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rolled back .fcs file import to restore naming convention and arbitar…
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…ty channel number suport
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@dwaithe
dwaithe committed Aug 26, 2021
1 parent 1eddd6b commit 1e3d379
Showing 1 changed file with 25 additions and 124 deletions.
149 changes: 25 additions & 124 deletions focuspoint/fimport_methods.py
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@ def fcs_import_method(fit_obj,file_path,feed=None):

read = True
ind = 0
channelnum =0
channelnum = 0

tdata_arr = []
tscale_arr = []
Expand Down Expand Up @@ -108,26 +108,38 @@ def fcs_import_method(fit_obj,file_path,feed=None):


num_of_channels = int(text[0].split(' = ')[1])

if channel_str == 'Auto-correlation detector Meta1':
channel = 0
chname = 'CH1'
ind +=1
if channel_str == 'Auto-correlation detector Meta2':
channel = 1
chname = 'CH2'
if channel_str == 'Cross-correlation detector Meta2 versus detector Meta1':
channel = 3
chname = 'CH21'
if channel_str == 'Cross-correlation detector Meta1 versus detector Meta2':
channel = 2
chname = 'CH12'

#Which channel is it. Found by matching string found earlier?? The only way I saw to do it.
for i in range(num_of_channels):
text = next(r_obj)[0].split(' = ')[1]
if text == channel_str:
this_is_ch = i

if len_of_seq >0:

#If a four channel file we want to skip until we have collected all channels.
if num_of_channels == 4 and tdata_arr.__len__() != 4:
continue


corrObj1 = corrObject(file_path,fit_obj)
corrObj1.siblings = None
corrObj1.autoNorm= np.array(tdata_arr[0]).astype(np.float64).reshape(-1)
corrObj1.autotime= np.array(tscale_arr[0]).astype(np.float64).reshape(-1)*1000


corrObj1.name = name+'-CH0'
corrObj1.name = name + '_'+str(ind)+'_'+chname
corrObj1.parent_name = '.fcs files'
corrObj1.parent_uqid = '0'

Expand Down Expand Up @@ -156,123 +168,12 @@ def fcs_import_method(fit_obj,file_path,feed=None):
fit_obj.objIdArr.append(corrObj1)


if num_of_channels == 1 or num_of_channels == 0:
tscale_arr = []
tdata_arr = []
cscale_arr = []
cdata_arr = []
continue;


if num_of_channels == 4 and tdata_arr.__len__() == 4:

corrObj2 = corrObject(file_path,fit_obj)
corrObj2.autoNorm= np.array(tdata_arr[1]).astype(np.float64).reshape(-1)
corrObj2.autotime= np.array(tscale_arr[1]).astype(np.float64).reshape(-1)*1000

corrObj2.name = name+'-CH1'
corrObj2.parent_name = '.fcs files'
corrObj2.parent_uqid = '0'
corrObj2.ch_type = 1;


#And to be in kHz we divide by 1000. This we found through comparison with Zeiss software.
if cscale_arr != []:
corrObj2.kcount = np.average(np.array(cdata_arr[1]))/1000

corrObj2.param = copy.deepcopy(fit_obj.def_param)

corrObj2.calculate_suitability()
corrObj2.max = np.max(corrObj2.autoNorm)
corrObj2.min = np.min(corrObj2.autoNorm)
corrObj2.tmax = np.max(corrObj2.autotime)
corrObj2.tmin = np.min(corrObj2.autotime)

if fit_obj.def_options['Diff_eq'] == 4:
VD.calc_param_fcs(fit_obj,corrObj2)
elif fit_obj.def_options['Diff_eq'] == 3:
GS.calc_param_fcs(fit_obj,corrObj2)
else:
SE.calc_param_fcs(fit_obj,corrObj2)

fit_obj.objIdArr.append(corrObj2)

corrObj3 = corrObject(file_path,fit_obj)
corrObj3.autoNorm= np.array(tdata_arr[2]).astype(np.float64).reshape(-1)
corrObj3.autotime= np.array(tscale_arr[2]).astype(np.float64).reshape(-1)*1000

corrObj3.ch_type = 2;
corrObj3.name = name+'-CH01'
corrObj3.parent_name = '.fcs files'
corrObj3.parent_uqid = '0'


#And to be in kHz we divide by 1000.
#corrObj3.kcount = np.average(np.array(int_tdata3)/unit)/1000
corrObj3.param = copy.deepcopy(fit_obj.def_param)
tscale_arr = []
tdata_arr = []
cscale_arr = []
cdata_arr = []



corrObj3.calculate_suitability()
corrObj3.max = np.max(corrObj3.autoNorm)
corrObj3.min = np.min(corrObj3.autoNorm)
corrObj3.tmax = np.max(corrObj3.autotime)
corrObj3.tmin = np.min(corrObj3.autotime)
if fit_obj.def_options['Diff_eq'] == 4:
VD.calc_param_fcs(fit_obj,corrObj3)
elif fit_obj.def_options['Diff_eq'] == 3:
GS.calc_param_fcs(fit_obj,corrObj3)
else:
SE.calc_param_fcs(fit_obj,corrObj3)
fit_obj.objIdArr.append(corrObj3)


corrObj4 = corrObject(file_path,fit_obj)
corrObj4.autoNorm= np.array(tdata_arr[3]).astype(np.float64).reshape(-1)
corrObj4.autotime= np.array(tscale_arr[3]).astype(np.float64).reshape(-1)*1000

corrObj4.ch_type = 3;
corrObj4.name = name+'-CH10'
corrObj4.parent_name = '.fcs files'
corrObj4.parent_uqid = '0'


#And to be in kHz we divide by 1000.
#corrObj4.kcount = np.average(np.array(int_tdata4)/unit)/1000
corrObj4.param = copy.deepcopy(fit_obj.def_param)

corrObj1.siblings = [corrObj2,corrObj3,corrObj4]
corrObj2.siblings = [corrObj1,corrObj3,corrObj4]
corrObj3.siblings = [corrObj1,corrObj2,corrObj4]
corrObj4.siblings = [corrObj1,corrObj2,corrObj3]

corrObj4.calculate_suitability()
corrObj4.max = np.max(corrObj4.autoNorm)
corrObj4.min = np.min(corrObj4.autoNorm)
corrObj4.tmax = np.max(corrObj4.autotime)
corrObj4.tmin = np.min(corrObj4.autotime)

if fit_obj.def_options['Diff_eq'] == 4:
VD.calc_param_fcs(fit_obj,corrObj4)
elif fit_obj.def_options['Diff_eq'] == 3:
GS.calc_param_fcs(fit_obj,corrObj4)
else:
SE.calc_param_fcs(fit_obj,corrObj4)
fit_obj.objIdArr.append(corrObj4)


tscale_arr = []
tdata_arr = []
cscale_arr = []
cdata_arr = []





#for i in range():
# corrObj.name = corrObj.name+'_'+str(ind)+'_'+str(name)

def sin_import_method(fit_obj,file_path,feed=None):

if feed == None:
Expand Down Expand Up @@ -329,7 +230,7 @@ def sin_import_method(fit_obj,file_path,feed=None):
corrObj1.autoNorm= np.array(tdata).astype(np.float64).reshape(-1)
corrObj1.autotime= np.array(tscale).astype(np.float64).reshape(-1)*1000

corrObj1.name = corrObj1.name+'-CH0'
corrObj1.name = corrObj1.name+'-CH1'
corrObj1.parent_name = '.sin files'
corrObj1.parent_uqid = '0'

Expand Down Expand Up @@ -362,7 +263,7 @@ def sin_import_method(fit_obj,file_path,feed=None):
corrObj2.autoNorm= np.array(tdata2).astype(np.float64).reshape(-1)
corrObj2.autotime= np.array(tscale).astype(np.float64).reshape(-1)*1000

corrObj2.name = corrObj2.name+'-CH1'
corrObj2.name = corrObj2.name+'-CH2'
corrObj2.parent_name = '.sin files'
corrObj2.parent_uqid = '0'
corrObj2.ch_type = 1
Expand Down Expand Up @@ -393,7 +294,7 @@ def sin_import_method(fit_obj,file_path,feed=None):
corrObj3.autoNorm= np.array(tdata3).astype(np.float64).reshape(-1)
corrObj3.autotime= np.array(tscale).astype(np.float64).reshape(-1)*1000
corrObj3.ch_type = 2;
corrObj3.name = corrObj3.name+'-CH01'
corrObj3.name = corrObj3.name+'-CH12'

corrObj3.parent_name = '.sin files'
corrObj3.parent_uqid = '0'
Expand Down Expand Up @@ -425,7 +326,7 @@ def sin_import_method(fit_obj,file_path,feed=None):
corrObj4.autotime= np.array(tscale).astype(np.float64).reshape(-1)*1000

corrObj4.ch_type = 3;
corrObj4.name = corrObj4.name+'-CH10'
corrObj4.name = corrObj4.name+'-CH21'

corrObj4.parent_name = '.sin files'
corrObj4.parent_uqid = '0'
Expand Down

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