Add Fig8 scripts to repo and Fig8 workflow to snakemake file

figures/Schmidt2018_dyn/Fig7_temporal_hierarchy.py

@@ -621,7 +621,7 @@ pl.savefig('Fig7_temporal_hierarchy_mpl.eps')
 
 
 """
-Merge eps files
+Merge surface plots
 """
 pyx.text.set(cls=pyx.text.LatexRunner)
 pyx.text.preamble(r"\usepackage{helvet}")

figures/Schmidt2018_dyn/Fig8_alluvial_sim_exp.eps

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+0.392157 0.709804 0.803922 rg
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+121.457 11 m 357.457 11 357.457 31 593.457 31 c S Q
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+(MSTd)Tj
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+10 0 0 10 13.964844 168.472388 Tm
+/f-1-0 1 Tf
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+[(dors)-3(al ar)-3(eas)]TJ
+9.5 0 0 9.5 91.36355 238.884644 Tm
+/f-0-0 1 Tf
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+10 0 0 10 4.697266 213.930676 Tm
+/f-1-0 1 Tf
+[(Dorsa)-3(l strea)-3(m)]TJ
+9.5 0 0 9.5 91.36355 107.009521 Tm
+/f-0-0 1 Tf
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+0 -1.25 Td
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+10 0 0 10 4.697266 51.513745 Tm
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+9.5 0 0 9.5 91.36355 393.325586 Tm
+/f-0-0 1 Tf
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+0 -1.25 Td
+[(V)18(OT)]TJ
+10 0 0 10 3.168945 393.552124 Tm
+/f-1-0 1 Tf
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+0 -1.25 Td
+[(+ ventra)-3(l area)]TJ
+9.5 0 0 9.5 91.36355 363.504883 Tm
+/f-0-0 1 Tf
+(V1)Tj
+0 -1.25 Td
+(V2)Tj
+10 0 0 10 18.925781 358.894946 Tm
+/f-1-0 1 Tf
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+9.5 0 0 9.5 91.465601 336.129883 Tm
+/f-0-0 1 Tf
+(V4)Tj
+0 -1.25 Td
+(PITv)Tj
+0 -1.25 Td
+[(CITv)]TJ
+0 -1.25 Td
+[(CITd)]TJ
+0 -1.25 Td
+(AITv)Tj
+0 -1.25 Td
+(AITd)Tj
+0 -1.25 Td
+(TH)Tj
+0 -1.25 Td
+(TF)Tj
+10 0 0 10 -0.0488281 295.403931 Tm
+/f-1-0 1 Tf
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+4.407227 -16.706753 Td
+[(F)64(ront)-3(al)]TJ
+24 0 0 24 552.461182 314.361511 Tm
+/f-0-0 1 Tf
+(1S)Tj
+0.0366211 -9.322224 Td
+(2S)Tj
+ET
+Q Q
+showpage
+%%Trailer
+end restore
+%%EOF

figures/Schmidt2018_dyn/Fig8_interactions.py

+import csv
+import correlation_toolbox.helper as ch
+import json
+import numpy as np
+import os
+import pyx
+import scipy.io
+
+from helpers import original_data_path
+from multiarea_model.multiarea_model import MultiAreaModel
+from plotcolors import myred, myblue
+from scipy.spatial.distance import pdist
+from scipy.spatial.distance import squareform
+
+import matplotlib.pyplot as pl
+from matplotlib import gridspec
+from matplotlib import rc_file
+rc_file('plotstyle.rc')
+
+import sys
+sys.path.append('../Schmidt2018')
+from graph_helpers import apply_map_equation
+
+"""
+Figure layout
+"""
+cmap = pl.cm.coolwarm
+cmap = cmap.from_list('mycmap', [myblue, 'white', myred], N=256)
+cmap2 = cmap.from_list('mycmap', ['white', myred], N=256)
+
+
+width = 7.0866
+n_horz_panels = 2.
+n_vert_panels = 3.
+
+axes = {}
+gs1 = gridspec.GridSpec(1, 3)
+gs1.update(left=0.05, right=0.95, top=0.95,
+           bottom=0.52, wspace=0., hspace=0.4)
+axes['A'] = pl.subplot(gs1[:, 0])
+axes['B'] = pl.subplot(gs1[:, 1])
+axes['C'] = pl.subplot(gs1[:, 2])
+
+gs1 = gridspec.GridSpec(1, 1)
+gs1.update(left=0.18, right=0.8, top=0.44,
+           wspace=0., bottom=0.27, hspace=0.2)
+axes['D'] = pl.subplot(gs1[:, :])
+
+gs1 = gridspec.GridSpec(1, 1)
+gs1.update(left=0.165, right=0.6, top=0.15,
+           wspace=0., bottom=0.075, hspace=0.2)
+axes['E'] = pl.subplot(gs1[:, :])
+
+gs1 = gridspec.GridSpec(1, 1)
+gs1.update(left=0.688, right=0.95, top=0.15,
+           wspace=0., bottom=0.075, hspace=0.2)
+axes['F'] = pl.subplot(gs1[:, :])
+
+for label in ['A', 'B', 'C', 'D', 'E', 'F']:
+    if label in ['E', 'F']:
+        label_pos = [-0.08, 1.01]
+    else:
+        label_pos = [-0.2, 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)
+    axes[label].spines['right'].set_color('none')
+    axes[label].spines['top'].set_color('none')
+    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
+"""
+
+"""
+Create MultiAreaModel instance to have access to data structures
+"""
+M = MultiAreaModel({})
+
+
+# Load experimental functional connectivity
+func_conn_data = {}
+with open('Fig8_exp_func_conn.csv', 'r') as f:
+    myreader = csv.reader(f, delimiter='\t')
+    # Skip first 3 lines
+    next(myreader)
+    next(myreader)
+    next(myreader)
+    areas = next(myreader)
+    for line in myreader:
+        dict_ = {}
+        for i in range(len(line)):
+            dict_[areas[i]] = float(line[i])
+        func_conn_data[areas[myreader.line_num - 5]] = dict_
+
+exp_FC = np.zeros((len(M.area_list),
+                   len(M.area_list)))
+for i, area1 in enumerate(M.area_list):
+    for j, area2 in enumerate(M.area_list):
+        exp_FC[i][j] = func_conn_data[area1][area2]
+
+
+"""
+Simulation data
+"""
+LOAD_ORIGINAL_DATA = True
+
+if LOAD_ORIGINAL_DATA:
+    tmin = 500.
+    tmax = 10000.
+
+    cc_weights_factor = [1.0, 1.4, 1.5, 1.6, 1.7, 1.75, 1.8, 1.9, 2., 2.1, 2.5]
+    labels = ['33fb5955558ba8bb15a3fdce49dfd914682ef3ea',
+              '783cedb0ff27240133e3daa63f5d0b8d3c2e6b79',
+              '380856f3b32f49c124345c08f5991090860bf9a3',
+              '5a7c6c2d6d48a8b687b8c6853fb4d98048681045',
+              'c1876856b1b2cf1346430cf14e8d6b0509914ca1',
+              'a30f6fba65bad6d9062e8cc51f5483baf84a46b7',
+              '1474e1884422b5b2096d3b7a20fd4bdf388af7e0',
+              '99c0024eacc275d13f719afd59357f7d12f02b77',
+              'f18158895a5d682db5002489d12d27d7a974146f',
+              '08a3a1a88c19193b0af9d9d8f7a52344d1b17498',
+              '5bdd72887b191ec22a5abcc04ca4a488ea216e32']
+
+    sim_FC = {}
+    for label in labels:
+        fn = os.path.join(original_data_path,
+                          label,
+                          'Analysis',
+                          'functional_connectivity_synaptic_input.npy')
+        sim_FC[label] = np.load(fn)
+
+    label = '99c0024eacc275d13f719afd59357f7d12f02b77'
+    fn = os.path.join(original_data_path,
+                      label,
+                      'Analysis',
+                      'FC_synaptic_input_communities.json')
+    with open(fn, 'r') as f:
+        part_sim = json.load(f)
+    part_sim_list = [part_sim[area] for area in M.area_list]
+    part_sim_index = np.argsort(part_sim_list, kind='heapsort')
+
+
+# """
+# Load bold signals
+# """
+# label = '99c0024eacc275d13f719afd59357f7d12f02b77'
+# bold_load_path = '../data/'
+# bold = {}
+# base_fn = 'bold_signal_syn_input/bold_signal_syn_input_{}'.format(label)
+# for area in M.area_list:
+#     bold[area] = np.load('{}_{}.npy'.format(base_fn, area))
+
+# data[label].bold_signal = bold
+
+
+def matrix_plot(ax, matrix, index, vlim, pos=None):
+    ax.yaxis.set_ticks_position('none')
+    ax.xaxis.set_ticks_position('none')
+
+    x = np.arange(0, len(M.area_list) + 1)
+    y = np.arange(0, len(M.area_list[::-1]) + 1)
+    X, Y = np.meshgrid(x, y)
+
+    ax.set_xlim((0, 32))
+    ax.set_ylim((0, 32))
+
+    ax.set_aspect(1. / ax.get_data_ratio())
+
+    vmax = vlim
+    vmin = -vlim
+
+    # , norm = LogNorm(1e-8,1.))
+    im = ax.pcolormesh(matrix[index][:, index][::-1],
+                       cmap=cmap, vmin=vmin, vmax=vmax)
+
+    cbticks = [-1., -0.5, 0., 0.5, 1.0]
+    cb = pl.colorbar(im, ax=ax, ticks=cbticks, fraction=0.046)
+    cb.ax.tick_params(labelsize=12)
+    ax.set_yticks([i + 0.5 for i in np.arange(0, len(M.area_list) + 1)])
+    ax.set_yticklabels(np.array(M.area_list)[index][::-1], size=8.)
+
+    if pos != (0, 2):
+        cb.remove()
+    else:
+        ax.text(1.25, 0.52, r'FC', rotation=90,
+                transform=ax.transAxes, size=12)
+    ax.set_xticks([i + 0.5 for i in np.arange(0, len(M.area_list) + 1)])
+    ax.set_xticklabels(np.array(M.area_list)[index], rotation=90, size=8.)
+    ax.tick_params(pad=1.5)
+
+
+"""
+Plotting
+"""
+ax = axes['A']
+label = '99c0024eacc275d13f719afd59357f7d12f02b77'
+
+
+matrix_plot(ax, sim_FC[label],
+            part_sim_index[::-1], 1., pos=(0, 0))
+
+ax = axes['B']
+matrix_plot(ax, FC_exp,
+            louvain_sim_mat_index[::-1], 1., pos=(0, 1))
+
+# ax = axes['C']
+
+# matrix_plot(ax, exp_FC, louvain_sim_mat_index[::-1], 1., pos=(0, 2))
+
+# indices = np.array(1. - np.eye(32), dtype=np.bool)
+
+
+# def compute_cc(label, cmp_matrix, measure='synaptic_input'):
+#     ts = []
+#     if measure == 'synaptic_input':
+#         d = data[label].synaptic_input
+#     elif measure == 'bold':
+#         d = data[label].bold_signal
+#     for area in M.area_list:
+#         ts.append(ch.centralize(d[area], units=True))
+
+#     D = pdist(ts, metric='correlation')
+#     correlation_matrix = 1. - squareform(D)
+#     for i in range(32):
+#         correlation_matrix[i][i] = 0.
+
+#     cc = np.corrcoef(correlation_matrix[indices].flatten(),
+#                      cmp_matrix[indices].flatten())[0][1]
+#     return cc
+
+# for k in labels:
+#     cc_exp = compute_cc(k, func_conn)
+#     corrcoeffs['sim_exp'].append(cc_exp)
+#     cc_struct = compute_cc(label, conn_matrix)
+#     corrcoeffs['sim_struct'].append(cc_struct)
+
+# label = '99c0024eacc275d13f719afd59357f7d12f02b77'
+# cc_exp = compute_cc(label, func_conn)
+# cc_struct = compute_cc(label, conn_matrix)
+# ##########################################################################
+
+# 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")
+
+# ax.plot(cc_weights_factor_100[0], cc_exp, '.',
+#         ms=10, markeredgecolor='none', color='k')
+
+# ax.plot(cc_weights_factor[1:], corrcoeffs[
+#         'sim_exp'][1:], '.', ms=10, markeredgecolor='none', label='Sim. vs. Exp.', color='k')
+# ax.plot(cc_weights_factor[0], corrcoeffs[
+#         'sim_exp'][0], '^', ms=5, markeredgecolor='none', label='Sim. vs. Exp.', color='k')
+
+# cc_bold = compute_cc('99c0024eacc275d13f719afd59357f7d12f02b77', func_conn, measure='bold')
+# ax.plot(cc_weights_factor_100[0], cc_bold, '.',
+#         ms=10, markeredgecolor='none', color=myred)
+
+# print(("Corr. with HiRes",
+#        np.corrcoef(correlation_matrix[indices].flatten(),
+#                    func_conn[indices].flatten())[0][1]))
+# print(("Corr. with HiRes", corrcoeffs['sim_exp']))
+# print(("Corr. of structur with HiRes",
+#        np.corrcoef(conn_matrix[indices].flatten(),
+#                    func_conn[indices].flatten())[0][1]))
+
+# ax.hlines(corrcoeffs['struct_exp'], -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=16)
+# 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.
+# """
+# infomap_path = None
+# filename = 'Fig8_structural_clusters'
+# modules, modules_areas, index = apply_map_equation(M.K_matrix,
+#                                                    M.area_list,
+#                                                    filename='stab',
+#                                                    infomap_path=infomap_path)
+# files = 'Fig8_structural_clusters.map'
+# map_equation_dict = {}
+# 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[label] = dict(
+#         list(zip(map_equation_areas, map_equation)))
+
+# To create the alluvial input, we rename the simulated clusters
+# 1S --> 2S, 2S ---> 1S
+# 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_mat[i] == 1:
+#         psm = 2
+#     elif part_sim_mat[i] == 2:
+#         psm = 1
+#     s = '{}, {}, {}, {}, {}'.format(area,
+#                                     map_equation_dict[area],
+#                                     psm,
+#                                     part_exp_mat[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
+"""
+pyx.text.set(cls=pyx.text.LatexRunner)
+pyx.text.preamble(r"\usepackage{helvet}")
+
+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(
+    1.7, 1., "Fig8_alluvial_struct_sim.eps", width=8.8))
+c.insert(pyx.epsfile.epsfile(
+    11.5, 1.3, "Fig8_alluvial_sim_exp.eps", width=5.4))
+
+c.writeEPSfile("Fig8_interactions.eps")

figures/Schmidt2018_dyn/Snakefile

@@ -38,7 +38,18 @@ ORIGINAL_SIMULATIONS = {'all': ['533d73357fbe99f6178029e6054b571b485f40f6',
                                  '5bdd72887b191ec22a5abcc04ca4a488ea216e32',
                                  '99c0024eacc275d13f719afd59357f7d12f02b77',
                                  '3afaec94d650c637ef8419611c3f80b3cb3ff539'],
-                        'Fig7': ['99c0024eacc275d13f719afd59357f7d12f02b77']}
+                        'Fig7': ['99c0024eacc275d13f719afd59357f7d12f02b77'],
+                        'Fig8': ['33fb5955558ba8bb15a3fdce49dfd914682ef3ea',
+                                 '783cedb0ff27240133e3daa63f5d0b8d3c2e6b79',
+                                 '380856f3b32f49c124345c08f5991090860bf9a3',
+                                 '5a7c6c2d6d48a8b687b8c6853fb4d98048681045',
+                                 'c1876856b1b2cf1346430cf14e8d6b0509914ca1',
+                                 'a30f6fba65bad6d9062e8cc51f5483baf84a46b7',
+                                 '1474e1884422b5b2096d3b7a20fd4bdf388af7e0',
+                                 'f18158895a5d682db5002489d12d27d7a974146f',
+                                 '08a3a1a88c19193b0af9d9d8f7a52344d1b17498',
+                                 '5bdd72887b191ec22a5abcc04ca4a488ea216e32',
+                                 '99c0024eacc275d13f719afd59357f7d12f02b77']}
 
 
 if LOAD_ORIGINAL_DATA:
@@ -53,7 +64,8 @@ rule all:
         'Fig4_metastability.eps',
         'Fig5_ground_state.eps',
         'Fig6_comparison_exp_spiking_data.eps',
-        'Fig7_temporal_hierarchy.eps'
+        'Fig7_temporal_hierarchy.eps',
+        'Fig8_interactions.eps'
 
 include: './Snakefile_preprocessing'
 
@@ -150,3 +162,17 @@ rule Fig7_temporal_hierarchy:
         'Fig7_temporal_hierarchy.eps'
     shell:
         'python3 Fig7_temporal_hierarchy.py'
+
+rule Fig8_interactions:
+    input:
+        expand(os.path.join(DATA_DIR, '{simulation}', 'Analysis', 'functional_connectivity_synaptic_input.npy'),
+               simulation=SIMULATIONS['Fig8']),
+        expand(os.path.join(DATA_DIR, '{simulation}', 'Analysis', 'FC_synaptic_input_communities.json'),
+               simulation=SIMULATIONS['Fig8'][-1]),
+        expand(os.path.join(DATA_DIR, '{simulation}', 'Analysis', 'functional_connectivity_bold_signal.npy'),
+               simulation=SIMULATIONS['Fig8'][-1])
+    output:
+        'Fig8_interactions.eps'
+    shell:
+        'python3 Fig8_interactions.py'
+        

figures/Schmidt2018_dyn/Snakefile_preprocessing

@@ -80,3 +80,40 @@ rule cross_correlation:
                      'cross_correlation_{area1}_{area2}.npy')
     shell:
         'python3 compute_cross_correlation.py {} {{wildcards.simulation}} {{wildcards.area1}} {{wildcards.area2}}'.format(DATA_DIR)
+
+rule synaptic_input:
+    input:
+        expand(os.path.join(DATA_DIR, '{{simulation}}', 'Analysis',
+                            'rate_time_series_full',
+                            'rate_time_series_full_{{area}}_{pop}.npy'),
+               pop=population_list),
+    output:
+        os.path.join(DATA_DIR, '{simulation}', 'Analysis', 'synaptic_input', 'synaptic_input_{area}.npy')
+    shell:
+        'python3 compute_synaptic_input.py {} {{wildcards.simulation}} {{wildcards.area}}'.format(DATA_DIR)
+
+rule functional_connectivity:
+    input:
+        expand(os.path.join(DATA_DIR, '{{simulation}}', 'Analysis', '{{method}}', '{{method}}_{area}.npy'),
+               area=area_list)
+    output:
+        os.path.join(DATA_DIR, '{simulation}', 'Analysis', 'functional_connectivity_{method}.npy')
+    shell:
+        'python3 compute_functional_connectivity.py {} {{wildcards.simulation}} {{wildcards.method}}'.format(DATA_DIR)
+
+rule communities_FC:
+    input:
+        os.path.join(DATA_DIR, '{simulation}', 'Analysis', 'functional_connectivity.npy')
+    output:
+        os.path.join(DATA_DIR, '{simulation}', 'Analysis', 'FC_synaptic_input_communities.json')
+    shell:
+        'python3 compute_louvain_communities.py {} {{wildcards.simulation}}'.format(DATA_DIR)
+
+rule bold_signal:
+    input:
+        os.path.join(DATA_DIR, '{simulation}', 'Analysis', 'synaptic_input', 'synaptic_input_{area}.npy')
+    output:
+        os.path.join(DATA_DIR, '{simulation}', 'Analysis', 'bold_signal', 'bold_signal_{area}.npy')
+    shell:
+        'python3 compute_bold_signal.py {} {{wildcards.simulation}} {{wildcards.area}}'.format(DATA_DIR)
+

figures/Schmidt2018_dyn/compute_bold_signal.R

+library('neuRosim')
+args <- commandArgs(trailingOnly=TRUE)
+print(args)
+
+x <- read.table(args[1])
+d <- data.matrix(x)
+
+T <- 100
+it <- 0.001
+
+out <- balloon(d, T, it)
+write.table(out, args[2])
\ No newline at end of file

figures/Schmidt2018_dyn/compute_bold_signal.py

+import numpy as np
+import os
+import sys
+
+
+data_path = sys.argv[1]
+label = sys.argv[2]
+area = sys.argv[3]
+
+load_path = os.path.join(data_path,
+                         label,
+                         'Analysis',
+                         'synaptic_input')
+
+save_path = os.path.join(data_path,
+                         label,
+                         'Analysis',
+                         'bold_signal')
+
+try:
+    os.mkdir(save_path)
+except FileExistsError:
+    pass
+
+fn = os.path.join(load_path,
+                  'synaptic_input_{}.npy'.format(area))
+synaptic_input = np.load(fn)
+
+
+def bold_R_parser(fn):
+    f = open(fn, 'r')
+    # skip first line
+    f.readline()
+
+    bold_signal = []
+    for l in f:
+        bold_signal.append(float(l.split(' ')[-1]))
+    f.close()
+    return np.array(bold_signal)
+
+
+fn = os.path.join(save_path,
+                  'syn_input_{}.txt'.format(area))
+out_fn = os.path.join(save_path,
+                      'bold_syn_input_{}.txt'.format(area))
+
+np.savetxt(fn, synaptic_input / np.max(synaptic_input))
+os.system('Rscript --vanilla compute_bold_signal.R {} {}'.format(fn, out_fn))
+
+bold_signal = bold_R_parser(out_fn)
+fn = os.path.join(save_path,
+                  'bold_signal_{}.npy'.format(area))
+np.save(fn, bold_signal)
+

figures/Schmidt2018_dyn/compute_functional_connectivity.py

+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
+
+data_path = sys.argv[1]
+label = sys.argv[2]
+method = sys.argv[3]
+
+load_path = os.path.join(data_path,
+                         label,
+                         'Analysis',
+                         method)
+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:
+    fn = os.path.join(load_path,
+                      '{}_{}.npy'.format(method, area))
+    si = np.load(fn)
+    time_series.append(ch.centralize(si, units=True))
+
+D = pdist(time_series, metric='correlation')
+correlation_matrix = 1. - squareform(D)
+
+np.save(os.path.join(save_path,
+                     'functional_connectivity_{}.npy'.format(method)),
+        correlation_matrix)

figures/Schmidt2018_dyn/compute_louvain_communities.py

+import community
+import json
+import networkx as nx
+import numpy as np
+import os
+import sys
+
+from multiarea_model.multiarea_model import MultiAreaModel
+
+data_path = sys.argv[1]
+label = sys.argv[2]
+
+
+"""
+Create MultiAreaModel instance to have access to data structures
+"""
+M = MultiAreaModel({})
+
+
+load_path = os.path.join(data_path,
+                         label,
+                         'Analysis',
+                         'functional_connectivity_synaptic_input.npy')
+
+
+FC = np.load(load_path)
+for i in range(FC.shape[0]):
+    FC[i][i] = 0.
+    
+G = nx.Graph()
+for area in M.area_list:
+    G.add_node(area)
+
+edges = []
+for i, area in enumerate(M.area_list):
+    for j, area2 in enumerate(M.area_list):
+        edges.append((area, area2, FC[i][j]))
+G.add_weighted_edges_from(edges)
+
+part = community.best_partition(G)
+
+fn = os.path.join(data_path,
+                  label,
+                  'Analysis',
+                  'FC_synaptic_input_communities.json')
+
+with open(fn, 'w') as f:
+    json.dump(part, f)

figures/Schmidt2018_dyn/compute_synaptic_input.py

+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
+
+data_path = sys.argv[1]
+label = sys.argv[2]
+area = sys.argv[3]
+
+
+load_path = os.path.join(data_path,
+                         label,
+                         'Analysis',
+                         'rate_time_series_full')
+save_path = os.path.join(data_path,
+                         label,
+                         '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']
+
+
+"""
+Create MultiAreaModel instance to have access to data structures
+"""
+connection_params = {'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'}
+
+M = MultiAreaModel({})
+
+"""
+Synaptic filtering kernel
+"""
+t = np.arange(0., 20., 1.)
+tau_syn = M.params['neuron_params']['single_neuron_dict']['tau_syn_ex']
+kernel = np.exp(-t / tau_syn)
+
+    
+"""
+Load rate time series
+"""
+rate_time_series = {}
+for source_area in M.area_list:
+    rate_time_series[source_area] = {}
+    for source_pop in M.structure[source_area]:
+        fn = os.path.join(load_path,
+                          'rate_time_series_full_{}_{}.npy'.format(source_area, source_pop))
+        dat = np.load(fn)
+        rate_time_series[source_area][source_pop] = dat
+
+
+synaptic_input_list = []
+N_list = []
+for pop in M.structure[area]:
+    time_series = np.zeros(int((sim_params['T'] - 500.)))
+    for source_area in M.area_list:
+        for source_pop in M.structure[source_area]:
+            weight = M.W[area][pop][source_area][source_pop]
+            time_series += (rate_time_series[source_area][source_pop] *
+                            abs(weight) *
+                            M.K[area][pop][source_area][source_pop])
+    syn_current = np.convolve(kernel, time_series, mode='same')
+    synaptic_input_list.append(time_series)
+    N_list.append(M.N[area][pop])
+
+    fp = '_'.join(('synaptic_input',
+                   area,
+                   pop))
+    try:
+        os.mkdir(save_path)
+    except FileExistsError:
+        pass
+    np.save('{}/{}.npy'.format(save_path, fp), time_series)
+
+synaptic_input_list = np.array(synaptic_input_list)
+area_time_series = np.average(synaptic_input_list, axis=0, weights=N_list)
+
+fp = '_'.join(('synaptic_input',
+               area))
+np.save('{}/{}.npy'.format(save_path, fp), area_time_series)
+
+par = {'areas': M.area_list,
+       'pops': 'complete',
+       'resolution': 1.,
+       't_min': 500.,
+       't_max': T}
+fp = '_'.join(('synaptic_input',
+               'Parameters.json'))
+with open('{}/{}'.format(save_path, fp), 'w') as f:
+    json.dump(par, f)