From 976a9436ee404823e708dd7e3cc3f261e57840e5 Mon Sep 17 00:00:00 2001
From: Maximilian Schmidt <max.schmidt@fz-juelich.de>
Date: Fri, 25 May 2018 18:28:31 +0900
Subject: [PATCH] Add documentation to scripts
---
figures/Schmidt2018_dyn/compute_bold_signal.py | 6 ++++++
figures/Schmidt2018_dyn/compute_corrcoeff.py | 7 ++++++-
figures/Schmidt2018_dyn/compute_cross_correlation.py | 5 +++++
.../compute_functional_connectivity.py | 6 ++++++
figures/Schmidt2018_dyn/compute_granger_causality.py | 7 +++++++
.../Schmidt2018_dyn/compute_louvain_communities.py | 7 +++++++
figures/Schmidt2018_dyn/compute_pop_LvR.py | 5 +++++
figures/Schmidt2018_dyn/compute_pop_rates.py | 7 ++++++-
figures/Schmidt2018_dyn/compute_power_spectrum.py | 12 ++++++++++++
figures/Schmidt2018_dyn/compute_rate_histogram.py | 6 ++++++
figures/Schmidt2018_dyn/compute_rate_time_series.py | 12 +++++++++++-
11 files changed, 77 insertions(+), 3 deletions(-)
diff --git a/figures/Schmidt2018_dyn/compute_bold_signal.py b/figures/Schmidt2018_dyn/compute_bold_signal.py
index a10f5a5..ac72a79 100644
--- a/figures/Schmidt2018_dyn/compute_bold_signal.py
+++ b/figures/Schmidt2018_dyn/compute_bold_signal.py
@@ -3,6 +3,12 @@ import os
import sys
+"""
+Compute BOLD signal for a given area from the time series of
+population-averaged spike rates of a given simulation using the
+neuRosim package of R (see Schmidt et al. 2018 for more details).
+"""
+
data_path = sys.argv[1]
label = sys.argv[2]
area = sys.argv[3]
diff --git a/figures/Schmidt2018_dyn/compute_corrcoeff.py b/figures/Schmidt2018_dyn/compute_corrcoeff.py
index 34ced53..4bd7f88 100644
--- a/figures/Schmidt2018_dyn/compute_corrcoeff.py
+++ b/figures/Schmidt2018_dyn/compute_corrcoeff.py
@@ -6,6 +6,11 @@ import correlation_toolbox.helper as ch
from multiarea_model import MultiAreaModel
import sys
+"""
+Compute correlation coefficients for a subsample
+of neurons for the entire network from raw spike files of a given simulation.
+"""
+
data_path = sys.argv[1]
label = sys.argv[2]
@@ -31,7 +36,7 @@ M = MultiAreaModel({})
spike_data = {}
cc_dict = {}
-for area in ['V1', 'V2', 'FEF']:
+for area in M.area_list:
cc_dict[area] = {}
LvR_list = []
N = []
diff --git a/figures/Schmidt2018_dyn/compute_cross_correlation.py b/figures/Schmidt2018_dyn/compute_cross_correlation.py
index fe607f7..905ebb9 100644
--- a/figures/Schmidt2018_dyn/compute_cross_correlation.py
+++ b/figures/Schmidt2018_dyn/compute_cross_correlation.py
@@ -5,6 +5,11 @@ import numpy as np
import os
import sys
+"""
+Compute the cross-correlation betwen two given areas from their
+time series of population-averaged spike rates of a given simulation.
+"""
+
data_path = sys.argv[1]
label = sys.argv[2]
area1 = sys.argv[3]
diff --git a/figures/Schmidt2018_dyn/compute_functional_connectivity.py b/figures/Schmidt2018_dyn/compute_functional_connectivity.py
index 87eb487..14afe4c 100644
--- a/figures/Schmidt2018_dyn/compute_functional_connectivity.py
+++ b/figures/Schmidt2018_dyn/compute_functional_connectivity.py
@@ -7,6 +7,12 @@ from multiarea_model import MultiAreaModel
from scipy.spatial.distance import pdist
from scipy.spatial.distance import squareform
+"""
+Compute the functional connectivity between all areas of a given
+simulation based on their time series of spiking rates or their
+estimated BOLD signal.
+"""
+
data_path = sys.argv[1]
label = sys.argv[2]
method = sys.argv[3]
diff --git a/figures/Schmidt2018_dyn/compute_granger_causality.py b/figures/Schmidt2018_dyn/compute_granger_causality.py
index b93d17d..e457582 100644
--- a/figures/Schmidt2018_dyn/compute_granger_causality.py
+++ b/figures/Schmidt2018_dyn/compute_granger_causality.py
@@ -9,6 +9,13 @@ from multiarea_model.multiarea_helpers import create_mask
from scipy.stats import levene
from statsmodels.tsa.vector_ar.var_model import VAR
+
+"""
+Compute the conditional Granger causality to a given population of an
+area based on the population-averaged spike rates from a given
+simulation.
+"""
+
data_path = sys.argv[1]
label = sys.argv[2]
area = sys.argv[3]
diff --git a/figures/Schmidt2018_dyn/compute_louvain_communities.py b/figures/Schmidt2018_dyn/compute_louvain_communities.py
index f3913fd..5d55872 100644
--- a/figures/Schmidt2018_dyn/compute_louvain_communities.py
+++ b/figures/Schmidt2018_dyn/compute_louvain_communities.py
@@ -8,6 +8,13 @@ import sys
from multiarea_model.multiarea_model import MultiAreaModel
+"""
+Determines communities in the functional connectivity of either the
+experimental fMRI data used in Schmidt et al. 2018 or of a given
+simulation (the functional connectivity being based either on spike
+rates or an estimated BOLD signal).
+"""
+
data_path = sys.argv[1]
label = sys.argv[2]
method = sys.argv[3]
diff --git a/figures/Schmidt2018_dyn/compute_pop_LvR.py b/figures/Schmidt2018_dyn/compute_pop_LvR.py
index 9128ffd..cd0d070 100644
--- a/figures/Schmidt2018_dyn/compute_pop_LvR.py
+++ b/figures/Schmidt2018_dyn/compute_pop_LvR.py
@@ -6,6 +6,11 @@ from multiarea_model.analysis_helpers import pop_LvR
from multiarea_model import MultiAreaModel
import sys
+"""
+Compute LvR for the entire network from raw spike
+files of a given simulation.
+"""
+
data_path = sys.argv[1]
label = sys.argv[2]
diff --git a/figures/Schmidt2018_dyn/compute_pop_rates.py b/figures/Schmidt2018_dyn/compute_pop_rates.py
index 784f9a0..88a9945 100644
--- a/figures/Schmidt2018_dyn/compute_pop_rates.py
+++ b/figures/Schmidt2018_dyn/compute_pop_rates.py
@@ -6,6 +6,11 @@ from multiarea_model.analysis_helpers import pop_rate
from multiarea_model import MultiAreaModel
import sys
+"""
+Compute stationary spike rates for the entire network from raw spike
+files of a given simulation.
+"""
+
data_path = sys.argv[1]
label = sys.argv[2]
@@ -27,7 +32,7 @@ M = MultiAreaModel({})
spike_data = {}
pop_rates = {}
-for area in ['V1', 'V2', 'FEF']:
+for area in M.area_list:
pop_rates[area] = {}
rate_list = []
N = []
diff --git a/figures/Schmidt2018_dyn/compute_power_spectrum.py b/figures/Schmidt2018_dyn/compute_power_spectrum.py
index 3cba554..3555077 100644
--- a/figures/Schmidt2018_dyn/compute_power_spectrum.py
+++ b/figures/Schmidt2018_dyn/compute_power_spectrum.py
@@ -6,6 +6,18 @@ from multiarea_model import MultiAreaModel
from multiarea_model.analysis_helpers import centralize
from scipy.signal import welch
+
+"""
+Compute the power spectrum time series for a given area of
+population-averaged spike rates of a given simulation.
+
+The spike rates can be based on three different methods:
+- binned spike histograms on all neurons ('full')
+- binned spike histograms on a subsample of 140 neurons ('subsample')
+- spike histograms convolved with a Gaussian kernel of optimal width
+ after Shimazaki et al. (2010)
+"""
+
# Parameters for Welch Power Spectral density and spectrogram
noverlap = 1000
nperseg = 1024
diff --git a/figures/Schmidt2018_dyn/compute_rate_histogram.py b/figures/Schmidt2018_dyn/compute_rate_histogram.py
index c351b33..6e5dec0 100644
--- a/figures/Schmidt2018_dyn/compute_rate_histogram.py
+++ b/figures/Schmidt2018_dyn/compute_rate_histogram.py
@@ -6,6 +6,12 @@ import sys
from multiarea_model import MultiAreaModel
from multiarea_model.analysis_helpers import pop_rate_distribution
+
+"""
+Compute histogram of spike rates over single neurons for a given area
+from raw spike files of a given simulation.
+"""
+
assert(len(sys.argv) == 4)
data_path = sys.argv[1]
label = sys.argv[2]
diff --git a/figures/Schmidt2018_dyn/compute_rate_time_series.py b/figures/Schmidt2018_dyn/compute_rate_time_series.py
index 4a2ebfc..c4956af 100644
--- a/figures/Schmidt2018_dyn/compute_rate_time_series.py
+++ b/figures/Schmidt2018_dyn/compute_rate_time_series.py
@@ -2,7 +2,6 @@ import json
import neo
import numpy as np
import os
-import pandas as pd
import quantities as pq
from multiarea_model.analysis_helpers import pop_rate_time_series
@@ -10,6 +9,17 @@ from elephant.statistics import instantaneous_rate
from multiarea_model import MultiAreaModel
import sys
+"""
+Compute time series of population-averaged spike rates for a given
+area from raw spike files of a given simulation.
+
+Implements three different methods:
+- binned spike histograms on all neurons ('full')
+- binned spike histograms on a subsample of 140 neurons ('subsample')
+- spike histograms convolved with a Gaussian kernel of optimal width
+ after Shimazaki et al. (2010)
+"""
+
assert(len(sys.argv) == 5)
data_path = sys.argv[1]
--
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