def test_natural_movie_one_a(nm1a, nwb_a, analysis_a_new):
nm1a_new = NaturalMovie.from_analysis_file(BODS(nwb_a), analysis_a_new, si.NATURAL_MOVIE_ONE)
#assert np.allclose(nm1a.sweep_response, nm1a_new.sweep_response)
assert np.allclose(nm1a.binned_cells_sp, nm1a_new.binned_cells_sp, equal_nan=True)
assert np.allclose(nm1a.binned_cells_vis, nm1a_new.binned_cells_vis, equal_nan=True)
assert np.allclose(nm1a.binned_dx_sp, nm1a_new.binned_dx_sp, equal_nan=True)
assert np.allclose(nm1a.binned_dx_vis, nm1a_new.binned_dx_vis, equal_nan=True)
def session_c(self, plot_flag=False, save_flag=True):
lsn = LocallySparseNoise(self.nwb)
nm2 = NaturalMovie(self.nwb, 'natural_movie_two')
nm1 = NaturalMovie(self.nwb, 'natural_movie_one', speed_tuning=True)
SessionAnalysis._log.info("Session C analyzed")
peak = multi_dataframe_merge([nm1.peak_run, nm1.peak, nm2.peak])
self.append_metadata(peak)
self.metrics_c["roi_id"] = nm1.roi_id
if save_flag:
self.save_session_c(lsn, nm1, nm2, peak)
if plot_flag:
cp._plot_3sc(lsn, nm1, nm2, self.save_dir)
cp.plot_lsn_traces(lsn, self.save_dir)
def test_natural_movie_one_a(nm1a, nwb_a, analysis_a_new):
nm1a_new = NaturalMovie.from_analysis_file(BODS(nwb_a), analysis_a_new, si.NATURAL_MOVIE_ONE)
#assert np.allclose(nm1a.sweep_response, nm1a_new.sweep_response)
assert np.allclose(nm1a.binned_cells_sp, nm1a_new.binned_cells_sp, equal_nan=True)
assert np.allclose(nm1a.binned_cells_vis, nm1a_new.binned_cells_vis, equal_nan=True)
assert np.allclose(nm1a.binned_dx_sp, nm1a_new.binned_dx_sp, equal_nan=True)
assert np.allclose(nm1a.binned_dx_vis, nm1a_new.binned_dx_vis, equal_nan=True)
def test_harness(dataset, trigger):
movie_name = "Mock Movie Name"
nm = NaturalMovie(dataset, movie_name)
assert nm._stim_table is StimulusAnalysis._PRELOAD
assert nm._sweeplength is StimulusAnalysis._PRELOAD
assert nm._sweep_response is StimulusAnalysis._PRELOAD
assert nm._peak is StimulusAnalysis._PRELOAD
for attr in trigger:
print(getattr(nm, attr))
assert nm._stim_table is not StimulusAnalysis._PRELOAD
assert nm._sweeplength is not StimulusAnalysis._PRELOAD
assert nm._sweep_response is not StimulusAnalysis._PRELOAD
assert nm._peak is not StimulusAnalysis._PRELOAD
# check super properties weren't preloaded
dataset.get_corrected_fluorescence_traces.assert_called_once_with()
assert nm._timestamps is not NaturalMovie._PRELOAD
assert nm._celltraces is not NaturalMovie._PRELOAD
assert nm._numbercells is not NaturalMovie._PRELOAD
assert not dataset.get_roi_ids.called
assert nm._roi_id is NaturalMovie._PRELOAD
assert dataset.get_cell_specimen_ids.called
assert nm._cell_id is NaturalMovie._PRELOAD
assert not dataset.get_dff_traces.called
assert nm._dfftraces is NaturalMovie._PRELOAD
assert nm._dxcm is NaturalMovie._PRELOAD
assert nm._dxtime is NaturalMovie._PRELOAD
def build_type(nwb_file, data_file, configs, output_dir, type_name):
data_set = BrainObservatoryNwbDataSet(nwb_file)
try:
if type_name == "dg":
dga = DriftingGratings.from_analysis_file(data_set, data_file)
build_drifting_gratings(dga, configs, output_dir)
elif type_name == "sg":
sga = StaticGratings.from_analysis_file(data_set, data_file)
build_static_gratings(sga, configs, output_dir)
elif type_name == "nm1":
nma = NaturalMovie.from_analysis_file(data_set, data_file, si.NATURAL_MOVIE_ONE)
build_natural_movie(nma, configs, output_dir, si.NATURAL_MOVIE_ONE)
elif type_name == "nm2":
nma = NaturalMovie.from_analysis_file(data_set, data_file, si.NATURAL_MOVIE_TWO)
build_natural_movie(nma, configs, output_dir, si.NATURAL_MOVIE_TWO)
elif type_name == "nm3":
nma = NaturalMovie.from_analysis_file(data_set, data_file, si.NATURAL_MOVIE_THREE)
build_natural_movie(nma, configs, output_dir, si.NATURAL_MOVIE_THREE)
elif type_name == "ns":
nsa = NaturalScenes.from_analysis_file(data_set, data_file)
build_natural_scenes(nsa, configs, output_dir)
elif type_name == "sp":
nma = NaturalMovie.from_analysis_file(data_set, data_file, si.NATURAL_MOVIE_ONE)
build_speed_tuning(nma, configs, output_dir)
elif type_name == "lsn_on":
lsna = lsna_check_hvas(data_set, data_file)
build_locally_sparse_noise(lsna, configs, output_dir, True)
elif type_name == "lsn_off":
lsna = lsna_check_hvas(data_set, data_file)
build_locally_sparse_noise(lsna, configs, output_dir, False)
elif type_name == "rf":
lsna = lsna_check_hvas(data_set, data_file)
build_receptive_field(lsna, configs, output_dir)
elif type_name == "corr":
build_correlation_plots(data_set, data_file, configs, output_dir)
elif type_name == "eye":
build_eye_tracking_plots(data_set, configs, output_dir)
except MissingStimulusException as e:
logging.warning("could not load stimulus (%s)", type_name)
except Exception as e:
traceback.print_exc()
logging.critical("error running stimulus (%s)", type_name)
raise e
def session_a(self, plot_flag=False, save_flag=True):
nm1 = NaturalMovie(self.nwb, 'natural_movie_one', speed_tuning=True)
nm3 = NaturalMovie(self.nwb, 'natural_movie_three')
dg = DriftingGratings(self.nwb)
SessionAnalysis._log.info("Session A analyzed")
peak = multi_dataframe_merge(
[nm1.peak_run, dg.peak, nm1.peak, nm3.peak])
self.append_metrics_drifting_grating(self.metrics_a, dg)
self.metrics_a["roi_id"] = dg.roi_id
self.append_metadata(peak)
if save_flag:
self.save_session_a(dg, nm1, nm3, peak)
if plot_flag:
cp._plot_3sa(dg, nm1, nm3, self.save_dir)
cp.plot_drifting_grating_traces(dg, self.save_dir)
def session_b(self, plot_flag=False, save_flag=True):
ns = NaturalScenes(self.nwb)
sg = StaticGratings(self.nwb)
nm1 = NaturalMovie(self.nwb, 'natural_movie_one', speed_tuning=True)
SessionAnalysis._log.info("Session B analyzed")
peak = multi_dataframe_merge(
[nm1.peak_run, sg.peak, ns.peak, nm1.peak])
self.append_metadata(peak)
self.append_metrics_static_grating(self.metrics_b, sg)
self.append_metrics_natural_scene(self.metrics_b, ns)
self.verify_roi_lists_equal(sg.roi_id, ns.roi_id)
self.metrics_b["roi_id"] = sg.roi_id
if save_flag:
self.save_session_b(sg, nm1, ns, peak)
if plot_flag:
cp._plot_3sb(sg, nm1, ns, self.save_dir)
cp.plot_ns_traces(ns, self.save_dir)
cp.plot_sg_traces(sg, self.save_dir)
def nm2(nwb_c, analysis_c):
return NaturalMovie.from_analysis_file(BODS(nwb_c), analysis_c, si.NATURAL_MOVIE_TWO)
def nm1b(nwb_b, analysis_b):
return NaturalMovie.from_analysis_file(BODS(nwb_b), analysis_b, si.NATURAL_MOVIE_ONE)
def nm3(nwb_a, analysis_a):
return NaturalMovie.from_analysis_file(BODS(nwb_a), analysis_a, si.NATURAL_MOVIE_THREE)
print ' Part III' print '##############\n' # goal: Figure out how to get the cell specimen data specifically for nm3 session A stimuli associated # with ophy_experiment_id: 570305847, experiment_container_id: 566759225 ## Remember: you have downloaded the NWB data for an experiment. You need to get the fluorecent traces ## of the cells from this data # 1: begin analysis using the # natural_movie class. nm3 will be a NaturalMovie object. ## It will be important to ensure whether or not passing in the moview_name arguement ## is necessary for data analysis of information 'I know' is recorded from the ## natural movie three stimulus. I believe the 'movie_name' param is neccessary ## because of the whole tuning analysis to a specific movie which is a big part of this class nm3_object = NaturalMovie(session_A_data, stim_info.NATURAL_MOVIE_THREE) # 2: grab the sweep response from the nm3_object and convert it to a pandas dataframe sweep_response = nm3_object.get_sweep_response() sweep_response = pd.DataFrame.from_records(sweep_response) # print len(sweep_response.iloc[0][0]) # print sweep_response.iloc[1][0] # __________________________________________________________________________________ print '\n##############' print ' Part IV' print '##############\n' # goal: using the sweep responce information, create traces of a cell for all 10 trials # remember: the sweep_response dataframe is a trial(i) X cell matrix(j), 0 = i < 10, and 0 = j < 181.
p.pprint(cell_experiment)
data_set = boc.get_ophys_experiment_data(cell_experiment['id'])
print '\nhere is the metadata set for the cell experiment above:'
p.pprint(data_set.get_metadata())
# print '\nhere is the stimuli available for this file:'
# print(data_set.list_stimuli())
# ___________________________________________________________________________________________________
# PART III: Mess around with the Natural Movie class
print '\n******************'
print ' PART III '
print '******************\n'
# access the NaturalMovie class
nm = NaturalMovie(data_set, stim_info.NATURAL_MOVIE_THREE)
# print 'Natural Movie class has the following characteristics'
# print nm.movie_name
# print nm._sweeplength
# print nm._sweep_response
# print str(nm.__dict__)
# print '----------------------------'
sweep_response = pd.DataFrame.from_records(
nm.get_sweep_response()) #turned the numpy array into a pandas dataframe
# print sweep_response #the sweep response numpy array contains the dF/F response for each cell
# print 'sweep length is: ' + str(nm.sweeplength)
# print ' lets try this peak thing again \n' + str(pd.DataFrame.from_records(nm.get_peak()))
# with sweep response, we can export that to a csv file to get a good look at it
sweep_response.to_csv('Sweep_Response_Data.csv', index=True, header=True)
def nm1c(nwb_c, analysis_c):
return NaturalMovie.from_analysis_file(BODS(nwb_c), analysis_c, si.NATURAL_MOVIE_ONE)
def nm1b(nwb_b, analysis_b):
return NaturalMovie.from_analysis_file(BODS(nwb_b), analysis_b, si.NATURAL_MOVIE_ONE)
def nm3(nwb_a, analysis_a):
return NaturalMovie.from_analysis_file(BODS(nwb_a), analysis_a, si.NATURAL_MOVIE_THREE)
