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Ramona
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188
code/NixFrame.py
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188
code/NixFrame.py
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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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@ -48,6 +48,7 @@ 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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<<<<<<< HEAD
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#fig = plt.figure()
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eod_cuts = [];
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#for i, idx in enumerate(eod_idx)-1:
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@ -56,6 +57,18 @@ eod_cuts = [];
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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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#plt.show()
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data = NixToFrame(data_dir)
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=======
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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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plt.show()
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>>>>>>> 477fa15dc430b3d9c42ac3e40c59d67b3075c007
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embed()
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43
code/liste.py
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43
code/liste.py
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# 9.11.18
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aa: quality: poor, depth: -1341
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ab: quality: poor, depth: -1341
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ac: quality: good, depth: -1341
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ad: quality: good, depth: -200
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ae: quality: good, depth: -200
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af: quality: good, depth: -200
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ag: no info.dat
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# 13.11.18
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aa: good, -30 µm, maybe no reaction
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ab: good, -309 µm
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ac: poor, -309 µm
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ad: fair, -360 µm
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ae: fair, -350 µm
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af: good, -440 µm, bursting
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ag: fair, -174 µm
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ah: good, -209 µm
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ai: good, -66.9 µm
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aj: good, -132 µm
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ak: good, -284 µm
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al: good, -286 µm
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# 14.11.18
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aa: good, -184 µm
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ab: fair, -279 µm, no reaction
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ac: fair, -60 µm
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ad: good, -357 µm
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ae: fair, -357 µm
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af: fair, -527 µm, only baseline
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ag: fair, -533 µm
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ah: poor, -505 µm
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ai: good, -500 µm, still same cell 3x
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aj: poor, -314 µm,no modulation
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ak: good, -140 µm
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al: good, -280 µm
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am: good, -320 µm
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an: good, -434 µm
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@ -5,4 +5,14 @@ def zero_crossing(eod,time):
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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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return eod_times
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sampling_rate = 40000.0
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eod_idx = eod_times*sampling_rate
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return eod_idx
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def vector_strength(spike_times, eod_durations)
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alphas = spike_times/ eod_durations
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cs = (1/len(spike_times))*np.sum(np.cos(alphas))^2
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sn = (1/len(spike_times))*np.sum(np.sin(alphas))^2
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vs = np.sprt(cs+sn)
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return vs
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