Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/jgrewe/gp_neurobio
This commit is contained in:
commit
d257383613
188
code/NixFrame.py
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188
code/NixFrame.py
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@ -0,0 +1,188 @@
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import nixio as nix
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from IPython import embed
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import numpy as np
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import os
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import pandas as pd
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import pickle
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def DataFrame(nixfile, savefile=False, saveto='./'):
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'''
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opens a nix file, extracts the data and converts it to a pandas.DataFrame
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:param nixfile (string): path and name of .nix file
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:param savefile (string): if not False, the dataframe will be saved as <savefile>.pickle
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:param saveto (string): path to save the files in NOT IMPLEMENTED YET
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:return dataframe (pandas.DataFrame): pandas.DataFrame with available nix data
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'''
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block = nix.File.open(nixfile,'r').blocks[0]
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data_arrays = block.data_arrays
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names = [data_arrays[i].name for i in range(len(data_arrays))]
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shapes = [x.shape for x in data_arrays]
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data_names = np.array([[x,i] for i,x in enumerate(names) if (shapes[i][0] >= 0.999*shapes[0][0])])
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data_traces = np.array([data_arrays[name][:] for name,idx in data_names])
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time = data_arrays[1].dimensions[0].axis(data_arrays[1].shape[0])
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dt = time[1]-time[0]
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block_metadata = {}
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block_metadata[block.id] = getMetadataDict(block.metadata)
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tag = block.tags
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tag_metadata = {}
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tag_id_times = {}
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for i in range(len(tag)):
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meta = tag[i].metadata
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tag_metadata[meta.id] = getMetadataDict(meta)
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tag_id_times[meta.id] = [tag[i].position[0], tag[i].position[0]+tag[i].extent[0]]
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data = []
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stim_num = -1
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protocol_idcs = np.where([' onset times' in name for name in names])[0]
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for i in range(len(protocol_idcs)):
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# print(names[int(protocol_idcs[i])].split(' onset times')[0])
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protocol = names[protocol_idcs[i]].split(' onset times')[0]
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#skip certain protocols
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if 'VC=' in protocol:
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# print('skip this protocol')
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continue
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#number of meta data entries
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if i == len(protocol_idcs)-1:
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meta_len = len(names) - protocol_idcs[i]
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else:
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meta_len = protocol_idcs[i+1] - protocol_idcs[i]
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#get new line for every sweep and save the data, make a pn subtraction if necessary
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if any([protocol + '_pn' == string for string in names[protocol_idcs[i]:protocol_idcs[i]+meta_len]]):
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pn = data_arrays[protocol + '_pn'][0]
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sweeps = np.arange(np.abs(pn),len(data_arrays[int(protocol_idcs[i])][:]),(np.abs(pn)+1), dtype=int)
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else:
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pn = np.nan
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sweeps = np.arange(len(data_arrays[int(protocol_idcs[i])][:]), dtype=int)
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for sweep in sweeps:
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stim_num +=1
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data.append({})
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# save protocol names
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split_vec = protocol.split('-')
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if len(split_vec)>2:
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prot_name = split_vec[0]
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prot_num = int(split_vec[-1])
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for j in range(len(split_vec)-2):
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prot_name += '-' + split_vec[j+1]
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else:
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prot_name = split_vec[0]
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prot_num = split_vec[-1]
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data[stim_num]['protocol'] = prot_name
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data[stim_num]['protocol_number'] = prot_num
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#save id
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data[stim_num]['id'] = data_arrays[int(protocol_idcs[i])].id
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#save rest of stored data
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for idx in range(meta_len):
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j = int(protocol_idcs[i] + idx)
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if (' durations' in names[j]) or (' onset times' in names[j]):
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continue
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if len(data_arrays[j][sweep]) == 1:
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data[stim_num][names[j].split(protocol + '_')[-1]] = data_arrays[j][sweep][0]
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else:
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data[stim_num][names[j].split(protocol+'_')[-1]] = data_arrays[j][sweep]
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data[stim_num]['samplingrate'] = 1/dt
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#save data arrays
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onset = data_arrays[protocol + ' onset times'][sweep]
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dur = data_arrays[protocol + ' durations'][sweep]
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t0 = int(onset/dt)
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t1 = int((onset+dur)/dt+1)
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data[stim_num]['onset time'] = onset
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data[stim_num]['duration'] = dur
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for name,idx in data_names:
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data[stim_num][name] = data_traces[int(idx)][t0:t1]
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for j in np.arange(int(idx)+1,protocol_idcs[0]):
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bool_vec = (data_arrays[names[j]][:]>=onset) & (data_arrays[names[j]][:]<=onset+dur)
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data[stim_num][names[j]] = np.array(data_arrays[names[j]])[bool_vec]
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data[stim_num]['time'] = time[t0:t1] - data[stim_num]['onset time']
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#pn-subtraction (if necessary)
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'''
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change the location of the pn (its already in the metadata, you dont need it as option
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'''
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if pn != np.nan and np.abs(pn)>0:
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pn_curr = np.zeros(len(data[stim_num][name]))
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idx = np.where(data_names[:,0] == 'Current-1')[0][0]
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for j in range(int(np.abs(pn))):
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onset = data_arrays[protocol + ' onset times'][sweep-j-1]
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t0 = int(onset / dt)
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t1 = int(onset/dt + len(data[stim_num]['Current-1']))
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pn_curr += data_traces[int(idx),t0:t1]
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data[stim_num]['Current-2'] = data[stim_num]['Current-1'] - pn/np.abs(pn)*pn_curr #- data[stim_num][name][0] - pn_curr[0]
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'''
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this one saves the complete metadata in EVERY line
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!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!THINK OF SOMETHING BETTER!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
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'''
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tag_id = None
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for key in tag_id_times.keys():
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if (data[stim_num]['onset time'] >= tag_id_times[key][0]) and (data[stim_num]['onset time'] <= tag_id_times[key][1]):
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tag_id = key
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# # save metadata
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data[stim_num]['block_meta'] = block_metadata[list(block_metadata.keys())[0]]
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data[stim_num]['tag_meta'] = tag_metadata[tag_id]
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# add block id
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data[stim_num]['block_id'] = list(block_metadata.keys())[0]
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data[stim_num]['tag_id'] = tag_id
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data = pd.DataFrame(data)
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if savefile != False:
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if savefile == True:
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savefile = nixfile.split('/')[-1].split('.nix')[0]
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with open(savefile + '_dataframe.pickle', 'wb') as f:
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pickle.dump(data, f, -1) # create pickle-files, using the highest pickle-protocol
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# embed()
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return data
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def NixToFrame(folder):
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'''
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searches subfolders of folder to convert .nix files to a pandas dataframe and saves them in the folder
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:param folder: path to folder that contains subfolders of year-month-day-aa style that contain .nix files
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'''
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if folder[-1] != '/':
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folder = folder + '/'
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dirlist = os.listdir(folder)
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for dir in dirlist:
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if os.path.isdir(folder + dir):
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for file in os.listdir(folder+dir):
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if '.nix' in file:
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print(file)
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DataFrame(folder+dir+'/'+file, True, folder)
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def load_data(filename):
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with open(filename, 'rb') as f:
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data = pickle.load(f) # load data with pickle
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return data
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def getMetadataDict(metadata):
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def unpackMetadata(sec):
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metadata = dict()
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metadata = {prop.name: sec[prop.name] for prop in sec.props}
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if hasattr(sec, 'sections') and len(sec.sections) > 0:
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metadata.update({subsec.name: unpackMetadata(subsec) for subsec in sec.sections})
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return metadata
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return unpackMetadata(metadata)
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@ -1,60 +1,94 @@
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import numpy as np
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import matplotlib.pyplot as plt
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from read_baseline_data import *
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from NixFrame import *
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from utility import *
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from IPython import embed
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# plot and data values
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inch_factor = 2.54
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data_dir = '../data'
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dataset = '2018-11-09-ad-invivo-1'
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# read eod and time of baseline
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time, eod = read_baseline_eod(os.path.join(data_dir, dataset))
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fig = plt.figure(figsize=(12/inch_factor, 8/inch_factor))
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ax = fig.add_subplot(111)
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fig, ax = plt.subplots(figsize=(12/inch_factor, 8/inch_factor))
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ax.plot(time[:1000], eod[:1000])
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ax.set_xlabel('time [ms]', fontsize=12)
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ax.set_ylabel('voltage [mV]', fontsize=12)
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plt.xticks(fontsize = 8)
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plt.yticks(fontsize = 8)
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plt.xticks(fontsize=8)
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plt.yticks(fontsize=8)
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fig.tight_layout()
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plt.savefig('eod.pdf')
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#interspikeintervalhistogram, windowsize = 1 ms
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#plt.hist
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#coefficient of variation
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#embed()
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#exit()
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#plt.savefig('eod.pdf')
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plt.show()
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# read spikes during baseline activity
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spikes = read_baseline_spikes(os.path.join(data_dir, dataset))
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# calculate interpike intervals and plot them
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interspikeintervals = np.diff(spikes)
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fig = plt.figure()
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fig, ax = plt.subplots(figsize=(12/inch_factor, 8/inch_factor))
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plt.hist(interspikeintervals, bins=np.arange(0, np.max(interspikeintervals), 0.0001))
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plt.show()
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# calculate coefficient of variation
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mu = np.mean(interspikeintervals)
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sigma = np.std(interspikeintervals)
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cv = sigma/mu
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print(cv)
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# calculate zero crossings of the eod
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# plot mean of eod circles
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# plot std of eod circles
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# plot psth into the same plot
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# calculate vector strength
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threshold = 0;
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# calculate eod times and indices by zero crossings
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threshold = 0
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shift_eod = np.roll(eod, 1)
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eod_times = time[(eod >= threshold) & (shift_eod < threshold)]
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sampling_rate = 40000.0
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eod_idx = eod_times*sampling_rate
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fig = plt.figure()
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for i, idx in enumerate(eod_idx):
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#embed()
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#exit()
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plt.plot(time[int(idx):int(eod_idx[i+1])], eod[int(idx):int(eod_idx[i+1])])
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# align eods and spikes to eods
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max_cut = int(np.max(np.diff(eod_idx)))
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eod_cuts = np.zeros([len(eod_idx)-1, max_cut])
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spike_times = []
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eod_durations = []
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for i, idx in enumerate(eod_idx[:-1]):
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eod_cut = eod[int(idx):int(eod_idx[i+1])]
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eod_cuts[i, :len(eod_cut)] = eod_cut
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eod_cuts[i, len(eod_cut):] = np.nan
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time_cut = time[int(idx):int(eod_idx[i+1])]
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spike_cut = spikes[(spikes > time_cut[0]) & (spikes < time_cut[-1])]
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spike_time = spike_cut - time_cut[0]
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if len(spike_time) > 0:
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spike_times.append(spike_time[:][0]*1000)
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eod_durations.append(len(eod_cut)/sampling_rate*1000)
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# calculate vector strength
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vs = vector_strength(spike_times, eod_durations)
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# determine means and stds of eod for plot
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# determine time axis
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mu_eod = np.nanmean(eod_cuts, axis=0)
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std_eod = np.nanstd(eod_cuts, axis=0)*3
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time_axis = np.arange(max_cut)/sampling_rate*1000
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# plot eod form and spike histogram
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fig, ax1 = plt.subplots(figsize=(12/inch_factor, 8/inch_factor))
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ax1.hist(spike_times, color='crimson')
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ax1.set_xlabel('time [ms]', fontsize=12)
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ax1.set_ylabel('number', fontsize=12)
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ax1.tick_params(axis='y', labelcolor='crimson')
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plt.yticks(fontsize=8)
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ax1.spines['top'].set_visible(False)
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ax2 = ax1.twinx()
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ax2.fill_between(time_axis, mu_eod+std_eod, mu_eod-std_eod, color='dodgerblue', alpha=0.5)
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ax2.plot(time_axis, mu_eod, color='black', lw=2)
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ax2.set_ylabel('voltage [mV]', fontsize=12)
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ax2.tick_params(axis='y', labelcolor='dodgerblue')
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plt.xticks(fontsize=8)
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plt.yticks(fontsize=8)
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fig.tight_layout()
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plt.show()
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#NixToFrame(data_dir)
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75
code/base_chirps.py
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75
code/base_chirps.py
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@ -0,0 +1,75 @@
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from read_chirp_data import *
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#import nix_helpers as nh
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import matplotlib.pyplot as plt
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import numpy as np
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from IPython import embed
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data_dir = "../data"
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dataset = "2018-11-09-ad-invivo-1"
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data = ("2018-11-09-ad-invivo-1", "2018-11-09-ae-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ac-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-ah-invivo-1", "2018-11-14-ai-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1")
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#for dataset in data:
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eod = read_chirp_eod(os.path.join(data_dir, dataset))
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times = read_chirp_times(os.path.join(data_dir, dataset))
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df_map = {} #Keys werden nach df sortiert ausgegeben
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for k in eod.keys():
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df = k[1]
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ch = k[3]
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if df in df_map.keys():
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df_map[df].append(k)
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else:
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df_map[df] = [k]
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||||
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||||
print(ch) #die Chirphöhe wird ausgegeben, um zu bestimmen, ob Chirps oder Chirps large benutzt wurde
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||||
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||||
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||||
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||||
#die äußere Schleife geht für alle Keys durch und somit durch alle dfs
|
||||
#die innnere Schleife bildet die 16 Wiederholungen einer Frequenz in 4 Subplots ab
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for idx in df_map.keys():
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||||
freq = list(df_map[idx])
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||||
fig,axs = plt.subplots(2, 2, sharex = True, sharey = True)
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||||
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for idx, k in enumerate(freq):
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ct = times[k]
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||||
e1 = eod[k]
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||||
zeit = e1[0]
|
||||
eods = e1[1]
|
||||
|
||||
if idx <= 3:
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axs[0, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
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||||
axs[0, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
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elif 4<= idx <= 7:
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axs[0, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
|
||||
axs[0, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
|
||||
elif 8<= idx <= 11:
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||||
axs[1, 0].plot(zeit, eods, color= 'blue', linewidth = 0.25)
|
||||
axs[1, 0].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
|
||||
else:
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||||
axs[1, 1].plot(zeit, eods, color= 'blue', linewidth = 0.25)
|
||||
axs[1, 1].scatter(np.asarray(ct), np.ones(len(ct))*3, color = 'green', s= 22)
|
||||
|
||||
|
||||
fig.suptitle('EOD for chirps', fontsize = 16)
|
||||
plt.show()
|
||||
|
||||
|
||||
|
||||
#Problem: axs hat keine label-Funktion, also müsste axes nochmal definiert werden. Momentan erscheint Schrift nur auf einem der Subplots
|
||||
|
||||
#ax = plt.gca()
|
||||
#ax.set_ylabel('Time [ms]')
|
||||
#ax.set_xlabel('Amplitude [mV]')
|
||||
#ax.label_outer()
|
||||
|
||||
|
||||
|
||||
|
||||
#next Step: relative Amplitudenmodulation berechnen, Max und Min der Amplitude bestimmen, EOD und Chirps zuordnen, Unterschied berechnen
|
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21
code/base_eod.py
Normal file
21
code/base_eod.py
Normal file
@ -0,0 +1,21 @@
|
||||
from read_baseline_data import *
|
||||
#import nix_helpers as nh
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from IPython import embed #Funktionen importieren
|
||||
|
||||
|
||||
data_dir = "../data"
|
||||
dataset = "2018-11-09-aa-invivo-1"
|
||||
#data = ("2018-11-09-aa-invivo-1", "2018-11-09-ab-invivo-1", "2018-11-09-ac-invivo-1", "2018-11-09-ad-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ab-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-09-af-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-ab-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-ae-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-aj-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1")
|
||||
time,eod = read_baseline_eod(os.path.join(data_dir, dataset))
|
||||
zeit = np.asarray(time)
|
||||
|
||||
|
||||
plt.plot(zeit[0:1000], eod[0:1000])
|
||||
plt.title('A.lepto EOD')#Plottitelk
|
||||
plt.xlabel('Time [ms]', fontsize = 12)#Achsentitel
|
||||
plt.ylabel('Amplitude[mv]', fontsize = 12)#Achsentitel
|
||||
plt.xticks(fontsize = 12)
|
||||
plt.yticks(fontsize = 12)
|
||||
plt.show()
|
||||
34
code/base_spikes.py
Normal file
34
code/base_spikes.py
Normal file
@ -0,0 +1,34 @@
|
||||
from read_baseline_data import *
|
||||
#import nix_helpers as nh
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from IPython import embed #Funktionen importieren
|
||||
|
||||
data_dir = "../data"
|
||||
dataset = "2018-11-09-aa-invivo-1"
|
||||
#data = ("2018-11-09-aa-invivo-1", "2018-11-09-ab-invivo-1", "2018-11-09-ac-invivo-1", "2018-11-09-ad-invivo-1", "2018-11-13-aa-invivo-1", "2018-11-13-ab-invivo-1", "2018-11-13-ad-invivo-1", "2018-11-09-af-invivo-1", "2018-11-09-ag-invivo-1", "2018-11-13-ah-invivo-1", "2018-11-13-ai-invivo-1", "2018-11-13-aj-invivo-1", "2018-11-13-ak-invivo-1", "2018-11-13-al-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-ab-invivo-1", "2018-11-14-ac-invivo-1", "2018-11-14-ad-invivo-1", "2018-11-14-ae-invivo-1", "2018-11-14-af-invivo-1", "2018-11-14-ag-invivo-1", "2018-11-14-aa-invivo-1", "2018-11-14-aj-invivo-1", "2018-11-14-ak-invivo-1", "2018-11-14-al-invivo-1", "2018-11-14-am-invivo-1", "2018-11-14-an-invivo-1")
|
||||
spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
|
||||
|
||||
|
||||
#spike_frequency = len(spike_times) / spike_times[-1]
|
||||
#inst_frequency = 1. / np.diff(spike_times)
|
||||
spike_rate = np.diff(spike_times)
|
||||
|
||||
|
||||
x = np.arange(0.001, 0.01, 0.0001)
|
||||
plt.hist(spike_rate,x)
|
||||
|
||||
mu = np.mean(spike_rate)
|
||||
sigma = np.std(spike_rate)
|
||||
cv = sigma/mu
|
||||
print(cv)
|
||||
|
||||
plt.title('A.lepto ISI Histogramm', fontsize = 14)
|
||||
plt.xlabel('duration ISI[ms]', fontsize = 12)
|
||||
plt.ylabel('number of ISI', fontsize = 12)
|
||||
|
||||
plt.xticks(fontsize = 12)
|
||||
plt.yticks(fontsize = 12)
|
||||
plt.show()
|
||||
|
||||
|
||||
@ -1,33 +0,0 @@
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
freq = 800
|
||||
freq2 = 820
|
||||
dt = 0.00001
|
||||
x = np.arange(0.0, 1.0, dt)
|
||||
eod = np.sin(x * 2 * np.pi * freq) + np.sin(x * 2 * np.pi * freq * 2) * 0.5
|
||||
eod2 = np.sin(x * 2 * np.pi * freq2) + np.sin(x * 2 * np.pi * freq2 * 2) * 0.5
|
||||
|
||||
fig = plt.figure(figsize=(5., 7.5))
|
||||
ax= fig.add_subplot(311)
|
||||
ax.plot(x, eod, color="darkgreen", linewidth = 1.0)
|
||||
ax.set_xlim(0.0, 0.1)
|
||||
ax.set_ylabel("voltage [mV]")
|
||||
|
||||
|
||||
ax= fig.add_subplot(312)
|
||||
ax.plot(x, eod2, color="crimson", linewidth = 1.0)
|
||||
ax.set_xlim(0.0, 0.1)
|
||||
ax.set_ylabel("voltage [mV]")
|
||||
|
||||
ax= fig.add_subplot(313)
|
||||
ax.plot(x, eod + eod2 * 0.05, color="lightblue", linewidth = 1.0)
|
||||
ax.set_xlim(0.0, 0.1)
|
||||
ax.set_xlabel("time [s]")
|
||||
ax.set_ylabel("voltage [mV]")
|
||||
|
||||
plt.tight_layout()
|
||||
plt.savefig("eods.pdf")
|
||||
plt.show()
|
||||
|
||||
|
||||
43
code/liste.py
Normal file
43
code/liste.py
Normal file
@ -0,0 +1,43 @@
|
||||
# 9.11.18
|
||||
|
||||
aa: quality: poor, depth: -1341, base
|
||||
ab: quality: poor, depth: -1341, base
|
||||
ac: quality: good, depth: -1341, base
|
||||
ad: quality: good, depth: -200, base, chirps
|
||||
ae: quality: good, depth: -200, chirps
|
||||
af: quality: good, depth: -200
|
||||
ag: no info.dat, chirps
|
||||
|
||||
|
||||
# 13.11.18
|
||||
|
||||
aa: good, -30 µm, maybe no reaction, base, chirps
|
||||
ab: good, -309 µm, base
|
||||
ac: poor, -309 µm, chirps
|
||||
ad: fair, -360 µm, base, chirps
|
||||
ae: fair, -350 µm
|
||||
af: good, -440 µm, bursting, base
|
||||
ag: fair, -174 µm, base
|
||||
ah: good, -209 µm, base, chirps, FI, SAM
|
||||
ai: good, -66.9 µm, base, chirps, SAM
|
||||
aj: good, -132 µm, base, chirps
|
||||
ak: good, -284 µm, base, chirps
|
||||
al: good, -286 µm, base, chirps, SAM
|
||||
|
||||
|
||||
# 14.11.18
|
||||
|
||||
aa: good, -184 µm, base, chirps, FI, SAM, noise
|
||||
ab: fair, -279 µm, no reaction, base
|
||||
ac: fair, -60 µm, base, chirps
|
||||
ad: good, -357 µm, base, chirps
|
||||
ae: fair, -357 µm, base
|
||||
af: fair, -527 µm, base, (chirps)
|
||||
ag: fair, -533 µm, base, chirps
|
||||
ah: poor, -505 µm, chirps
|
||||
ai: good, -500 µm, still same cell 3x, chirps, FI, noise
|
||||
aj: poor, -314 µm, no modulation, base
|
||||
ak: good, -140 µm, base, chirps, FI, SAM, noise
|
||||
al: good, -280 µm, base, chirps, SAM
|
||||
am: good, -320 µm, base, chirps, FI, SAM, noise
|
||||
an: good, -434 µm, base, chirps, FI, SAM, noise
|
||||
@ -7,7 +7,13 @@ def read_baseline_eod(dataset):
|
||||
base = dataset.split(os.path.sep)[-1] + ".nix"
|
||||
nix_file = nix.File.open(os.path.join(dataset, base), nix.FileMode.ReadOnly)
|
||||
b = nix_file.blocks[0]
|
||||
t = b.tags["BaselineActivity_1"]
|
||||
if 'BaselineActivity_1' in b.tags:
|
||||
t = b.tags["BaselineActivity_1"]
|
||||
elif "BaselineActivity_2" in b.tags:
|
||||
t = b.tags["BaselineActivity_2"]
|
||||
else:
|
||||
f.close()
|
||||
return [],[]
|
||||
eod_da = b.data_arrays["LocalEOD-1"]
|
||||
eod = t.retrieve_data("LocalEOD-1")[:]
|
||||
time = np.asarray(eod_da.dimensions[0].axis(len(eod)))
|
||||
@ -19,7 +25,13 @@ def read_baseline_spikes(dataset):
|
||||
base = dataset.split(os.path.sep)[-1] + ".nix"
|
||||
nix_file = nix.File.open(os.path.join(dataset, base), nix.FileMode.ReadOnly)
|
||||
b = nix_file.blocks[0]
|
||||
t = b.tags["BaselineActivity_1"]
|
||||
if 'BaselineActivity_1' in b.tags:
|
||||
t = b.tags["BaselineActivity_1"]
|
||||
elif "BaselineActivity_2" in b.tags:
|
||||
t = b.tags["BaselineActivity_2"]
|
||||
else:
|
||||
f.close()
|
||||
return [],[]
|
||||
spikes_da = b.data_arrays["Spikes-1"]
|
||||
spike_times = spikes_da[:spikes_da.shape[0]-5000]
|
||||
baseline_spikes = spike_times[(spike_times > t.position[0]) & (spike_times < (t.position[0] + t.extent[0]))]
|
||||
|
||||
@ -2,7 +2,7 @@ import numpy as np
|
||||
import os
|
||||
|
||||
|
||||
def load_chirp_spikes(dataset):
|
||||
def read_chirp_spikes(dataset):
|
||||
spikes_file = os.path.join(dataset, "chirpspikess1.dat")
|
||||
if not os.path.exists(spikes_file):
|
||||
print("found no chirps!")
|
||||
@ -15,11 +15,11 @@ def load_chirp_spikes(dataset):
|
||||
if "index" in l and "chirp" not in l:
|
||||
index = int(l.split(":")[-1])
|
||||
if "deltaf" in l and "true" not in l:
|
||||
df = l.split(":")[-1]
|
||||
df = l.split(":")[-1].strip()
|
||||
if "contrast" in l and "true" not in l:
|
||||
contrast = l.split(":")[-1]
|
||||
contrast = l.split(":")[-1].strip()
|
||||
if "chirpsize" in l:
|
||||
cs = l.split(":")[-1]
|
||||
cs = l.split(":")[-1].strip()
|
||||
if "#Key" in l:
|
||||
spikes[(index, df, contrast, cs)] = {}
|
||||
if "chirp index" in l:
|
||||
@ -32,7 +32,7 @@ def load_chirp_spikes(dataset):
|
||||
return spikes
|
||||
|
||||
|
||||
def load_chirp_eod(dataset):
|
||||
def read_chirp_eod(dataset):
|
||||
eod_file = os.path.join(dataset, "chirpeodampls.dat")
|
||||
if not os.path.exists(eod_file):
|
||||
print("found no chirpeodampls.dat!")
|
||||
@ -45,11 +45,11 @@ def load_chirp_eod(dataset):
|
||||
if "index" in l and "chirp" not in l:
|
||||
index = int(l.split(":")[-1])
|
||||
if "deltaf" in l and "true" not in l:
|
||||
df = l.split(":")[-1]
|
||||
df = l.split(":")[-1].strip()
|
||||
if "contrast" in l and "true" not in l:
|
||||
contrast = l.split(":")[-1]
|
||||
contrast = l.split(":")[-1].strip()
|
||||
if "chirpsize" in l:
|
||||
cs = l.split(":")[-1]
|
||||
cs = l.split(":")[-1].strip()
|
||||
if "#Key" in l:
|
||||
chirp_eod[(index, df, contrast, cs)] = ([], [])
|
||||
if len(l.strip()) != 0 and "#" not in l:
|
||||
@ -60,7 +60,7 @@ def load_chirp_eod(dataset):
|
||||
return chirp_eod
|
||||
|
||||
|
||||
def load_chirp_times(dataset):
|
||||
def read_chirp_times(dataset):
|
||||
chirp_times_file = os.path.join(dataset, "chirpss.dat")
|
||||
if not os.path.exists(chirp_times_file):
|
||||
print("found no chirpss.dat!")
|
||||
@ -73,15 +73,15 @@ def load_chirp_times(dataset):
|
||||
if "index" in l and "chirp" not in l:
|
||||
index = int(l.split(":")[-1])
|
||||
if "deltaf" in l and "true" not in l:
|
||||
df = l.split(":")[-1]
|
||||
df = l.split(":")[-1].strip()
|
||||
if "contrast" in l and "true" not in l:
|
||||
contrast = l.split(":")[-1]
|
||||
contrast = l.split(":")[-1].strip()
|
||||
if "chirpsize" in l:
|
||||
cs = l.split(":")[-1]
|
||||
cs = l.split(":")[-1].strip()
|
||||
if "#Key" in l:
|
||||
chirp_times[(index, df, contrast, cs)] = []
|
||||
if len(l.strip()) != 0 and "#" not in l:
|
||||
chirp_times[(index, df, contrast, cs)].append(float(l.split()[1]))
|
||||
chirp_times[(index, df, contrast, cs)].append(float(l.split()[1]) * 1000.)
|
||||
return chirp_times
|
||||
|
||||
|
||||
|
||||
51
code/spikes_analysis.py
Normal file
51
code/spikes_analysis.py
Normal file
@ -0,0 +1,51 @@
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from read_chirp_data import *
|
||||
from utility import *
|
||||
from IPython import embed
|
||||
|
||||
data_dir = "../data"
|
||||
dataset = "2018-11-09-ad-invivo-1"
|
||||
|
||||
spikes = read_chirp_spikes(os.path.join(data_dir, dataset))
|
||||
eod = read_chirp_eod(os.path.join(data_dir, dataset))
|
||||
times = read_chirp_times(os.path.join(data_dir, dataset))
|
||||
|
||||
df_map = {}
|
||||
for k in spikes.keys():
|
||||
df = k[1]
|
||||
if df in df_map.keys():
|
||||
df_map[df].append(k)
|
||||
else:
|
||||
df_map[df] = [k]
|
||||
|
||||
# make phases together, 12 phases
|
||||
spikes_mat = {}
|
||||
for deltaf in df_map.keys():
|
||||
for rep in df_map[deltaf]:
|
||||
for phase in spikes[rep]:
|
||||
#print(phase)
|
||||
spikes_one_chirp = spikes[rep][phase]
|
||||
if deltaf == '-50Hz' and phase == (9, 0.54):
|
||||
spikes_mat[deltaf, rep, phase] = spikes_one_chirp
|
||||
|
||||
plot_spikes = spikes[(0, '-50Hz', '20%', '100Hz')][(0, 0.789)]
|
||||
|
||||
mu = 1
|
||||
sigma = 1
|
||||
time_gauss = np.arange(-4, 4, 1)
|
||||
gauss = gaussian(time_gauss, mu, sigma)
|
||||
# spikes during time vec (00010000001)?
|
||||
smoothed_spikes = np.convolve(plot_spikes, gauss, 'same')
|
||||
window = np.mean(np.diff(plot_spikes))
|
||||
time_vec = np.arange(plot_spikes[0], plot_spikes[-1]+window, window)
|
||||
|
||||
fig, ax = plt.subplots()
|
||||
ax.scatter(plot_spikes, np.ones(len(plot_spikes))*10, marker='|', color='k')
|
||||
ax.plot(time_vec, smoothed_spikes)
|
||||
plt.show()
|
||||
|
||||
#embed()
|
||||
#exit()
|
||||
#hist_data = plt.hist(plot_spikes, bins=np.arange(-200, 400, 20))
|
||||
#ax.plot(hist_data[1][:-1], hist_data[0])
|
||||
@ -1,8 +1,8 @@
|
||||
import numpy as np
|
||||
|
||||
|
||||
def zero_crossing(eod,time):
|
||||
threshold = 0;
|
||||
def zero_crossing(eod, time):
|
||||
threshold = 0
|
||||
shift_eod = np.roll(eod, 1)
|
||||
eod_times = time[(eod >= threshold) & (shift_eod < threshold)]
|
||||
sampling_rate = 40000.0
|
||||
@ -10,9 +10,16 @@ def zero_crossing(eod,time):
|
||||
return eod_idx
|
||||
|
||||
|
||||
def vector_strength(spike_times, eod_durations)
|
||||
alphas = spike_times/ eod_durations
|
||||
cs = (1/len(spike_times))*np.sum(np.cos(alphas))^2
|
||||
sn = (1/len(spike_times))*np.sum(np.sin(alphas))^2
|
||||
vs = np.sprt(cs+sn)
|
||||
def vector_strength(spike_times, eod_durations):
|
||||
n = len(spike_times)
|
||||
phase_times = np.zeros(len(spike_times))
|
||||
for i, idx in enumerate(spike_times):
|
||||
phase_times[i] = (spike_times[i] / eod_durations[i]) * 2 * np.pi
|
||||
vs = np.sqrt((1/n*sum(np.cos(phase_times)))**2 + (1/n*sum(np.sin(phase_times)))**2)
|
||||
return vs
|
||||
|
||||
|
||||
def gaussian(x, mu, sig):
|
||||
y = np.exp(-np.power(x - mu, 2.) / (2 * np.power(sig, 2.)))
|
||||
return y
|
||||
|
||||
|
||||
25
code/vector_phase.py
Normal file
25
code/vector_phase.py
Normal file
@ -0,0 +1,25 @@
|
||||
from read_baseline_data import *
|
||||
from utility import *
|
||||
#import nix_helpers as nh
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
from IPython import embed #Funktionen importieren
|
||||
|
||||
|
||||
#Zeitpunkte einer EOD über Zero-crossings finden, die in einer Steigung liegen
|
||||
data_dir = "../data"
|
||||
dataset = "2018-11-09-ad-invivo-1"
|
||||
time,eod = read_baseline_eod(os.path.join(data_dir, dataset))
|
||||
spike_times = read_baseline_spikes(os.path.join(data_dir, dataset))
|
||||
print(len(spike_times))
|
||||
|
||||
eod_times = zero_crossing(eod,time)
|
||||
eod_durations = np.diff(eod_times)
|
||||
print(len(spike_times))
|
||||
print(len(eod_durations))
|
||||
|
||||
#for st in spike_times:
|
||||
#et = eod_times[eod_times < st]
|
||||
#dt = st - et
|
||||
|
||||
#vs = vector_strength(spike_times, eod_durations)
|
||||
Loading…
Reference in New Issue
Block a user