def load_fnames(md_path):
md = Metadata(md_path)
md.image_table = md.image_table[[
True if (('hybe' in i) or ('nucstain' in i)) else False
for i in md.image_table.acq
]]
posnames = md.posnames
print('posnames loaded')
hybe_list = sorted([
i.split('_')[0] for i in md.acqnames
if ('hybe' in i) or ('nucstain' in i)
])
if zindexes == -1:
Input = [{
'fname_dicts':
md.stkread(Channel='DeepBlue',
Position=pos,
fnames_only=True,
groupby='acq',
hybe=hybe_list),
'posnames':
pos
} for pos in posnames]
else:
Input = [{
'fname_dicts':
md.stkread(Channel='DeepBlue',
Position=pos,
fnames_only=True,
groupby='acq',
hybe=hybe_list,
Zindex=zindexes),
'posnames':
pos
} for pos in posnames]
print('fnames loaded')
return Input
if not os.path.exists(deconvolved_path):
print('analysis path doesnt have your metadata')
print('add path to metadata after analysis path')
else:
deconvolved_path = md_path
bead_path = os.path.join(analysis_path, 'beads')
if not os.path.exists(bead_path):
os.makedirs(bead_path)
results_path = os.path.join(analysis_path, 'results')
if not os.path.exists(results_path):
os.makedirs(results_path)
#Retreiving file names and position names
md = Metadata(deconvolved_path)
md.image_table = md.image_table[[
True if 'hybe' in i else False for i in md.image_table.acq
]]
posnames = md.posnames
print('posnames loaded')
hybe_list = sorted([i.split('_')[0] for i in md.acqnames if 'hybe' in i])
if zindexes == -1:
fnames_dicts = [
md.stkread(Channel='DeepBlue',
Position=pos,
fnames_only=True,
groupby='acq',
hybe=hybe_list) for pos in posnames
]
else:
fnames_dicts = [
md.stkread(Channel='DeepBlue',
deconvolved_path = os.path.join(analysis_path,'deconvolved')
if not os.path.exists(deconvolved_path):
print('analysis path doesnt have your metadata')
print('add path to metadata after analysis path')
else:
deconvolved_path = md_path
# bead_path = os.path.join(analysis_path,'beads')
# if not os.path.exists(bead_path):
# os.makedirs(bead_path)
results_path = os.path.join(analysis_path,'results')
if not os.path.exists(results_path):
os.makedirs(results_path)
#Retreiving file names and position names
md = Metadata(deconvolved_path)
md.image_table = md.image_table[[True if (('hybe' in i) or ('nucstain' in i)) else False for i in md.image_table.acq]]
posnames = md.posnames
print('posnames loaded')
hybe_list = sorted([i.split('_')[0] for i in md.acqnames if ('hybe' in i) or ('nucstain' in i)])
if zindexes == -1:
Input = [ {'fname_dicts': md.stkread(Channel='DeepBlue', Position=pos,
fnames_only=True, groupby='acq',
hybe=hybe_list), 'posnames': pos} for pos in posnames]
else:
Input = [{'fname_dicts': md.stkread(Channel='DeepBlue', Position=pos,
fnames_only=True, groupby='acq',
hybe=hybe_list, Zindex=zindexes), 'posnames': pos} for pos in posnames]
# print(Input[0]['fname_dicts'])
print('fnames loaded')
# Ave_Bead = pickle.load(open(ave_bead_path, 'rb'))
def load_fnames(md_path, zindexes):
md = Metadata(md_path)
md.image_table = md.image_table[[
True if (('hybe' in i) or ('nucstain' in i)) else False
for i in md.image_table.acq
]]
posnames = md.posnames
# Speed up checking if a position is already fully done
not_finished = []
finished_pos = []
for pos in posnames:
try:
processing = pickle.load(
open(
os.path.join(md_path, 'codestacks', pos, 'processing.pkl'),
'rb'))
if len(processing
) == 18: # Check to make sure position is finished
finished_pos.append(pos)
else:
not_finished.append(pos)
except:
not_finished.append(pos)
random_posnames = np.random.choice(not_finished,
size=len(not_finished),
replace=False)
hybe_list = sorted([
i.split('_')[0] for i in md.acqnames
if ('hybe' in i) or ('nucstain' in i)
])
if zindexes == -1:
Input = [{
'fname_dicts':
md.stkread(Channel='DeepBlue',
Position=pos,
fnames_only=True,
groupby='acq',
hybe=hybe_list),
'posnames':
pos
} for pos in random_posnames]
else:
Input = [{
'fname_dicts':
md.stkread(Channel='DeepBlue',
Position=pos,
fnames_only=True,
groupby='acq',
hybe=hybe_list,
Zindex=list(range(8, 25))),
'posnames':
pos
} for pos in random_posnames]
# Input = []
# for pos in random_posnames:
# pos_df = {}
# for hybe in hybe_list:
# pos_df[hybe] = []
# for z in range(1,zindexes):
# fnames = md.stkread(Channel='DeepBlue', Position=pos,
# fnames_only=True, groupby='acq',
# hybe=hybe_list,Zindex=z)
# for h,f in fnames.items():
# pos_df[h.split('_')[0]].append(f)
# Input.append({'fname_dicts':pos_df,'posnames': pos})
return Input
