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
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 = []
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
