Source code for allensdk.brain_observatory.natural_movie

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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()
@staticmethod
[docs] 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