diff --git a/.github/workflows/ebrains-push.yml b/.github/workflows/ebrains-push.yml
index a7e08d550336e76fe56c6b516755c8667f67a6b9..d9c177388b06b1365898e8ba2b66c1838624625c 100644
--- a/.github/workflows/ebrains-push.yml
+++ b/.github/workflows/ebrains-push.yml
@@ -9,12 +9,6 @@ jobs:
runs-on: ubuntu-latest
if: ${{ github.repository_owner == 'INM-6' }}
steps:
- - name: Harden Runner
- uses: step-security/harden-runner@63c24ba6bd7ba022e95695ff85de572c04a18142 # v2.7.0
- with:
- egress-policy: audit
- disable-telemetry: true
-
- name: sycnmaster
uses: wei/git-sync@55c6b63b4f21607da0e9877ca9b4d11a29fc6d83
with:
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
new file mode 100644
index 0000000000000000000000000000000000000000..633ad20875755e2a13645ce6e85436e762256dc7
--- /dev/null
+++ b/.gitlab-ci.yml
@@ -0,0 +1,37 @@
+# Specify stages in this CI workflow
+stages:
+ - build
+ - test
+ - deploy
+
+# List variables and they can be called by the variable names
+# variables:
+ # GITLAB_BUILD_ENV_DOCKER_IMAGE: docker-registry.ebrains.eu/tc/ebrains-spack-build-env/gitlab_runners_nfs:gitlab_runners_nfs_23.06
+ # SYSTEMNAME: ebrainslab
+
+run_notebooks:
+ stage: test
+ tags:
+ # Add tags for runner to select runners that meet the requirement
+ - docker-runner
+
+ before_script:
+ # - cat /mnt/ebrains_env/ebrains-23.09/bin/load_env.sh
+ - . /mnt/ebrains_env/ebrains-23.09/bin/load_env.sh
+
+
+ script:
+ - echo "Starting testing..."
+ # List all kernels
+ # - jupyter kernelspec list
+ - echo "Testing multi-area-model.ipynb..."
+ # Replace all ebrains-23.09 with python3 as the kernel ebrains-23.09 is now actually named as python3
+ - sed -i -e "s/ebrains-23.09/python3/" multi-area-model.ipynb
+ # Convert jupyter notebook to python file and execute it
+ - jupyter nbconvert --to notebook --execute ./multi-area-model.ipynb
+ - echo "Testing M2E_statistical_test.ipynb..."
+ # Replace all ebrains-23.09 with python3 as the kernel ebrains-23.09 is now actually named as python3
+ - sed -i -e "s/ebrains-23.09/python3/" M2E_statistical_test.ipynb
+ # Convert jupyter notebook to python file and execute it
+ - jupyter nbconvert --to notebook --execute ./M2E_statistical_test.ipynb
+ - echo "Testing are finished and passed!"
diff --git a/figures/MAM2EBRAINS/M2E_compute_corrcoeff.py b/figures/MAM2EBRAINS/M2E_compute_corrcoeff.py
index ba37d1bd069eb1502f246c46a5384a61b3c41769..1b486296c04d90927b025043cbc08c16185da0c2 100644
--- a/figures/MAM2EBRAINS/M2E_compute_corrcoeff.py
+++ b/figures/MAM2EBRAINS/M2E_compute_corrcoeff.py
@@ -1,17 +1,22 @@
import json
import numpy as np
import os
-
import correlation_toolbox.helper as ch
-from multiarea_model import MultiAreaModel
-import sys
-
-"""
-Compute correlation coefficients for a subsample
-of neurons for the entire network from raw spike files of a given simulation.
-"""
def compute_corrcoeff(M, data_path, label):
+ """
+ Compute the correlation coefficient between
+ the spiking rates of different populations
+ in different areas.
+
+ Parameters:
+ - M (MultiAreaModel): The MultiAreaModel instance.
+ - data_path (str): The path to the directory where the data is stored.
+ - label (str): The label used to identify the specific data set.
+
+ Returns:
+ None
+ """
load_path = os.path.join(data_path,
label,
'recordings')
@@ -19,9 +24,6 @@ def compute_corrcoeff(M, data_path, label):
label,
'Analysis')
- # with open(os.path.join(data_path, label, 'custom_params_{}'.format(label)), 'r') as f:
- # sim_params = json.load(f)
- # T = sim_params['T']
T = M.simulation.params["t_sim"]
areas_simulated = M.simulation.params["areas_simulated"]
@@ -29,18 +31,9 @@ def compute_corrcoeff(M, data_path, label):
subsample = 2000
resolution = 1.
- """
- Create MultiAreaModel instance to have access to data structures
- """
- # M = MultiAreaModel({})
-
- spike_data = {}
cc_dict = {}
- # for area in M.area_list:
for area in areas_simulated:
cc_dict[area] = {}
- LvR_list = []
- N = []
for pop in M.structure[area]:
fp = '-'.join((label,
'spikes', # assumes that the default label for spike files was used
@@ -48,20 +41,18 @@ def compute_corrcoeff(M, data_path, label):
pop))
fn = '{}/{}.npy'.format(load_path, fp)
# +1000 to ensure that we really have subsample non-silent neurons in the end
- # spikes = np.load(fn)
spikes = np.load(fn, allow_pickle=True)
ids = np.unique(spikes[:, 0])
dat = ch.sort_gdf_by_id(spikes, idmin=ids[0], idmax=ids[0]+subsample+1000)
bins, hist = ch.instantaneous_spike_count(dat[1], resolution, tmin=tmin, tmax=T)
rates = ch.strip_binned_spiketrains(hist)[:subsample]
- # test if only 1 of the neurons is firing, if yes, print warning message and continue
+ # Test if only 1 of the neurons is firing, if yes, print warning message and continue
if rates.shape[0] < 2:
print(f"WARNING: There are less than 2 neurons firing in the population: {area} {pop}, the corresponding cross-correlation will not be computed.")
- # cc_dict[area][pop] = 0.
continue
- # compute cross correlation coefficient
+ # Compute cross correlation coefficient
cc = np.corrcoef(rates)
cc = np.extract(1-np.eye(cc[0].size), cc)
cc[np.where(np.isnan(cc))] = 0.
diff --git a/figures/MAM2EBRAINS/M2E_compute_fc.py b/figures/MAM2EBRAINS/M2E_compute_fc.py
index 57e390cf7471c5ed80d1ec34818d8a083a90853f..8cdd103b4214d1dc5ef08fd933dc05aa533ae42a 100644
--- a/figures/MAM2EBRAINS/M2E_compute_fc.py
+++ b/figures/MAM2EBRAINS/M2E_compute_fc.py
@@ -1,26 +1,25 @@
import correlation_toolbox.helper as ch
import numpy as np
import os
-import sys
-from multiarea_model import MultiAreaModel
from scipy.spatial.distance import pdist
from scipy.spatial.distance import squareform
from M2E_compute_synaptic_input import compute_synaptic_input
-"""
-Compute the functional connectivity between all areas of a given
-simulation based on their time series of spiking rates or their
-estimated BOLD signal.
-"""
-
-# data_path = sys.argv[1]
-# label = sys.argv[2]
-# method = sys.argv[3]
-
def compute_fc(M, data_path, label):
- # compute synaptic input
+ """
+ Compute the functional connectivity between different areas of the brain using synaptic input.
+
+ Parameters:
+ - M (MultiAreaModel): An instance of the MultiAreaModel class representing the brain model.
+ - data_path (str): The path to the directory where the data is stored.
+ - label (str): The label used to identify the specific data set.
+
+ Returns:
+ None
+ """
+ # Compute synaptic input
for area in M.area_list:
compute_synaptic_input(M, data_path, label, area)
@@ -33,11 +32,6 @@ def compute_fc(M, data_path, label):
save_path = os.path.join(data_path,
label,
'Analysis')
-
- # """
- # Create MultiAreaModel instance to have access to data structures
- # """
- # M = MultiAreaModel({})
time_series = []
for area in M.area_list:
diff --git a/figures/MAM2EBRAINS/M2E_compute_louvain_communities.py b/figures/MAM2EBRAINS/M2E_compute_louvain_communities.py
index ec7e92362d1351ed53740351ea8ca6f889f99497..629e7bd2915b1f7014a97f47c15d30df749dd6dd 100644
--- a/figures/MAM2EBRAINS/M2E_compute_louvain_communities.py
+++ b/figures/MAM2EBRAINS/M2E_compute_louvain_communities.py
@@ -5,27 +5,22 @@ import json
import networkx as nx
import numpy as np
import os
-import sys
-from multiarea_model.multiarea_model import MultiAreaModel
-
-"""
-Determines communities in the functional connectivity of either the
-experimental fMRI data used in Schmidt et al. 2018 or of a given
-simulation (the functional connectivity being based either on spike
-rates or an estimated BOLD signal).
-"""
-
-# data_path = sys.argv[1]
-# label = sys.argv[2]
-# method = sys.argv[3]
+def compute_communities(M, data_path, label):
+ """
+ Determines communities in the functional connectivity of either the
+ experimental fMRI data used in Schmidt et al. 2018 or of a given
+ simulation (the functional connectivity being based either on spike
+ rates or an estimated BOLD signal).
-# """
-# Create MultiAreaModel instance to have access to data structures
-# """
-# M = MultiAreaModel({})
+ Parameters:
+ - M (MultiAreaModel): The M object containing the area list.
+ - data_path (str): The path to the data directory.
+ - label (str): The label for the data.
-def compute_communities(M, data_path, label):
+ Returns:
+ None
+ """
method = "synaptic_input"
if label == 'exp':
@@ -73,7 +68,6 @@ def compute_communities(M, data_path, label):
edges.append((area, area2, FC[i][j]))
G.add_weighted_edges_from(edges)
- # part = community.best_partition(G)
part = community_louvain.best_partition(G)
if label == 'exp':
diff --git a/figures/MAM2EBRAINS/M2E_compute_pop_LvR.py b/figures/MAM2EBRAINS/M2E_compute_pop_LvR.py
index 54799aba7bb698b2beb3c0551a1810a0bd9537cb..0cb91a6e4e5fcc16416f08546415d851985060ed 100644
--- a/figures/MAM2EBRAINS/M2E_compute_pop_LvR.py
+++ b/figures/MAM2EBRAINS/M2E_compute_pop_LvR.py
@@ -3,18 +3,20 @@ import numpy as np
import os
from multiarea_model.analysis_helpers import pop_LvR
-from multiarea_model import MultiAreaModel
-import sys
-
-"""
-Compute LvR for the entire network from raw spike
-files of a given simulation.
-"""
-
-# data_path = sys.argv[1]
-# label = sys.argv[2]
def compute_pop_LvR(M, data_path, label):
+ """
+ Compute LvR for the entire network from raw spike
+ files of a given simulation.
+
+ Parameters:
+ - M (MultiAreaModel): MultiAreaModel instance
+ - data_path (str): path to the data
+ - label (str): label for the data
+
+ Returns:
+ None
+ """
load_path = os.path.join(data_path,
label,
'recordings')
@@ -22,33 +24,21 @@ def compute_pop_LvR(M, data_path, label):
label,
'Analysis')
- # with open(os.path.join(data_path, label, 'custom_params_{}'.format(label)), 'r') as f:
- # sim_params = json.load(f)
- # T = sim_params['T']
T = M.simulation.params["t_sim"]
areas_simulated = M.simulation.params["areas_simulated"]
- # """
- # Create MultiAreaModel instance to have access to data structures
- # """
- # M = MultiAreaModel({})
- spike_data = {}
pop_LvR_dict = {}
- # for area in ['V1']: # , 'V2', 'FEF']:
+
for area in areas_simulated:
pop_LvR_dict[area] = {}
- LvR_list = []
- N = []
for pop in M.structure[area]:
fp = '-'.join((label,
- 'spikes', # assumes that the default label for spike files was used
+ 'spikes', # Assumes that the default label for spike files was used
area,
pop))
fn = '{}/{}.npy'.format(load_path, fp)
- # dat = np.load(fn)
dat = np.load(fn, allow_pickle=True)
- # print(area, pop)
pop_LvR_dict[area][pop] = pop_LvR(dat, 2., 500., T, round(M.N[area][pop]))[0]
fn = os.path.join(save_path,
diff --git a/figures/MAM2EBRAINS/M2E_compute_pop_rates.py b/figures/MAM2EBRAINS/M2E_compute_pop_rates.py
index bc71e0fe5dc342f74c9f91c4254a6a79c6c25ec4..a5b79623defc7496566e8d3682ec593fd55c183f 100644
--- a/figures/MAM2EBRAINS/M2E_compute_pop_rates.py
+++ b/figures/MAM2EBRAINS/M2E_compute_pop_rates.py
@@ -3,18 +3,20 @@ import numpy as np
import os
from multiarea_model.analysis_helpers import pop_rate
-from multiarea_model import MultiAreaModel
-import sys
-
-"""
-Compute stationary spike rates for the entire network from raw spike
-files of a given simulation.
-"""
-
-# data_path = sys.argv[1]
-# label = sys.argv[2]
def compute_pop_rates(M, data_path, label):
+ """
+ Compute stationary spike rates for the entire network from raw spike
+ files of a given simulation.
+
+ Parameters:
+ - M (MultiAreaModel): The MultiAreaModel instance containing the simulation data.
+ - data_path (str): The path to the directory where the simulation data is stored.
+ - label (str): The label used to identify the simulation data.
+
+ Returns:
+ None
+ """
load_path = os.path.join(data_path,
label,
'recordings')
@@ -22,20 +24,10 @@ def compute_pop_rates(M, data_path, label):
label,
'Analysis')
- # with open(os.path.join(data_path, label, 'custom_params_{}'.format(label)), 'r') as f:
- # sim_params = json.load(f)
- # T = sim_params['T']
T = M.simulation.params["t_sim"]
areas_simulated = M.simulation.params["areas_simulated"]
- # """
- # Create MultiAreaModel instance to have access to data structures
- # """
- # M = MultiAreaModel({})
-
- spike_data = {}
pop_rates = {}
- # for area in M.area_list:
for area in areas_simulated:
pop_rates[area] = {}
rate_list = []
@@ -46,9 +38,7 @@ def compute_pop_rates(M, data_path, label):
area,
pop))
fn = '{}/{}.npy'.format(load_path, fp)
- # dat = np.load(fn)
dat = np.load(fn, allow_pickle=True)
- # print(area, pop)
pop_rates[area][pop] = pop_rate(dat, 500., T, M.N[area][pop])
rate_list.append(pop_rates[area][pop])
N.append(M.N[area][pop])
diff --git a/figures/MAM2EBRAINS/M2E_compute_rate_time_series.py b/figures/MAM2EBRAINS/M2E_compute_rate_time_series.py
index 2ef7908b8e507d780acf0c960883720271097ea3..ce83fd1b9dc46edfe01d732c88b3f93d10dae32d 100644
--- a/figures/MAM2EBRAINS/M2E_compute_rate_time_series.py
+++ b/figures/MAM2EBRAINS/M2E_compute_rate_time_series.py
@@ -6,28 +6,27 @@ import quantities as pq
from multiarea_model.analysis_helpers import pop_rate_time_series
from elephant.statistics import instantaneous_rate
-from multiarea_model import MultiAreaModel
-import sys
-
-"""
-Compute time series of population-averaged spike rates for a given
-area from raw spike files of a given simulation.
-
-Implements three different methods:
-- binned spike histograms on all neurons ('full')
-- binned spike histograms on a subsample of 140 neurons ('subsample')
-- spike histograms convolved with a Gaussian kernel of optimal width
- after Shimazaki et al. (2010)
-"""
-
-# assert(len(sys.argv) == 5)
-
-# data_path = sys.argv[1]
-# label = sys.argv[2]
-# area = sys.argv[3]
-# method = sys.argv[4]
def compute_rate_time_series(M, data_path, label, area, method):
+ """
+ Compute time series of population-averaged spike rates for a given
+ area from raw spike files of a given simulation.
+
+ Implements three different methods:
+ - binned spike histograms on all neurons ('full')
+ - binned spike histograms on a subsample of 140 neurons ('subsample')
+ - spike histograms convolved with a Gaussian kernel of optimal width after Shimazaki et al. (2010)
+
+ Parameters:
+ - M (MultiAreaModel): The MultiAreaModel instance representing the model.
+ - data_path (str): The path to the data directory.
+ - label (str): The label for the data.
+ - area (str): The area for which to compute the rate time series.
+ - method (str): The method to use for computing the rate time series. Must be one of 'subsample', 'full', or 'auto_kernel'.
+
+ Returns:
+ None
+ """
assert(method in ['subsample', 'full', 'auto_kernel'])
# subsample : subsample spike data to 140 neurons to match the Chu 2014 data
# full : use spikes of all neurons and compute spike histogram with bin size 1 ms
@@ -47,26 +46,16 @@ def compute_rate_time_series(M, data_path, label, area, method):
except FileExistsError:
pass
- # with open(os.path.join(data_path, label, 'custom_params_{}'.format(label)), 'r') as f:
- # sim_params = json.load(f)
- # T = sim_params['T']
T = M.simulation.params["t_sim"]
- areas_simulated = M.simulation.params["areas_simulated"]
-
- """
- Create MultiAreaModel instance to have access to data structures
- """
- # M = MultiAreaModel({})
time_series_list = []
N_list = []
for pop in M.structure[area]:
fp = '-'.join((label,
- 'spikes', # assumes that the default label for spike files was used
+ 'spikes', # Assumes that the default label for spike files was used
area,
pop))
fn = '{}/{}.npy'.format(load_path, fp)
- # spike_data = np.load(fn)
spike_data = np.load(fn, allow_pickle=True)
spike_data = spike_data[np.logical_and(spike_data[:, 1] > 500.,
spike_data[:, 1] <= T)]
diff --git a/figures/MAM2EBRAINS/M2E_compute_synaptic_input.py b/figures/MAM2EBRAINS/M2E_compute_synaptic_input.py
index bf871af1fe37d33b08451826ae147ee8710c100b..fad8f63b40bd9e3d0fbe1b321201dcad9d2ef9bb 100644
--- a/figures/MAM2EBRAINS/M2E_compute_synaptic_input.py
+++ b/figures/MAM2EBRAINS/M2E_compute_synaptic_input.py
@@ -1,17 +1,20 @@
-import correlation_toolbox.helper as ch
-import correlation_toolbox.correlation_analysis as corr
import json
import numpy as np
import os
-import sys
-from multiarea_model.multiarea_model import MultiAreaModel
+def compute_synaptic_input(M, data_path, label, area):
+ """
+ Compute the synaptic input for a given area.
-# data_path = sys.argv[1]
-# label = sys.argv[2]
-# area = sys.argv[3]
+ Parameters:
+ - M (MultiAreaModel): An instance of the MultiAreaModel class.
+ - data_path (str): The path to the data directory.
+ - label (str): The label for the data.
+ - area (str): The area for which to compute the synaptic input.
-def compute_synaptic_input(M, data_path, label, area):
+ Returns:
+ None
+ """
load_path = os.path.join(data_path,
label,
'Analysis',
@@ -21,23 +24,8 @@ def compute_synaptic_input(M, data_path, label, area):
'Analysis',
'synaptic_input')
- with open(os.path.join(data_path, label, 'custom_params_{}'.format(label)), 'r') as f:
- sim_params = json.load(f)
- # T = sim_params['T']
T = M.simulation.params['t_sim']
-
- # """
- # Create MultiAreaModel instance to have access to data structures
- # """
- # connection_params = {'g': -11.,
- # 'cc_weights_factor': sim_params['cc_weights_factor'],
- # 'cc_weights_I_factor': sim_params['cc_weights_I_factor'],
- # 'K_stable': '../SchueckerSchmidt2017/K_prime_original.npy'}
- # network_params = {'connection_params': connection_params}
- # M = MultiAreaModel(network_params)
-
-
"""
Synaptic filtering kernel
"""
diff --git a/figures/MAM2EBRAINS/M2E_firing_rate.py b/figures/MAM2EBRAINS/M2E_firing_rate.py
index 66db04dd95dde2742d7930d63783c90f7cb7a8c5..c89214eab201aaccd8a6e35641db117de711d2be 100644
--- a/figures/MAM2EBRAINS/M2E_firing_rate.py
+++ b/figures/MAM2EBRAINS/M2E_firing_rate.py
@@ -2,14 +2,21 @@ import numpy as np
import matplotlib.pyplot as pl
import os
import json
-from matplotlib.colors import LogNorm
-from matplotlib.ticker import FixedLocator
from M2E_compute_pop_rates import compute_pop_rates
from M2E_compute_rate_time_series import compute_rate_time_series
-# Function for computing and printing the mean firing rate for all and only all simulated populations
def mean_firing_rate(M, data_path):
+ """
+ Calculate the mean firing rate for all simulated populations.
+
+ Parameters:
+ - M (MultiAreaModel): The simulation object.
+ - data_path (str): The path to the data directory.
+
+ Returns:
+ None
+ """
label = M.simulation.label
# Compute firing rate for all simulated populations
@@ -24,7 +31,6 @@ def mean_firing_rate(M, data_path):
rates = np.zeros((len(M.area_list), 8))
for i, area in enumerate(M.area_list):
for j, pop in enumerate(M.structure[area][::-1]):
- # rate = pop_rates[area][pop][0]
rate = pop_rates[area][pop]
if rate == 0.0:
rate = 1e-5
@@ -38,39 +44,41 @@ def mean_firing_rate(M, data_path):
print("The mean firing rate over all simulated populations is {0:.3f} spikes/s.".format(mfr))
-# Function for visualizing the instantaneous firing rate over all simulated areas
def plot_firing_rate_over_areas(M, data_path):
+ """
+ Generate a plot of firing rate over different areas based on the provided simulation data.
+
+ Parameters:
+ - M: object containing simulation data
+ - data_path: path to the data directory
+
+ Returns:
+ None
+ """
area_list = M.simulation.params["areas_simulated"]
label = M.simulation.label
for area in area_list:
compute_rate_time_series(M, data_path, label, area, 'full')
- # time series of firing rates
+ # Time series of firing rates
rate_time_series = {}
for area in area_list:
fn = os.path.join(data_path, label,
'Analysis',
'rate_time_series_full',
'rate_time_series_full_{}.npy'.format(area))
- # fn = os.path.join(data_path, label,
- # 'Analysis',
- # 'rate_time_series-{}.npy'.format(area))
rate_time_series[area] = np.load(fn)
- # print(rate_time_series)
t_min = 500.
t_max = M.simulation.params['t_sim']
- time = np.arange(t_min, t_max)
matrix = []
for area in area_list:
- # print(area)
binned_spikes = rate_time_series[area][500:600]
matrix.append(binned_spikes)
matrix = np.array(matrix)
- # normalized_matrix = (matrix - np.min(matrix)) / (np.max(matrix) - np.min(matrix))
fig = pl.figure(figsize=(12, 5))
fig.suptitle('Instantaneous firing rate over simulated areas', fontsize=16, x=0.45, y=0.95)
@@ -78,7 +86,6 @@ def plot_firing_rate_over_areas(M, data_path):
cmap = pl.get_cmap('YlOrBr')
- # masked_matrix = np.ma.masked_where(np.isnan(matrix), matrix)
ax.patch.set_hatch('x')
im = ax.pcolormesh(matrix, cmap=cmap, edgecolors='None', vmin=0)
ax.set_xlim(0, matrix[0].size)
@@ -89,6 +96,5 @@ def plot_firing_rate_over_areas(M, data_path):
ax.set_yticklabels(area_list)
ax.set_ylabel('Area', size=13)
ax.set_xlabel('Time (ms)', size=13)
- # t = FixedLocator([0.01, 0.1, 1., 10., 100.])
pl.colorbar(im)
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS/M2E_visualize_dynamics.py b/figures/MAM2EBRAINS/M2E_visualize_dynamics.py
index 4ec7caa3574f8f930cfbe8691581ad13877f20a0..946a6fcc19487422785e439407ca80e7eb63b539 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_dynamics.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_dynamics.py
@@ -5,16 +5,11 @@ import os
import sys
sys.path.append('./figures/Schmidt2018_dyn')
-# from helpers import original_data_path, population_labels
from helpers import population_labels
-from multiarea_model import MultiAreaModel
-from multiarea_model import Analysis
from plotcolors import myred, myblue
import matplotlib.pyplot as pl
from matplotlib import gridspec
-# from matplotlib import rc_file
-# rc_file('plotstyle.rc')
from M2E_compute_pop_LvR import compute_pop_LvR
from M2E_compute_corrcoeff import compute_corrcoeff
@@ -24,6 +19,15 @@ icolor = myred
ecolor = myblue
def set_boxplot_props(d):
+ """
+ Sets the properties of a boxplot.
+
+ Parameters:
+ - d (dict): A dictionary containing the boxplot elements.
+
+ Returns:
+ None
+ """
for i in range(len(d['boxes'])):
if i % 2 == 0:
d['boxes'][i].set_facecolor(icolor)
@@ -39,6 +43,19 @@ def set_boxplot_props(d):
markerfacecolor='k', markeredgecolor='k', markersize=3.)
def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
+ """
+ Visualize the network dynamics for the given simulation,
+ including raster plots, mean firing rates,
+ correlation coefficients, and rate time series.
+
+ Parameters:
+ - M (MultiAreaModel): The MultiAreaModel instance containing the simulation data.
+ - data_path (str): The path to the directory where the simulation data is stored.
+ - raster_areas (list): The list of areas to plot the raster plots for.
+
+ Returns:
+ None
+ """
label_spikes = M.simulation.label
# Compute pop_LvR for simulated area
@@ -82,7 +99,6 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
nrows = 4
ncols = 4
- # width = 7.0866
width = 10
panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2. # golden ratio
@@ -95,9 +111,6 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
gs1 = gridspec.GridSpec(1, 3)
gs1.update(left=0.06, right=0.72, top=0.95, wspace=0.4, bottom=0.35)
- # axes['A'] = pl.subplot(gs1[:-1, :1])
- # axes['B'] = pl.subplot(gs1[:-1, 1:2])
- # axes['C'] = pl.subplot(gs1[:-1, 2:])
axes['A'] = pl.subplot(gs1[:1, :1])
axes['B'] = pl.subplot(gs1[:1, 1:2])
axes['C'] = pl.subplot(gs1[:1, 2:])
@@ -116,10 +129,6 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
labels = ['A', 'B', 'C']
for area, label in zip(raster_areas, labels):
label_pos = [-0.2, 1.01]
- # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label + ': ' + area,
- # fontdict={'fontsize': 10, 'weight': 'bold',
- # 'horizontalalignment': 'left', 'verticalalignment':
- # 'bottom'}, transform=axes[label].transAxes)
pl.text(label_pos[0], label_pos[1], label + ': ' + area,
fontdict={'fontsize': 10, 'weight': 'bold',
'horizontalalignment': 'left', 'verticalalignment':
@@ -127,10 +136,6 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
label = 'G'
label_pos = [-0.1, 0.92]
- # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
- # fontdict={'fontsize': 10, 'weight': 'bold',
- # 'horizontalalignment': 'left', 'verticalalignment':
- # 'bottom'}, transform=axes[label].transAxes)
pl.text(label_pos[0], label_pos[1], label,
fontdict={'fontsize': 10, 'weight': 'bold',
'horizontalalignment': 'left', 'verticalalignment':
@@ -139,10 +144,6 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
labels = ['E', 'D', 'F']
for label in labels:
label_pos = [-0.2, 1.05]
- # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
- # fontdict={'fontsize': 10, 'weight': 'bold',
- # 'horizontalalignment': 'left', 'verticalalignment':
- # 'bottom'}, transform=axes[label].transAxes)
pl.text(label_pos[0], label_pos[1], label,
fontdict={'fontsize': 10, 'weight': 'bold',
'horizontalalignment': 'left', 'verticalalignment':
@@ -159,33 +160,8 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
for label in ['A', 'B', 'C']:
axes[label].yaxis.set_ticks_position('none')
-
-
-# """
-# Load data
-# """
-# LOAD_ORIGINAL_DATA = True
-
-
-# if LOAD_ORIGINAL_DATA:
-# # use T=10500 simulation for spike raster plots
-# label_spikes = '3afaec94d650c637ef8419611c3f80b3cb3ff539'
-# # and T=100500 simulation for all other panels
-# label = '99c0024eacc275d13f719afd59357f7d12f02b77'
-# data_path = original_data_path
-# else:
-# from network_simulations import init_models
-# from config import data_path
-# models = init_models('Fig5')
-# label_spikes = models[0].simulation.label
-# label = models[1].simulation.label
-
- # """
- # Create MultiAreaModel instance to have access to data structures
- # """
- # M = MultiAreaModel({})
- # spike data
+ # Spike data
spike_data = {}
for area in areas_simulated:
spike_data[area] = {}
@@ -197,24 +173,21 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
# area, pop)))
area, pop)), allow_pickle=True)
- # stationary firing rates
+ # Stationary firing rates
fn = os.path.join(data_path, label_spikes, 'Analysis', 'pop_rates.json')
with open(fn, 'r') as f:
pop_rates = json.load(f)
- # time series of firing rates
+ # Time series of firing rates
rate_time_series = {}
for area in raster_areas:
fn = os.path.join(data_path, label_spikes,
'Analysis',
'rate_time_series_full',
'rate_time_series_full_{}.npy'.format(area))
- # fn = os.path.join(data_path, label_spikes,
- # 'Analysis',
- # 'rate_time_series-{}.npy'.format(area))
rate_time_series[area] = np.load(fn)
- # time series of firing rates convolved with a kernel
+ # Time series of firing rates convolved with a kernel
if rate_auto_kernel == True:
rate_time_series_auto_kernel = {}
for area in raster_areas:
@@ -224,12 +197,12 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
'rate_time_series_auto_kernel_{}.npy'.format(area))
rate_time_series_auto_kernel[area] = np.load(fn)
- # local variance revised (LvR)
+ # Local variance revised (LvR)
fn = os.path.join(data_path, label_spikes, 'Analysis', 'pop_LvR.json')
with open(fn, 'r') as f:
pop_LvR = json.load(f)
- # correlation coefficients
+ # Correlation coefficients
fn = os.path.join(data_path, label_spikes, 'Analysis', 'corrcoeff.json')
with open(fn, 'r') as f:
corrcoeff = json.load(f)
@@ -237,17 +210,12 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
"""
Plotting
"""
- # print("Raster plots")
-
- # t_min = 3000.
- # t_max = 3500.
t_min = t_sim - 500
t_max = t_sim
icolor = myred
ecolor = myblue
- # frac_neurons = 0.03
frac_neurons = 1
for i, area in enumerate(raster_areas):
@@ -296,29 +264,14 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
ax.set_yticks(yticklocs)
ax.set_xlabel('Time (s)', labelpad=-0.1)
ax.set_xticks([t_min, t_min + 250., t_max])
- # ax.set_xticklabels([r'$3.$', r'$3.25$', r'$3.5$'])
l = t_min/1000
m = (t_min + t_max)/2000
r = t_max/1000
ax.set_xticklabels([f'{l:.2f}', f'{m:.2f}', f'{r:.2f}'])
-
- # print("plotting Population rates")
-
- # rates = np.zeros((len(M.area_list), 8))
- # for i, area in enumerate(M.area_list):
- # for j, pop in enumerate(M.structure[area][::-1]):
- # rate = pop_rates[area][pop][0]
- # if rate == 0.0:
- # rate = 1e-5
- # if area == 'TH' and j > 3: # To account for missing layer 4 in TH
- # rates[i][j + 2] = rate
- # else:
- # rates[i][j] = rate
rates = np.zeros((len(areas_simulated), 8))
for i, area in enumerate(areas_simulated):
for j, pop in enumerate(M.structure[area][::-1]):
- # rate = pop_rates[area][pop][0]
rate = pop_rates[area][pop]
if rate == 0.0:
rate = 1e-5
@@ -341,25 +294,10 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))
ax.set_ylim((0., len(M.structure['V1']) + .5))
- # x_max = 220.
x_max = 100.
ax.set_xlim((-1., x_max))
ax.set_xlabel(r'Rate (spikes/s)', labelpad=-0.1)
- # ax.set_xticks([0., 25., 50.])
ax.set_xticks([0., 25., 50., 75., 100.])
-
- # print("plotting Synchrony")
-
- # syn = np.zeros((len(M.area_list), 8))
- # for i, area in enumerate(M.area_list):
- # for j, pop in enumerate(M.structure[area][::-1]):
- # value = corrcoeff[area][pop]
- # if value == 0.0:
- # value = 1e-5
- # if area == 'TH' and j > 3: # To account for missing layer 4 in TH
- # syn[i][j + 2] = value
- # else:
- # syn[i][j] = value
syn = np.zeros((len(areas_simulated), 8))
for i, area in enumerate(areas_simulated):
@@ -367,7 +305,6 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
try:
value = corrcoeff[area][pop]
if value == 0.0:
- # print(i, area, j, pop, value)
value = 1e-5
except:
value = 0.0
@@ -376,45 +313,22 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
syn[i][j + 2] = value
else:
syn[i][j] = value
- # print(syn)
syn = np.transpose(syn)
masked_syn = np.ma.masked_where(syn == 0, syn)
- # print(masked_syn)
ax = axes['E']
- # d = ax.boxplot(np.transpose(syn), vert=False,
- # patch_artist=True, whis=1.5, showmeans=True)
d = ax.boxplot(np.transpose(masked_syn), vert=False,
patch_artist=True, whis=1.5, showmeans=True)
set_boxplot_props(d)
- # ax.plot(np.mean(syn, axis=1), np.arange(
- # 1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)
ax.plot(np.mean(masked_syn, axis=1), np.arange(
1., len(M.structure['V1']) + 1., 1.), 'x', color='k', markersize=3)
ax.set_yticklabels(population_labels[::-1], size=8)
ax.set_yticks(np.arange(1., len(M.structure['V1']) + 1., 1.))
ax.set_ylim((0., len(M.structure['V1']) + .5))
- # ax.set_xticks(np.arange(0.0, 0.601, 0.2))
- # ax.set_xticks([0., 0.005, 0.02])
ax.set_xlabel('Correlation coefficient', labelpad=-0.1)
- # ax.set_xlabel('Synchrony', labelpad=-0.1)
-
-
- # print("plotting Irregularity")
-
- # LvR = np.zeros((len(M.area_list), 8))
- # for i, area in enumerate(M.area_list):
- # for j, pop in enumerate(M.structure[area][::-1]):
- # value = pop_LvR[area][pop]
- # if value == 0.0:
- # value = 1e-5
- # if area == 'TH' and j > 3: # To account for missing layer 4 in TH
- # LvR[i][j + 2] = value
- # else:
- # LvR[i][j] = value
LvR = np.zeros((len(areas_simulated), 8))
for i, area in enumerate(areas_simulated):
@@ -456,8 +370,6 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
axes['G'].set_xticks([])
axes['G'].set_yticks([])
-
- # print("Plotting rate time series")
pos = axes['G'].get_position()
ax = []
h = pos.y1 - pos.y0
@@ -468,11 +380,8 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
colors = ['0.5', '0.3', '0.0']
- # t_min = 500.
- # t_max = 10500.
t_min = 500.
t_max = t_sim
- # time = np.arange(500., t_max)
time = np.arange(500, t_max)
for i, area in enumerate(raster_areas[::-1]):
ax[i].spines['right'].set_color('none')
@@ -484,7 +393,6 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
np.logical_and(time >= t_min, time < t_max))]
ax[i].plot(time, binned_spikes, color=colors[0], label=area)
if rate_auto_kernel == True:
- # rate = rate_time_series_auto_kernel[area]
rate = rate_time_series_auto_kernel[area][np.where(
np.logical_and(time >= t_min, time < t_max))]
ax[i].plot(time, rate, color=colors[2], label=area)
@@ -497,14 +405,11 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
ax[i].set_xticks([])
ax[i].set_yticks([0., 30.])
else:
- # ax[i].set_xticks([1000., 5000., 10000.])
ax[i].set_xticks([t_min, (t_min+t_max)/2, t_max])
l = t_min/1000
m = (t_min + t_max)/2000
r = t_max/1000
- # ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])
ax[i].set_xticklabels([f'{l:.2f}', f'{m:.2f}', f'{r:.2f}'])
- # ax[i].set_yticks([0., 5.])
ax[i].set_yticks([0., 30.])
if i == 1:
ax[i].set_ylabel(r'Rate (spikes/s)')
@@ -512,6 +417,4 @@ def visual_dynamics(M, data_path, raster_areas=['V1', 'V2', 'FEF']):
ax[0].set_xlabel('Time (s)', labelpad=-0.05)
fig.subplots_adjust(left=0.05, right=0.95, top=0.95,
- bottom=0.075, wspace=1., hspace=.5)
-
- # pl.savefig('Fig5_ground_state.eps')
\ No newline at end of file
+ bottom=0.075, wspace=1., hspace=.5)
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS/M2E_visualize_fc.py b/figures/MAM2EBRAINS/M2E_visualize_fc.py
index f9a8caa0282137d838f6bd2a1cf9d5653c496786..749144ed1a5b08610f9c30d27dd481017b9a26fa 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_fc.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_fc.py
@@ -3,22 +3,16 @@ import copy
import json
import numpy as np
import os
-# import pyx
import sys
sys.path.append('./figures/Schmidt2018_dyn')
-# from helpers import original_data_path, infomap_path
-from multiarea_model.multiarea_model import MultiAreaModel
from plotcolors import myred, myblue
import matplotlib.pyplot as pl
from matplotlib import gridspec
-from matplotlib import rc_file
-# rc_file('./figures/Schmidt2018_dyn/plotstyle.rc')
sys.path.append('./figures/Schmidt2018')
-# from graph_helpers import apply_map_equation
from M2E_compute_fc import compute_fc
from M2E_compute_louvain_communities import compute_communities
@@ -27,23 +21,15 @@ cmap = pl.cm.coolwarm
cmap = cmap.from_list('mycmap', [myblue, 'white', myred], N=256)
cmap2 = cmap.from_list('mycmap', ['white', myred], N=256)
-# """
-# Create MultiAreaModel instance to have access to data structures
-# """
-# conn_params = {'g': -11.,
-# 'fac_nu_ext_TH': 1.2,
-# 'fac_nu_ext_5E': 1.125,
-# 'fac_nu_ext_6E': 1.41666667,
-# 'av_indegree_V1': 3950.,
-# 'K_stable': '../SchueckerSchmidt2017/K_prime_original.npy'}
-# network_params = {'N_scaling': 1.,
-# 'K_scaling': 1.,
-# 'connection_params': conn_params}
-# M = MultiAreaModel(network_params)
-
def zero_diagonal(matrix):
"""
Return copy of a matrix with diagonal set to zero.
+
+ Parameters:
+ - matrix (ndarray): Matrix to copy.
+
+ Returns:
+ - M (ndarray): Matrix with diagonal set to zero.
"""
M = copy.copy(matrix)
for i in range(M.shape[0]):
@@ -54,6 +40,17 @@ def zero_diagonal(matrix):
def matrix_plot(M, ax, matrix, index, vlim, pos=None):
"""
Plot matrix into matplotlib axis sorted according to index.
+
+ Parameters:
+ - M (MultiAreaModel): Object containing simulation data.
+ - ax (matplotlib axis): Axis to plot matrix into.
+ - matrix (ndarray): Matrix to plot.
+ - index (int): Index of matrix to plot.
+ - vlim (float): Value limit.
+ - pos (tuple): Position of matrix in figure.
+
+ Returns:
+ None
"""
ax.yaxis.set_ticks_position('none')
ax.xaxis.set_ticks_position('none')
@@ -70,7 +67,6 @@ def matrix_plot(M, ax, matrix, index, vlim, pos=None):
vmax = vlim
vmin = -vlim
- # , norm = LogNorm(1e-8,1.))
im = ax.pcolormesh(matrix[index][:, index][::-1],
cmap=cmap, vmin=vmin, vmax=vmax)
@@ -78,73 +74,46 @@ def matrix_plot(M, ax, matrix, index, vlim, pos=None):
cb = pl.colorbar(im, ax=ax, ticks=cbticks, fraction=0.046)
cb.ax.tick_params(labelsize=14)
ax.set_yticks([])
-
- # if pos != (0, 2):
- # cb.remove()
- # else:
- # ax.text(1.25, 0.52, r'FC', rotation=90,
- # transform=ax.transAxes, size=14)
ax.set_xticks([])
-
ax.set_xlabel('Cortical area', size=14)
ax.set_ylabel('Cortical area', size=14)
def visualize_fc(M, data_path):
+ """
+ Visualize functional connectivity.
+
+ Parameters:
+ - M ((MultiAreaModel)): Object containing simulation data.
+ - data_path (str): Path to the data directory
+
+ Returns:
+ None
+ """
label = M.simulation.label
- # compute functional connectivity
+ # Compute functional connectivity
compute_fc(M, data_path, label)
compute_communities(M, data_path, label)
"""
Figure layout
"""
- # width = 7.0866
- # n_horz_panels = 2.
- n_horz_panels = 1.
- # n_vert_panels = 3.
- n_vert_panels = 2.
-
fig = pl.figure(figsize=(10, 4))
fig.suptitle('Simulated functional connectivity (left) and FC of macaque resting-state fMRI',
fontsize=17, x=0.5, y=1.1)
axes = {}
- # gs1 = gridspec.GridSpec(1, 3)
gs1 = gridspec.GridSpec(1, 2)
- # gs1.update(left=0.05, right=0.95, top=0.95,
- # bottom=0.52, wspace=0., hspace=0.4)
gs1.update(left=0.05, right=0.95, top=1,
bottom=0, wspace=0.3, hspace=0)
axes['A'] = pl.subplot(gs1[:1, :1])
axes['B'] = pl.subplot(gs1[:1, 1:2])
- # axes['C'] = pl.subplot(gs1[:, 2])
-
- # gs1 = gridspec.GridSpec(1, 1)
- # gs1.update(left=0.18, right=0.8, top=0.35,
- # wspace=0., bottom=0.13, hspace=0.2)
- # axes['D'] = pl.subplot(gs1[:, :])
-
- # gs1 = gridspec.GridSpec(1, 1)
- # gs1.update(left=0.165, right=0.6, top=0.04,
- # wspace=0., bottom=0.0, hspace=0.2)
- # axes['E'] = pl.subplot(gs1[:, :])
- # gs1 = gridspec.GridSpec(1, 1)
- # gs1.update(left=0.688, right=0.95, top=0.04,
- # wspace=0., bottom=0.0, hspace=0.2)
- # axes['F'] = pl.subplot(gs1[:, :])
-
- # for label in ['A', 'B', 'C', 'D', 'E', 'F']:
for label in ['A', 'B']:
if label in ['E', 'F']:
label_pos = [-0.08, 1.01]
else:
label_pos = [-0.1, 1.01]
- # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
- # fontdict={'fontsize': 16, 'weight': 'bold',
- # 'horizontalalignment': 'left', 'verticalalignment':
- # 'bottom'}, transform=axes[label].transAxes)
pl.text(label_pos[0], label_pos[1], label,
fontdict={'fontsize': 16, 'weight': 'bold',
'horizontalalignment': 'left', 'verticalalignment':
@@ -154,21 +123,9 @@ def visualize_fc(M, data_path):
axes[label].yaxis.set_ticks_position("left")
axes[label].xaxis.set_ticks_position("bottom")
- # for label in ['E', 'F']:
- # axes[label].spines['right'].set_color('none')
- # axes[label].spines['top'].set_color('none')
- # axes[label].spines['left'].set_color('none')
- # axes[label].spines['bottom'].set_color('none')
-
- # axes[label].yaxis.set_ticks_position("none")
- # axes[label].xaxis.set_ticks_position("none")
- # axes[label].set_yticks([])
- # axes[label].set_xticks([])
-
"""
Load data
"""
-
# Load experimental functional connectivity
func_conn_data = {}
with open('./figures/Schmidt2018_dyn/Fig8_exp_func_conn.csv', 'r') as f:
@@ -190,39 +147,9 @@ def visualize_fc(M, data_path):
for j, area2 in enumerate(M.area_list):
exp_FC[i][j] = func_conn_data[area1][area2]
- # fn = './figures/Schmidt2018_dyn/FC_exp_communities.json'
- # with open(fn, 'r') as f:
- # part_exp = json.load(f)
- # part_exp_list = [part_exp[area] for area in M.area_list]
-
-
"""
Simulation data
"""
- # LOAD_ORIGINAL_DATA = True
-
- # cc_weights_factor = [1.0, 1.4, 1.5, 1.6, 1.7, 1.75, 1.8, 2., 2.1, 2.5, 1.9]
-
- # if LOAD_ORIGINAL_DATA:
- # labels = ['33fb5955558ba8bb15a3fdce49dfd914682ef3ea',
- # '783cedb0ff27240133e3daa63f5d0b8d3c2e6b79',
- # '380856f3b32f49c124345c08f5991090860bf9a3',
- # '5a7c6c2d6d48a8b687b8c6853fb4d98048681045',
- # 'c1876856b1b2cf1346430cf14e8d6b0509914ca1',
- # 'a30f6fba65bad6d9062e8cc51f5483baf84a46b7',
- # '1474e1884422b5b2096d3b7a20fd4bdf388af7e0',
- # 'f18158895a5d682db5002489d12d27d7a974146f',
- # '08a3a1a88c19193b0af9d9d8f7a52344d1b17498',
- # '5bdd72887b191ec22a5abcc04ca4a488ea216e32',
- # '99c0024eacc275d13f719afd59357f7d12f02b77']
- # data_path = original_data_path
- # label_plot = labels[-1] # chi=1.9
- # else:
- # from network_simulations import init_models
- # from config import data_path
- # models = init_models('Fig8')
- # labels = [M.simulation.label for M in models]
-
labels = [M.simulation.label]
sim_FC = {}
@@ -233,14 +160,6 @@ def visualize_fc(M, data_path):
'functional_connectivity_synaptic_input.npy')
sim_FC[label] = np.load(fn)
- # sim_FC_bold = {}
- # for label in [label_plot]:
- # fn = os.path.join(data_path,
- # label,
- # 'Analysis',
- # 'functional_connectivity_bold_signal.npy')
- # sim_FC_bold[label] = np.load(fn)
-
label = M.simulation.label
fn = os.path.join(data_path,
label,
@@ -262,22 +181,11 @@ def visualize_fc(M, data_path):
Plotting
"""
ax = axes['A']
- # label = label_plot
-
matrix_plot(M, ax, zero_diagonal(sim_FC[label]),
part_sim_index, 1., pos=(0, 0))
- # ax = axes['B']
- # label = label_plot
-
- # matrix_plot(ax, zero_diagonal(sim_FC_bold[label]),
- # part_sim_index, 1., pos=(0, 0))
-
- # ax = axes['C']
ax = axes['B']
- # matrix_plot(M, ax, zero_diagonal(exp_FC),
- # part_sim_index, 1., pos=(0, 2))
matrix_plot(M, ax, zero_diagonal(exp_FC),
part_sim_index, 1., pos=(0, 0))
@@ -286,131 +194,3 @@ def visualize_fc(M, data_path):
for area in areas[1:]:
area_string += ' '
area_string += area
-
- # # pl.text(0.02, 0.45, r'\bfseries{}Order of cortical areas:', transform=fig.transFigure, fontsize=12)
- # pl.text(0.02, 0.3, 'Order of cortical areas:', transform=fig.transFigure, fontsize=17)
- # pl.text(0.02, 0.25, area_string,
- # transform=fig.transFigure, fontsize=15)
-
-
- # ax = axes['D']
- # ax.spines['right'].set_color('none')
- # ax.spines['top'].set_color('none')
- # ax.yaxis.set_ticks_position("left")
- # ax.xaxis.set_ticks_position("bottom")
-
- # cc_list = []
- # for i, label in enumerate(labels):
- # cc = np.corrcoef(zero_diagonal(sim_FC[label]).flatten(),
- # zero_diagonal(exp_FC).flatten())[0][1]
- # cc_list.append(cc)
-
-
- # ax.plot(cc_weights_factor[1:], cc_list[1:], '.', ms=10,
- # markeredgecolor='none', label='Sim. vs. Exp.', color='k')
- # ax.plot(cc_weights_factor[0], cc_list[0], '^', ms=5,
- # markeredgecolor='none', label='Sim. vs. Exp.', color='k')
-
- # label = label_plot
- # cc_bold = np.corrcoef(zero_diagonal(sim_FC_bold[label]).flatten(),
- # zero_diagonal(exp_FC).flatten())[0][1]
- # ax.plot([1.9], cc_bold, '.',
- # ms=10, markeredgecolor='none', color=myred)
-
- # # Correlation between exp. FC and structural connectivity
- # # Construct the structural connectivity as the matrix of relative
- # conn_matrix = np.zeros((len(M.area_list), len(M.area_list)))
- # for i, area1 in enumerate(M.area_list):
- # s = np.sum(list(M.K_areas[area1].values()))
- # for j, area2 in enumerate(M.area_list):
- # value = M.K_areas[area1][area2] / s
- # conn_matrix[i][j] = value
-
-
- # cc = np.corrcoef(zero_diagonal(conn_matrix).flatten(),
- # zero_diagonal(exp_FC).flatten())[0][1]
-
- # # Formatting
- # ax.hlines(cc, -0.1, 2.5, linestyle='dashed', color='k')
- # ax.set_xlabel(r'Cortico-cortical weight factor $\chi$',
- # labelpad=-0.1, size=16)
- # ax.set_ylabel(r'$r_{\mathrm{Pearson}}$', size=20)
- # ax.set_xlim((0.9, 2.7))
- # ax.set_ylim((-0.1, 0.6))
- # ax.set_yticks([0., 0.2, 0.4])
- # ax.set_yticklabels([0., 0.2, 0.4], size=13)
- # ax.set_xticks([1., 1.5, 2., 2.5])
- # ax.set_xticklabels([1., 1.5, 2., 2.5], size=13)
-
-
- # """
- # Save figure
- # """
- # pl.savefig('Fig8_interactions_mpl.eps')
-
- # """
- # We compare the clusters found in the functional connectivity to
- # clusters found in the structural connectivity of the network. To
- # detect the clusters in the structural connectivity, we repeat the the
- # procedure from Fig. 7 of Schmidt et al. 'Multi-scale account of the
- # network structure of macaque visual cortex' and apply the map equation
- # method (see Materials & Methods in Schmidt et al. 2018) to the
- # structural connectivity of the network.
-
- # This requires installation of the infomap executable and defining the
- # path to the executable.
- # """
- # fn = 'Fig8_structural_clusters'
- # modules, modules_areas, index = apply_map_equation(conn_matrix,
- # M.area_list,
- # filename=fn,
- # infomap_path=infomap_path)
- # with open('{}.map'.format(fn), 'r') as f:
- # line = ''
- # while '*Nodes' not in line:
- # line = f.readline()
- # line = f.readline()
- # map_equation = []
- # map_equation_areas = []
- # while "*Links" not in line:
- # map_equation.append(int(line.split(':')[0]))
- # map_equation_areas.append(line.split('"')[1])
- # line = f.readline()
- # f.close()
- # map_equation = np.array(map_equation)
- # map_equation_dict = dict(
- # list(zip(map_equation_areas, map_equation)))
-
- # # To create the alluvial input, we rename the simulated clusters
- # # 1S --> 2S, 2S ---> 1S for visual purposes
- # f = open('alluvial_input.txt', 'w')
- # f.write("area,map_equation, louvain, louvain_exp\n")
- # for i, area in enumerate(M.area_list):
- # if part_sim_list[i] == 1:
- # psm = 2
- # elif part_sim_list[i] == 0:
- # psm = 1
- # s = '{}, {}, {}, {}'.format(area,
- # map_equation_dict[area],
- # psm,
- # part_exp_list[i])
- # f.write(s)
- # f.write('\n')
- # f.close()
-
- # # The alluvial plot cannot be created with a script. To reproduce the alluvial
- # # plot, go to http://app.rawgraphs.io/ and proceed from there.
-
- # """
- # Merge with alluvial plot
- # """
- # c = pyx.canvas.canvas()
- # c.fill(pyx.path.rect(0, 0., 17.9, 17.), [pyx.color.rgb.white])
-
- # c.insert(pyx.epsfile.epsfile(0., 6., "Fig8_interactions_mpl.eps", width=17.9))
- # c.insert(pyx.epsfile.epsfile(
- # 0.1, -1., "Fig8_alluvial_struct_sim.eps", width=11.))
- # c.insert(pyx.epsfile.epsfile(
- # 11.2, -0.6, "Fig8_alluvial_sim_exp.eps", width=6.5))
-
- # c.writeEPSfile("Fig8_interactions.eps")
diff --git a/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
index d18b7e05dc64e8137be2012a878ab997f73247b5..bb111c2cd7535efe4a7577aa2cf739e9154ad568 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_interareal_connectivity.py
@@ -1,7 +1,5 @@
-import json
import numpy as np
import matplotlib.pyplot as pl
-import os
import sys
sys.path.append('./figures/Schmidt2018')
@@ -10,56 +8,46 @@ from helpers import area_list, datapath
from matplotlib import gridspec
from matplotlib.colors import LogNorm
from matplotlib.ticker import FixedLocator
-from matplotlib import rc_file
from multiarea_model import MultiAreaModel
-from plotcolors import myblue
-from scipy import stats
-
-# rc_file('plotstyle.rc')
def visualize_interareal_connectivity(M):
- # full-scale model
+ """
+ Visualize inter-area connectivity for a comparison of the full-scale model and the down-scaled model
+
+ Parameters:
+ - M ((MultiAreaModel)): Object containing simulation data.
+
+ Returns:
+ None
+ """
+ # Full-scale model
M_full_scale = MultiAreaModel({})
"""
Figure layout
"""
- # nrows = 2
nrows = 1
ncols = 2
- # width = 6.8556
width = 12
panel_wh_ratio = 0.7 * (1. + np.sqrt(5)) / 2. # golden ratio
height = width / panel_wh_ratio * float(nrows) / ncols
- # print(width, height)
pl.rcParams['figure.figsize'] = (width, height)
fig = pl.figure()
fig.suptitle('Area-level connectivity of the full-scale and down-scaled MAM expressed as relative indegrees for each target area', fontsize=15, x=0.5, y=1.05)
axes = {}
- # gs1 = gridspec.GridSpec(2, 2)
gs1 = gridspec.GridSpec(1, 2)
- # gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.1, hspace=0.3)
gs1.update(left=0.1, right=0.95, top=0.95, bottom=0.1, wspace=0.3, hspace=0.3)
- # axes['A'] = pl.subplot(gs1[:1, :1])
- # axes['B'] = pl.subplot(gs1[:1, 1:2])
axes['B'] = pl.subplot(gs1[:1, :1])
- # axes['D'] = pl.subplot(gs1[:1, 2:])
axes['D'] = pl.subplot(gs1[:1, 1:2])
- # pos = axes['A'].get_position()
pos2 = axes['D'].get_position()
- # axes['C'] = pl.axes([pos.x0 + 0.01, pos2.y0, pos.x1 - pos.x0 - 0.025, 0.23])
-
- # print(pos.x1 - pos.x0 - 0.025)
- # labels = ['A', 'B', 'C', 'D']
labels = ['B', 'D']
labels_display = ['Full-scale model', 'Down-scaled model']
- # for label in labels:
for i in range(len(labels)):
label = labels[i]
label_display = labels_display[i]
@@ -67,113 +55,11 @@ def visualize_interareal_connectivity(M):
label_pos = [-0.045, 1.18]
else:
label_pos = [-0.2, 1.04]
- # pl.text(label_pos[0], label_pos[1], r'\bfseries{}' + label,
- # fontdict={'fontsize': 10, 'weight': 'bold',
- # 'horizontalalignment': 'left', 'verticalalignment':
- # 'bottom'}, transform=axes[label].transAxes)
pl.text(label_pos[0], label_pos[1], label_display,
fontdict={'fontsize': 12, 'weight': 'bold',
'horizontalalignment': 'left', 'verticalalignment':
'bottom'}, transform=axes[label].transAxes)
-# """
-# Load data
-# """
-# # M = MultiAreaModel({})
-# M_full_scale = MultiAreaModel({})
-
-# datapath = './multiarea_model/data_multiarea/'
-# with open(os.path.join(datapath, 'viscortex_processed_data.json'), 'r') as f:
-# proc = json.load(f)
-# with open(os.path.join(datapath, 'viscortex_raw_data.json'), 'r') as f:
-# raw = json.load(f)
-
-# FLN_Data_FV91 = proc['FLN_Data_FV91']
-
-# cocomac_data = raw['cocomac_data']
-# median_distance_data = raw['median_distance_data']
-
-# cocomac = np.zeros((32, 32))
-# conn_matrix = np.zeros((32, 32))
-# for i, area1 in enumerate(area_list[::-1]):
-# for j, area2 in enumerate(area_list):
-# # if M.K_areas[area1][area2] > 0. and area2 in cocomac_data[area1]:
-# if M_full_scale.K_areas[area1][area2] > 0. and area2 in cocomac_data[area1]:
-# cocomac[i][j] = 1.
-# if area2 in FLN_Data_FV91[area1]:
-# conn_matrix[i][j] = FLN_Data_FV91[area1][area2]
-
-# """
-# Panel A: CoCoMac Data
-# """
-# ax = axes['A']
-# # ax.yaxis.set_ticks_position("left")
-# # ax.xaxis.set_ticks_position("bottom")
-
-# ax.set_aspect(1. / ax.get_data_ratio())
-# ax.yaxis.set_ticks_position("none")
-# ax.xaxis.set_ticks_position("none")
-
-# masked_matrix = np.ma.masked_values(cocomac, 0.0)
-# cmap = pl.cm.binary
-# cmap.set_bad('w', 1.0)
-
-# x = np.arange(0, len(area_list) + 1)
-# y = np.arange(0, len(area_list[::-1]) + 1)
-# X, Y = np.meshgrid(x, y)
-
-# ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
-# ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
-# # ax.set_xticklabels(area_list, rotation=90, size=6.)
-# ax.set_xticklabels(area_list, rotation=90, size=10.)
-
-# # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
-# ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
-# # ax.set_yticklabels(area_list[::-1], size=6.)
-# ax.set_yticklabels(area_list[::-1], size=6.)
-
-# ax.set_ylabel('Target area')
-# ax.set_xlabel('Source area')
-
-# im = ax.pcolormesh(masked_matrix, cmap=cmap,
-# edgecolors='None', vmin=0., vmax=1.)
-
-# t = FixedLocator([])
-# cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
-# cbar.set_alpha(0.)
- # cbar.remove()
-
-# """
-# Panel B: Data from Markov et al. (2014) "A weighted and directed
-# interareal connectivity matrix for macaque cerebral cortex."
-# Cerebral Cortex, 24(1), 17–36.
-# """
-# ax = axes['B']
-# ax.set_aspect(1. / ax.get_data_ratio())
-# ax.yaxis.set_ticks_position("none")
-# ax.xaxis.set_ticks_position("none")
-
-# masked_matrix = np.ma.masked_values(conn_matrix, 0.0)
-# cmap = pl.get_cmap('inferno')
-# cmap.set_bad('w', 1.0)
-
-# x = np.arange(0, len(area_list) + 1)
-# y = np.arange(0, len(area_list[::-1]) + 1)
-# X, Y = np.meshgrid(x, y)
-
-# ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
-# ax.set_xticklabels(area_list, rotation=90, size=6.)
-
-# ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
-# ax.set_yticklabels(area_list[::-1], size=6.)
-
-# im = ax.pcolormesh(masked_matrix, cmap=cmap,
-# edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
-
-# t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
-# cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
-# cbar.set_alpha(0.)
-
"""
Panel B: Interareal connectivity of full-scaling multi-area model
"""
@@ -190,7 +76,7 @@ def visualize_interareal_connectivity(M):
ax.set_aspect(1. / ax.get_data_ratio())
masked_matrix_full_scale = np.ma.masked_values(conn_matrix_full_scale, 0.0)
- # cmap = pl.get_cmap('inferno')
+
cmap = pl.get_cmap('YlOrBr')
cmap.set_bad('w', 1.0)
@@ -198,18 +84,12 @@ def visualize_interareal_connectivity(M):
y = np.arange(0, len(area_list[::-1]) + 1)
X, Y = np.meshgrid(x, y)
- # ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
- # ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_xticklabels(area_list, rotation=90, size=10.)
- # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
- # ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_yticklabels(area_list[::-1], size=10.)
- # ax.set_ylabel('Target area')
- # ax.set_xlabel('Source area')
ax.set_ylabel('Target area', fontsize=15)
ax.set_xlabel('Source area', fontsize=15)
im = ax.pcolormesh(masked_matrix_full_scale, cmap=cmap,
@@ -219,93 +99,6 @@ def visualize_interareal_connectivity(M):
cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
cbar.set_alpha(0.)
- # """
- # Panel C: Exponential decay of FLN with distance
- # """
- # FLN_values_FV91 = np.array([])
- # distances_FV91 = np.array([])
-
- # for target_area in FLN_Data_FV91:
- # for source_area in FLN_Data_FV91[target_area]:
- # if target_area in median_distance_data and source_area in median_distance_data:
- # if FLN_Data_FV91[target_area][source_area]:
- # FLN_values_FV91 = np.append(FLN_values_FV91, FLN_Data_FV91[
- # target_area][source_area])
- # distances_FV91 = np.append(distances_FV91, median_distance_data[
- # target_area][source_area])
-
- # # Linear fit of distances vs. log FLN
- # print("\n \n Linear fit to logarithmic values")
- # gradient, intercept, r_value, p_value, std_err = stats.linregress(
- # distances_FV91, np.log(FLN_values_FV91))
- # print("Raw parameters: ", gradient, intercept)
- # print("Transformed parameters: ", -gradient, np.exp(intercept))
- # print('r_value**2', r_value ** 2)
- # print('p_value', p_value)
- # print('std_err', std_err)
-
- # ax = axes['C']
- # ax.yaxis.set_ticks_position("left")
- # ax.xaxis.set_ticks_position("bottom")
-
- # ax.yaxis.set_ticks_position("left")
- # ax.xaxis.set_ticks_position("bottom")
-
- # ax.spines['right'].set_color('none')
- # ax.spines['top'].set_color('none')
- # ax.yaxis.set_ticks_position("left")
- # ax.xaxis.set_ticks_position("bottom")
-
- # ax.plot(distances_FV91, np.log10(FLN_values_FV91), '.', color=myblue)
- # x = np.arange(np.min(distances_FV91), np.max(distances_FV91), 1)
- # ax.plot(x, (intercept + gradient * x) / np.log(10), linewidth=2.0,
- # color='Black', label='Linear regression fit')
-
- # ax.set_xlabel('Distance (mm)', labelpad=7)
- # ax.set_ylabel(r'$\log(FLN)$')
- # ax.set_yticks([-6, -4, -2, 0])
-
- # print("log fit")
- # print(np.corrcoef(gradient * distances_FV91 + intercept, np.log(FLN_values_FV91))[0][1])
-
- # """
- # Panel D: Resulting connectivity matrix
- # """
- # conn_matrix = np.zeros((32, 32))
- # for i, area1 in enumerate(area_list[::-1]):
- # for j, area2 in enumerate(area_list):
- # conn_matrix[i][j] = M.K_areas[area1][
- # area2] / np.sum(list(M.K_areas[area1].values()))
-
- # ax = axes['D']
- # ax.yaxis.set_ticks_position("none")
- # ax.xaxis.set_ticks_position("none")
-
- # ax.set_aspect(1. / ax.get_data_ratio())
-
- # masked_matrix = np.ma.masked_values(conn_matrix, 0.0)
- # cmap = pl.get_cmap('inferno')
- # cmap.set_bad('w', 1.0)
-
- # x = np.arange(0, len(area_list) + 1)
- # y = np.arange(0, len(area_list[::-1]) + 1)
- # X, Y = np.meshgrid(x, y)
-
- # ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
- # ax.set_xticklabels(area_list, rotation=90, size=6.)
-
- # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
- # ax.set_yticklabels(area_list[::-1], size=6.)
-
- # ax.set_ylabel('Target area')
- # ax.set_xlabel('Source area')
- # im = ax.pcolormesh(masked_matrix, cmap=cmap,
- # edgecolors='None', norm=LogNorm(vmin=1e-6, vmax=1.))
-
- # t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
- # cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
- # cbar.set_alpha(0.)
-
"""
Panel D: Interareal connectivity of down-scaling multi-area model
"""
@@ -322,21 +115,16 @@ def visualize_interareal_connectivity(M):
ax.set_aspect(1. / ax.get_data_ratio())
masked_matrix_down_scale = np.ma.masked_values(conn_matrix_down_scale, 0.0)
- # cmap = pl.get_cmap('inferno')
cmap.set_bad('w', 1.0)
x = np.arange(0, len(area_list) + 1)
y = np.arange(0, len(area_list[::-1]) + 1)
X, Y = np.meshgrid(x, y)
- # ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
- # ax.set_xticklabels(area_list, rotation=90, size=6.)
ax.set_xticklabels(area_list, rotation=90, size=10.)
- # ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list) + 1, 1)])
ax.set_yticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
- # ax.set_yticklabels(area_list[::-1], size=6.)
ax.set_yticklabels(area_list[::-1], size=10.)
ax.set_ylabel('Target area', fontsize=15)
@@ -346,9 +134,4 @@ def visualize_interareal_connectivity(M):
t = FixedLocator([1e-6, 1e-4, 1e-2, 1])
cbar = pl.colorbar(im, ticks=t, fraction=0.046, ax=ax)
- cbar.set_alpha(0.)
-
- # """
- # Save figure
- # """
- # pl.savefig('Fig4_connectivity.eps')
+ cbar.set_alpha(0.)
\ No newline at end of file
diff --git a/figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py b/figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py
index 54576bfa3615eb6202832b098f09b09f3ddefad9..973855e090c815a8425d6dae9bc86731e01baaee 100644
--- a/figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py
+++ b/figures/MAM2EBRAINS/M2E_visualize_time_ave_pop_rates.py
@@ -1,49 +1,38 @@
import numpy as np
import os
import json
-
-from multiarea_model import Analysis
import matplotlib.pyplot as plt
-from matplotlib.colors import LogNorm
-from matplotlib.ticker import FixedLocator
def plot_time_averaged_population_rates(M, data_path, area_list=None, **keywords):
"""
Plot overview over time-averaged population rates encoded in colors
with areas along x-axis and populations along y-axis.
- Parameters
- ----------
- area_list : list, optional
- Specifies with areas are plotted in which order.
- Default to None, leading to plotting of all areas ordered by architectural type.
- output : {'pdf', 'png', 'eps'}, optional
- If given, the function stores the plot to a file of the given format.
+ Parameters:
+ - M (MultiAreaModel): The simulation object.
+ - data_path (str): The path to the data directory.
+ - area_list (list): List of areas to plot. If None, all areas are plotted.
+ - **keywords: Additional keyword arguments.
+
+ Returns:
+ - fig (object): The figure object.
"""
-
- # with open(os.path.join(data_path, M.simulation.label, 'custom_params_{}'.format(M.simulation.label)), 'r') as f:
- # sim_params = json.load(f)
- # areas_simulated = sim_params['sim_params']['areas_simulated']
area_list = M.simulation.params["areas_simulated"]
- # matrix = np.zeros((len(area_list), len(A.network.structure['V1'])))
matrix = np.zeros((len(area_list), len(M.structure['V1'])))
fig = plt.figure(figsize=(12, 3))
fig.suptitle('Time-averaged firing rate over simulated populations', fontsize=15, x=0.43)
ax = fig.add_subplot(111)
- # stationary firing rates
+ # Stationary firing rates
fn = os.path.join(data_path, M.simulation.label, 'Analysis', 'pop_rates.json')
with open(fn, 'r') as f:
pop_rates = json.load(f)
for i, area in enumerate(area_list):
- # print(i, area)
- # for j, pop in enumerate(A.network.structure_reversed['V1']):
for j, pop in enumerate(M.structure['V1'][::-1]):
if pop in M.structure[area]:
- # rate = A.pop_rates[area][pop][0]
rate = pop_rates[area][pop]
if rate == 0.0:
rate = 1e-5 # To distinguish zero-rate from non-existing populations
@@ -51,42 +40,26 @@ def plot_time_averaged_population_rates(M, data_path, area_list=None, **keywords
rate = np.nan
matrix[i][j] = rate
- # cm = plt.cm.jet
- # cm = cm.from_list('mycmap', [(0., 64./255., 192./255.), # custom dark blue
- # (0., 128./255., 192./255.), # custom light blue
- # 'white',
- # (245./255., 157./255., 115./255.), # custom light red
- # (192./255., 64./255., 0.)], N=256) # custom dark red
cm = plt.get_cmap('YlOrBr')
cm.set_under('0.3')
- # cm.set_bad('k')
cm.set_bad('white')
matrix = np.transpose(matrix)
masked_matrix = np.ma.masked_where(np.isnan(matrix), matrix)
ax.patch.set_hatch('x')
- # im = ax.pcolormesh(masked_matrix, cmap=cm, edgecolors='None', norm=LogNorm(
- # vmin=0.01, vmax=100.))
im = ax.pcolormesh(masked_matrix, cmap=cm, edgecolors='None', vmin=0)
ax.set_xlim(0, matrix[0].size)
x_index = np.arange(4.5, 31.6, 5.0)
x_ticks = [int(a + 0.5) for a in x_index]
- # y_index = list(range(len(A.network.structure['V1'])))
y_index = list(range(len(M.structure['V1'])))
y_index = [a + 0.5 for a in y_index]
- # ax.set_xticks(x_index)
ax.set_xticks([i + 0.5 for i in np.arange(0, len(area_list), 1)])
- # ax.set_xticklabels(x_ticks)
ax.set_xticklabels(area_list, rotation=90, size=10.)
ax.set_yticks(y_index)
- # ax.set_yticklabels(A.network.structure_reversed['V1'])
- # ax.set_yticklabels(M.structure['V1'][::-1])
ax.set_yticklabels(['6I', '6E', '5I', '5E', '4I', '4E', '2/3I', '2/3E'])
ax.set_ylabel('Population', size=13)
ax.set_xlabel('Area index', size=13)
- # t = FixedLocator([0.01, 0.1, 1., 10., 100.])
- # t = FixedLocator([0, 10, 20, 30, 40, 50])
# Iterate over the data and add 'X' for masked cells
for i in range(masked_matrix.shape[0]):
@@ -94,12 +67,9 @@ def plot_time_averaged_population_rates(M, data_path, area_list=None, **keywords
if masked_matrix.mask[i, j]:
ax.text(j + 0.5, i + 0.5, 'X', va='center', ha='center', color='black', fontsize=23)
- # plt.colorbar(im, ticks=t)
plt.colorbar(im)
if 'output' in keywords:
- # plt.savefig(os.path.join(A.output_dir, '{}_rates.{}'.format(A.simulation.label,
- # keywords['output'])))
plt.savefig(os.path.join(M.output_dir, '{}_rates.{}'.format(M.simulation.label,
keywords['output'])))
else:
diff --git a/multi-area-model.ipynb b/multi-area-model.ipynb
index 78acefdc3f3231b57be1d3045e61068409cf4377..c7017a7d8772f884ff568e556ce4864bd241b902 100644
--- a/multi-area-model.ipynb
+++ b/multi-area-model.ipynb
@@ -177,7 +177,7 @@
"|Parameter |Default value|Value range/options|Value assigned|Description|\n",
"|:---------------:|:-----------:|:-----------------:|:------------:|:---------:|\n",
"|scale_down_to |$1.0$ |$(0, 1.0]$ |$0.006$ |$^1$ |\n",
- "|cc_weights_factor|$1.9$ |$[1.0, 2.5]$ |$1.9$ |$^2$ |\n",
+ "|cc_weights_factor|$1.9$ |$\\geq 1.0$ |$1.9$ |$^2$ |\n",
"|areas_simulated |complete_area_list|Sublists of complete_area_list|complete_area_list|$^3$|\n",
"|replace_non_simulated_areas|None|None, 'hom_poisson_stat', 'het_poisson_stat', 'het_current_nonstat'|'het_poisson_stat'|$^4$ |\n",
"|g |$-11.0$|$\\leq -1.0$ |$-11.0$ |$^5$ |\n",
diff --git a/multiarea_model/data_multiarea/Model.py b/multiarea_model/data_multiarea/Model.py
index 8e5cc5e0fdcb3d4dc404f429fcfcf32fbd613823..2a7a0f88964c46c5e0d3d1ea93b0b25ca2a1f056 100644
--- a/multiarea_model/data_multiarea/Model.py
+++ b/multiarea_model/data_multiarea/Model.py
@@ -646,7 +646,7 @@ def compute_Model_params(out_label='', mode='default'):
# if there is laminar information in CoCoMac, use it
if Coco_Data[target_area][source_area]['source_pattern'] is not None:
sp = np.array(Coco_Data[target_area][source_area][
- 'source_pattern'], dtype=np.float)
+ 'source_pattern'], dtype=float)
# Manually determine SLN, based on CoCoMac:
# from supragranular, then SLN=0.,