diff --git a/figures/Schmidt2018/Fig2_anatomy.py b/figures/Schmidt2018/Fig2_anatomy.py
index 7f88a20cd8cecaaa8bdccdbe8f982a36eb92ffcf..4d535c5264cba012918930bbe580ce1361ec74cd 100644
--- a/figures/Schmidt2018/Fig2_anatomy.py
+++ b/figures/Schmidt2018/Fig2_anatomy.py
@@ -17,8 +17,8 @@ if NEURON_DENSITIES_AVAILABLE:
architecture_completed = proc['architecture_completed']
categories = {}
- for ii in np.arange(0, 9, 1):
- categories[ii] = []
+ for i in np.arange(0, 9, 1):
+ categories[i] = []
for area in architecture_completed:
categories[architecture_completed[area]].append(area)
@@ -171,12 +171,12 @@ if NEURON_DENSITIES_AVAILABLE:
for l in layers[:]:
bottom += rho[l]
- for ii, l in enumerate(layers):
+ for i, l in enumerate(layers):
print(l, rho[l][4], bottom[4])
bottom -= rho[l]
print("LAYER", l, bottom)
ax.bar(x - 0.4, rho[l], bottom=bottom,
- color=colors[ii], label='L' + layer_labels[ii],
+ color=colors[i], label='L' + layer_labels[i],
edgecolor='k')
ax.set_xlabel('Architectural type', labelpad=0.3)
@@ -188,7 +188,7 @@ if NEURON_DENSITIES_AVAILABLE:
ax.set_ylim((0., 200000.))
ax.set_xlim((-0.5, 9))
ax.set_xticks(np.arange(2, 9, 1) - 0.4)
- ax.set_xticklabels([r'${}$'.format(ii) for ii in list(range(2, 9))])
+ ax.set_xticklabels([r'${}$'.format(i) for i in list(range(2, 9))])
ax.legend(loc=(0.035, 0.45), edgecolor='k')
diff --git a/figures/Schmidt2018/Fig4_connectivity.py b/figures/Schmidt2018/Fig4_connectivity.py
index 586e7fe25b8568657bea24c588dd068ca961f87e..b724915930e2ac27dbe4600e9ffaa3b14a2fccd2 100644
--- a/figures/Schmidt2018/Fig4_connectivity.py
+++ b/figures/Schmidt2018/Fig4_connectivity.py
@@ -70,12 +70,12 @@ median_distance_data = raw['median_distance_data']
cocomac = np.zeros((32, 32))
conn_matrix = np.zeros((32, 32))
-for ii, area1 in enumerate(area_list[::-1]):
- for jj, area2 in enumerate(area_list):
+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]:
- cocomac[ii][jj] = 1.
+ cocomac[i][j] = 1.
if area2 in FLN_Data_FV91[area1]:
- conn_matrix[ii][jj] = FLN_Data_FV91[area1][area2]
+ conn_matrix[i][j] = FLN_Data_FV91[area1][area2]
"""
Panel A: CoCoMac Data
@@ -197,9 +197,9 @@ print(np.corrcoef(gradient * distances_FV91 + intercept, np.log(FLN_values_FV91)
Panel D: Resulting connectivity matrix
"""
conn_matrix = np.zeros((32, 32))
-for ii, area1 in enumerate(area_list[::-1]):
- for jj, area2 in enumerate(area_list):
- conn_matrix[ii][jj] = M.K_areas[area1][
+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']
diff --git a/figures/Schmidt2018/Fig5_cc_laminar_pattern.py b/figures/Schmidt2018/Fig5_cc_laminar_pattern.py
index b8753834a242517a3d950e4ee7243f56c2291738..15bf87ffa17563840c3d5156a1cf92024cc2fafb 100644
--- a/figures/Schmidt2018/Fig5_cc_laminar_pattern.py
+++ b/figures/Schmidt2018/Fig5_cc_laminar_pattern.py
@@ -127,8 +127,8 @@ if NEURON_DENSITIES_AVAILABLE:
def probit(x,):
if isinstance(x, np.ndarray):
- res = [integrate.quad(integrand, -1000., ii,
- args=(0., 1.))[0] for ii in x]
+ res = [integrate.quad(integrand, -1000., i,
+ args=(0., 1.))[0] for i in x]
else:
res = integrate.quad(integrand, -1000., x, args=(0., 1.))[0]
return res
@@ -202,7 +202,6 @@ if NEURON_DENSITIES_AVAILABLE:
target_high_SLN_unweighted[layer] += int(int(cocomac[target][
source]['target_pattern'][layer]) > 0)
-
SLN_labels = [r' $\mathbf{SLN < 0.35}$',
r'$\mathbf{0.35\leq SLN \leq 0.65}$',
r'$\mathbf{SLN > 0.65}$']
@@ -224,7 +223,6 @@ if NEURON_DENSITIES_AVAILABLE:
ax.set_ylabel('Target layer', size=10)
ax.yaxis.set_label_coords(-0.15, 0.5)
-
# Resulting patterns in the connectivity matrix
M = MultiAreaModel({})
@@ -232,8 +230,8 @@ if NEURON_DENSITIES_AVAILABLE:
FB_conns = []
lateral_conns = []
- for ii, target_area in enumerate(area_list):
- for jj, source_area in enumerate(area_list):
+ for target_area in area_list:
+ for source_area in area_list:
mask = create_mask(M.structure,
target_areas=[target_area],
source_areas=[source_area],
@@ -282,9 +280,9 @@ if NEURON_DENSITIES_AVAILABLE:
matrix = np.mean(data, axis=0)
ind = [0, 4, 6]
- for ii in range(3):
- ax.barh(np.arange(8), matrix[:, ind][:, ii][
- ::-1], left=ii * factor, color=myred, edgecolor='none')
+ for i in range(3):
+ ax.barh(np.arange(8), matrix[:, ind][:, i][
+ ::-1], left=i * factor, color=myred, edgecolor='none')
ax.text(0.1, 1.1, label, transform=ax.transAxes)
ax.set_yticks(np.arange(8) + 0.3)
diff --git a/figures/Schmidt2018/Fig6_connectivity_measures.py b/figures/Schmidt2018/Fig6_connectivity_measures.py
index c3d8234da3cf58908ec9e0334bb1c9e90a94c0c6..177921aa7e7f389e3d0be536188255f69d1bfccc 100644
--- a/figures/Schmidt2018/Fig6_connectivity_measures.py
+++ b/figures/Schmidt2018/Fig6_connectivity_measures.py
@@ -71,9 +71,9 @@ for area in area_list:
num_vector[index] = M.N[area][pop]
index += 1
-for ii in range(254):
- Npre[ii] = num_vector
- Npost[:, ii] = num_vector
+for i in range(254):
+ Npre[i] = num_vector
+ Npost[:, i] = num_vector
C = 1. - (1. - 1. / (Npre * Npost))**(M.K_matrix[:, :-1] * Npost)
Nsyn = M.K_matrix[:, :-1] * Npost
@@ -98,7 +98,7 @@ ticks_r = []
for area in plot_areas:
ticks.append(t_index + 0.5 * len(M.structure[area]))
ticks_r.append(new_size - (t_index + 0.5 * len(M.structure[area])))
- for ii, pop in enumerate(M.structure[area]):
+ for pop in M.structure[area]:
t_index += 1
diff --git a/figures/Schmidt2018/Fig7_community_structure.py b/figures/Schmidt2018/Fig7_community_structure.py
index bfbbcb65792d0e4711c029410f06450e790c84c9..bcf605dcd1bd7c607fd723730d867250c5401594 100644
--- a/figures/Schmidt2018/Fig7_community_structure.py
+++ b/figures/Schmidt2018/Fig7_community_structure.py
@@ -42,13 +42,13 @@ Construct matrix of relative and absolute outdegrees
"""
conn_matrix = np.zeros((32, 32))
conn_matrix_abs = np.zeros((32, 32))
-for ii, area1 in enumerate(area_list):
- for jj, area2 in enumerate(area_list):
+for i, area1 in enumerate(area_list):
+ for j, area2 in enumerate(area_list):
value = outa[area1][area2] / np.sum(list(outa[area1].values()))
value_abs = outa[area1][area2]
if area1 != area2:
- conn_matrix[ii][jj] = value
- conn_matrix_abs[ii][jj] = value_abs
+ conn_matrix[i][j] = value
+ conn_matrix_abs[i][j] = value_abs
"""
@@ -80,8 +80,8 @@ while "*Links" not in line:
# sort map_equation lists
index = []
-for ii in range(32):
- index.append(map_equation_areas.index(area_list[ii]))
+for i in range(32):
+ index.append(map_equation_areas.index(area_list[i]))
map_equation = np.array(map_equation)
@@ -109,10 +109,10 @@ mod_list = []
# For each column, we shuffle the rows and therefore conserve the
# total outdegree of each area.
-for ii in range(1000):
- for jj in range(32):
- ind = np.extract(np.arange(32) != jj, np.arange(32))
- null_model[:, jj][ind] = null_model[:, jj][ind][np.random.shuffle(ind)]
+for i in range(1000):
+ for j in range(32):
+ ind = np.extract(np.arange(32) != j, np.arange(32))
+ null_model[:, j][ind] = null_model[:, j][ind][np.random.shuffle(ind)]
modules, modules_areas, index = apply_map_equation(null_model, area_list,
filename='null',
infomap_path=infomap_path)
diff --git a/figures/Schmidt2018/Fig8_laminar_paths.py b/figures/Schmidt2018/Fig8_laminar_paths.py
index 8e4f2de215519fcc9d879d5b1318b1366c1a32d9..25ab09b995c050a8abdb594b98c3d568e244537c 100644
--- a/figures/Schmidt2018/Fig8_laminar_paths.py
+++ b/figures/Schmidt2018/Fig8_laminar_paths.py
@@ -48,12 +48,12 @@ for area in area_list:
paths[target_pop][source_pop] = []
path_length_matrix = np.zeros((254, 254))
-for ii, source in enumerate(M.structure_vec):
- for jj, target in enumerate(M.structure_vec):
+for i, source in enumerate(M.structure_vec):
+ for j, target in enumerate(M.structure_vec):
if target in path_lengths[source]:
- path_length_matrix[jj][ii] = path_lengths[source][target]
+ path_length_matrix[j][i] = path_lengths[source][target]
else:
- path_length_matrix[jj][ii] = np.inf
+ path_length_matrix[j][i] = np.inf
# Create dictionary containing the shortest path between any pair of areas
CC_paths = {area: {} for area in area_list}
@@ -73,9 +73,9 @@ for target_area in area_list:
# Create area-level graph
K_area = np.zeros((32, 32))
-for ii, target in enumerate(area_list):
- for jj, source in enumerate(area_list):
- K_area[ii][jj] = M.K_areas[target][source]
+for i, target in enumerate(area_list):
+ for j, source in enumerate(area_list):
+ K_area[i][j] = M.K_areas[target][source]
area_gain_matrix = K_area * np.ones_like(K_area) * M.J_matrix[0][0]
eig = np.linalg.eig(area_gain_matrix)
diff --git a/figures/Schmidt2018/graph_helpers.py b/figures/Schmidt2018/graph_helpers.py
index a25be729307b73591fc1dfb4002e40830f5b463c..8b66a7a7d59675d1f7bddab96c6063e4b7958d5f 100644
--- a/figures/Schmidt2018/graph_helpers.py
+++ b/figures/Schmidt2018/graph_helpers.py
@@ -43,19 +43,19 @@ def apply_map_equation(graph_matrix, node_list, filename='', infomap_path=None):
# nodes
f = open(net_fn, 'w')
f.write('*Vertices ' + str(len(node_list)) + '\n')
- for ii, area in enumerate(node_list):
- f.write(str(ii + 1) + ' "' + area + '"\n')
+ for i, area in enumerate(node_list):
+ f.write(str(i + 1) + ' "' + area + '"\n')
# Determine number of vertices in the network
k = np.where(graph_matrix != 0)[0].size
f.write('*Arcs ' + str(k) + '\n')
# edges
- for ii in range(len(node_list)):
- for jj in range(len(node_list)):
- if graph_matrix[ii][jj] > 0.:
- f.write(str(jj + 1) + ' ' + str(ii + 1) +
- ' ' + str(graph_matrix[ii][jj]) + '\n')
+ for i in range(len(node_list)):
+ for j in range(len(node_list)):
+ if graph_matrix[i][j] > 0.:
+ f.write(str(j + 1) + ' ' + str(i + 1) +
+ ' ' + str(graph_matrix[i][j]) + '\n')
f.close()
"""
@@ -91,8 +91,8 @@ def apply_map_equation(graph_matrix, node_list, filename='', infomap_path=None):
# sort map_equation lists
index = []
- for ii in range(32):
- index.append(map_equation_areas.index(node_list[ii]))
+ for i in range(32):
+ index.append(map_equation_areas.index(node_list[i]))
map_equation = np.array(map_equation)
@@ -123,12 +123,12 @@ def modularity(g, membership):
m = np.sum(g.es['weight'])
Q = 0.
- for ii, area in enumerate(g.vs):
- k_out = np.sum(g.es.select(_source=ii)['weight'])
- for jj, area2 in enumerate(g.vs):
- k_in = np.sum(g.es.select(_target=jj)['weight'])
- if membership[ii] == membership[jj]:
- weight = g.es.select(_source=ii, _target=jj)['weight']
+ for i, area in enumerate(g.vs):
+ k_out = np.sum(g.es.select(_source=i)['weight'])
+ for j, area2 in enumerate(g.vs):
+ k_in = np.sum(g.es.select(_target=j)['weight'])
+ if membership[i] == membership[j]:
+ weight = g.es.select(_source=i, _target=j)['weight']
if len(weight) > 0:
Q += weight[0] - k_out * k_in / m
else:
@@ -241,11 +241,11 @@ def plot_clustered_graph(g, g_abs, membership, filename, center_of_masses, color
layout = gcopy.layout("kk", **layout_params)
coords = np.array(copy.copy(layout.coords))
# For better visibility, place clusters at defined positions
- for ii in range(np.max(membership)):
- coo = coords[np.where(membership == ii + 1)]
+ for i in range(np.max(membership)):
+ coo = coords[np.where(membership == i + 1)]
com = np.mean(coo, axis=0)
- coo = np.array(coo) - (com - center_of_masses[ii])
- coords[np.where(membership == ii + 1)] = coo
+ coo = np.array(coo) - (com - center_of_masses[i])
+ coords[np.where(membership == i + 1)] = coo
# Define layout parameters
gplot.es["color"] = edges_colors
visual_style = {}
@@ -295,10 +295,10 @@ def create_graph(matrix, area_list):
g = igraph.Graph(directed=True)
g.add_vertices(area_list)
- for ii in range(32):
- for jj in range(32):
- if matrix[ii][jj] != 0:
- g.add_edge(jj, ii, weight=matrix[ii][jj])
+ for i in range(32):
+ for j in range(32):
+ if matrix[i][j] != 0:
+ g.add_edge(j, i, weight=matrix[i][j])
return g
@@ -326,13 +326,13 @@ def create_networkx_graph(matrix, complete_population_list, relative=False):
g = nx.DiGraph()
g.add_nodes_from(complete_population_list)
- for ii, target in enumerate(complete_population_list):
- for jj, source in enumerate(complete_population_list):
- if matrix[ii][jj] != 0:
+ for i, target in enumerate(complete_population_list):
+ for j, source in enumerate(complete_population_list):
+ if matrix[i][j] != 0:
if relative:
- weight = matrix[ii][jj] / np.sum(matrix[ii][:-1])
+ weight = matrix[i][j] / np.sum(matrix[i][:-1])
else:
- weight = matrix[ii][jj]
+ weight = matrix[i][j]
g.add_edge(source, target, weight=weight,
distance=np.log10(1. / weight))
return g
@@ -362,13 +362,13 @@ def create_networkx_area_graph(matrix, area_list, relative=False):
G = nx.DiGraph()
G.add_nodes_from(area_list)
- for ii, target in enumerate(area_list):
- for jj, source in enumerate(area_list):
- if matrix[ii][jj] > 0:
+ for i, target in enumerate(area_list):
+ for j, source in enumerate(area_list):
+ if matrix[i][j] > 0:
if relative:
- weight = matrix[ii][jj] / np.sum(matrix[ii])
+ weight = matrix[i][j] / np.sum(matrix[i])
else:
- weight = matrix[ii][jj]
+ weight = matrix[i][j]
G.add_edge(source, target, weight=weight,
distance=np.log10(1. / weight))
return G
diff --git a/figures/Schmidt2018_dyn/Fig2_bistability.py b/figures/Schmidt2018_dyn/Fig2_bistability.py
index dcf78beb698a82df52b805a6d2874c8a0d0dc77b..bd16f49c5bc4206d94596abac957822d2e411bee 100644
--- a/figures/Schmidt2018_dyn/Fig2_bistability.py
+++ b/figures/Schmidt2018_dyn/Fig2_bistability.py
@@ -47,9 +47,9 @@ if LOAD_ORIGINAL_DATA:
labels = ['A', 'B', 'C']
-for ii, k in enumerate(['LA', 'HA', 'LA_post']):
- ax = axes[labels[ii]]
- ax2 = axes[labels[ii] + '2']
+for i, k in enumerate(['LA', 'HA', 'LA_post']):
+ ax = axes[labels[i]]
+ ax2 = axes[labels[i] + '2']
print(k)
matrix = np.zeros((len(M.area_list), 8))
@@ -66,11 +66,11 @@ for ii, k in enumerate(['LA', 'HA', 'LA_post']):
matrix = np.transpose(matrix)
- if ii == 0:
+ if i == 0:
matrix_plot(panel_factory.figure, ax, matrix, position='left')
rate_histogram_plot(panel_factory.figure, ax2,
matrix, position='left')
- elif ii == 1:
+ elif i == 1:
matrix_plot(panel_factory.figure, ax, matrix, position='center')
rate_histogram_plot(panel_factory.figure, ax2,
matrix, position='center')
diff --git a/figures/Schmidt2018_dyn/Fig3_ground_state_chi1.py b/figures/Schmidt2018_dyn/Fig3_ground_state_chi1.py
index 998fd0bddaa0d27762ca8a15f3c85253dfbc5dbb..0e0781b002945daf9b297e120fdcfce38b1f1dee 100644
--- a/figures/Schmidt2018_dyn/Fig3_ground_state_chi1.py
+++ b/figures/Schmidt2018_dyn/Fig3_ground_state_chi1.py
@@ -150,8 +150,8 @@ ecolor = myblue
frac_neurons = 0.03
-for ii, area in enumerate(areas):
- ax = axes[labels[ii]]
+for i, area in enumerate(areas):
+ ax = axes[labels[i]]
if area in spike_data:
n_pops = len(spike_data[area])
@@ -166,10 +166,10 @@ for ii, area in enumerate(areas):
prev_pop = ''
yticks = []
yticklocs = []
- for jj, pop in enumerate(M.structure[area]):
+ for j, pop in enumerate(M.structure[area]):
if pop[0:-1] != prev_pop:
prev_pop = pop[0:-1]
- yticks.append('L' + population_labels[jj][0:-1])
+ yticks.append('L' + population_labels[j][0:-1])
yticklocs.append(offset - 0.5 * n_to_plot[pop])
ind = np.where(np.logical_and(
spike_data[area][pop][:, 1] <= t_max, spike_data[area][pop][:, 1] >= t_min))
@@ -198,16 +198,15 @@ for ii, area in enumerate(areas):
ax.set_xticks([t_min, t_min + 250., t_max])
ax.set_xticklabels([r'$3.$', r'$3.25$', r'$3.5$'])
-
def set_boxplot_props(d):
- for ii in range(len(d['boxes'])):
- if ii % 2 == 0:
- d['boxes'][ii].set_facecolor(icolor)
- d['boxes'][ii].set_color(icolor)
+ for i in range(len(d['boxes'])):
+ if i % 2 == 0:
+ d['boxes'][i].set_facecolor(icolor)
+ d['boxes'][i].set_color(icolor)
else:
- d['boxes'][ii].set_facecolor(ecolor)
- d['boxes'][ii].set_color(ecolor)
+ d['boxes'][i].set_facecolor(ecolor)
+ d['boxes'][i].set_color(ecolor)
pl.setp(d['whiskers'], color='k')
pl.setp(d['fliers'], color='k', markerfacecolor='k', marker='+')
pl.setp(d['medians'], color='none')
@@ -341,32 +340,32 @@ colors = ['0.5', '0.3', '0.0']
t_min = 500.
t_max = 10000.
time = np.arange(500., t_max)
-for ii, area in enumerate(areas[::-1]):
- ax[ii].spines['right'].set_color('none')
- ax[ii].spines['top'].set_color('none')
- ax[ii].yaxis.set_ticks_position("left")
- ax[ii].xaxis.set_ticks_position("none")
+for i, area in enumerate(areas[::-1]):
+ ax[i].spines['right'].set_color('none')
+ ax[i].spines['top'].set_color('none')
+ ax[i].yaxis.set_ticks_position("left")
+ ax[i].xaxis.set_ticks_position("none")
binned_spikes = rate_time_series[area][np.where(
np.logical_and(time >= t_min, time < t_max))]
- ax[ii].plot(time, binned_spikes, color=colors[0], label=area)
+ ax[i].plot(time, binned_spikes, color=colors[0], label=area)
rate = rate_time_series_auto_kernel[area]
- ax[ii].plot(time, rate, color=colors[2], label=area)
- ax[ii].set_xlim((500., t_max))
+ ax[i].plot(time, rate, color=colors[2], label=area)
+ ax[i].set_xlim((500., t_max))
- ax[ii].text(9000., 4.7, area)
+ ax[i].text(9000., 4.7, area)
- if ii > 0:
- ax[ii].spines['bottom'].set_color('none')
- ax[ii].set_xticks([])
+ if i > 0:
+ ax[i].spines['bottom'].set_color('none')
+ ax[i].set_xticks([])
else:
- ax[ii].set_xticks([1000., 5000., 10000.])
- ax[ii].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])
- if ii == 1:
- ax[ii].set_ylabel(r'Rate (spikes/s)')
+ ax[i].set_xticks([1000., 5000., 10000.])
+ ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])
+ if i == 1:
+ ax[i].set_ylabel(r'Rate (spikes/s)')
- ax[ii].set_yticks([0., 10.])
- ax[ii].set_ylim((0., 11.))
+ ax[i].set_yticks([0., 10.])
+ ax[i].set_ylim((0., 11.))
ax[0].set_xlabel('Time (s)', labelpad=-0.05)
fig.subplots_adjust(left=0.05, right=0.95, top=0.95,
diff --git a/figures/Schmidt2018_dyn/Fig4_metastability.py b/figures/Schmidt2018_dyn/Fig4_metastability.py
index 756f3f86eddc2c275f29a9de0b0e8f3cc6d1379c..820c714216db76178108b29f520cb0fad096f0c4 100644
--- a/figures/Schmidt2018_dyn/Fig4_metastability.py
+++ b/figures/Schmidt2018_dyn/Fig4_metastability.py
@@ -170,8 +170,8 @@ Plotting
"""
print("Plotting rate time series")
area = 'V1'
-for ii, (cc, label) in enumerate(zip(chi_list, labels)):
- ax = ax_rates[ii]
+for i, (cc, label) in enumerate(zip(chi_list, labels)):
+ ax = ax_rates[i]
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.yaxis.set_ticks_position("left")
@@ -182,7 +182,7 @@ for ii, (cc, label) in enumerate(zip(chi_list, labels)):
ax.set_ylim((-5., 60.))
ax.set_yticks([5., 40.])
ax.text(51500., 48., r'$\chi = $' + str(cc))
- if ii == len(labels) - 1:
+ if i == len(labels) - 1:
ax.vlines(0., 0., 40.)
ax.hlines(0., 0., 2.)
@@ -191,7 +191,7 @@ for ii, (cc, label) in enumerate(zip(chi_list, labels)):
ax.set_xticklabels([0, 10, 20, 30, 40, 50])
else:
ax.set_xticks([])
- if ii == 3:
+ if i == 3:
ax.set_ylabel(r'Rate $(\mathrm{spikes/s})$')
print("Plotting critical eigenvalues")
@@ -246,13 +246,13 @@ ax.set_yticklabels(chi_list[:-1])
load_path = 'Fig4_theory_data'
-for ii, cc_weights_factor in enumerate(chi_list):
- ax = ax_phasespace[ii]
+for i, cc_weights_factor in enumerate(chi_list):
+ ax = ax_phasespace[i]
ax.spines['right'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['top'].set_color('none')
ax.set_xticks([0., 5., 35.])
- if ii != len(labels) - 1:
+ if i != len(labels) - 1:
ax.set_xticklabels([])
else:
ax.set_xlabel(r'Average activity ($\mathrm{spikes/s}$)')
diff --git a/figures/Schmidt2018_dyn/Fig5_ground_state.py b/figures/Schmidt2018_dyn/Fig5_ground_state.py
index 7c7230dd447bc5748096faefae132629799bd863..e5384ebc3c47d9733403ad8f5d04d280050cdfc4 100644
--- a/figures/Schmidt2018_dyn/Fig5_ground_state.py
+++ b/figures/Schmidt2018_dyn/Fig5_ground_state.py
@@ -153,8 +153,8 @@ ecolor = myblue
frac_neurons = 0.03
-for ii, area in enumerate(areas):
- ax = axes[labels[ii]]
+for i, area in enumerate(areas):
+ ax = axes[labels[i]]
if area in spike_data:
n_pops = len(spike_data[area])
@@ -204,13 +204,13 @@ for ii, area in enumerate(areas):
def set_boxplot_props(d):
- for ii in range(len(d['boxes'])):
- if ii % 2 == 0:
- d['boxes'][ii].set_facecolor(icolor)
- d['boxes'][ii].set_color(icolor)
+ for i in range(len(d['boxes'])):
+ if i % 2 == 0:
+ d['boxes'][i].set_facecolor(icolor)
+ d['boxes'][i].set_color(icolor)
else:
- d['boxes'][ii].set_facecolor(ecolor)
- d['boxes'][ii].set_color(ecolor)
+ d['boxes'][i].set_facecolor(ecolor)
+ d['boxes'][i].set_color(ecolor)
pl.setp(d['whiskers'], color='k')
pl.setp(d['fliers'], color='k', markerfacecolor='k', marker='+')
pl.setp(d['medians'], color='none')
@@ -340,31 +340,31 @@ colors = ['0.5', '0.3', '0.0']
t_min = 500.
t_max = 10000.
time = np.arange(500., t_max)
-for ii, area in enumerate(areas[::-1]):
- ax[ii].spines['right'].set_color('none')
- ax[ii].spines['top'].set_color('none')
- ax[ii].yaxis.set_ticks_position("left")
- ax[ii].xaxis.set_ticks_position("none")
+for i, area in enumerate(areas[::-1]):
+ ax[i].spines['right'].set_color('none')
+ ax[i].spines['top'].set_color('none')
+ ax[i].yaxis.set_ticks_position("left")
+ ax[i].xaxis.set_ticks_position("none")
binned_spikes = rate_time_series[area][np.where(
np.logical_and(time >= t_min, time < t_max))]
- ax[ii].plot(time, binned_spikes, color=colors[0], label=area)
+ ax[i].plot(time, binned_spikes, color=colors[0], label=area)
rate = rate_time_series_auto_kernel[area]
- ax[ii].plot(time, rate, color=colors[2], label=area)
- ax[ii].set_xlim((500., t_max))
+ ax[i].plot(time, rate, color=colors[2], label=area)
+ ax[i].set_xlim((500., t_max))
- ax[ii].text(0.8, 0.7, area, transform=ax[ii].transAxes)
+ ax[i].text(0.8, 0.7, area, transform=ax[i].transAxes)
- if ii > 0:
- ax[ii].spines['bottom'].set_color('none')
- ax[ii].set_xticks([])
- ax[ii].set_yticks([0., 30.])
+ if i > 0:
+ ax[i].spines['bottom'].set_color('none')
+ ax[i].set_xticks([])
+ ax[i].set_yticks([0., 30.])
else:
- ax[ii].set_xticks([1000., 5000., 10000.])
- ax[ii].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])
- ax[ii].set_yticks([0., 5.])
- if ii == 1:
- ax[ii].set_ylabel(r'Rate (spikes/s)')
+ ax[i].set_xticks([1000., 5000., 10000.])
+ ax[i].set_xticklabels([r'$1.$', r'$5.$', r'$10.$'])
+ ax[i].set_yticks([0., 5.])
+ if i == 1:
+ ax[i].set_ylabel(r'Rate (spikes/s)')
ax[0].set_xlabel('Time (s)', labelpad=-0.05)
diff --git a/figures/Schmidt2018_dyn/Fig8_interactions.py b/figures/Schmidt2018_dyn/Fig8_interactions.py
index 7f88acb523ad03b2aba49af7ddf77d3125eced2b..528dadf6291826326356a0f10d7e587d1b044485 100644
--- a/figures/Schmidt2018_dyn/Fig8_interactions.py
+++ b/figures/Schmidt2018_dyn/Fig8_interactions.py
@@ -275,11 +275,11 @@ ax.plot([1.9], cc_bold, '.',
# 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 ii, area1 in enumerate(M.area_list):
+for i, area1 in enumerate(M.area_list):
s = np.sum(list(M.K_areas[area1].values()))
- for jj, area2 in enumerate(M.area_list):
+ for j, area2 in enumerate(M.area_list):
value = M.K_areas[area1][area2] / s
- conn_matrix[ii][jj] = value
+ conn_matrix[i][j] = value
cc = np.corrcoef(zero_diagonal(conn_matrix).flatten(),
diff --git a/figures/Schmidt2018_dyn/Fig9_laminar_interactions.py b/figures/Schmidt2018_dyn/Fig9_laminar_interactions.py
index 67860a9156dd6c406238378a9acc00a070f597db..0bab2ddd11b38e78c1b9caf81ed2563fbfbc734e 100644
--- a/figures/Schmidt2018_dyn/Fig9_laminar_interactions.py
+++ b/figures/Schmidt2018_dyn/Fig9_laminar_interactions.py
@@ -79,8 +79,8 @@ Bottom row
gs1 = gridspec.GridSpec(2, 3)
gs1.update(left=0.1, right=0.95, top=0.95, wspace=0.4, bottom=0.3)
-for ii, ax_label in enumerate(['A', 'B']):
- ax = pl.subplot(gs1[ii:ii + 1, :])
+for i, ax_label in enumerate(['A', 'B']):
+ ax = pl.subplot(gs1[i:i + 1, :])
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['left'].set_color('none')
@@ -122,10 +122,10 @@ for target_area in gc:
prop[grad]['significant'] += s_sign
colors = ['0.1', '0.1', '0.1', mypurple]
-for ii, typ in enumerate(['HL', 'HZ', 'HL', 'same-area']):
- ax.bar([(ii + 1) / 5.], [prop[typ]['significant'] / prop[typ]['total']],
+for i, typ in enumerate(['HL', 'HZ', 'HL', 'same-area']):
+ ax.bar([(i + 1) / 5.], [prop[typ]['significant'] / prop[typ]['total']],
width=0.2,
- color=colors[ii])
+ color=colors[i])
s_total_overall = 0
s_sign_overall = 0
@@ -159,7 +159,7 @@ ax.xaxis.set_ticks_position("none")
balance_EI = {}
NI_overall = 0
NE_overall = 0
-for ii, typ in enumerate(['HL', 'HZ', 'HL']):
+for i, typ in enumerate(['HL', 'HZ', 'HL']):
C = Counter(significant_channels[typ])
NI, NE = 0, 0
for channel in C:
@@ -172,7 +172,7 @@ for ii, typ in enumerate(['HL', 'HZ', 'HL']):
balance_EI[typ] = {'E': float(NE) / (NE + NI),
'I': float(NI) / (NE + NI)}
- ax.bar(np.array([0, 1]) + (ii + 1) / 5., [balance_EI[typ]['E'], balance_EI[typ]['I']],
+ ax.bar(np.array([0, 1]) + (i + 1) / 5., [balance_EI[typ]['E'], balance_EI[typ]['I']],
width=0.2,
color=[myblue, myred],
edgecolor='1.')
diff --git a/figures/Schmidt2018_dyn/Fig9_path_analysis.py b/figures/Schmidt2018_dyn/Fig9_path_analysis.py
index 63f2100dd42ea0c0f9469c7bd7e8632457aebcfb..84f383e679204ddc07fc5808a542cc594290e43e 100644
--- a/figures/Schmidt2018_dyn/Fig9_path_analysis.py
+++ b/figures/Schmidt2018_dyn/Fig9_path_analysis.py
@@ -78,12 +78,12 @@ for area in M.area_list:
paths[target_pop][source_pop] = []
path_length_matrix = np.zeros((254, 254))
-for ii, source in enumerate(M.structure_vec):
- for jj, target in enumerate(M.structure_vec):
+for i, source in enumerate(M.structure_vec):
+ for j, target in enumerate(M.structure_vec):
if target in path_lengths[source]:
- path_length_matrix[jj][ii] = path_lengths[source][target]
+ path_length_matrix[j][i] = path_lengths[source][target]
else:
- path_length_matrix[jj][ii] = np.inf
+ path_length_matrix[j][i] = np.inf
# Create dictionary containing the shortest path between any pair of areas
CC_paths = {area: {} for area in M.area_list}
diff --git a/figures/Schmidt2018_dyn/Fig9_significant_channels.py b/figures/Schmidt2018_dyn/Fig9_significant_channels.py
index e57f53af445d0a121c7e94e72e9e5f41b467aaca..beb495df19d933247a85e442431cd0ac54ff398a 100644
--- a/figures/Schmidt2018_dyn/Fig9_significant_channels.py
+++ b/figures/Schmidt2018_dyn/Fig9_significant_channels.py
@@ -42,19 +42,18 @@ for area in M.area_list:
def area_pair_matrix(target_area, source_area):
matrix = np.nan * np.zeros((len(M.structure[target_area]),
len(M.structure[source_area])))
- for ii, target_pop in enumerate(M.structure[target_area]):
- for jj, source_pop in enumerate(M.structure[source_area]):
+ for i, target_pop in enumerate(M.structure[target_area]):
+ for j, source_pop in enumerate(M.structure[source_area]):
if source_area in gc[target_area][target_pop]:
if source_pop in gc[target_area][target_pop][source_area]:
- matrix[ii][jj] = gc[target_area][target_pop][source_area][source_pop][1]
+ matrix[i][j] = gc[target_area][target_pop][source_area][source_pop][1]
return np.ma.masked_where(np.isnan(matrix), matrix)
def significant_pop_pairs(target_area, source_area):
significant_pop_pairs = []
- # for ii, target_pop in enumerate(M.structure[target_area]):
- for ii, target_pop in enumerate(gc[target_area].keys()):
- for jj, source_pop in enumerate(M.structure[source_area]):
+ for i, target_pop in enumerate(gc[target_area].keys()):
+ for j, source_pop in enumerate(M.structure[source_area]):
if source_area in gc[target_area][target_pop]:
if source_pop in gc[target_area][target_pop][source_area]:
if gc[target_area][target_pop][source_area][source_pop][1] < 0.05:
diff --git a/figures/Schmidt2018_dyn/process_chu2014_data.py b/figures/Schmidt2018_dyn/process_chu2014_data.py
index 1e172d4a6f2edbbbdf227f867da6d20aa00dc559..4bf4fc85b824abd7e491bf78f7e2c75609fa159a 100644
--- a/figures/Schmidt2018_dyn/process_chu2014_data.py
+++ b/figures/Schmidt2018_dyn/process_chu2014_data.py
@@ -55,9 +55,9 @@ Take only neurons of the first 113 electrodes into account which are
less than 1 mm apart
"""
ind_mm = []
-for ii, id in enumerate(data_pvc5['ids']):
+for i, id in enumerate(data_pvc5['ids']):
if int(id_to_channel[id]) <= 112:
- ind_mm.append(ii)
+ ind_mm.append(i)
np.save(os.path.join(save_path,
'spike_data_1mm.npy'),
@@ -135,8 +135,8 @@ for ti in t[ind_high]:
time_ind_high += list(np.where(np.logical_and(time > 1e3*(ti - 5.),
time <= 1e3*(ti+5.)))[0])
-for ii, (phase, times) in enumerate(zip(['low_fluct', 'high_fluct', 'full'],
- [time_ind_low, time_ind_high, None])):
+for phase, times in zip(['low_fluct', 'high_fluct', 'full'],
+ [time_ind_low, time_ind_high, None]):
if times is not None:
rate = pop_rate[times]
else:
diff --git a/multiarea_model/analysis_helpers.py b/multiarea_model/analysis_helpers.py
index 12a6ea961c54ad5861505f65379cdd79d9e3354f..ce4c2e47e49425a9d85e0334354af7db95ed864c 100644
--- a/multiarea_model/analysis_helpers.py
+++ b/multiarea_model/analysis_helpers.py
@@ -415,13 +415,13 @@ def pop_rate_distribution(data_array, t_min, t_max, num_neur):
else: # No spikes in [t_min, t_max]
n = None
rates = np.zeros(int(num_neur))
- ii = 0
+ s = 0
for i in range(neurons.size):
if neurons[i] == n:
- rates[ii] += 1
+ rates[s] += 1
else:
n = neurons[i]
- ii += 1
+ s += 1
rates /= (t_max - t_min) / 1000.
vals, bins = np.histogram(rates, bins=100)
vals = vals / float(np.sum(vals))
@@ -476,10 +476,10 @@ def pop_rate_time_series(data_array, num_neur, t_min, t_max,
rates = np.array([])
last_time_step = times[0]
- for ii in range(1, times.size):
+ for i in range(1, times.size):
rates = np.append(
- rates, rate[ii - 1] * np.ones_like(np.arange(last_time_step, times[ii], 1.0)))
- last_time_step = times[ii]
+ rates, rate[i - 1] * np.ones_like(np.arange(last_time_step, times[i], 1.0)))
+ last_time_step = times[i]
time_series = rates
else:
diff --git a/multiarea_model/data_multiarea/Model.py b/multiarea_model/data_multiarea/Model.py
index 11c14f504683174d5b1c5535546eab04c37d4c4b..6d68fba7906b192ba3631f498599ababfcf5f8b3 100644
--- a/multiarea_model/data_multiarea/Model.py
+++ b/multiarea_model/data_multiarea/Model.py
@@ -690,9 +690,9 @@ def compute_Model_params(out_label='', mode='default'):
elif source_pop in origin_patterns['I']:
X = 1.0 - SLN_Data[target_area][source_area]
infra_neurons = 0.0
- for ii in origin_patterns['I']:
+ for i in origin_patterns['I']:
infra_neurons += neuronal_numbers_fullscale[
- source_area][ii]
+ source_area][i]
Y = num_source / infra_neurons
# target side
@@ -721,12 +721,12 @@ def compute_Model_params(out_label='', mode='default'):
p_T = np.sum(10 ** tp[np.where(tp > 0.)[0]])
Nsyn = 0.0
su = 0.
- for ii in range(len(T)):
- if T[ii] in [2, 3]:
+ for i in range(len(T)):
+ if T[i] in [2, 3]:
syn_layer = '23'
else:
- syn_layer = str(T[ii])
- Z = 10 ** tp[np.where(tp > 0.)[0]][ii] / p_T
+ syn_layer = str(T[i])
+ Z = 10 ** tp[np.where(tp > 0.)[0]][i] / p_T
if target_pop in synapse_to_cell_body[target_area][syn_layer]:
Nsyn += synapse_to_cell_body[target_area][syn_layer][
target_pop] * Nsyn_tot * FLN_BA * X * Y * Z
@@ -744,9 +744,9 @@ def compute_Model_params(out_label='', mode='default'):
T = termination_layers['F']
p_T = 0.0
- for ii in T:
- if ii != '1':
- p_T += laminar_thicknesses[target_area][ii]
+ for i in T:
+ if i != '1':
+ p_T += laminar_thicknesses[target_area][i]
Nsyn = 0.0
for syn_layer in T:
@@ -863,23 +863,23 @@ def compute_Model_params(out_label='', mode='default'):
for area in area_list:
nonvisual_fraction_matrix = np.zeros(
(len(structure[area]) + 1, len(structure[area])))
- for ii in range(len(structure[area])):
+ for i in range(len(structure[area])):
nonvisual_fraction_matrix[
- ii] = 1. / len(structure[area]) * np.ones(len(structure[area]))
- nonvisual_fraction_matrix[ii][ii] -= 1
+ i] = 1. / len(structure[area]) * np.ones(len(structure[area]))
+ nonvisual_fraction_matrix[i][i] -= 1
- for ii in range(len(structure[area])):
+ for i in range(len(structure[area])):
nonvisual_fraction_matrix[-1][
- ii] = neuronal_numbers[area][structure[area][ii]]
+ i] = neuronal_numbers[area][structure[area][i]]
vector = np.zeros(len(structure[area]) + 1)
ext_syn = External_synapses[area]
vector[-1] = ext_syn
solution, residues, rank, s = np.linalg.lstsq(
nonvisual_fraction_matrix, vector)
- for ii, pop in enumerate(structure[area]):
+ for i, pop in enumerate(structure[area]):
synapse_numbers[area][pop]['external'] = {
- 'external': solution[ii] * neuronal_numbers[area][pop]}
+ 'external': solution[i] * neuronal_numbers[area][pop]}
synapse_numbers['TH']['4E']['external'] = {'external': 0.0}
synapse_numbers['TH']['4I']['external'] = {'external': 0.0}
diff --git a/multiarea_model/data_multiarea/VisualCortex_Data.py b/multiarea_model/data_multiarea/VisualCortex_Data.py
index 60d5ee03a8017ff0339d2d569884a25e8d74404f..4f062e47317377b79388807bd69fcd81ef413ac3 100644
--- a/multiarea_model/data_multiarea/VisualCortex_Data.py
+++ b/multiarea_model/data_multiarea/VisualCortex_Data.py
@@ -129,9 +129,9 @@ def process_raw_data():
names=['area', 'level'])
hierarchy = {area: level for area, level in hier_temp.values}
- for ii in hierarchy.keys():
- if hierarchy[ii] != '?':
- hierarchy[ii] = float(hierarchy[ii])
+ for i in hierarchy.keys():
+ if hierarchy[i] != '?':
+ hierarchy[i] = float(hierarchy[i])
hierarchy['MIP'] = .5
hierarchy['MDP'] = .5
@@ -142,9 +142,9 @@ def process_raw_data():
names=['area', 'level', 'rescaled level'])
hierarchy_markov = {}
- for ii in range(len(hier_temp)):
- hierarchy_markov[hier_temp.iloc[ii]['area']] = {'level': hier_temp.iloc[ii][
- 'level'], 'rescaled level': hier_temp.iloc[ii]['rescaled level']}
+ for i in range(len(hier_temp)):
+ hierarchy_markov[hier_temp.iloc[i]['area']] = {'level': hier_temp.iloc[i][
+ 'level'], 'rescaled level': hier_temp.iloc[i]['rescaled level']}
"""
2. Neuronal densities
@@ -203,9 +203,9 @@ def process_raw_data():
skiprows=2,
names=['area', 'structure'])
architecture = {area: architecture for area, architecture in temp.values}
- for ii in architecture:
- if architecture[ii] != '?':
- architecture[ii] = int(architecture[ii])
+ for i in architecture:
+ if architecture[i] != '?':
+ architecture[i] = int(architecture[i])
"""
4. Distances
@@ -216,12 +216,12 @@ def process_raw_data():
temp = next(myreader)
areas = temp
median_distance_data = {}
- for ii in range(1, 82, 1):
+ for i in range(1, 82, 1):
temp = next(myreader)
dict_ = {}
for i in range(1, 82, 1):
dict_[areas[i]] = float(temp[i])
- median_distance_data[areas[ii]] = dict_
+ median_distance_data[areas[i]] = dict_
with open(os.path.join(datapath, 'Thom_Distances.csv'), 'rt') as f:
myreader = csv.reader(f, delimiter='\t')
@@ -229,12 +229,12 @@ def process_raw_data():
temp = next(myreader)
areas = temp
thom_distance_data = {}
- for ii in range(1, 33, 1):
+ for i in range(1, 33, 1):
temp = next(myreader)
dict_ = {}
for i in range(1, 33, 1):
dict_[areas[i]] = float(temp[i])
- thom_distance_data[areas[ii]] = dict_
+ thom_distance_data[areas[i]] = dict_
# Distances for area in the parcellation used by Markov et al. (2014)
with open(os.path.join(datapath, 'Thom_Distances_MERetal12.csv'), 'rt') as f:
@@ -243,12 +243,12 @@ def process_raw_data():
temp = next(myreader)
areas = temp
thom_distance_data_markov = {}
- for ii in range(1, 93, 1):
+ for i in range(1, 93, 1):
temp = next(myreader)
dict_ = {}
for i in range(1, 93, 1):
dict_[areas[i]] = float(temp[i])
- thom_distance_data_markov[areas[ii]] = dict_
+ thom_distance_data_markov[areas[i]] = dict_
with open(os.path.join(datapath, 'Euclidean_Distances.csv'), 'rt') as f:
myreader = csv.reader(f, delimiter='\t')
@@ -256,12 +256,12 @@ def process_raw_data():
temp = next(myreader)
areas = temp
euclidean_distance_data = {}
- for ii in range(1, 33, 1):
+ for i in range(1, 33, 1):
temp = next(myreader)
dict_ = {}
for i in range(1, 33, 1):
dict_[areas[i]] = float(temp[i])
- euclidean_distance_data[areas[ii]] = dict_
+ euclidean_distance_data[areas[i]] = dict_
"""
5. Surface areas
@@ -392,7 +392,7 @@ def process_raw_data():
myreader = csv.reader(f, delimiter='\t')
skip_header()
Intrinsic_FLN_Data = {}
- for ii in range(4):
+ for i in range(4):
temp = next(myreader)
dict_ = {'mean': float(temp[1]), 'error': float(temp[2])}
Intrinsic_FLN_Data[temp[0]] = dict_
@@ -445,12 +445,12 @@ def process_raw_data():
areas = temp
- for ii in range(1, 9, 1):
+ for i in range(1, 9, 1):
temp = next(myreader)
dict_ = {}
for i in range(1, 10, 1):
dict_[areas[i]] = float(temp[i])
- intrinsic_connectivity[areas[ii]] = dict_
+ intrinsic_connectivity[areas[i]] = dict_
"""
11. Numbers of neurons and external inputs in V1
@@ -459,7 +459,7 @@ def process_raw_data():
myreader = csv.reader(f, delimiter='\t')
skip_header()
num_V1 = {}
- for ii in range(0, 9, 1):
+ for i in range(0, 9, 1):
temp = next(myreader)
num_V1[temp[0]] = {'neurons': float(
temp[1]), 'ext_inputs': float(temp[2])}
@@ -473,7 +473,7 @@ def process_raw_data():
skip_header()
Laminar_Thickness_cat = {}
- for ii in range(10):
+ for i in range(10):
temp = next(myreader)
Laminar_Thickness_cat[temp[0]] = {
'thickness': float(temp[1]), 'error': float(temp[2])}
@@ -484,9 +484,9 @@ def process_raw_data():
myreader = csv.reader(f, delimiter='\t')
skip_header()
names = next(myreader)[1:16]
- for ii in range(0, len(names)):
- names[ii] = re.sub('L', '', names[ii])
- names[ii] = re.sub('/', '', names[ii])
+ for i in range(0, len(names)):
+ names[i] = re.sub('L', '', names[i])
+ names[i] = re.sub('/', '', names[i])
laminar_thicknesses = {}
line = True
while line:
@@ -496,29 +496,29 @@ def process_raw_data():
line = False
if temp[0] in laminar_thicknesses:
if np.isscalar(laminar_thicknesses[temp[0]][names[0]]):
- for jj in range(len(temp) - 3):
- if temp[jj + 1]:
- laminar_thicknesses[temp[0]][names[jj]] = [
- laminar_thicknesses[temp[0]][names[jj]]] + [float(temp[jj + 1])]
+ for j in range(len(temp) - 3):
+ if temp[j + 1]:
+ laminar_thicknesses[temp[0]][names[j]] = [
+ laminar_thicknesses[temp[0]][names[j]]] + [float(temp[j + 1])]
else:
- laminar_thicknesses[temp[0]][names[jj]] = [
- laminar_thicknesses[temp[0]][names[jj]]] + [np.nan]
+ laminar_thicknesses[temp[0]][names[j]] = [
+ laminar_thicknesses[temp[0]][names[j]]] + [np.nan]
else:
- for jj in range(len(temp) - 3):
- if temp[jj + 1]:
- laminar_thicknesses[temp[0]][names[jj]] = laminar_thicknesses[
- temp[0]][names[jj]] + [float(temp[jj + 1])]
+ for j in range(len(temp) - 3):
+ if temp[j + 1]:
+ laminar_thicknesses[temp[0]][names[j]] = laminar_thicknesses[
+ temp[0]][names[j]] + [float(temp[j + 1])]
else:
- laminar_thicknesses[temp[0]][names[jj]] = laminar_thicknesses[
- temp[0]][names[jj]] + [np.nan]
+ laminar_thicknesses[temp[0]][names[j]] = laminar_thicknesses[
+ temp[0]][names[j]] + [np.nan]
else:
laminar_thicknesses[temp[0]] = {}
- for jj in range(len(temp) - 3):
- if temp[jj + 1]:
+ for j in range(len(temp) - 3):
+ if temp[j + 1]:
laminar_thicknesses[temp[0]
- ][names[jj]] = float(temp[jj + 1])
+ ][names[j]] = float(temp[j + 1])
else:
- laminar_thicknesses[temp[0]][names[jj]] = np.nan
+ laminar_thicknesses[temp[0]][names[j]] = np.nan
"""
13. Total cortical thicknesses from Barbas lab
@@ -531,7 +531,7 @@ def process_raw_data():
for area in area_list:
total_thickness_data[area] = np.nan
- for ii in range(0, 30, 1):
+ for i in range(0, 30, 1):
temp = next(myreader)
if temp[4]:
total_thickness_data[temp[0]] = float(
@@ -550,8 +550,8 @@ def process_raw_data():
else:
translation[i] = np.array([j])
- for ii in translation:
- translation[ii] = list(np.unique(translation[ii]))
+ for i in translation:
+ translation[i] = list(np.unique(translation[i]))
f = open(os.path.join(datapath, 'overlap.json'), 'r')
overlap = json.load(f)
@@ -566,7 +566,7 @@ def process_raw_data():
pre_cells = next(myreader)
binzegger_data = {}
- for ii in range(1, 38, 1):
+ for i in range(1, 38, 1):
temp = next(myreader)
if temp[0] != '':
subdict = {}
@@ -574,11 +574,11 @@ def process_raw_data():
syn_layer = re.sub("\D", "", re.sub(".*\(", "", temp[0]))
else:
syn_layer = re.sub("\D", "", temp[1])
- for jj in range(3, len(temp), 1):
+ for j in range(3, len(temp), 1):
try:
- subdict[pre_cells[jj]] = float(temp[jj])
+ subdict[pre_cells[j]] = float(temp[j])
except:
- subdict[pre_cells[jj]] = temp[jj]
+ subdict[pre_cells[j]] = temp[j]
if temp[0] in binzegger_data:
binzegger_data[temp[0]]['syn_dict'].update(
{syn_layer: subdict})
@@ -672,26 +672,26 @@ def process_raw_data():
# map Neuronal density data to FV91 scheme
neuronal_density_data_updated_FV91 = {}
- for ii in list(neuronal_density_data_updated.keys()):
- if ii not in area_list:
- if ii in translation:
- areas_FV91 = translation[ii]
+ for i in list(neuronal_density_data_updated.keys()):
+ if i not in area_list:
+ if i in translation:
+ areas_FV91 = translation[i]
for kk in areas_FV91:
neuronal_density_data_updated_FV91[
- kk] = neuronal_density_data_updated[ii]
+ kk] = neuronal_density_data_updated[i]
else:
neuronal_density_data_updated_FV91[
- ii] = neuronal_density_data_updated[ii]
+ i] = neuronal_density_data_updated[i]
neuronal_density_data_FV91 = {}
- for ii in list(neuronal_density_data.keys()):
- if ii not in area_list:
- areas_FV91 = translation[ii]
+ for i in list(neuronal_density_data.keys()):
+ if i not in area_list:
+ areas_FV91 = translation[i]
for kk in areas_FV91:
- neuronal_density_data_FV91[kk] = neuronal_density_data[ii].copy(
+ neuronal_density_data_FV91[kk] = neuronal_density_data[i].copy(
)
else:
- neuronal_density_data_FV91[ii] = neuronal_density_data[ii].copy()
+ neuronal_density_data_FV91[i] = neuronal_density_data[i].copy()
# map Neuronal density data to 4 layers by dividing
neuronal_density_data_FV91_4layers = {}
@@ -700,28 +700,28 @@ def process_raw_data():
'4': {'value': 0.0, 'error': 0.0},
'5': 0., '6': 0.}})
- for ii in list(neuronal_density_data_FV91_4layers.keys()):
+ for i in list(neuronal_density_data_FV91_4layers.keys()):
for layer in ['23', '4', '56']:
- if neuronal_density_data_FV91[ii][layer]['value'] > 0.:
+ if neuronal_density_data_FV91[i][layer]['value'] > 0.:
# Assign equal density to layers 5 and 6
if layer == '56':
- neuronal_density_data_FV91_4layers[ii]['5'] = neuronal_density_data_FV91[
- ii]['56']['value'] * gradient + intercept
- neuronal_density_data_FV91_4layers[ii]['6'] = neuronal_density_data_FV91[
- ii]['56']['value'] * gradient + intercept
+ neuronal_density_data_FV91_4layers[i]['5'] = neuronal_density_data_FV91[
+ i]['56']['value'] * gradient + intercept
+ neuronal_density_data_FV91_4layers[i]['6'] = neuronal_density_data_FV91[
+ i]['56']['value'] * gradient + intercept
else:
- neuronal_density_data_FV91_4layers[ii][layer] = neuronal_density_data_FV91[
- ii][layer]['value'] * gradient + intercept
+ neuronal_density_data_FV91_4layers[i][layer] = neuronal_density_data_FV91[
+ i][layer]['value'] * gradient + intercept
else:
- neuronal_density_data_FV91_4layers[ii][layer] = 0.
+ neuronal_density_data_FV91_4layers[i][layer] = 0.
# if there is Nissl data, then take it, otherwise
# transform NeuN data with the linear fit
- if ii in neuronal_density_data_updated_FV91:
- neuronal_density_data_FV91_4layers[ii][
- 'overall'] = neuronal_density_data_updated_FV91[ii]
+ if i in neuronal_density_data_updated_FV91:
+ neuronal_density_data_FV91_4layers[i][
+ 'overall'] = neuronal_density_data_updated_FV91[i]
else:
- neuronal_density_data_FV91_4layers[ii]['overall'] = neuronal_density_data_FV91[
- ii]['overall']['value'] * gradient + intercept
+ neuronal_density_data_FV91_4layers[i]['overall'] = neuronal_density_data_FV91[
+ i]['overall']['value'] * gradient + intercept
"""
We build a dictionary containing neuron densities
@@ -760,11 +760,11 @@ def process_raw_data():
for x in range(8):
dict_ = {}
- for ii in list(neuronal_density_list[0].keys()):
- if len(neuronal_density_list[x][ii]) == 0:
- dict_[ii] = np.nan
+ for i in list(neuronal_density_list[0].keys()):
+ if len(neuronal_density_list[x][i]) == 0:
+ dict_[i] = np.nan
else:
- dict_[ii] = np.mean(neuronal_density_list[x][ii])
+ dict_[i] = np.mean(neuronal_density_list[x][i])
category_density[x + 1] = dict_
# Step 2: For areas with out experimental data,
@@ -1092,24 +1092,24 @@ def process_raw_data():
sp = cocomac_data[target][source]['source_pattern']
tp = cocomac_data[target][source]['target_pattern']
if sp is not None:
- for ii in range(6):
- if sp[ii] == 'X':
- sp[ii] = 2
- if sp[ii] == '?' and ii in [0, 3]:
- sp[ii] = 0
- if sp[ii] == '?' and ii in [1, 2, 4, 5]:
+ for i in range(6):
+ if sp[i] == 'X':
+ sp[i] = 2
+ if sp[i] == '?' and i in [0, 3]:
+ sp[i] = 0
+ if sp[i] == '?' and i in [1, 2, 4, 5]:
# Dummy value to enable transformation of this list into a
# numpy array
- sp[ii] = -1
+ sp[i] = -1
if tp is not None:
- for ii in range(6):
- if tp[ii] == 'X':
- tp[ii] = 2.
- if tp[ii] == '?':
+ for i in range(6):
+ if tp[i] == 'X':
+ tp[i] = 2.
+ if tp[i] == '?':
# Dummy value to enable transformation of this list into a
# numpy array
- tp[ii] = -1
+ tp[i] = -1
cocomac_completed[target][source] = {
'source_pattern': sp, 'target_pattern': tp}
@@ -1322,8 +1322,8 @@ def process_raw_data():
def probit(x,):
if isinstance(x, np.ndarray):
- res = [integrate.quad(integrand, -1000., ii,
- args=(0., 1.))[0] for ii in x]
+ res = [integrate.quad(integrand, -1000., i,
+ args=(0., 1.))[0] for i in x]
else:
res = integrate.quad(integrand, -1000., x, args=(0., 1.))[0]
return res
diff --git a/multiarea_model/multiarea_helpers.py b/multiarea_model/multiarea_helpers.py
index f32527b99f428b4d1af320abc7c516787440164d..2b9cadd0e7a01e10050d3cb2d614420c7177ff39 100644
--- a/multiarea_model/multiarea_helpers.py
+++ b/multiarea_model/multiarea_helpers.py
@@ -227,11 +227,11 @@ def matrix_to_dict(m, area_list, structure, external=None):
x = np.insert(x, 2, np.zeros((2, 8), dtype=float), axis=0)
else:
x = x.reshape((8, 8))
- for ii, pop in enumerate(population_list):
- for jj, pop2 in enumerate(population_list):
- if x[ii][jj] < 1e-20:
- x[ii][jj] = 0.
- dic[area][pop][area2][pop2] = x[ii][jj]
+ for i, pop in enumerate(population_list):
+ for j, pop2 in enumerate(population_list):
+ if x[i][j] < 1e-20:
+ x[i][j] = 0.
+ dic[area][pop][area2][pop2] = x[i][j]
if external is not None:
if isinstance(external, np.ndarray):
for area in dic:
@@ -272,8 +272,8 @@ def vector_to_dict(v, area_list, structure, external=None):
dic = nested_dict()
for area in area_list:
vmask = create_vector_mask(structure, areas=[area])
- for ii, pop in enumerate(structure[area]):
- dic[area][pop] = v[vmask][ii]
+ for i, pop in enumerate(structure[area]):
+ dic[area][pop] = v[vmask][i]
for pop in population_list:
if pop not in structure[area]:
dic[area][pop] = 0.
diff --git a/multiarea_model/sumatra_helpers.py b/multiarea_model/sumatra_helpers.py
index 9d04d7a59bc6579e267161ee74fd5042337eab9f..94c29c36ac43c1cd338a920ea99be0679ece35e6 100644
--- a/multiarea_model/sumatra_helpers.py
+++ b/multiarea_model/sumatra_helpers.py
@@ -60,10 +60,10 @@ def register_runtime(label):
'runtime_*')
files = glob.glob(fp)
- for ii, fn in enumerate(files):
+ for i, fn in enumerate(files):
with open(fn, 'r') as f:
d = json.load(f)
- if ii == 0:
+ if i == 0:
data = {key: [value] for key, value in d.items()}
else:
for key, value in d.items():
diff --git a/multiarea_model/theory.py b/multiarea_model/theory.py
index f4e93620365ef6558c6bbbaae51cccd6ba6f37bc..4780f49bcc3e4e81fb885541fa80e91a7984502b 100644
--- a/multiarea_model/theory.py
+++ b/multiarea_model/theory.py
@@ -143,19 +143,19 @@ class Theory():
# initial rates are explicitly defined in self.params
elif isinstance(self.params['initial_rates'], np.ndarray):
num_iter = 1
- gen = (self.params['initial_rates'] for ii in range(num_iter))
+ gen = (self.params['initial_rates'] for i in range(num_iter))
# if initial rates are not defined, set them 0
else:
num_iter = 1
- gen = (np.zeros(dim) for ii in range(num_iter))
+ gen = (np.zeros(dim) for i in range(num_iter))
rates = []
# Loop over all iterations of different initial conditions
for nsim in range(num_iter):
print('Iteration: {}'.format(nsim))
initial_rates = next(gen)
- for ii in range(dim):
- nest.SetStatus([neurons[ii]], {'rate': initial_rates[ii]})
+ for i in range(dim):
+ nest.SetStatus([neurons[i]], {'rate': initial_rates[i]})
# create recording device
multimeter = nest.Create('multimeter', params={'record_from':
@@ -173,8 +173,8 @@ class Theory():
data = nest.GetStatus(multimeter)[0]['events']
res = np.array([np.insert(data['rate'][np.where(data['senders'] == n)],
0,
- initial_rates[ii])
- for ii, n in enumerate(neurons)])
+ initial_rates[i])
+ for i, n in enumerate(neurons)])
if full_output:
rates.append(res)
@@ -322,16 +322,16 @@ class Theory():
1.e-3*self.NP['t_ref'],
self.NP['theta'],
self.NP['V_reset'],
- mu[ii], sigma[ii]) for ii in range(len(mu))])
+ mu[i], sigma[i]) for i in range(len(mu))])
# Unit: 1/(mV)**2
d_nu_d_sigma = np.array([d_nu_d_sigma_fb_numeric(1.e-3*self.NP['tau_m'],
1.e-3*self.NP['tau_syn'],
1.e-3*self.NP['t_ref'],
self.NP['theta'],
self.NP['V_reset'],
- mu[ii], sigma[ii])*1/(
- 2. * sigma[ii])
- for ii in range(len(mu))])
+ mu[i], sigma[i])*1/(
+ 2. * sigma[i])
+ for i in range(len(mu))])
return d_nu_d_mu, d_nu_d_sigma
def gain_matrix(self, rates, matrix_filter=None,
@@ -368,9 +368,9 @@ class Theory():
N_pre = np.zeros_like(K)
N_post = np.zeros_like(K)
- for ii in range(N.size):
- N_pre[ii] = N
- N_post[:, ii] = N
+ for i in range(N.size):
+ N_pre[i] = N
+ N_post[:, i] = N
# Connection probabilities between populations
mu, sigma = self.mu_sigma(rates)
@@ -383,9 +383,9 @@ class Theory():
slope_mu_matrix = np.zeros_like(J)
slope_sigma_matrix = np.zeros_like(J)
- for ii in range(N.size):
- slope_mu_matrix[:, ii] = d_nu_d_mu
- slope_sigma_matrix[:, ii] = d_nu_d_sigma
+ for i in range(N.size):
+ slope_mu_matrix[:, i] = d_nu_d_mu
+ slope_sigma_matrix[:, i] = d_nu_d_sigma
G = self.NP['tau_m'] * 1e-3 * (slope_mu_matrix * K * J +
slope_sigma_matrix * K * J**2)
if full_output:
diff --git a/start_jobs.py b/start_jobs.py
index 54896778244c1a174df743d72f3d34e4caca2140..89e4ad2c86b6ff8d6d2539d394f8d2e868c29865 100644
--- a/start_jobs.py
+++ b/start_jobs.py
@@ -53,8 +53,8 @@ def start_job(label, submit_cmd, jobscript_template, sumatra=False, reason=None,
nested_update(sim_params, custom_params['sim_params'])
# Copy custom param file for each MPI process
- for ii in range(sim_params['num_processes']):
- shutil.copy(fn, '_'.join((fn, str(ii))))
+ for i in range(sim_params['num_processes']):
+ shutil.copy(fn, '_'.join((fn, str(i))))
# Collect relevant arguments for job script
num_vp = sim_params['num_processes'] * sim_params[
'local_num_threads']