def get_data(data_files):
data_ranges = []
data_names = []
new_data_files = []
row_ranges = []
headers = []
for df in data_files:
if type(df) == list:
df, ranges = df
else:
ranges = None
# read the data file, and don't try to convert column 0 -- the contour names --
# to anything other than a string.
data = datafile.DataFile(df, skip_empty = False, type_dict = {0:str})
header, rows = data.get_header_and_data()
headers.append(header)
if ranges is None:
data_ranges.append(rows)
data_names.append(path.path(df).namebase)
new_data_files.append(df)
if header is None: start = 0
else: start = 1
row_ranges.append(range(1+start, len(data.data)+start))
else:
for low, high, name in ranges:
try:
# recall that low, high give an inclusive, one-indexed range...
data_ranges.append(data.data[low-1:high])
except:
raise ValueError('Cannot get row range (%d, %d) from data file "%s" -- not enough rows?')
data_names.append(name)
new_data_files.append(df)
row_ranges.append(range(low, high+1))
return headers, data_ranges, data_names, new_data_files, row_ranges
def read_files(files, data_column):
names = []
data_out = []
for df in files:
names.append(path.path(df).namebase)
data = datafile.DataFile(df, skip_empty=False, type_dict={0: str})
header, rows = data.get_header_and_data()
data_out.append(numpy.array([row[data_column] for row in rows]))
return names, data_out
import numpy
from celltool.utility.path import path
import celltool.utility.datafile as datafile
import matplotlib.image as mpimg
from PIL import Image
import glob
import os
t = 0.1
bins = numpy.arange(0, 4, t)
"""Sets up some paths..."""
parent_dir = 'C:/Users/vymak_i7/Desktop/New_ER/Screen/Tm3/flashes/lobula/'
parent_path = path(
'C:/Users/vymak_i7/Desktop/New_ER/Screen/Tm3/flashes/lobula/')
"""I'll explain this in person, ignore through line 54."""
header, rows = datafile.DataFile(parent_path /
'directories.csv').get_header_and_data()
dir_list = [parent_dir + '/' + row[1] for row in rows]
path_list = [parent_path / row[1] for row in rows]
for directory, p in zip(dir_list[:], path_list[:]):
print(directory)
plot_dir = directory + '/plots/'
print(plot_dir)
image_dir1 = directory + '/RGECO'
print(image_dir1)
"""This IDs and sorts image file names for one channel."""
imagefiles1 = glob.glob(os.path.join(image_dir1, '*tif'))
unmap_mdir = mlb_mdir + '/unmappable/'
#input flashes directory name
flashes_dir = 'C:/Users/vymak_i7/Desktop/New_ER/Screen/Mi1/10s_flashes/'
f_mdir = flashes_dir + '/measurements/'
f_pdir = flashes_dir + '/plots/'
# #input gratings directory name
# gratings_dir = 'C:/Users/vymak_i7/Desktop/New_ER/Screen/Mi1/gratings/'
# g_mdir = gratings_dir + '/measurements/'
# g_pdir = gratings_dir +'/plots/'
for br in brain_regions[:]:
#import flash ER and RGECO data w/ mapped RF centers and separate headers and rows from each dataset
header, rows = datafile.DataFile(f_mdir + 'mapped/newavg_flash-' + br +
'-ER210.csv').get_header_and_data()
flash_ER = np.asarray(rows)
mapf_ER = np.asarray(flash_ER[:, 6:], dtype='float')
header, rows = datafile.DataFile(f_mdir + 'mapped/newavg_flash-' + br +
'-RGECO.csv').get_header_and_data()
flash_CYT = np.asarray(rows)
mapf_CYT = np.asarray(flash_CYT[:, 6:], dtype='float')
print(len(mapf_CYT))
# #import flash ER and RGECO data w/ unmapped RF centers and separate headers and rows from each dataset
# header,rows = datafile.DataFile(f_mdir+'unmapped/newavg_flash-'+br+'-ER210.csv').get_header_and_data()
# flash_ER = np.asarray(rows)
# umapf_ER = np.asarray(flash_ER[:,6:],dtype = 'float')
# header,rows = datafile.DataFile(f_mdir+'unmapped/newavg_flash-'+br+'-RGECO.csv').get_header_and_data()
T = numpy.arange(0, 5, t) #5 for flashes, 31 for gratings
threshold = 0.14 #0.6 for flashes; 0.13 for sum>0
threshold_inverted = -0.1
colors = ['#9900cc', '#00cc66', '#808080']
alphas = [1, 0.6]
brain_regions = ['M1', 'M5', 'M9-M10']
directory = 'C:/Users/vymak_i7/Desktop/New_ER/Screen/Mi1/2s_flashes/'
plot_dir = directory + '/plots/wo_screen'
m_dir = directory + '/measurements/'
for br in brain_regions[:]:
header, rows = datafile.DataFile(m_dir + 'average_responses-' + br +
'-RGECO.csv').get_header_and_data()
R = numpy.asarray(rows)
R = numpy.asarray(R[:, 6:], dtype='float')
print(len(R) / 2)
header, rows = datafile.DataFile(m_dir + 'average_responses-' + br +
'-ER210.csv').get_header_and_data()
ER = numpy.asarray(rows)
ER = numpy.asarray(ER[:, 6:], dtype='float')
OFF = []
ON = []
OFFer = []
ONer = []
OFF_inverted = []
ON_inverted = []
to_unmap_axons=[0,1,2,3,4,5,24,28,95,107,112,113,114,115,116,\
117,118,119,120,121,122,123,124,125,126,127,128,129,\
137,138,143,144,145,146,147,148,240,254,255,262,263,264]
to_map_axons = [45, 47, 51, 60, 64, 69, 118, 119, 120, 121, 122, 123, 206, 209]
to_unmap_indexes = [to_unmap_M1, to_unmap_M5, to_unmap_axons]
to_map_indexes = [to_map_M1, to_map_M5, to_map_axons]
for br, tounmap, tomap in zip(brain_regions[:], to_unmap_indexes[:],
to_map_indexes[:]):
print('BRAIN REGION ' + br)
#______SCREEN RF CENTERS____________
#import MLB data w/ mapped and unmapped RF centers and separate headers and rows from each dataset
header, rows = datafile.DataFile(map_mdir + 'RF_centers-' + br +
'.csv').get_header_and_data() #mapped
mapRF = np.asarray(rows)
header, rows = datafile.DataFile(unmap_mdir + 'RF_centers-' + br +
'.csv').get_header_and_data() #unmapped
unmapRF = np.asarray(rows)
print('RF centers total: ' + str(len(mapRF) + len(unmapRF)))
#import flash ER and RGECO data w/ mapped RF centers and separate headers and rows from each dataset
headerf, rows = datafile.DataFile(f_mdir + '/mapped/average_responses-' +
br + '-ER210.csv').get_header_and_data()
fmapER = np.asarray(rows)
headerf, rows = datafile.DataFile(f_mdir + '/mapped/average_responses-' +
br + '-RGECO.csv').get_header_and_data()
fmapCYT = np.asarray(rows)
#import flash ER and RGECO data w/ unmapped RF centers and separate headers and rows from each dataset
import os
import shutil
from celltool.utility.path import path
import celltool.utility.datafile as datafile
stim = '10s_flashes'
"""Set up path"""
directory = 'C:/Users/vymak_i7/Desktop/New_ER/Screen/Tm3/medulla/' + stim
parent_dir = path(directory)
directories = os.listdir(parent_dir)
t_dir = 'C:/Users/vymak_i7/Desktop/New_ER/Screen/Tm3/medulla/google_drive/'
target_dir = path(t_dir)
dirs = os.listdir(target_dir)
header, rows = datafile.DataFile(parent_dir /
'directories.csv').get_header_and_data()
path_list = [parent_dir / row[1] for row in rows]
##0) to rename files (optional)
# for row in directories[1:19]:
# prev=os.path.join(directory+row,'mask-M3-M4.tif')
# new = os.path.join(directory+row,'mask-M2-M5.tif')
# os.rename(prev,new)
# 1) CREATE DIRECTORIES FOR GOOGLE DRIVE FOLDER
# i=1
# for row in rows[:]:
# targetname=row[1][2:13]+'-z'+str(i)
out_mdir = MLB_dir + '/measurements/2s'
map_mdir = out_mdir + '/mappable/'
unmap_mdir = out_mdir + '/unmappable/'
plot_dir = MLB_dir + '/plots/2s'
map_pdir = plot_dir + '/mappable/'
unmap_pdir = plot_dir + '/unmappable/'
for br in brain_regions[:]:
print('BRAIN REGION ' + str(br))
#RGECO moving light bars data
OUT = [] #for ROIS w/ mappable RFs
OUT2 = [] #for ROIs w/ unmappable RFs
header, rows = datafile.DataFile(out_mdir + '/average_responses-' + br +
'.csv').get_header_and_data()
R = numpy.asarray(rows)
print('mlb count: ' + str(len(R) / 4))
#RGECO flash responses data
norm_cytf = [] #empty list for RFs pointing at the visual screen
abn_cytf = [
] #empty list for RFs not pointing at the screen or RFs having weak responses
flash_header, rows = datafile.DataFile(flash_mdir + '/average_responses-' +
br +
'-RGECO.csv').get_header_and_data()
flash_cyt = numpy.asarray(rows)
print('flash count: ' + str(len(flash_cyt) / 2))
#ER210 flash responses data
norm_ERf = [
subfont = 12
supfont = 16
xo = numpy.arange(0,240,0.3)
x = numpy.arange(0,241,30)
xti = numpy.arange(0,241,30)
#t = 0.2855
#bins = numpy.arange(0,29.95,t)
brain_regions = ['Lo2','Lo4']
color = ['#9900cc','#00cc66','#808080']
parent_dir = 'C:/Users/vymak_i7/Desktop/New_ER/Screen/Tm3/gratings/lobula/'
parent_path = path('C:/Users/vymak_i7/Desktop/New_ER/Screen/Tm3/gratings/lobula/')
header,rows = datafile.DataFile(parent_path/'directories.csv').get_header_and_data()
parent_mdir = parent_dir+'/measurements/'
dir_list = [parent_dir+'/'+row[1] for row in rows]
path_list = [parent_path/row[1] for row in rows]
for br in brain_regions[:]:
out = []
out2 = []
#import gratings ER and RGECO data and separate headers and rows from each dataset
header,rows = datafile.DataFile(parent_mdir+'all_responses-'+br+'-ER210.csv').get_header_and_data()
out = numpy.asarray(rows)
out = numpy.asarray(out[:,3:],dtype = 'float')
