def makeDF(camdir):
# creates data frame for CAM experiments, and returns paths to all experiments
list_of_dirs = os.walk(camdir).next()[1]
list_of_paths = []
for i in list_of_dirs:
nwb_path = camdir + i + '/' + i + '.nwb'
list_of_paths.append(nwb_path)
CAM_df = pd.DataFrame(index=range(len(list_of_paths),),columns=cn.getMetaData(list_of_paths[0]))
row_counter = 0
for path in list_of_paths:
CAM_df.loc[row_counter] = pd.Series(cn.getMetaData(path))
row_counter += 1
# add a metric of running speed to that data frame
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
return list_of_paths, CAM_df
NTRIALS = 15 # number of trials per sweep
PTHRESH = 0.05 # p-value cutoff above which results are not significant
DTHRESH = 0.01 # D-statistic for K-S test below which results are not significant
NBINS = 20 # number of bins for histogram plot
DF_THRESH = 15 # percent threshold (dF/F) for cutting off non-responsive cells
# get experiment data and path to experiments
list_of_dirs = os.walk(CAMDIR).next()[1]
list_of_paths = []
for i in list_of_dirs:
nwb_path = CAMDIR + i + '/' + i + '.nwb'
list_of_paths.append(nwb_path)
CAM_df = pd.DataFrame(index=range(len(list_of_paths),),
columns=cn.getMetaData(list_of_paths[0]))
row_counter = 0
for path in list_of_paths:
CAM_df.loc[row_counter] = pd.Series(cn.getMetaData(path))
row_counter += 1
# add a metric of running speed to that data frame
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 = []
NPERMS = 100 # number of permutations to use for permutation test of significance for LVs
NBOOTS = 100 # number of iterations to perform for bootsrapping procedure for salience stabilities
# 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
NBOOTS = 100 # number of iterations to perform for bootsrapping procedure for salience stabilities
# 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)
