diff --git a/figures/Schmidt2018/.ipynb_checkpoints/Fig4_connectivity-checkpoint.py b/figures/Schmidt2018/.ipynb_checkpoints/Fig4_connectivity-checkpoint.py
deleted file mode 100644
index b724915930e2ac27dbe4600e9ffaa3b14a2fccd2..0000000000000000000000000000000000000000
--- a/figures/Schmidt2018/.ipynb_checkpoints/Fig4_connectivity-checkpoint.py
+++ /dev/null
@@ -1,237 +0,0 @@
-import json
-import numpy as np
-import matplotlib.pyplot as pl
-import os
-
-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')
-
-"""
-Figure layout
-"""
-nrows = 2
-ncols = 2
-width = 6.8556
-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()
-axes = {}
-
-gs1 = gridspec.GridSpec(2, 2)
-gs1.update(left=0.06, right=0.95, top=0.95, bottom=0.1, wspace=0.1, hspace=0.3)
-
-axes['A'] = pl.subplot(gs1[:1, :1])
-axes['B'] = pl.subplot(gs1[:1, 1:2])
-axes['D'] = pl.subplot(gs1[1:2, 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']
-for label in labels:
- if label in ['C']:
- 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)
-
-"""
-Load data
-"""
-M = MultiAreaModel({})
-
-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]:
- 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_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', 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 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.)
-
-"""
-Save figure
-"""
-pl.savefig('Fig4_connectivity.eps')
diff --git a/figures/Schmidt2018/.ipynb_checkpoints/helpers-checkpoint.py b/figures/Schmidt2018/.ipynb_checkpoints/helpers-checkpoint.py
deleted file mode 100644
index 1c2de1285bfe0086d1a9768babd1c00b555dadbe..0000000000000000000000000000000000000000
--- a/figures/Schmidt2018/.ipynb_checkpoints/helpers-checkpoint.py
+++ /dev/null
@@ -1,164 +0,0 @@
-import numpy as np
-
-"""
-Helper file collecting a number of necessary
-imports for the plot scripts
-"""
-
-area_list = ['V1', 'V2', 'VP', 'V3', 'V3A', 'MT', 'V4t', 'V4', 'VOT', 'MSTd',
- 'PIP', 'PO', 'DP', 'MIP', 'MDP', 'VIP', 'LIP', 'PITv', 'PITd',
- 'MSTl', 'CITv', 'CITd', 'FEF', 'TF', 'AITv', 'FST', '7a', 'STPp',
- 'STPa', '46', 'AITd', 'TH']
-
-
-population_list = ['23E', '23I', '4E', '4I', '5E', '5I', '6E', '6I']
-
-datapath = '../../multiarea_model/data_multiarea'
-raw_datapath = '../../multiarea_model/data_multiarea/raw_data/'
-
-population_labels = ['2/3E', '2/3I', '4E', '4I', '5E', '5I', '6E', '6I']
-layer_labels = ['L1', 'L2', 'L3', 'L4', 'L5', 'L6']
-tex_names = {'23': 'twothree', '4': 'four', '5': 'five', '6': 'six'}
-
-# This path determines the location of the infomap
-# installation and needs to be provided to execute the script for Fig. 7
-infomap_path = None
-
-
-def hierarchical_relation(target_area, source_area, SLN_completed, thresh=(0.35, 0.65)):
- """
- Returns the hierarchical relation between
- two areas based on their SLN value (data + estimated).
-
- Parameters
- ----------
- target_area : str
- Name of target area.
- source_area : str
- Name of source area.
- SLN_completed : dict
- Dictionary of SLN values for pairs of areas.
- thresh : tuple of floats
- Threshold values to classify connections
- as FF/FB/lateral.
-
- Returns
- -------
- hierarchical_relation : str
- Hierarchical relation between source
- and target area.
- """
-
- if (target_area != source_area and
- source_area in SLN_completed[target_area]):
- if SLN_completed[target_area][source_area] > thresh[1]:
- return 'FF'
- elif SLN_completed[target_area][source_area] < thresh[0]:
- return 'FB'
- else:
- return 'lateral'
- else:
- return 'same-area'
-
-
-def structural_gradient(target_area, source_area, arch_types):
- """
- Returns the structural gradient between two areas
- See Schmidt, M., Bakker, R., Hilgetag, C.C. et al.
- Brain Structure and Function (2018), 223:1409,
- for a definition.
-
- Parameters
- ----------
- target_area : str
- Name of target area.
- source_area : str
- Name of source area.
- arch_types : dict
- Dictionary containing the architectural type for each area.
- """
- if target_area != source_area:
- if arch_types[target_area] < arch_types[source_area]:
- return 'HL'
- elif arch_types[target_area] > arch_types[source_area]:
- return 'LH'
- else:
- return 'HZ'
- else:
- return 'same-area'
-
-
-def write_out_lw(fn, C, std=False):
- """
- Stores line widths for arrows in path figures
- generated by pstricks to a txt file.
-
- Parameters
- ----------
- fn : str
- Filename of output file.
- C : dict
- Dictionary with line width values.
- std : bool
- Whether to write out mean or std values.
- """
- if not std:
- max_lw = 0.3 # This is an empirically determined value
- scale_factor = max_lw / np.max(list(C.values()))
- with open(fn, 'w') as f:
- for pair, count in list(C.items()):
- s = '\setboolean{{DRAW{}{}{}{}}}{{true}}'.format(tex_names[pair[0][:-1]],
- pair[0][-1],
- tex_names[pair[1][:-1]],
- pair[1][-1])
- f.write(s)
- f.write('\n')
- s = '\def\{}{}{}{}{{{}}}'.format(tex_names[pair[0][:-1]],
- pair[0][-1],
- tex_names[pair[1][:-1]],
- pair[1][-1],
- float(count) * scale_factor)
- f.write(s)
- f.write('\n')
- else:
- max_lw = 0.3
- scale_factor = max_lw / np.max(list(C['mean'].values()))
- with open(fn, 'w') as f:
- for pair, count in list(C['mean'].items()):
- s = '\setboolean{{DRAW\{}{}{}{}}}{{true}}'.format(tex_names[pair[0][:-1]],
- pair[0][-1],
- tex_names[pair[1][:-1]],
- pair[1][-1])
- f.write('\n')
- s = '\def\{}{}{}{}{{{}}}'.format(tex_names[pair[0][:-1]],
- pair[0][-1],
- tex_names[pair[1][:-1]],
- pair[1][-1],
- float(count) * scale_factor)
- f.write('\n')
-
- for pair, count in list(C['1sigma'].items()):
- f.write('\n')
- s = '\def\{}{}{}{}sigma{{{}}}'.format(tex_names[pair[0][:-1]],
- pair[0][-1],
- tex_names[pair[1][:-1]],
- pair[1][-1],
- float(count) * scale_factor)
- f.write('\n')
-
-
-def area_population_list(structure, area):
- """
- Construct list of all populations in an area.
-
- Parameters
- ----------
- structure : dict
- Dictionary defining the structure of each area.
- area : str
- Area to construct list for.
- """
- complete = []
- for pop in structure[area]:
- complete.append(area + '-' + pop)
- return complete