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import scipy.stats as st
import pandas as pd
import numpy as np
import h5py
from .stimulus_analysis import StimulusAnalysis
from .brain_observatory_exceptions import MissingStimulusException
from . import stimulus_info as stiminfo
from . import circle_plots as cplots
[docs]class NaturalMovie(StimulusAnalysis):
""" Perform tuning analysis specific to natural movie stimulus.
Parameters
----------
data_set: BrainObservatoryNwbDataSet object
movie_name: string
one of [ stimulus_info.NATURAL_MOVIE_ONE, stimulus_info.NATURAL_MOVIE_TWO,
stimulus_info.NATURAL_MOVIE_THREE ]
"""
def __init__(self, data_set, movie_name, **kwargs):
super(NaturalMovie, self).__init__(data_set, **kwargs)
self.movie_name = movie_name
self._sweeplength = NaturalMovie._PRELOAD
self._sweep_response = NaturalMovie._PRELOAD
@property
def sweeplength(self):
if self._sweeplength is NaturalMovie._PRELOAD:
self.populate_stimulus_table()
return self._sweeplength
@property
def sweep_response(self):
if self._sweep_response is NaturalMovie._PRELOAD:
self._sweep_response = self.get_sweep_response()
return self._sweep_response
[docs] def populate_stimulus_table(self):
stimulus_table = self.data_set.get_stimulus_table(self.movie_name)
self._stim_table = stimulus_table[stimulus_table.frame == 0]
self._sweeplength = \
self.stim_table.start.iloc[1] - self.stim_table.start.iloc[0]
[docs] def get_sweep_response(self):
''' Returns the dF/F response for each cell
Returns
-------
Numpy array
'''
sweep_response = pd.DataFrame(index=self.stim_table.index.values, columns=np.array(
range(self.numbercells)).astype(str))
for index, row in self.stim_table.iterrows():
start = row.start
end = start + self.sweeplength
for nc in range(self.numbercells):
sweep_response[str(nc)][index] = self.dfftraces[nc, start:end]
return sweep_response
[docs] def get_peak(self):
''' Computes properties of the peak response condition for each cell.
Returns
-------
Pandas data frame with the below fields. A suffix of "nm1", "nm2" or "nm3" is appended to the field name depending
on which of three movie clips was presented.
* peak_nm1 (frame with peak response)
* response_variability_nm1
'''
peak_movie = pd.DataFrame(index=range(self.numbercells), columns=(
'peak', 'response_reliability', 'cell_specimen_id'))
cids = self.data_set.get_cell_specimen_ids()
mask = np.ones((10,10))
for i in range(10):
for j in range(10):
if i>=j:
mask[i,j] = np.NaN
for nc in range(self.numbercells):
peak_movie.cell_specimen_id.iloc[nc] = cids[nc]
meanresponse = self.sweep_response[str(nc)].mean()
# movie_len = len(meanresponse) / 30
# output = np.empty((movie_len, 10))
# for tr in range(10):
# test = self.sweep_response[str(nc)].iloc[tr]
# for i in range(movie_len):
# _, p = st.ks_2samp(
# test[i * 30:(i + 1) * 30], test[(i + 1) * 30:(i + 2) * 30])
# output[i, tr] = p
# output = np.where(output < 0.05, 1, 0)
# ptime = np.sum(output, axis=1)
# ptime *= 10
peak = np.argmax(meanresponse)
# if peak > 30:
# peak_movie.response_reliability.iloc[
# nc] = ptime[(peak - 30) / 30]
# else:
# peak_movie.response_reliability.iloc[nc] = ptime[0]
peak_movie.peak.iloc[nc] = peak
#reliability
corr_matrix = np.empty((10,10))
for i in range(10):
for j in range(10):
r,p = st.pearsonr(self.sweep_response[str(nc)].iloc[i], self.sweep_response[str(nc)].iloc[j])
corr_matrix[i,j] = r
corr_matrix*=mask
peak_movie.response_reliability.iloc[nc] = np.nanmean(corr_matrix)
if self.movie_name == stiminfo.NATURAL_MOVIE_ONE:
peak_movie.rename(columns={
'peak': 'peak_'+stiminfo.NATURAL_MOVIE_ONE_SHORT,
'response_reliability': 'response_reliability_'+stiminfo.NATURAL_MOVIE_ONE_SHORT},
inplace=True)
elif self.movie_name == stiminfo.NATURAL_MOVIE_TWO:
peak_movie.rename(columns={
'peak': 'peak_'+stiminfo.NATURAL_MOVIE_TWO_SHORT,
'response_reliability': 'response_reliability_'+stiminfo.NATURAL_MOVIE_TWO_SHORT},
inplace=True)
elif self.movie_name == stiminfo.NATURAL_MOVIE_THREE:
peak_movie.rename(columns={
'peak': 'peak_'+stiminfo.NATURAL_MOVIE_THREE_SHORT,
'response_reliability': 'response_reliability_'+stiminfo.NATURAL_MOVIE_THREE_SHORT},
inplace=True)
return peak_movie
[docs] def open_track_plot(self, cell_specimen_id=None, cell_index=None):
cell_index = self.row_from_cell_id(cell_specimen_id, cell_index)
cell_rows = self.sweep_response[str(cell_index)]
data = []
for i in range(len(cell_rows)):
data.append(cell_rows.iloc[i])
data = np.vstack(data)
tp = cplots.TrackPlotter(ring_length=360)
tp.plot(data,
clim=[0, data.mean() + data.std()*3])
tp.show_arrow()
[docs] @staticmethod
def from_analysis_file(data_set, analysis_file, movie_name):
nm = NaturalMovie(data_set, movie_name)
nm.populate_stimulus_table()
# TODO: deal with this properly
suffix_map = {
stiminfo.NATURAL_MOVIE_ONE: '_'+stiminfo.NATURAL_MOVIE_ONE_SHORT,
stiminfo.NATURAL_MOVIE_TWO: '_'+stiminfo.NATURAL_MOVIE_TWO_SHORT,
stiminfo.NATURAL_MOVIE_THREE: '_'+stiminfo.NATURAL_MOVIE_THREE_SHORT
}
try:
suffix = suffix_map[movie_name]
nm._sweep_response = pd.read_hdf(analysis_file, "analysis/sweep_response"+suffix)
nm._peak = pd.read_hdf(analysis_file, "analysis/peak")
with h5py.File(analysis_file, "r") as f:
nm._binned_dx_sp = f["analysis/binned_dx_sp"].value
nm._binned_cells_sp = f["analysis/binned_cells_sp"].value
nm._binned_dx_vis = f["analysis/binned_dx_vis"].value
nm._binned_cells_vis = f["analysis/binned_cells_vis"].value
except Exception as e:
raise MissingStimulusException(e.args)
return nm