diff --git a/README.md b/README.md
index 63d8edb5863da2dda858d8798e654a3e9f440ba0..98ab245b5a0575fc74d30ac7c7d6b8ed23a57809 100644
--- a/README.md
+++ b/README.md
@@ -87,7 +87,7 @@ basic analysis and plotting.
## Analysis and figure scripts for [1-3]
The `figures` folder contains subfolders with all scripts necessary to produce
-the figures from [1-3]. If Snakemake (Köster J & Rahmann S, Bioinformatics (2012) 28(19): 2520-2522)
+the figures from [1-3]. If Snakemake (Köster J & Rahmann S, Bioinformatics (2012) 28(19): 2520-2522)
is installed, the figures can be produced by executing
`snakemake` in the respective folder, e.g.:
@@ -203,7 +203,7 @@ conveniently run by executing `pytest` in the `tests/` folder:
## Requirements
Python 3, python\_dicthash ([https://github.com/INM-6/python-dicthash](https://github.com/INM-6/python-dicthash)),
correlation\_toolbox ([https://github.com/INM-6/correlation-toolbox](https://github.com/INM-6/correlation-toolbox)),
-pandas, numpy, nested_dict, matplotlib (2.1.2), scipy, NEST 2.14.0
+pandas, numpy, nested_dict, matplotlib (2.1.2), scipy, pytest, NEST 2.14.0
Optional: seaborn, Sumatra
diff --git a/figures/Schmidt2018_dyn/Snakefile b/figures/Schmidt2018_dyn/Snakefile
index 10ec12318b913e420321b2e605a5a8c0d1dbf0de..5b7edcbebd6c5a8093d2afc5de0cca9413f7c731 100644
--- a/figures/Schmidt2018_dyn/Snakefile
+++ b/figures/Schmidt2018_dyn/Snakefile
@@ -26,7 +26,7 @@ ORIGINAL_SIM_LABELS = {'all': ['533d73357fbe99f6178029e6054b571b485f40f6',
'Fig2': ['533d73357fbe99f6178029e6054b571b485f40f6',
'0adda4a542c3d5d43aebf7c30d876b6c5fd1d63e',
'33fb5955558ba8bb15a3fdce49dfd914682ef3ea'],
- 'Fig3': '33fb5955558ba8bb15a3fdce49dfd914682ef3ea',
+ 'Fig3': ['33fb5955558ba8bb15a3fdce49dfd914682ef3ea'],
'Fig4': ['33fb5955558ba8bb15a3fdce49dfd914682ef3ea',
'1474e1884422b5b2096d3b7a20fd4bdf388af7e0',
'99c0024eacc275d13f719afd59357f7d12f02b77',
@@ -88,17 +88,17 @@ rule Fig2_bistability:
'Fig2_bistability.eps'
shell:
'python3 Fig2_bistability.py'
-
+
rule Fig3_ground_state_chi1:
input:
- os.path.join(DATA_DIR, SIM_LABELS['Fig3'], 'Analysis/pop_rates.json'),
- os.path.join(DATA_DIR, SIM_LABELS['Fig3'], 'Analysis/pop_LvR.json'),
- os.path.join(DATA_DIR, SIM_LABELS['Fig3'], 'Analysis/corrcoeff.json'),
- expand(os.path.join(DATA_DIR, SIM_LABELS['Fig3'], 'recordings', '-'.join((SIM_LABELS['Fig3'], 'spikes-{area}-{pop}.npy'))),
+ os.path.join(DATA_DIR, SIM_LABELS['Fig3'][0], 'Analysis/pop_rates.json'),
+ os.path.join(DATA_DIR, SIM_LABELS['Fig3'][0], 'Analysis/pop_LvR.json'),
+ os.path.join(DATA_DIR, SIM_LABELS['Fig3'][0], 'Analysis/corrcoeff.json'),
+ expand(os.path.join(DATA_DIR, SIM_LABELS['Fig3'][0], 'recordings', '-'.join((SIM_LABELS['Fig3'][0], 'spikes-{area}-{pop}.npy'))),
area=['V1', 'V2', 'FEF'], pop=population_list),
- expand(os.path.join(DATA_DIR, SIM_LABELS['Fig3'], 'Analysis', 'rate_time_series_full', 'rate_time_series_full_{area}.npy'),
+ expand(os.path.join(DATA_DIR, SIM_LABELS['Fig3'][0], 'Analysis', 'rate_time_series_full', 'rate_time_series_full_{area}.npy'),
area=['V1', 'V2', 'FEF']),
- expand(os.path.join(DATA_DIR, SIM_LABELS['Fig3'], 'Analysis', 'rate_time_series_auto_kernel', 'rate_time_series_auto_kernel_{area}.npy'), area=['V1', 'V2', 'FEF']),
+ expand(os.path.join(DATA_DIR, SIM_LABELS['Fig3'][0], 'Analysis', 'rate_time_series_auto_kernel', 'rate_time_series_auto_kernel_{area}.npy'), area=['V1', 'V2', 'FEF']),
output:
'Fig3_ground_state_chi1.eps'
shell:
@@ -108,7 +108,7 @@ rule Fig4_theory_calculations:
output:
'Fig4_theory_data/results_{cc_weights_factor}.npy'
shell:
- 'python3 Fig4_theory.py {wildcards.cc_weights_factor}'
+ 'python3 Fig4_theory.py {wildcards.cc_weights_factor}'
rule Fig4_metastability:
input:
@@ -120,7 +120,7 @@ rule Fig4_metastability:
'Fig4_metastability.eps'
shell:
'python3 Fig4_metastability.py'
-
+
rule Fig5_ground_state:
input:
os.path.join(DATA_DIR, SIM_LABELS['Fig5'][1], 'Analysis', 'pop_rates.json'),
@@ -189,7 +189,7 @@ rule Fig8_interactions:
'Fig8_interactions.eps'
shell:
'python3 Fig8_interactions.py'
-
+
rule Fig9_laminar_interactions:
input:
expand(os.path.join(DATA_DIR, '{simulation}', 'Analysis', 'granger_causality', 'granger_causality_{area}_{pop}.json'),
@@ -242,7 +242,7 @@ rule Fig9_path_analysis:
'Fig9_tex_files/{simulation}_lw_HZ_paths.tex'
shell:
'python3 Fig9_path_analysis.py {} {{wildcards.simulation}}'.format(DATA_DIR)
-
+
rule Fig9_paths:
input:
'Fig9_tex_files/{simulation}_lw_{type}_paths.tex'
diff --git a/figures/Schmidt2018_dyn/compute_granger_causality.py b/figures/Schmidt2018_dyn/compute_granger_causality.py
index e4575826e9bf083a7fc50a8da1fbd1b923e062cd..6d725aba9abc10e35cbee45b1f52d9c0b511d469 100644
--- a/figures/Schmidt2018_dyn/compute_granger_causality.py
+++ b/figures/Schmidt2018_dyn/compute_granger_causality.py
@@ -49,7 +49,7 @@ connection_params = {'g': -11.,
network_params = {'connection_params': connection_params}
M = MultiAreaModel(network_params)
# We exclude external input from the analysis
-K = M.K_matrix[:, :-1]
+K = M.K[:, :-1]
def indices_to_population(structure, indices):
@@ -150,7 +150,7 @@ else:
test = (np.nan, np.nan)
res_red = np.nan
- gc[source_area][source_pop] = (cause, test[1])
+ gc[source_area][source_pop] = (cause, test[1])
fn = os.path.join(save_path,
'granger_causality_{}_{}.json'.format(area, pop))
diff --git a/multiarea_model/analysis_helpers.py b/multiarea_model/analysis_helpers.py
index ce4c2e47e49425a9d85e0334354af7db95ed864c..546ef8012fd3a32101ffb2f2bbf306bf3c1ecfd4 100644
--- a/multiarea_model/analysis_helpers.py
+++ b/multiarea_model/analysis_helpers.py
@@ -87,7 +87,7 @@ def model_iter(mode='single',
Cartesian product of 2 ('single' mode) or 4 ('double' mode) lists
"""
if mode == 'single':
- assert((areas2 is None) and (pops2 is 'complete'))
+ assert((areas2 is None) and (pops2 == 'complete'))
if pops is None or pops2 is None:
assert((pops is None) and (pops2 is None) or mode == 'single')
if pops == 'complete':
@@ -538,7 +538,7 @@ def pop_cv_isi(data_array, t_min, t_max):
for i in np.unique(data_array[:, 0]):
intervals = np.diff(data_array[indices][
np.where(data_array[indices, 0] == i), 1])
- if (len(intervals) > 0):
+ if intervals.size > 0:
cv_isi.append(np.std(intervals) / np.mean(intervals))
if len(cv_isi) > 0:
return np.mean(cv_isi)
@@ -668,8 +668,11 @@ def synchrony(data_array, num_neur, t_min, t_max, resolution=1.0):
spike_count_histogramm = pop_rate_time_series(
data_array, num_neur, t_min, t_max, resolution=resolution)
mean = np.mean(spike_count_histogramm)
- variance = np.var(spike_count_histogramm)
- synchrony = variance / mean
+ std_dev = np.std(spike_count_histogramm)
+ try:
+ synchrony = std_dev / mean
+ except ZeroDivisionError:
+ synchrony = np.inf
return synchrony
diff --git a/multiarea_model/data_multiarea/Model.py b/multiarea_model/data_multiarea/Model.py
index 6d68fba7906b192ba3631f498599ababfcf5f8b3..8e5cc5e0fdcb3d4dc404f429fcfcf32fbd613823 100644
--- a/multiarea_model/data_multiarea/Model.py
+++ b/multiarea_model/data_multiarea/Model.py
@@ -20,7 +20,6 @@ Sacha van Albada
import numpy as np
import json
import re
-import sys
import os
import scipy
import scipy.integrate
@@ -262,8 +261,8 @@ def compute_Model_params(out_label='', mode='default'):
"""
def integrand(r, R, sig):
gauss = np.exp(-r ** 2 / (2 * sig ** 2))
- x1 = scipy.arctan(np.sqrt((2 * R - r) / (2 * R + r)))
- x2 = scipy.sin(4 * scipy.arctan(np.sqrt((2 * R - r) / (2 * R + r))))
+ x1 = np.arctan(np.sqrt((2 * R - r) / (2 * R + r)))
+ x2 = np.sin(4 * np.arctan(np.sqrt((2 * R - r) / (2 * R + r))))
factor = 4 * x1 - x2
return r * gauss * factor
@@ -876,7 +875,7 @@ def compute_Model_params(out_label='', mode='default'):
ext_syn = External_synapses[area]
vector[-1] = ext_syn
solution, residues, rank, s = np.linalg.lstsq(
- nonvisual_fraction_matrix, vector)
+ nonvisual_fraction_matrix, vector, rcond=-1)
for i, pop in enumerate(structure[area]):
synapse_numbers[area][pop]['external'] = {
'external': solution[i] * neuronal_numbers[area][pop]}
diff --git a/multiarea_model/default_params.py b/multiarea_model/default_params.py
index d1158c4db3d1cea7087392a1f22779850edc6a45..499bfaeffd8ca0ab699e4200ad6dd768928190d9 100644
--- a/multiarea_model/default_params.py
+++ b/multiarea_model/default_params.py
@@ -122,11 +122,11 @@ General connection parameters
"""
connection_params = {
# Whether to apply the stabilization method of
- # Schuecker, Schmidt et al. (2017). Default is False.
+ # Schuecker, Schmidt et al. (2017). Default is None.
# Options are True to perform the stabilization or
# a string that specifies the name of a binary
# numpy file containing the connectivity matrix
- 'K_stable': False,
+ 'K_stable': None,
# Whether to replace all cortico-cortical connections by stationary
# Poisson input with population-specific rates (het_poisson_stat)
diff --git a/multiarea_model/multiarea_helpers.py b/multiarea_model/multiarea_helpers.py
index 7bf2fe3a5738d49ae07df0cc8325ef48aadf6b60..787af3d79ae081f13702d2a3136f482b89396da3 100644
--- a/multiarea_model/multiarea_helpers.py
+++ b/multiarea_model/multiarea_helpers.py
@@ -32,7 +32,10 @@ import json
import numpy as np
import os
from itertools import product
-import collections
+try:
+ from collections.abc import Iterable
+except ImportError:
+ from collections import Iterable
from config import base_path
from .default_params import complete_area_list, population_list
@@ -229,7 +232,7 @@ def matrix_to_dict(m, area_list, structure, external=None):
x = x.reshape((8, 8))
for i, pop in enumerate(population_list):
for j, pop2 in enumerate(population_list):
- if x[i][j] < 1e-20:
+ if np.isclose(0., x[i][j]):
x[i][j] = 0.
dic[area][pop][area2][pop2] = x[i][j]
if external is not None:
@@ -306,7 +309,7 @@ def dict_to_vector(d, area_list, structure):
for target_area in area_list:
if target_area in structure:
for target_pop in structure[target_area]:
- if isinstance(d[target_area][target_pop], collections.Iterable):
+ if isinstance(d[target_area][target_pop], Iterable):
V[i] = d[target_area][target_pop][0]
else:
V[i] = d[target_area][target_pop]
diff --git a/requirements.txt b/requirements.txt
index 334dadee1be7c79946682bab225daab7af9f1f8a..8e4d8fb814917e5c4f3877817701810bcb215559 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,5 +3,6 @@ numpy
matplotlib
pandas
scipy
+pytest
nested_dict
dicthash