linux goes windows

ephys_analysis.py

@@ -7,17 +7,12 @@ from IPython import embed
 
 
 def stimulus_collector(filename):
-    try:
-        stims = []
-        f = nix.File.open(filename, nix.FileMode.ReadOnly)
-        b = f.blocks[0]
-        for g in b.groups:
-            # embed()
-            # exit()
-            stims.append(g.multi_tags[0].name)
-    except KeyError:
-        embed()
-        exit()
+    stims = []
+    f = nix.File.open(filename, nix.FileMode.ReadOnly)
+    b = f.blocks[0]
+    embed()
+    exit()
+
     return stims
 
 
@@ -26,22 +21,18 @@ def analyze_sams(filename):
     b = f.blocks[0]
     b.metadata.pprint(max_depth=-1) # max_depth=-1: alles rausschreiben
     for g in b.groups:
-
         if 'sam' in g.name.lower(): # go through loop, until 'sam' is found
-             break
-    # embed()
-    # exit()
-    rtag_data = g.tags[0]
+            break
+
+    rtag_data = g.tags[0]   # rtag_data = tags within this group
     rtag_data.metadata.pprint(max_depth=-1)
 
     print(40*'*')
 
     stim_tag = g.multi_tags[0]
     stim_type = g.multi_tags[0].name
-    stim_pos = stim_tag.positions[:] # beginnings of stimulations
-    stim_extent = stim_tag.extents[:] # duration of stimulations
-    # for r in rtag_data.references:
-    #     print(r.name, r.type)
+    stim_pos = stim_tag.positions[:]    # beginnings of stimuli
+    stim_extent = stim_tag.extents[:]   # duration of stimuli
 
     voltage_trace = rtag_data.references['V-1']
 
@@ -56,15 +47,16 @@ def analyze_sams(filename):
         stim_y = [np.max(voltage_trace)+10, np.max(voltage_trace)+10]
         plt.plot([stim_pos[i], stim_pos[i] + stim_extent[i]], stim_y, 'k')
 
+    os.chdir('/home/lisa/Dropbox/Masterarbeit/')
     for j in range(len(spike_data)):
         for k in range(len(spike_data[j])):
-            plt.plot(spike_data[j][k], np.max(voltage_trace)+5, 'o', color='k')
-        plt.xlim([stim_pos[0]-1, stim_extent[-1]+1])
-        plt.title(filename[-26:-13] + ': ' + stim_type)
-        # plt.show()
+            plt.plot(spike_data[j][k], np.max(voltage_trace)+2.5, 'o', color='k')
+        plt.xlim([stim_pos[0]-.5, stim_pos[0] + stim_extent[-1] + .5])
+        plt.xlabel('time [s]')
+        plt.ylabel('voltage [mV]')
+        plt.show()
         print('saving...')
-        os.chdir('/home/lisa/Dropbox/Masterarbeit/')
-        plt.savefig(filename[-26:-13] + ': ' + stim_type + '.png')
+        plt.savefig(filename + stim_type + '.png')
         plt.close()
         # f.close()
         # embed()
@@ -73,13 +65,61 @@ def analyze_sams(filename):
         return
 
 
+def analyze_fis(filename):
+    f = nix.File.open(filename, nix.FileMode.ReadOnly)
+    b = f.blocks[0]
+    b.metadata.pprint(max_depth=-1)     # max_depth=-1: alles rausschreiben
+    for g in b.groups:
+        # if 'FICurve' in g.name:     # go through loop, until 'sam' is found
+        #     break
+
+        rtag_data = g.tags[0]   # rtag_data = tags within this group
+        rtag_data.metadata.pprint(max_depth=-1)
+
+        print(40*'*')
+
+        stim_tag = g.multi_tags[0]
+        stim_type = g.multi_tags[0].name
+        stim_pos = stim_tag.positions[:]    # beginnings of stimuli
+        stim_extent = stim_tag.extents[:]   # duration of stimuli
+
+        voltage_trace = rtag_data.references['V-1']
+        for i in range(len(voltage_trace)):
+            print(voltage_trace[i])
+        spike_data = []
+
+        for idx in range(len(stim_pos)):
+            spike_data.append(stim_tag.retrieve_data(idx, 'Spikes-1')[:])
+
+
+        # embed()
+        # exit()
+        dims = voltage_trace.dimensions[0].axis(len(voltage_trace))
+
+        for i in range(len(stim_pos)):
+            plt.plot(dims[:-1], voltage_trace[:-1], color='#3673A4')
+            stim_y = [np.max(voltage_trace)+1.25, np.max(voltage_trace)+1.25]
+            plt.plot([stim_pos[i], stim_pos[i] + stim_extent[i]], stim_y, 'k')
+            xmin = stim_pos[i] - .1
+            xmax = stim_pos[i] + stim_extent[i] + .1
+            y = np.ones(len(spike_data[i]))*(np.max(voltage_trace)+2.5)
+            plt.plot(spike_data[i], y, 'o', color='#DC143C')
+            plt.xlim(xmin, xmax)
+            plt.xlabel('time [s]')
+            plt.ylabel('voltage [mV]')
+            plt.show()
+
+        return
+
 if __name__ == '__main__':
     data_dir = '/home/lisa/data/'
     os.chdir(data_dir)
-    data_set = glob.glob('2019-*')
+    data_set = glob.glob('2019*')    # only look at single cells
+    # data_set = '2019*'    # only look at single cells
     for i in range(len(data_set)):
-        # data_set = '2019-08-22-aa-invivo-1'
-        print(data_dir + data_set[i] + '/' + data_set[i])
+        if '08' in data_set[i]:
+            continue
+    # print(data_dir + data_set + '/' + data_set)
         analyze_sams(data_dir + data_set[i] + '/' + data_set[i] + '.nix')
-        # analyze_sams(data_dir + data_set + '/' + data_set + '.nix')
+
 

frame_analysis.py

@@ -0,0 +1,32 @@
+import numpy as np
+import matplotlib.pyplot as plt
+import glob
+import os
+import NixFrame as nf
+from IPython import embed
+
+if __name__ == '__main__':
+    data_dir = '/home/lisa/data/'
+    os.chdir(data_dir)
+    data_sets = glob.glob('2019-*')
+    for data_set in data_sets:
+        print(data_set)
+        df = nf.load_data(data_dir + data_set + '/' + data_set + '_dataframe.pickle')
+        embed()
+        exit()
+        for i in range(len(df)):
+
+            stim_type = df['tag_meta']['RePro'][i]
+            smpl_rt = df['samplingrate'][i]
+            # t = df['time'][i+1]
+            voltage = df['V-1'][i]
+            t = np.arange(0, len(voltage)*1./smpl_rt, 1./smpl_rt)
+            print(len(t))
+            spiketimes = df['Spikes-1'][i]
+            stim_onset = df['onset_times'][i]
+            stim_dur = df['durations'][i]
+
+            plt.plot(t, voltage, color='#BA2D22')
+            plt.plot([stim_onset, stim_dur], [np.max(voltage)+0.75, np.max(voltage)+0.75], color='#53379B')
+            plt.plot([spiketimes, spiketimes], [np.max(voltage)+1, np.max(voltage)+1.5], color='black')
+            plt.show()

frame_maker.py

@@ -0,0 +1,8 @@
+from IPython import embed
+import NixFrame as nf
+
+folder = '../../data'
+
+df = nf.NixToFrame(folder, before=0.0, after=0.0, skipold=True, saveto=None, mindepth=0)
+
+print(df)