From cdc8aa038ca304d74f08a5fc3b6752a6e6854a4b Mon Sep 17 00:00:00 2001
From: didihou <didi.hou@rwth-aachen.de>
Date: Thu, 14 Sep 2023 13:50:33 +0200
Subject: [PATCH] /
---
.../.ipynb_checkpoints/helpers-checkpoint.py | 164 ++++++++++++++++++
1 file changed, 164 insertions(+)
create mode 100644 figures/Schmidt2018/.ipynb_checkpoints/helpers-checkpoint.py
diff --git a/figures/Schmidt2018/.ipynb_checkpoints/helpers-checkpoint.py b/figures/Schmidt2018/.ipynb_checkpoints/helpers-checkpoint.py
new file mode 100644
index 0000000..1c2de12
--- /dev/null
+++ b/figures/Schmidt2018/.ipynb_checkpoints/helpers-checkpoint.py
@@ -0,0 +1,164 @@
+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
--
GitLab