# get experiment data and path to experiments
path_list, CAM_df = pr.makeDF(CAMDIR)
# LOOP OVER ALL EXPERIMENTS AND CALCULATE HISTOGRAM OF LIFETIME SPARSENESSES FOR EACH
life_spar_list = []
pop_spar_list = []
meta_list = []
pval_list = []
sal_list = []
for path in path_list:
# extract interesting information
meta = cn.getMetaData(path) # get meta data for experiment of interest
proj = cn.getMaxProjection(
path
) # getMaxProjection returns a 512x512 array of the maximum projection of the 2P movie
print meta
meta_list.append(meta)
# returns an array of raw fluorescence traces for each ROI and the timestamps for each time point.
timestamps, celltraces = cn.getFluorescenceTraces(path)
number_cells = np.size(celltraces, 0)
acquisition_rate = 1 / (timestamps[1] - timestamps[0])
print "Number of cells: ", number_cells
print "Acquisition rate: %f Hz" % acquisition_rate
# returns data frame of stimulus conditions
stimulus_table = cn.getStimulusTable(path)
number_sweeps = len(stimulus_table)
mean_speed = []
for path in list_of_paths:
run_data = cn.getRunningSpeed(path)
mean_speed.append(np.nanmean(run_data[0]))
CAM_df['mean speed'] = mean_speed
CAM_df['path_to_exp'] = list_of_paths
# LOOP OVER ALL EXPERIMENTS AND CALCULATE HISTOGRAM OF LIFETIME SPARSENESSES FOR EACH
lift_spar_list = []
pop_spar_list = []
meta_list = []
for path in list_of_paths:
print path
# extract interesting information
meta = cn.getMetaData(path) # get meta data for experiment of interest
proj = cn.getMaxProjection(path) # getMaxProjection returns a 512x512 array of the
# maximum projection of the 2P movie
print meta
meta_list.append(meta)
# returns an array of raw fluorescence traces for each ROI and the timestamps for each
# time point.
timestamps, celltraces = cn.getFluorescenceTraces(path)
number_cells = np.size(celltraces, 0)
acquisition_rate = 1/(timestamps[1]-timestamps[0])
print "Number of cells: ", number_cells
print "Acquisition rate: %f Hz" % acquisition_rate
# returns data frame of stimulus conditions
stimulus_table = cn.getStimulusTable(path)
number_sweeps = len(stimulus_table)
# get experiment data and path to experiments
path_list, CAM_df = pr.makeDF(CAMDIR)
# LOOP OVER ALL EXPERIMENTS AND CALCULATE HISTOGRAM OF LIFETIME SPARSENESSES FOR EACH
life_spar_list = []
pop_spar_list = []
meta_list = []
pval_list = []
sal_list = []
for path in path_list:
# extract interesting information
meta = cn.getMetaData(path) # get meta data for experiment of interest
proj = cn.getMaxProjection(path) # getMaxProjection returns a 512x512 array of the maximum projection of the 2P movie
print meta
meta_list.append(meta)
# returns an array of raw fluorescence traces for each ROI and the timestamps for each time point.
timestamps, celltraces = cn.getFluorescenceTraces(path)
number_cells = np.size(celltraces,0)
acquisition_rate = 1/(timestamps[1]-timestamps[0])
print "Number of cells: ", number_cells
print "Acquisition rate: %f Hz" % acquisition_rate
# returns data frame of stimulus conditions
stimulus_table = cn.getStimulusTable(path)
number_sweeps = len(stimulus_table)
