diff --git a/multiarea_model/default_params.py b/multiarea_model/default_params.py
index 3cc31982786111bd22d5b5b25d9af46d4d47fca3..ccf891e8fc256d922a98b8358bf7f628970a8c22 100644
--- a/multiarea_model/default_params.py
+++ b/multiarea_model/default_params.py
@@ -269,9 +269,22 @@ Theory params
theory_params = {'neuron_params': neuron_params,
'input_params': input_params,
+ # Initial rates can be None (start integration at
+ # zero rates), a numpy.ndarray defining the initial
+ # rates or 'random_uniform' which leads to randomly
+ # drawn initial rates from a uniform distribution.
'initial_rates': None,
+ # If 'initial_rates' is set to 'random_uniform',
+ # 'initial_rates_iter' defines the number of
+ # different initial conditions
'initial_rates_iter': None,
+ # If 'initial_rates' is set to 'random_uniform',
+ # 'initial_rates_max' defines the maximum rate of the
+ # uniform distribution to draw the initial rates from
+ 'initial_rates_max': 1000.,
+ # The simulation time of the mean-field theory integration
'T': 50.,
+ # The time step of the mean-field theory integration
'dt': 0.1}
diff --git a/multiarea_model/theory.py b/multiarea_model/theory.py
index a1bf8d55cdfd7323067732b99cd487e18c678031..d584df91204fcd955eb305a456d426017208fa7a 100644
--- a/multiarea_model/theory.py
+++ b/multiarea_model/theory.py
@@ -138,7 +138,7 @@ class Theory():
gen = self.initial_rates(self.params['initial_rates_iter'],
dim,
mode=self.params['initial_rates'],
- rate_max=1000.)
+ rate_max=self.params['initial_rates_max'])
num_iter = self.params['initial_rates_iter']
# initial rates are explicitly defined in self.params
elif isinstance(self.params['initial_rates'], np.ndarray):
@@ -321,11 +321,27 @@ class Theory():
def initial_rates(self, num_iter, dim, mode='random_uniform', rate_max=100., rng_seed=123):
"""
Helper function to create generator for initial rates
+
+ Parameters
+ ----------
+ num_iter : int
+ Number of elements in generator
+ dim : int
+ Dimension of each element, i.e. number of populations in
+ the network
+ mode : str
+ Mode defining the process to generate each element.
+ Currently only 'random_uniform' is supported: each element
+ is drawn from a uniform distribution
+ rate_max : float
+ Maximal rate for uniform distribution.
+ rng_seed : int
+ Seed for random number generation. Defaults to 123.
"""
- np.random.seed(rng_seed)
+ rng = np.random.RandomState(seed=rng_seed)
n = 0
while n < num_iter:
- yield rate_max * np.random.rand(dim)
+ yield rate_max * rng.rand(dim)
n += 1
def mu_sigma(self, rates, external=True, matrix_filter=None,
@@ -333,6 +349,20 @@ class Theory():
"""
Calculates mean and variance according to the
theory.
+
+ Parameters
+ ----------
+ rates : numpy.ndarray
+ Stationary rates of the network.
+ external : bool
+ Whether to include external input into the calculation.
+ Defaults to False.
+ matrix_filter : numpy.ndarray
+ Filter to filter for a subset of the network. Defaults to
+ None.
+ vector_filter : numpy.ndarray
+ Filter to filter for a subset of the network. Defaults to
+ None.
"""
if matrix_filter is not None:
K = copy(self.network.K_matrix)
@@ -369,9 +399,23 @@ class Theory():
return mu, sigma
def stability_matrix(self, rates, matrix_filter=None,
- vector_filter=None, full_output=False, replace_cc=None):
+ vector_filter=None, full_output=False):
"""
Computes stability matrix on the population level.
+
+ Parameters
+ ----------
+ rates : numpy.ndarray
+ Stationary rates of the network.
+ matrix_filter : numpy.ndarray
+ Filter to filter for a subset of the network. Defaults to
+ None.
+ vector_filter : numpy.ndarray
+ Filter to filter for a subset of the network. Defaults to
+ None.
+ full_output : bool
+ Whether to return only the matrix itself or all variables
+ contributing. Defaults to False.
"""
if np.any(matrix_filter is not None):
assert(np.any(vector_filter is not None))
@@ -432,21 +476,34 @@ class Theory():
return M
def lambda_max(self, rates, matrix_filter=None,
- vector_filter=None, full_output=False, replace_cc=None):
+ vector_filter=None, full_output=False):
"""
Computes radius of eigenvalue spectrum of the stability matrix.
+
+ Parameters
+ ----------
+ rates : numpy.ndarray
+ Stationary rates of the network.
+ matrix_filter : numpy.ndarray
+ Filter to filter for a subset of the network. Defaults to
+ None.
+ vector_filter : numpy.ndarray
+ Filter to filter for a subset of the network. Defaults to
+ None.
+ full_output : bool
+ Whether to return only the value itself or all variables
+ contributing. Defaults to False.
"""
if full_output:
(M, slope, slope_sigma,
M, EV, C, V, G_N) = self.stability_matrix(rates,
matrix_filter=matrix_filter,
vector_filter=vector_filter,
- full_output=full_output,
- replace_cc=replace_cc)
+ full_output=full_output)
else:
M = self.stability_matrix(rates, matrix_filter=matrix_filter,
vector_filter=vector_filter,
- full_output=full_output, replace_cc=replace_cc)
+ full_output=full_output)
EV = np.linalg.eig(M)
lambda_max = np.sqrt(np.max(np.real(EV[0])))
if full_output: