In [1]:
%matplotlib inline
import pdb
import sys
import pandas as pd
import numpy as np
import scipy.io as sio
import matplotlib.pyplot as plt
import matplotlib
import scipy.signal as sg
import math
import scipy as sp
import socket
import os
matplotlib.style.use('ggplot')
comp_name=socket.gethostname()
if comp_name == 'Ezequiels-MacBook-Pro.local':
print 'Computer: ' + comp_name
sys.path.append('/Users/zeke/experiment/ephysDataManagement/ephysScripts')
experiment_folder = os.path.join('/Users','zeke','experiment')
else:
print 'Computer: ' + 'server'
sys.path.append('/experiment/ephysDataManagement/ephysScripts')
experiment_folder = os.path.join('/','experiment')
import unitToolsv2
from data_handling import ephys_names as en
from data_handling.basic_plot import decim, plot_raster, make_psth
from data_handling import data_load as dl
from analysis import stimulus as st
#load all the records, to not load all the records for every stimulus
fn = en.file_names(root = experiment_folder)
cells_path = os.path.join(fn.fold_exp_data, 'data_play')
all_records = dl.load_cells(cells_path)
## create a stimulus and initialize it (load its responses)
high_2hydroxy = st.Odor(['2-hydroxyacetophenone','2hydroxyacetophenone'], 0.0051)
st_1 = st.Stimulus(high_2hydroxy, records = all_records, tags = {'light':1} )
#get the first response and plot it
[r.plot() for r in list(st_1.responses.values())]
Computer: server
Computer: server
/experiment/export_data/data_play/ZKawakeM72_013_e_010_spikes.mat
/experiment/export_data/data_play/ZKawakeM72_013_f_010_spikes.mat
['ZKawakeM72_013_f_010', 'ZKawakeM72_013_e_010']
['/experiment/export_data/data_play/ZKawakeM72_013_f_trial.mat', '/experiment/export_data/data_play/ZKawakeM72_013_e_trial.mat']
/experiment/export_data/data_play/ZKawakeM72_013_f_trial.mat
/experiment/export_data/data_play/ZKawakeM72_013_e_trial.mat
['/experiment/export_data/data_play/ZKawakeM72_013_f_noStimSniff.mat', '/experiment/export_data/data_play/ZKawakeM72_013_e_noStimSniff.mat']
/experiment/export_data/data_play/ZKawakeM72_013_f_noStimSniff.mat
/experiment/export_data/data_play/ZKawakeM72_013_e_noStimSniff.mat
/experiment/export_data/data_play/ZKawakeM72_013_e_011_spikes.mat
['ZKawakeM72_013_e_011']
[]
[]
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-1-99e676652c6b> in <module>()
40 st_1 = st.Stimulus(high_2hydroxy, records = all_records, tags = {'light':1} )
41 #get the first response and plot it
---> 42 [r.plot() for r in list(st_1.responses.values())]
/experiment/ephysDataManagement/ephysScripts/analysis/stimulus.py in plot(self, t_pre, t_post, bin_size, warped)
133
134 #the raster of the response
--> 135 response = self.make_raster(t_pre=t_pre, t_post=t_post, warped=warped)
136 #get the baseline for the cell
137
/experiment/ephysDataManagement/ephysScripts/analysis/stimulus.py in make_raster(self, t_pre, t_post, warped)
189
190 all_trial_id = self.raster['trialId']
--> 191 all_spikes = self.raster['all_spikes']
192 all_sniffs = np.sort(self.baseline.sniff_data, order=['inh_len', 't_0'])
193
KeyError: 'all_spikes'
In [3]:
#just plot a sniff
plt.plot(all_records['base_sniff']['ZKawakeM72_013_f'][5]['flow'])
Out[3]:
[<matplotlib.lines.Line2D at 0x7fa538d51510>]
In [4]:
all_records['base_sniff']['ZKawakeM72_013_e']
Out[4]:
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dtype=[('flow', 'O'), ('t_0', '<i8'), ('t_zer', 'O'), ('t_zer_fit', 'O'), ('inh_len', '<i8'), ('exh_len', '<i8')])
In [7]:
## create a stimulus and initialize it (load its responses)
high_2hydroxy = st.Odor(['2-hydroxyacetophenone','2hydroxyacetophenone'], 0.0051)
st_1 = st.Stimulus(high_2hydroxy, records = all_records, tags = {'light':1} )
#get the first response and plot it
[r.plot() for r in list(st_1.responses.values())]
Out[7]:
[{'ax_stack': <matplotlib.figure.AxesStack at 0x7f9cad83da50>,
'fig': None,
'figure': <matplotlib.figure.Figure at 0x7f9cad013e10>},
{'ax_stack': <matplotlib.figure.AxesStack at 0x7f9cad758fd0>,
'fig': None,
'figure': <matplotlib.figure.Figure at 0x7f9cad95ef90>},
{'ax_stack': <matplotlib.figure.AxesStack at 0x7f9cad64c690>,
'fig': None,
'figure': <matplotlib.figure.Figure at 0x7f9cad803a90>}]
In [3]:
Out[3]:
[{'ax_stack': <matplotlib.figure.AxesStack at 0x7f163c743990>,
'fig': None,
'figure': <matplotlib.figure.Figure at 0x7f1659280510>},
{'ax_stack': <matplotlib.figure.AxesStack at 0x7f163c665850>,
'fig': None,
'figure': <matplotlib.figure.Figure at 0x7f163c768610>},
{'ax_stack': <matplotlib.figure.AxesStack at 0x7f163c422210>,
'fig': None,
'figure': <matplotlib.figure.Figure at 0x7f163c70ae10>}]
In [9]:
for property, value in vars(r).iteritems():
print property, ": "
raster :
baseline :
raster_plot :
base_sniff :
all_trials :
rec :
In [6]:
r.base_sniff.data.keys()
Out[6]:
['phase', 'start', 'trial_uid', 'flow', 'rec_id']
In [10]:
plt.plot(sniffs[10]['flow'])
sniffs[10]['t_zer_fit']
sniffs['t_zer_fit']
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-10-3dd6d8d7e3d7> in <module>()
----> 1 plt.plot(sniffs[10]['flow'])
2 sniffs[10]['t_zer_fit']
3 sniffs['t_zer_fit']
NameError: name 'sniffs' is not defined
In [9]:
sniff_data.keys()
Out[9]:
['noStimSniffs', '__version__', '__header__', '__globals__']
In [20]:
sniffs = np.ndarray((10,), dtype=np.dtype([('flow', np.ndarray), ('t_zer_fit',np.zeros([1,1]))] ))
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-20-b52c38fa27b9> in <module>()
----> 1 sniffs = np.ndarray((10,), dtype=np.dtype([('flow', np.ndarray), ('t_zer_fit',np.zeros([1,1]))] ))
/usr/local/anaconda/lib/python2.7/site-packages/numpy/core/numeric.pyc in array_repr(arr, max_line_width, precision, suppress_small)
1651 if arr.size > 0 or arr.shape==(0,):
1652 lst = array2string(arr, max_line_width, precision, suppress_small,
-> 1653 ', ', "array(")
1654 else: # show zero-length shape unless it is (0,)
1655 lst = "[], shape=%s" % (repr(arr.shape),)
/usr/local/anaconda/lib/python2.7/site-packages/numpy/core/arrayprint.pyc in array2string(a, max_line_width, precision, suppress_small, separator, prefix, style, formatter)
447 x = _convert_arrays(x)
448 lst = style(x)
--> 449 elif reduce(product, a.shape) == 0:
450 # treat as a null array if any of shape elements == 0
451 lst = "[]"
TypeError: data type not understood
In [31]:
arec = np.zeros((2,), dtype = np.dtype([('flow', np.ndarray), ('t_0',np.int)]))
In [43]:
arec[1]['flow']=np.array(50)
In [33]:
'ZKawakeM72_013_f_noStimSniff.mat'.split('_')[0:3]
Out[33]:
['ZKawakeM72', '013', 'f']
In [48]:
sent_str = ""
for i in 'ZKawakeM72_013_f_noStimSniff.mat'.split('_')[0:3]:
sent_str += str(i) + "_"
sent_str = sent_str[:-1]
print sent_str
ZKawakeM72_013_f
In [9]:
all_records['responses']['ZKawakeM72_013_e_010']['all_spikes']
Out[9]:
array([ 140, 144, 148, ..., 2853388, 2853391, 2853412])
In [17]:
mat_file_path='/Users/zeke/experiment/export_data/ZKawakeM72_013_e_011_spikes.mat'
assert(os.path.isfile(mat_file_path))
print (mat_file_path)
spike_data = sio.loadmat(mat_file_path, struct_as_record=False, squeeze_me=True)
spikes_loaded = spike_data['thisUnit']
all_spikes = np.array(spikes_loaded.times, dtype=np.float)
/Users/zeke/experiment/export_data/ZKawakeM72_013_e_011_spikes.mat
In [4]:
all_records['responses']['ZKawakeM72_013_e_010'].keys()
Out[4]:
['all_spikes', 'meta', 'rec_id', 'odor_resp', 'light_resp']
In [5]:
plt.plot(all_records['responses']['ZKawakeM72_013_e_011']['all_spikes'])
Out[5]:
[<matplotlib.lines.Line2D at 0x7f263f97bf90>]
In [39]:
mat_file_path='/experiment/export_data/ZKawakeM72_013_e_sniff.mat'
assert(os.path.isfile(mat_file_path))
print (mat_file_path)
data = sio.loadmat(mat_file_path, struct_as_record=False, squeeze_me=True)
sniff_data = np.array(data['Sniff'])
plt.plot(sniff_data)
/experiment/export_data/ZKawakeM72_013_e_sniff.mat
Out[39]:
[<matplotlib.lines.Line2D at 0x7f263f8dbb50>]
In [7]:
all_records['responses']['ZKawakeM72_013_f_010']['meta']
Out[7]:
{'comment': 'response to all weak ligands, 2 hydroxy first; stim set 2, waveform clu and responses look same as e4',
'id': 'ZKawakeM72_013_f_010',
'light': 1,
'mouse': 'ZKawakeM72',
'odor': 1,
'quality': 1,
'rec': 'f',
'sess': 13,
'sessCell': 10,
'u_id': 'ZKawakeM72_013_010'}
In [17]:
all_sniffs.dtype.names
all_sniffs[1]['t_zer_fit']
Out[17]:
array([ 147.93494044, 185.9690932 , 227.67827677, 280.54730338])
In [8]:
# make a baseline raster
all_spikes = all_records['responses']['ZKawakeM72_013_f_010']['all_spikes']
a = all_records['base_sniff']['ZKawakeM72_013_f']
all_sniffs = np.sort(a, order=['inh_len', 't_0'])
#naturally, get the t0 as the 150 ms after the start of the sniff structure (inhale)
# nxt matrix with 1 where there is a spikes.
t_pre = 0
t_post = 300
n_sniffs = all_sniffs.shape[0]
t_2 = round(np.mean([sniff['flow'][sniff['t_zer'][0]:-1].shape[0] for sniff in all_sniffs]))
t_1 = 0
#order by sniff lengths
t_range = t_2-t_1
raster = np.zeros((n_sniffs,t_range))
flows = np.zeros((t_range, n_sniffs))
i_f =0;
for flow in all_sniffs['flow']:
t_zer = all_sniffs[i_f]['t_zer'][0]
t_end = min(all_sniffs[i_f]['t_zer'][2]-all_sniffs[i_f]['t_zer'][0], t_2)
flows[0:t_end,i_f] = flow[t_zer:t_zer+t_end]
#get absolute timestamps of spikes in the corresp. sniff segment
t_inh = all_sniffs[i_f]['t_0']+t_zer
condition = (all_spikes>t_inh+t_1) & (all_spikes<t_inh+t_end)
spike_times = np.extract(condition, all_spikes) - t_inh - t_1
if spike_times.size>0:
raster[i_f, spike_times] = 1
i_f+=1
#complete periodically to fit in t_pre, t_post
if t_pre>0:
raster = np.append(raster[:,-t_pre:-1], raster, axis = 1)
if t_post > t_2:
raster = np.append(raster, raster[:,t_pre: t_post-t_2], axis = 1)
if t_post < t_2:
raster = raster[:,0:t_pre+t_post]
plt.plot((flows[0:t_range,1500:1520]));
plot_raster(raster)
plot_raster(raster, bin_size=1)
Out[8]:
([<matplotlib.lines.Line2D at 0x7f12617a0d10>],
<matplotlib.axes._axes.Axes at 0x7f1261891f10>)
In [89]:
print inh_spike_times
print spike_times
print t_mid
print t_zer
print (t_mid-t_zer)/inh_len
[ 29.1797736 32.09775096]
[100 110]
186.544076945
150
0.291797735989
In [94]:
Out[94]:
(<matplotlib.collections.PathCollection at 0x7f9ca5118d10>,
<matplotlib.axes._axes.Axes at 0x7f9c9bf8c950>)
In [16]:
plt.plot(flow[t_zer+t_1:t_zer+t_2])
Out[16]:
[<matplotlib.lines.Line2D at 0x7f263f85e3d0>]
In [29]:
t_zer = all_sniffs[i_f]['t_zer'][0]
t_end = min(all_sniffs[i_f]['t_zer'][2], t_2)
print t_end
flows[0:t_end,i_f] = flow[t_zer:t_zer+t_end]
#get absolute timestamps of spikes in the corresp. sniff segment
t_inh = all_sniffs[i_f]['t_0']+t_zer
condition = (all_spikes>t_inh+t_1) & (all_spikes<t_inh+t_end)
spike_times = np.extract(condition, all_spikes) - t_inh - t_1
if spike_times.size>0:
raster[spike_times,i_f] = 1
i_f+=1
270
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-29-daa76cd5c26d> in <module>()
3 print t_end
4
----> 5 flows[0:t_end,i_f] = flow[t_zer:t_zer+t_end]
6 #get absolute timestamps of spikes in the corresp. sniff segment
7 t_inh = all_sniffs[i_f]['t_0']+t_zer
ValueError: could not broadcast input array from shape (253) into shape (270)
In [35]:
flow[t_zer:t_zer+t_end].shape
Out[35]:
(253,)
In [12]:
all_records['responses']['ZKawakeM72_013_f_010']['odor_resp']['spikes'].shape
Out[12]:
(123, 2701)
In [37]:
min([(all_sniffs[x]['flow']).shape[0] for x in range(all_sniffs['flow'].shape[0])])
Out[37]:
348
In [49]:
[x['t_zer'] for x in all_sniffs['t_zer']]
Out[49]:
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array([150, 353, 759]),
array([150, 347, 753]),
array([150, 341, 793]),
array([150, 345, 637]),
array([150, 286, 497]),
array([150, 234, 506]),
array([150, 251, 693]),
array([150, 317, 730]),
array([150, 325, 732]),
array([150, 332, 714]),
array([150, 328, 746]),
array([150, 336, 756]),
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array([150, 339, 747]),
array([150, 322, 751]),
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array([133, 194, 285]),
array([150, 231, 298]),
array([148, 237, 300]),
array([150, 236, 299]),
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array([140, 192, 254]),
array([114, 185, 249]),
array([135, 217, 277]),
array([142, 199, 261]),
array([119, 185, 248]),
array([129, 218, 276]),
array([147, 209, 273]),
array([126, 183, 248]),
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array([133, 205, 269]),
array([136, 190, 250]),
array([114, 171, 230]),
array([116, 175, 234]),
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array([126, 180, 241]),
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array([150, 228, 299]),
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array([135, 212, 282]),
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array([150, 225, 303]),
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array([150, 238, 320]),
array([150, 260, 330]),
array([150, 218, 295]),
array([145, 210, 315]),
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array([137, 221, 311]),
array([150, 251, 318]),
array([150, 218, 304]),
array([150, 231, 290]),
array([140, 219, 291]),
array([150, 212, 310]),
array([150, 220, 288]),
array([138, 202, 274]),
array([136, 209, 280]),
array([144, 212, 284]),
array([140, 220, 287]),
array([147, 213, 288]),
array([141, 231, 301]),
array([150, 226, 296]),
array([146, 211, 304]),
array([150, 240, 309]),
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array([150, 243, 306]),
array([150, 235, 312]),
array([150, 239, 303]),
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array([144, 227, 296]),
array([150, 221, 293]),
array([143, 227, 295]),
array([150, 219, 293]),
array([143, 224, 295]),
array([150, 220, 295]),
array([145, 248, 312]),
array([150, 209, 330]),
array([150, 243, 308]),
array([150, 230, 312]),
array([150, 248, 314]),
array([150, 252, 325]),
array([150, 245, 312]),
array([150, 238, 313]),
array([150, 249, 314]),
array([150, 223, 294]),
array([144, 246, 314]),
array([150, 230, 304]),
array([150, 228, 301]),
array([150, 218, 288]),
array([138, 226, 292]),
array([150, 234, 303]),
array([150, 259, 332]),
array([150, 213, 301]),
array([150, 220, 311]),
array([150, 210, 341]),
array([150, 214, 308]),
array([150, 209, 323]),
array([150, 209, 316]),
array([150, 208, 337]),
array([150, 212, 340]),
array([150, 441, 601]),
array([150, 232, 319]),
array([150, 216, 286]),
array([136, 193, 288]),
array([150, 223, 297]),
array([147, 213, 290]),
array([143, 200, 261]),
array([118, 180, 423]),
array([150, 256, 445]),
array([150, 236, 375]),
array([150, 256, 337]),
array([150, 224, 301]),
array([150, 244, 318]),
array([150, 220, 299]),
array([149, 238, 306]),
array([150, 221, 300]),
array([150, 233, 299]),
array([149, 229, 298]),
array([149, 234, 305]),
array([150, 231, 304]),
array([150, 255, 333]),
array([150, 226, 300]),
array([150, 257, 327]),
array([150, 239, 313]),
array([150, 243, 316]),
array([150, 241, 315]),
array([150, 230, 295]),
array([145, 221, 298]),
array([150, 239, 302]),
array([150, 217, 285]),
array([135, 200, 269]),
array([134, 235, 303]),
array([150, 235, 310]),
array([150, 248, 310]),
array([150, 223, 292]),
array([150, 221, 332]),
array([150, 210, 313]),
array([150, 222, 315]),
array([150, 236, 314]),
array([150, 219, 440]),
array([150, 348, 570]),
array([150, 247, 320]),
array([150, 212, 284]),
array([134, 230, 299]),
array([150, 272, 353]),
array([150, 257, 335]),
array([150, 239, 316]),
array([150, 245, 325]),
array([150, 233, 312]),
array([150, 272, 342]),
array([150, 239, 309]),
array([150, 231, 297]),
array([147, 236, 303]),
array([150, 226, 298]),
array([148, 282, 336]),
array([150, 227, 288]),
array([138, 237, 330]),
array([150, 220, 288]),
array([138, 244, 317]),
array([150, 225, 296]),
array([146, 229, 319]),
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array([150, 256, 467]),
array([150, 225, 331]),
array([150, 228, 477]),
array([150, 242, 582]),
array([150, 278, 663]),
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array([150, 294, 672]),
array([150, 286, 679]),
array([150, 298, 710]),
array([150, 285, 452]),
array([150, 245, 456]),
array([150, 250, 353]),
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array([150, 235, 531]),
array([150, 240, 392]),
array([150, 240, 607]),
array([150, 263, 738]),
array([150, 296, 732]),
array([150, 289, 727]),
array([150, 288, 730]),
array([150, 314, 727]),
array([150, 299, 444]),
array([150, 260, 513]),
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array([150, 251, 322]),
array([150, 261, 326]),
array([150, 238, 304]),
array([150, 242, 307]),
array([150, 226, 284]),
array([134, 218, 278]),
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array([150, 230, 290]),
array([140, 234, 302]),
array([150, 231, 293]),
array([143, 220, 285]),
array([142, 226, 291]),
array([149, 248, 313]),
array([150, 223, 287]),
array([137, 264, 331]),
array([150, 220, 284]),
array([134, 241, 300]),
array([150, 243, 305]),
array([150, 265, 319]),
array([150, 228, 292]),
array([142, 224, 283]),
array([141, 215, 275]),
array([134, 214, 282]),
array([148, 268, 328]),
array([150, 222, 284]),
array([134, 443, 504]),
array([150, 275, 331]),
array([150, 251, 317]),
array([150, 218, 286]),
array([136, 243, 308]),
array([150, 288, 358]),
array([150, 242, 302]),
array([150, 209, 271]),
array([121, 183, 253]),
array([132, 281, 385]),
array([150, 255, 305]),
array([150, 249, 315]),
array([150, 256, 334]),
array([150, 271, 330]),
array([150, 237, 303]),
array([150, 216, 278]),
array([128, 201, 275]),
array([147, 253, 328]),
array([150, 259, 317]),
array([150, 219, 285]),
array([135, 204, 270]),
array([135, 216, 281]),
array([146, 332, 396]),
array([150, 266, 604]),
array([150, 215, 406]),
array([150, 248, 563]),
array([150, 336, 650]),
array([150, 338, 688]),
array([150, 335, 683]),
array([150, 338, 690]),
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array([150, 335, 684]),
array([150, 238, 609]),
array([150, 283, 736]),
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array([150, 319, 780]),
array([150, 318, 722]),
array([150, 341, 842]),
array([150, 342, 818]),
array([150, 342, 787]),
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array([150, 338, 784]),
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array([150, 333, 810]),
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array([150, 366, 768]),
array([150, 365, 799]),
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array([150, 372, 780]),
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array([150, 360, 739]),
array([150, 350, 778]),
array([150, 364, 785]),
array([150, 358, 777]),
array([150, 345, 757]),
array([150, 356, 780]),
array([150, 357, 810]),
array([150, 367, 764]),
array([150, 352, 750]),
array([150, 351, 766]),
array([150, 362, 822]),
array([150, 359, 818]),
array([150, 328, 499]),
array([150, 230, 337]),
array([150, 357, 734]),
array([150, 356, 730]),
array([150, 362, 734]),
array([150, 355, 725]),
array([150, 361, 728]),
array([150, 358, 743]),
array([150, 353, 718]),
array([150, 366, 760]),
array([150, 361, 730]),
array([150, 359, 812]),
array([150, 372, 797]),
array([150, 345, 718]),
array([150, 351, 709]),
array([150, 363, 724]),
array([150, 361, 725]),
array([150, 365, 740]),
array([150, 360, 765]),
array([150, 361, 733]),
array([150, 362, 746]),
array([150, 367, 742]),
array([150, 381, 818]),
array([150, 386, 826]),
array([150, 376, 798]),
array([150, 378, 807]),
array([150, 378, 834]),
array([150, 380, 817]),
array([150, 323, 682]),
array([150, 341, 713]),
array([150, 336, 715]),
array([150, 354, 747]),
array([150, 366, 758]),
array([150, 361, 766]),
array([150, 359, 773]),
array([150, 368, 790]),
array([150, 364, 813]),
array([150, 376, 792]),
array([150, 363, 778]),
array([150, 366, 794]),
array([150, 382, 833]),
array([150, 371, 822]),
array([150, 347, 847]),
array([150, 352, 766]),
array([150, 352, 818]),
array([150, 359, 791]),
array([150, 351, 831]),
array([150, 367, 793]),
array([150, 361, 880]),
array([150, 350, 905]),
array([150, 347, 768]),
array([150, 361, 807]),
array([150, 369, 758]),
array([150, 350, 772]),
array([150, 357, 798]),
array([150, 364, 872]),
array([150, 332, 706]),
array([150, 282, 472]),
array([150, 217, 602]),
array([150, 246, 579]),
array([150, 308, 631]),
array([ 150, 428, 1009]),
array([150, 317, 692]),
array([150, 344, 743]),
array([150, 349, 777]),
array([150, 359, 817]),
array([150, 352, 806]),
array([150, 357, 797]),
array([150, 369, 826]),
array([150, 360, 769]),
array([150, 364, 778]),
array([150, 330, 781]),
array([150, 344, 773]),
array([150, 357, 843]),
array([150, 366, 808]),
array([150, 361, 798]),
array([150, 345, 752]),
array([150, 352, 784]),
array([150, 358, 796]),
array([150, 364, 808]),
array([150, 372, 873]),
array([150, 363, 775]),
array([150, 367, 688]),
array([150, 348, 774]),
array([150, 333, 753]),
array([150, 330, 775]),
array([150, 358, 777]),
array([150, 370, 798]),
array([150, 362, 778]),
array([150, 371, 817]),
array([150, 364, 805]),
array([150, 366, 767]),
array([150, 348, 779]),
array([150, 353, 788]),
array([150, 353, 726]),
array([150, 346, 753]),
array([150, 355, 769]),
array([150, 356, 744]),
array([150, 374, 787]),
array([150, 357, 767]),
array([150, 353, 754]),
array([150, 359, 772]),
array([150, 368, 702]),
array([150, 308, 757]),
array([150, 337, 746]),
array([150, 354, 788]),
array([150, 352, 748]),
array([150, 358, 767]),
array([150, 374, 795]),
array([150, 360, 768]),
array([150, 369, 770]),
array([150, 371, 788]),
array([150, 329, 735]),
array([150, 348, 740]),
array([150, 352, 776]),
array([150, 338, 729]),
array([150, 360, 770]),
array([150, 356, 728]),
array([150, 341, 731]),
array([150, 346, 704]),
array([150, 338, 660]),
array([150, 334, 683]),
array([150, 337, 660]),
array([150, 340, 700]),
array([150, 348, 697]),
array([150, 328, 702]),
array([150, 331, 660]),
array([150, 337, 693]),
array([150, 340, 717]),
array([150, 337, 683]),
array([150, 337, 688]),
array([150, 321, 667]),
array([150, 218, 289]),
array([139, 234, 302]),
array([150, 222, 286]),
array([136, 221, 286]),
array([150, 233, 300]),
array([150, 238, 300]),
array([150, 240, 306]),
array([150, 226, 290]),
array([140, 214, 281]),
array([141, 248, 308]),
array([150, 226, 291]),
array([141, 237, 298]),
array([150, 223, 285]),
array([135, 211, 278]),
array([143, 227, 289]),
array([146, 238, 304]),
array([150, 230, 300]),
array([150, 240, 313]),
array([150, 227, 304]),
array([150, 245, 312]),
array([150, 234, 304]),
array([150, 229, 304]),
array([150, 239, 311]),
array([150, 250, 313]),
array([150, 225, 299]),
array([149, 231, 300]),
array([150, 218, 291]),
array([141, 220, 283]),
array([142, 217, 290]),
array([148, 270, 339]),
array([150, 242, 309]),
array([150, 232, 314]),
array([150, 242, 321]),
array([150, 243, 332]),
array([150, 222, 313]),
array([150, 260, 323]),
array([150, 246, 313]),
array([150, 273, 328]),
array([150, 295, 345]),
array([150, 264, 329]),
array([150, 259, 321]),
array([150, 234, 302]),
array([150, 231, 295]),
array([145, 229, 299]),
array([150, 240, 308]),
array([150, 230, 309]),
array([150, 251, 314]),
array([150, 246, 322]),
array([150, 310, 379]),
array([150, 264, 338]),
array([150, 506, 691]),
array([150, 321, 625]),
array([150, 316, 636]),
array([150, 312, 569]),
array([150, 270, 356]),
array([150, 267, 424]),
array([150, 262, 422]),
array([150, 277, 584]),
array([150, 310, 651]),
array([150, 313, 641]),
array([150, 328, 672]),
array([150, 286, 649]),
array([150, 307, 639]),
array([150, 265, 350]),
array([150, 235, 332]),
array([150, 258, 730]),
array([150, 382, 525]),
array([150, 270, 358]),
array([150, 260, 376]),
array([150, 292, 393]),
array([150, 278, 373]),
array([150, 370, 480]),
array([150, 321, 614]),
array([150, 360, 652]),
array([150, 383, 734]),
array([150, 282, 568]),
array([150, 332, 616]),
array([150, 322, 629]),
array([150, 328, 609]),
array([150, 252, 330]),
array([150, 296, 381]),
array([150, 266, 353]),
array([150, 224, 462]),
array([150, 319, 394]),
array([150, 271, 573]),
array([150, 261, 419]),
array([150, 505, 665]),
array([150, 324, 528]),
...]
In [44]:
all_sniffs[0]['flow'][0:40]
Out[44]:
array([-469.34870792, -473.90308697, -478.03242099, -481.70239113,
-484.89439146, -487.6032223 , -489.8354455 , -491.60954761,
-492.95532112, -493.91288103, -494.53337199, -494.88100965,
-495.03514827, -495.08996476, -495.15076217, -495.33036388,
-495.74626828, -496.51524302, -497.7457862 , -499.53211892,
-501.95253176, -505.07066414, -508.93551336, -513.58048891,
-519.0234273 , -525.26656604, -532.29720993, -540.09035273,
-548.61250273, -557.827167 , -567.70263022, -578.22030036,
-589.38116979, -601.20941317, -613.75411242, -627.08949292,
-641.31177782, -656.53312655, -672.87606992, -690.46920166])
In [ ]:
Content source: zekearneodo/ephys-tools
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