From ce2f5b00a37d7af4fae972e3b0b75ffcc920358a Mon Sep 17 00:00:00 2001
From: Maximilian Schmidt <max.schmidt@fz-juelich.de>
Date: Fri, 25 May 2018 18:14:53 +0900
Subject: [PATCH] Add script to compute correlation coefficients
---
figures/Schmidt2018_dyn/compute_corrcoeff.py | 58 ++++++++++++++++++++
1 file changed, 58 insertions(+)
create mode 100644 figures/Schmidt2018_dyn/compute_corrcoeff.py
diff --git a/figures/Schmidt2018_dyn/compute_corrcoeff.py b/figures/Schmidt2018_dyn/compute_corrcoeff.py
new file mode 100644
index 0000000..34ced53
--- /dev/null
+++ b/figures/Schmidt2018_dyn/compute_corrcoeff.py
@@ -0,0 +1,58 @@
+import json
+import numpy as np
+import os
+
+import correlation_toolbox.helper as ch
+from multiarea_model import MultiAreaModel
+import sys
+
+data_path = sys.argv[1]
+label = sys.argv[2]
+
+load_path = os.path.join(data_path,
+ label,
+ 'recordings')
+save_path = os.path.join(data_path,
+ label,
+ 'Analysis')
+
+with open(os.path.join(data_path, label, 'custom_params_{}'.format(label)), 'r') as f:
+ sim_params = json.load(f)
+T = sim_params['T']
+
+tmin = 500.
+subsample = 2000
+resolution = 1.
+
+"""
+Create MultiAreaModel instance to have access to data structures
+"""
+M = MultiAreaModel({})
+
+spike_data = {}
+cc_dict = {}
+for area in ['V1', 'V2', 'FEF']:
+ cc_dict[area] = {}
+ LvR_list = []
+ N = []
+ for pop in M.structure[area]:
+ fp = '-'.join((label,
+ 'spikes', # assumes that the default label for spike files was used
+ area,
+ pop))
+ fn = '{}/{}.npy'.format(load_path, fp)
+ # +1000 to ensure that we really have subsample non-silent neurons in the end
+ spikes = np.load(fn)
+ ids = np.unique(spikes[:, 0])
+ dat = ch.sort_gdf_by_id(spikes, idmin=ids[0], idmax=ids[0]+subsample+1000)
+ bins, hist = ch.instantaneous_spike_count(dat[1], resolution, tmin=tmin, tmax=T)
+ rates = ch.strip_binned_spiketrains(hist)[:subsample]
+ cc = np.corrcoef(rates)
+ cc = np.extract(1-np.eye(cc[0].size), cc)
+ cc[np.where(np.isnan(cc))] = 0.
+ cc_dict[area][pop] = np.mean(cc)
+
+fn = os.path.join(save_path,
+ 'corrcoeff.json')
+with open(fn, 'w') as f:
+ json.dump(cc_dict, f)
--
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