistics(self, theta, level=1):
+    def extract_summary_statistics(self, theta, level=0):
         """Return summary statistics for training the neural density
         estimator.
 
@@ -777,7 +777,7 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
                 if n_samples is None:
                     raise ValueError('Either provide `x` or `n_samples`.')
                 else:
-                    x = self.extract_summary_statistics(theta)
+                    x = self.extract_summary_statistics(theta, level=1)
             self.x = x
 
             # initialize inference object

From c3e6c6fac2a012faf38e40066ea6bf001ac65a8a Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 7 Aug 2021 11:54:26 +0200
Subject: [PATCH 062/121] rerun

---
 examples/hh_sbi_flexible_interface.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/hh_sbi_flexible_interface.py b/examples/hh_sbi_flexible_interface.py
index 20cd339..55dec84 100644
--- a/examples/hh_sbi_flexible_interface.py
+++ b/examples/hh_sbi_flexible_interface.py
@@ -63,12 +63,12 @@
     theta, x = inferencer.load_summary_statistics(path_to_data)
 else:
     # Generate training data
-    theta = inferencer.generate_training_data(n_samples=10_000,
+    theta = inferencer.generate_training_data(n_samples=1000,
                                               prior=prior)
     # Extract summary stats
     x = inferencer.extract_summary_statistics(theta)
     # Save the data for later use
-    inferencer.save_summary_statistics(path_to_data, theta, x)
+    #inferencer.save_summary_statistics(path_to_data, theta, x)
 
 # Amortized inference
 # Training the neural density estimator

From 9f9e29b971078e102abf8da1f66ffae0f549cb33 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 7 Aug 2021 11:55:15 +0200
Subject: [PATCH 063/121] fix handling args for multiround inference with snpe

---
 brian2modelfitting/inferencer.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 77ae541..e552721 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -592,7 +592,7 @@ def train(self, inference, theta, x, *args, **train_kwargs):
             Sampled prior.
         x : torch.tensor
             Summary statistics.
-        args : list, optional
+        args : tuple, optional
             Contains a uniformly distributed sbi.utils.BoxUniform
             proposal. Used only for SNPE, for SNLE and SNRE,
             ``proposal`` should not be passed to ``append_simulations``
@@ -791,16 +791,16 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
             # could provide them by using more flexible inferface via
             # `.infer_step` method
             if inference_method == 'SNPE':
-                args = [prior]
+                args = [prior, ]
             else:
-                args = []
+                args = [None, ]
         else:  # `.infer_step` has been called manually
             x_o = torch.tensor(self.x_o, dtype=torch.float32)
             prior = self.posterior.set_default_x(x_o)
             if self.posterior._method_family == 'snpe':
-                args = [prior]
+                args = [prior, ]
             else:
-                args = []
+                args = [None, ]
 
         # allocate empty list of posteriors
         posteriors = []

From 3b6a82a55f6608c84929e94befb9b5d97892bc63 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Mon, 9 Aug 2021 11:58:02 +0200
Subject: [PATCH 064/121] add restart parameter that enables amortized
 inference in case of a repeated function call

---
 brian2modelfitting/inferencer.py | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index e552721..83c07e6 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -700,7 +700,7 @@ def infer_step(self, proposal, inference,
     def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
               inference_method='SNPE', density_estimator_model='maf',
               inference_kwargs={}, train_kwargs={}, posterior_kwargs={},
-              **params):
+              restart=False, **params):
         """Return the trained posterior.
 
         If ``theta`` and ``x`` are not provided, ``n_samples`` has to
@@ -736,6 +736,11 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
             estimator.
         posterior_kwargs : dict, optional
             Additional keyword arguments for builing the posterior.
+        restart : bool, optional
+            When the method is called for a second time, set to True if
+            amortized inference should be performed. If False,
+            multi-round inference with the existing posterior will be
+            performed.
         params : dict
             Bounds for each parameter. Keys should correspond to names
             of parameters as defined in the model equaions, values
@@ -747,6 +752,8 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
         sbi.inference.NeuralPosterior
             Approximated posterior distribution over parameters.
         """
+        if restart:
+            self.posterior = None
         if self.posterior is None:
             # handle the number of rounds
             if not isinstance(n_rounds, int):

From 0465a21b87991b28a619b3cb8d285633efdade6f Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Mon, 9 Aug 2021 15:25:00 +0200
Subject: [PATCH 065/121] use tqdm instead of manually printing each round
 number

---
 brian2modelfitting/inferencer.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 83c07e6..0b79d29 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -24,6 +24,7 @@
 import torch
 
 from .simulator import RuntimeSimulator, CPPStandaloneSimulator
+from .utils import tqdm
 
 
 def configure_simulator():
@@ -816,8 +817,7 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
         proposal = prior
 
         # main inference loop
-        for round in range(n_rounds):
-            print(f'Round {round + 1}/{n_rounds}.')
+        for _ in tqdm(range(n_rounds)):
 
             # inference step
             posterior = self.infer_step(proposal, self.inference,
@@ -831,6 +831,7 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
             if n_rounds > 1:
                 x_o = torch.tensor(self.x_o, dtype=torch.float32)
                 proposal = posterior.set_default_x(x_o)
+
         self.posterior = posterior
         return posterior
 

From 92c6703809741d0e8a3015dc0785024410a1609e Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 10 Aug 2021 14:39:02 +0200
Subject: [PATCH 066/121] fix handling of features for different output
 variables

---
 brian2modelfitting/inferencer.py | 40 +++++++++++++++++++-------------
 1 file changed, 24 insertions(+), 16 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 0b79d29..59974d8 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -277,16 +277,19 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         self.refractory = refractory
 
         # observation the focus is on
-        for ov, o in zip(self.output_var, self.output):
-            o = np.array(o)
-            if features:
-                obs = []
+        obs = []
+        if features:
+            for ov, o in zip(self.output_var, self.output):
+                o = np.array(o)
                 for _o in o:
                     for feature in features[ov]:
                         obs.append(feature(_o))
-                x_o = np.array(obs, dtype=np.float32)
-            else:
-                x_o = o.ravel().astype(np.float32)
+            x_o = np.array(obs, dtype=np.float32)
+        else:
+            for o in self.output:
+                o = np.array(o)
+                obs.append(o.ravel().astype(np.float32))
+            x_o = np.concatenate(obs)
         self.x_o = x_o
         self.features = features
 
@@ -461,19 +464,24 @@ def extract_summary_statistics(self, theta, level=0):
 
         # extract features for each output variable and each trace
         obs = simulator.statemonitor.recorded_variables
-        for ov in self.output_var:
-            o = obs[ov].get_value()
-            o = o.T
-            if self.features:
+        x = []
+        if self.features:
+            for ov in self.output_var:
                 summary_statistics = []
+                o = obs[ov].get_value()
                 # TODO: should be vectorized
-                for _o in o:
+                for _o in o.T:
                     for feature in self.features[ov]:
                         summary_statistics.append(feature(_o))
-                x = np.array(summary_statistics, dtype=np.float32)
-                x = x.reshape(self.n_samples, -1)
-            else:
-                x = o.reshape(self.n_samples, -1).astype(np.float32)
+                _x = np.array(summary_statistics, dtype=np.float32)
+                _x = _x.reshape(self.n_samples, -1)
+                x.append(_x)
+            x = np.hstack(x)
+        else:
+            for ov in self.output_var:
+                o = obs[ov].get_value().T
+                x.append(o.reshape(self.n_samples, -1).astype(np.float32))
+            x = np.hstack(x)
         return x
 
     def save_summary_statistics(self, f, theta=None, x=None):

From 4e8533abc9393cc7ea65d5a62a7962d43193e8f8 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 10 Aug 2021 15:27:04 +0200
Subject: [PATCH 067/121] add tqdm to feature extraction loops

---
 brian2modelfitting/inferencer.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 59974d8..2ac3c10 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -466,7 +466,8 @@ def extract_summary_statistics(self, theta, level=0):
         obs = simulator.statemonitor.recorded_variables
         x = []
         if self.features:
-            for ov in self.output_var:
+            for ov in tqdm(self.output_var, desc='Extracting features',
+                           total=len(self.output), leave=True):
                 summary_statistics = []
                 o = obs[ov].get_value()
                 # TODO: should be vectorized
@@ -478,7 +479,8 @@ def extract_summary_statistics(self, theta, level=0):
                 x.append(_x)
             x = np.hstack(x)
         else:
-            for ov in self.output_var:
+            for ov in tqdm(self.output_var, desc='Aranging output traces',
+                           total=len(self.output)):
                 o = obs[ov].get_value().T
                 x.append(o.reshape(self.n_samples, -1).astype(np.float32))
             x = np.hstack(x)
@@ -825,7 +827,11 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
         proposal = prior
 
         # main inference loop
-        for _ in tqdm(range(n_rounds)):
+        if self.posterior or n_rounds > 1:
+            tqdm_desc = f'{n_rounds}-round focused inference'
+        else:
+            tqdm_desc = 'Amortized inference'
+        for _ in tqdm(range(n_rounds), desc=tqdm_desc):
 
             # inference step
             posterior = self.infer_step(proposal, self.inference,

From 385bf88be8294ff66f81c578cae0eb5c84179ccc Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 10 Aug 2021 18:44:32 +0200
Subject: [PATCH 068/121] add spike support

---
 brian2modelfitting/inferencer.py | 90 +++++++++++++++++++++++---------
 1 file changed, 66 insertions(+), 24 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 2ac3c10..2a12c15 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -9,6 +9,7 @@
 from brian2.equations.equations import Equations
 from brian2.groups.neurongroup import NeuronGroup
 from brian2.input.timedarray import TimedArray
+from brian2.monitors.spikemonitor import SpikeMonitor
 from brian2.monitors.statemonitor import StateMonitor
 from brian2.units.fundamentalunits import (DIMENSIONLESS,
                                            fail_for_dimension_mismatch,
@@ -220,7 +221,7 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         output_var = list(output.keys())
         output = list(output.values())
         for o_var in output_var:
-            if o_var not in model.names:
+            if o_var != 'spikes' and o_var not in model.names:
                 raise NameError(f'{o_var} is not a model variable')
         self.output_var = output_var
         self.output = output
@@ -235,11 +236,14 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         # handle multiple output variables
         self.output_dim = []
         for o_var, out in zip(self.output_var, self.output):
-            self.output_dim.append(model[o_var].dim)
-            fail_for_dimension_mismatch(out, self.output_dim[-1],
-                                        'The provided target values must have'
-                                        ' the same units as the variable'
-                                        f' {o_var}')
+            if o_var == 'spikes':
+                self.output_dim.append(DIMENSIONLESS)
+            else:
+                self.output_dim.append(model[o_var].dim)
+                fail_for_dimension_mismatch(out, self.output_dim[-1],
+                                            'The provided target values must'
+                                            ' have the same units as the'
+                                            f' variable {o_var}')
 
         # add input to equations
         self.model = model
@@ -251,11 +255,12 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         # add output to equations
         counter = 0
         for o_var, o_dim in zip(self.output_var, self.output_dim):
-            counter += 1
-            output_expr = f'output_var_{counter}(t, i % n_traces)'
-            output_dim = ('1' if o_dim is DIMENSIONLESS else repr(o_dim))
-            output_eqs = f'{o_var}_target = {output_expr} : {output_dim}'
-            self.model += output_eqs
+            if o_var != 'spikes':
+                counter += 1
+                output_expr = f'output_var_{counter}(t, i % n_traces)'
+                output_dim = ('1' if o_dim is DIMENSIONLESS else repr(o_dim))
+                output_eqs = f'{o_var}_target = {output_expr} : {output_dim}'
+                self.model += output_eqs
 
         # create ``TimedArray`` object for input w.r.t. a given time scale
         self.input_traces = TimedArray(input.transpose(), dt=self.dt)
@@ -281,9 +286,18 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         if features:
             for ov, o in zip(self.output_var, self.output):
                 o = np.array(o)
-                for _o in o:
-                    for feature in features[ov]:
-                        obs.append(feature(_o))
+                if ov == 'spikes':
+                    obs.extend(o.ravel())
+                else:
+                    for _o in o:
+                        for feature in features[ov]:
+                            obs.append(feature(_o))
+            x_o = np.array(obs, dtype=np.float32)
+        elif features is None and 'spikes' in self.output_var:
+            for ov, o in zip(self.output_var, self.output):
+                o = np.array(o)
+                if ov == 'spikes':
+                    obs.extend(o.ravel())
             x_o = np.array(obs, dtype=np.float32)
         else:
             for o in self.output:
@@ -357,10 +371,11 @@ def setup_simulator(self, network_name, n_neurons, output_var, param_init,
                                         'n_traces': self.n_traces},
                                        level=level+1)
         counter = 0
-        for out in self.output:
-            counter += 1
-            namespace[f'output_var_{counter}'] = TimedArray(out.transpose(),
-                                                            dt=self.dt)
+        for o_var, out in zip(self.output_var, self.output):
+            if o_var != 'spikes':
+                counter += 1
+                namespace[f'output_var_{counter}'] = TimedArray(out.T,
+                                                                dt=self.dt)
 
         # setup neuron group
         kwds = {}
@@ -377,9 +392,18 @@ def setup_simulator(self, network_name, n_neurons, output_var, param_init,
                               namespace=namespace,
                               name='neurons',
                               **kwds)
+
+        # create a network of neurons
         network = Network(neurons)
-        network.add(StateMonitor(source=neurons, variables=output_var,
-                                 record=True, dt=self.dt, name='statemonitor'))
+        if isinstance(output_var, str):
+            output_var = [output_var]
+        if 'spikes' in output_var:
+            network.add(SpikeMonitor(neurons, name='spikemonitor'))
+        record_vars = [v for v in output_var if v != 'spikes']
+        if len(record_vars):
+            network.add(StateMonitor(source=neurons, variables=record_vars,
+                                     record=True, dt=self.dt,
+                                     name='statemonitor'))
 
         # initialize the simulator
         simulator.initialize(network, param_init, name=network_name)
@@ -464,16 +488,34 @@ def extract_summary_statistics(self, theta, level=0):
 
         # extract features for each output variable and each trace
         obs = simulator.statemonitor.recorded_variables
+        if 'spikes' in self.output_var:
+            spike_trains = list(simulator.spikemonitor.spike_trains().values())
         x = []
         if self.features:
             for ov in tqdm(self.output_var, desc='Extracting features',
                            total=len(self.output), leave=True):
                 summary_statistics = []
-                o = obs[ov].get_value()
+                if ov != 'spikes':
+                    o = obs[ov].get_value()
+                # TODO: should be vectorized
+                for idx, _o in enumerate(o.T):
+                    if ov != 'spikes':
+                        for feature in self.features[ov]:
+                            summary_statistics.append(feature(_o))
+                    else:
+                        summary_statistics.append(spike_trains[idx].shape[0])
+                _x = np.array(summary_statistics, dtype=np.float32)
+                _x = _x.reshape(self.n_samples, -1)
+                x.append(_x)
+            x = np.hstack(x)
+        elif self.features is None and 'spikes' in self.output_var:
+            for ov in tqdm(self.output_var, desc='Extracting spikes',
+                           total=len(self.output), leave=True):
+                summary_statistics = []
                 # TODO: should be vectorized
-                for _o in o.T:
-                    for feature in self.features[ov]:
-                        summary_statistics.append(feature(_o))
+                if ov == 'spikes':
+                    for spike_train in spike_trains:
+                        summary_statistics.append(spike_train.shape[0])
                 _x = np.array(summary_statistics, dtype=np.float32)
                 _x = _x.reshape(self.n_samples, -1)
                 x.append(_x)

From ba913830150c7da3994c3d9b999d5a9db79467dd Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 10 Aug 2021 21:35:07 +0200
Subject: [PATCH 069/121] enable inferencer to work without statemonitor

---
 brian2modelfitting/inferencer.py | 10 +++++++++-
 1 file changed, 9 insertions(+), 1 deletion(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 2a12c15..46084c7 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -15,6 +15,7 @@
                                            fail_for_dimension_mismatch,
                                            get_dimensions,
                                            Quantity)
+from brian2.utils.logger import get_logger
 import numpy as np
 from sbi.utils.get_nn_models import (posterior_nn,
                                      likelihood_nn,
@@ -28,6 +29,9 @@
 from .utils import tqdm
 
 
+logger = get_logger(__name__)
+
+
 def configure_simulator():
     """Return the configured simulator, which can be either
     `.RuntimeSimulator`, object for the use with `.RuntimeDevice`, or
@@ -487,7 +491,11 @@ def extract_summary_statistics(self, theta, level=0):
                       name=network_name)
 
         # extract features for each output variable and each trace
-        obs = simulator.statemonitor.recorded_variables
+        try:
+            obs = simulator.statemonitor.recorded_variables
+        except KeyError:
+            logger.warn('The state monitor object is not defined.',
+                        name_suffix='statemonitor_definition')
         if 'spikes' in self.output_var:
             spike_trains = list(simulator.spikemonitor.spike_trains().values())
         x = []

From 41b33760f38232074107101a4d9a7c189a5a5b58 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 12 Aug 2021 10:36:17 +0200
Subject: [PATCH 070/121] secifiy sbi minimum version

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 98ac0bb..cfea664 100644
--- a/setup.py
+++ b/setup.py
@@ -27,7 +27,7 @@
       provides=['brian2modelfitting'],
       extras_require={'test': ['pytest'],
                       'docs': ['sphinx>=1.8'],
-                      'full': ['efel', 'lmfit', 'sbi'],
+                      'full': ['efel', 'lmfit', 'sbi>=0.16.0'],
                       },
       python_requires='>=3.6',
       use_2to3=False,

From 2bf189c566b3242cc0334bb34f56ae5da048caf4 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 12 Aug 2021 11:52:17 +0200
Subject: [PATCH 071/121] simplify feature extraction including spikes

---
 brian2modelfitting/inferencer.py | 44 ++++++++++----------------------
 1 file changed, 14 insertions(+), 30 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 46084c7..4eeb61d 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -16,6 +16,7 @@
                                            get_dimensions,
                                            Quantity)
 from brian2.utils.logger import get_logger
+from brian2modelfitting.fitter import get_spikes
 import numpy as np
 from sbi.utils.get_nn_models import (posterior_nn,
                                      likelihood_nn,
@@ -289,19 +290,9 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         obs = []
         if features:
             for ov, o in zip(self.output_var, self.output):
-                o = np.array(o)
-                if ov == 'spikes':
-                    obs.extend(o.ravel())
-                else:
-                    for _o in o:
-                        for feature in features[ov]:
-                            obs.append(feature(_o))
-            x_o = np.array(obs, dtype=np.float32)
-        elif features is None and 'spikes' in self.output_var:
-            for ov, o in zip(self.output_var, self.output):
-                o = np.array(o)
-                if ov == 'spikes':
-                    obs.extend(o.ravel())
+                for _o in o:
+                    for feature in features[ov]:
+                        obs.append(feature(_o))
             x_o = np.array(obs, dtype=np.float32)
         else:
             for o in self.output:
@@ -504,26 +495,15 @@ def extract_summary_statistics(self, theta, level=0):
                            total=len(self.output), leave=True):
                 summary_statistics = []
                 if ov != 'spikes':
-                    o = obs[ov].get_value()
+                    o = obs[ov].get_value().T
                 # TODO: should be vectorized
-                for idx, _o in enumerate(o.T):
+                for i in range(self.n_neurons):
                     if ov != 'spikes':
                         for feature in self.features[ov]:
-                            summary_statistics.append(feature(_o))
+                            summary_statistics.append(feature(o[i, :]))
                     else:
-                        summary_statistics.append(spike_trains[idx].shape[0])
-                _x = np.array(summary_statistics, dtype=np.float32)
-                _x = _x.reshape(self.n_samples, -1)
-                x.append(_x)
-            x = np.hstack(x)
-        elif self.features is None and 'spikes' in self.output_var:
-            for ov in tqdm(self.output_var, desc='Extracting spikes',
-                           total=len(self.output), leave=True):
-                summary_statistics = []
-                # TODO: should be vectorized
-                if ov == 'spikes':
-                    for spike_train in spike_trains:
-                        summary_statistics.append(spike_train.shape[0])
+                        for feature in self.features[ov]:
+                            summary_statistics.append(feature(spike_trains[i]))
                 _x = np.array(summary_statistics, dtype=np.float32)
                 _x = _x.reshape(self.n_samples, -1)
                 x.append(_x)
@@ -1290,7 +1270,11 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
         # create dictionary of traces for multiple observed output variables
         if len(output_var) > 1:
             for ov in output_var:
-                trace = getattr(simulator.statemonitor, ov)[:]
+                if ov == 'spikes':
+                    trace = get_spikes(simulator.spikemonitor, 1,
+                                       self.n_traces)[0]
+                else:
+                    trace = getattr(simulator.statemonitor, ov)[:]
                 traces = {ov: trace}
         else:
             traces = getattr(simulator.statemonitor, output_var[0])[:]

From b6432268e204e532c351af78f205930550f488a6 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 12 Aug 2021 14:28:37 +0200
Subject: [PATCH 072/121] add key error handling for the state monitor object
 in generate_traces method

---
 brian2modelfitting/inferencer.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 4eeb61d..89d6f95 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -1274,8 +1274,12 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
                     trace = get_spikes(simulator.spikemonitor, 1,
                                        self.n_traces)[0]
                 else:
-                    trace = getattr(simulator.statemonitor, ov)[:]
-                traces = {ov: trace}
+                    try:
+                        trace = getattr(simulator.statemonitor, ov)[:]
+                        traces = {ov: trace}
+                    except KeyError:
+                        logger.warn('No state monitor object found.'
+                                    ' Call again with specified `output_var`.')
         else:
             traces = getattr(simulator.statemonitor, output_var[0])[:]
         return traces

From 27370ac953d8bb05ba729c5e450e2fb59c1022de Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 12 Aug 2021 16:07:34 +0200
Subject: [PATCH 073/121] update examples before new PR covering spike support

---
 examples/IF_sbi.py                    | 100 ++-
 examples/hh_sbi_flexible_interface.py |  25 +-
 examples/hh_sbi_simple_interface.py   | 144 +++-
 examples/hh_sbi_synthetic_traces.py   |  37 +-
 examples/prototyping.ipynb            | 921 ++++++++++++++++++++++++++
 5 files changed, 1141 insertions(+), 86 deletions(-)
 create mode 100644 examples/prototyping.ipynb

diff --git a/examples/IF_sbi.py b/examples/IF_sbi.py
index a6284f7..99001b0 100644
--- a/examples/IF_sbi.py
+++ b/examples/IF_sbi.py
@@ -2,32 +2,39 @@
 from brian2modelfitting import *
 
 
-# Generate input and output data traces
+# Set parameters
 dt = 0.1*ms
-defaultclock.dt = dt
+sim_time = 60*ms
+El = -70*mV
+VT = -50*mV
+DeltaT = 2*mV
+ground_truth_params = {'gl': 30*nS, 'C': 1*nF}  # parameters to be inferred
+
+# Set input trace
 inp_trace = hstack([zeros(int(round(5*ms/dt))),
                     ones(int(round(25*ms/dt))),
                     zeros(int(round(30*ms/dt)))]) * 10*nA
 I = TimedArray(inp_trace, dt=dt)
-El = -70*mV
-VT = -50*mV
-DeltaT = 2*mV
+
+# Set model equations
 eqs = '''
     dv/dt = (gl*(El - v) + gl*DeltaT*exp((v - VT) / DeltaT) + I(t)) / C : volt
     gl    : siemens (constant)
     C     : farad (constant)
     '''
 
-neurons = NeuronGroup(1, eqs,
+# Run a model and create synthetic voltage traces
+neurons = NeuronGroup(1, eqs, dt=dt,
                       threshold='v > -50 * mV',
                       reset='v = -70 * mV',
                       method='exponential_euler')
 neurons.v = -70*mV
-neurons.set_states({'gl': 30*nS,
-                    'C': 1*nF})
-monitor = StateMonitor(neurons, 'v', record=True)
-run(60*ms)
-out_trace = monitor.v
+neurons.set_states(ground_truth_params)
+state_monitor = StateMonitor(neurons, 'v', record=True)
+spike_monitor = SpikeMonitor(neurons, record=True)
+run(sim_time)
+out_trace = state_monitor.v  # voltage traces in mV
+spike_times = array(spike_monitor.t_)  # spike events in s
 
 # Inference
 start_scope()
@@ -40,27 +47,64 @@
     C     : farad (constant)
     '''
 
+# Instantiate the inferencer object
+spike_features_list = [
+        lambda x: x.size,  # number of spikes
+        lambda x: 0. if diff(x).size == 0 else mean(diff(x)),  # mean ISI
+]
 inferencer = Inferencer(dt=dt, model=eqs_inf,
                         input={'I_syn': inp_trace.reshape(1, -1)},
-                        output={'v': out_trace},
+                        output={'spikes': [spike_times]},
+                        features={'spikes': spike_features_list},
                         method='exponential_euler',
-                        threshold='v > -50 * mV',
-                        reset='v = -70 * mV',
-                        param_init={'v': -70 * mV})
+                        threshold='v > -50*mV',
+                        reset='v = -70*mV',
+                        param_init={'v': -70*mV})
+
+# Infer parameter posteriors given bounds
 
-inferencer.infer(n_samples=10_000,
-                 gl=[10*nS, 100*nS],
-                 C=[0.1*nF, 10*nF])
+posterior = inferencer.infer(n_samples=3_000,
+                             n_rounds=3,
+                             gl=[10*nS, 100*nS],
+                             C=[0.1*nF, 10*nF])
 
-inferencer.sample((10000, ))
-inferencer.pairplot(labels={'gl': r'$\overline{g}_{l}$',
-                            'C': r'$C$'},
-                    points={'gl': 30*nS,
-                            'C': 1*nF},
-                    limits={'gl': [10*nS, 100*nS],
-                            'C': [0.1*nF, 10*nF]},
-                    ticks={'gl': [10*nS, 100*nS],
-                           'C': [0.1*nF, 10*nF]},
+# Sample from the posterior
+samples = inferencer.sample((3_000, ))
+
+# Create a pairplot
+labels = {'gl': r'$\overline{g}_{l}$', 'C': r'$C$'}
+ticks = {'gl': [10*nS, 100*nS], 'C': [0.1*nF, 10*nF]}
+inferencer.pairplot(labels=labels,
+                    limits=ticks,
+                    ticks=ticks,
+                    points=ground_truth_params,
                     points_offdiag={'markersize': 9},
-                    points_colors=['r'])
+                    points_colors=['r'], 
+                    figsize=(6, 6))
+
+# Visualize the trace by sampling trained posterior
+t = arange(0, inp_trace.size*dt/ms, dt/ms)
+t_start, t_end = t[where(inp_trace != 0)[0][[0, -1]]]
+
+# Identify spike events
+spike_v = (out_trace.min()/mV, out_trace.max()/mV)
+spike_i = []
+for spike_time in spike_times:
+    spike_i.append(where(isclose(spike_time * 1000, t))[0].item())
+
+# Generate traces from a single sample of parameters
+inf_trace = inferencer.generate_traces(output_var='v')
+
+fig, axs = subplots(nrows, 1, sharex=True,
+                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(7, 3))
+axs[0].plot(t, out_trace.T/mV, 'C3-', lw=3, label='recordings')
+axs[0].plot(t, inf_trace.T/mV, 'k--', lw=2, label='sampled traces')
+axs[0].vlines(t[spike_i], *spike_v, lw=3, color='C0', label='spike', zorder=3)
+axs[0].set_ylabel('$V$, mV')
+axs[0].legend()
+axs[1].plot(t, inp_trace.T/nA, lw=3, c='k', label='stimuli')
+axs[1].set_xlabel('$t$, ms')
+axs[1].set_ylabel('$I$, nA')
+axs[1].legend()
+tight_layout()
 show()
diff --git a/examples/hh_sbi_flexible_interface.py b/examples/hh_sbi_flexible_interface.py
index 55dec84..f9ff33d 100644
--- a/examples/hh_sbi_flexible_interface.py
+++ b/examples/hh_sbi_flexible_interface.py
@@ -36,22 +36,23 @@
 
 # Time domain
 t = arange(0, out_traces.shape[1]*dt/ms, dt/ms)
+t_start, t_end = t[where(inp_traces[0, :] != 0)[0][[0, -1]]]
 
 # Step-by-step inference
 # Start with the regular instatiation of the class
 inferencer = Inferencer(dt=dt, model=eqs,
                         input={'I': inp_traces*amp},
                         output={'v': out_traces*mV},
-                        features={'v': [lambda x: x.mean(),
-                                        lambda x: x[(t > 5) & (t < 10)].mean(),
-                                        lambda x: x[(t > 5) & (t < 10)].std(),
-                                        lambda x: x.ptp()]},
+                        features={'v': [lambda x: mean(x),
+                                        lambda x: mean(x[(t > t_start) & (t < t_end)]),
+                                        lambda x: std(x[(t > t_start) & (t < t_end)]),
+                                        lambda x: ptp(x)]},
                         method='exponential_euler',
                         threshold='m > 0.5',
                         refractory='m > 0.5',
                         param_init={'v': 'VT'})
 
-# Data generation
+# Training data generation
 # Initializing the prior
 prior = inferencer.init_prior(gl=[1e-09*siemens, 1e-07*siemens],
                               g_na=[2e-06*siemens, 2e-04*siemens],
@@ -63,12 +64,12 @@
     theta, x = inferencer.load_summary_statistics(path_to_data)
 else:
     # Generate training data
-    theta = inferencer.generate_training_data(n_samples=1000,
+    theta = inferencer.generate_training_data(n_samples=5_000,
                                               prior=prior)
     # Extract summary stats
     x = inferencer.extract_summary_statistics(theta)
     # Save the data for later use
-    #inferencer.save_summary_statistics(path_to_data, theta, x)
+    inferencer.save_summary_statistics(path_to_data, theta, x)
 
 # Amortized inference
 # Training the neural density estimator
@@ -104,11 +105,13 @@
 nrows = 2
 ncols = out_traces.shape[0]
 fig, axs = subplots(nrows, ncols, sharex=True,
-                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(15, 4))
+                    gridspec_kw={'height_ratios': [3, 1]},
+                    figsize=(ncols * 3, 3))
 for idx in range(ncols):
-    axs[0, idx].plot(t, out_traces[idx, :].T, label='measurements')
-    axs[0, idx].plot(t, inf_traces[idx, :].T/mV, label='fits')
-    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, 'k-', label='stimulus')
+    axs[0, idx].plot(t, out_traces[idx, :].T, 'C3-', lw=3, label='recordings')
+    axs[0, idx].plot(t, inf_traces[idx, :].T/mV, 'k--', lw=2,
+                     label='sampled traces')
+    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimuli')
     axs[1, idx].set_xlabel('$t$, ms')
     if idx == 0:
         axs[0, idx].set_ylabel('$V$, mV')
diff --git a/examples/hh_sbi_simple_interface.py b/examples/hh_sbi_simple_interface.py
index 42b2a5c..b42295c 100644
--- a/examples/hh_sbi_simple_interface.py
+++ b/examples/hh_sbi_simple_interface.py
@@ -1,15 +1,16 @@
 from brian2 import *
 from brian2modelfitting import *
 import pandas as pd
+from scipy.stats import kurtosis as kurt
 
 
 # Load input and output data traces
 df_inp_traces = pd.read_csv('input_traces_hh.csv')
 df_out_traces = pd.read_csv('output_traces_hh.csv')
 inp_traces = df_inp_traces.to_numpy()
-inp_traces = inp_traces[[0, 1, 3], 1:]
+inp_traces = inp_traces[[0, 1], 1:]
 out_traces = df_out_traces.to_numpy()
-out_traces = out_traces[[0, 1, 3], 1:]
+out_traces = out_traces[[0, 1], 1:]
 
 # Define model and its parameters
 area = 20_000*um**2
@@ -26,6 +27,7 @@
     dn/dt = 0.032*(mV**-1)*(15.*mV-v+VT)/
             (exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1
     dh/dt = 0.128*exp((17.*mV-v+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-v+VT)/(5.*mV)))/ms*h : 1
+
     g_na : siemens (constant)
     g_kd : siemens (constant)
     gl   : siemens (constant)
@@ -34,42 +36,124 @@
 
 # Time domain
 t = arange(0, out_traces.shape[1]*dt/ms, dt/ms)
+t_start, t_end = t[where(inp_traces[0, :] != 0)[0][[0, -1]]]
+
+# Visualize the recordings
+nrows = 2
+ncols = out_traces.shape[0]
+fig, axs = subplots(nrows, ncols, sharex=True,
+                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))
+for idx in range(ncols):
+    axs[0, idx].plot(t, out_traces[idx, :].T, lw=3, c='C3', label='recordings')
+    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimulus')
+    axs[1, idx].set_xlabel('$t$, ms')
+    if idx == 0:
+        axs[0, idx].set_ylabel('$V$, mV')
+        axs[1, idx].set_ylabel('$I$, nA')
+handles, labels = [(h + l) for h, l
+                   in zip(axs[0, idx].get_legend_handles_labels(),
+                   axs[1, idx].get_legend_handles_labels())]
+fig.legend(handles, labels)
+tight_layout()
+
+
+# Obtain spike times manually
+def get_spike_times(x):
+    x = x.copy()
+    # put everything to -40 mV that is below -40 mV or has negative slope
+    ind = where(x < -0.04)
+    x[ind] = -0.04
+    ind = where(diff(x) < 0)
+    x[ind] = -0.04
+
+    # remaining negative slopes are at spike peaks
+    ind = where(diff(x) < 0)
+    spike_times = array(t)[ind]
+
+    # spike times for active stimulus
+    spike_times_stim = spike_times[
+        (spike_times > t_start) & (spike_times < t_end)
+        ]
+
+    # number of spikes
+    if spike_times_stim.shape[0] > 0:
+        spike_times_stim = spike_times_stim[
+            append(1, diff(spike_times_stim)) > 0.5
+        ]
+    return spike_times_stim / 1000  # in seconds
+
+
+# store spike times for each trace into the list
+spike_times = [get_spike_times(out_trace) for out_trace in out_traces]
+
+# Visualize the recordings and spikes while stimulus is on
+nrows = 2
+ncols = out_traces.shape[0]
+fig, axs = subplots(nrows, ncols, sharex=True,
+                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))
+for idx in range(ncols):
+    spike_idx = in1d(t, spike_times[idx] * 1000).nonzero()[0]
+    spike_v = (out_traces[idx, :].min(), out_traces[idx, :].max())
+    axs[0, idx].plot(t, out_traces[idx, :].T, lw=3, c='C3',
+                     label='recordings', zorder=1)
+    axs[0, idx].vlines(t[spike_idx], *spike_v, lw=3, color='C0',
+                       label='spike event', zorder=2)
+    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimulus')
+    axs[1, idx].set_xlabel('$t$, ms')
+    if idx == 0:
+        axs[0, idx].set_ylabel('$V$, mV')
+        axs[1, idx].set_ylabel('$I$, nA')
+handles, labels = [(h + l) for h, l
+                   in zip(axs[0, idx].get_legend_handles_labels(),
+                   axs[1, idx].get_legend_handles_labels())]
+fig.legend(handles, labels)
+tight_layout()
+
+# Define features to create a summary statistics representation of traces
+v_features = [
+    lambda x: max(x[(t > t_start) & (t < t_end)]),  # AP max
+    lambda x: mean(x[(t > t_start) & (t < t_end)]),  # AP mean
+    lambda x: std(x[(t > t_start) & (t < t_end)]),  # AP std
+    lambda x: kurt(x[(t > t_start) & (t < t_end)], fisher=False),  # AP kurt
+    lambda x: mean(x[(t > .25 * t_start) & (t < .75 * t_start)]),  # resting
+]
+s_features = [lambda x: x.size]  # numbe of spikes in a spike train
 
 # Simulation-based inference object instantiation
 inferencer = Inferencer(dt=dt, model=eqs,
                         input={'I': inp_traces*amp},
-                        output={'v': out_traces*mV},
-                        features={'v': [lambda x: x[(t > 5) & (t < 10)].mean(),
-                                        lambda x: x[(t > 5) & (t < 10)].std(),
-                                        lambda x: x.max()]},
+                        output={'v': out_traces*mV, 'spikes': spike_times},
+                        features={'v': v_features, 'spikes': s_features},
                         method='exponential_euler',
                         threshold='m > 0.5',
                         refractory='m > 0.5',
                         param_init={'v': 'VT'})
 
-# Generate prior and train the neural density estimator
-inferencer.infer(n_samples=10_000,
-                 n_rounds=1,
-                 gl=[1e-09*siemens, 1e-07*siemens],
-                 g_na=[2e-06*siemens, 2e-04*siemens],
-                 g_kd=[6e-07*siemens, 6e-05*siemens],
-                 Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])
+# Multi-round inference
+posterior = inferencer.infer(n_samples=5_000,
+                             n_rounds=3,
+                             inference_method='SNPE',
+                             density_estimator_model='mdn',
+                             gl=[1e-09*siemens, 1e-07*siemens],
+                             g_na=[2e-06*siemens, 2e-04*siemens],
+                             g_kd=[6e-07*siemens, 6e-05*siemens],
+                             Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])
 
 # Draw samples from posterior
-inferencer.sample((10_000, ))
+samples = inferencer.sample((5_000, ))
 
 # Visualize pairplot and conditional pairplot
-limits = {'gl': [1e-09*siemens, 1e-07*siemens],
+limits = {'gl': [1e-9*siemens, 1e-07*siemens],
           'g_na': [2e-06*siemens, 2e-04*siemens],
           'g_kd': [6e-07*siemens, 6e-05*siemens],
           'Cm': [0.1*uF*cm**-2*area, 2*uF*cm**-2*area]}
-labels = {'gl': r'$\overline{g}_\mathrm{l}$',
-          'g_na': r'$\overline{g}_\mathrm{Na}$',
-          'g_kd': r'$\overline{g}_\mathrm{K}$',
-          'Cm': r'$\overline{C}_{m}$'}
+labels = {'gl': r'$\overline{g}_{l}$',
+          'g_na': r'$\overline{g}_{Na}$',
+          'g_kd': r'$\overline{g}_{K}$',
+          'Cm': r'$C_{m}$'}
 inferencer.pairplot(limits=limits,
-                    ticks=limits,
                     labels=labels,
+                    ticks=limits,
                     figsize=(6, 6))
 condition = inferencer.sample((1, ))
 inferencer.conditional_pairplot(condition=condition,
@@ -81,25 +165,27 @@
 # Construct and visualize conditional coefficient matrix
 cond_coeff_mat = inferencer.conditional_corrcoeff(condition=condition,
                                                   limits=limits)
-fig, ax = subplots(1, 1, figsize=(6, 6))
+fig, ax = subplots(1, 1, figsize=(4, 4))
 im = imshow(cond_coeff_mat, clim=[-1, 1])
 _ = fig.colorbar(im)
 
-# Generate and visualize traces by sampling posterior
-inf_traces = inferencer.generate_traces()
+# Generate traces and visualize from a single sample of parameters
+inf_traces = inferencer.generate_traces(output_var='v')
 
-# Visualize traces
 nrows = 2
 ncols = out_traces.shape[0]
 fig, axs = subplots(nrows, ncols, sharex=True,
-                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(15, 4))
+                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))
 for idx in range(ncols):
-    axs[0, idx].plot(t, out_traces[idx, :], lw=3, label='measurements')
-    axs[0, idx].plot(t, inf_traces[idx, :]/mV, '--', lw=3,label='fits')
-    axs[1, idx].plot(t, inp_traces[idx, :]/nA, 'k-', label='stimulus')
+    spike_idx = in1d(t, spike_times[idx]).nonzero()[0]
+    spike_v = (out_traces[idx, :].min(), out_traces[idx, :].max())
+    axs[0, idx].plot(t, out_traces[idx, :].T, 'C3-', lw=3, label='recordings')
+    axs[0, idx].plot(t, inf_traces[idx, :].T/mV, 'k--', lw=2,
+                     label='sampled traces')
+    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimuli')
     axs[1, idx].set_xlabel('$t$, ms')
     if idx == 0:
-        axs[0, idx].set_ylabel('$v$, mV')
+        axs[0, idx].set_ylabel('$V$, mV')
         axs[1, idx].set_ylabel('$I$, nA')
 handles, labels = [(h + l) for h, l
                    in zip(axs[0, idx].get_legend_handles_labels(),
diff --git a/examples/hh_sbi_synthetic_traces.py b/examples/hh_sbi_synthetic_traces.py
index 94116b5..ee75a44 100644
--- a/examples/hh_sbi_synthetic_traces.py
+++ b/examples/hh_sbi_synthetic_traces.py
@@ -4,7 +4,6 @@
 
 # Input and output data traces simulation
 dt = 0.05*ms
-defaultclock.dt = dt
 t_on = 50*ms
 t_total = 350*ms
 t_off = t_total - t_on
@@ -13,9 +12,9 @@
 Eleak = -70*mV
 VT = -60*mV
 C = 200*pF
-gNa = 32*uS
+gNa = 32*uS  # 1st "unknown" parameter to infer
 ENa = 53*mV
-gK = 1*uS
+gK = 1*uS  # 2nd "unknown" parameter to infer
 EK = -107*mV
 eqs = '''
     dVm/dt = -(gNa*m**3*h*(Vm - ENa) + gK*n**4*(Vm - EK) + gleak*(Vm - Eleak) - I_inj) / C : volt
@@ -58,7 +57,7 @@
     alphan = (-0.032/mV) * (Vm - VT - 15.*mV) / (exp((-(Vm - VT - 15.*mV)) / (5.*mV)) - 1)/ms : Hz
     betan = 0.5*exp(-(Vm - VT - 10.*mV) / (40.*mV))/ms : Hz
 
-    # The parameters to fit
+    # parameters to fit
     gNa : siemens (constant)
     gK : siemens (constant)
     '''
@@ -72,7 +71,7 @@
                                     'h': '1/(1 + betah/alphah)',
                                     'n': '1/(1 + betan/alphan)'})
 
-# Amortized inference
+# Amortized inference with automatic feature extraction by using a default MLP
 inferencer.infer(n_samples=10_000,
                  inference_method='SNPE',
                  density_estimator_model='mdn',
@@ -83,12 +82,13 @@
 inferencer.sample((10_000, ))
 
 # Visualize estimated posterior distribution
-inferencer.pairplot(limits={'gNa': [.5*uS, 80.*uS],
-                            'gK': [1e-4*uS, 15.*uS]},
-                    ticks={'gNa': [.5*uS, 80.*uS],
-                           'gK': [1e-4*uS, 15.*uS]},
-                    labels={'gNa': r'$\overline{g}_\mathrm{Na}$',
-                            'gK': r'$\overline{g}_\mathrm{K}$'},
+limits = {'gNa': [.5*uS, 80.*uS],
+          'gK': [1e-4*uS, 15.*uS]}
+labels= {'gNa': r'$\overline{g}_\mathrm{Na}$',
+         'gK': r'$\overline{g}_\mathrm{K}$'}
+inferencer.pairplot(limits=limits,
+                    ticks=limits,
+                    labels=labels,
                     points={'gNa': 32*uS,
                             'gK': 1*uS},
                     points_offdiag={'markersize': 6},
@@ -97,14 +97,15 @@
 
 # Visualize traces for a single sample from posterior
 inf_trace = inferencer.generate_traces()
+
 fig, axs = subplots(2, 1, sharex=True,
-                    gridspec_kw={'height_ratios': [3, 1]},
-                    figsize=(6, 4))
-axs[0].plot(t, out_trace.ravel()/mV, label='simulated recordings')
-axs[0].plot(t, inf_trace.ravel()/mV, '--', label='posterior sample')
-axs[0].set(ylabel='Vm, mV')
+                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(7, 3))
+axs[0].plot(t, out_trace.ravel()/mV, 'C3-', lw=3, label='simulated recordings')
+axs[0].plot(t, inf_trace.ravel()/mV, 'k--', lw=2, label='posterior sample')
+axs[0].set(ylabel='$V_m$, mV')
 axs[0].legend()
-axs[1].plot(t, I_inj.ravel()/nA, 'k-', label='stimulus')
-axs[1].set(xlabel='t, ms', ylabel='I, nA')
+axs[1].plot(t, I_inj.ravel()/nA, 'k-', lw=3, label='stimulus')
+axs[1].set(xlabel='$t$, s', ylabel='I, nA')
 axs[1].legend()
+tight_layout()
 show()
diff --git a/examples/prototyping.ipynb b/examples/prototyping.ipynb
new file mode 100644
index 0000000..a8fcee3
--- /dev/null
+++ b/examples/prototyping.ipynb
@@ -0,0 +1,921 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "28e8bc5d-26a6-4ac6-83c4-97b2a816c1c5",
+   "metadata": {},
+   "source": [
+    "# Hodgkin-Huxley model: traces+spikes inference from recordings"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2f3614b7-b6ac-43a3-a0c6-d2b3ab32e929",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "from brian2 import *\n",
+    "from brian2modelfitting import Inferencer\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import kurtosis as kurt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9d3fbd95-785a-4e0a-8a37-2feb0d86eb9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# pretty figures :3\n",
+    "%config InlineBackend.figure_format = 'retina'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "cdc3ea42",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load few recorded traces\n",
+    "df_inp_traces = pd.read_csv('input_traces_hh.csv')\n",
+    "df_out_traces = pd.read_csv('output_traces_hh.csv')\n",
+    "inp_traces = df_inp_traces.to_numpy()\n",
+    "inp_traces = inp_traces[[0, 1], 1:]\n",
+    "out_traces = df_out_traces.to_numpy()\n",
+    "out_traces = out_traces[[0, 1], 1:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "fb22019e-62b0-4bda-bc17-e2b1b7dc4ce3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set parameters and define the Hodgkin-Huxley model\n",
+    "area = 20_000*um**2\n",
+    "El = -65*mV\n",
+    "EK = -90*mV\n",
+    "ENa = 50*mV\n",
+    "VT = -63*mV\n",
+    "dt = 0.01*ms\n",
+    "eqs = '''\n",
+    "    dv/dt = (gl*(El-v) - g_na*(m*m*m)*h*(v-ENa) - g_kd*(n*n*n*n)*(v-EK) + I)/Cm : volt\n",
+    "    dm/dt = 0.32*(mV**-1)*(13.*mV-v+VT)/\n",
+    "            (exp((13.*mV-v+VT)/(4.*mV))-1.)/ms*(1-m)-0.28*(mV**-1)*(v-VT-40.*mV)/\n",
+    "            (exp((v-VT-40.*mV)/(5.*mV))-1.)/ms*m : 1\n",
+    "    dn/dt = 0.032*(mV**-1)*(15.*mV-v+VT)/\n",
+    "            (exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1\n",
+    "    dh/dt = 0.128*exp((17.*mV-v+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-v+VT)/(5.*mV)))/ms*h : 1\n",
+    "    g_na : siemens (constant)\n",
+    "    g_kd : siemens (constant)\n",
+    "    gl   : siemens (constant)\n",
+    "    Cm   : farad (constant)\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8a515f25-b94a-4082-97a5-e76fe3887140",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up time domain\n",
+    "t = np.arange(0, inp_traces.shape[1]*dt/ms, dt/ms)\n",
+    "t_start, t_end = t[np.where(inp_traces[0, :] != 0)[0][[0, -1]]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "9f2c4326-a247-49f9-829d-9559c4db1eae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x216 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 228,
+       "width": 618
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the recordings\n",
+    "nrows = 2\n",
+    "ncols = out_traces.shape[0]\n",
+    "fig, axs = subplots(nrows, ncols, sharex=True,\n",
+    "                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))\n",
+    "for idx in range(ncols):\n",
+    "    axs[0, idx].plot(t, out_traces[idx, :].T, lw=3, c='C3', label='recordings')\n",
+    "    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimuli')\n",
+    "    axs[1, idx].set_xlabel('$t$, ms')\n",
+    "    if idx == 0:\n",
+    "        axs[0, idx].set_ylabel('$V$, mV')\n",
+    "        axs[1, idx].set_ylabel('$I$, nA')\n",
+    "handles, labels = [(h + l) for h, l\n",
+    "                   in zip(axs[0, idx].get_legend_handles_labels(),\n",
+    "                   axs[1, idx].get_legend_handles_labels())]\n",
+    "fig.legend(handles, labels)\n",
+    "tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4bec8cef-8df3-4f55-9db2-a408a0898d71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# get spike times manually\n",
+    "def get_spike_times(x):\n",
+    "    x = x.copy()\n",
+    "    # put everything to -40 mV that is below -40 mV or has negative slope\n",
+    "    ind = np.where(x < -0.04)\n",
+    "    x[ind] = -0.04\n",
+    "    ind = np.where(np.diff(x) < 0)\n",
+    "    x[ind] = -0.04\n",
+    "\n",
+    "    # remaining negative slopes are at spike peaks\n",
+    "    ind = np.where(np.diff(x) < 0)\n",
+    "    spike_times = np.array(t)[ind]\n",
+    "    \n",
+    "    # spike times for active stimulus\n",
+    "    spike_times_stim = spike_times[\n",
+    "        (spike_times > t_start) & (spike_times < t_end)\n",
+    "        ]\n",
+    "    \n",
+    "    # number of spikes\n",
+    "    if spike_times_stim.shape[0] > 0:\n",
+    "        spike_times_stim = spike_times_stim[\n",
+    "            np.append(1, np.diff(spike_times_stim)) > 0.5\n",
+    "        ]\n",
+    "    return spike_times_stim / 1000  # in seconds\n",
+    "\n",
+    "# store spike times for each trace into the list\n",
+    "spike_times = [get_spike_times(out_trace) for out_trace in out_traces]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "3927d071-390b-4e20-8d3e-bfd7012ec762",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x216 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 228,
+       "width": 618
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the recordings and spikes while stimulus is on\n",
+    "nrows = 2\n",
+    "ncols = out_traces.shape[0]\n",
+    "fig, axs = subplots(nrows, ncols, sharex=True,\n",
+    "                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))\n",
+    "for idx in range(ncols):\n",
+    "    spike_idx = np.in1d(t, spike_times[idx] * 1000).nonzero()[0]\n",
+    "    spike_v = (out_traces[idx, :].min(), out_traces[idx, :].max())\n",
+    "    axs[0, idx].plot(t, out_traces[idx, :].T, lw=3, c='C3',\n",
+    "                     label='recordings', zorder=1)\n",
+    "    axs[0, idx].vlines(t[spike_idx], *spike_v, lw=3, color='C0',\n",
+    "                       label='spike event', zorder=2)\n",
+    "    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimuli')\n",
+    "    axs[1, idx].set_xlabel('$t$, ms')\n",
+    "    if idx == 0:\n",
+    "        axs[0, idx].set_ylabel('$V$, mV')\n",
+    "        axs[1, idx].set_ylabel('$I$, nA')\n",
+    "handles, labels = [(h + l) for h, l\n",
+    "                   in zip(axs[0, idx].get_legend_handles_labels(),\n",
+    "                   axs[1, idx].get_legend_handles_labels())]\n",
+    "fig.legend(handles, labels)\n",
+    "tight_layout;"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "25a040ac-3dee-42cf-82fd-0e1af5c47f53",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# a few features to create a summary statistics representation of traces\n",
+    "v_features = [\n",
+    "    lambda x: np.max(x[(t > t_start) & (t < t_end)]),  # max active potential\n",
+    "    lambda x: np.mean(x[(t > t_start) & (t < t_end)]),  # mean active potential\n",
+    "    lambda x: np.std(x[(t > t_start) & (t < t_end)]),  # std of active potential\n",
+    "    lambda x: kurt(x[(t > t_start) & (t < t_end)], fisher=False),  # kurtosis of active potential\n",
+    "    lambda x: np.mean(x[(t > 0.25 * t_start) & (t < 0.75 * t_start)]),  # membrane resting potential\n",
+    "]\n",
+    "\n",
+    "s_features = [\n",
+    "    lambda x: x.size,  # count of spikes in the train\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "a4550ead",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inference object instantiation\n",
+    "inferencer = Inferencer(dt=dt, model=eqs,\n",
+    "                        input={'I': inp_traces*amp},\n",
+    "                        output={'v': out_traces*mV,\n",
+    "                                'spikes': spike_times},\n",
+    "                        features={'v': v_features,\n",
+    "                                  'spikes': s_features},\n",
+    "                        method='exponential_euler',\n",
+    "                        threshold='m > 0.5',\n",
+    "                        refractory='m > 0.5',\n",
+    "                        param_init={'v': 'VT'})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "9fe4d1cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd6893517f754a8d8a63ee879b594705",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Extracting features:   0%|          | 0/2 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "96c3e458cdd743d39d087a1ae2693073",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "3-round focused inference:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural network successfully converged after 197 epochs.\n",
+      "Neural network successfully converged after 217 epochs.\n",
+      "Neural network successfully converged after 23 epochs.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# posterior estimation\n",
+    "posterior = inferencer.infer(n_samples=5_000,\n",
+    "                             n_rounds=3,\n",
+    "                             inference_method='SNPE',\n",
+    "                             density_estimator_model='mdn',\n",
+    "                             restart=True,\n",
+    "                             gl=[1e-09*siemens, 1e-07*siemens],\n",
+    "                             g_na=[2e-06*siemens, 2e-04*siemens],\n",
+    "                             g_kd=[6e-07*siemens, 6e-05*siemens],\n",
+    "                             Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "b7e4d8db",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bbc0b97ead1c443a873da2e70939a238",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Only 0% posterior samples are within the\n",
+      "                        prior support. It may take a long time to collect the remaining\n",
+      "                        4998 samples. Consider interrupting (Ctrl-C)\n",
+      "                        and switching to `sample_with_mcmc=True`.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# sampling the posterior\n",
+    "samples = inferencer.sample((5_000, ))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "66c3c4ce",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 386,
+       "width": 378
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# pairplotting space of sampled parameters from posterior\n",
+    "limits = {'gl': [1e-9*siemens, 1e-07*siemens],\n",
+    "          'g_na': [2e-06*siemens, 2e-04*siemens],\n",
+    "          'g_kd': [6e-07*siemens, 6e-05*siemens],\n",
+    "          'Cm': [0.1*uF*cm**-2*area, 2*uF*cm**-2*area]}\n",
+    "labels = {'gl': r'$\\overline{g}_{l}$',\n",
+    "          'g_na': r'$\\overline{g}_{Na}$',\n",
+    "          'g_kd': r'$\\overline{g}_{K}$',\n",
+    "          'Cm': r'$C_{m}$'}\n",
+    "\n",
+    "inferencer.pairplot(limits=limits,\n",
+    "                    labels=labels,\n",
+    "                    ticks=limits,\n",
+    "                    figsize=(6, 6));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "1a87fdae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7534eaf0020d41259f5afb2eb7160e84",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 1 posterior samples:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Only 0% posterior samples are within the\n",
+      "                        prior support. It may take a long time to collect the remaining\n",
+      "                        -2 samples. Consider interrupting (Ctrl-C)\n",
+      "                        and switching to `sample_with_mcmc=True`.\n",
+      "WARNING:root:Drawing samples from posterior to estimate the normalizing\n",
+      "                        constant for `log_prob()`. However, only 0%\n",
+      "                        posterior samples are within the prior support. It may take a\n",
+      "                        long time to collect the remaining 9996 samples.\n",
+      "                        Consider interrupting (Ctrl-C) and either basing the estimate\n",
+      "                        of the normalizing constant on fewer samples (by calling\n",
+      "                        `posterior.leakage_correction(x_o, num_rejection_samples=N)`,\n",
+      "                        where `N` is the number of samples you want to base the\n",
+      "                        estimate on (default N=10000), or not estimating the\n",
+      "                        normalizing constant at all\n",
+      "                        (`log_prob(..., norm_posterior=False)`. The latter will result\n",
+      "                        in an unnormalized `log_prob()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 16 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 386,
+       "width": 378
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# conditional pairplot\n",
+    "condition = inferencer.sample((1, ))\n",
+    "inferencer.conditional_pairplot(condition=condition,\n",
+    "                                limits=limits,\n",
+    "                                labels=labels,\n",
+    "                                ticks=limits,\n",
+    "                                figsize=(6, 6));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "2f195b76",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 288x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 241,
+       "width": 284
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# computing conditional coefficient matrix using the same condition\n",
+    "cond_coeff_mat = inferencer.conditional_corrcoeff(condition=condition, limits=limits)\n",
+    "\n",
+    "fig, ax = plt.subplots(1,1, figsize=(4, 4))\n",
+    "im = plt.imshow(cond_coeff_mat, clim=[-1, 1])\n",
+    "_ = fig.colorbar(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "7ed79a8f-187d-41bf-abbe-cac82160dbbb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aea058a947a944fc9f73e669a38cd97e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 1 posterior samples:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Only 0% posterior samples are within the\n",
+      "                        prior support. It may take a long time to collect the remaining\n",
+      "                        -3 samples. Consider interrupting (Ctrl-C)\n",
+      "                        and switching to `sample_with_mcmc=True`.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 648x216 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 228,
+       "width": 618
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# generate traces from a single sample of parameters\n",
+    "inf_traces = inferencer.generate_traces(output_var='v')\n",
+    "\n",
+    "nrows = 2\n",
+    "ncols = out_traces.shape[0]\n",
+    "fig, axs = subplots(nrows, ncols, sharex=True,\n",
+    "                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))\n",
+    "for idx in range(ncols):\n",
+    "    spike_idx = np.in1d(t, spike_times[idx]).nonzero()[0]\n",
+    "    spike_v = (out_traces[idx, :].min(), out_traces[idx, :].max())\n",
+    "    axs[0, idx].plot(t, out_traces[idx, :].T, 'C3-', lw=3, label='recordings')\n",
+    "    axs[0, idx].plot(t, inf_traces[idx, :].T/mV, 'k--', lw=2,\n",
+    "                     label='sampled traces')\n",
+    "    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimuli')\n",
+    "    axs[1, idx].set_xlabel('$t$, ms')\n",
+    "    if idx == 0:\n",
+    "        axs[0, idx].set_ylabel('$V$, mV')\n",
+    "        axs[1, idx].set_ylabel('$I$, nA')\n",
+    "handles, labels = [(h + l) for h, l\n",
+    "                   in zip(axs[0, idx].get_legend_handles_labels(),\n",
+    "                   axs[1, idx].get_legend_handles_labels())]\n",
+    "fig.legend(handles, labels)\n",
+    "tight_layout;"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "59d9e91e-7e22-4e9a-9ff0-7ddb2207b030",
+   "metadata": {},
+   "source": [
+    "# Integrate-and-Fire model: spike inference from synthetic trace"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "64968695-ad82-450e-bea7-1a2eb5ebd1e0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from brian2 import *\n",
+    "from brian2modelfitting import Inferencer\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "73fc4301-ffab-4675-b68e-6534d1af5b1a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%config InlineBackend.figure_format = 'retina'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ad802d6b-2976-4cae-86aa-f7bd276c802b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set parameters\n",
+    "dt = 0.1*ms\n",
+    "defaultclock.dt = dt\n",
+    "El = -70*mV\n",
+    "VT = -50*mV\n",
+    "DeltaT = 2*mV\n",
+    "ground_truth_params = {'gl': 30*nS, 'C': 1*nF}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d4635f4e-63d9-4d77-af5e-261cebb496cb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set input trace\n",
+    "inp_trace = np.hstack([np.zeros(int(round(5*ms/dt))),\n",
+    "                       np.ones(int(round(25*ms/dt))),\n",
+    "                       np.zeros(int(round(30*ms/dt)))]) * 10*nA\n",
+    "I = TimedArray(inp_trace, dt=dt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "c83542d6-1d02-4ac7-9748-4cb1c5395a20",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set the model equations\n",
+    "eqs = '''\n",
+    "    dv/dt = (gl*(El - v) + gl*DeltaT*exp((v - VT) / DeltaT) + I(t)) / C : volt\n",
+    "    gl    : siemens (constant)\n",
+    "    C     : farad (constant)\n",
+    "    '''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "e1b40eec-43f7-472a-83b6-154546c77b0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# compute traces and spike trains\n",
+    "neurons = NeuronGroup(1, eqs,\n",
+    "                      threshold='v > -50*mV',\n",
+    "                      reset='v = -70*mV',\n",
+    "                      method='exponential_euler')\n",
+    "neurons.v = -70*mV\n",
+    "neurons.set_states(ground_truth_params)\n",
+    "monitor = StateMonitor(neurons, 'v', record=True)\n",
+    "smonitor = SpikeMonitor(neurons, record=True)\n",
+    "run(60*ms)\n",
+    "out_trace = monitor.v\n",
+    "spike_times = array(smonitor.t_)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "3c53dbc9-ac36-436a-8970-e8972541b361",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# inference procedure\n",
+    "start_scope()\n",
+    "El = -70*mV\n",
+    "VT = -50*mV\n",
+    "DeltaT = 2*mV\n",
+    "eqs_inf = '''\n",
+    "    dv/dt = (gl*(El - v) + gl*DeltaT*exp((v - VT) / DeltaT) + I_syn) / C : volt\n",
+    "    gl    : siemens (constant)\n",
+    "    C     : farad (constant)\n",
+    "    '''\n",
+    "\n",
+    "features = [\n",
+    "    lambda x: x.size,  # number of spikes\n",
+    "    lambda x: 0. if np.diff(x).size == 0 else np.mean(np.diff(x)),  # mean inter-spike interval\n",
+    "    ]\n",
+    "\n",
+    "inferencer = Inferencer(dt=dt, model=eqs_inf,\n",
+    "                        input={'I_syn': inp_trace.reshape(1, -1)},\n",
+    "                        output={'spikes': [spike_times]},\n",
+    "                        features={'spikes': features},\n",
+    "                        method='exponential_euler',\n",
+    "                        threshold='v > -50*mV',\n",
+    "                        reset='v = -70*mV',\n",
+    "                        param_init={'v': -70*mV})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "54451813-a222-4ff1-aa22-b31e3fda411f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The state monitor object is not defined.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "98d56a92a892417c8c218c7e5f8c01a3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Extracting features:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b59dce1567504d69b34b684869f7d7c9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "3-round focused inference:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Neural network successfully converged after 120 epochs.\n",
+      "Neural network successfully converged after 49 epochs.\n",
+      "Neural network successfully converged after 47 epochs.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train the inferencer\n",
+    "posterior = inferencer.infer(n_samples=1_000,\n",
+    "                             n_rounds=3,\n",
+    "                             gl=[10*nS, 100*nS],\n",
+    "                             C=[0.1*nF, 10*nF])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "52119cae-6e82-48ba-a6ca-b6f28f791401",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f6c219c5290d4b4382e9af0323b44dbd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x432 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 386,
+       "width": 378
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# sample and pairplot\n",
+    "samples = inferencer.sample((7_000, ))\n",
+    "labels = {'gl': r'$\\overline{g}_{l}$', 'C': r'$C$'}\n",
+    "ticks = {'gl': [10*nS, 100*nS], 'C': [0.1*nF, 10*nF]}\n",
+    "inferencer.pairplot(labels=labels,\n",
+    "                    limits=ticks,\n",
+    "                    ticks=ticks,\n",
+    "                    points=ground_truth_params,\n",
+    "                    points_offdiag={'markersize': 9},\n",
+    "                    points_colors=['r'], \n",
+    "                    figsize=(6, 6));"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b39ca278-0e53-41f2-b1b5-6a781020cb24",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up time domain\n",
+    "t = np.arange(0, inp_trace.size*dt/ms, dt/ms)\n",
+    "t_start, t_end = t[np.where(inp_trace != 0)[0][[0, -1]]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "fdc1c61e-759e-4d15-ab73-5a2cb6ff594e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# identify spike events\n",
+    "spike_v = (out_trace.min()/mV, out_trace.max()/mV)\n",
+    "spike_idx = []\n",
+    "for spike_time in spike_times:\n",
+    "    spike_idx.append(np.where(np.isclose(spike_time * 1000, t))[0].item())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "b5776e70-8424-4590-94df-a604ba97db5e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "9bb3352f6e0f416785bc36f6ec7da8ba",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Drawing 1 posterior samples:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 504x216 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 205,
+       "width": 493
+      },
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# generate traces from a single sample of parameters\n",
+    "inf_trace = inferencer.generate_traces(output_var='v')\n",
+    "\n",
+    "nrows = 2\n",
+    "fig, axs = subplots(nrows, 1, sharex=True,\n",
+    "                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(7, 3))\n",
+    "axs[0].plot(t, out_trace.T/mV, 'C3-', lw=3, label='recordings')\n",
+    "axs[0].plot(t, inf_trace.T/mV, 'k--', lw=2, label='sampled traces')\n",
+    "axs[0].vlines(t[spike_idx], *spike_v, lw=3, color='C0', label='spike event', zorder=3)\n",
+    "axs[0].set_ylabel('$V$, mV')\n",
+    "axs[0].legend()\n",
+    "axs[1].plot(t, inp_trace.T/nA, lw=3, c='k', label='stimuli')\n",
+    "axs[1].set_xlabel('$t$, ms')\n",
+    "axs[1].set_ylabel('$I$, nA')\n",
+    "axs[1].legend()\n",
+    "tight_layout();"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "926141b7-3567-44b5-82c8-d156f6cebbdc",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "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": "3.8.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From ee73d296f0948e10c9291df5d1f5533044f01a7d Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 12 Aug 2021 16:08:19 +0200
Subject: [PATCH 074/121] remove uncecessary files

---
 examples/prototyping.ipynb | 921 -------------------------------------
 1 file changed, 921 deletions(-)
 delete mode 100644 examples/prototyping.ipynb

diff --git a/examples/prototyping.ipynb b/examples/prototyping.ipynb
deleted file mode 100644
index a8fcee3..0000000
--- a/examples/prototyping.ipynb
+++ /dev/null
@@ -1,921 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "28e8bc5d-26a6-4ac6-83c4-97b2a816c1c5",
-   "metadata": {},
-   "source": [
-    "# Hodgkin-Huxley model: traces+spikes inference from recordings"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "2f3614b7-b6ac-43a3-a0c6-d2b3ab32e929",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# imports\n",
-    "from brian2 import *\n",
-    "from brian2modelfitting import Inferencer\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "from scipy.stats import kurtosis as kurt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "9d3fbd95-785a-4e0a-8a37-2feb0d86eb9e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# pretty figures :3\n",
-    "%config InlineBackend.figure_format = 'retina'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "cdc3ea42",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# load few recorded traces\n",
-    "df_inp_traces = pd.read_csv('input_traces_hh.csv')\n",
-    "df_out_traces = pd.read_csv('output_traces_hh.csv')\n",
-    "inp_traces = df_inp_traces.to_numpy()\n",
-    "inp_traces = inp_traces[[0, 1], 1:]\n",
-    "out_traces = df_out_traces.to_numpy()\n",
-    "out_traces = out_traces[[0, 1], 1:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "fb22019e-62b0-4bda-bc17-e2b1b7dc4ce3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set parameters and define the Hodgkin-Huxley model\n",
-    "area = 20_000*um**2\n",
-    "El = -65*mV\n",
-    "EK = -90*mV\n",
-    "ENa = 50*mV\n",
-    "VT = -63*mV\n",
-    "dt = 0.01*ms\n",
-    "eqs = '''\n",
-    "    dv/dt = (gl*(El-v) - g_na*(m*m*m)*h*(v-ENa) - g_kd*(n*n*n*n)*(v-EK) + I)/Cm : volt\n",
-    "    dm/dt = 0.32*(mV**-1)*(13.*mV-v+VT)/\n",
-    "            (exp((13.*mV-v+VT)/(4.*mV))-1.)/ms*(1-m)-0.28*(mV**-1)*(v-VT-40.*mV)/\n",
-    "            (exp((v-VT-40.*mV)/(5.*mV))-1.)/ms*m : 1\n",
-    "    dn/dt = 0.032*(mV**-1)*(15.*mV-v+VT)/\n",
-    "            (exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1\n",
-    "    dh/dt = 0.128*exp((17.*mV-v+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-v+VT)/(5.*mV)))/ms*h : 1\n",
-    "    g_na : siemens (constant)\n",
-    "    g_kd : siemens (constant)\n",
-    "    gl   : siemens (constant)\n",
-    "    Cm   : farad (constant)\n",
-    "'''"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "8a515f25-b94a-4082-97a5-e76fe3887140",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set up time domain\n",
-    "t = np.arange(0, inp_traces.shape[1]*dt/ms, dt/ms)\n",
-    "t_start, t_end = t[np.where(inp_traces[0, :] != 0)[0][[0, -1]]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "9f2c4326-a247-49f9-829d-9559c4db1eae",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 648x216 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 228,
-       "width": 618
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# visualize the recordings\n",
-    "nrows = 2\n",
-    "ncols = out_traces.shape[0]\n",
-    "fig, axs = subplots(nrows, ncols, sharex=True,\n",
-    "                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))\n",
-    "for idx in range(ncols):\n",
-    "    axs[0, idx].plot(t, out_traces[idx, :].T, lw=3, c='C3', label='recordings')\n",
-    "    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimuli')\n",
-    "    axs[1, idx].set_xlabel('$t$, ms')\n",
-    "    if idx == 0:\n",
-    "        axs[0, idx].set_ylabel('$V$, mV')\n",
-    "        axs[1, idx].set_ylabel('$I$, nA')\n",
-    "handles, labels = [(h + l) for h, l\n",
-    "                   in zip(axs[0, idx].get_legend_handles_labels(),\n",
-    "                   axs[1, idx].get_legend_handles_labels())]\n",
-    "fig.legend(handles, labels)\n",
-    "tight_layout();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "4bec8cef-8df3-4f55-9db2-a408a0898d71",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# get spike times manually\n",
-    "def get_spike_times(x):\n",
-    "    x = x.copy()\n",
-    "    # put everything to -40 mV that is below -40 mV or has negative slope\n",
-    "    ind = np.where(x < -0.04)\n",
-    "    x[ind] = -0.04\n",
-    "    ind = np.where(np.diff(x) < 0)\n",
-    "    x[ind] = -0.04\n",
-    "\n",
-    "    # remaining negative slopes are at spike peaks\n",
-    "    ind = np.where(np.diff(x) < 0)\n",
-    "    spike_times = np.array(t)[ind]\n",
-    "    \n",
-    "    # spike times for active stimulus\n",
-    "    spike_times_stim = spike_times[\n",
-    "        (spike_times > t_start) & (spike_times < t_end)\n",
-    "        ]\n",
-    "    \n",
-    "    # number of spikes\n",
-    "    if spike_times_stim.shape[0] > 0:\n",
-    "        spike_times_stim = spike_times_stim[\n",
-    "            np.append(1, np.diff(spike_times_stim)) > 0.5\n",
-    "        ]\n",
-    "    return spike_times_stim / 1000  # in seconds\n",
-    "\n",
-    "# store spike times for each trace into the list\n",
-    "spike_times = [get_spike_times(out_trace) for out_trace in out_traces]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "3927d071-390b-4e20-8d3e-bfd7012ec762",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 648x216 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 228,
-       "width": 618
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# visualize the recordings and spikes while stimulus is on\n",
-    "nrows = 2\n",
-    "ncols = out_traces.shape[0]\n",
-    "fig, axs = subplots(nrows, ncols, sharex=True,\n",
-    "                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))\n",
-    "for idx in range(ncols):\n",
-    "    spike_idx = np.in1d(t, spike_times[idx] * 1000).nonzero()[0]\n",
-    "    spike_v = (out_traces[idx, :].min(), out_traces[idx, :].max())\n",
-    "    axs[0, idx].plot(t, out_traces[idx, :].T, lw=3, c='C3',\n",
-    "                     label='recordings', zorder=1)\n",
-    "    axs[0, idx].vlines(t[spike_idx], *spike_v, lw=3, color='C0',\n",
-    "                       label='spike event', zorder=2)\n",
-    "    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimuli')\n",
-    "    axs[1, idx].set_xlabel('$t$, ms')\n",
-    "    if idx == 0:\n",
-    "        axs[0, idx].set_ylabel('$V$, mV')\n",
-    "        axs[1, idx].set_ylabel('$I$, nA')\n",
-    "handles, labels = [(h + l) for h, l\n",
-    "                   in zip(axs[0, idx].get_legend_handles_labels(),\n",
-    "                   axs[1, idx].get_legend_handles_labels())]\n",
-    "fig.legend(handles, labels)\n",
-    "tight_layout;"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "25a040ac-3dee-42cf-82fd-0e1af5c47f53",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# a few features to create a summary statistics representation of traces\n",
-    "v_features = [\n",
-    "    lambda x: np.max(x[(t > t_start) & (t < t_end)]),  # max active potential\n",
-    "    lambda x: np.mean(x[(t > t_start) & (t < t_end)]),  # mean active potential\n",
-    "    lambda x: np.std(x[(t > t_start) & (t < t_end)]),  # std of active potential\n",
-    "    lambda x: kurt(x[(t > t_start) & (t < t_end)], fisher=False),  # kurtosis of active potential\n",
-    "    lambda x: np.mean(x[(t > 0.25 * t_start) & (t < 0.75 * t_start)]),  # membrane resting potential\n",
-    "]\n",
-    "\n",
-    "s_features = [\n",
-    "    lambda x: x.size,  # count of spikes in the train\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "a4550ead",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# inference object instantiation\n",
-    "inferencer = Inferencer(dt=dt, model=eqs,\n",
-    "                        input={'I': inp_traces*amp},\n",
-    "                        output={'v': out_traces*mV,\n",
-    "                                'spikes': spike_times},\n",
-    "                        features={'v': v_features,\n",
-    "                                  'spikes': s_features},\n",
-    "                        method='exponential_euler',\n",
-    "                        threshold='m > 0.5',\n",
-    "                        refractory='m > 0.5',\n",
-    "                        param_init={'v': 'VT'})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "9fe4d1cc",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fd6893517f754a8d8a63ee879b594705",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Extracting features:   0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "96c3e458cdd743d39d087a1ae2693073",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "3-round focused inference:   0%|          | 0/3 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Neural network successfully converged after 197 epochs.\n",
-      "Neural network successfully converged after 217 epochs.\n",
-      "Neural network successfully converged after 23 epochs.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# posterior estimation\n",
-    "posterior = inferencer.infer(n_samples=5_000,\n",
-    "                             n_rounds=3,\n",
-    "                             inference_method='SNPE',\n",
-    "                             density_estimator_model='mdn',\n",
-    "                             restart=True,\n",
-    "                             gl=[1e-09*siemens, 1e-07*siemens],\n",
-    "                             g_na=[2e-06*siemens, 2e-04*siemens],\n",
-    "                             g_kd=[6e-07*siemens, 6e-05*siemens],\n",
-    "                             Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "b7e4d8db",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "bbc0b97ead1c443a873da2e70939a238",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Drawing 5000 posterior samples:   0%|          | 0/5000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Only 0% posterior samples are within the\n",
-      "                        prior support. It may take a long time to collect the remaining\n",
-      "                        4998 samples. Consider interrupting (Ctrl-C)\n",
-      "                        and switching to `sample_with_mcmc=True`.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# sampling the posterior\n",
-    "samples = inferencer.sample((5_000, ))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "66c3c4ce",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 386,
-       "width": 378
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# pairplotting space of sampled parameters from posterior\n",
-    "limits = {'gl': [1e-9*siemens, 1e-07*siemens],\n",
-    "          'g_na': [2e-06*siemens, 2e-04*siemens],\n",
-    "          'g_kd': [6e-07*siemens, 6e-05*siemens],\n",
-    "          'Cm': [0.1*uF*cm**-2*area, 2*uF*cm**-2*area]}\n",
-    "labels = {'gl': r'$\\overline{g}_{l}$',\n",
-    "          'g_na': r'$\\overline{g}_{Na}$',\n",
-    "          'g_kd': r'$\\overline{g}_{K}$',\n",
-    "          'Cm': r'$C_{m}$'}\n",
-    "\n",
-    "inferencer.pairplot(limits=limits,\n",
-    "                    labels=labels,\n",
-    "                    ticks=limits,\n",
-    "                    figsize=(6, 6));"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "1a87fdae",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7534eaf0020d41259f5afb2eb7160e84",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Drawing 1 posterior samples:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Only 0% posterior samples are within the\n",
-      "                        prior support. It may take a long time to collect the remaining\n",
-      "                        -2 samples. Consider interrupting (Ctrl-C)\n",
-      "                        and switching to `sample_with_mcmc=True`.\n",
-      "WARNING:root:Drawing samples from posterior to estimate the normalizing\n",
-      "                        constant for `log_prob()`. However, only 0%\n",
-      "                        posterior samples are within the prior support. It may take a\n",
-      "                        long time to collect the remaining 9996 samples.\n",
-      "                        Consider interrupting (Ctrl-C) and either basing the estimate\n",
-      "                        of the normalizing constant on fewer samples (by calling\n",
-      "                        `posterior.leakage_correction(x_o, num_rejection_samples=N)`,\n",
-      "                        where `N` is the number of samples you want to base the\n",
-      "                        estimate on (default N=10000), or not estimating the\n",
-      "                        normalizing constant at all\n",
-      "                        (`log_prob(..., norm_posterior=False)`. The latter will result\n",
-      "                        in an unnormalized `log_prob()`.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 16 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 386,
-       "width": 378
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# conditional pairplot\n",
-    "condition = inferencer.sample((1, ))\n",
-    "inferencer.conditional_pairplot(condition=condition,\n",
-    "                                limits=limits,\n",
-    "                                labels=labels,\n",
-    "                                ticks=limits,\n",
-    "                                figsize=(6, 6));"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "2f195b76",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 288x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 241,
-       "width": 284
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# computing conditional coefficient matrix using the same condition\n",
-    "cond_coeff_mat = inferencer.conditional_corrcoeff(condition=condition, limits=limits)\n",
-    "\n",
-    "fig, ax = plt.subplots(1,1, figsize=(4, 4))\n",
-    "im = plt.imshow(cond_coeff_mat, clim=[-1, 1])\n",
-    "_ = fig.colorbar(im)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "7ed79a8f-187d-41bf-abbe-cac82160dbbb",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "aea058a947a944fc9f73e669a38cd97e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Drawing 1 posterior samples:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Only 0% posterior samples are within the\n",
-      "                        prior support. It may take a long time to collect the remaining\n",
-      "                        -3 samples. Consider interrupting (Ctrl-C)\n",
-      "                        and switching to `sample_with_mcmc=True`.\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 648x216 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 228,
-       "width": 618
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# generate traces from a single sample of parameters\n",
-    "inf_traces = inferencer.generate_traces(output_var='v')\n",
-    "\n",
-    "nrows = 2\n",
-    "ncols = out_traces.shape[0]\n",
-    "fig, axs = subplots(nrows, ncols, sharex=True,\n",
-    "                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))\n",
-    "for idx in range(ncols):\n",
-    "    spike_idx = np.in1d(t, spike_times[idx]).nonzero()[0]\n",
-    "    spike_v = (out_traces[idx, :].min(), out_traces[idx, :].max())\n",
-    "    axs[0, idx].plot(t, out_traces[idx, :].T, 'C3-', lw=3, label='recordings')\n",
-    "    axs[0, idx].plot(t, inf_traces[idx, :].T/mV, 'k--', lw=2,\n",
-    "                     label='sampled traces')\n",
-    "    axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimuli')\n",
-    "    axs[1, idx].set_xlabel('$t$, ms')\n",
-    "    if idx == 0:\n",
-    "        axs[0, idx].set_ylabel('$V$, mV')\n",
-    "        axs[1, idx].set_ylabel('$I$, nA')\n",
-    "handles, labels = [(h + l) for h, l\n",
-    "                   in zip(axs[0, idx].get_legend_handles_labels(),\n",
-    "                   axs[1, idx].get_legend_handles_labels())]\n",
-    "fig.legend(handles, labels)\n",
-    "tight_layout;"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "59d9e91e-7e22-4e9a-9ff0-7ddb2207b030",
-   "metadata": {},
-   "source": [
-    "# Integrate-and-Fire model: spike inference from synthetic trace"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "64968695-ad82-450e-bea7-1a2eb5ebd1e0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from brian2 import *\n",
-    "from brian2modelfitting import Inferencer\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "73fc4301-ffab-4675-b68e-6534d1af5b1a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%config InlineBackend.figure_format = 'retina'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "ad802d6b-2976-4cae-86aa-f7bd276c802b",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set parameters\n",
-    "dt = 0.1*ms\n",
-    "defaultclock.dt = dt\n",
-    "El = -70*mV\n",
-    "VT = -50*mV\n",
-    "DeltaT = 2*mV\n",
-    "ground_truth_params = {'gl': 30*nS, 'C': 1*nF}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "d4635f4e-63d9-4d77-af5e-261cebb496cb",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set input trace\n",
-    "inp_trace = np.hstack([np.zeros(int(round(5*ms/dt))),\n",
-    "                       np.ones(int(round(25*ms/dt))),\n",
-    "                       np.zeros(int(round(30*ms/dt)))]) * 10*nA\n",
-    "I = TimedArray(inp_trace, dt=dt)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "c83542d6-1d02-4ac7-9748-4cb1c5395a20",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set the model equations\n",
-    "eqs = '''\n",
-    "    dv/dt = (gl*(El - v) + gl*DeltaT*exp((v - VT) / DeltaT) + I(t)) / C : volt\n",
-    "    gl    : siemens (constant)\n",
-    "    C     : farad (constant)\n",
-    "    '''"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e1b40eec-43f7-472a-83b6-154546c77b0c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# compute traces and spike trains\n",
-    "neurons = NeuronGroup(1, eqs,\n",
-    "                      threshold='v > -50*mV',\n",
-    "                      reset='v = -70*mV',\n",
-    "                      method='exponential_euler')\n",
-    "neurons.v = -70*mV\n",
-    "neurons.set_states(ground_truth_params)\n",
-    "monitor = StateMonitor(neurons, 'v', record=True)\n",
-    "smonitor = SpikeMonitor(neurons, record=True)\n",
-    "run(60*ms)\n",
-    "out_trace = monitor.v\n",
-    "spike_times = array(smonitor.t_)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "3c53dbc9-ac36-436a-8970-e8972541b361",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# inference procedure\n",
-    "start_scope()\n",
-    "El = -70*mV\n",
-    "VT = -50*mV\n",
-    "DeltaT = 2*mV\n",
-    "eqs_inf = '''\n",
-    "    dv/dt = (gl*(El - v) + gl*DeltaT*exp((v - VT) / DeltaT) + I_syn) / C : volt\n",
-    "    gl    : siemens (constant)\n",
-    "    C     : farad (constant)\n",
-    "    '''\n",
-    "\n",
-    "features = [\n",
-    "    lambda x: x.size,  # number of spikes\n",
-    "    lambda x: 0. if np.diff(x).size == 0 else np.mean(np.diff(x)),  # mean inter-spike interval\n",
-    "    ]\n",
-    "\n",
-    "inferencer = Inferencer(dt=dt, model=eqs_inf,\n",
-    "                        input={'I_syn': inp_trace.reshape(1, -1)},\n",
-    "                        output={'spikes': [spike_times]},\n",
-    "                        features={'spikes': features},\n",
-    "                        method='exponential_euler',\n",
-    "                        threshold='v > -50*mV',\n",
-    "                        reset='v = -70*mV',\n",
-    "                        param_init={'v': -70*mV})"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "54451813-a222-4ff1-aa22-b31e3fda411f",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "The state monitor object is not defined.\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "98d56a92a892417c8c218c7e5f8c01a3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Extracting features:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "b59dce1567504d69b34b684869f7d7c9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "3-round focused inference:   0%|          | 0/3 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Neural network successfully converged after 120 epochs.\n",
-      "Neural network successfully converged after 49 epochs.\n",
-      "Neural network successfully converged after 47 epochs.\n"
-     ]
-    }
-   ],
-   "source": [
-    "# train the inferencer\n",
-    "posterior = inferencer.infer(n_samples=1_000,\n",
-    "                             n_rounds=3,\n",
-    "                             gl=[10*nS, 100*nS],\n",
-    "                             C=[0.1*nF, 10*nF])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "52119cae-6e82-48ba-a6ca-b6f28f791401",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f6c219c5290d4b4382e9af0323b44dbd",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Drawing 10000 posterior samples:   0%|          | 0/10000 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x432 with 4 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 386,
-       "width": 378
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# sample and pairplot\n",
-    "samples = inferencer.sample((7_000, ))\n",
-    "labels = {'gl': r'$\\overline{g}_{l}$', 'C': r'$C$'}\n",
-    "ticks = {'gl': [10*nS, 100*nS], 'C': [0.1*nF, 10*nF]}\n",
-    "inferencer.pairplot(labels=labels,\n",
-    "                    limits=ticks,\n",
-    "                    ticks=ticks,\n",
-    "                    points=ground_truth_params,\n",
-    "                    points_offdiag={'markersize': 9},\n",
-    "                    points_colors=['r'], \n",
-    "                    figsize=(6, 6));"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b39ca278-0e53-41f2-b1b5-6a781020cb24",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# set up time domain\n",
-    "t = np.arange(0, inp_trace.size*dt/ms, dt/ms)\n",
-    "t_start, t_end = t[np.where(inp_trace != 0)[0][[0, -1]]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "fdc1c61e-759e-4d15-ab73-5a2cb6ff594e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# identify spike events\n",
-    "spike_v = (out_trace.min()/mV, out_trace.max()/mV)\n",
-    "spike_idx = []\n",
-    "for spike_time in spike_times:\n",
-    "    spike_idx.append(np.where(np.isclose(spike_time * 1000, t))[0].item())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "b5776e70-8424-4590-94df-a604ba97db5e",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9bb3352f6e0f416785bc36f6ec7da8ba",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Drawing 1 posterior samples:   0%|          | 0/1 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 504x216 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "image/png": {
-       "height": 205,
-       "width": 493
-      },
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# generate traces from a single sample of parameters\n",
-    "inf_trace = inferencer.generate_traces(output_var='v')\n",
-    "\n",
-    "nrows = 2\n",
-    "fig, axs = subplots(nrows, 1, sharex=True,\n",
-    "                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(7, 3))\n",
-    "axs[0].plot(t, out_trace.T/mV, 'C3-', lw=3, label='recordings')\n",
-    "axs[0].plot(t, inf_trace.T/mV, 'k--', lw=2, label='sampled traces')\n",
-    "axs[0].vlines(t[spike_idx], *spike_v, lw=3, color='C0', label='spike event', zorder=3)\n",
-    "axs[0].set_ylabel('$V$, mV')\n",
-    "axs[0].legend()\n",
-    "axs[1].plot(t, inp_trace.T/nA, lw=3, c='k', label='stimuli')\n",
-    "axs[1].set_xlabel('$t$, ms')\n",
-    "axs[1].set_ylabel('$I$, nA')\n",
-    "axs[1].legend()\n",
-    "tight_layout();"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "926141b7-3567-44b5-82c8-d156f6cebbdc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "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": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From c10397ccde7c18720a4142bec147e184ae1fb635 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Thu, 12 Aug 2021 16:08:57 +0200
Subject: [PATCH 075/121] add minor updates

---
 brian2modelfitting/inferencer.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 89d6f95..ed99ae7 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -1227,7 +1227,7 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
             If a single output variable is observed, 2-D array of
             traces generated by using a set of parameters sampled from
             the trained posterior distribution of shape
-            (``n.traces``, number of time steps). Otherwise, a
+            (``self.n_traces``, number of time steps). Otherwise, a
             dictionary with keys set to names of output variables, and
             values to generated traces of respective output variables.
         """
@@ -1273,6 +1273,7 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
                 if ov == 'spikes':
                     trace = get_spikes(simulator.spikemonitor, 1,
                                        self.n_traces)[0]
+                    traces = {ov: trace}
                 else:
                     try:
                         trace = getattr(simulator.statemonitor, ov)[:]

From 3fe11f717507ad8ebfb8ef2652bf2c83e19a610e Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 13 Aug 2021 15:22:12 +0200
Subject: [PATCH 076/121] add inferencer api reference

---
 docs_sphinx/api/brian2modelfitting.inferencer.rst | 7 +++++++
 1 file changed, 7 insertions(+)
 create mode 100644 docs_sphinx/api/brian2modelfitting.inferencer.rst

diff --git a/docs_sphinx/api/brian2modelfitting.inferencer.rst b/docs_sphinx/api/brian2modelfitting.inferencer.rst
new file mode 100644
index 0000000..30cf9c6
--- /dev/null
+++ b/docs_sphinx/api/brian2modelfitting.inferencer.rst
@@ -0,0 +1,7 @@
+brian2modelfitting.inferencer module
+======================================
+
+.. automodule:: brian2modelfitting.inferencer
+   :members:
+   :undoc-members:
+   :show-inheritance:

From f1215460cad64ea36ef985dd69cbf7abdb8a53ac Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 14:15:14 +0200
Subject: [PATCH 077/121] update README file

---
 README.md | 32 +++++++++++++++++++++-----------
 1 file changed, 21 insertions(+), 11 deletions(-)

diff --git a/README.md b/README.md
index 31e29c7..278c316 100644
--- a/README.md
+++ b/README.md
@@ -1,28 +1,38 @@
 brian2modelfitting
 ==================
 
+Model fitting toolbox for Brian 2 simulator.
 
-Model Fitting Toolbox for Brian 2 simulator, to allow the user to find the best fit of the parameters for recorded traces and spike trains.
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4601961.svg)](https://doi.org/10.5281/zenodo.4601961)
 
-By default, we support a range of global derivative-free optimization methods, that include popular methods for model fitting, such as: Differential Evolution, Particle Swarm Optimization and Covariance Matrix Adaptation (provided by the Nevergrad) as well as Bayesian Optimization for black box functions (provided by Scikit-Optimize).
 
-Documentation for Brian2modelfitting can be found at http://brian2modelfitting.readthedocs.org
 
-Brian2modelfitting is released under the terms of the CeCILL 2.1 license.
+The package [`brian2modelfitting`](https://brian2modelfitting.readthedocs.io) allows the user to find the best fit of the unknown free parameters for recorded traces and spike trains. It also supports simulation-based inference, where instead of point-estimated parameter values, a full posterior distribution over the parameters is computed.
 
-Please report issues at the github issue tracker (https://github.com/brian-team/brian2modelfitting/issues) or at the brian discussion forum (https://brian.discourse.group)
+By default, the toolbox supports a range of global derivative-free optimization methods, that include popular methods for model fitting: differential evolution, particle swarm optimization and covariance matrix adaptation (provided by the  [`Nevergrad`](https://facebookresearch.github.io/nevergrad/), a gradient-free optimization platform) as well as Bayesian optimization for black box functions (provided by [`scikit-optimize`](https://scikit-optimize.github.io/stable/), a sequential model-based optimization library). On the other hand, simulation-based inference is the process of finding parameters of a simulator from observations by taking a Bayesian approach via sequential neural posterior estimation, likelihood estimation or ration estimation (provided by the [`sbi`](https://www.mackelab.org/sbi/)), where neural densitiy estimator, a deep neural network allowing probabilistic association between the data and underlying parameter space, is trained. After the network is trained, the approximated posterior distribution is available.
 
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4601961.svg)](https://doi.org/10.5281/zenodo.4601961)
+Documentation
+-------------
+[![Documentation Status](https://readthedocs.org/projects/ansicolortags/badge/?version=latest)](https://brian2modelfitting.readthedocs.io)
+
+The full documentation is available at http://brian2modelfitting.readthedocs.org.
+
+Testing status
+--------------
+[![Build Status](https://github.com/brian-team/brian2modelfitting/workflows/Tests/badge.svg)](https://github.com/brian-team/brian2modelfitting/actions) 
+[![Coverage Status](https://coveralls.io/repos/github/brian-team/brian2modelfitting/badge.svg?branch=master)](https://coveralls.io/github/brian-team/brian2modelfitting?branch=master)
 
 Installation
 ------------
-Module can be installed with pypi:
+Install `brian2modelfitting` from the Python package index via pip:
 ```
 pip install brian2modelfitting
 ```
 
+License
+-------
+The model fitting toolbox is released under the terms of the CeCILL 2.1 license and is available [here](https://github.com/brian-team/brian2modelfitting/blob/master/LICENSE).
 
-Testing status
---------------
-[![Build Status](https://github.com/brian-team/brian2modelfitting/workflows/Tests/badge.svg)](https://github.com/brian-team/brian2modelfitting/actions) 
-[![Coverage Status](https://coveralls.io/repos/github/brian-team/brian2modelfitting/badge.svg?branch=master)](https://coveralls.io/github/brian-team/brian2modelfitting?branch=master)
+Use
+---
+Please report issues at the GitHub [issue tracker](https://github.com/brian-team/brian2modelfitting/issues) or at the [Brian 2 discussion forum](https://brian.discourse.group).

From 5f0651b5de78b65f3f101498a79c7fa8ba78d020 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 15:02:31 +0200
Subject: [PATCH 078/121] update the front webpage

---
 docs_sphinx/index.rst | 40 ++++++++++++++++++++++++++++------------
 1 file changed, 28 insertions(+), 12 deletions(-)

diff --git a/docs_sphinx/index.rst b/docs_sphinx/index.rst
index a4876c9..4c466ec 100644
--- a/docs_sphinx/index.rst
+++ b/docs_sphinx/index.rst
@@ -1,24 +1,40 @@
 brian2modelfitting
 ==================
 
-The package `.brian2modelfitting` is a tool for parameter fitting of neuron
-models in the `Brian 2 simulator <https://brian2.readthedocs.org>`_.
+The package `.brian2modelfitting` is a tool for parameter identification of
+neuron models defined in the `Brian 2 simulator <https://brian2.readthedocs.org>`_.
 
-Please report bugs at the `github issue tracker <https://github.com/brian-team/brian2modelfitting/issues>`_
-or at the `Brian discussion forum <https://groups.google.com/forum/#!forum/briansupport>`_. The latter is
-also a could place to discuss feature requests or potential contributions.
+Please report bugs at the `GitHub issue tracker <https://github.com/brian-team/brian2modelfitting/issues>`_
+or at the `Brian 2 discussion forum <https://groups.google.com/forum/#!forum/briansupport>`_.
+The latter is also a place to discuss feature requests or potential
+contributions.
 
 
 Model fitting
 -------------
 
-The ``brian2modelfitting`` toolbox offers allows the user to perform data driven
-optimization for custom neuronal models specified with
-`Brian 2 <https://brian2.readthedocs.org>`_.
+This toolbox allows the user to find the best fit of the unknown free
+parameters for recorded traces and spike trains.
+It also supports simulation-based inference, where instead of point-estimated
+parameter values, a full posterior distribution over the parameters is
+computed.
 
-The toolbox allows the user to find the best fit of the parameters for recorded traces and
-spike trains. Just like Brian itself, the Model Fitting Toolbox is designed to
-be easy to use and save time through automatic parallelization of the
+By default, the toolbox supports a range of global derivative-free
+optimization methods, that include popular methods for model fitting:
+differential evolution, particle swarm optimization and covariance matrix
+adaptation (provided by the ``Nevergrad``, a gradient-free optimization
+platform) as well as Bayesian optimization for black box functions (provided
+by ``scikit-optimize``, a sequential model-based optimization library). On the
+other hand, simulation-based inference is the process of finding parameters of
+a simulator from observations by taking a Bayesian approach via sequential
+neural posterior estimation, likelihood estimation or ration estimation
+(provided by the ``sbi``), where neural densitiy estimator, a deep neural
+network allowing probabilistic association between the data and underlying
+parameter space, is trained. After the network is trained, the approximated
+posterior distribution is available.
+
+Just like Brian itself, the `brian2modelfitting` toolbox is designed to be
+easy to use and to save time through automatic parallelization of the
 simulations using code generation.
 
 
@@ -32,6 +48,7 @@ Contents
    introduction/index
    optimizer/index
    metric/index
+   inferencer/index
    features/index
    examples/index
 
@@ -48,4 +65,3 @@ Indices and tables
 
 * :ref:`genindex`
 * :ref:`modindex`
-* :ref:`search`

From 90bfad952bac08d7ef0d3b3fafbe163e77dd498a Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 15:02:43 +0200
Subject: [PATCH 079/121] small changes

---
 docs_sphinx/conf.py | 27 ++++++++++++++++-----------
 1 file changed, 16 insertions(+), 11 deletions(-)

diff --git a/docs_sphinx/conf.py b/docs_sphinx/conf.py
index 6cb4846..fc49303 100644
--- a/docs_sphinx/conf.py
+++ b/docs_sphinx/conf.py
@@ -10,6 +10,7 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
+
 import os
 import sys
 sys.path.insert(0, os.path.abspath('..'))
@@ -17,11 +18,12 @@
 needs_sphinx = '1.8'
 
 
-brian2modelfitting_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),
-                                               '..', 'brian2modelfitting'))
+brian2modelfitting_dir = os.path.abspath(os.path.join(
+    os.path.dirname(__file__), '..', 'brian2modelfitting')
+    )
 # -- Project information -----------------------------------------------------
 
-project = 'Brian2modelfitting'
+project = 'brian2modelfitting'
 copyright = '2019, brian-team'
 author = 'brian-team'
 
@@ -66,7 +68,8 @@
 autodoc_default_options = {'inherited-members': True}
 
 # -- Options for HTML output -------------------------------------------------
-# on_rtd is whether we are on readthedocs.org, this line of code grabbed from docs.readthedocs.org
+# on_rtd is whether we are on readthedocs.org, this line of code grabbed from
+# docs.readthedocs.org
 on_rtd = os.environ.get('READTHEDOCS', None) == 'True'
 
 if not on_rtd:
@@ -92,10 +95,12 @@
 
 
 # -- Extension configuration -------------------------------------------------
-intersphinx_mapping = {'python': ('https://docs.python.org/', None),
-                       'brian2': ('https://brian2.readthedocs.org/en/stable/', None),
-                       'matplotlib': ('http://matplotlib.org/', None),
-                       'numpy': ('http://docs.scipy.org/doc/numpy/', None),
-                       'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
-                       'pandas': ('https://pandas.pydata.org/pandas-docs/stable/', None),
-                       'lmfit': ('https://lmfit.github.io/lmfit-py/', None)}
+intersphinx_mapping = {
+    'python': ('https://docs.python.org/', None),
+    'brian2': ('https://brian2.readthedocs.org/en/stable/', None),
+    'matplotlib': ('http://matplotlib.org/', None),
+    'numpy': ('http://docs.scipy.org/doc/numpy/', None),
+    'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None),
+    'pandas': ('https://pandas.pydata.org/pandas-docs/stable/', None),
+    'lmfit': ('https://lmfit.github.io/lmfit-py/', None),
+    }

From 8510f1d957c3482f6ac754debfc3af70f0d1bf5e Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 15:03:16 +0200
Subject: [PATCH 080/121] add inferencer to api references

---
 docs_sphinx/api/brian2modelfitting.rst | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/docs_sphinx/api/brian2modelfitting.rst b/docs_sphinx/api/brian2modelfitting.rst
index d09c81a..4a10106 100644
--- a/docs_sphinx/api/brian2modelfitting.rst
+++ b/docs_sphinx/api/brian2modelfitting.rst
@@ -8,9 +8,10 @@ Subpackages and -modules
 
 .. toctree::
 
-   brian2modelfitting.tests
-   brian2modelfitting.metric
    brian2modelfitting.fitter
+   brian2modelfitting.inferencer
+   brian2modelfitting.metric
    brian2modelfitting.optimizer
    brian2modelfitting.simulator
+   brian2modelfitting.tests
    brian2modelfitting.utils

From f6ae7aa73dfe210316c472cec901ac3474556f7b Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 15:12:46 +0200
Subject: [PATCH 081/121] update simple examples with Inferencer

---
 docs_sphinx/examples/base.rst | 33 ++++++++++++++++++++++++++++++---
 1 file changed, 30 insertions(+), 3 deletions(-)

diff --git a/docs_sphinx/examples/base.rst b/docs_sphinx/examples/base.rst
index a6f4c7d..9c03418 100644
--- a/docs_sphinx/examples/base.rst
+++ b/docs_sphinx/examples/base.rst
@@ -1,7 +1,8 @@
 Simple Examples
 ===============
 
-Following pieces of code show an example of Fitter class calls with possible inputs.
+Following pieces of code show an example of two ``Fitter`` class calls and an
+``Inferencer`` class call with possible inputs.
 
 
 TraceFitter
@@ -27,8 +28,6 @@ TraceFitter
                               g_na=[1*mS*cm**-2 * area, 2000*mS*cm**-2 * area],
                               g_kd=[1*mS*cm**-2 * area, 1000*mS*cm**-2 * area])
 
-
-
 SpikeFitter
 -----------
 
@@ -51,3 +50,31 @@ SpikeFitter
                    metric=metric,
                    gL=[20*nS, 40*nS],
                    C = [0.5*nF, 1.5*nF])
+
+Inferencer
+----------
+
+.. code:: python
+
+  v_features = [
+      lambda x: max(x[(t > t_start) & (t < t_end)]),  # AP max
+      lambda x: mean(x[(t > t_start) & (t < t_end)]),  # AP mean
+      lambda x: std(x[(t > t_start) & (t < t_end)]),  # AP std
+      lambda x: mean(x[(t > .25 * t_start) & (t < .75 * t_start)]),  # resting
+  ]
+  s_features = [lambda x: x.size]  # number of spikes in a spike train
+
+  inferencer = Inferencer(model=eqs, dt=0.1*ms,
+                          input={'I': inp_traces},
+                          output={'v': out_traces, 'spike': spike_times}, 
+                          features={'v': v_features, 'spikes': s_features},
+                          method='exponential_euler',
+                          threshold='m > 0.5',
+                          refractory='m > 0.5',
+                          param_init={'v': 'VT'})
+
+  posterior = inferencer.infer(n_samples=1_000,
+                               n_rounds=2,
+                               inference_method='SNPE',
+                               gL=[20*nS, 40*nS],
+                               C = [0.5*nF, 1.5*nF])

From bfb00946200fa48c13b5eaf9a79593468262934b Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 15:13:06 +0200
Subject: [PATCH 082/121] update table of contents in examples

---
 docs_sphinx/examples/index.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/docs_sphinx/examples/index.rst b/docs_sphinx/examples/index.rst
index 25f0830..957a08a 100644
--- a/docs_sphinx/examples/index.rst
+++ b/docs_sphinx/examples/index.rst
@@ -6,3 +6,5 @@ Examples
 
    base
    hh_multirun
+   hh_sbi_simple
+   hh_sbi_flex

From 7d9278eee612981502877fe220e4a13f675736da Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 15:13:41 +0200
Subject: [PATCH 083/121] change the title of the example using Fitter

---
 docs_sphinx/examples/hh_multirun.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs_sphinx/examples/hh_multirun.rst b/docs_sphinx/examples/hh_multirun.rst
index e155a3b..d1ed895 100644
--- a/docs_sphinx/examples/hh_multirun.rst
+++ b/docs_sphinx/examples/hh_multirun.rst
@@ -1,5 +1,5 @@
-Multirun of Hodgkin-Huxley
-==========================
+Multirun fitting of Hodgkin-Huxley
+==================================
 
 Here you can download the data:
 :download:`input_traces <../../examples/input_traces_hh.csv>`

From ef99912a853123330b57e8e17aff32cc55c47fe3 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 15:14:01 +0200
Subject: [PATCH 084/121] add placehoders for examples using Inferencer

---
 docs_sphinx/examples/hh_sbi_flex.rst   | 4 ++++
 docs_sphinx/examples/hh_sbi_simple.rst | 4 ++++
 2 files changed, 8 insertions(+)
 create mode 100644 docs_sphinx/examples/hh_sbi_flex.rst
 create mode 100644 docs_sphinx/examples/hh_sbi_simple.rst

diff --git a/docs_sphinx/examples/hh_sbi_flex.rst b/docs_sphinx/examples/hh_sbi_flex.rst
new file mode 100644
index 0000000..e1e4b69
--- /dev/null
+++ b/docs_sphinx/examples/hh_sbi_flex.rst
@@ -0,0 +1,4 @@
+Inference on Hodgin-Huxley model: flexible interface
+====================================================
+
+tba
\ No newline at end of file
diff --git a/docs_sphinx/examples/hh_sbi_simple.rst b/docs_sphinx/examples/hh_sbi_simple.rst
new file mode 100644
index 0000000..6b4d331
--- /dev/null
+++ b/docs_sphinx/examples/hh_sbi_simple.rst
@@ -0,0 +1,4 @@
+Inference on Hodgin-Huxley model: simple interface
+==================================================
+
+tba
\ No newline at end of file

From b3274fc347a99727af6f7246741001e05398059b Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 15:18:23 +0200
Subject: [PATCH 085/121] update introduction section

---
 docs_sphinx/introduction/howitworks.rst   | 63 +++++++++++++----------
 docs_sphinx/introduction/index.rst        |  1 +
 docs_sphinx/introduction/introduction.rst | 20 ++++---
 docs_sphinx/introduction/tutorial_sbi.rst |  4 ++
 4 files changed, 56 insertions(+), 32 deletions(-)
 create mode 100644 docs_sphinx/introduction/tutorial_sbi.rst

diff --git a/docs_sphinx/introduction/howitworks.rst b/docs_sphinx/introduction/howitworks.rst
index 39466c0..01769b6 100644
--- a/docs_sphinx/introduction/howitworks.rst
+++ b/docs_sphinx/introduction/howitworks.rst
@@ -1,10 +1,14 @@
+============
 How it works
 ============
 
+Fitting
+=======
+
 Model fitting script requires three components:
- - A **Fitter** of choice: object that will perform the optimization
- - A **metric**: to compare results and decide which one is the best
- - An **optimization** algorithm: to decide which parameter combinations to try
+ - a **fitter**: object that will perform the optimization
+ - a **metric**: objective function
+ - an **optimizer**: optimization algorithm
 
 All of which need to be initialized for fitting application.
 Each optimization works with a following scheme:
@@ -13,61 +17,63 @@ Each optimization works with a following scheme:
 
   opt = Optimizer()
   metric = Metric()
-
   fitter = Fitter(...)
   result, error = fitter.fit(metric=metric, optimizer=opt, ...)
 
 
-The proposed solution is developed using a modular approach, where both the optimization
-method and metric to be optimized can be easily swapped out by a custom implementation.
+The proposed solution is developed using a modular approach, where both the
+optimization method and the objective function can be easily swapped out by a
+user-defined custom implementation.
 
-`~.Fitter` objects require 'model' defined as an
+`~.Fitter` objects require a model defined as an
 `~brian2.equations.equations.Equations` object or as a string, that has
 parameters that will be optimized specified as constants in the following way:
 
 .. code:: python
 
   model = '''
-  ...
-  g_na : siemens (constant)
-  g_kd : siemens (constant)
-  gl   : siemens (constant)
-  '''
+      ...
+      g_na : siemens (constant)
+      g_kd : siemens (constant)
+      gl   : siemens (constant)
+      '''
 
 Initialization of Fitter requires:
-  - ``dt`` - time step
-  - ``input`` - a dictionary with the name of the input variable and a set of input traces (list or array)
-  - ``output`` - a dictionary with the name of the output variable(s) and a set of goal output (traces/spike trains) (list or array)
-  - ``n_samples`` - number of samples to draw in each round (limited by method)
-  - ``reset``, and ``threshold`` in case of spiking neurons (can take refractory as well)
+  - ``dt`` - the time step
+  - ``input`` - a dictionary with the name of the input variable and a set of
+  input traces (list or array)
+  - ``output`` - a dictionary with the name of the output variable(s) and a
+  set of goal output (traces/spike trains) (list or array)
+  - ``n_samples`` - a number of samples to draw in each round (limited by
+  method)
+  - ``reset`` and ``threshold`` in case of spiking neurons (can take
+  refractory as well)
 
 Additionally, upon call of `~brian2modelfitting.fitter.Fitter.fit()`,
 object requires:
 
- - ``n_rounds`` - number of rounds to optimize over
+ - ``n_rounds`` - a number of rounds to optimize over
  - parameters with ranges to be optimized over
 
-...as well as an ``optimizer`` and a ``metric``
-
 Each free parameter of the model that shall be fitted is defined by two values:
 
 .. code:: python
 
-  param_name = [min, max]
+  param_name = [lower_bound, upper_bound]
 
 Ready to use elements
 ---------------------
 
-Alongside three optimization classes:
+Optimization classes:
  - `~brian2modelfitting.fitter.TraceFitter`
  - `~brian2modelfitting.fitter.SpikeFitter`
  - `~brian2modelfitting.fitter.OnlineTraceFitter`
 
-We also provide ready optimizers:
+Optimization algorithms:
  - `~brian2modelfitting.optimizer.NevergradOptimizer`
  - `~brian2modelfitting.optimizer.SkoptOptimizer`
 
-and metrics:
+Metrics:
  - `~brian2modelfitting.metric.MSEMetric` (for `~brian2modelfitting.fitter.TraceFitter`)
  - `~brian2modelfitting.metric.GammaFactor` (for `~brian2modelfitting.fitter.SpikeFitter`)
 
@@ -85,11 +91,11 @@ Example of `~brian2modelfitting.fitter.TraceFitter` with all of the necessary ar
   result, error = fitter.fit(optimizer=optimizer,
                              metric=metric,
                              n_rounds=1,
-                             gl=[1e-8*siemens*cm**-2 * area, 1e-3*siemens*cm**-2 * area],)
+                             gl=[1e-8*siemens*cm**-2 * area, 1e-3*siemens*cm**-2 * area])
 
 Remarks
 -------
-- After performing first fitting, user can continue the optimization
+- After performing first fitting round, user can continue the optimization
   with another `~brian2modelfitting.fitter.Fitter.fit()` run.
 
 - Number of samples can not be changed between rounds or `~brian2modelfitting.fitter.Fitter.fit()`
@@ -101,3 +107,8 @@ Remarks
 
 - When using the `~brian2modelfitting.fitter.TraceFitter`, users can use a standard
   curve fitting algorithm for refinement by calling `~brian2modelfitting.fitter.TraceFitter.refine`.
+
+Simulation-based inference
+==========================
+
+tba
\ No newline at end of file
diff --git a/docs_sphinx/introduction/index.rst b/docs_sphinx/introduction/index.rst
index fedf7e1..139929e 100644
--- a/docs_sphinx/introduction/index.rst
+++ b/docs_sphinx/introduction/index.rst
@@ -7,3 +7,4 @@ Model Fitting
    introduction
    howitworks
    tutorial_hh
+   tutorial_sbi
diff --git a/docs_sphinx/introduction/introduction.rst b/docs_sphinx/introduction/introduction.rst
index ae1feb0..b0b8041 100644
--- a/docs_sphinx/introduction/introduction.rst
+++ b/docs_sphinx/introduction/introduction.rst
@@ -6,11 +6,19 @@ The `.brian2modelfitting` toolbox provides three optimization classes:
  - `~brian2modelfitting.fitter.SpikeFitter`
  - `~brian2modelfitting.fitter.OnlineTraceFitter`
 
-The classes expect a model and data as an input and returns the best fit of
-parameters and the corresponding error. The toolbox can optimize over multiple
-traces (e.g. input currents) at the same time.
+and a simulation-based inference class:
+ - `~brian2modelfitting.inferencer.Inferencer`
 
-In following documentation we assume that ``brian2modelfitting`` has been imported like this:
+All classes expect a model and the data as an input and return either the best
+fit of each parameter with the corresponding error, or a posterior
+distribution over unknown parameters.
+The toolbox can optimize over multiple traces (e.g. input currents) at the
+same time.
+It also allows the possiblity of simultaneous fitting/inferencing by taking
+into account multiple output variables including spike trains.
+
+In following documentation we assume that ``brian2modelfitting`` has been
+installed and imported as follows:
 
 .. code:: python
 
@@ -20,7 +28,7 @@ In following documentation we assume that ``brian2modelfitting`` has been import
 Installation
 ------------
 
-To install Model Fitting alongside Brian2 you can use pip, by using
+To install the toolbox alongside Brian 2 simulator, use `pip` as follows:
 a pip utility:
 
 .. code:: python
@@ -33,7 +41,7 @@ Testing Model Fitting
 
 Version on master branch gets automatically tested with Travis services.
 To test the code yourself, you will need to have ``pytest`` installed and run
-the command:
+the following command inside the `brian2modelfitting` root directory:
 
 
 .. code:: python
diff --git a/docs_sphinx/introduction/tutorial_sbi.rst b/docs_sphinx/introduction/tutorial_sbi.rst
new file mode 100644
index 0000000..3dad431
--- /dev/null
+++ b/docs_sphinx/introduction/tutorial_sbi.rst
@@ -0,0 +1,4 @@
+Tutorial: Inferencer
+====================
+
+tba
\ No newline at end of file

From 50f7192fa5960a459e9a5653f591298a30c46016 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 15:18:42 +0200
Subject: [PATCH 086/121] small typo fix

---
 docs_sphinx/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs_sphinx/index.rst b/docs_sphinx/index.rst
index 4c466ec..d87651d 100644
--- a/docs_sphinx/index.rst
+++ b/docs_sphinx/index.rst
@@ -22,7 +22,7 @@ computed.
 By default, the toolbox supports a range of global derivative-free
 optimization methods, that include popular methods for model fitting:
 differential evolution, particle swarm optimization and covariance matrix
-adaptation (provided by the ``Nevergrad``, a gradient-free optimization
+adaptation (provided by the ``nevergrad``, a gradient-free optimization
 platform) as well as Bayesian optimization for black box functions (provided
 by ``scikit-optimize``, a sequential model-based optimization library). On the
 other hand, simulation-based inference is the process of finding parameters of

From f086c7045393e0a639d349ad8cbabfe95954c674 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 15:20:02 +0200
Subject: [PATCH 087/121] placeholder for detailed info on Inferencer

---
 docs_sphinx/inferencer/index.rst | 4 ++++
 1 file changed, 4 insertions(+)
 create mode 100644 docs_sphinx/inferencer/index.rst

diff --git a/docs_sphinx/inferencer/index.rst b/docs_sphinx/inferencer/index.rst
new file mode 100644
index 0000000..67980ff
--- /dev/null
+++ b/docs_sphinx/inferencer/index.rst
@@ -0,0 +1,4 @@
+Inferencer
+==========
+
+tba
\ No newline at end of file

From 01ce180c16f3ca9900b6e6f5fa479053969d4db6 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 15:26:08 +0200
Subject: [PATCH 088/121] finilize the front page

---
 docs_sphinx/index.rst | 18 +++++++++---------
 1 file changed, 9 insertions(+), 9 deletions(-)

diff --git a/docs_sphinx/index.rst b/docs_sphinx/index.rst
index d87651d..644b7d8 100644
--- a/docs_sphinx/index.rst
+++ b/docs_sphinx/index.rst
@@ -26,16 +26,16 @@ adaptation (provided by the ``nevergrad``, a gradient-free optimization
 platform) as well as Bayesian optimization for black box functions (provided
 by ``scikit-optimize``, a sequential model-based optimization library). On the
 other hand, simulation-based inference is the process of finding parameters of
-a simulator from observations by taking a Bayesian approach via sequential
-neural posterior estimation, likelihood estimation or ration estimation
-(provided by the ``sbi``), where neural densitiy estimator, a deep neural
-network allowing probabilistic association between the data and underlying
-parameter space, is trained. After the network is trained, the approximated
-posterior distribution is available.
+a simulator from observations by taking a Bayesian approach, in our case, via
+sequential neural posterior estimation, likelihood estimation or ratio
+estimation (provided by the ``sbi``), where neural densitiy estimator, a deep
+neural network allowing probabilistic association between the data and
+underlying parameter space, is trained. After the network is trained, the
+approximated posterior distribution is available.
 
-Just like Brian itself, the `brian2modelfitting` toolbox is designed to be
-easy to use and to save time through automatic parallelization of the
-simulations using code generation.
+Just like Brian 2 simulator itself, the `.brian2modelfitting` toolbox is
+designed to be easy to use and to save time through automatic parallelization
+of the simulations using code generation.
 
 
 Contents

From 0e936cb17334480d445fe1a6e38f3bda829c1ad8 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 14 Aug 2021 15:31:32 +0200
Subject: [PATCH 089/121] double instead of single backticks

---
 docs_sphinx/introduction/introduction.rst | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/docs_sphinx/introduction/introduction.rst b/docs_sphinx/introduction/introduction.rst
index b0b8041..a849dc8 100644
--- a/docs_sphinx/introduction/introduction.rst
+++ b/docs_sphinx/introduction/introduction.rst
@@ -17,7 +17,7 @@ same time.
 It also allows the possiblity of simultaneous fitting/inferencing by taking
 into account multiple output variables including spike trains.
 
-In following documentation we assume that ``brian2modelfitting`` has been
+In following documentation we assume that `.brian2modelfitting` has been
 installed and imported as follows:
 
 .. code:: python
@@ -28,10 +28,9 @@ installed and imported as follows:
 Installation
 ------------
 
-To install the toolbox alongside Brian 2 simulator, use `pip` as follows:
-a pip utility:
+To install the toolbox alongside Brian 2 simulator, use ``pip`` as follows:
 
-.. code:: python
+.. code::
 
   pip install brian2modelfitting
 
@@ -41,9 +40,9 @@ Testing Model Fitting
 
 Version on master branch gets automatically tested with Travis services.
 To test the code yourself, you will need to have ``pytest`` installed and run
-the following command inside the `brian2modelfitting` root directory:
+the following command inside the `.brian2modelfitting` root directory:
 
 
-.. code:: python
+.. code::
 
     pytest

From 50c61971b1ef85f2b3624750a22ccbb08b663666 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sun, 15 Aug 2021 12:07:59 +0200
Subject: [PATCH 090/121] enable gpu support

---
 brian2modelfitting/inferencer.py | 99 +++++++++++++++++++++++---------
 1 file changed, 72 insertions(+), 27 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index ed99ae7..01eced8 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -1,5 +1,6 @@
 from numbers import Number
 from typing import Mapping
+import warnings
 
 from brian2.core.functions import Function
 from brian2.core.namespace import get_local_namespace
@@ -129,7 +130,7 @@ def calc_prior(param_names, **params):
     Return
     ------
     sbi.utils.torchutils.BoxUniform
-        ``sbi`` compatible object that contains a uniform prior
+        ``sbi``-compatible object that contains a uniform prior
         distribution over a given set of parameters.
     """
     for param_name in param_names:
@@ -151,9 +152,7 @@ class Inferencer(object):
     It offers an interface similar to that of the `.Fitter` class but
     instead of fitting, neural density estimator is trained using a
     generative model which ultimately provides the posterior
-    distribution over free parameters.
-    This class serves as a wrapper for ``sbi`` library for inferencing
-    posterior over unknown parameters of a given model.
+    distribution over the unknown parameters.
 
     Parameters
     ----------
@@ -309,6 +308,7 @@ def __init__(self, dt, model, input, output, features=None, method=None,
         self.posterior = None
         self.theta = None
         self.x = None
+        self.sbi_device = 'cpu'
 
     @property
     def n_neurons(self):
@@ -574,7 +574,7 @@ def load_summary_statistics(self, f):
         return (theta, x)
 
     def init_inference(self, inference_method, density_estimator_model, prior,
-                       **inference_kwargs):
+                       sbi_device='cpu', **inference_kwargs):
         """Return instantiated inference object.
 
         Parameters
@@ -587,13 +587,28 @@ def init_inference(self, inference_method, density_estimator_model, prior,
             ``linear``, ``mlp``, ``resnet`` for SNRE.
         prior : sbi.utils.BoxUniform
             Uniformly distributed prior over given parameters.
+        sbi_device : str, optional
+            Device on which the ``sbi`` will operate. By default this
+            is set to CPU and it is advisable to remain so for most
+            cases. In cases where the user provide custom embedding
+            network through ``inference_kwargs`` argument, which will
+            be trained more efficiently by using GPU, device should be
+            set accordingly.
         inference_kwargs : dict, optional
-            Additional keyword arguments for
-            ``sbi.utils.get_nn_models.posterior_nn`` method. The user
-            is free to provide own embedding network to learn features
-            from potentially high-dimensional simulation outputs. By
-            default multi-layer perceptron is used if no user defined
-            embedding network is provided.
+            Additional keyword arguments for different density
+            estimator builder functions:
+            ``sbi.utils.get_nn_models.posterior_nn`` for SNPE,
+            ``sbi.utils.get_nn_models.classifier_nn`` for SNRE, and
+            ``sbi.utils.get_nn_models.likelihood_nn`` for SNLE. For
+            details check the ``sbi`` documentation. The most important
+            additional keyword augment the user is able to pass is
+            custom embedding network to learn features from potentially
+            high-dimensional simulation outputs. By default multi-layer
+            perceptron is used if no custom embedding network is
+            provided. For SNPE and SNLE,the user can pass an embedding
+            network for simulation outputs, while for SNRE, the user
+            may pass two embedding networks, one for parameters and one
+            for simulation outputs, respectively.
 
         Returns
         -------
@@ -616,9 +631,26 @@ def init_inference(self, inference_method, density_estimator_model, prior,
             else:
                 density_estimator_builder = classifier_nn(
                     model=density_estimator_model, **inference_kwargs)
-        inference = inference_method_fun(prior, density_estimator_builder,
-                                         device='cpu',
-                                         show_progress_bars=True)
+
+        sbi_device = str.lower(sbi_device)
+        if sbi_device in ['cuda', 'gpu']:
+            if torch.cuda.is_available():
+                sbi_device = 'gpu'
+                self.sbi_device = 'cuda'
+            else:
+                logger.warn(f'Device {sbi_device} is not available.'
+                            ' Falling back to CPU.')
+                sbi_device = 'cpu'
+                self.sbi_device = sbi_device
+        else:
+            sbi_device = 'cpu'
+            self.sbi_device = sbi_device
+
+        with warnings.catch_warnings():
+            warnings.filterwarnings('ignore')
+            inference = inference_method_fun(prior, density_estimator_builder,
+                                             device=sbi_device,
+                                             show_progress_bars=True)
         return inference
 
     def train(self, inference, theta, x, *args, **train_kwargs):
@@ -646,7 +678,14 @@ def train(self, inference, theta, x, *args, **train_kwargs):
             True inside the `train_kwargs`.
         train_kwargs : dict, optional
             Additional keyword arguments for ``train`` method of
-            ``sbi.inference.NeuralInference`` object.
+            ``sbi.inference.NeuralInference`` object. The user is able
+            to gain full control over training process by tuning
+            hyperparameters, i.e. batch size (by specifiying
+            ``training_batch_size`` argument), learning rate
+            (``learning_rate``), validation fraction
+            (``validation_fraction``), number of training epochs
+            (``max_num_epochs``), etc. For details, check the ``sbi``
+            documentation.
 
         Returns
         -------
@@ -667,7 +706,8 @@ def build_posterior(self, inference, **posterior_kwargs):
             simulation outputs prepared for training.
         posterior_kwargs : dict, optional
             Additional keyword arguments for ``build_posterior`` method
-            of ``sbi.inference.NeuralInference`` object.
+            in ``sbi.inference.NeuralInference`` classes. For details,
+            check the ``sbi`` documentation.
 
         Returns
         -------
@@ -710,7 +750,7 @@ def infer_step(self, proposal, inference,
         sbi.inference.NeuralPosterior
             Trained posterior.
         """
-        # extract the training data and make adjustments for ``sbi``
+        # extract the training data and make adjustments for the ``sbi``
         if theta is None:
             if n_samples is None:
                 raise ValueError('Either provide `theta` or `n_samples`.')
@@ -719,7 +759,7 @@ def infer_step(self, proposal, inference,
         self.theta = theta
         theta = torch.tensor(theta, dtype=torch.float32)
 
-        # extract the summary statistics and make adjustments for ``sbi``
+        # extract the summary statistics and make adjustments for the ``sbi``
         if x is None:
             if n_samples is None:
                 raise ValueError('Either provide `x` or `n_samples`.')
@@ -741,7 +781,7 @@ def infer_step(self, proposal, inference,
     def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
               inference_method='SNPE', density_estimator_model='maf',
               inference_kwargs={}, train_kwargs={}, posterior_kwargs={},
-              restart=False, **params):
+              restart=False, device='cpu', **params):
         """Return the trained posterior.
 
         If ``theta`` and ``x`` are not provided, ``n_samples`` has to
@@ -770,18 +810,23 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
             ``mdn``, ``made``, ``maf``, ``nsf`` for SNPE and SNLE, or
             ``linear``, ``mlp``, ``resnet`` for SNRE.
         inference_kwargs : dict, optional
-            Additional keyword arguments for the inferencer method
-            definition.
+            Additional keyword arguments for the `.init_inference`.
         train_kwargs : dict, optional
-            Additional keyword arguments for training the posterior
-            estimator.
+            Additional keyword arguments for `.train`.
         posterior_kwargs : dict, optional
-            Additional keyword arguments for builing the posterior.
+            Additional keyword arguments for `.build_posterior`.
         restart : bool, optional
             When the method is called for a second time, set to True if
             amortized inference should be performed. If False,
             multi-round inference with the existing posterior will be
             performed.
+        sbi_device : str, optional
+            Device on which the ``sbi`` will operate. By default this
+            is set to CPU and it is advisable to remain so for most
+            cases. In cases where the user provide custom embedding
+            network through ``inference_kwargs`` argument, which will
+            be trained more efficiently by using GPU, device should be
+            set accordingly.
         params : dict
             Bounds for each parameter. Keys should correspond to names
             of parameters as defined in the model equaions, values
@@ -812,7 +857,7 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
             # initialize prior
             prior = self.init_prior(**params)
 
-            # extract the training data and make adjustments for ``sbi``
+            # extract the training data
             if theta is None:
                 if n_samples is None:
                     raise ValueError('Either provide `theta` or `n_samples`.')
@@ -820,7 +865,7 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
                     theta = self.generate_training_data(n_samples, prior)
             self.theta = theta
 
-            # extract the summary statistics and make adjustments for ``sbi``
+            # extract the summary statistics
             if x is None:
                 if n_samples is None:
                     raise ValueError('Either provide `x` or `n_samples`.')
@@ -831,7 +876,7 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
             # initialize inference object
             self.inference = self.init_inference(inference_method,
                                                  density_estimator_model,
-                                                 prior,
+                                                 prior, device,
                                                  **inference_kwargs)
 
             # args for SNPE in `.train`

From 2cb7886d68a559b3dc8501e52c16998703fa91b3 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sun, 15 Aug 2021 15:45:12 +0200
Subject: [PATCH 091/121] add technical details on sbi

---
 docs_sphinx/inferencer/index.rst | 72 +++++++++++++++++++++++++++++++-
 1 file changed, 71 insertions(+), 1 deletion(-)

diff --git a/docs_sphinx/inferencer/index.rst b/docs_sphinx/inferencer/index.rst
index 67980ff..d2c437b 100644
--- a/docs_sphinx/inferencer/index.rst
+++ b/docs_sphinx/inferencer/index.rst
@@ -1,4 +1,74 @@
 Inferencer
 ==========
 
-tba
\ No newline at end of file
+Unlike more traditional inverse identification procedures that rely either on
+gradient or gradient-free methods, the ``Inferencer`` class supports
+simulation-based inference that has been established as a powerful alternative
+approach.
+
+The simulation-based inference is data-driven procedure supported by the
+`sbi <https://www.mackelab.org/sbi/>`_, ``PyTorch``-based toolbox by Macke lab.
+
+In general, this method yields twofold improvement over point-estimate fitting
+procedures:
+
+#. Simulation-based inference acts as if the actual statistical inference is
+   performed, even in cases of extremly complex models with untractable
+   likelihood function. Thus, instead of returning a single set of optimal
+   parameters, it results in the approximated posterior distribution over
+   unknown parameters. This is achieved by training a neural density estimator,
+   details of which will be explained in depth later in the documentation.
+#. Simulation-based inference uses prior system knowledge sparsely, using 
+   only the most important features to identify mechanistic models that are 
+   consistent with the recordings. This is achieved either by providing the 
+   predifend set of features, or by automatically extraciting summary features 
+   by using deep neural networks which is trained in parallel with neural 
+   density estimator. 
+
+The ``Inferencer`` class, in its core, is a fancy wrapper around the ``sbi``
+package where the focus is put on inferring the unknown parameters of the
+single-cell neuron models defined in Brian 2 simulator.
+
+Neural density estimator
+------------------------
+
+There are three main estimation techniques supported in ``sbi`` that the user
+can take the full control over seamlesly by using the ``Inferencer``:
+
+#. sequential neural posterior estimation (SNPE)
+#. sequential neural likelihood estimation (SNLE)
+#. sequential neural ratio estimator (SNRE)
+
+Simulation-based inference workflow
+----------------------------------- 
+
+The inferencer procedure is defined via three main steps:
+
+#. step
+   Prior over unknown parameters needs to be defined, where the simplest
+   choice would be uniform distribution given lower and upper bound
+   (currently, this is only prior distribution supported through
+   ``brian2modelfitting`` toolbox).
+   After that, simulated data are generated given a mechanistic model with
+   unknown parameters set as constants.
+   Instead of taking the full output of the model, the neural network takes
+   in summary data statistics of the output, e.g. instead of voltage trace as
+   the output from a neuron model, we would feed a neural network with
+   relevant electrophysiology features that outline the gist of the output
+   sufficiently well.
+#. step
+   A neural network learns association between the summary data statistics
+   and unknown parameters (given the prior distribution over parameters).
+   The learning method is heavily dependent on the choice of the inference
+   technique.
+#. step
+   The trained neural network is applied to the empirical data to infer
+   posterior distribution over unknown parameters.  Optionally, this process
+   can be repeated by using the trained posterior distribution over parameters
+   as the prior distribution proposal for a refined optimization.
+
+Implementation
+--------------
+
+Go to :doc:`the tutorial section <../introduction/tutorial_sbi.rst>` for the
+in-depth implementation analysis.
\ No newline at end of file

From 9c538881667a0b1fbab5a86b80289455c88fe9e2 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sun, 15 Aug 2021 15:53:01 +0200
Subject: [PATCH 092/121] fix typos

---
 docs_sphinx/inferencer/index.rst | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs_sphinx/inferencer/index.rst b/docs_sphinx/inferencer/index.rst
index d2c437b..a4e3ff3 100644
--- a/docs_sphinx/inferencer/index.rst
+++ b/docs_sphinx/inferencer/index.rst
@@ -44,7 +44,7 @@ Simulation-based inference workflow
 
 The inferencer procedure is defined via three main steps:
 
-#. step
+#. step.
    Prior over unknown parameters needs to be defined, where the simplest
    choice would be uniform distribution given lower and upper bound
    (currently, this is only prior distribution supported through
@@ -56,12 +56,12 @@ The inferencer procedure is defined via three main steps:
    the output from a neuron model, we would feed a neural network with
    relevant electrophysiology features that outline the gist of the output
    sufficiently well.
-#. step
+#. step.
    A neural network learns association between the summary data statistics
    and unknown parameters (given the prior distribution over parameters).
    The learning method is heavily dependent on the choice of the inference
    technique.
-#. step
+#. step.
    The trained neural network is applied to the empirical data to infer
    posterior distribution over unknown parameters.  Optionally, this process
    can be repeated by using the trained posterior distribution over parameters
@@ -70,5 +70,5 @@ The inferencer procedure is defined via three main steps:
 Implementation
 --------------
 
-Go to :doc:`the tutorial section <../introduction/tutorial_sbi.rst>` for the
-in-depth implementation analysis.
\ No newline at end of file
+Go to `the tutorial section <https://brian2modelfitting.readthedocs.io/en/stable/introduction/tutorial_sbi.html>`_
+in  for the in-depth implementation analysis.
\ No newline at end of file

From 598d9a9fc29342ecae1a896ca5f0b2dde1bd7974 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sun, 15 Aug 2021 18:10:07 +0200
Subject: [PATCH 093/121] update howitworks page

---
 docs_sphinx/introduction/howitworks.rst | 157 +++++++++++++++++++++++-
 1 file changed, 156 insertions(+), 1 deletion(-)

diff --git a/docs_sphinx/introduction/howitworks.rst b/docs_sphinx/introduction/howitworks.rst
index 01769b6..c4c8bfe 100644
--- a/docs_sphinx/introduction/howitworks.rst
+++ b/docs_sphinx/introduction/howitworks.rst
@@ -111,4 +111,159 @@ Remarks
 Simulation-based inference
 ==========================
 
-tba
\ No newline at end of file
+The `~brian2modelfitting.inferencer.Inferencer` class has to be initialized
+within the script that will perform a simulation-based inference procedure.
+
+Initialization of `~brian2modelfitting.inferencer.Inferencer` requires:
+
+- ``dt`` - the time step in Brian 2 units.
+- ``model`` - single cell model equations, defined as either string or as 
+  ``brian2.Equation`` object.
+- ``input`` - a dictionary where key corresponds to the name of the input
+  variable as defined in ``model`` and value corresponds to an array of
+  input traces.
+- ``output`` - a dictionary where key corresponds to the name of the output
+  variable as defined in ``model`` and value corresponds to an array of
+  recorded traces and/or spike trains.
+
+.. code:: python
+
+  inferencer = Inferencer(dt=0.1*ms, model=eqs,
+                          input={'I': inp_traces*amp},
+                          output={'v': out_traces*mV})
+
+Optionally, arguments to be passed to the constructor are:
+
+- ``features`` - a dictionary of callables that take the voltage
+  trace and/or spike trains and output summary statistics. Keys correspond to
+  output variable names, while values are lists of callables. If features are
+  not provided, automatic feature extraction will be performed either by using
+  the default multi-layer perceptron or by using the user-provided embedding
+  network.
+- ``method`` - a string that defines an integration method.
+- ``threshold`` - optional string that defines the condition which produces
+  spikes. It should be a single line boolean expression.
+- ``reset`` - an optional (multi-line) string that that holds the code to
+  execute on reset.
+- ``refractory`` - can be either Boolean expression or string. Defines either
+  the length of the refractory period (e.g., ``2*ms``), a string expression
+  that evaluates to the length of the refractory period after each spike,
+  e.g., ``'(1 + rand())*ms'``, or a string expression evaluating to a boolean
+  value, given the condition under which the neuron stays refractory after a
+  spike, e.g., ``'v > -20*mV'``.
+- ``param_init`` - a dictionary of state variables to be initialized with
+  respective values, i.e., initial conditions.
+
+.. code:: python
+
+  inferencer = Inferencer(dt=dt, model=eqs_inf,
+                          input={'I': inp_trace*amp},
+                          output={'v': out_traces*mV},
+                          features={'v': voltage_feature_list},
+                          method='exponential_euler',
+                          threshold='v > -50*mV',
+                          reset='v = -70*mV',
+                          param_init={'v': -70*mV})
+
+Inference
+---------
+
+After the `~brian2modelfitting.inferencer.Inferencer` class is
+instantiated, the simplest and the most convenient way to start with the
+inferencer procedure is by calling `~brian2modelfitting.inferencer.Inferencer.infer`
+method on `~brian2modelfitting.inferencer.Inferencer` object.
+
+In the nutshell, infer method returns the trained neural posterior object,
+which may or may not be used by the user, but it has to exist. There are two
+possible approaches:
+
+- amortized inference
+- multi-round inference
+
+If the number of inference rounds is 1, then amortized inference will be
+performed. Otherwise if the number of inference rounds is 2 or above, the
+focused multi-round inference will be performed. Multi-round inference,
+unlike the amortized one, is focused on a particular observation, where in
+each new round of inference, samples are drawn from the posterior distribution
+conditioned exactly by this observation. This process can be repeated
+aribtrarily many times to get increasingly better approximations of the the
+posterior distribution.
+
+The infer method requires:
+
+- ``n_samples`` - the number of samples from which the neural posterior will
+  be learnt.
+  
+or:
+
+- ``theta`` -  sampled prior.
+- and ``x`` - summary statistics.
+
+along with the:
+
+- ``params`` - a dictionary of bounds for each free parameter defined in the
+  ``model``. Keys should correspond to names of parameters as defined in the
+  model equations, values are lists with lower and upper bounds with
+  quantities of respective parameter. 
+
+The simplest way to start the inference process is by calling:
+
+.. code:: python
+
+  posterior = inferencer.infer(n_samples=1000,
+                               gl=[10*nS, 100*nS],
+                               C=[0.1*nF, 10*nF])
+
+Optionally, user can defined the following arguments:
+
+- ``n_rounds`` - if it is set to 1, amortized inference will be performed.
+  Otherwise, if ``n_rounds`` is integer larger than 1, multi-round inference
+  will be performed. This is only valid if the posterior has not yet been
+  defined. Otherwise, if this method is called after the posterior has already
+  been built, multi-round inference is performed, e.g. repeated calling of
+  ``~brian2modelfitting.inferencer.Inferencer.infer`` method or manually
+  building the posterior by approaching the inference with flexible inference.
+- ``inference_method`` - either SNPE, SNLE or SNRE.
+- ``density_estimator_model`` - string that defines the type of density
+  estimator to be created. Either ``mdn``, ``made``, ``maf``, ``nsf`` for SNPE
+  and SNLE, or ``linear``, ``mlp``, ``resnet`` for SNRE.
+- ``inference_kwargs`` - a dictionary that holds additional keyword arguments
+  for the `~brian2modelfitting.inferencer.Inferencer.init_inference`.
+- ``train_kwargs`` - a dictionary that holds additional keyword arguments for
+  `~brian2modelfitting.inferencer.Inferencer.train`.
+- ``posterior_kwargs`` - a dictionary that holds additional keyword arguments
+  for `~brian2modelfitting.inferencer.Inferencer.build_posterior`.
+- ``restart`` - when the method is called for a second time, set to True if
+  amortized inference should be performed. If False, multi-round inference
+  with the existing posterior will be performed.
+- ``sbi_device`` a string that defines the device on which the ``sbi`` and
+  subseqently the ``torch`` will operate. By default this is set to ``cpu``
+  and it is advisable to remain so for most cases. In cases where the user
+  provides custom embedding network through ``inference_kwargs`` argument,
+  which will be trained more efficiently by using GPU, device should be set
+  accordingly to ``gpu``.
+
+A bit more comprehensive specification of the infer call is showcased below:
+
+.. code:: python
+
+  posterior = inferencer.infer(n_samples=5_000,
+                               n_rounds=3,
+                               inference_method='SNPE',
+                               density_estimator_model='mdn',
+                               restart=True,
+                               sbi_device='cpu',
+                               gl=[10*nS, 100*nS],
+                               C=[0.1*nF, 10*nF])
+
+Remarks
+-------
+
+For a better understanding, please go through examples that go step-by-step
+through the entire process. Currently, there are two tutorials: the one that
+is covering `simple interface <https://brian2modelfitting.readthedocs.io/en/stable/examples/hh_sbi_simple.html>`_,
+appropriate for the regular user, and the one that goes a bit more in-depth by
+using `flexible interface <https://brian2modelfitting.readthedocs.io/en/stable/examples/hh_sbi_flex.html>`_,
+and shows how to manually go through the process of inference, storing/loading
+the training data and the trained neural density estimator, parameter space
+visualization, conditioning, etc.
\ No newline at end of file

From 98666a63386ccbecf1e42f30e79f3bb16aa349de Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Mon, 16 Aug 2021 11:16:36 +0200
Subject: [PATCH 094/121] update docstrings

---
 brian2modelfitting/inferencer.py | 314 +++++++++++++++++--------------
 1 file changed, 169 insertions(+), 145 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 01eced8..f57e0e9 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -92,11 +92,12 @@ def get_param_dict(param_values, param_names, n_values):
 
     Parameters
     ----------
-    param_values : iterable
-        Iterable of size (``n_samples``, ``len(param_names)``)
-        containing parameter values.
-    param_names : iterable
-        Iterable containing parameter names
+    param_values : numpy.ndarray
+        Parameter values in a 2-dimensional array with the number of
+        rows corresponding to a number of samples and the number of
+        columns corresponding to ``len(param_names).
+    param_names : list
+        List containing parameter names.
     n_values : int
         Total number of given values for a single parameter.
 
@@ -116,16 +117,17 @@ def get_param_dict(param_values, param_names, n_values):
 def calc_prior(param_names, **params):
     """Return the prior distribution over given parameters.
 
-    N.B. The only currently supported prior distribution is
-    the multi-dimensional uniform distribution defined on a box.
+    Note that the only currently supported prior distribution is the
+    multi-dimensional uniform distribution defined on a box.
 
     Parameters
     ----------
-    param_names : iterable
-        Iterable containing parameter names.
+    param_names : list
+        List containing parameter names.
     params : dict
-        Dictionary with keys that correspond to parameter names, values
-        are a single dimensional lists or arrays.
+        Dictionary with keys that correspond to parameter names, and
+        the respective values are 2-element lists that hold the upper
+        and the lower bound of a distribution.
 
     Return
     ------
@@ -147,12 +149,12 @@ def calc_prior(param_names, **params):
 
 
 class Inferencer(object):
-    """Class for simulation-based inference.
+    """Class for a simulation-based inference.
 
     It offers an interface similar to that of the `.Fitter` class but
-    instead of fitting, neural density estimator is trained using a
+    instead of fitting, a neural density estimator is trained using a
     generative model which ultimately provides the posterior
-    distribution over the unknown parameters.
+    distribution over unknown free parameters.
 
     Parameters
     ----------
@@ -161,25 +163,26 @@ class Inferencer(object):
     model : str or brian2.equations.equations.Equations
         Single cell model equations.
     input : dict
-        Input traces in dictionary format where key corresponds to the
-        name of the input variable as defined in ``model`` and value
-        corresponds to an array of input traces.
+        Input traces in the dictionary format where key corresponds to
+        the name of the input variable as defined in ``model``, and
+        value corresponds to an array of input traces.
     output : dict
         Dictionary of recorded (or simulated) output data traces, where
         key corresponds to the name of the output variable as defined
-        in ``model`` and value corresponds to a an array of recorded
+        in ``model``, and value corresponds to an array of recorded
         traces.
     features : dict, optional
-        Dictionary of callables that take the 1-dimensional voltage
-        trace and output summary statistics. Keys correspond to output
-        variable names, while values are lists of callables.
-        If features are not provided, automatic feature extraction will
-        be performed either by using the default multi-layer perceptron
-        or by using the user-provided embedding network.
+        Dictionary of callables that take a 1-dimensional voltage
+        trace or a spike train and output summary statistics. Keys
+        correspond to output variable names, while values are lists of
+        callables. If ``features`` are set to None, automatic feature
+        extraction process will occur instead either by using the
+        default multi-layer perceptron or by using the custom embedding
+        network.
     method : str, optional
         Integration method.
     threshold : str, optional
-        The condition which produces spikes. Should be a single line
+        The condition which produces spikes. It should be a single line
         boolean expression.
     reset : str, optional
         The (possibly multi-line) string with the code to execute on
@@ -193,7 +196,7 @@ class Inferencer(object):
         e.g., ``'v > -20*mV'``.
     param_init : dict, optional
         Dictionary of state variables to be initialized with respective
-        values.
+        values, i.e., initial conditions for the model.
     """
     def __init__(self, dt, model, input, output, features=None, method=None,
                  threshold=None, reset=None, refractory=False,
@@ -317,8 +320,8 @@ class while generating data for training the neural density
         estimator.
 
         Unlike the `.Fitter` class, `.Inferencer` does not take the
-        total number of samples in the constructor. Thus, this property
-        becomes available only after the simulation is performed.
+        total number of samples directly in the constructor. Thus, this
+        property becomes available only after the simulation is performed.
 
         Parameters
         ----------
@@ -331,7 +334,7 @@ class while generating data for training the neural density
         """
         if self.n_samples is None:
             raise ValueError('Number of samples have not been yet defined.'
-                             'Call `generate_training_data` method first.')
+                             ' Call `generate_training_data` method first.')
         return self.n_traces * self.n_samples
 
     def setup_simulator(self, network_name, n_neurons, output_var, param_init,
@@ -405,17 +408,20 @@ def setup_simulator(self, network_name, n_neurons, output_var, param_init,
         return simulator
 
     def init_prior(self, **params):
-        """Return the prior uniform distribution over parameters.
+        """Return the prior uniform distribution over the parameters.
 
         Parameters
         ----------
         params : dict
-            Bounds for each parameter.
+            Dictionary with keys that correspond to parameter names,
+            and the respective values are 2-element lists that hold
+            the upper and the lower bound of a distribution.
 
         Returns
         -------
-        sbi.utils.BoxUniform
-            Uniformly distributed prior over given parameters.
+        sbi.utils.torchutils.BoxUniform
+            ``sbi``-compatible object that contains a uniform prior
+            distribution over a given set of parameters.
         """
         for param in params:
             if param not in self.param_names:
@@ -438,7 +444,9 @@ def generate_training_data(self, n_samples, prior):
         Returns
         -------
         numpy.ndarray
-            Sampled prior of shape (``n_samples``, -1).
+            Sampled prior with the number of rows that corresponds to
+            the ``n_samples``, while the number of columns depends on
+            the number of free parameters.
         """
         # set n_samples to class variable to be able to call self.n_neurons
         self.n_samples = n_samples
@@ -449,14 +457,14 @@ def generate_training_data(self, n_samples, prior):
         return theta
 
     def extract_summary_statistics(self, theta, level=0):
-        """Return summary statistics for training the neural density
-        estimator.
+        """Return the summary statistics for the process of training
+        of the neural density estimator.
 
         Parameters
         ----------
         theta : numpy.ndarray
-            Sampled prior with ``n_samples`` rows, while then umber of
-            columns is equal to the number of free parameters.
+            Sampled prior with ``n_samples`` rows, and the number of
+            columns corresponds to the number of free parameters.
         level : int, optional
             How far to go back to get the locals/globals.
 
@@ -517,8 +525,8 @@ def extract_summary_statistics(self, theta, level=0):
         return x
 
     def save_summary_statistics(self, f, theta=None, x=None):
-        """Save sampled prior, and extracted summary statistics into a
-        single file in compressed ``.npz`` format.
+        """Save sampled prior and the extracted summary statistics into
+        a single compressed ``.npz`` file.
 
         Parameters
         ----------
@@ -551,8 +559,8 @@ def save_summary_statistics(self, f, theta=None, x=None):
         np.savez_compressed(f, theta=t, x=x)
 
     def load_summary_statistics(self, f):
-        """Load samples from a prior and extracted summary statistics
-        from a compressed ``.npz`` file.
+        """Load samples from a prior and the extracted summary
+        statistics from a compressed ``.npz`` file.
 
         Parameters
         ----------
@@ -563,7 +571,7 @@ def load_summary_statistics(self, f):
         Returns
         -------
         tuple
-            Sampled prior and summary statistics arrays.
+            Sampled prior and the summary statistics arrays.
         """
         loaded = np.load(f, allow_pickle=True)
         if set(loaded.files) == {'theta', 'x'}:
@@ -571,7 +579,7 @@ def load_summary_statistics(self, f):
             x = loaded['x']
         self.theta = theta
         self.x = x
-        return (theta, x)
+        return theta, x
 
     def init_inference(self, inference_method, density_estimator_model, prior,
                        sbi_device='cpu', **inference_kwargs):
@@ -580,35 +588,37 @@ def init_inference(self, inference_method, density_estimator_model, prior,
         Parameters
         ----------
         inference_method : str
-            Inference method. Either of SNPE, SNLE or SNRE.
+            Inference method. Either SNPE, SNLE or SNRE.
         density_estimator_model : str
             The type of density estimator to be created. Either
             ``mdn``, ``made``, ``maf``, ``nsf`` for SNPE and SNLE, or
             ``linear``, ``mlp``, ``resnet`` for SNRE.
         prior : sbi.utils.BoxUniform
-            Uniformly distributed prior over given parameters.
+            Uniformly distributed prior over free parameters.
         sbi_device : str, optional
             Device on which the ``sbi`` will operate. By default this
-            is set to CPU and it is advisable to remain so for most
-            cases. In cases where the user provide custom embedding
+            is set to ``cpu`` and it is advisable to remain so for most
+            cases. In cases where the user provides custom embedding
             network through ``inference_kwargs`` argument, which will
             be trained more efficiently by using GPU, device should be
-            set accordingly.
+            set accordingly to either ``gpu`` or ``cuda``.
         inference_kwargs : dict, optional
             Additional keyword arguments for different density
             estimator builder functions:
             ``sbi.utils.get_nn_models.posterior_nn`` for SNPE,
             ``sbi.utils.get_nn_models.classifier_nn`` for SNRE, and
             ``sbi.utils.get_nn_models.likelihood_nn`` for SNLE. For
-            details check the ``sbi`` documentation. The most important
-            additional keyword augment the user is able to pass is
-            custom embedding network to learn features from potentially
+            details check the official ``sbi`` documentation. A single
+            highlighted keyword argument is a custom embedding network
+            that serves a purpose to learn features from potentially
             high-dimensional simulation outputs. By default multi-layer
             perceptron is used if no custom embedding network is
-            provided. For SNPE and SNLE,the user can pass an embedding
-            network for simulation outputs, while for SNRE, the user
-            may pass two embedding networks, one for parameters and one
-            for simulation outputs, respectively.
+            provided. For SNPE and SNLE, the user may pass an embedding
+            network for simulation outputs through ``embedding_net``
+            argument, while for SNRE, the user may pass two embedding
+            networks, one for parameters through
+            ``embedding_net_theta`` argument, and the other for
+            simulation outputs through ``embedding_net_x`` argument.
 
         Returns
         -------
@@ -654,38 +664,38 @@ def init_inference(self, inference_method, density_estimator_model, prior,
         return inference
 
     def train(self, inference, theta, x, *args, **train_kwargs):
-        """Return trained neural inference object.
+        """Return the trained neural inference object.
 
         Parameters
         ----------
         inference : sbi.inference.NeuralInference
             Instantiated inference object with stored paramaters and
-            simulation outputs prepared for training.
+            simulation outputs prepared for the training process.
         theta : torch.tensor
             Sampled prior.
         x : torch.tensor
             Summary statistics.
         args : tuple, optional
-            Contains a uniformly distributed sbi.utils.BoxUniform
-            proposal. Used only for SNPE, for SNLE and SNRE,
-            ``proposal`` should not be passed to ``append_simulations``
-            method, thus ``args`` should not be passed. The args should
-            be provided only if the parameters were not sampled from
-            the prior, e.g., during the multi-round inference.
-            For SNLE and SNRE, args can hold the number of round from
-            which the data is stemmed from. Round 0 means from prior.
-            This is used only if the `discard_prior_samples` are set to
-            True inside the `train_kwargs`.
+            Contains a uniformly distributed proposal. Used only for
+            SNPE, for SNLE and SNRE, proposal should not be passed to
+            inference object, thus ``args`` should not be passed. The
+            additional arguments should be passed only if the
+            parameters were not sampled from the prior, e.g., during
+            the multi-round inference. For SNLE and SNRE, this can be
+            the number of round from which the data is stemmed from,
+            e.g., 0 means from the prior. This is used only if the
+            ``discard_prior_samples`` is set to True inside the
+            ``train_kwargs``.
         train_kwargs : dict, optional
-            Additional keyword arguments for ``train`` method of
-            ``sbi.inference.NeuralInference`` object. The user is able
-            to gain full control over training process by tuning
-            hyperparameters, i.e. batch size (by specifiying
-            ``training_batch_size`` argument), learning rate
-            (``learning_rate``), validation fraction
-            (``validation_fraction``), number of training epochs
-            (``max_num_epochs``), etc. For details, check the ``sbi``
-            documentation.
+            Additional keyword arguments for ``train`` method in the
+            ``sbi.inference.NeuralInference`` class. The user is able
+            to gain the full control over the training process by
+            tuning hyperparameters, e.g., the batch size (by specifiying
+            ``training_batch_size`` argument), the learning rate
+            (``learning_rate``), the validation fraction
+            (``validation_fraction``), the number of training epochs
+            (``max_num_epochs``), etc. For details, check the official
+            ``sbi`` documentation.
 
         Returns
         -------
@@ -697,37 +707,37 @@ def train(self, inference, theta, x, *args, **train_kwargs):
         return inference
 
     def build_posterior(self, inference, **posterior_kwargs):
-        """Return inference and neural posterior objects.
+        """Return the updated inference and the neural posterior
+        objects.
 
         Parameters
         ----------
         inference : sbi.inference.NeuralInference
             Instantiated inference object with stored paramaters and
-            simulation outputs prepared for training.
+            simulation outputs.
         posterior_kwargs : dict, optional
             Additional keyword arguments for ``build_posterior`` method
-            in ``sbi.inference.NeuralInference`` classes. For details,
-            check the ``sbi`` documentation.
+            in all ``sbi.inference.NeuralInference``-type classes. For
+            details, check the official ``sbi`` documentation.
 
         Returns
         -------
         tuple
             ``sbi.inference.NeuralInference`` object with stored
             paramaters and simulation outputs prepared for training and
-            ``sbi.inference.NeuralPosterior`` object.
+            the neural posterior object.
         """
         posterior = inference.build_posterior(**posterior_kwargs)
-        return (inference, posterior)
+        return inference, posterior
 
-    def infer_step(self, proposal, inference,
-                   n_samples=None, theta=None, x=None,
-                   train_kwargs={}, posterior_kwargs={}, *args):
+    def infer_step(self, proposal, inference, n_samples=None, theta=None,
+                   x=None, train_kwargs={}, posterior_kwargs={}, *args):
         """Return the trained neural density estimator.
 
-        Parameter
-        ---------
+        Parameters
+        ----------
         proposal : ``sbi.utils.torchutils.BoxUniform``
-            Prior over parameters for current inference round.
+            Prior over parameters for the current round of inference.
         inference : ``sbi.inference.NeuralInference``
             Inference object obtained via `.init_inference` method.
         n_samples : int, optional
@@ -737,17 +747,15 @@ def infer_step(self, proposal, inference,
         x : numpy.ndarray, optional
             Summary statistics.
         train_kwargs : dict, optional
-            Additional keyword arguments for training the posterior
-            estimator.
+            Additional keyword arguments for `.train`.
         posterior_kwargs : dict, optional
-            Dictionary of arguments for `.build_posterior` method.
+            Additional keyword arguments for `.build_posterior`.
         args : list, optional
-            Additional arguments for `.train` method if SNPE is used as
-            an inference method.
+            Additional arguments for `.train`.
 
         Returns
         -------
-        sbi.inference.NeuralPosterior
+        sbi.inference.posteriors.base_posterior.NeuralPosterior
             Trained posterior.
         """
         # extract the training data and make adjustments for the ``sbi``
@@ -784,12 +792,13 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
               restart=False, device='cpu', **params):
         """Return the trained posterior.
 
-        If ``theta`` and ``x`` are not provided, ``n_samples`` has to
-        be defined. Otherwise, if ``n_samples`` is provided, neither
-        ``theta`` nor ``x`` needs to be provided.
+        Note that if ``theta`` and ``x`` are not provided,
+        ``n_samples`` has to be defined. Otherwise, if ``n_samples`` is
+        provided, neither ``theta`` nor ``x`` are needed and will be
+        ignored.
 
-        Parameter
-        ---------
+        Parameters
+        ----------
         n_samples : int, optional
             The number of samples.
         theta : numpy.ndarray, optional
@@ -804,7 +813,7 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
             Otherwise, if this method is called after posterior has
             already been built, multi-round inference is performed.
         inference_method : str, optional
-            Inference method. Either of SNPE, SNLE or SNRE.
+            Inference method. Either SNPE, SNLE or SNRE.
         density_estimator_model : str, optional
             The type of density estimator to be created. Either
             ``mdn``, ``made``, ``maf``, ``nsf`` for SNPE and SNLE, or
@@ -822,20 +831,20 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
             performed.
         sbi_device : str, optional
             Device on which the ``sbi`` will operate. By default this
-            is set to CPU and it is advisable to remain so for most
+            is set to ``cpu`` and it is advisable to remain so for most
             cases. In cases where the user provide custom embedding
             network through ``inference_kwargs`` argument, which will
             be trained more efficiently by using GPU, device should be
-            set accordingly.
+            set accordingly to either ``gpu`` or ``cuda``.
         params : dict
             Bounds for each parameter. Keys should correspond to names
-            of parameters as defined in the model equaions, values
-            are lists with lower and upper bounds with quantities of
-            respective parameter.
+            of parameters as defined in the model equations, while
+            values are lists with the lower and the upper bound with
+            corresponding quantities of the parameter.
 
         Returns
         -------
-        sbi.inference.NeuralPosterior
+        sbi.inference.posteriors.base_posterior.NeuralPosterior
             Approximated posterior distribution over parameters.
         """
         if restart:
@@ -925,12 +934,12 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
         return posterior
 
     def save_posterior(self, f):
-        """Saves the density estimator state dictionary to a disk file.
+        """Save the density estimator state dictionary to a disk file.
 
         Parameters
         ----------
-        posterior : sbi.inference.NeuralPosterior, optional
-            Posterior distribution.
+        posterior: neural posterior object, optional
+            Posterior distribution over parameters.
         f : str or os.PathLike
             Path to file either as string or ``os.PathLike`` object
             that contains file name.
@@ -942,7 +951,8 @@ def save_posterior(self, f):
         torch.save(self.posterior, f)
 
     def load_posterior(self, f):
-        """Loads the density estimator state dictionary from a disk file.
+        """Loads the density estimator state dictionary from a disk
+        file.
 
         Parameters
         ----------
@@ -952,10 +962,10 @@ def load_posterior(self, f):
 
         Returns
         -------
-        sbi.inference.NeuralPosterior
+        sbi.inference.posteriors.base_posterior.NeuralPosterior
             Loaded neural posterior with defined method family, density
-            estimator state dictionary, prior over parameters and
-            output shape of the simulator.
+            estimator state dictionary, the prior over parameters and
+            the output shape of the simulator.
         """
         p = torch.load(f)
         self.posterior = p
@@ -968,15 +978,16 @@ def sample(self, shape, posterior=None, **kwargs):
         ----------
         shape : tuple
             Desired shape of samples that are drawn from posterior.
-        posterior : sbi.inference.NeuralPosterior, optional
+        posterior: neural posterior object, optional
             Posterior distribution.
         kwargs : dict, optional
             Additional keyword arguments for ``sample`` method of
-            ``sbi.inference.NeuralPosterior`` object.
+            the neural posterior object.
         Returns
         -------
         numpy.ndarray
-            Samples from posterior of the shape as given in ``shape``.
+            Samples taken from the posterior of the shape as given in
+            ``shape``.
         """
         if posterior:
             p = posterior
@@ -992,28 +1003,34 @@ def sample(self, shape, posterior=None, **kwargs):
 
     def pairplot(self, samples=None, points=None, limits=None, subset=None,
                  labels=None, ticks=None, **kwargs):
-        """Plot samples in a 2-D grid with marginals and pairwise
-        marginals.
+        """Plot samples in a 2-dimensional grid with marginals and
+        pairwise marginals.
 
         Check ``sbi.analysis.plot.pairplot`` for more details.
 
         Parameters
         ----------
-        samples : iterable, optional
+        samples : list or numpy.ndarray, optional
             Samples used to build the pairplot.
         points : dict, optional
             Additional points to scatter, e.g., true parameter values,
             if known.
         limits : dict, optional
-            Limits for each parameter. If None, min and max of the
-            given samples will be used.
+            Limits for each parameter. Keys correspond to parameter
+            names as defined in the model, while values are lists with
+            limits defined as the Brian 2 quantity objects. If None,
+            min and max of the given samples will be used.
         subset : list, optional
-            Which parameters to plot.
+            The names as strings of parameters to plot.
         labels : dict, optional
-            Names for each parameter.
+            Names for each parameter. Keys correspond to parameter
+            names as defined in the model, while values are lists of
+            strings.
         ticks : dict, optional
-            Position of the ticks. If None, default ticks positions
-            will be used.
+            Position of the ticks. Keys correspond to parameter names
+            as defined in the model, while values are lists with ticks
+            defined as the Brian 2 quantity objects. If None, default
+            ticks positions will be used.
         kwargs : dict, optional
             Additional keyword arguments for the
             ``sbi.analysis.pairplot`` function.
@@ -1021,7 +1038,7 @@ def pairplot(self, samples=None, points=None, limits=None, subset=None,
         Returns
         -------
         tuple
-            Figure and axis of posterior distribution plot.
+            Figure and axis of the posterior distribution plot.
         """
         if samples is not None:
             s = samples
@@ -1083,29 +1100,34 @@ def conditional_pairplot(self, condition, density=None, points=None,
                              ticks=None, **kwargs):
         """Plot conditional distribution given all other parameters.
 
-        Check ``sbi.analysis.plot.conditional_pairplot`` for more
-        details.
+        Check ``sbi.analysis.conditional_pairplot`` for more details.
 
         Parameters
         ----------
         condition : numpy.ndarray
             Condition that all but the one/two regarded parameters are
             fixed to.
-        density : sbi.inference.NeuralPosterior, optional
+        density : neural posterior object, optional
             Posterior probability density.
         points : dict, optional
             Additional points to scatter, e.g., true parameter values,
             if known.
         limits : dict, optional
-            Limits for each parameter. If None, min and max of the
-            given samples will be used.
+            Limits for each parameter. Keys correspond to parameter
+            names as defined in the model, while values are lists with
+            limits defined as the Brian 2 quantity objects. If None,
+            min and max of the given samples will be used.
         subset : list, optional
-            Which parameters to plot.
+            The names as strings of parameters to plot.
         labels : dict, optional
-            Names for each parameter.
+            Names for each parameter. Keys correspond to parameter
+            names as defined in the model, while values are lists of
+            strings.
         ticks : dict, optional
-            Position of the ticks. If None, default ticks positions
-            will be used.
+            Position of the ticks. Keys correspond to parameter names
+            as defined in the model, while values are lists with ticks
+            defined as the Brian 2 quantity objects. If None, default
+            ticks positions will be used.
         kwargs : dict, optional
             Additional keyword arguments for the
             ``sbi.analysis.conditional_pairplot`` function.
@@ -1192,11 +1214,13 @@ def conditional_corrcoeff(self, condition, density=None, limits=None,
         condition : numpy.ndarray
             Condition that all but the one/two regarded parameters are
             fixed to.
-        density : sbi.inference.NeuralPosterior, optional
+        density : neural posterior object, optional
             Posterior probability density.
         limits : dict, optional
-            Limits for each parameter. If None, min and max of the
-            given samples will be used.
+            Limits for each parameter. Keys correspond to parameter
+            names as defined in the model, while values are lists with
+            limits defined as the Brian 2 quantity objects. If None,
+            min and max of the given samples will be used.
         subset : list, optional
             Parameters that are taken for conditional distribution, if
             None all parameters are considered.
@@ -1256,11 +1280,11 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
 
         Parameters
         ----------
-        posterior : sbi.inference.NeuralPosterior, optional
+        posterior: neural posterior object, optional
             Posterior distribution.
-        output_var: str or sequence of str
+        output_var: str or list
             Name of the output variable to be monitored, it can also be
-            a sequence of names to record multiple variables.
+            a list of names to record multiple variables.
         param_init : dict
             Dictionary of initial values for the model.
         level : int, optional
@@ -1269,9 +1293,9 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
         Returns
         -------
         brian2.units.fundamentalunits.Quantity or dict
-            If a single output variable is observed, 2-D array of
-            traces generated by using a set of parameters sampled from
-            the trained posterior distribution of shape
+            If a single output variable is observed, 2-dimensional
+            array of traces generated by using a set of parameters
+            sampled from the trained posterior distribution of shape
             (``self.n_traces``, number of time steps). Otherwise, a
             dictionary with keys set to names of output variables, and
             values to generated traces of respective output variables.

From 590ccb13ab6d369697d266b7a986f258f3bdbb54 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Mon, 16 Aug 2021 15:54:29 +0200
Subject: [PATCH 095/121] sbi example with simple interface

---
 docs_sphinx/examples/hh_sbi_simple.rst | 157 ++++++++++++++++++++++++-
 1 file changed, 156 insertions(+), 1 deletion(-)

diff --git a/docs_sphinx/examples/hh_sbi_simple.rst b/docs_sphinx/examples/hh_sbi_simple.rst
index 6b4d331..3350ab4 100644
--- a/docs_sphinx/examples/hh_sbi_simple.rst
+++ b/docs_sphinx/examples/hh_sbi_simple.rst
@@ -1,4 +1,159 @@
 Inference on Hodgin-Huxley model: simple interface
 ==================================================
 
-tba
\ No newline at end of file
+Here you can download the data:
+:download:`input_traces <../../examples/input_traces_hh.csv>`
+:download:`output_traces <../../examples/output_traces_hh.csv>`
+
+.. code:: python
+
+  from brian2 import *
+  from brian2modelfitting import *
+  import pandas as pd
+
+
+To load the data, use the following:
+
+.. code:: python
+
+  df_inp_traces = pd.read_csv('input_traces_hh.csv')
+  df_out_traces = pd.read_csv('output_traces_hh.csv')
+  inp_traces = df_inp_traces.to_numpy()
+  inp_traces = inp_traces[[0, 1], 1:]
+  out_traces = df_out_traces.to_numpy()
+  out_traces = out_traces[[0, 1], 1:]
+
+Then we have to define the model and its parameters:
+
+.. code:: python
+
+  area = 20_000*um**2
+  El = -65*mV
+  EK = -90*mV
+  ENa = 50*mV
+  VT = -63*mV
+  dt = 0.01*ms
+  eqs = '''
+      dv/dt = (gl*(El-v) - g_na*(m*m*m)*h*(v-ENa) - g_kd*(n*n*n*n)*(v-EK) + I)/Cm : volt
+      dm/dt = 0.32*(mV**-1)*(13.*mV-v+VT)/
+              (exp((13.*mV-v+VT)/(4.*mV))-1.)/ms*(1-m)-0.28*(mV**-1)*(v-VT-40.*mV)/
+              (exp((v-VT-40.*mV)/(5.*mV))-1.)/ms*m : 1
+      dn/dt = 0.032*(mV**-1)*(15.*mV-v+VT)/
+              (exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1
+      dh/dt = 0.128*exp((17.*mV-v+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-v+VT)/(5.*mV)))/ms*h : 1
+
+      # free parameters
+      g_na : siemens (constant)
+      g_kd : siemens (constant)
+      gl   : siemens (constant)
+      Cm   : farad (constant)
+'''
+
+Let's also specify time domain for more convenient plotting afterwards:
+
+.. code:: python
+
+  t = arange(0, out_traces.shape[1]*dt/ms, dt/ms)
+  stim_start, stim_end = t[where(inp_traces[0, :] != 0)[0][[0, -1]]]
+
+Now, we have to define features in order to create a summary statistics representation of the output data traces:
+
+.. code:: python
+  
+    list_of_features = [
+        lambda x: max(x[(t > stim_start) & (t < stim_end)]),  # max active potential
+        lambda x: mean(x[(t > stim_start) & (t < stim_end)]),  # mean active potential
+        lambda x: std(x[(t > stim_start) & (t < stim_end)]),  # std active potential
+        lambda x: mean(x[(t > .25 * stim_start) & (t < .75 * stim_start)]),  # resting
+    ]
+
+We have to instantiate the object by using the class ``Inferencer`` in which the data and the list of features should be passed:
+
+.. code:: python
+
+  inferencer = Inferencer(dt=dt, model=eqs,
+                          input={'I': inp_traces*amp},
+                          output={'v': out_traces*mV},
+                          features={'v': list_of_features},
+                          method='exponential_euler',
+                          threshold='m > 0.5',
+                          refractory='m > 0.5',
+                          param_init={'v': 'VT'})
+
+
+
+Be sure that the names of parameters passed to the ``infer`` method correspond to the names of unknown parameters defined as constatns in the model equations.
+
+.. code:: python
+
+  posterior = inferencer.infer(n_samples=5_000,
+                               n_rounds=3,
+                               inference_method='SNPE',
+                               density_estimator_model='mdn',
+                               gl=[1e-09*siemens, 1e-07*siemens],
+                               g_na=[2e-06*siemens, 2e-04*siemens],
+                               g_kd=[6e-07*siemens, 6e-05*siemens],
+                               Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])
+
+After the training of the neural density estimator stored accessible through ``posterior`` is done, we can draw samples from the approximated posterior distribution as follows:
+
+.. code:: python
+
+  samples = inferencer.sample((5_000, ))
+
+In order to analyze the sampled data further, we can use the embedded ``pairplot`` method which visualizes the pairwise relationship between each two parameters:
+
+.. code:: python
+
+  limits = {'gl': [1e-9*siemens, 1e-07*siemens],
+            'g_na': [2e-06*siemens, 2e-04*siemens],
+            'g_kd': [6e-07*siemens, 6e-05*siemens],
+            'Cm': [0.1*uF*cm**-2*area, 2*uF*cm**-2*area]}
+  labels = {'gl': r'$\overline{g}_{l}$',
+            'g_na': r'$\overline{g}_{Na}$',
+            'g_kd': r'$\overline{g}_{K}$',
+            'Cm': r'$C_{m}$'}
+  inferencer.pairplot(limits=limits,
+                      labels=labels,
+                      ticks=limits,
+                      figsize=(6, 6))
+  condition = inferencer.sample((1, ))
+  inferencer.conditional_pairplot(condition=condition,
+                                  limits=limits,
+                                  labels=labels,
+                                  ticks=limits,
+                                  figsize=(6, 6))
+
+To obtain a simulated trace from a single sample of parameters drawn from posterior distribution, use the following code:
+
+.. code:: python
+
+  inf_traces = inferencer.generate_traces(output_var='v')
+
+Let us now visualize the recordings and simulated traces:
+
+.. code:: python
+
+  inf_traces = inferencer.generate_traces(output_var='v')
+
+  nrows = 2
+  ncols = out_traces.shape[0]
+  fig, axs = subplots(nrows, ncols, sharex=True,
+                      gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))
+  for idx in range(ncols):
+      spike_idx = in1d(t, spike_times[idx]).nonzero()[0]
+      spike_v = (out_traces[idx, :].min(), out_traces[idx, :].max())
+      axs[0, idx].plot(t, out_traces[idx, :].T, 'C3-', lw=3, label='recordings')
+      axs[0, idx].plot(t, inf_traces[idx, :].T/mV, 'k--', lw=2,
+                       label='sampled traces')
+      axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimuli')
+      axs[1, idx].set_xlabel('$t$, ms')
+      if idx == 0:
+          axs[0, idx].set_ylabel('$V$, mV')
+          axs[1, idx].set_ylabel('$I$, nA')
+  handles, labels = [(h + l) for h, l
+                     in zip(axs[0, idx].get_legend_handles_labels(),
+                     axs[1, idx].get_legend_handles_labels())]
+  fig.legend(handles, labels)
+  tight_layout()
+  show()

From a7d168fd36ead46b6f8e5ec21a620f6d3fa72bb0 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Mon, 16 Aug 2021 16:05:54 +0200
Subject: [PATCH 096/121] minor fixes

---
 docs_sphinx/examples/hh_sbi_simple.rst | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/docs_sphinx/examples/hh_sbi_simple.rst b/docs_sphinx/examples/hh_sbi_simple.rst
index 3350ab4..c6a8ab5 100644
--- a/docs_sphinx/examples/hh_sbi_simple.rst
+++ b/docs_sphinx/examples/hh_sbi_simple.rst
@@ -1,9 +1,12 @@
 Inference on Hodgin-Huxley model: simple interface
 ==================================================
 
+You can also download and run this example by clicking here:
+:download:`hh_sbi_simple_interface.py <../../examples/hh_sbi_simple_interface.py>`
+
 Here you can download the data:
-:download:`input_traces <../../examples/input_traces_hh.csv>`
-:download:`output_traces <../../examples/output_traces_hh.csv>`
+:download:`input traces <../../examples/input_traces_hh.csv>`
+:download:`output traces <../../examples/output_traces_hh.csv>`
 
 .. code:: python
 
@@ -11,7 +14,6 @@ Here you can download the data:
   from brian2modelfitting import *
   import pandas as pd
 
-
 To load the data, use the following:
 
 .. code:: python
@@ -47,7 +49,7 @@ Then we have to define the model and its parameters:
       g_kd : siemens (constant)
       gl   : siemens (constant)
       Cm   : farad (constant)
-'''
+  '''
 
 Let's also specify time domain for more convenient plotting afterwards:
 

From 4eb23f09e4721ce9381a1914c0df068d06e6391e Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Mon, 16 Aug 2021 18:52:35 +0200
Subject: [PATCH 097/121] add small changes to examples

---
 examples/IF_sbi.py                  |  8 ++++----
 examples/hh_sbi_simple_interface.py | 28 ++++++++++++----------------
 2 files changed, 16 insertions(+), 20 deletions(-)

diff --git a/examples/IF_sbi.py b/examples/IF_sbi.py
index 99001b0..3f5a501 100644
--- a/examples/IF_sbi.py
+++ b/examples/IF_sbi.py
@@ -49,9 +49,9 @@
 
 # Instantiate the inferencer object
 spike_features_list = [
-        lambda x: x.size,  # number of spikes
-        lambda x: 0. if diff(x).size == 0 else mean(diff(x)),  # mean ISI
-]
+    lambda x: x.size,  # number of spikes
+    lambda x: 0. if diff(x).size == 0 else mean(diff(x)),  # mean ISI
+    ]
 inferencer = Inferencer(dt=dt, model=eqs_inf,
                         input={'I_syn': inp_trace.reshape(1, -1)},
                         output={'spikes': [spike_times]},
@@ -95,7 +95,7 @@
 # Generate traces from a single sample of parameters
 inf_trace = inferencer.generate_traces(output_var='v')
 
-fig, axs = subplots(nrows, 1, sharex=True,
+fig, axs = subplots(2, 1, sharex=True,
                     gridspec_kw={'height_ratios': [3, 1]}, figsize=(7, 3))
 axs[0].plot(t, out_trace.T/mV, 'C3-', lw=3, label='recordings')
 axs[0].plot(t, inf_trace.T/mV, 'k--', lw=2, label='sampled traces')
diff --git a/examples/hh_sbi_simple_interface.py b/examples/hh_sbi_simple_interface.py
index b42295c..e4eb53f 100644
--- a/examples/hh_sbi_simple_interface.py
+++ b/examples/hh_sbi_simple_interface.py
@@ -8,9 +8,9 @@
 df_inp_traces = pd.read_csv('input_traces_hh.csv')
 df_out_traces = pd.read_csv('output_traces_hh.csv')
 inp_traces = df_inp_traces.to_numpy()
-inp_traces = inp_traces[[0, 1], 1:]
+inp_traces = inp_traces[[0, 1, 3, 4], 1:]
 out_traces = df_out_traces.to_numpy()
-out_traces = out_traces[[0, 1], 1:]
+out_traces = out_traces[[0, 1, 3, 4], 1:]
 
 # Define model and its parameters
 area = 20_000*um**2
@@ -42,7 +42,7 @@
 nrows = 2
 ncols = out_traces.shape[0]
 fig, axs = subplots(nrows, ncols, sharex=True,
-                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))
+                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(12, 3))
 for idx in range(ncols):
     axs[0, idx].plot(t, out_traces[idx, :].T, lw=3, c='C3', label='recordings')
     axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimulus')
@@ -57,7 +57,7 @@
 tight_layout()
 
 
-# Obtain spike times manually
+# Obtain spike times manually from recordings
 def get_spike_times(x):
     x = x.copy()
     # put everything to -40 mV that is below -40 mV or has negative slope
@@ -70,17 +70,12 @@ def get_spike_times(x):
     ind = where(diff(x) < 0)
     spike_times = array(t)[ind]
 
-    # spike times for active stimulus
-    spike_times_stim = spike_times[
-        (spike_times > t_start) & (spike_times < t_end)
-        ]
-
     # number of spikes
-    if spike_times_stim.shape[0] > 0:
-        spike_times_stim = spike_times_stim[
-            append(1, diff(spike_times_stim)) > 0.5
+    if spike_times.shape[0] > 0:
+        spike_times = spike_times[
+            append(1, diff(spike_times)) > 0.5
         ]
-    return spike_times_stim / 1000  # in seconds
+    return spike_times / 1000  # in seconds
 
 
 # store spike times for each trace into the list
@@ -90,7 +85,7 @@ def get_spike_times(x):
 nrows = 2
 ncols = out_traces.shape[0]
 fig, axs = subplots(nrows, ncols, sharex=True,
-                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))
+                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(12, 3))
 for idx in range(ncols):
     spike_idx = in1d(t, spike_times[idx] * 1000).nonzero()[0]
     spike_v = (out_traces[idx, :].min(), out_traces[idx, :].max())
@@ -116,8 +111,9 @@ def get_spike_times(x):
     lambda x: std(x[(t > t_start) & (t < t_end)]),  # AP std
     lambda x: kurt(x[(t > t_start) & (t < t_end)], fisher=False),  # AP kurt
     lambda x: mean(x[(t > .25 * t_start) & (t < .75 * t_start)]),  # resting
+    lambda x: mean(x[(t > t_end) & (t <= max(t))]),  # steady-state
 ]
-s_features = [lambda x: x.size]  # numbe of spikes in a spike train
+s_features = [lambda x: x.size]  # the number of spikes in a spike train
 
 # Simulation-based inference object instantiation
 inferencer = Inferencer(dt=dt, model=eqs,
@@ -175,7 +171,7 @@ def get_spike_times(x):
 nrows = 2
 ncols = out_traces.shape[0]
 fig, axs = subplots(nrows, ncols, sharex=True,
-                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(9, 3))
+                    gridspec_kw={'height_ratios': [3, 1]}, figsize=(12, 3))
 for idx in range(ncols):
     spike_idx = in1d(t, spike_times[idx]).nonzero()[0]
     spike_v = (out_traces[idx, :].min(), out_traces[idx, :].max())

From fafcced33ae18634f212ac14839b8fad9e14ec3c Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 17 Aug 2021 18:16:36 +0200
Subject: [PATCH 098/121] fix new data generation in multi-round inference

---
 brian2modelfitting/inferencer.py | 47 +++++++++++++++++++++-----------
 1 file changed, 31 insertions(+), 16 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index f57e0e9..d9e3ccd 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -499,8 +499,8 @@ def extract_summary_statistics(self, theta, level=0):
             spike_trains = list(simulator.spikemonitor.spike_trains().values())
         x = []
         if self.features:
-            for ov in tqdm(self.output_var, desc='Extracting features',
-                           total=len(self.output), leave=True):
+            for ov in self.output_var:
+                print(f'Extracting features for \'{ov}\'...')
                 summary_statistics = []
                 if ov != 'spikes':
                     o = obs[ov].get_value().T
@@ -517,8 +517,8 @@ def extract_summary_statistics(self, theta, level=0):
                 x.append(_x)
             x = np.hstack(x)
         else:
-            for ov in tqdm(self.output_var, desc='Aranging output traces',
-                           total=len(self.output)):
+            print('Aranging traces for automatic feature extraction...')
+            for ov in self.output_var:
                 o = obs[ov].get_value().T
                 x.append(o.reshape(self.n_samples, -1).astype(np.float32))
             x = np.hstack(x)
@@ -661,6 +661,7 @@ def init_inference(self, inference_method, density_estimator_model, prior,
             inference = inference_method_fun(prior, density_estimator_builder,
                                              device=sbi_device,
                                              show_progress_bars=True)
+        self.inference = inference
         return inference
 
     def train(self, inference, theta, x, *args, **train_kwargs):
@@ -883,10 +884,8 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
             self.x = x
 
             # initialize inference object
-            self.inference = self.init_inference(inference_method,
-                                                 density_estimator_model,
-                                                 prior, device,
-                                                 **inference_kwargs)
+            _ = self.init_inference(inference_method, density_estimator_model,
+                                    prior, device, **inference_kwargs)
 
             # args for SNPE in `.train`
             # args for SNRE and SNLE are not supported here, if needed the user
@@ -903,6 +902,9 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
                 args = [prior, ]
             else:
                 args = [None, ]
+            # generate data if the posterior already exist given proposal
+            self.theta = self.generate_training_data(self.n_samples, prior)
+            self.x = self.extract_summary_statistics(self.theta, level=1)
 
         # allocate empty list of posteriors
         posteriors = []
@@ -915,8 +917,7 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
             tqdm_desc = f'{n_rounds}-round focused inference'
         else:
             tqdm_desc = 'Amortized inference'
-        for _ in tqdm(range(n_rounds), desc=tqdm_desc):
-
+        for round in tqdm(range(n_rounds), desc=tqdm_desc):
             # inference step
             posterior = self.infer_step(proposal, self.inference,
                                         n_samples, self.theta, self.x,
@@ -926,9 +927,16 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
             posteriors.append(posterior)
 
             # update the proposal given the observation
-            if n_rounds > 1:
+            if n_rounds > 1 and round < n_rounds - 1:
                 x_o = torch.tensor(self.x_o, dtype=torch.float32)
                 proposal = posterior.set_default_x(x_o)
+                if posterior._method_family == 'snpe':
+                    args = [proposal, ]
+                else:
+                    args = [None, ]
+                self.theta = self.generate_training_data(self.n_samples,
+                                                         proposal)
+                self.x = self.extract_summary_statistics(self.theta, level=1)
 
         self.posterior = posterior
         return posterior
@@ -1273,16 +1281,19 @@ def conditional_corrcoeff(self, condition, density=None, limits=None,
                                                         **kwargs)
         return cond_coeff.numpy()
 
-    def generate_traces(self, posterior=None, output_var=None, param_init=None,
-                        level=0):
+    def generate_traces(self, n_samples=1, posterior=None, output_var=None,
+                        param_init=None, level=0):
         """Generates traces for a single drawn sample from the trained
         posterior and all inputs.
 
         Parameters
         ----------
-        posterior: neural posterior object, optional
+        n_samples : int, optional
+            The number of parameters samples. If n_samples is larger
+            than 1, the mean value of sampled set will be used.
+        posterior : neural posterior object, optional
             Posterior distribution.
-        output_var: str or list
+        output_var : str or list
             Name of the output variable to be monitored, it can also be
             a list of names to record multiple variables.
         param_init : dict
@@ -1300,6 +1311,10 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
             dictionary with keys set to names of output variables, and
             values to generated traces of respective output variables.
         """
+        if not isinstance(n_samples, int):
+            raise ValueError('`n_samples` argument needs to be a positive'
+                             ' integer.')
+
         # sample a single set of parameters from posterior distribution
         if posterior:
             p = posterior
@@ -1310,7 +1325,7 @@ def generate_traces(self, posterior=None, output_var=None, param_init=None,
                              ' posterior has been calculated by the `infere`'
                              ' method.')
         x_o = torch.tensor(self.x_o, dtype=torch.float32)
-        params = p.sample((1, ), x=x_o)
+        params = p.sample((n_samples, ), x=x_o).mean(axis=0)
 
         # set output variable that is monitored
         if output_var is None:

From 55eb009c8e9f45b871212f0919b3234f5eb63503 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 17 Aug 2021 18:27:19 +0200
Subject: [PATCH 099/121] update examples after multi-round bug has been fixed

---
 examples/IF_sbi.py                    | 18 +++++++-------
 examples/hh_sbi_flexible_interface.py | 25 +++++++++-----------
 examples/hh_sbi_simple_interface.py   | 12 +++++-----
 examples/hh_sbi_synthetic_traces.py   | 34 +++++++++++++++++----------
 4 files changed, 47 insertions(+), 42 deletions(-)

diff --git a/examples/IF_sbi.py b/examples/IF_sbi.py
index 3f5a501..3dc182f 100644
--- a/examples/IF_sbi.py
+++ b/examples/IF_sbi.py
@@ -2,7 +2,7 @@
 from brian2modelfitting import *
 
 
-# Set parameters
+# Set parameters for integrate-and-fire model
 dt = 0.1*ms
 sim_time = 60*ms
 El = -70*mV
@@ -23,7 +23,7 @@
     C     : farad (constant)
     '''
 
-# Run a model and create synthetic voltage traces
+# Run a model and create synthetic voltage traces and obtain spike trains
 neurons = NeuronGroup(1, eqs, dt=dt,
                       threshold='v > -50 * mV',
                       reset='v = -70 * mV',
@@ -42,7 +42,7 @@
 VT = -50*mV
 DeltaT = 2*mV
 eqs_inf = '''
-    dv/dt = (gl*(El - v) + gl*DeltaT*exp((v - VT) / DeltaT) + I_syn) / C : volt
+    dv/dt = (gl*(El - v) + gl*DeltaT*exp((v - VT) / DeltaT) + I) / C : volt
     gl    : siemens (constant)
     C     : farad (constant)
     '''
@@ -53,7 +53,7 @@
     lambda x: 0. if diff(x).size == 0 else mean(diff(x)),  # mean ISI
     ]
 inferencer = Inferencer(dt=dt, model=eqs_inf,
-                        input={'I_syn': inp_trace.reshape(1, -1)},
+                        input={'I': inp_trace.reshape(1, -1)},
                         output={'spikes': [spike_times]},
                         features={'spikes': spike_features_list},
                         method='exponential_euler',
@@ -61,8 +61,7 @@
                         reset='v = -70*mV',
                         param_init={'v': -70*mV})
 
-# Infer parameter posteriors given bounds
-
+# Infer parameter posteriors for given parameters
 posterior = inferencer.infer(n_samples=3_000,
                              n_rounds=3,
                              gl=[10*nS, 100*nS],
@@ -79,7 +78,7 @@
                     ticks=ticks,
                     points=ground_truth_params,
                     points_offdiag={'markersize': 9},
-                    points_colors=['r'], 
+                    points_colors=['r'],
                     figsize=(6, 6))
 
 # Visualize the trace by sampling trained posterior
@@ -94,8 +93,9 @@
 
 # Generate traces from a single sample of parameters
 inf_trace = inferencer.generate_traces(output_var='v')
-
-fig, axs = subplots(2, 1, sharex=True,
+nrows = 2
+ncols = 1
+fig, axs = subplots(nrows, ncols, sharex=True,
                     gridspec_kw={'height_ratios': [3, 1]}, figsize=(7, 3))
 axs[0].plot(t, out_trace.T/mV, 'C3-', lw=3, label='recordings')
 axs[0].plot(t, inf_trace.T/mV, 'k--', lw=2, label='sampled traces')
diff --git a/examples/hh_sbi_flexible_interface.py b/examples/hh_sbi_flexible_interface.py
index f9ff33d..c3e8fe5 100644
--- a/examples/hh_sbi_flexible_interface.py
+++ b/examples/hh_sbi_flexible_interface.py
@@ -32,7 +32,7 @@
     g_kd : siemens (constant)
     gl   : siemens (constant)
     Cm   : farad (constant)
-'''
+    '''
 
 # Time domain
 t = arange(0, out_traces.shape[1]*dt/ms, dt/ms)
@@ -43,10 +43,9 @@
 inferencer = Inferencer(dt=dt, model=eqs,
                         input={'I': inp_traces*amp},
                         output={'v': out_traces*mV},
-                        features={'v': [lambda x: mean(x),
+                        features={'v': [lambda x: max(x),
                                         lambda x: mean(x[(t > t_start) & (t < t_end)]),
-                                        lambda x: std(x[(t > t_start) & (t < t_end)]),
-                                        lambda x: ptp(x)]},
+                                        lambda x: std(x[(t > t_start) & (t < t_end)])]},
                         method='exponential_euler',
                         threshold='m > 0.5',
                         refractory='m > 0.5',
@@ -64,28 +63,25 @@
     theta, x = inferencer.load_summary_statistics(path_to_data)
 else:
     # Generate training data
-    theta = inferencer.generate_training_data(n_samples=5_000,
+    theta = inferencer.generate_training_data(n_samples=10_000,
                                               prior=prior)
     # Extract summary stats
     x = inferencer.extract_summary_statistics(theta)
     # Save the data for later use
-    inferencer.save_summary_statistics(path_to_data, theta, x)
+    # inferencer.save_summary_statistics(path_to_data, theta, x)
 
 # Amortized inference
 # Training the neural density estimator
 inference = inferencer.init_inference(inference_method='SNPE',
                                       density_estimator_model='mdn',
-                                      prior=prior,
-                                      hidden_features=50,
-                                      num_components=10)
+                                      prior=prior)
 # First round of inference where no observation data is set to posterior
 posterior_amortized = inferencer.infer_step(proposal=prior,
                                             inference=inference,
-                                            theta=theta, x=x,
-                                            train_kwargs={'num_atoms': 10})
+                                            theta=theta, x=x)
 # Storing the trained posterior without a default observation
-path_to_posterior = __file__[:-3] + '_posterior.pth'
-inferencer.save_posterior(path_to_posterior)
+path_to_posterior = __file__[:-3] + '_posterior_amortized.pth'
+# inferencer.save_posterior(path_to_posterior)
 
 # Sampling from the posterior given observations, ...
 inferencer.sample((10_000, ))
@@ -95,8 +91,9 @@
           'g_kd': r'$\overline{g}_\mathrm{K}$',
           'Cm': r'$\overline{C}_{m}$'}
 inferencer.pairplot(labels=labels)
+
 # ...and optionally, continue the multiround inference using ``infer`` method
-posterior_multiround = inferencer.infer(n_rounds=2)
+posterior_multiround = inferencer.infer()
 inferencer.sample((10_000, ))
 inferencer.pairplot(labels=labels)
 
diff --git a/examples/hh_sbi_simple_interface.py b/examples/hh_sbi_simple_interface.py
index e4eb53f..5fb4039 100644
--- a/examples/hh_sbi_simple_interface.py
+++ b/examples/hh_sbi_simple_interface.py
@@ -32,7 +32,7 @@
     g_kd : siemens (constant)
     gl   : siemens (constant)
     Cm   : farad (constant)
-'''
+    '''
 
 # Time domain
 t = arange(0, out_traces.shape[1]*dt/ms, dt/ms)
@@ -126,8 +126,8 @@ def get_spike_times(x):
                         param_init={'v': 'VT'})
 
 # Multi-round inference
-posterior = inferencer.infer(n_samples=5_000,
-                             n_rounds=3,
+posterior = inferencer.infer(n_samples=10_000,
+                             n_rounds=2,
                              inference_method='SNPE',
                              density_estimator_model='mdn',
                              gl=[1e-09*siemens, 1e-07*siemens],
@@ -136,9 +136,9 @@ def get_spike_times(x):
                              Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])
 
 # Draw samples from posterior
-samples = inferencer.sample((5_000, ))
+samples = inferencer.sample((10_000, ))
 
-# Visualize pairplot and conditional pairplot
+# Visualize conditional pairwise relationships between parameter distributions
 limits = {'gl': [1e-9*siemens, 1e-07*siemens],
           'g_na': [2e-06*siemens, 2e-04*siemens],
           'g_kd': [6e-07*siemens, 6e-05*siemens],
@@ -166,7 +166,7 @@ def get_spike_times(x):
 _ = fig.colorbar(im)
 
 # Generate traces and visualize from a single sample of parameters
-inf_traces = inferencer.generate_traces(output_var='v')
+inf_traces = inferencer.generate_traces(n_samples=10_000, output_var='v')
 
 nrows = 2
 ncols = out_traces.shape[0]
diff --git a/examples/hh_sbi_synthetic_traces.py b/examples/hh_sbi_synthetic_traces.py
index ee75a44..f24f594 100644
--- a/examples/hh_sbi_synthetic_traces.py
+++ b/examples/hh_sbi_synthetic_traces.py
@@ -12,10 +12,9 @@
 Eleak = -70*mV
 VT = -60*mV
 C = 200*pF
-gNa = 32*uS  # 1st "unknown" parameter to infer
 ENa = 53*mV
-gK = 1*uS  # 2nd "unknown" parameter to infer
 EK = -107*mV
+ground_truth_params = {'gNa': 32*uS, 'gK': 1*uS}
 eqs = '''
     dVm/dt = -(gNa*m**3*h*(Vm - ENa) + gK*n**4*(Vm - EK) + gleak*(Vm - Eleak) - I_inj) / C : volt
     I_inj = int(t >= t_on and t < t_off)*I : amp (shared)
@@ -28,12 +27,18 @@
     betah = 4/(1 + exp((-(Vm - VT - 40.*mV)) / (5.*mV)))/ms : Hz
     alphan = (-0.032/mV) * (Vm - VT - 15.*mV) / (exp((-(Vm - VT - 15.*mV)) / (5.*mV)) - 1)/ms : Hz
     betan = 0.5*exp(-(Vm - VT - 10.*mV) / (40.*mV))/ms : Hz
+
+    # parameters
+    gNa : siemens (constant)
+    gK : siemens (constant)
     '''
 neurons = NeuronGroup(1, eqs, dt=dt,
                       threshold='m>0.5', refractory='m>0.5',
                       method='exponential_euler')
 state_monitor = StateMonitor(neurons, 'Vm', record=True)
 spike_monitor = SpikeMonitor(neurons, record=False)
+neurons.gNa = ground_truth_params['gNa']
+neurons.gK = ground_truth_params['gK']
 neurons.Vm = 'Eleak'
 neurons.m = '1/(1 + betam/alpham)'
 neurons.h = '1/(1 + betah/alphah)'
@@ -45,22 +50,26 @@
 
 # Inference
 start_scope()
+
+# A set of equations for the Inferencer class
 eqs = '''
     dVm/dt = -(gNa*m**3*h*(Vm - ENa) + gK*n**4*(Vm - EK) + gleak*(Vm - Eleak) - I_inj) / C : volt
     dm/dt = alpham*(1-m) - betam*m : 1
     dn/dt = alphan*(1-n) - betan*n : 1
     dh/dt = alphah*(1-h) - betah*h : 1
-    alpham = (-0.32/mV) * (Vm - VT - 13.*mV) / (exp((-(Vm - VT - 13.*mV)) / (4.*mV)) - 1)/ms : Hz
-    betam = (0.28/mV) * (Vm - VT - 40.*mV) / (exp((Vm - VT - 40.*mV) / (5.*mV)) - 1)/ms : Hz
+    alpham = (-0.32/mV) * (Vm - VT - 13.*mV) / (exp((-(Vm - VT - 13.*mV))/(4.*mV)) - 1)/ms : Hz
+    betam = (0.28/mV) * (Vm - VT - 40.*mV) / (exp((Vm - VT - 40.*mV)/(5.*mV)) - 1)/ms : Hz
     alphah = 0.128 * exp(-(Vm - VT - 17.*mV) / (18.*mV))/ms : Hz
     betah = 4/(1 + exp((-(Vm - VT - 40.*mV)) / (5.*mV)))/ms : Hz
     alphan = (-0.032/mV) * (Vm - VT - 15.*mV) / (exp((-(Vm - VT - 15.*mV)) / (5.*mV)) - 1)/ms : Hz
     betan = 0.5*exp(-(Vm - VT - 10.*mV) / (40.*mV))/ms : Hz
 
-    # parameters to fit
+    # parameters
     gNa : siemens (constant)
     gK : siemens (constant)
     '''
+
+# Instantiating the inferencer object with input and output data traces
 inferencer = Inferencer(dt=dt, model=eqs,
                         input={'I_inj': I_inj.reshape(1, -1)},
                         output={'Vm': out_trace},
@@ -71,7 +80,7 @@
                                     'h': '1/(1 + betah/alphah)',
                                     'n': '1/(1 + betan/alphan)'})
 
-# Amortized inference with automatic feature extraction by using a default MLP
+# Amortized inference performed with automatic feature extraction
 inferencer.infer(n_samples=10_000,
                  inference_method='SNPE',
                  density_estimator_model='mdn',
@@ -84,18 +93,17 @@
 # Visualize estimated posterior distribution
 limits = {'gNa': [.5*uS, 80.*uS],
           'gK': [1e-4*uS, 15.*uS]}
-labels= {'gNa': r'$\overline{g}_\mathrm{Na}$',
-         'gK': r'$\overline{g}_\mathrm{K}$'}
+labels = {'gNa': r'$\overline{g}_\mathrm{Na}$',
+          'gK': r'$\overline{g}_\mathrm{K}$'}
 inferencer.pairplot(limits=limits,
                     ticks=limits,
                     labels=labels,
-                    points={'gNa': 32*uS,
-                            'gK': 1*uS},
+                    points=ground_truth_params,
                     points_offdiag={'markersize': 6},
                     points_colors=['r'],
                     figsize=(6, 6))
 
-# Visualize traces for a single sample from posterior
+# Visualize sampled traces
 inf_trace = inferencer.generate_traces()
 
 fig, axs = subplots(2, 1, sharex=True,
@@ -103,9 +111,9 @@
 axs[0].plot(t, out_trace.ravel()/mV, 'C3-', lw=3, label='simulated recordings')
 axs[0].plot(t, inf_trace.ravel()/mV, 'k--', lw=2, label='posterior sample')
 axs[0].set(ylabel='$V_m$, mV')
-axs[0].legend()
+axs[0].legend(loc='upper right')
 axs[1].plot(t, I_inj.ravel()/nA, 'k-', lw=3, label='stimulus')
 axs[1].set(xlabel='$t$, s', ylabel='I, nA')
-axs[1].legend()
+axs[1].legend(loc='upper right')
 tight_layout()
 show()

From 58c9b7273b120ef382278494bba1c870a722dcaf Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 17 Aug 2021 20:59:28 +0200
Subject: [PATCH 100/121] add examples of using the toolbox for
 simulation-based inferencer

---
 docs_sphinx/examples/hh_sbi_flex.rst   | 186 ++++++++++++++++++++++++-
 docs_sphinx/examples/hh_sbi_simple.rst |   2 +-
 2 files changed, 186 insertions(+), 2 deletions(-)

diff --git a/docs_sphinx/examples/hh_sbi_flex.rst b/docs_sphinx/examples/hh_sbi_flex.rst
index e1e4b69..37b58ea 100644
--- a/docs_sphinx/examples/hh_sbi_flex.rst
+++ b/docs_sphinx/examples/hh_sbi_flex.rst
@@ -1,4 +1,188 @@
 Inference on Hodgin-Huxley model: flexible interface
 ====================================================
 
-tba
\ No newline at end of file
+You can also download and run this example by clicking here:
+:download:`hh_sbi_simple_interface.py <../../examples/hh_sbi_flexible_interface.py>`
+
+Here you can download the data:
+:download:`input traces <../../examples/input_traces_hh.csv>`
+:download:`output traces <../../examples/output_traces_hh.csv>`
+
+.. code:: python
+
+  from brian2 import *
+  from brian2modelfitting import *
+  import pandas as pd
+
+
+To load the data, use the following:
+
+.. code:: python
+  
+  df_inp_traces = pd.read_csv('input_traces_hh.csv')
+  df_out_traces = pd.read_csv('output_traces_hh.csv')
+  inp_traces = df_inp_traces.to_numpy()
+  inp_traces = inp_traces[[0, 1, 3], 1:]
+  out_traces = df_out_traces.to_numpy()
+  out_traces = out_traces[[0, 1, 3], 1:]
+
+The model used for this example is the Hodgkin-Huxley neuron model.
+The parameters of the model are defined as follows:
+
+.. code:: python
+
+  area = 20_000*um**2
+  El = -65*mV
+  EK = -90*mV
+  ENa = 50*mV
+  VT = -63*mV
+  dt = 0.01*ms
+  eqs = '''
+      dv/dt = (gl*(El-v) - g_na*(m*m*m)*h*(v-ENa) - g_kd*(n*n*n*n)*(v-EK) + I)/Cm : volt
+      dm/dt = 0.32*(mV**-1)*(13.*mV-v+VT)/
+              (exp((13.*mV-v+VT)/(4.*mV))-1.)/ms*(1-m)-0.28*(mV**-1)*(v-VT-40.*mV)/
+              (exp((v-VT-40.*mV)/(5.*mV))-1.)/ms*m : 1
+      dn/dt = 0.032*(mV**-1)*(15.*mV-v+VT)/
+              (exp((15.*mV-v+VT)/(5.*mV))-1.)/ms*(1.-n)-.5*exp((10.*mV-v+VT)/(40.*mV))/ms*n : 1
+      dh/dt = 0.128*exp((17.*mV-v+VT)/(18.*mV))/ms*(1.-h)-4./(1+exp((40.*mV-v+VT)/(5.*mV)))/ms*h : 1
+
+      # unknown parameters
+      g_na : siemens (constant)
+      g_kd : siemens (constant)
+      gl   : siemens (constant)
+      Cm   : farad (constant)
+      '''
+
+Now, let's define the time domain and start with the inferencer procedure
+manually:
+
+.. code:: python
+    
+  t = arange(0, out_traces.shape[1]*dt/ms, dt/ms)
+  t_start, t_end = t[where(inp_traces[0, :] != 0)[0][[0, -1]]]
+
+  # Start with the regular instatiation of the class
+  inferencer = Inferencer(dt=dt, model=eqs,
+                          input={'I': inp_traces*amp},
+                          output={'v': out_traces*mV},
+                          features={'v': [lambda x: max(x),
+                                          lambda x: mean(x[(t > t_start) & (t < t_end)]),
+                                          lambda x: std(x[(t > t_start) & (t < t_end)])]},
+                          method='exponential_euler',
+                          threshold='m > 0.5',
+                          refractory='m > 0.5',
+                          param_init={'v': 'VT'})
+
+The prior should be initialized by defining the upper and lower bounds for
+each unknown parameter:
+
+.. code:: python
+  
+  prior = inferencer.init_prior(gl=[1e-09*siemens, 1e-07*siemens],
+                                g_na=[2e-06*siemens, 2e-04*siemens],
+                                g_kd=[6e-07*siemens, 6e-05*siemens],
+                                Cm=[0.1*uF*cm**-2*area, 2*uF*cm**-2*area])
+
+If the input and output data for the training of the neural density estimator
+already exists, we can load it as follows:
+
+.. code:: python
+
+  path_to_data = ...
+  theta, x = inferencer.load_summary_statistics(path_to_data)
+
+Otherwise, we have to generate training data and summary statistics from a
+given list of features:
+
+.. code:: python
+
+  theta = inferencer.generate_training_data(n_samples=10_000,
+                                            prior=prior)
+  x = inferencer.extract_summary_statistics(theta)
+
+And the data can be saved for the later use:
+
+.. code:: python
+ 
+  inferencer.save_summary_statistics(path_to_data, theta, x)
+
+Finally, let's get our hands dirty and let's perform a single step of
+inference:
+
+.. code:: python
+
+  # amortized inference
+  inference = inferencer.init_inference(inference_method='SNPE',
+                                        density_estimator_model='mdn',
+                                        prior=prior)
+  # first round of inference where no observation data is set to posterior
+  posterior_amortized = inferencer.infer_step(proposal=prior,
+                                              inference=inference,
+                                              theta=theta, x=x)
+
+After the posterior has been built, it can be stored as follows:                                              
+
+.. code:: python
+  
+  # storing the trained posterior without a default observation
+  path_to_posterior = ...
+  inferencer.save_posterior(path_to_posterior)
+
+Now, as in the simple interface example, sampling can be performed via
+``sample`` method where it is enough to define a number of parameters to
+be drawn from the posterior:
+
+.. code:: python
+
+  inferencer.sample((10_000, ))
+
+
+Creating the pairwise relationship visualizations using the approximated
+posterior distribution
+
+.. code:: python
+
+  # define the label for each parameter
+  labels = {'gl': r'$\overline{g}_\mathrm{l}$',
+            'g_na': r'$\overline{g}_\mathrm{Na}$',
+            'g_kd': r'$\overline{g}_\mathrm{K}$',
+            'Cm': r'$\overline{C}_{m}$'}
+  inferencer.pairplot(labels=labels)
+
+
+It is possible to continue with the focused inference (to draw parameters
+from the posterior and to perform the training of a neural network to
+estimate the posterior distribution by focusing on a particular observation)
+by using a standard approach through ``infer`` method:
+
+.. code:: python
+
+  posterior_focused = inferencer.infer()
+
+For every future call of ``inferencer``, the last trained posterior will be
+used by default, e.g., when generating traces by using a single sample of
+parameters from a now non-amortized approximated posterior distribution:
+
+.. code:: python
+
+  inf_traces = inferencer.generate_traces()
+  nrows = 2
+  ncols = out_traces.shape[0]
+  fig, axs = subplots(nrows, ncols, sharex=True,
+                      gridspec_kw={'height_ratios': [3, 1]},
+                      figsize=(ncols * 3, 3))
+  for idx in range(ncols):
+      axs[0, idx].plot(t, out_traces[idx, :].T, 'C3-', lw=3, label='recordings')
+      axs[0, idx].plot(t, inf_traces[idx, :].T/mV, 'k--', lw=2,
+                       label='sampled traces')
+      axs[1, idx].plot(t, inp_traces[idx, :].T/nA, lw=3, c='k', label='stimuli')
+     axs[1, idx].set_xlabel('$t$, ms')
+      if idx == 0:
+          axs[0, idx].set_ylabel('$V$, mV')
+          axs[1, idx].set_ylabel('$I$, nA')
+  handles, labels = [(h + l) for h, l
+                     in zip(axs[0, idx].get_legend_handles_labels(),
+                     axs[1, idx].get_legend_handles_labels())]
+  fig.legend(handles, labels)
+  tight_layout()
+  show()
diff --git a/docs_sphinx/examples/hh_sbi_simple.rst b/docs_sphinx/examples/hh_sbi_simple.rst
index c6a8ab5..ef8d650 100644
--- a/docs_sphinx/examples/hh_sbi_simple.rst
+++ b/docs_sphinx/examples/hh_sbi_simple.rst
@@ -1,7 +1,7 @@
 Inference on Hodgin-Huxley model: simple interface
 ==================================================
 
-You can also download and run this example by clicking here:
+You can also download and run a similar example available here:
 :download:`hh_sbi_simple_interface.py <../../examples/hh_sbi_simple_interface.py>`
 
 Here you can download the data:

From 5aff366f50d798f90af2c166ad15ff8e57c5ffef Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Tue, 17 Aug 2021 21:00:09 +0200
Subject: [PATCH 101/121] small updates

---
 examples/hh_sbi_flexible_interface.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/hh_sbi_flexible_interface.py b/examples/hh_sbi_flexible_interface.py
index c3e8fe5..bdc95b1 100644
--- a/examples/hh_sbi_flexible_interface.py
+++ b/examples/hh_sbi_flexible_interface.py
@@ -68,7 +68,7 @@
     # Extract summary stats
     x = inferencer.extract_summary_statistics(theta)
     # Save the data for later use
-    # inferencer.save_summary_statistics(path_to_data, theta, x)
+    inferencer.save_summary_statistics(path_to_data, theta, x)
 
 # Amortized inference
 # Training the neural density estimator
@@ -81,7 +81,7 @@
                                             theta=theta, x=x)
 # Storing the trained posterior without a default observation
 path_to_posterior = __file__[:-3] + '_posterior_amortized.pth'
-# inferencer.save_posterior(path_to_posterior)
+inferencer.save_posterior(path_to_posterior)
 
 # Sampling from the posterior given observations, ...
 inferencer.sample((10_000, ))

From ccd8ce69557ad13929d8288c44dc2d3155e4843b Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Wed, 18 Aug 2021 14:56:32 +0200
Subject: [PATCH 102/121] update advanced features list

---
 docs_sphinx/features/index.rst | 413 +++++++++++++++++++++++----------
 1 file changed, 290 insertions(+), 123 deletions(-)

diff --git a/docs_sphinx/features/index.rst b/docs_sphinx/features/index.rst
index 9fc3245..1f2d63d 100644
--- a/docs_sphinx/features/index.rst
+++ b/docs_sphinx/features/index.rst
@@ -1,8 +1,10 @@
 Advanced Features
 =================
 
-This part of documentation list other features provided alongside or inside `~brian2modelfitting.fitter.Fitter`
-objects, to help the user with easier and more flexible applications.
+This part of documentation lists other features provided alongside or inside
+`~brian2modelfitting.fitter.Fitter` and `~brian2modelfitting.inferencer.Inferencer`
+objects to allow users easier and a more flexible development when working on
+their own problems.
 
 .. contents::
     :local:
@@ -11,108 +13,199 @@ objects, to help the user with easier and more flexible applications.
 Parameters initialization
 -------------------------
 
-Whilst running `~brian2modelfitting.fitter.Fitter` user can specify values with which model evaluation
-of differential equations start.
+Whilst running `~brian2modelfitting.fitter.Fitter` or `~brian2modelfitting.inferencer.Inferencer`,
+the user is able to pass the values of the parameters and variables that will
+be used as initial conditions when solving the differential equations defined
+in the neuron model.
 
-The fitting functions accept additional dictionary input to address that. To do so,
-dictionary argument has to be added to `~brian2modelfitting.fitter.Fitter` initialization:
+Initial conditions should be passed by using an additional dictionary to the
+constructor:
 
 .. code:: python
 
-   param_init = {'v': -30*mV}
+  init_conds = {'v': -30*mV}
 
 
 .. code:: python
 
-   fitter = TraceFitter(..., param_init = {'v': -30*mV})
+  fitter = TraceFitter(..., param_init = init_conds)
 
+or
+
+.. code:: python
+
+  inferencer = Inferencer(..., param_init = init_conds)
 
 Restart
 -------
-By default any `~brian2modelfitting.fitter.Fitter` works in continuous optimization mode between run, where all of the
-parameters drawn are being evaluated.
+By default any `~brian2modelfitting.fitter.Fitter` object works in continuous
+optimization mode between run, where all of the parameters drawn are being
+evaluated.
 
-Through changing the input flag in `~brian2modelfitting.fitter.Fitter.fit()`: ``restart`` to ``True``, user can reset the optimizer and
-start the optimization from scratch.
+By setting the ``restart`` argument in `~brian2modelfitting.fitter.Fitter.fit()`
+to ``True``, the user can restart the optimizer and the optimization will
+start from scratch.
 
-Used by Fitter optimizer and metric can only be changed when the flat is `True`.
+Used by Fitter optimizer and metric can only be changed when the flat is
+`True`.
 
+The previously outlined ``restart`` argument is used in the similar fashion
+in `~brian2modelfitting.inferencer.Inferencer.infer()` method. It is set to
+``False`` by default, and each following re-call of the method will result
+in the multi-round inference. If the user wants amortized inference without
+using any knowledge from the previous round of optimization instead, the
+``restart`` argument should be set to ``True``.
 
-Multiobjective normalization
+Multi-objective optimization
 ----------------------------
-It is possible to fit more than one output variable at the same time, by combining the errors for each variable. To do
-so, users can specify several output variables during the initialization::
+In the case of `~brian2modelfitting.fitter.Fitter` classes, it is possible to
+fit more than one output variable at the same time by combining the errors for
+each variable. To do so, the user can specify several output variables during
+the initialization as follows:
+
+.. code:: python
+
+  fitter = TraceFitter(...,
+                       output={'x': target_x,
+                               'y': target_y})
+
+If the fitter function uses a single metric, it is applied to both variables.
+
+.. note::
+ 
+  This approach requires that the resulting error has the same units for all
+  variables, i.e., it would not be possible to use the same `.MSEMetric` on
+  variables with different units, since the errors cannot be simply added up.
+
+As a more general solution, the user can specify a metric for each variable
+and utilize their normalization arguments to make the units compatible (most
+commonly by turning both errors into dimensionless quantities). The
+normalization also defines the relative weights of all errors. For example, if
+the variable ``x`` has dimensions of mV and the variable ``y`` is
+dimensionless, the following metrics can be used to make an error of 10 mV in
+``x`` to be weighed as much as an error of 0.1 in ``y``
+
+.. code:: python
+
+  metrics = {'x': MSEMetric(normalization=10*mV),
+             'y': MSEMetric(normalization=0.1)}
+
+This has to be passed as the ``metric`` argument of the `~brian2modelfitting.fitter.Fitter.fit`
+function.
+
+In the case of the `~brian2modelfitting.inferencer.Inferencer` class,
+switching from a single- to multi-objective optimization is seamless. The user
+has to provide multiple output variables during the initialization process the
+same way as for `~brian2modelfitting.fitter.Fitter` classes:
+
+.. code:: python
 
-    fitter = TraceFitter(..., output={'x': target_x,
-                                      'y': target_y},
-                         ...)
+  inferencer = Inferencer(...,
+                          output={'x': target_x,
+                                  'y': target_y})
 
-If the fit uses a single metric, it applies to both variables. Note that this requires that the resulting error has the
-same units for both variables – for example, it would not be possible to use the same `.MSEMetric` on variables with
-different units, since the errors cannot be simply added up. As a more general solution, users can specify a metric for
-each variable and use their normalization arguments to make the units compatible (most commonly by turning both errors
-into dimensionless quantities). This normalization also defines the relative weights of both errors. For example, if the
-variable ``x`` has dimensions of volt and the variable ``y`` is dimensionless, the following metrics can be used to make
-an error of 10mV in ``x`` to be weighed as much as an error of 0.1 in ``y``::
+Later, during the inference process, the user has to define feautres for each
+output variable as follows:
 
-    metrics = {'x': MSEMetric(normalization=10*mV)
-               'y': MSEMetric(normalization=0.1)}
+.. code:: python
+
+  posterior = inferencer.infer(...,
+                               features={'x': list_of_features_for_x,
+                                         'y': list_of_features_for_y})
 
-This dictionary then has to be provided as the ``metric`` argument of the `~.Fitter.fit` function.
+If the user prefers automatic feature extraction, the ``features`` argument
+should not be defined (it should stay set to None).
 
+.. warning:: *The number of features - be aware*
+ 
+  If the user chooses to define a list of features for extracting the summary
+  features, it is important to keep in mind that the total number of features
+  will be increased as many times as there are output variables set for
+  multi-objective optimization.
 
 Callback function
 -----------------
 
-To visualize the progress of the optimization we provided few possibilities of feedback
-inside `~brian2modelfitting.fitter.Fitter`.
-
+To visualize the progress of the optimization we provided few possibilities of
+the feedback inside the `~brian2modelfitting.fitter.Fitter`.
 
 The 'callback' input provides few default options, updated in each round:
- - ``'text'`` (default) that prints out the parameters of the best fit and corresponding error
- - ``'progressbar'`` that uses ``tqdm.autonotebook`` to provide a progress bar
- - ``None`` for non-verbose option
+ - ``'text'`` (default) - prints out the parameters of the best fit and
+   corresponding error;
+ - ``'progressbar'`` - uses ``tqdm.autonotebook`` to provide a progress bar;
+ - ``None`` - non-verbose;
 
 as well as **customized feedback option**. User can provide
-a *callable* (i.e. function), that will provide an output or printout. If callback returns
-``True`` the fitting execution is interrupted.
+a *callable* (i.e., a function), that ensures either returning an output or
+printout. If callback returns ``True``, the fitting execution will be
+interrupted.
 
 User gets four arguments to customize over:
- - ``params`` - set of parameters from current round
- - ``errors`` - set of errors from current round
- - ``best_params`` - best parameters globally, from all rounds
- - ``best_error`` - best parameters globally, from all rounds
- - ``index`` - index of current round
+ - ``params`` - set of parameters from current round;
+ - ``errors`` - set of errors from current round;
+ - ``best_params`` - best parameters globally, from all rounds;
+ - ``best_error`` - best parameters globally, from all rounds;
+ - ``index`` - index of current round.
 
-An example function:
+An example callback function:
 
 .. code:: python
 
- def callback(params, errors, best_params, best_error, index):
-    print('index {} errors minimum: {}'.format(index, min(errors)))
-
-.. code:: python
+  def callback_fun(params, errors, best_params, best_error, index):
+      print('index {} errors minimum: {}'.format(index, min(errors)))
 
-   fitter = TraceFitter(...)
-   result, error  = fitter.fit(..., callback=...)
+  ...
 
+  fitter = TraceFitter(...)
+  result, error  = fitter.fit(..., callback=callback_fun)
 
+OnlineTraceFitter
+-----------------
+  
+`~brian2modelfitting.fitter.OnlineTraceFitter` was created to work with long
+traces or large-scale optimization problems. This `~brian2modelfitting.fitter.Fitter`
+class uses online mean square error as a metric.
+When the `~brian2modelfitting.fitter.OnlineTraceFitter.fit()` method is called
+there is no need of specifying a metric, which is by default set to None.
+The errors are instead calculated with `~brian2.groups.group.Group.run_regularly`
+for each simulation.
+  
+  .. code:: python
+  
+  fitter = OnlineTraceFitter(model=model,
+                             input={'I': inp_traces},
+                             output={'v': out_traces},
+                             dt=0.1*ms,
+                             n_samples=5)
+  
+  result, error = fitter.fit(optimizer=optimizer,
+                             n_rounds=1,
+                             gl=[1e-8*siemens*cm**-2 * area, 1e-3*siemens*cm**-2 * area])
+  
+Reference the target values in the equations
+--------------------------------------------
+  
+A model can refer to the target output values within the equations. For
+example, if the membrane potential trace *v* (i.e. `output_var='v'`) is used
+for the optimization, equations can refer to the target trace as `v_target`.
+This allows adding a coupling term such as: `coupling*(v_target - v)` to
+the equation that corresponds to state variable `v`, pulling the trajectory
+towards the correct solution.
 
 Generate Traces
 ---------------
 
-With the same `~brian2modelfitting.fitter.Fitter` class user can also generate the traces with newly
-optimized parameters.
+`~brian2modelfitting.fitter.Fitter` and `~brian2modelfitting.inferencer.Inferencer`
+classes allow the user can to generate the traces with optimized parameters.
 
-To simulate and visualize the traces or spikes for the parameters of choice.
-For a quick access to best fitted set of parameters Fitter classes provided
-ready to use functions:
+For a quick access to best fitted set of parameters `~brian2modelfitting.fitter.Fitter`
+classes provide ready to use functions:
 
  - `~brian2modelfitting.fitter.TraceFitter.generate_traces` inside `~brian2modelfitting.fitter.TraceFitter`
  - `~brian2modelfitting.fitter.SpikeFitter.generate_spikes` inside `~brian2modelfitting.fitter.SpikeFitter`
 
-Functions can be called after fitting in the following manner, without
-any input arguments:
+These functions can be called after the fitting procedure is finalized in the
+following manner, without any input arguments:
 
 .. code:: python
 
@@ -126,27 +219,46 @@ any input arguments:
     results, error = fitter.fit(...)
     spikes = fitter.generate_spikes()
 
+On the other hand, since the `~brian2modelfitting.inferencer.Inferencer` class
+is able to perform the inference of the unknown parameter distribution by
+utilizing output traces and spike trains simultaneously, ``generate_traces``
+is used for both.
+
+Once the approximated posterior distribution is built, the user is allowed to
+call ``generate_traces`` on `~brian2modelfitting.inferencer.Inferencer`
+object. If only one output variable is used for the optimization of the
+parameters, the user does not have to specifiy output variable in the 
+``generate_traces`` method through ``output_var`` argument. If, for example,
+the multi-objective optimization is performed by using both output traces and
+spike trains and the user is interested in only times of spike events,
+``output_var`` should be set to ``'spike'``. Otherwise, if the user specifies
+a list of names or the ``output_var`` is not specified, a dictionary with keys
+set to output variable names and with their respective values, will be
+returned instead.
 
-Custom generate
-~~~~~~~~~~~~~~~
 
-To create traces for other parameters, or generate traces after spike
-train fitting, user can call the - `~brian2modelfitting.fitter.Fitter.generate` call, that takes in following
-arguments:
+Customize ``generate`` for `~brian2modelfitting.fitter.Fitter`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To create traces for other parameters, or generate traces after the spike
+train fitting, user can call the `~brian2modelfitting.fitter.Fitter.generate`
+method, which takes in the following arguments:
 
 .. code:: python
 
-  fitter.generate(params=None, output_var=None, param_init=None, level=0)
+  fitter.generate(params=..., output_var=..., param_init=..., level=0)
 
-Where ``params`` is a dictionary of parameters for which the traces we generate.
-``output_var`` provides an option to pick one or more variable for visualization. With
-``param_init``, user can define the initial values for differential equations.
-``level`` allows for specification of namespace level from which we get
-the constant parameters of the model.
+where ``params`` should be a dictionary of parameters for which we generate
+the traces; ``output_var`` provides an option to pick one or more variables
+for visualization; with ``param_init``, the user is able to define the initial
+values for differential equations in the model; and ``level`` allows for
+specification of the namespace level from which we are able to get the
+constant parameters of the model.
 
-If ``output_var`` is the name of a single variable name (or the special name ``'spikes'``), a single `~.Quantity`
-(for normal variables) or a list of spikes time arrays (for ``'spikes'``) will be returned. If a list of names is
-provided, then the result is a dictionary with all the results.
+If ``output_var`` is the name of a single variable name (or the special name
+``'spikes'``), a single `~.Quantity` (for variables) or a list of spikes time
+arrays (for ``'spikes'``) will be returned. If a list of names is provided,
+then the result is a dictionary with all the results.
 
 .. code:: python
 
@@ -155,43 +267,44 @@ provided, then the result is a dictionary with all the results.
     traces = fitter.generate(output_var=['v', 'h', 'n', 'm'])
     v_trace = traces['v']
     h_trace = traces['h']
-    ...
-
 
 Results
 -------
 
-Fitter class stores all of the parameters examined by the optimizer as well
-as the corresponding error. To retrieve them you can call the - `~brian2modelfitting.fitter.Fitter.results`.
+`~brian2modelfitting.fitter.Fitter` classes store all of the parameters
+used by the optimizer as well as the corresponding errors. To retrieve them
+you can call the `~brian2modelfitting.fitter.Fitter.results`.
 
 
 .. code:: python
 
-    fitter = TraceFitter(...)
-    ...
-    traces = fitter.generate_traces()
+  fitter = TraceFitter(...)
+  ...
+  traces = fitter.generate_traces()
 
 .. code:: python
 
-    fitter = SpikeFitter(...)
-    ...
-    results = fitter.results(format='dataframe')
+  fitter = SpikeFitter(...)
+  ...
+  results = fitter.results(format='dataframe')
 
 
 Results can be returned in one of the following formats:
 
- - ``'list'`` (default) returns a list of dictionaries with corresponding parameters (including units) and errors
- - ``'dict'`` returns a dictionary of arrays with corresponding parameters (including units) and errors
- - ``'dataframe'`` returns a `~pandas.DataFrame` (without units)
+ - ``'list'`` (default) - returns a list of dictionaries with corresponding
+   parameters (including units) and errors;
+ - ``'dict'`` - returns a dictionary of arrays with corresponding parameters
+   (including units) and errors;
+ - ``'dataframe'`` - returns a `~pandas.DataFrame` (without units).
 
-The use of units (only relevant for formats ``'list'`` and ``'dict'``) can be switched
-on or off with the ``use_units`` argument. If it is not specified, it will default to
-the value used during the initialization of the `Fitter` (which itself defaults to
-``True``).
+The use of units (only relevant for formats ``'list'`` and ``'dict'``) can be
+switched on or off with the ``use_units`` argument. If it is not specified, it
+will default to the value used during the initialization of the `Fitter`
+(which itself defaults to ``True``).
 
 Example output:
 ~~~~~~~~~~~~~~~
-``'list'``:
+- ``'list'``:
 
 .. code:: python
 
@@ -200,7 +313,7 @@ Example output:
    ...]
 
 
-``'dict'``:
+- ``'dict'``:
 
 .. code:: python
 
@@ -210,68 +323,122 @@ Example output:
    'errors': array([1.16788502, 0.5253008 , ...])}
 
 
-``'dataframe'``:
+- ``'dataframe'``:
 
 .. code:: python
 
-   g_na            gl      g_kd    errors
-   0  0.000280  8.870238e-08  0.000047  0.521425
-   1  0.000192  1.121861e-07  0.000118  0.387140
-   ...
+     g_na      gl            g_kd      errors
+  0  0.000280  8.870238e-08  0.000047  0.521425
+  1  0.000192  1.121861e-07  0.000118  0.387140
+  ...
+
 
+Posterior distribution analysis
+-------------------------------
 
+Unlike `~brian2modelfitting.fitter.Fitter` classes, the `~brian2modelfitting.inferencer.Inferencer`
+class does not keep track of all parameter values. Rather, it stores all
+training data for neural density estimator which will later be used for
+building the posterior distribution of each unknown parameter. Thus, the `~brian2modelfitting.inferencer.Inferencer`
+does not returns best-fit values and corresponding errors, but the entire
+posterior distribution that can be used to draw samples from, compute
+descriptive statistics of parameters, analyize pairwise relationship between
+each to parameters, etc.
+
+There are three methods that enable the comprehensive analysis of the
+posterior:
+
+- `~brian2modelfitting.inferencer.Inferencer.pairplot` - returns axes of drawn
+  samples from the posterior in a 2-dimenstional grid with marginals and
+  pairwise marginals. Using this method, the user is able to inspect the
+  relationship for all combinations of distributions for each parameter;
+- `~brian2modelfitting.inferencer.Inferencer.conditional_pairplot` -
+  visualizes the conditional pairplot;
+- `~brian2modelfitting.inferencer.Inferencer.conditional_corrcoeff` - returns
+  the correlation matrix of a distribution conditioned with the user-specified
+  condition.
+
+To see this in action, go to our tutorial page and learn how to use each of
+the previouly outlined methods.
 
 Standalone mode
 ---------------
 
-Just like with regular Brian script, modelfitting computations can be performed in
-``Runtime`` mode (default) or ``Standalone`` mode.
-(https://brian2.readthedocs.io/en/stable/user/computation.html)
+Just like with regular Brian 2 scripts, all computations in the toolbox can be
+performed in ``Runtime`` mode (default) or ``Standalone`` mode. For details,
+please check the official Brian 2 documentation: https://brian2.readthedocs.io/en/stable/user/computation.html
 
-To enable this mode, add the following line after your Brian import, but before your simulation code:
+To enable the ``Standalone`` mode, and to allowthe source code generation to
+C++ code, add the following code right after Brian 2 is imported, but before
+the simulation code:
 
 .. code:: python
 
   set_device('cpp_standalone')
 
-
 Important notes:
 ~~~~~~~~~~~~~~~~
 
 .. warning::
-    In standlone mode one script can not be used to contain multiple - `~brian2modelfitting.fitter.Fitter`, use separate scripts!
+
+  In the ``Standalone`` mode, a single script should not contain multiple
+  `~brian2modelfitting.fitter.Fitter` or `~brian2modelfitting.inferencer.Inferencer`
+  classes. Please, use separate scripts.
 
 Note that the generation of traces or spikes via `~brian2modelfitting.fitter.Fitter.generate`
 will always use runtime mode, even when the fitting procedure uses standalone mode.
 
+Embedding network for automatic feature extraction
+--------------------------------------------------
 
-OnlineTraceFitter
------------------
-
-`~brian2modelfitting.fitter.OnlineTraceFitter` was created to work with long traces or big optimization.
-This `~brian2modelfitting.fitter.Fitter` uses online Mean Square Error as a metric.
-When `~brian2modelfitting.fitter.Fitter.fit()` is called there is no need of specifying a metric, that is by
-default set to None. Instead the errors are calculated with use of brian's `~brian2.groups.group.Group.run_regularly`,
-with each simulation.
+If the ``features`` argument of the `~brian2modelfitting.inferencer.Inferencer`
+class is not defined, automatic feature extraction from the given output
+traces will occur. By default, this is done by using the multi-layer
+perceptron that is trained in parallel with the neural density estimator of
+choice during the inference process. If the user wants to specify their own
+custom embedding network, it is possible to do so by creating a neural
+network by using ``PyTorch`` library and passing the instance of that neural
+network as an additional keyword argument as follows:
 
 .. code:: python
+  
+  import torch
+  from torch import nn
+  
+  ...
 
-  fitter = OnlineTraceFitter(model=model,
-                             input={'I': inp_traces},
-                             output={'v': out_traces},
-                             dt=0.1*ms,
-                             n_samples=5)
+  class CustomEmbeddingNet(nn.Module):
 
-  result, error = fitter.fit(optimizer=optimizer,
-                             n_rounds=1,
-                             gl=[1e-8*siemens*cm**-2 * area, 1e-3*siemens*cm**-2 * area],)
+      def __init__(self, in_features, out_features, ...):
+          ...
 
+      def forward(self, x):
+          ...
 
-Reference the target values in the equations
---------------------------------------------
+  
+  in_features = out_traces.shape[1]
+  out_features = ...
+  embedding_net = CustomEmbeddingNet(in_features, out_features, ...)
+
+  ...
+
+  inferencer = Inferencer(...)
+  inferencer.infer(...,
+                   inference_kwargs={'embedding_net': embedding_net})
+
+GPU usage for inference
+-----------------------
+
+It is possible to use the GPU for training the sdensity estimator. It is enough
+to specify the ``sbi_device`` to ``'gpu'`` or ``'cuda'``.  Otherwise, if not
+specified, or if set to ``'cpu'``, training will be done by using the CPU.
+
+.. note::
+
+  For default density estimators that are used either for SNPE, SNLE and SNRE,
+  there are no significant speed-ups expected if the training is translocated
+  to the GPU.
 
-A model can refer to the target output values within the equations. For example, if you
-are fitting a membrane potential trace *v* (i.e. `output_var='v'`), then the equations
-can refer to the target trace as `v_target`. This allows you for example to add a coupling
-term like `coupling*(v_target - v)` to the equation for `v`, pulling the trajectory towards the
-correct solution.
\ No newline at end of file
+It is, however, possible to achieve a significant speed-up if the custom
+embedding network relies on convolutions to extract feautres. Such operations
+are known to achieve improvement in compuation time multifold.

From 97163b6d4c8ef59db76f9ebc39006189243e9355 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Wed, 18 Aug 2021 14:57:02 +0200
Subject: [PATCH 103/121] fix docstrings for conditional_corrcoeff

---
 brian2modelfitting/inferencer.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index d9e3ccd..77cd7d5 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -1108,6 +1108,9 @@ def conditional_pairplot(self, condition, density=None, points=None,
                              ticks=None, **kwargs):
         """Plot conditional distribution given all other parameters.
 
+        The conditionals can be interpreted as slices through the
+        density at a location given by condition.
+
         Check ``sbi.analysis.conditional_pairplot`` for more details.
 
         Parameters
@@ -1212,7 +1215,15 @@ def conditional_pairplot(self, condition, density=None, points=None,
 
     def conditional_corrcoeff(self, condition, density=None, limits=None,
                               subset=None, **kwargs):
-        """Plot conditional distribution given all other parameters.
+        """Return the conditional correlation matrix of a distribution.
+
+        All but two parameters are conditioned with the condition as
+        defined in the ``condition`` argument and the Pearson
+        correlation coefficient is computed between the remaining two
+        parameters under the distribution. This is performed for all
+        pairs of parameters given whose names are defined in the
+        ``subset`` argument. The conditional correlation matrix is
+        stored in the 2-dimenstional array.
 
         Check ``sbi.analysis.conditional_density.conditional_corrcoeff``
         for more details.

From 160de3ca841bf365fd4c9a9e772b8a0c3d659d79 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Wed, 18 Aug 2021 15:06:28 +0200
Subject: [PATCH 104/121] fix typos

---
 docs_sphinx/features/index.rst | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/docs_sphinx/features/index.rst b/docs_sphinx/features/index.rst
index 1f2d63d..806ec6b 100644
--- a/docs_sphinx/features/index.rst
+++ b/docs_sphinx/features/index.rst
@@ -34,7 +34,7 @@ or
 
 .. code:: python
 
-  inferencer = Inferencer(..., param_init = init_conds)
+  inferencer = Inferencer(..., param_init=init_conds)
 
 Restart
 -------
@@ -47,7 +47,7 @@ to ``True``, the user can restart the optimizer and the optimization will
 start from scratch.
 
 Used by Fitter optimizer and metric can only be changed when the flat is
-`True`.
+``True``.
 
 The previously outlined ``restart`` argument is used in the similar fashion
 in `~brian2modelfitting.inferencer.Inferencer.infer()` method. It is set to
@@ -116,7 +116,7 @@ output variable as follows:
 If the user prefers automatic feature extraction, the ``features`` argument
 should not be defined (it should stay set to None).
 
-.. warning:: *The number of features - be aware*
+.. warning::
  
   If the user chooses to define a list of features for extracting the summary
   features, it is important to keep in mind that the total number of features
@@ -170,7 +170,7 @@ there is no need of specifying a metric, which is by default set to None.
 The errors are instead calculated with `~brian2.groups.group.Group.run_regularly`
 for each simulation.
   
-  .. code:: python
+.. code:: python
   
   fitter = OnlineTraceFitter(model=model,
                              input={'I': inp_traces},
@@ -201,8 +201,8 @@ classes allow the user can to generate the traces with optimized parameters.
 For a quick access to best fitted set of parameters `~brian2modelfitting.fitter.Fitter`
 classes provide ready to use functions:
 
- - `~brian2modelfitting.fitter.TraceFitter.generate_traces` inside `~brian2modelfitting.fitter.TraceFitter`
- - `~brian2modelfitting.fitter.SpikeFitter.generate_spikes` inside `~brian2modelfitting.fitter.SpikeFitter`
+ - `~brian2modelfitting.fitter.TraceFitter.generate_traces` inside `~brian2modelfitting.fitter.TraceFitter`;
+ - `~brian2modelfitting.fitter.SpikeFitter.generate_spikes` inside `~brian2modelfitting.fitter.SpikeFitter`.
 
 These functions can be called after the fitting procedure is finalized in the
 following manner, without any input arguments:
@@ -237,8 +237,8 @@ set to output variable names and with their respective values, will be
 returned instead.
 
 
-Customize ``generate`` for `~brian2modelfitting.fitter.Fitter`
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Customize the ``generate`` method for `~brian2modelfitting.fitter.Fitter`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 To create traces for other parameters, or generate traces after the spike
 train fitting, user can call the `~brian2modelfitting.fitter.Fitter.generate`
@@ -304,7 +304,7 @@ will default to the value used during the initialization of the `Fitter`
 
 Example output:
 ~~~~~~~~~~~~~~~
-- ``'list'``:
+- ``format='list'``:
 
 .. code:: python
 
@@ -313,7 +313,7 @@ Example output:
    ...]
 
 
-- ``'dict'``:
+- ``format='dict'``:
 
 .. code:: python
 
@@ -323,7 +323,7 @@ Example output:
    'errors': array([1.16788502, 0.5253008 , ...])}
 
 
-- ``'dataframe'``:
+- ``format='dataframe'``:
 
 .. code:: python
 
@@ -359,7 +359,7 @@ posterior:
   condition.
 
 To see this in action, go to our tutorial page and learn how to use each of
-the previouly outlined methods.
+these methods.
 
 Standalone mode
 ---------------

From a82670b3aa4df3944b2168ad6e6c44e46bf1b845 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 20 Aug 2021 15:54:13 +0200
Subject: [PATCH 105/121] figures for the sbi tutorial

---
 .../hh_sbi_4params_3features_pairplot.png       | Bin 0 -> 68237 bytes
 .../hh_sbi_4params_8features_pairplot.png       | Bin 0 -> 32189 bytes
 ...i_4params_default_automatic_ext_pairplot.png | Bin 0 -> 32469 bytes
 .../_static/hh_sbi_4params_inf_traces.png       | Bin 0 -> 87247 bytes
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From f60c27871d9259eb6ef0ca6e6b73292d32a4127c Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 20 Aug 2021 15:54:35 +0200
Subject: [PATCH 106/121] add tutorial on sbi

---
 docs_sphinx/introduction/tutorial_sbi.rst | 679 +++++++++++++++++++++-
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 Tutorial: Inferencer
 ====================
 
-tba
\ No newline at end of file
+In this tutorial, we use simulation-based inference on the Hodgkin-Huxley
+neuron model, where different scenarios are considered. The data are
+synthetically generated from a model by using the script available `here <https://github.com/brian-team/brian2/blob/master/examples/advanced/modelfitting_sbi.py>`_.
+This enables the comparisson of the inferred parameter values with the ground
+truth parameter values as used in the generative model.
+
+We start with importing basic packages and modules. Note that
+``tutorial_sbi_helpers`` contains three different functions that are used
+extensively throughout this tutorial. Namely, ``plot_traces`` is used for
+visualization of the input current and the output voltage traces, and optional
+time series of spikes or sampled traces obtained by using an estimated
+posterior. In order to detect and capture spike events in a given voltage
+trace, we use ``spike_times`` function. Finally, ``plot_cond_coeff_mat`` is
+used to visualize conditional coerrelation matrix. To check how each of these
+functions works behind the scenes, download the script and go throguh the code
+yourself: :download:`tutorial_sbi_helpers.py <../../examples/tutorial_sbi_helpers.py>`
+
+.. code:: python
+
+    from brian2 import *
+    from brian2modelfitting import Inferencer
+    from scipy.stats import kurtosis as kurt
+    from tutorial_sbi_helpers import (spike_times,
+                                      plot_traces,
+                                      plot_cond_coeff_mat)
+
+Now, let's load the input current and output voltage traces by using ``NumPy``.
+Not that all functions available in ``NumPy``, as well as in ``Matplotlib``,
+are implicitly available via ``brian2`` import.
+
+.. code:: python
+
+    inp_traces = load('input_traces_synthetic.npy').reshape(1, -1)
+    out_traces = load('output_traces_synthetic.npy').reshape(1, -1)
+
+You can download the ``.npy`` files loaded above:
+:download:`input_traces_synthetic.npy <../../examples/output_traces_synthetic.npy>`,
+:download:`output_traces_synthetic.npy <../../examples/output_traces_synthetic.npy>`
+
+
+By setting the time step, we are able to set up the time domain:
+
+.. code:: python
+
+    dt = 0.05*ms
+    t = arange(0, inp_traces.size*dt/ms, dt/ms)
+    stim_start, stim_end = t[where(inp_traces[0, :] != 0)[0][[0, -1]]]
+
+By calling a ``plot_traces`` and passing an array of time steps, input current
+and output voltage traces, we obtain the visualization of a synthetic recording
+of neural activity:
+
+.. code:: python
+
+    fig, ax = plot_traces(t, inp_traces, out_traces)
+
+.. image:: ../_static/hh_sbi_recording_traces.png
+
+
+Toy-example: infer two free parameters
+--------------------------------------
+
+The first scenario we cover is a simple inference procedure of two unknown
+parameters in the Hodgkin-Huxley neuron model. The parameters to infer are
+the maximal value of sodium and potassium electrical conductances.
+
+As a standard practice in Brian 2 simulator, we have to define parameters of
+the model, initial conditions for differential equations that describe the
+model, and the model itself:
+
+.. code:: python
+
+    # set parameters of the model
+    E_Na = 53*mV
+    E_K = -107*mV
+    E_l = -70*mV
+    VT = -60.0*mV
+    g_l = 10*nS
+    Cm = 200*pF
+
+    # set ground truth parameters, which are unknown from the model's perspective
+    ground_truth_params = {'g_Na': 32*uS,
+                           'g_K': 1*uS}
+
+    # define initial conditions
+    init_conds = {'v': 'E_l',
+                 'm': '1 / (1 + beta_m / alpha_m)',
+                 'h': '1 / (1 + beta_h / alpha_h)',
+                 'n': '1 / (1 + beta_n / alpha_n)'}
+
+    # define the Hodgkin-Huxley neuron model
+    eqs = '''
+        # non-linear set of ordinary differential equations
+        dv/dt = - (g_Na * m ** 3 * h * (v - E_Na)
+                   + g_K * n ** 4 * (v - E_K)
+                   + g_l * (v - E_l) - I) / Cm : volt
+        dm/dt = alpha_m * (1 - m) - beta_m * m : 1
+        dn/dt = alpha_n * (1 - n) - beta_n * n : 1
+        dh/dt = alpha_h * (1 - h) - beta_h * h : 1
+        
+        # time independent rate constants for a channel activation/inactivation
+        alpha_m = ((-0.32 / mV) * (v - VT - 13.*mV))
+                   / (exp((-(v - VT - 13.*mV)) / (4.*mV)) - 1) / ms : Hz
+        beta_m = ((0.28/mV) * (v - VT - 40.*mV))
+                  / (exp((v - VT - 40.*mV) / (5.*mV)) - 1) / ms : Hz
+        alpha_h = 0.128 * exp(-(v - VT - 17.*mV) / (18.*mV)) / ms : Hz
+        beta_h = 4 / (1 + exp((-(v - VT - 40.*mV)) / (5.*mV))) / ms : Hz
+        alpha_n = ((-0.032/mV) * (v - VT - 15.*mV))
+                   / (exp((-(v - VT - 15.*mV)) / (5.*mV)) - 1) / ms : Hz
+        beta_n = 0.5 * exp(-(v - VT - 10.*mV) / (40.*mV)) / ms : Hz
+            
+        # free parameters
+        g_Na : siemens (constant)
+        g_K : siemens (constant)
+        '''
+
+Since the output of the model is very high-dimensional, and since we are
+interested only in a few hand-picked features that will capture the gist of
+the neuronal activity, we start the inference process by defining a list of
+summary functions.
+
+Each summary feature is obtained by calling a single-valued function on each
+trace generated by using a sampled prior distribution over unknown parameters.
+In this case, we consider the maximal value of the action potential, the mean
+action potential and the standard deviatian of the action potential.
+
+.. code:: python
+
+    v_features = [
+        # max action potential
+        lambda x: np.max(x[(t > stim_start) & (t < stim_end)]),
+        # mean action potential
+        lambda x: np.mean(x[(t > stim_start) & (t < stim_end)]),
+        # std of action potential
+        lambda x: np.std(x[(t > stim_start) & (t < stim_end)]),
+        ]
+
+
+``Inferencer``
+~~~~~~~~~~~~~~
+
+The minimum set of arguments for the ``Inferencer`` class constructor are the
+time step, ``dt``, input data traces, ``input``, output data traces, ``output``
+and the model that will be used for the inference process, ``model``.
+Input and output traces should be shaped so that the number of rows corresponds
+to each individual observed trace and the number of columns is equal to the
+number of time steps. 
+
+Here, we define additional arguments such as ``method`` to define an
+integration technique used for solving the set of differential equations,
+``threshold`` to define a condition that produce a single spike, ``refractory``
+to define a condition under which a neuron remains refractory, and
+``param_init`` to define a set of initial conditions. We also define the set of
+summary features that is used to represent the data instead of using the entire
+trace. Summary features are passed to the inference algorithm via the
+``features`` argument.
+
+.. code:: python
+
+    inferencer = Inferencer(dt=dt, model=eqs,
+                            input={'I': inp_traces*amp},
+                            output={'v': out_traces*mV},
+                            features={'v': v_features},
+                            method='exponential_euler',
+                            threshold='m > 0.5',
+                            refractory='m > 0.5',
+                            param_init=init_conds)
+
+After the ``Inferencer`` object is created, we begin the inference process by
+calling ``infer`` method and defining the total number of samples that are used
+for the learning of a neural density estimator.
+
+
+Posterior
+~~~~~~~~~
+
+Neural density estimator learns the probabilistic mapping of the input data,
+i.e., sampled parameter values given a prior distribution, and the output data,
+i.e., summary features extracted from the traces, obtained by solving the model
+with the corresponding set of sampled parameters from the input data.
+
+We can choose the inference method and the estimator model , but the only
+arguments that are required to be passed to ``infer`` method are the number of
+samples (in case of running the inference process for the first time),
+``n_samples``, and upper and lower bounds for each unknown parameter.
+
+.. code:: python
+
+    posterior = inferencer.infer(n_samples=10_000,
+                                 n_rounds=1,
+                                 inference_method='SNPE',
+                                 density_estimator_model='maf',
+                                 g_Na=[1*uS, 100*uS],
+                                 g_K=[0.1*uS, 10*uS])
+
+After the inference is completed, the estimated posterior distribution over
+unkown parameters can be analyzed by observing the pairwise relationship
+between each pair of parameters.
+But before, we have to draw samples from the estimated posterior as follows:
+
+.. code:: python
+
+    samples = inferencer.sample((10_000, ))
+
+The samples are stored inside the ``Inferencer``, and are available through
+the ``samples`` variable.
+We create a visual representation of the pairwise relationship of the posterior
+as follows:
+
+.. code:: python
+
+    limits = {'g_Na': [1*uS, 100*uS],
+              'g_K': [0.1*uS, 10*uS]}
+    labels = {'g_Na': r'$\overline{g}_{Na}$',
+              'g_K': r'$\overline{g}_{K}$'}
+    fig, ax = inferencer.pairplot(limits=limits,
+                                  labels=labels,
+                                  ticks=limits,
+                                  points=ground_truth_params,
+                                  points_offdiag={'markersize': 5},
+                                  points_colors=['C3'],
+                                  figsize=(6, 6))
+
+.. image:: ../_static/hh_sbi_toy_pairplot.png
+
+The inferred posterior is plotted against the ground truth parameters, and as
+can be seen, the ground truth parameters are located in a high-probability
+region of the estimated distribution.
+
+To further substantiate this, let's now see the traces simulated from a single
+set of parameters sampled from the posterior:
+
+.. code:: python
+
+    inf_traces = inferencer.generate_traces()
+
+We again use the ``plot_traces`` function as follows:
+
+.. code:: python
+
+    fig, ax = plot_traces(t, inp_traces, out_traces, inf_traces=array(inf_traces/mV))
+
+.. image:: ../_static/hh_sbi_toy_inf_traces.png
+
+
+Additional free parameters
+--------------------------
+
+The simple scenarios where only two parameters are considered unkwon works
+quite well on the synthetic data. What if we have a larger number of unkown
+parameters? Let us now consider leakage additional unkown parameters for the
+same model as before. In addition to the unknown maximal values of the
+electrical conductance of the sodium and potassium channels, the membrane
+capacity and the maximal value of the electrical conductance of the leakage
+ion channel are also unknown.
+
+We can try to do the same as before:
+
+.. code:: python
+    del Cm, g_l
+
+    # set parameters, initial condition and the model
+    E_Na = 53*mV
+    E_K = -107*mV
+    E_l = -70*mV
+    VT = -60.0*mV
+
+    ground_truth_params = {'g_Na': 32*uS,
+                           'g_K': 1*uS,
+                           'g_l': 10*nS,
+                           'Cm': 200*pF}
+
+    init_conds = {'v': 'E_l',
+                  'm': '1 / (1 + beta_m / alpha_m)',
+                  'h': '1 / (1 + beta_h / alpha_h)',
+                  'n': '1 / (1 + beta_n / alpha_n)'}
+
+    eqs = '''
+        # non-linear set of ordinary differential equations
+        dv/dt = - (g_Na * m ** 3 * h * (v - E_Na)
+                   + g_K * n ** 4 * (v - E_K)
+                   + g_l * (v - E_l) - I) / Cm : volt
+        dm/dt = alpha_m * (1 - m) - beta_m * m : 1
+        dn/dt = alpha_n * (1 - n) - beta_n * n : 1
+        dh/dt = alpha_h * (1 - h) - beta_h * h : 1
+        
+        # time independent rate constants for activation and inactivation
+        alpha_m = ((-0.32 / mV) * (v - VT - 13.*mV))
+                   / (exp((-(v - VT - 13.*mV)) / (4.*mV)) - 1) / ms : Hz
+        beta_m = ((0.28/mV) * (v - VT - 40.*mV))
+                  / (exp((v - VT - 40.*mV) / (5.*mV)) - 1) / ms : Hz
+        alpha_h = 0.128 * exp(-(v - VT - 17.*mV) / (18.*mV)) / ms : Hz
+        beta_h = 4 / (1 + exp((-(v - VT - 40.*mV)) / (5.*mV))) / ms : Hz
+        alpha_n = ((-0.032/mV) * (v - VT - 15.*mV))
+                   / (exp((-(v - VT - 15.*mV)) / (5.*mV)) - 1) / ms : Hz
+        beta_n = 0.5 * exp(-(v - VT - 10.*mV) / (40.*mV)) / ms : Hz
+            
+        # free parameters
+        g_Na : siemens (constant)
+        g_K : siemens (constant)
+        g_l : siemens (constant)
+        Cm : farad (constant)
+        '''
+
+    # infer the posterior using the same configuration as before
+    inferencer = Inferencer(dt=dt, model=eqs,
+                            input={'I': inp_traces*amp},
+                            output={'v': out_traces*mV},
+                            features={'v': v_features},
+                            method='exponential_euler',
+                            threshold='m > 0.5',
+                            refractory='m > 0.5',
+                            param_init=init_conds)
+
+    posterior = inferencer.infer(n_samples=10_000,
+                                 n_rounds=1,
+                                 inference_method='SNPE',
+                                 density_estimator_model='maf',
+                                 g_Na=[1*uS, 100*uS],
+                                 g_K=[0.1*uS, 10*uS],
+                                 g_l=[1*nS, 100*nS],
+                                 Cm=[20*pF, 2*nF])
+
+    # finally, sample and visualize the posterior distribution
+    samples = inferencer.sample((10_000, ))
+
+    limits = {'g_Na': [1*uS, 100*uS],
+            'g_K': [0.1*uS, 10*uS],
+            'g_l': [1*nS, 100*nS],
+            'Cm': [20*pF, 2*nF]}
+    labels = {'g_Na': r'$\overline{g}_{Na}$',
+            'g_K': r'$\overline{g}_{K}$',
+            'g_l': r'$\overline{g}_{l}$',
+            'Cm': r'$C_{m}$'}
+    fig, ax = inferencer.pairplot(limits=limits,
+                                  labels=labels,
+                                  ticks=limits,
+                                  points=ground_truth_params,
+                                  points_offdiag={'markersize': 5},
+                                  points_colors=['C3'],
+                                  figsize=(6, 6))
+
+.. image:: ../_static/hh_sbi_4params_3features_pairplot.png
+
+This could have been expected. The posterior distribution is estimated poorly
+using a simple approach as in the toy example.
+
+Yes, we can play around with the hyper-parameters and tuning the neural density
+estimator, but with this apporach we will not get far.
+
+We can, however, try with the non-amortized (or focused) approach, meaning we
+perform multi-round inference, where each following round will use the
+posterior from the previous one to sample new input data for the training,
+rather than using the same prior distribution as defined in the beginning.
+This approach yields additional advantage - the number of samples may be
+considerably lower, but it will lead to the posterior that is no longer being
+amortized, it is accurate only for a specific observation.
+
+.. code:: python
+
+    # note that the only difference is the number of rounds of inference
+    posterior = inferencer.infer(n_samples=5_000,
+                                 n_rounds=2,
+                                inference_method='SNPE',
+                                 density_estimator_model='maf',
+                                 restart=True,
+                                 g_Na=[1*uS, 100*uS],
+                                 g_K=[0.1*uS, 10*uS],
+                                 g_l=[1*nS, 100*nS],
+                                 Cm=[20*pF, 2*nF])
+
+    samples = inferencer.sample((10_000, ))
+
+    fig, ax = inferencer.pairplot(limits=limits,
+                                  labels=labels,
+                                  ticks=limits,
+                                  points=ground_truth_params,
+                                  points_offdiag={'markersize': 5},
+                                  points_colors=['C3'],
+                                  figsize=(6, 6))
+                        
+.. image:: ../_static/hh_sbi_4params_multiround_3features_pairplot.png
+
+This seems as a promising approach for parameters that already have the
+high-probability regions of the posterior distribution around ground-truth
+values. For other parameters, this leads to further deterioration of posterior
+estimates.
+
+So, we may wonder, how else can we improve the neural density estimator
+accuracy?
+
+Currently, we use only three features to describe extremely complex output of a
+neural model and should probably create a more comprehensive and more
+descriptive set of summary features. If we want to include data related to
+spikes in summary statistics, it is necessary to perform multi-objective
+optimization since we will observe spike trains as outputs in addition to
+voltage traces.
+
+Multi-objective optimization
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In order to use spikes, we have to have some observation to pass to the
+``inferencer``. We can utilize ``spike_times`` function as follows:
+
+.. code:: python
+
+    spike_times_list = [spike_times(t, out_trace) for out_trace in out_traces]
+
+To visually prove that the spikes times are indeed correct, we use
+``plot_traces`` again:
+
+.. code:: python
+
+    fig, ax = plot_traces(t, inp_traces, out_traces, spike_times_list[0])
+
+.. image:: ../_static/hh_sbi_4params_spike_train.png
+
+Now, let us create additional features that will be applied to voltage traces,
+and a few features that will be applied to spike trains:
+
+.. code:: python
+
+    def voltage_deflection(x):
+        voltage_base = np.mean(x[t < stim_start])
+        stim_end_idx = np.where(t >= stim_end)[0][0]
+        steady_state_voltage_stimend = np.mean(x[stim_end_idx-10:stim_end_idx-5])
+        return steady_state_voltage_stimend - voltage_base
+
+
+    v_features = [
+        # max action potential
+        lambda x: np.max(x[(t > stim_start) & (t < stim_end)]),
+        # mean action potential
+        lambda x: np.mean(x[(t > stim_start) & (t < stim_end)]),
+        # std of action potential
+        lambda x: np.std(x[(t > stim_start) & (t < stim_end)]),
+        # kurtosis of action potential
+        lambda x: kurt(x[(t > stim_start) & (t < stim_end)], fisher=False),
+        # membrane resting potential
+        lambda x: np.mean(x[(t > 0.1 * stim_start) & (t < 0.9 * stim_start)]),
+        # the voltage deflection between base and steady-state voltage
+        voltage_deflection,
+        ]
+
+    s_features = [
+        # number of spikes in a train
+        lambda x: x.size,
+        # mean inter-spike interval
+        lambda x: 0. if np.diff(x).size == 0 else np.mean(diff(x)),
+        ]
+
+The rest of the inference process stays pretty much the same:
+
+.. code:: python
+
+    inferencer = Inferencer(dt=dt, model=eqs,
+                            input={'I': inp_traces*amp},
+                            output={'v': out_traces*mV, 'spikes': spike_times_list},
+                            features={'v': v_features, 'spikes': s_features},
+                            method='exponential_euler',
+                            threshold='m > 0.5',
+                            refractory='m > 0.5',
+                            param_init=init_conds)
+
+    posterior = inferencer.infer(n_samples=15_000,
+                                 n_rounds=1,
+                                 inference_method='SNPE',
+                                 density_estimator_model='maf',
+                                 g_Na=[1*uS, 100*uS],
+                                 g_K=[0.1*uS, 10*uS],
+                                 g_l=[1*nS, 100*nS],
+                                 Cm=[20*pF, 2*nF])
+
+    samples = inferencer.sample((10_000, ))
+
+    fig, ax = inferencer.pairplot(limits=limits,
+                                 labels=labels,
+                                 ticks=limits,
+                                 points=ground_truth_params,
+                                 points_offdiag={'markersize': 5},
+                                 points_colors=['C3'],
+                                 figsize=(6, 6))
+
+.. image:: ../_static/hh_sbi_4params_8features_pairplot.png
+
+Let's also visualize the sampled trace, this time using the mean of ten
+thousands drawn samples:
+
+.. code:: python
+
+    inf_traces = inferencer.generate_traces(n_samples=10_000, output_var='v')
+
+    fig, ax = plot_traces(t, inp_traces, out_traces, inf_traces=array(inf_traces/mV))
+
+.. image:: ../_static/hh_sbi_4params_inf_traces.png
+
+Okay, now we are clearly getting somewhere and this should be a strong
+indicatior of the importance of crafting quality summary statistics.
+
+Still, the summary statistics can be a huge bottleneck and can set back the
+training of a neural density estimator. For this reason automatic learning
+summary features should be considered instead.
+
+
+Automatic feature extraction
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+To enable automatic feature extraction, the ``features`` argument simly should
+not be defined when instantiating an inferencer object. And that's it.
+Everything else happens behind the scenes without any need for additional user
+intervention. If the user wants to gain additional control over the extraction
+process, in addition to changing the hyperparameters, they can also define
+their own embedding neural network.
+
+Default settings
+################
+
+.. code:: python
+
+    inferencer = Inferencer(dt=dt, model=eqs,
+                            input={'I': inp_traces*amp},
+                            output={'v': out_traces*mV},
+                            method='exponential_euler',
+                            threshold='m > 0.5',
+                            refractory='m > 0.5',
+                            param_init=init_conds)
+
+    posterior = inferencer.infer(n_samples=15_000,
+                                 n_rounds=1,
+                                 inference_method='SNPE',
+                                 density_estimator_model='maf',
+                                 g_Na=[1*uS, 100*uS],
+                                 g_K=[0.1*uS, 10*uS],
+                                 g_l=[1*nS, 100*nS],
+                                 Cm=[20*pF, 2*nF])
+
+    samples = inferencer.sample((10_000, ))
+
+    fig, ax = inferencer.pairplot(limits=limits,
+                                  labels=labels,
+                                  ticks=limits,
+                                  points=ground_truth_params,
+                                  points_offdiag={'markersize': 5},
+                                  points_colors=['C3'],
+                                  figsize=(6, 6))
+
+.. image:: ../_static/hh_sbi_4params_default_automatic_ext_pairplot.png
+
+
+Custom embedding network
+########################
+
+Here, we demonstrate how to build a custom summary feature extractor and how
+to exploit the GPU processing power to speed up the inference process.
+
+Note that the use of the GPU will result in the speed-up of computation time
+only if a custom automatic feature extractor uses techniques that are actually
+faster to compute on the GPU.
+
+For this case, we use the YuleNet, a convolutional neural network, proposed in
+the paper by Rodrigues and Gramfort 2020, titled *Learning summary features of
+time series for likelihood free inference*, preprint available at: https://arxiv.org/abs/2012.02807.
+The authors outline impresive results where the automatic feature extraction by
+using the YuleNet is capable of outperforming carefully hand-crafted features.
+
+.. code:: python
+
+    import torch
+    from torch import nn
+
+
+    class YuleNet(nn.Module):
+        """The summary feature extractor proposed in Rodrigues, Pedro L. C.
+        and Gramfort, Alexandre: Learning summary features of time series
+        for likelihood free inference, in proceedings of the Third workshop
+        on machine learning and the physical sciences (NeurIPS 2020). The
+        preprint is available at: https://arxiv.org/abs/2012.02807
+        
+        Parameters
+        ----------
+        in_features : int
+            Number of input features should correspond to the size of a
+            single output voltage trace.
+        out_features : int
+            Number of the features that are used for the inference process.
+            
+        Returns
+        -------
+        None
+        """
+        def __init__(self, in_features, out_features):
+            super().__init__()
+            self.conv1 = nn.Conv1d(in_channels=1, out_channels=8, kernel_size=64,
+                                   stride=1, padding=32, bias=True)
+            self.relu1 = nn.ReLU()
+            pooling1 = 16
+            self.pool1 = nn.AvgPool1d(kernel_size=pooling1)
+
+            self.conv2 = nn.Conv1d(in_channels=8, out_channels=8, kernel_size=64,
+                                   stride=1, padding=32, bias=True)
+            self.relu2 = nn.ReLU()
+            pooling2 = int((in_features // pooling1) // 16)
+            self.pool2 = nn.AvgPool1d(kernel_size=pooling2)
+
+            self.dropout = nn.Dropout(p=0.50)
+            linear_in = 8 * in_features // (pooling1 * pooling2) - 1
+            self.linear = nn.Linear(in_features=linear_in,
+                                    out_features=out_features)
+            self.relu3 = nn.ReLU()
+
+        def forward(self, x):
+            if x.ndim == 1:
+                x = x.view(1, 1, -1)
+            else:
+                x = x.view(len(x), 1, -1)
+            x_conv1 = self.conv1(x)
+            x_relu1 = self.relu1(x_conv1)
+            x_pool1 = self.pool1(x_relu1)
+
+            x_conv2 = self.conv2(x_pool1)
+            x_relu2 = self.relu2(x_conv2)
+            x_pool2 = self.pool2(x_relu2)
+            
+            x_flatten = x_pool2.view(len(x), 1, -1)
+            x_dropout = self.dropout(x_flatten)
+
+            x = self.relu3(self.linear(x_dropout))
+            return x.view(len(x), -1)
+
+In the following code, we also demonstrate how to control the hyperparameters
+of the density estimator using additional keyword arguments in `~brian2modelfitting.inferencer.Inferencer.infer` method:
+
+.. code:: python
+
+    in_features = out_traces.shape[1]
+    out_features = 10
+
+    inferencer = Inferencer(dt=dt, model=eqs,
+                            input={'I': inp_traces*amp},
+                            output={'v': out_traces*mV},
+                            method='exponential_euler',
+                            threshold='m > 0.5',
+                            refractory='m > 0.5',
+                            param_init=init_conds)
+
+    posterior = inferencer.infer(n_samples=15_000,
+                                 n_rounds=1,
+                                 inference_method='SNPE',
+                                 density_estimator_model='maf',
+                                 inference_kwargs={'embedding_net': YuleNet(in_features, out_features)},
+                                 train_kwargs={'num_atoms': 10,
+                                              'training_batch_size': 100,
+                                              'use_combined_loss': True,
+                                              'discard_prior_samples': True},
+                                 device='gpu',
+                                 g_Na=[1*uS, 100*uS],
+                                 g_K=[0.1*uS, 10*uS],
+                                 g_l=[1*nS, 100*nS],
+                                 Cm=[20*pF, 2*nF])
+
+    samples = inferencer.sample((10_000, ))
+
+    fig, ax = inferencer.pairplot(limits=limits,
+                                  labels=labels,
+                                  ticks=limits,
+                                  points=ground_truth_params,
+                                  points_offdiag={'markersize': 5},
+                                  points_colors=['C3'],
+                                  figsize=(6, 6))
+
+.. image:: ../_static/hh_sbi_4params_yulenet_pairplot.png
+
+
+Next steps
+----------
+
+To learn more go through the reference API and check out examples on our GitHub
+repository: https://github.com/brian-team/brian2modelfitting/tree/master/examples
\ No newline at end of file
diff --git a/examples/tutorial_sbi_helpers.py b/examples/tutorial_sbi_helpers.py
new file mode 100644
index 0000000..422a4c2
--- /dev/null
+++ b/examples/tutorial_sbi_helpers.py
@@ -0,0 +1,107 @@
+from brian2 import *
+
+
+def spike_times(t, x):
+    """Return time points in seconds in which a spike occurs.
+
+    Parameters
+    ----------
+    t : numpy.ndarray
+        Time steps in ms.
+    x : numpy.ndarray
+        Voltage trace in mV.
+
+    Returns
+    -------
+    numpy.ndarray
+        Spike times in seconds.
+    """
+    x = x.copy()
+
+    ind = where(x < -0.04)
+    x[ind] = -0.04
+    ind = where(diff(x) < 0)
+    x[ind] = -0.04
+
+    ind = where(diff(x) < 0)
+    spike_times = array(t)[ind]
+
+    if spike_times.shape[0] > 0:
+        spike_times = spike_times[append(1, diff(spike_times)) > 0.5]
+    return spike_times / 1000
+
+
+def plot_traces(t, inp_traces, out_traces, spike_times=None, inf_traces=None):
+    """Visualize input current and output voltage traces.
+
+    Parameters
+    ----------
+    t : numpy.ndarray
+        Time steps in ms.
+    inp_traces : numpy.ndarray
+        Synaptic stimulus data in Amperes.
+    inp_traces : numpy.ndarray
+        Recorded voltage traces in mV.
+    spike_times : numpy.ndarray
+        Spike times in s.
+    inf_traces : numpy.ndarray, optional
+        Sampled voltage traces from the approximated posterior in mV.
+
+    Returns
+    -------
+    tuple
+        Figure and axes.
+    """
+    if (inp_traces.ndim, out_traces.ndim) != (2, 2):
+        inp_traces = inp_traces.reshape(1, -1)
+        out_traces = out_traces.reshape(1, -1)
+    if inf_traces is not None:
+        if inf_traces.ndim != 2:
+            inf_traces = inf_traces.reshape(1, -1)
+    if spike_times is not None:
+        spike_times = spike_times.ravel()
+        spike_i = []
+        for spike_time in spike_times:
+            spike_i.append(where(isclose(spike_time * 1000, t))[0].item())
+        spike_v = (out_traces.min(), out_traces.max())
+
+    fig, ax = subplots(2, 1, sharex=True,
+                       gridspec_kw={'height_ratios': [3, 1]}, figsize=(6, 3))
+
+    # voltage traces
+    ax[0].plot(t, out_traces.T, 'C0', lw=3, label='recordings', zorder=1)
+    if inf_traces is not None:
+        ax[0].plot(t, inf_traces.T, 'C1--', lw=2, label='sampled traces',
+                   zorder=2)
+    if spike_times is not None:
+        ax[0].vlines(t[spike_i], *spike_v, lw=2, color='C3', label='spikes',
+                     zorder=3)
+    ax[0].set(ylabel='$V$ [mV]')
+    ax[0].legend(loc='upper right')
+
+    # input stimulus current
+    ax[1].plot(t, inp_traces.T * 1e9, lw=2, c='k', label='stimulus')
+    ax[1].set(xlabel='$t$ [ms]', ylabel='$I$, nA')
+    ax[1].legend(loc='upper right')
+
+    tight_layout()
+    return fig, ax
+
+
+def plot_cond_coeff_mat(cond_coeff_mat):
+    """Visualize conditional coerrelation matrix.
+
+    Parameters
+    ----------
+    cond_coeff_mat : numpy.ndarray
+        Average conditional correlation matrix.
+
+    Returnss
+    -------
+    tuple
+        Figure and axes.
+    """
+    fig, ax = subplots(1, 1, figsize=(4, 4))
+    im = imshow(cond_coeff_mat, clim=[-1, 1])
+    _ = fig.colorbar(im)
+    return fig, ax

From a4560973b56c0bf899a6e02c724e4e7849ccad95 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 20 Aug 2021 16:05:03 +0200
Subject: [PATCH 107/121] fix formating issues and resize images

---
 docs_sphinx/introduction/tutorial_sbi.rst | 42 ++++++++++++++---------
 1 file changed, 26 insertions(+), 16 deletions(-)

diff --git a/docs_sphinx/introduction/tutorial_sbi.rst b/docs_sphinx/introduction/tutorial_sbi.rst
index 6addc07..1bf4946 100644
--- a/docs_sphinx/introduction/tutorial_sbi.rst
+++ b/docs_sphinx/introduction/tutorial_sbi.rst
@@ -58,6 +58,7 @@ of neural activity:
     fig, ax = plot_traces(t, inp_traces, out_traces)
 
 .. image:: ../_static/hh_sbi_recording_traces.png
+    :width: 70 %
 
 
 Toy-example: infer two free parameters
@@ -139,8 +140,8 @@ action potential and the standard deviatian of the action potential.
         ]
 
 
-``Inferencer``
-~~~~~~~~~~~~~~
+``~brian2modelfitting.inferencer.Inferencer``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The minimum set of arguments for the ``Inferencer`` class constructor are the
 time step, ``dt``, input data traces, ``input``, output data traces, ``output``
@@ -169,7 +170,7 @@ trace. Summary features are passed to the inference algorithm via the
                             refractory='m > 0.5',
                             param_init=init_conds)
 
-After the ``Inferencer`` object is created, we begin the inference process by
+After the ``inferencer`` object is created, we begin the inference process by
 calling ``infer`` method and defining the total number of samples that are used
 for the learning of a neural density estimator.
 
@@ -225,6 +226,7 @@ as follows:
                                   figsize=(6, 6))
 
 .. image:: ../_static/hh_sbi_toy_pairplot.png
+    :width: 70 %
 
 The inferred posterior is plotted against the ground truth parameters, and as
 can be seen, the ground truth parameters are located in a high-probability
@@ -244,6 +246,7 @@ We again use the ``plot_traces`` function as follows:
     fig, ax = plot_traces(t, inp_traces, out_traces, inf_traces=array(inf_traces/mV))
 
 .. image:: ../_static/hh_sbi_toy_inf_traces.png
+    :width: 70 %
 
 
 Additional free parameters
@@ -328,13 +331,13 @@ We can try to do the same as before:
     samples = inferencer.sample((10_000, ))
 
     limits = {'g_Na': [1*uS, 100*uS],
-            'g_K': [0.1*uS, 10*uS],
-            'g_l': [1*nS, 100*nS],
-            'Cm': [20*pF, 2*nF]}
+              'g_K': [0.1*uS, 10*uS],
+              'g_l': [1*nS, 100*nS],
+              'Cm': [20*pF, 2*nF]}
     labels = {'g_Na': r'$\overline{g}_{Na}$',
-            'g_K': r'$\overline{g}_{K}$',
-            'g_l': r'$\overline{g}_{l}$',
-            'Cm': r'$C_{m}$'}
+              'g_K': r'$\overline{g}_{K}$',
+              'g_l': r'$\overline{g}_{l}$',
+              'Cm': r'$C_{m}$'}
     fig, ax = inferencer.pairplot(limits=limits,
                                   labels=labels,
                                   ticks=limits,
@@ -344,6 +347,7 @@ We can try to do the same as before:
                                   figsize=(6, 6))
 
 .. image:: ../_static/hh_sbi_4params_3features_pairplot.png
+    :width: 70 %
 
 This could have been expected. The posterior distribution is estimated poorly
 using a simple approach as in the toy example.
@@ -364,7 +368,7 @@ amortized, it is accurate only for a specific observation.
     # note that the only difference is the number of rounds of inference
     posterior = inferencer.infer(n_samples=5_000,
                                  n_rounds=2,
-                                inference_method='SNPE',
+                                 inference_method='SNPE',
                                  density_estimator_model='maf',
                                  restart=True,
                                  g_Na=[1*uS, 100*uS],
@@ -383,6 +387,7 @@ amortized, it is accurate only for a specific observation.
                                   figsize=(6, 6))
                         
 .. image:: ../_static/hh_sbi_4params_multiround_3features_pairplot.png
+    :width: 70 %
 
 This seems as a promising approach for parameters that already have the
 high-probability regions of the posterior distribution around ground-truth
@@ -417,6 +422,7 @@ To visually prove that the spikes times are indeed correct, we use
     fig, ax = plot_traces(t, inp_traces, out_traces, spike_times_list[0])
 
 .. image:: ../_static/hh_sbi_4params_spike_train.png
+    :width: 70 %
 
 Now, let us create additional features that will be applied to voltage traces,
 and a few features that will be applied to spike trains:
@@ -477,14 +483,15 @@ The rest of the inference process stays pretty much the same:
     samples = inferencer.sample((10_000, ))
 
     fig, ax = inferencer.pairplot(limits=limits,
-                                 labels=labels,
-                                 ticks=limits,
-                                 points=ground_truth_params,
-                                 points_offdiag={'markersize': 5},
-                                 points_colors=['C3'],
-                                 figsize=(6, 6))
+                                  labels=labels,
+                                  ticks=limits,
+                                  points=ground_truth_params,
+                                  points_offdiag={'markersize': 5},
+                                  points_colors=['C3'],
+                                  figsize=(6, 6))
 
 .. image:: ../_static/hh_sbi_4params_8features_pairplot.png
+    :width: 70 %
 
 Let's also visualize the sampled trace, this time using the mean of ten
 thousands drawn samples:
@@ -496,6 +503,7 @@ thousands drawn samples:
     fig, ax = plot_traces(t, inp_traces, out_traces, inf_traces=array(inf_traces/mV))
 
 .. image:: ../_static/hh_sbi_4params_inf_traces.png
+    :width: 70 %
 
 Okay, now we are clearly getting somewhere and this should be a strong
 indicatior of the importance of crafting quality summary statistics.
@@ -548,6 +556,7 @@ Default settings
                                   figsize=(6, 6))
 
 .. image:: ../_static/hh_sbi_4params_default_automatic_ext_pairplot.png
+    :width: 70 %
 
 
 Custom embedding network
@@ -672,6 +681,7 @@ of the density estimator using additional keyword arguments in `~brian2modelfitt
                                   figsize=(6, 6))
 
 .. image:: ../_static/hh_sbi_4params_yulenet_pairplot.png
+    :width: 70 %
 
 
 Next steps

From b562c8045c0d50bdf366c06870b5bd8aa2d44cc1 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 20 Aug 2021 16:11:20 +0200
Subject: [PATCH 108/121] fix ref to Inferencer

---
 docs_sphinx/introduction/tutorial_sbi.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs_sphinx/introduction/tutorial_sbi.rst b/docs_sphinx/introduction/tutorial_sbi.rst
index 1bf4946..4662ccd 100644
--- a/docs_sphinx/introduction/tutorial_sbi.rst
+++ b/docs_sphinx/introduction/tutorial_sbi.rst
@@ -140,8 +140,8 @@ action potential and the standard deviatian of the action potential.
         ]
 
 
-``~brian2modelfitting.inferencer.Inferencer``
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+`~brian2modelfitting.inferencer.Inferencer`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 The minimum set of arguments for the ``Inferencer`` class constructor are the
 time step, ``dt``, input data traces, ``input``, output data traces, ``output``

From 7c731069faf3b627b9af5d0ac377161dc9ffa9fb Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Fri, 20 Aug 2021 16:15:59 +0200
Subject: [PATCH 109/121] remove ref to class from subtitle

---
 docs_sphinx/introduction/tutorial_sbi.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/docs_sphinx/introduction/tutorial_sbi.rst b/docs_sphinx/introduction/tutorial_sbi.rst
index 4662ccd..d3f7671 100644
--- a/docs_sphinx/introduction/tutorial_sbi.rst
+++ b/docs_sphinx/introduction/tutorial_sbi.rst
@@ -140,8 +140,8 @@ action potential and the standard deviatian of the action potential.
         ]
 
 
-`~brian2modelfitting.inferencer.Inferencer`
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+`Inferencer`
+~~~~~~~~~~~~
 
 The minimum set of arguments for the ``Inferencer`` class constructor are the
 time step, ``dt``, input data traces, ``input``, output data traces, ``output``

From 15ac38da7401d55edd838d698620ee0785f0d67f Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 21 Aug 2021 14:28:45 +0200
Subject: [PATCH 110/121] update figures for sbi tutorial

---
 .../hh_sbi_4params_3features_pairplot.png     | Bin 68237 -> 0 bytes
 .../hh_sbi_4params_4features_pairplot.png     | Bin 0 -> 42159 bytes
 .../hh_sbi_4params_8features_pairplot.png     | Bin 32189 -> 0 bytes
 .../hh_sbi_4params_9features_pairplot.png     | Bin 0 -> 22603 bytes
 ...4params_default_automatic_ext_pairplot.png | Bin 32469 -> 25811 bytes
 .../_static/hh_sbi_4params_inf_traces.png     | Bin 87247 -> 89963 bytes
 ..._4params_multiround_3features_pairplot.png | Bin 37113 -> 0 bytes
 ..._4params_multiround_4features_pairplot.png | Bin 0 -> 30216 bytes
 .../_static/hh_sbi_4params_spike_train.png    | Bin 45097 -> 50402 bytes
 .../hh_sbi_4params_yulenet_pairplot.png       | Bin 46221 -> 42997 bytes
 .../_static/hh_sbi_recording_traces.png       | Bin 46203 -> 50179 bytes
 docs_sphinx/_static/hh_sbi_toy_inf_traces.png | Bin 92486 -> 88171 bytes
 docs_sphinx/_static/hh_sbi_toy_pairplot.png   | Bin 12212 -> 11088 bytes
 .../_static/hh_sbi_toy_pairplot_zoom_in.png   | Bin 0 -> 18166 bytes
 14 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 docs_sphinx/_static/hh_sbi_4params_3features_pairplot.png
 create mode 100644 docs_sphinx/_static/hh_sbi_4params_4features_pairplot.png
 delete mode 100644 docs_sphinx/_static/hh_sbi_4params_8features_pairplot.png
 create mode 100644 docs_sphinx/_static/hh_sbi_4params_9features_pairplot.png
 delete mode 100644 docs_sphinx/_static/hh_sbi_4params_multiround_3features_pairplot.png
 create mode 100644 docs_sphinx/_static/hh_sbi_4params_multiround_4features_pairplot.png
 create mode 100644 docs_sphinx/_static/hh_sbi_toy_pairplot_zoom_in.png

diff --git a/docs_sphinx/_static/hh_sbi_4params_3features_pairplot.png b/docs_sphinx/_static/hh_sbi_4params_3features_pairplot.png
deleted file mode 100644
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From f4857b5583b045b64bff944ba773531b08a42bbc Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 21 Aug 2021 14:29:34 +0200
Subject: [PATCH 111/121] add reference to sbi lib and rename device to
 sbi_device in infer

---
 brian2modelfitting/inferencer.py | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 77cd7d5..2f84e63 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -156,6 +156,9 @@ class Inferencer(object):
     generative model which ultimately provides the posterior
     distribution over unknown free parameters.
 
+    To utilize simulation-based inference, this class uses a ``sbi``
+    library, for details see Tejero-Cantero 2020.
+
     Parameters
     ----------
     dt : brian2.units.fundamentalunits.Quantity
@@ -197,6 +200,12 @@ class Inferencer(object):
     param_init : dict, optional
         Dictionary of state variables to be initialized with respective
         values, i.e., initial conditions for the model.
+
+    References
+    ----------
+    * Tejero-Cantero, A., Boelts, J. et al. "sbi: A toolkit for
+      simulation-based inference" Journal of Open Source Software
+      (JOOS), 5(52):2505. 2020.
     """
     def __init__(self, dt, model, input, output, features=None, method=None,
                  threshold=None, reset=None, refractory=False,
@@ -790,7 +799,7 @@ def infer_step(self, proposal, inference, n_samples=None, theta=None,
     def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
               inference_method='SNPE', density_estimator_model='maf',
               inference_kwargs={}, train_kwargs={}, posterior_kwargs={},
-              restart=False, device='cpu', **params):
+              restart=False, sbi_device='cpu', **params):
         """Return the trained posterior.
 
         Note that if ``theta`` and ``x`` are not provided,
@@ -885,7 +894,7 @@ def infer(self, n_samples=None, theta=None, x=None, n_rounds=1,
 
             # initialize inference object
             _ = self.init_inference(inference_method, density_estimator_model,
-                                    prior, device, **inference_kwargs)
+                                    prior, sbi_device, **inference_kwargs)
 
             # args for SNPE in `.train`
             # args for SNRE and SNLE are not supported here, if needed the user

From c79128d6b568fda3f2a34d8f050ba6935ffae7b0 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 21 Aug 2021 14:30:37 +0200
Subject: [PATCH 112/121] add simulating script for sbi tutorial

---
 examples/generate_traces_synthetic.py | 52 +++++++++++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 examples/generate_traces_synthetic.py

diff --git a/examples/generate_traces_synthetic.py b/examples/generate_traces_synthetic.py
new file mode 100644
index 0000000..97e6fa6
--- /dev/null
+++ b/examples/generate_traces_synthetic.py
@@ -0,0 +1,52 @@
+from brian2 import *
+
+
+defaultclock.dt = 0.1*ms
+
+
+I1 = 0.1*nA
+I2 = 0.5*nA
+
+t_on = 100*ms
+t_total = 1000*ms
+t_off = t_total - t_on
+
+C = 200*pF
+VT = -60.0*mV
+E_Na = 53*mV
+g_Na = 32*uS
+E_K = -107*mV
+g_K = 1*uS
+E_l = -70*mV
+g_l = 10*nS
+E_l = -70*mV
+
+eqs = '''
+    dVm/dt = - (g_Na * m ** 3 * h * (Vm - E_Na) 
+                + g_K * n ** 4 * (Vm - E_K)
+                + g_l * (Vm - E_l) - I_inj) / C : volt
+    I_inj = ((I2 - I1) / (t_off - t_on) * (t - t_on) + I1) * int((t >= t_on) and (t < t_off)) : amp (shared)
+    dm/dt = alpha_m * (1-m) - beta_m * m : 1
+    dn/dt = alpha_n * (1-n) - beta_n * n : 1
+    dh/dt = alpha_h * (1-h) - beta_h * h : 1
+    alpha_m = ((-0.32/mV) * (Vm - VT - 13.*mV)
+               / (exp((-(Vm - VT - 13.*mV)) / (4.*mV)) - 1))/ms : Hz
+    beta_m = ((0.28/mV) * (Vm - VT - 40.*mV)
+              / (exp((Vm - VT - 40.*mV) / (5.*mV)) - 1))/ms : Hz
+    alpha_h = 0.128 * exp(-(Vm - VT - 17.*mV) / (18.*mV))/ms : Hz
+    beta_h = 4 / (1 + exp((-(Vm - VT - 40.*mV)) / (5.*mV)))/ms : Hz
+    alpha_n = ((-0.032/mV) * (Vm - VT - 15.*mV)
+               / (exp((-(Vm - VT - 15.*mV)) / (5.*mV)) - 1))/ms : Hz
+    beta_n = 0.5 * exp(-(Vm - VT - 10.*mV) / (40.*mV))/ms : Hz
+    '''
+neurons = NeuronGroup(1, eqs, threshold='m>0.5', refractory='m>0.5',
+                      method='exponential_euler', name='neurons')
+neurons.Vm = 'E_l'
+neurons.m = '1 / (1 + beta_m / alpha_m)'
+neurons.h = '1 / (1 + beta_h / alpha_h)'
+neurons.n = '1 / (1 + beta_n / alpha_n)'
+Vm_mon = StateMonitor(neurons, ['Vm', 'I_inj'], record=True, name='Vm_mon')
+run(t_total)
+
+save('input_traces_synthetic.npy', Vm_mon.I_inj.ravel()/amp)
+save('output_traces_synthetic.npy', Vm_mon.Vm.ravel()/mV)

From b30cb555221dcdb40922467cad82d168514ac196 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 21 Aug 2021 14:40:43 +0200
Subject: [PATCH 113/121] add citation for sbi

---
 docs_sphinx/inferencer/index.rst | 29 +++++++++++++++++++----------
 1 file changed, 19 insertions(+), 10 deletions(-)

diff --git a/docs_sphinx/inferencer/index.rst b/docs_sphinx/inferencer/index.rst
index a4e3ff3..8906817 100644
--- a/docs_sphinx/inferencer/index.rst
+++ b/docs_sphinx/inferencer/index.rst
@@ -2,12 +2,13 @@ Inferencer
 ==========
 
 Unlike more traditional inverse identification procedures that rely either on
-gradient or gradient-free methods, the ``Inferencer`` class supports
-simulation-based inference that has been established as a powerful alternative
-approach.
+gradient or gradient-free methods, the `~brian2modelfitting.inferencer.Inferencer`
+class supports simulation-based inference that has been established as a
+powerful alternative approach.
 
 The simulation-based inference is data-driven procedure supported by the
-`sbi <https://www.mackelab.org/sbi/>`_, ``PyTorch``-based toolbox by Macke lab.
+`sbi <https://www.mackelab.org/sbi/>`_, ``PyTorch``-based toolbox by Macke
+lab, [Tejero-Cantero2020]_.
 
 In general, this method yields twofold improvement over point-estimate fitting
 procedures:
@@ -25,15 +26,16 @@ procedures:
    by using deep neural networks which is trained in parallel with neural 
    density estimator. 
 
-The ``Inferencer`` class, in its core, is a fancy wrapper around the ``sbi``
-package where the focus is put on inferring the unknown parameters of the
-single-cell neuron models defined in Brian 2 simulator.
+The `~brian2modelfitting.inferencer.Inferencer` class, in its core, is a fancy
+wrapper around the ``sbi`` package,  where the focus is put on inferring the
+unknown parameters of the single-cell neuron models defined in Brian 2
+simulator.
 
 Neural density estimator
 ------------------------
 
 There are three main estimation techniques supported in ``sbi`` that the user
-can take the full control over seamlesly by using the ``Inferencer``:
+can take the full control over seamlesly by using the `~brian2modelfitting.inferencer.Inferencer`:
 
 #. sequential neural posterior estimation (SNPE)
 #. sequential neural likelihood estimation (SNLE)
@@ -70,5 +72,12 @@ The inferencer procedure is defined via three main steps:
 Implementation
 --------------
 
-Go to `the tutorial section <https://brian2modelfitting.readthedocs.io/en/stable/introduction/tutorial_sbi.html>`_
-in  for the in-depth implementation analysis.
\ No newline at end of file
+Go to :doc:`the tutorial section <../introduction/tutorial_sbi>` for the
+in-depth implementation analysis.
+
+References
+----------
+
+.. [Tejero-Cantero2020] Tejero-Cantero, A., Boelts, J. et al. "sbi: A toolkit
+                        for simulation-based inference" Journal of Open Source
+                        Software (JOOS), 5(52):2505. 2020.
\ No newline at end of file

From fa402990fd7077bfa5a914f2c97dbf5f8281c38a Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 21 Aug 2021 14:41:02 +0200
Subject: [PATCH 114/121] revision of the tutorial

---
 docs_sphinx/introduction/tutorial_sbi.rst | 208 ++++++++++++----------
 1 file changed, 112 insertions(+), 96 deletions(-)

diff --git a/docs_sphinx/introduction/tutorial_sbi.rst b/docs_sphinx/introduction/tutorial_sbi.rst
index d3f7671..b2eec2d 100644
--- a/docs_sphinx/introduction/tutorial_sbi.rst
+++ b/docs_sphinx/introduction/tutorial_sbi.rst
@@ -3,20 +3,20 @@ Tutorial: Inferencer
 
 In this tutorial, we use simulation-based inference on the Hodgkin-Huxley
 neuron model, where different scenarios are considered. The data are
-synthetically generated from a model by using the script available `here <https://github.com/brian-team/brian2/blob/master/examples/advanced/modelfitting_sbi.py>`_.
-This enables the comparisson of the inferred parameter values with the ground
-truth parameter values as used in the generative model.
+synthetically generated from the model which enables the comparisson of the
+optimized parameter values with the ground truth parameter values as used in
+the generative model.
 
 We start with importing basic packages and modules. Note that
 ``tutorial_sbi_helpers`` contains three different functions that are used
 extensively throughout this tutorial. Namely, ``plot_traces`` is used for
-visualization of the input current and the output voltage traces, and optional
-time series of spikes or sampled traces obtained by using an estimated
-posterior. In order to detect and capture spike events in a given voltage
-trace, we use ``spike_times`` function. Finally, ``plot_cond_coeff_mat`` is
-used to visualize conditional coerrelation matrix. To check how each of these
-functions works behind the scenes, download the script and go throguh the code
-yourself: :download:`tutorial_sbi_helpers.py <../../examples/tutorial_sbi_helpers.py>`
+visualization of the input current and output voltage traces, and optionally
+for visualization of spike trains or sampled traces obtained by using an
+estimated posterior. In order to detect and capture spike events in a given
+voltage trace, we use ``spike_times`` function. Finally,
+``plot_cond_coeff_mat`` is used to visualize conditional coerrelation matrix.
+For a detailed overview of the mechanism of each of the functions, download
+the script: :download:`tutorial_sbi_helpers.py <../../examples/tutorial_sbi_helpers.py>`
 
 .. code:: python
 
@@ -28,20 +28,21 @@ yourself: :download:`tutorial_sbi_helpers.py <../../examples/tutorial_sbi_helper
                                       plot_cond_coeff_mat)
 
 Now, let's load the input current and output voltage traces by using ``NumPy``.
-Not that all functions available in ``NumPy``, as well as in ``Matplotlib``,
-are implicitly available via ``brian2`` import.
+Note that all functions available in ``NumPy``, as well as in ``Matplotlib``,
+are implicitly available after ``brian2``  was imported.
 
 .. code:: python
 
     inp_traces = load('input_traces_synthetic.npy').reshape(1, -1)
     out_traces = load('output_traces_synthetic.npy').reshape(1, -1)
 
-You can download the ``.npy`` files loaded above:
-:download:`input_traces_synthetic.npy <../../examples/output_traces_synthetic.npy>`,
-:download:`output_traces_synthetic.npy <../../examples/output_traces_synthetic.npy>`
+The data is generated by running ``generate_traces_synthetic.py`` script:
+:download:`generate_traces_synthetic.py <../../examples/generate_traces_synthetic.py>`
 
-
-By setting the time step, we are able to set up the time domain:
+By setting the time step, we are able to set up the time domain. From the
+array of time steps, we also define the exact time when the stimulus starts
+and when it ends by computing ``stim_start`` and ``stim_end``, respectively.
+This will come handy later during the feature extraction process.
 
 .. code:: python
 
@@ -49,9 +50,9 @@ By setting the time step, we are able to set up the time domain:
     t = arange(0, inp_traces.size*dt/ms, dt/ms)
     stim_start, stim_end = t[where(inp_traces[0, :] != 0)[0][[0, -1]]]
 
-By calling a ``plot_traces`` and passing an array of time steps, input current
-and output voltage traces, we obtain the visualization of a synthetic recording
-of neural activity:
+By calling ``plot_traces`` and passing the array of time steps, the input
+current and output voltage traces, we obtain the visualization of a synthetic
+neural activity recordings:
 
 .. code:: python
 
@@ -66,11 +67,12 @@ Toy-example: infer two free parameters
 
 The first scenario we cover is a simple inference procedure of two unknown
 parameters in the Hodgkin-Huxley neuron model. The parameters to infer are
-the maximal value of sodium and potassium electrical conductances.
+the maximal value of sodium, :math:`\overline{g}_\mathrm{Na}`, and potassium
+electrical conductances, :math:`\overline{g}_\mathrm{K}`.
 
-As a standard practice in Brian 2 simulator, we have to define parameters of
-the model, initial conditions for differential equations that describe the
-model, and the model itself:
+By following the standard practice from Brian 2 simulator, we have to define
+parameters of the model, initial conditions for differential equations that
+describe the model, and the model itself:
 
 .. code:: python
 
@@ -88,9 +90,9 @@ model, and the model itself:
 
     # define initial conditions
     init_conds = {'v': 'E_l',
-                 'm': '1 / (1 + beta_m / alpha_m)',
-                 'h': '1 / (1 + beta_h / alpha_h)',
-                 'n': '1 / (1 + beta_n / alpha_n)'}
+                  'm': '1 / (1 + beta_m / alpha_m)',
+                  'h': '1 / (1 + beta_h / alpha_h)',
+                  'n': '1 / (1 + beta_n / alpha_n)'}
 
     # define the Hodgkin-Huxley neuron model
     eqs = '''
@@ -118,15 +120,15 @@ model, and the model itself:
         g_K : siemens (constant)
         '''
 
-Since the output of the model is very high-dimensional, and since we are
+Since the output of the model is extremely high-dimensional, and since we are
 interested only in a few hand-picked features that will capture the gist of
 the neuronal activity, we start the inference process by defining a list of
 summary functions.
 
 Each summary feature is obtained by calling a single-valued function on each
 trace generated by using a sampled prior distribution over unknown parameters.
-In this case, we consider the maximal value of the action potential, the mean
-action potential and the standard deviatian of the action potential.
+In this case, we consider the maximal value, mean and standard deviatian of
+action potential, and the membrane resting potential.
 
 .. code:: python
 
@@ -137,27 +139,29 @@ action potential and the standard deviatian of the action potential.
         lambda x: np.mean(x[(t > stim_start) & (t < stim_end)]),
         # std of action potential
         lambda x: np.std(x[(t > stim_start) & (t < stim_end)]),
-        ]
+        # membrane resting potential
+        lambda x: np.mean(x[(t > 0.1 * stim_start) & (t < 0.9 * stim_start)])
+    ]
 
 
-`Inferencer`
-~~~~~~~~~~~~
+Inferencer
+~~~~~~~~~~
 
-The minimum set of arguments for the ``Inferencer`` class constructor are the
-time step, ``dt``, input data traces, ``input``, output data traces, ``output``
-and the model that will be used for the inference process, ``model``.
-Input and output traces should be shaped so that the number of rows corresponds
-to each individual observed trace and the number of columns is equal to the
-number of time steps. 
+The minimum set of arguments for the `~brian2modelfitting.inferencer.Inferencer`
+class constructor are the time step, ``dt``, input data traces, ``input``,
+output data traces, ``output``, and the model that will be used for the
+inference process, ``model``. Input and output traces should have the number
+of rows that corresponds to the number of observed traces, and the number of
+columns should be equal to the number of time steps in each trace. 
 
-Here, we define additional arguments such as ``method`` to define an
+Here, we define additional arguments such as: ``method`` to define an
 integration technique used for solving the set of differential equations,
 ``threshold`` to define a condition that produce a single spike, ``refractory``
 to define a condition under which a neuron remains refractory, and
 ``param_init`` to define a set of initial conditions. We also define the set of
 summary features that is used to represent the data instead of using the entire
-trace. Summary features are passed to the inference algorithm via the
-``features`` argument.
+trace. Summary features are passed to the inference algorithm via ``features``
+argument.
 
 .. code:: python
 
@@ -170,9 +174,10 @@ trace. Summary features are passed to the inference algorithm via the
                             refractory='m > 0.5',
                             param_init=init_conds)
 
-After the ``inferencer`` object is created, we begin the inference process by
-calling ``infer`` method and defining the total number of samples that are used
-for the learning of a neural density estimator.
+After the ``inferencer`` is instantiated, we may begin the inference process
+by calling `~brian2modelfitting.inferencer.Inferencer.infer` and defining the
+total number of samples that are used for the training of a neural density
+estimator.
 
 
 Posterior
@@ -183,31 +188,31 @@ i.e., sampled parameter values given a prior distribution, and the output data,
 i.e., summary features extracted from the traces, obtained by solving the model
 with the corresponding set of sampled parameters from the input data.
 
-We can choose the inference method and the estimator model , but the only
-arguments that are required to be passed to ``infer`` method are the number of
-samples (in case of running the inference process for the first time),
-``n_samples``, and upper and lower bounds for each unknown parameter.
+We can choose the inference method and the estimator model , but only
+arguments that `~brian2modelfitting.inferencer.Inferencer.infer` requires are
+the number of samples (in case of running the inference process for the first
+time), ``n_samples``, and upper and lower bounds for each unknown parameter.
 
 .. code:: python
 
-    posterior = inferencer.infer(n_samples=10_000,
+    posterior = inferencer.infer(n_samples=15_000,
                                  n_rounds=1,
                                  inference_method='SNPE',
                                  density_estimator_model='maf',
                                  g_Na=[1*uS, 100*uS],
                                  g_K=[0.1*uS, 10*uS])
 
-After the inference is completed, the estimated posterior distribution over
-unkown parameters can be analyzed by observing the pairwise relationship
-between each pair of parameters.
-But before, we have to draw samples from the estimated posterior as follows:
+After inference is completed, the estimated posterior distribution can be
+analyzed by observing the pairwise relationship between each pair of
+parameters. But before, we have to draw samples from the estimated posterior
+as follows:
 
 .. code:: python
 
     samples = inferencer.sample((10_000, ))
 
-The samples are stored inside the ``Inferencer``, and are available through
-the ``samples`` variable.
+The samples are stored inside the `~brian2modelfitting.inferencer.Inferencer`
+object and are available through ``samples`` variable.
 We create a visual representation of the pairwise relationship of the posterior
 as follows:
 
@@ -228,9 +233,14 @@ as follows:
 .. image:: ../_static/hh_sbi_toy_pairplot.png
     :width: 70 %
 
+Let's zoom in a bit:
+
+.. image:: ../_static/hh_sbi_toy_pairplot_zoom_in.png
+    :width: 70 %
+
 The inferred posterior is plotted against the ground truth parameters, and as
-can be seen, the ground truth parameters are located in a high-probability
-region of the estimated distribution.
+can be seen, the ground truth parameters are located in high-probability
+regions of the estimated distribution.
 
 To further substantiate this, let's now see the traces simulated from a single
 set of parameters sampled from the posterior:
@@ -252,17 +262,18 @@ We again use the ``plot_traces`` function as follows:
 Additional free parameters
 --------------------------
 
-The simple scenarios where only two parameters are considered unkwon works
-quite well on the synthetic data. What if we have a larger number of unkown
-parameters? Let us now consider leakage additional unkown parameters for the
-same model as before. In addition to the unknown maximal values of the
+The simple scenarios where only 2 parameters are considered works quite well
+using synthetic data traces. What if we have a larger number of unkown
+parameters? Let's now consider additional unkown parameters for the same model
+as before. In addition to the unknown maximal values of the
 electrical conductance of the sodium and potassium channels, the membrane
-capacity and the maximal value of the electrical conductance of the leakage
-ion channel are also unknown.
+capacity, :math:`C_m`, and the maximal value of the electrical conductance of
+the leakage ion channel, :math:`\overline{g}_{l}`, are also unknown.
 
-We can try to do the same as before:
+We can try to do the same as before with a bit more training data:
 
 .. code:: python
+    
     del Cm, g_l
 
     # set parameters, initial condition and the model
@@ -318,7 +329,7 @@ We can try to do the same as before:
                             refractory='m > 0.5',
                             param_init=init_conds)
 
-    posterior = inferencer.infer(n_samples=10_000,
+    posterior = inferencer.infer(n_samples=20_000,
                                  n_rounds=1,
                                  inference_method='SNPE',
                                  density_estimator_model='maf',
@@ -346,13 +357,13 @@ We can try to do the same as before:
                                   points_colors=['C3'],
                                   figsize=(6, 6))
 
-.. image:: ../_static/hh_sbi_4params_3features_pairplot.png
+.. image:: ../_static/hh_sbi_4params_4features_pairplot.png
     :width: 70 %
 
 This could have been expected. The posterior distribution is estimated poorly
 using a simple approach as in the toy example.
 
-Yes, we can play around with the hyper-parameters and tuning the neural density
+Yes, we can play around with hyper-parameters and tuning the neural density
 estimator, but with this apporach we will not get far.
 
 We can, however, try with the non-amortized (or focused) approach, meaning we
@@ -361,12 +372,12 @@ posterior from the previous one to sample new input data for the training,
 rather than using the same prior distribution as defined in the beginning.
 This approach yields additional advantage - the number of samples may be
 considerably lower, but it will lead to the posterior that is no longer being
-amortized, it is accurate only for a specific observation.
+amortized - it is accurate only for a specific observation.
 
 .. code:: python
 
     # note that the only difference is the number of rounds of inference
-    posterior = inferencer.infer(n_samples=5_000,
+    posterior = inferencer.infer(n_samples=10_000,
                                  n_rounds=2,
                                  inference_method='SNPE',
                                  density_estimator_model='maf',
@@ -386,7 +397,7 @@ amortized, it is accurate only for a specific observation.
                                   points_colors=['C3'],
                                   figsize=(6, 6))
                         
-.. image:: ../_static/hh_sbi_4params_multiround_3features_pairplot.png
+.. image:: ../_static/hh_sbi_4params_multiround_4features_pairplot.png
     :width: 70 %
 
 This seems as a promising approach for parameters that already have the
@@ -397,25 +408,25 @@ estimates.
 So, we may wonder, how else can we improve the neural density estimator
 accuracy?
 
-Currently, we use only three features to describe extremely complex output of a
+Currently, we use only four features to describe extremely complex output of a
 neural model and should probably create a more comprehensive and more
 descriptive set of summary features. If we want to include data related to
 spikes in summary statistics, it is necessary to perform multi-objective
-optimization since we will observe spike trains as outputs in addition to
+optimization since we will observe spike trains as an output in addition to
 voltage traces.
 
 Multi-objective optimization
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 In order to use spikes, we have to have some observation to pass to the
-``inferencer``. We can utilize ``spike_times`` function as follows:
+`~brian2modelfitting.inferencer.Inferencer`. We can utilize ``spike_times`` as
+follows:
 
 .. code:: python
 
     spike_times_list = [spike_times(t, out_trace) for out_trace in out_traces]
 
-To visually prove that the spikes times are indeed correct, we use
-``plot_traces`` again:
+To visually prove that spike times are indeed correct, we use ``plot_traces``:
 
 .. code:: python
 
@@ -424,7 +435,7 @@ To visually prove that the spikes times are indeed correct, we use
 .. image:: ../_static/hh_sbi_4params_spike_train.png
     :width: 70 %
 
-Now, let us create additional features that will be applied to voltage traces,
+Now, let's create additional features that will be applied to voltage traces,
 and a few features that will be applied to spike trains:
 
 .. code:: python
@@ -449,16 +460,18 @@ and a few features that will be applied to spike trains:
         lambda x: np.mean(x[(t > 0.1 * stim_start) & (t < 0.9 * stim_start)]),
         # the voltage deflection between base and steady-state voltage
         voltage_deflection,
-        ]
+    ]
 
     s_features = [
         # number of spikes in a train
         lambda x: x.size,
         # mean inter-spike interval
         lambda x: 0. if np.diff(x).size == 0 else np.mean(diff(x)),
-        ]
+        # time to first spike
+        lambda x: 0. if x.size == 0 else x[0]
+    ]
 
-The rest of the inference process stays pretty much the same:
+The rest of the inference process stays pretty much the same as before:
 
 .. code:: python
 
@@ -471,7 +484,7 @@ The rest of the inference process stays pretty much the same:
                             refractory='m > 0.5',
                             param_init=init_conds)
 
-    posterior = inferencer.infer(n_samples=15_000,
+    posterior = inferencer.infer(n_samples=20_000,
                                  n_rounds=1,
                                  inference_method='SNPE',
                                  density_estimator_model='maf',
@@ -490,7 +503,7 @@ The rest of the inference process stays pretty much the same:
                                   points_colors=['C3'],
                                   figsize=(6, 6))
 
-.. image:: ../_static/hh_sbi_4params_8features_pairplot.png
+.. image:: ../_static/hh_sbi_4params_9features_pairplot.png
     :width: 70 %
 
 Let's also visualize the sampled trace, this time using the mean of ten
@@ -509,14 +522,14 @@ Okay, now we are clearly getting somewhere and this should be a strong
 indicatior of the importance of crafting quality summary statistics.
 
 Still, the summary statistics can be a huge bottleneck and can set back the
-training of a neural density estimator. For this reason automatic learning
-summary features should be considered instead.
+training of a neural density estimator. For this reason automatic feature
+extraction can be considered instead.
 
 
 Automatic feature extraction
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~
 
-To enable automatic feature extraction, the ``features`` argument simly should
+To enable automatic feature extraction, ``features`` argument simly should
 not be defined when instantiating an inferencer object. And that's it.
 Everything else happens behind the scenes without any need for additional user
 intervention. If the user wants to gain additional control over the extraction
@@ -536,7 +549,7 @@ Default settings
                             refractory='m > 0.5',
                             param_init=init_conds)
 
-    posterior = inferencer.infer(n_samples=15_000,
+    posterior = inferencer.infer(n_samples=20_000,
                                  n_rounds=1,
                                  inference_method='SNPE',
                                  density_estimator_model='maf',
@@ -582,11 +595,7 @@ using the YuleNet is capable of outperforming carefully hand-crafted features.
 
 
     class YuleNet(nn.Module):
-        """The summary feature extractor proposed in Rodrigues, Pedro L. C.
-        and Gramfort, Alexandre: Learning summary features of time series
-        for likelihood free inference, in proceedings of the Third workshop
-        on machine learning and the physical sciences (NeurIPS 2020). The
-        preprint is available at: https://arxiv.org/abs/2012.02807
+        """The summary feature extractor proposed in Rodrigues 2020.
         
         Parameters
         ----------
@@ -599,6 +608,12 @@ using the YuleNet is capable of outperforming carefully hand-crafted features.
         Returns
         -------
         None
+        
+        References
+        ----------
+        * Rodrigues, P. L. C. and Gramfort, A. "Learning summary features
+          of time series for likelihood free inference" 3rd Workshop on
+          Machine Learning and the Physical Sciences (NeurIPS 2020). 2020.
         """
         def __init__(self, in_features, out_features):
             super().__init__()
@@ -640,7 +655,8 @@ using the YuleNet is capable of outperforming carefully hand-crafted features.
             return x.view(len(x), -1)
 
 In the following code, we also demonstrate how to control the hyperparameters
-of the density estimator using additional keyword arguments in `~brian2modelfitting.inferencer.Inferencer.infer` method:
+of the density estimator by using additional keyword arguments in `~brian2modelfitting.inferencer.Inferencer.infer`
+method:
 
 .. code:: python
 
@@ -655,7 +671,7 @@ of the density estimator using additional keyword arguments in `~brian2modelfitt
                             refractory='m > 0.5',
                             param_init=init_conds)
 
-    posterior = inferencer.infer(n_samples=15_000,
+    posterior = inferencer.infer(n_samples=20_000,
                                  n_rounds=1,
                                  inference_method='SNPE',
                                  density_estimator_model='maf',
@@ -664,7 +680,7 @@ of the density estimator using additional keyword arguments in `~brian2modelfitt
                                               'training_batch_size': 100,
                                               'use_combined_loss': True,
                                               'discard_prior_samples': True},
-                                 device='gpu',
+                                 sbi_device='gpu',
                                  g_Na=[1*uS, 100*uS],
                                  g_K=[0.1*uS, 10*uS],
                                  g_l=[1*nS, 100*nS],
@@ -687,5 +703,5 @@ of the density estimator using additional keyword arguments in `~brian2modelfitt
 Next steps
 ----------
 
-To learn more go through the reference API and check out examples on our GitHub
-repository: https://github.com/brian-team/brian2modelfitting/tree/master/examples
\ No newline at end of file
+To learn more read the reference API and check out more examples available
+:doc:`here <../examples/index>`.
\ No newline at end of file

From 570d570a44b49e1ca3da14fd3c6e40b6a798d3b6 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 21 Aug 2021 17:58:30 +0200
Subject: [PATCH 115/121] add doi to sbi ref

---
 docs_sphinx/inferencer/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs_sphinx/inferencer/index.rst b/docs_sphinx/inferencer/index.rst
index 8906817..f3fbc8d 100644
--- a/docs_sphinx/inferencer/index.rst
+++ b/docs_sphinx/inferencer/index.rst
@@ -80,4 +80,4 @@ References
 
 .. [Tejero-Cantero2020] Tejero-Cantero, A., Boelts, J. et al. "sbi: A toolkit
                         for simulation-based inference" Journal of Open Source
-                        Software (JOOS), 5(52):2505. 2020.
\ No newline at end of file
+                        Software 5(52):2505. 2020. doi: `10.21105/joss.02505 <https://doi.org/10.21105/joss.02505>`_
\ No newline at end of file

From 1a7e93f6e4c89cb58fc4cdc6247cf2a86f008222 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sat, 21 Aug 2021 17:58:39 +0200
Subject: [PATCH 116/121] add citations

---
 docs_sphinx/introduction/tutorial_sbi.rst | 38 +++++++++++++++++------
 1 file changed, 28 insertions(+), 10 deletions(-)

diff --git a/docs_sphinx/introduction/tutorial_sbi.rst b/docs_sphinx/introduction/tutorial_sbi.rst
index b2eec2d..a848499 100644
--- a/docs_sphinx/introduction/tutorial_sbi.rst
+++ b/docs_sphinx/introduction/tutorial_sbi.rst
@@ -2,10 +2,10 @@ Tutorial: Inferencer
 ====================
 
 In this tutorial, we use simulation-based inference on the Hodgkin-Huxley
-neuron model, where different scenarios are considered. The data are
-synthetically generated from the model which enables the comparisson of the
-optimized parameter values with the ground truth parameter values as used in
-the generative model.
+neuron model, [Hodgkin1952]_, where different scenarios are considered. The
+data are synthetically generated from the model which enables the comparisson
+of the optimized parameter values with the ground truth parameter values as
+used in the generative model.
 
 We start with importing basic packages and modules. Note that
 ``tutorial_sbi_helpers`` contains three different functions that are used
@@ -177,7 +177,8 @@ argument.
 After the ``inferencer`` is instantiated, we may begin the inference process
 by calling `~brian2modelfitting.inferencer.Inferencer.infer` and defining the
 total number of samples that are used for the training of a neural density
-estimator.
+estimator. We use the sequential neural posterior estimation algorithm (SNPE),
+proposed in [Greenberg2019]_. 
 
 
 Posterior
@@ -583,10 +584,9 @@ only if a custom automatic feature extractor uses techniques that are actually
 faster to compute on the GPU.
 
 For this case, we use the YuleNet, a convolutional neural network, proposed in
-the paper by Rodrigues and Gramfort 2020, titled *Learning summary features of
-time series for likelihood free inference*, preprint available at: https://arxiv.org/abs/2012.02807.
-The authors outline impresive results where the automatic feature extraction by
-using the YuleNet is capable of outperforming carefully hand-crafted features.
+[Rodrigues2020]_. The authors outline impresive results where the automatic
+feature extraction by using the YuleNet is capable of outperforming carefully
+hand-crafted features.
 
 .. code:: python
 
@@ -704,4 +704,22 @@ Next steps
 ----------
 
 To learn more read the reference API and check out more examples available
-:doc:`here <../examples/index>`.
\ No newline at end of file
+:doc:`here <../examples/index>`.
+
+References
+----------
+
+.. [Hodgkin1952] Hodgkin, A. L., and Huxley, A. F. "A quantitative description
+                 of membrane current and its application to conduction and
+                 excitation in nerve" Journal of Physiology 117(4):500-44. 1952.
+                 doi: `10.1113/jphysiol.1952.sp004764 <https://doi.org/10.1113/jphysiol.1952.sp004764>`_
+
+.. [Greenberg2019] Greenberg, D. S., Nonnenmacher, M. et al. "Automatic
+                   posterior transformation for likelihood-free inference"
+                   36th International Conference on Machine Learning
+                   (ICML 2019). 2019. Available online: `arXiv:1905.07488 <https://arxiv.org/abs/1905.07488>`_
+
+.. [Rodrigues2020] Rodrigues, P. L. C. and Gramfort, A. "Learning summary
+                   features of time series for likelihood free inference" 3rd
+                   Workshop on Machine Learning and the Physical Sciences
+                   (NeurIPS 2020). 2020. Available online: `arXiv:2012.02807 <https://arxiv.org/abs/2012.02807>`_
\ No newline at end of file

From d57b1c53f13c2ee790edc4b9a930d4e2c0ca5560 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sun, 22 Aug 2021 13:36:30 +0200
Subject: [PATCH 117/121] update gitignore

---
 .gitignore | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index 09ffcc7..8f674e0 100644
--- a/.gitignore
+++ b/.gitignore
@@ -83,11 +83,16 @@ brian2modelfitting.egg-info
 
 # sbi logs
 examples/sbi-logs/
+brian2modelfitting/tests/sbi-logs/
 
 # other sbi and torch stuff
 examples/*.pt
 examples/*.pth
+brian2modelfitting/tests/*.pt
+brian2modelfitting/tests/*.pth
 
 # numpy files
 examples/*.npy
-examples/*.npz
\ No newline at end of file
+examples/*.npz
+brian2modelfitting/tests/*.npy
+brian2modelfitting/tests/*.npz
\ No newline at end of file

From 2df6dc299784c972f4a21ff385a16623c78c26a9 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sun, 22 Aug 2021 13:37:07 +0200
Subject: [PATCH 118/121] fix docstrings

---
 brian2modelfitting/inferencer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/brian2modelfitting/inferencer.py b/brian2modelfitting/inferencer.py
index 2f84e63..3cc4154 100644
--- a/brian2modelfitting/inferencer.py
+++ b/brian2modelfitting/inferencer.py
@@ -95,7 +95,7 @@ def get_param_dict(param_values, param_names, n_values):
     param_values : numpy.ndarray
         Parameter values in a 2-dimensional array with the number of
         rows corresponding to a number of samples and the number of
-        columns corresponding to ``len(param_names).
+        columns corresponding to a number of parameters.
     param_names : list
         List containing parameter names.
     n_values : int

From 2bf03d2bb37ac928779641e70e6cbd576ccd1c2e Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sun, 22 Aug 2021 13:38:31 +0200
Subject: [PATCH 119/121] testing inferencer

---
 brian2modelfitting/tests/test_inferencer.py | 334 ++++++++++++++++++++
 1 file changed, 334 insertions(+)
 create mode 100644 brian2modelfitting/tests/test_inferencer.py

diff --git a/brian2modelfitting/tests/test_inferencer.py b/brian2modelfitting/tests/test_inferencer.py
new file mode 100644
index 0000000..af20591
--- /dev/null
+++ b/brian2modelfitting/tests/test_inferencer.py
@@ -0,0 +1,334 @@
+from brian2 import nS, siemens, mV, volt, ms, TimedArray, Equations
+from brian2.devices.device import reinit_devices
+from brian2modelfitting.inferencer import (Inferencer,
+                                           get_param_dict,
+                                           calc_prior)
+import numpy as np
+from numpy.testing import assert_equal, assert_almost_equal
+import pytest
+
+
+def test_get_param_dict():
+    param_names = ['a', 'b']
+    n_samples = 3
+    n_traces = 3
+    n_neurons = n_samples * n_traces
+
+    # check theta
+    theta = np.array([[1, 2],
+                      [3, 4],
+                      [5, 6]])
+    _theta = np.array([[1, 2],
+                       [1, 2],
+                       [1, 2],
+                       [3, 4],
+                       [3, 4],
+                       [3, 4],
+                       [5, 6],
+                       [5, 6],
+                       [5, 6]])
+    assert_equal(_theta, np.repeat(theta, repeats=n_traces, axis=0))
+
+    # check dictionary type and values
+    theta = np.repeat(theta, repeats=n_traces, axis=0)
+    d_param = {'a': np.array([1., 1., 1., 3., 3., 3., 5., 5., 5.]),
+               'b': np.array([2., 2., 2., 4., 4., 4., 6., 6., 6.])}
+    d_param_ret = get_param_dict(param_names=param_names,
+                                 param_values=_theta,
+                                 n_values=n_neurons)
+    assert isinstance(d_param_ret, dict)
+    assert_equal(d_param, d_param_ret)
+
+
+def test_calc_prior():
+    n_samples = 10
+    param_names = ['a', 'b']
+    a_lower_bound = 1
+    a_upper_bound = 10
+    a_mean = (a_lower_bound + a_upper_bound) / 2.
+    b_lower_bound = 0
+    b_upper_bound = 1
+    b_mean = (b_lower_bound + b_upper_bound) / 2.
+    prior_mean = np.array([a_mean, b_mean], dtype=np.float32)
+    a_std = (a_upper_bound - a_lower_bound) / np.sqrt(12)
+    b_std = (b_upper_bound - b_lower_bound) / np.sqrt(12)
+    prior_std = np.array([a_std, b_std], dtype=np.float32)
+
+    prior_sbi = calc_prior(param_names=param_names,
+                           a=[a_lower_bound*volt, a_upper_bound*volt],
+                           b=[b_lower_bound*volt, b_upper_bound*volt])
+
+    # check mean value
+    prior_sbi_mean = prior_sbi.mean.numpy()
+    assert_equal(prior_mean, prior_sbi_mean)
+
+    # check standard deviation
+    prior_sbi_std = prior_sbi.stddev.numpy()
+    assert_equal(prior_std, prior_sbi_std)
+
+    # check the type and shape of the prior
+    prior_sbi_sampled = prior_sbi.sample((n_samples, )).numpy()
+    assert isinstance(prior_sbi_sampled[0, 0], np.float32)
+    assert_equal(prior_sbi_sampled.shape, (n_samples, len(param_names)))
+
+E = 40*mV
+input_traces = np.zeros((10, 5))*volt
+for i in range(5):
+    input_traces[5:, i] = i * 10*mV
+input_traces = input_traces.T
+output_traces = input_traces * 10*nS
+output_traces = output_traces.T
+model = '''
+    I = g * (v - E) : amp
+    g : siemens (constant)
+    '''
+model_full = '''
+    I = g * (v - 40*mV) : amp
+    g : siemens (constant)
+    '''
+
+
+@pytest.fixture
+def setup(request):
+    dt = 0.01 * ms
+    inferencer = Inferencer(dt=dt,
+                            model=model,
+                            input={'v': input_traces},
+                            output={'I': output_traces})
+
+    def fin():
+        reinit_devices()
+    request.addfinalizer(fin)
+
+    return dt, inferencer
+
+
+@pytest.fixture
+def setup_feautres(request):
+    dt = 0.01 * ms
+    inferencer = Inferencer(dt=dt,
+                            model=model,
+                            input={'v': input_traces},
+                            output={'I': output_traces},
+                            features={'I': [lambda x: x.mean()]})
+
+    def fin():
+        reinit_devices()
+    request.addfinalizer(fin)
+
+    return dt, inferencer
+
+
+@pytest.fixture
+def setup_full(request):
+    dt = 0.01 * ms
+    inferencer = Inferencer(dt=dt,
+                            model=model_full,
+                            input={'v': input_traces},
+                            output={'I': output_traces},
+                            features={'I': [lambda x: x.mean()]})
+
+    def fin():
+        reinit_devices()
+    request.addfinalizer(fin)
+
+    return dt, inferencer
+
+
+def test_inferencer_init(setup):
+    _, inferencer = setup
+    attr_inferencer = ['dt', 'output_var', 'output', 'param_names',
+                       'n_traces', 'sim_time', 'output_dim', 'model',
+                       'input_traces', 'param_init', 'method',
+                       'threshold', 'reset', 'refractory', 'x_o',
+                       'features', 'params', 'n_samples', 'samples',
+                       'posterior', 'theta', 'x', 'sbi_device']
+    for attr in attr_inferencer:
+        assert hasattr(inferencer, attr)
+
+    assert inferencer.params is None
+    assert inferencer.n_samples is None
+    assert inferencer.samples is None
+    assert inferencer.posterior is None
+    assert inferencer.theta is None
+    assert inferencer.x is None
+
+    assert isinstance(inferencer.input_traces, TimedArray)
+    assert isinstance(inferencer.model, Equations)
+
+    target_var = f'{inferencer.output_var[0]}_target'
+    assert target_var in inferencer.model
+    assert inferencer.model[target_var].dim is inferencer.output_dim[0]
+
+    assert inferencer.features is None
+
+    observations = []
+    for o in output_traces:
+        o = np.array(o)
+        observations.append(o.ravel().astype(np.float32))
+    x_o = np.concatenate(observations)
+    assert_almost_equal(inferencer.x_o, x_o)
+
+
+def test_inferencer_init_features(setup_feautres):
+    _, inferencer = setup_feautres
+    attr_inferencer = ['dt', 'output_var', 'output', 'param_names',
+                       'n_traces', 'sim_time', 'output_dim', 'model',
+                       'input_traces', 'param_init', 'method',
+                       'threshold', 'reset', 'refractory', 'x_o',
+                       'features', 'params', 'n_samples', 'samples',
+                       'posterior', 'theta', 'x', 'sbi_device']
+    for attr in attr_inferencer:
+        assert hasattr(inferencer, attr)
+
+    assert inferencer.params is None
+    assert inferencer.n_samples is None
+    assert inferencer.samples is None
+    assert inferencer.posterior is None
+    assert inferencer.theta is None
+    assert inferencer.x is None
+
+    assert isinstance(inferencer.input_traces, TimedArray)
+    assert isinstance(inferencer.model, Equations)
+
+    target_var = f'{inferencer.output_var[0]}_target'
+    assert target_var in inferencer.model
+    assert inferencer.model[target_var].dim is inferencer.output_dim[0]
+
+    assert callable(inferencer.features['I'][0])
+
+    observations = []
+    for o in output_traces:
+        o = np.array(o)
+        print(o)
+        observations.append(o.mean())
+    x_o = np.array(observations, dtype=np.float32)
+    print(x_o)
+    print(inferencer.x_o)
+    assert_almost_equal(inferencer.x_o, x_o)
+
+
+def test_init_prior(setup):
+    _, inferencer = setup
+    lower_bound = 1e-9
+    upper_bound = 100e-9
+    g_mean = np.array((lower_bound + upper_bound) / 2, dtype=np.float32)
+    g_std = np.array((upper_bound - lower_bound) / np.sqrt(12),
+                     dtype=np.float32)
+    g_std = g_std.astype(np.float32)
+    prior = inferencer.init_prior(g=[lower_bound*siemens, upper_bound*siemens])
+
+    # check mean value
+    prior_mean = prior.mean.numpy()
+    assert_almost_equal(g_mean, prior_mean)
+
+    # check standard deviation
+    prior_std = prior.stddev.numpy()
+    assert_almost_equal(g_std, prior_std)
+
+
+def test_generate_training_data(setup):
+    _, inferencer = setup
+    lower_bound = 1*nS
+    upper_bound = 100*nS
+    n_samples = 100
+    prior = inferencer.init_prior(g=[lower_bound, upper_bound])
+    theta = inferencer.generate_training_data(n_samples, prior)
+    assert theta.shape == (100, 1)
+
+
+def test_extract_summary_statistics(setup):
+    _, inferencer = setup
+    lower_bound = 1*nS
+    upper_bound = 100*nS
+    n_samples = 100
+    n_traces = 5
+    prior = inferencer.init_prior(g=[lower_bound, upper_bound])
+    theta = inferencer.generate_training_data(n_samples, prior)
+    x = inferencer.extract_summary_statistics(theta)
+    assert x.shape == (100, n_traces * 10)
+
+
+def test_extract_summary_statistics_errors(setup):
+    _, inferencer = setup
+    lower_bound = 1*nS
+    upper_bound = 100*nS
+    n_samples = 100
+    prior = inferencer.init_prior(g=[lower_bound, upper_bound])
+    theta = inferencer.generate_training_data(n_samples, prior)
+    with pytest.raises(Exception):
+        _ = inferencer.extract_summary_statistics(theta, level=2)
+
+
+def test_save_summary_statistics_errors(setup):
+    _, inferencer = setup
+    with pytest.raises(AttributeError):
+        inferencer.save_summary_statistics('summary_stats')
+
+    theta = np.array([[1, 2], [3, 4]])
+    x = np.arange(2).reshape(2, 1)
+    with pytest.raises(TypeError):
+        inferencer.save_summary_statistics(1, theta=theta, x=x)
+        inferencer.save_summary_statistics(1, theta='theta', x=x)
+        inferencer.save_summary_statistics(1, theta=theta, x='x')
+        inferencer.save_summary_statistics(1, theta='theta', x='x')
+
+
+def test_load_summary_statistics(setup):
+    _, inferencer = setup
+    theta = np.array([[1, 2], [3, 4]])
+    x = np.arange(2).reshape(2, 1)
+    inferencer.save_summary_statistics('summ_stats.npz', theta=theta, x=x)
+    theta_load, x_load = inferencer.load_summary_statistics('summ_stats.npz')
+    assert_equal(theta_load, theta)
+    assert_equal(x_load, x)
+
+
+def test_init_inference_errors(setup):
+    _, inferencer = setup
+    prior = inferencer.init_prior(g=[1*nS, 100*nS])
+    with pytest.raises(NameError):
+        _ = inferencer.init_inference(inference_method='test',
+                                      density_estimator_model='mdn',
+                                      prior=prior,
+                                      sbi_device='cpu')
+        _ = inferencer.init_inference(inference_method='SNPE',
+                                      density_estimator_model='test',
+                                      prior=prior,
+                                      sbi_device='cpu')
+
+    # sbi_device should be automatically set to 'cpu'
+    _ = inferencer.init_inference(inference_method='SNPE',
+                                  density_estimator_model='mdn',
+                                  prior=prior,
+                                  sbi_device='test')
+    assert inferencer.sbi_device == 'cpu'
+
+
+def test_infer_step(setup_full):
+    _, inferencer = setup_full
+    prior = inferencer.init_prior(g=[1*nS, 100*nS])
+    inference = inferencer.init_inference(inference_method='SNPE',
+                                          density_estimator_model='mdn',
+                                          prior=prior,
+                                          sbi_device='cpu')
+    posterior = inferencer.infer_step(proposal=prior,
+                                      n_samples=10,
+                                      inference=inference)
+
+
+def test_infer_step_errors(setup_full):
+    _, inferencer = setup_full
+    prior = inferencer.init_prior(g=[1*nS, 100*nS])
+    inference = inferencer.init_inference(inference_method='SNPE',
+                                          density_estimator_model='mdn',
+                                          prior=prior,
+                                          sbi_device='cpu')
+    with pytest.raises(ValueError):
+        _ = inferencer.infer_step(proposal=prior,
+                                  inference=inference)
+
+
+def test_infer(setup_features):
+    _, inferncer = setup_features
+    posterior = inferencer.infer(n_samples=10)

From 0c8776f625bdb057536ccaf7ab7bc023b08550d6 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Sun, 22 Aug 2021 14:53:36 +0200
Subject: [PATCH 120/121] update tests

---
 brian2modelfitting/tests/test_inferencer.py | 29 ++++++++++++++++-----
 1 file changed, 23 insertions(+), 6 deletions(-)

diff --git a/brian2modelfitting/tests/test_inferencer.py b/brian2modelfitting/tests/test_inferencer.py
index af20591..819f8a2 100644
--- a/brian2modelfitting/tests/test_inferencer.py
+++ b/brian2modelfitting/tests/test_inferencer.py
@@ -71,6 +71,7 @@ def test_calc_prior():
     assert isinstance(prior_sbi_sampled[0, 0], np.float32)
     assert_equal(prior_sbi_sampled.shape, (n_samples, len(param_names)))
 
+
 E = 40*mV
 input_traces = np.zeros((10, 5))*volt
 for i in range(5):
@@ -104,7 +105,7 @@ def fin():
 
 
 @pytest.fixture
-def setup_feautres(request):
+def setup_features(request):
     dt = 0.01 * ms
     inferencer = Inferencer(dt=dt,
                             model=model,
@@ -170,8 +171,8 @@ def test_inferencer_init(setup):
     assert_almost_equal(inferencer.x_o, x_o)
 
 
-def test_inferencer_init_features(setup_feautres):
-    _, inferencer = setup_feautres
+def test_inferencer_init_features(setup_features):
+    _, inferencer = setup_features
     attr_inferencer = ['dt', 'output_var', 'output', 'param_names',
                        'n_traces', 'sim_time', 'output_dim', 'model',
                        'input_traces', 'param_init', 'method',
@@ -315,6 +316,8 @@ def test_infer_step(setup_full):
     posterior = inferencer.infer_step(proposal=prior,
                                       n_samples=10,
                                       inference=inference)
+    assert posterior._method_family == 'snpe'
+    assert_equal(np.array(posterior._x_shape), np.array([1, 5]))
 
 
 def test_infer_step_errors(setup_full):
@@ -329,6 +332,20 @@ def test_infer_step_errors(setup_full):
                                   inference=inference)
 
 
-def test_infer(setup_features):
-    _, inferncer = setup_features
-    posterior = inferencer.infer(n_samples=10)
+def test_infer(setup):
+    _, inferencer = setup
+    posterior = inferencer.infer(n_samples=10, g=[1*nS, 100*nS])
+    assert posterior == inferencer.posterior
+
+    posterior_multi_round = inferencer.infer(n_samples=10, n_rounds=2,
+                                             restart=True, g=[1*nS, 100*nS])
+    assert posterior_multi_round != posterior
+    assert posterior_multi_round == inferencer.posterior
+
+
+def test_load_posterior(setup):
+    _, inferencer = setup
+    _ = inferencer.infer(n_samples=10, g=[1*nS, 100*nS])
+    inferencer.save_posterior('posterior.pth')
+    posterior_load = inferencer.load_posterior('posterior.pth')
+    assert inferencer.posterior.net == posterior_load.net

From cc3c6b17862c102fea8dbec144e0ef8b0b6b16b9 Mon Sep 17 00:00:00 2001
From: antelk <alojic00@fesb.hr>
Date: Mon, 23 Aug 2021 11:26:53 +0200
Subject: [PATCH 121/121] The last commit for GSoC 2021 project!

---
 setup.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index cfea664..ba3db00 100644
--- a/setup.py
+++ b/setup.py
@@ -46,6 +46,7 @@
           'Operating System :: OS Independent',
           'Programming Language :: Python',
           'Programming Language :: Python :: 3',
-          'Topic :: Scientific/Engineering :: Bio-Informatics'],
+          'Topic :: Scientific/Engineering :: Bio-Informatics'
+          ],
       keywords='model fitting computational neuroscience',
       )