def main(name, arguments):
parser.prog = name
options, args = parser.parse_args(arguments)
args = cli_tools.glob_args(args)
if options.help:
print_help()
if len(args) == 0:
raise ValueError('Some contour files must be specified!')
contours = simple_interface.load_contours(args, show_progress = options.show_progress)
measurements = [m(**kws) for m, kws in options.measurements]
header, rows = simple_interface.measure_contours(contours, options.show_progress, *measurements)
datafile.write_data_file([header]+rows, options.output_file)
def main(name, arguments):
parser.prog = name
options, args = parser.parse_args(arguments)
args = cli_tools.glob_args(args)
if len(args) == 0:
raise ValueError('Some contour files must be specified!')
contours = simple_interface.load_contours(args, show_progress = options.show_progress)
shape_model, header, rows, norm_header, norm_rows = simple_interface.make_shape_model(contours, options.variance_explained)
shape_model.to_file(options.output_prefix + '.contour')
if options.write_data:
datafile.write_data_file([header]+rows, options.output_prefix + '-positions.csv')
datafile.write_data_file([norm_header]+norm_rows, options.output_prefix + '-normalized-positions.csv')
def main(name, arguments):
parser.prog = name
options, args = parser.parse_args(arguments)
args = cli_tools.glob_args(args)
reference = cli_tools.glob_args(options.reference)
if (len(args) < 2) or (len(args) + len(reference) < 3):
raise ValueError(
'A data column and at least two datasets (at least one of which must not be a reference) must be provided!'
)
data_column, datasets = args[0], args[1:]
# if the data column is convertible to an integer, do so and
# then convert from 1-indexed to 0-indexed
try:
data_column = int(data_column)
data_column -= 1
except:
pass
if options.show_progress:
datasets = terminal_tools.progress_list(datasets, "Reading input data")
names, pops = read_files(datasets, data_column)
if options.show_progress and len(reference) > 0:
reference = terminal_tools.progress_list(reference,
"Reading reference data")
ref_names, ref_pops = read_files(reference, data_column)
if options.show_progress:
pb = terminal_tools.IndeterminantProgressBar("Resampling data")
if len(ref_pops) > 0:
pvals = ks_resample.compare_to_ref(pops, ref_pops, options.n)
if len(ref_names) > 1:
ref_name = 'reference (%s)' % (', '.join(ref_names))
else:
ref_name = ref_names[0]
rows = [[None, 'difference from %s' % ref_name]]
for name, p in zip(names, pvals):
rows.append([name, format_pval(p, options.n)])
else: # no ref pops
pvals = ks_resample.symmetric_comparison(pops, options.n)
rows = [[None] + names]
for i, (name, p_row) in enumerate(zip(names, pvals)):
rows.append([name] + [format_pval(p, options.n) for p in p_row])
rows[-1][i +
1] = None # empty out the self-self comparison diagonal
datafile.write_data_file(rows, options.output_file)
def main(name, arguments):
parser.prog = name
options, args = parser.parse_args(arguments)
args = cli_tools.glob_args(args)
if options.help:
print_help()
if len(args) == 0:
raise ValueError('Some contour files must be specified!')
contours = simple_interface.load_contours(
args, show_progress=options.show_progress)
measurements = [m(**kws) for m, kws in options.measurements]
header, rows = simple_interface.measure_contours(contours,
options.show_progress,
*measurements)
datafile.write_data_file([header] + rows, options.output_file)
# directory = 'D:/Image_files/Mi9_strongER210+RGECO/'+stim
# parent_dir = path(directory)
# dirs = sorted(os.listdir(parent_dir))
t_dir = 'C:/Users/vymak_i7/Desktop/New_ER/Screen/Mi9_strong/'+stim
target_dir = path(t_dir)
#1) CREATE FILES FROM IMAGE_FILES TO NEW_ER/SCREEN FOLDERS
# for file in dirs[:]:
# targetname=file
# os.mkdir(os.path.join(t_dir,targetname))
# # """2) Create directories.csv file"""
target_list = sorted(os.listdir(target_dir))
header = ['fly','directory']
dir_list = []
for file in target_list:
i=1
if 'fly' in file:
i = file.find('fly') + 3
fly_no = file[i]
dir_list.append([fly_no,file])
datafile.write_data_file([header]+dir_list,target_dir/'directories.csv')
count_off = count_off-1
F_off.extend([count_off])
F_on.extend([F_on])
else:
F_on.extend([count_on])
F_off.extend([count_off])
frame_labels.extend([count_on*(count_on+count_off)])
n=n+1
frame_labels.extend([0])
# print(frame_labels.index(max(frame_labels)))
# print(F_on)
# print(F_off)
plt.plot(frame_labels)
plt.savefig(directory/'plots/frame_numbers.png')
plt.show()
plt.close()
print(count_on)
print(count_off)
output_array[:,:R.shape[1]] = R
output_array[:,-1] = frame_labels
OAS = output_array[output_array[:,-1].argsort()]
i1 = numpy.searchsorted(OAS[:,-1],1)
datafile.write_data_file([header]+list(OAS[i1:,:]),directory/stim_file.namebase+'-frames.csv')
print('# of total mapped RF: ' + str(len(newmap)))
print('# of total mapped flash: ' + str(len(newfmapCYT) / 2))
#add unmapped RFs to updated unmapped RFs array
sub_unmap = np.asarray(sub_unmap)
sub_funmapCYT = np.asarray(sub_funmapCYT)
sub_funmapER = np.asarray(sub_funmapER)
[newunmap.append(sub_unmap[a, :]) for a in range(len(sub_unmap))]
[
newfunmapCYT.append(sub_funmapCYT[a, :])
for a in range(len(sub_funmapCYT))
]
[newfunmapER.append(sub_funmapER[a, :]) for a in range(len(sub_funmapER))]
#save new mapped and unmapped RFs arrays as .csv files
datafile.write_data_file([header] + newmap, map_mdir + '/newRF_centers-' +
br + '.csv') #MLB RF centers .csv
datafile.write_data_file([header] + newunmap,
unmap_mdir + '/newRF_centers-' + br +
'.csv') #MLB RF centers .csv
datafile.write_data_file(
[headerf] + newfmapER, f_mdir + '/mapped/newavg_flash-' + br +
'-ER210.csv') #ER210 flash responses .csv (mapped RFs)
datafile.write_data_file(
[headerf] + newfmapCYT, f_mdir + '/mapped/newavg_flash-' + br +
'-RGECO.csv') #RGECO flash responses .csv (mapped RFs)
datafile.write_data_file(
[headerf] + newfunmapER, f_mdir + '/unmapped/newavg_flash-' + br +
'-ER210.csv') #ER210 flash responses .csv (unmapped RFs)
datafile.write_data_file([headerf] + newfunmapCYT, f_mdir +
'/unmapped/newavg_flash-' + br + '-RGECO.csv')
pylab.xticks(numpy.arange(0, T[-1], 5), fontsize=12)
# pylab.yticks(numpy.arange(ytmin, ytmax, yti), fontsize=12)
pylab.xlabel('seconds', fontsize=14)
pylab.ylabel('DF/F', fontsize=14)
DFF = [row[1], br, n, centx, centy, 'light']
roi_A = A
roi_A = numpy.asarray(roi_A)
# roi_A = smooth(roi_A)
# roi_A = numpy.asarray(roi_A[:-4])
baseline = numpy.median(roi_A[:10])
dff = (roi_A - baseline) / baseline
DFF.extend(dff)
pylab.plot(T, dff, color=color, alpha=alphas[1])
OUT_DFF.append(DFF)
O_DFF.append(DFF)
pylab.savefig(plot_dir + '/' + br + '-ROI-' + str(n) +
'-ER210.png',
dpi=300,
bbox_inches='tight')
#pylab.show()
pylab.close()
n = n + 1
datafile.write_data_file([header_out] + OUT_DFF, m_dir +
'average_responses-' + br + '-ER210.csv')
datafile.write_data_file([header_out] + O_DFF, parent_m_dir +
'average_responses-' + br + '-ER210.csv')
plt.savefig(unmap_pdir + br + '/' + str(count_unmap).zfill(4) +
'-' + str(R[n, 0]) + '-' + br + '-ROI_' +
str(R[n, 2]) + '.png',
dpi=300,
bbox_inches='tight')
count_unmap = count_unmap + 1
# plt.show()
plt.close()
f = f + 2
g = g + 1
n = n + 4
#Write and save .csv files
datafile.write_data_file([header_out] + OUT, map_mdir + '/RF_centers-' +
br + '.csv') #MLB RF centers .csv
datafile.write_data_file([flash_header] + norm_cytf,
flash_mdir + '/mapped/average_responses-' + br +
'-RGECO.csv') #flash responses .csv RGECO
datafile.write_data_file([flash_header] + norm_ERf,
flash_mdir + '/mapped/average_responses-' + br +
'-ER210.csv') #flash responses .csv ER210
#Write up .csv files for unmappable RF centers
datafile.write_data_file([header_out] + OUT2, unmap_mdir + '/RF_centers-' +
br + '.csv') #MLB RF centers .csv
datafile.write_data_file([flash_header] + abn_cytf,
flash_mdir + '/unmapped/average_responses-' + br +
'-RGECO.csv') #flash responses .csv RGECO
datafile.write_data_file([flash_header] + abn_ERf,
flash_mdir + '/unmapped/average_responses-' + br +
plt.figure(figsize=(fs, fs))
ax = plt.subplot(111)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
# plt.ylim(ymin,ymax)
plt.xlim(0,T[-1])
plt.xticks(numpy.arange(0,T[-1],1), fontsize=12)
# plt.yticks(numpy.arange(ytmin, ytmax, yti), fontsize=12)
plt.yticks(fontsize=12)
plt.xlabel('seconds',fontsize = 14)
plt.ylabel('DF/F',fontsize = 14)
for i,c,epoch_num in zip(indices,colors,ENUM):
DFF=[row[1],br,n,centx,centy,epoch_num]
roi_A = A[i[0]:i[1]]
baseline = numpy.median(roi_A[:10])
dff = (roi_A-baseline)/baseline
DFF.extend(dff)
ax.plot(T,dff,color = c)
OUT_DFF.append(DFF)
plt.savefig(plot_dir+'/'+br+'-RGECO-'+str(n)+'.png',dpi=300,bbox_inches = 'tight')
# plt.show()
plt.close()
n=n+1
print(br+' MLB count: '+str(len(OUT_DFF)/4))
datafile.write_data_file([header_out]+OUT_DFF,mdir+'/average_responses-'+br+'.csv')
