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@biosciflo
biosciflo authored Jul 25, 2024
1 parent 17e87b5 commit e7bed96
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Showing 9 changed files with 12,524 additions and 74 deletions.
9 changes: 5 additions & 4 deletions Python/AllFunctions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1437,9 +1437,10 @@ def makemask(imageR, Lambdachannel, varlist, MaskParams, colocalization):
idx_col = [Lambdachannel.index(colocalization)]
else:
idx_col = []

notnoisemask, Thr_abovenoise = abovenoise(imageR, varidxs, MaskParams['S_N'], MaskParams['stddev'],
MaskParams['bgmean'], idx_col)


combinedmask, satperc = nosaturated(imageR, notnoisemask, varidxs, MaskParams['PixelDepth']) # > S/N + no sat

Expand Down Expand Up @@ -1878,22 +1879,22 @@ def exportframes(filename, savingpath, frames, dims):
try:
if sum(np.array(dims) > 1) == 2 or sum(np.array(dims) > 1) == 3:
frames.to_excel(excelname + '.xlsx', sheet_name='Global', index=False, header=False, index_label=False)
frames.to_csv(excelname, index=False, header=False, index_label=False)
frames.to_csv(excelname + '.csv', index=False, header=False, index_label=False)

elif sum(np.array(dims) > 1) == 4:
with pd.ExcelWriter(excelname + '.xlsx', engine='xlsxwriter') as writer:
for z in frames.keys():
frames[z].to_excel(writer, sheet_name='slice_{0}'.format(z),
index=False, header=False, index_label=False)
frames[z].to_csv(excelname + '_slice_{0}'.format(z), index=False, header=False, index_label=False)
frames[z].to_csv(excelname + '_slice_{0}'.format(z) + '.csv' , index=False, header=False, index_label=False)

elif sum(np.array(dims) > 1) == 5:
with pd.ExcelWriter(excelname + '.xlsx', engine='xlsxwriter') as writer:
for t in frames.keys():
for z in frames[t].keys():
frames[t][z].to_excel(writer, sheet_name='slice_t{0}_z{1}'.format(t, z),
index=False, header=False, index_label=False)
frames[t][z].to_csv(excelname + '_t{0}_z{1}'.format(t, z), index=False, header=False,
frames[t][z].to_csv(excelname + '_t{0}_z{1}'.format(t, z) + '.csv', index=False, header=False,
index_label=False)
except:
print('Nothing to save')
Expand Down
226 changes: 226 additions & 0 deletions Python/Analyzer_V1_0_3.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,226 @@
# -*- coding: utf-8 -*-

from Packages import np
import AllFunctions as F



def GP_Analyser(mode, filename, Lambdachannel, image, dims, varlist, ObjectDetection, profiler,
profilershape, autoff, PDiamCutoff, proDim1, proDim2, text, histpars, histpars_cyto,
text_cyto, profile_cyto, varlist_cyto, n_debranch, tol0, tol1, savecroppedmembrane, savecroppedcyto,
savelinearized, savepath, objlinear, recentering, dim_line, MaskParams_mem, MaskParams_cyto,
colocalization, savephasors, radius):

Profpars = [profilershape, (proDim1, proDim2), PDiamCutoff]
if dims[0] <= 1 and dims[1] <= 1: # 3dim (t and z are 1)
if mode == 0:
Dict = {'datamask': []}
imageR = image

slice_Thr, Threshold, Recmask, IntSum, Meta, Reccoors, notempty, varidxs, Thr_abovenoise = \
F.makemask(imageR, Lambdachannel, varlist, MaskParams_mem, colocalization)

if profile_cyto:
_, _, Recmask_cyto, _, Meta_cyto, _, _, varidxs_cyto, *_ = \
F.makemask(imageR, Lambdachannel, varlist_cyto, MaskParams_cyto, colocalization)
Recmask_cyto[Recmask] = False

else:
Recmask_cyto, Meta_cyto, Reccoors_cyto, varidxs_cyto = [], [], [], []

Dict.update(
{'datamask': [slice_Thr, Threshold, Recmask, IntSum, Meta, Reccoors, notempty, varidxs, Recmask_cyto]})
return Dict

else:
Dict = {'datamask': [], 'analysis': []}
imageR = image

slice_Thr, Threshold, Recmask, IntSum, Meta, Reccoors, notempty, varidxs, Thr_abovenoise = \
F.makemask(imageR, Lambdachannel, varlist, MaskParams_mem, colocalization)

if profile_cyto:
_, _, Recmask_cyto, _, Meta_cyto, _, _, varidxs_cyto, *_ = \
F.makemask(imageR, Lambdachannel, varlist_cyto, MaskParams_cyto, colocalization)
Recmask_cyto[Recmask] = False
Reccoors_cyto = np.transpose(np.nonzero(Recmask_cyto))

else:
Recmask_cyto, Meta_cyto, Reccoors_cyto, varidxs_cyto = [], [], [], []

Dict.update(
{'datamask': [slice_Thr, Threshold, Recmask, IntSum, Meta, Reccoors, notempty, varidxs, Recmask_cyto]})

if np.sum(Recmask_cyto) == 0:
profile_cyto = False

if notempty:
Results, frames = F.measuremask(imageR, Reccoors, Reccoors_cyto, Meta, varidxs, varidxs_cyto, text,
histpars, ObjectDetection, Recmask,
Recmask_cyto, n_debranch, tol0, tol1, Lambdachannel, profile_cyto,
varlist_cyto, text_cyto, savecroppedmembrane,
savecroppedcyto, savepath, profiler, Profpars, autoff, objlinear, radius,
savelinearized, histpars_cyto, recentering, MaskParams_mem['pxlSize'],
MaskParams_mem['PixelDepth'], Thr_abovenoise, dim_line, colocalization,
savephasors)

F.exportframes(filename, savepath, frames, dims)

else:
print('Image is empty')
Results = []

Dict.update({'analysis': Results})

return Dict

elif dims[0] > 1 or dims[1] > 1: #4dim (t or z is bigger than 1)
if mode == 0:
preDict_z = {z: [] for z in range(image.shape[0])}
for z in range(0, image.shape[0]):
Dict = {'datamask': []}
imageR = image[z, :, :, :]

slice_Thr, Threshold, Recmask, IntSum, Meta, Reccoors, notempty, varidxs, Thr_abovenoise = \
F.makemask(imageR, Lambdachannel, varlist, MaskParams_mem[z], colocalization)

if profile_cyto:
_, _, Recmask_cyto, _, Meta_cyto, _, _, varidxs_cyto, *_ = \
F.makemask(imageR, Lambdachannel, varlist_cyto, MaskParams_cyto[z], colocalization)
Recmask_cyto[Recmask] = False

else:
Recmask_cyto, Meta_cyto, Reccoors_cyto, varidxs_cyto = [], [], [], []

Dict.update(
{'datamask': [slice_Thr, Threshold, Recmask, IntSum, Meta, Reccoors, notempty, varidxs,
Recmask_cyto]})
preDict_z[z] = Dict

return preDict_z

else:
preDict_z = {z: [] for z in range(image.shape[0])}
frames_z = {z: [] for z in range(image.shape[0])}
for z in range(0, image.shape[0]):
print('Analyzing slice n{0}'.format(z + 1))
Dict = {'datamask': [], 'analysis': []}
imageR = image[z, :, :, :]

slice_Thr, Threshold, Recmask, IntSum, Meta, Reccoors, notempty, varidxs, Thr_abovenoise = \
F.makemask(imageR, Lambdachannel, varlist, MaskParams_mem[z], colocalization)

if profile_cyto:
_, _, Recmask_cyto, _, Meta_cyto, _, _, varidxs_cyto, *_ = \
F.makemask(imageR, Lambdachannel, varlist_cyto, MaskParams_cyto[z], colocalization)
Recmask_cyto[Recmask] = False
Reccoors_cyto = np.transpose(np.nonzero(Recmask_cyto))

else:
Recmask_cyto, Meta_cyto, Reccoors_cyto, varidxs_cyto = [], [], [], []

Dict.update(
{'datamask': [slice_Thr, Threshold, Recmask, IntSum, Meta, Reccoors, notempty, varidxs, Recmask_cyto]})

if np.sum(Recmask_cyto) == 0:
profile_cyto = False

if notempty:
Results, frames_z[z] = F.measuremask(imageR, Reccoors, Reccoors_cyto, Meta, varidxs, varidxs_cyto, text,
histpars, ObjectDetection, Recmask,
Recmask_cyto, n_debranch, tol0, tol1, Lambdachannel, profile_cyto,
varlist_cyto, text_cyto, savecroppedmembrane,
savecroppedcyto, savepath, profiler, Profpars, autoff, objlinear,
radius,
savelinearized, histpars_cyto, recentering, MaskParams_mem[z]['pxlSize'],
MaskParams_mem[z]['PixelDepth'], Thr_abovenoise, dim_line, colocalization,
savephasors)

else:
print('Image is empty')
Results = []

Dict.update({'analysis': Results})

preDict_z[z] = Dict

F.exportframes(filename, savepath, frames_z, dims)

return preDict_z

elif dims[0] > 1 and dims[1] > 1: #5dim (t and z is bigger than 1)
if mode == 0:
preDict_zt = {t: {z: [] for z in range(image.shape[1])} for t in range(image.shape[0])}
for t in range(0, image.shape[0]):
for z in range(0, image.shape[1]):
Dict = {'datamask': []}
imageR = image[t, z, :, :, :]

slice_Thr, Threshold, Recmask, IntSum, Meta, Reccoors, notempty, varidxs, Thr_abovenoise = \
F.makemask(imageR, Lambdachannel, varlist, MaskParams_mem[t][z], colocalization)

if profile_cyto:
_, _, Recmask_cyto, _, Meta_cyto, _, _, varidxs_cyto, *_ = \
F.makemask(imageR, Lambdachannel, varlist_cyto, MaskParams_cyto[t][z], colocalization)
Recmask_cyto[Recmask] = False

else:
Recmask_cyto, Meta_cyto, Reccoors_cyto, varidxs_cyto = [], [], [], []

Dict.update(
{'datamask': [slice_Thr, Threshold, Recmask, IntSum, Meta, Reccoors, notempty, varidxs,
Recmask_cyto]})
preDict_zt[t][z] = Dict

return preDict_zt

else:
preDict_zt = {t: {z: [] for z in range(image.shape[1])} for t in range(image.shape[0])}
frames_zt = {t: {z: [] for z in range(image.shape[1])} for t in range(image.shape[0])}
for t in range(0, image.shape[0]):
for z in range(0, image.shape[1]):
print('Analyzing slice t{0}z{1}'.format(t + 1, z + 1))
Dict = {'datamask': [], 'analysis': []}
imageR = image[t, z, :, :, :]

slice_Thr, Threshold, Recmask, IntSum, Meta, Reccoors, notempty, varidxs, Thr_abovenoise = \
F.makemask(imageR, Lambdachannel, varlist, MaskParams_mem[t][z], colocalization)

if profile_cyto:
_, _, Recmask_cyto, _, Meta_cyto, _, _, varidxs_cyto, *_ = \
F.makemask(imageR, Lambdachannel, varlist_cyto, MaskParams_cyto[t][z], colocalization)
Recmask_cyto[Recmask] = False
Reccoors_cyto = np.transpose(np.nonzero(Recmask_cyto))

else:
Recmask_cyto, Meta_cyto, Reccoors_cyto, varidxs_cyto = [], [], [], []

Dict.update(
{'datamask': [slice_Thr, Threshold, Recmask, IntSum, Meta, Reccoors, notempty, varidxs,
Recmask_cyto]})

if np.sum(Recmask_cyto) == 0:
profile_cyto = False

if notempty:
Results, frames_zt[t][z] = F.measuremask(imageR, Reccoors, Reccoors_cyto, Meta, varidxs, varidxs_cyto, text,
histpars, ObjectDetection, Recmask,
Recmask_cyto, n_debranch, tol0, tol1, Lambdachannel, profile_cyto,
varlist_cyto, text_cyto, savecroppedmembrane,
savecroppedcyto, savepath, profiler, Profpars, autoff, objlinear,
radius,
savelinearized, histpars_cyto, recentering,
MaskParams_mem[t][z]['pxlSize'],
MaskParams_mem[t][z]['PixelDepth'], Thr_abovenoise, dim_line,
colocalization, savephasors)
else:
print('Image is empty')
Results = []

Dict.update({'analysis': Results})

preDict_zt[t][z] = Dict

F.exportframes(filename, savepath, frames_zt, dims)

return preDict_zt
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