def fitToParameters(self):
#Populate param for lmfit.
param = Parameters()
#self.def_param.add('A1', value=1.0, min=0,max=1.0, vary=False)
for art in self.param:
if self.param[art]['to_show'] == True and self.param[art][
'calc'] == False:
param.add(art,
value=float(self.param[art]['value']),
min=float(self.param[art]['minv']),
max=float(self.param[art]['maxv']),
vary=self.param[art]['vary'])
#Find the index of the nearest point in the scale.
data = np.array(self.autoNorm).astype(np.float64).reshape(-1)
scale = np.array(self.autotime).astype(np.float64).reshape(-1)
self.indx_L = int(np.argmin(np.abs(scale - self.parentFn.dr.xpos)))
self.indx_R = int(np.argmin(np.abs(scale - self.parentFn.dr1.xpos)))
print('idxl', self.indx_L, self.indx_R)
if self.parentFn.bootstrap_enable_toggle == True:
num_of_straps = self.parentFn.bootstrap_samples.value()
aver_data = {}
lim_scale = scale[self.indx_L:self.indx_R + 1].astype(np.float64)
lim_data = data[self.indx_L:self.indx_R + 1].astype(np.float64)
#Populate a container which will store our output variables from the bootstrap.
for art in self.param:
if self.param[art]['to_show'] == True and self.param[art][
'calc'] == False:
aver_data[art] = []
for i in range(0, num_of_straps):
#Bootstrap our sample, but remove duplicates.
boot_ind = np.random.choice(np.arange(0, lim_data.shape[0]),
size=lim_data.shape[0],
replace=True)
#boot_ind = np.arange(0,lim_data.shape[0])#np.sort(boot_ind)
boot_scale = lim_scale[boot_ind]
boot_data = lim_data[boot_ind]
res = minimize(self.residual,
param,
args=(boot_scale, boot_data,
self.parentFn.def_options))
for art in self.param:
if self.param[art]['to_show'] == True and self.param[art][
'calc'] == False:
aver_data[art].append(res.params[art].value)
for art in self.param:
if self.param[art]['to_show'] == True and self.param[art][
'calc'] == False:
self.param[art]['value'] = np.average(aver_data[art])
self.param[art]['stderr'] = np.std(aver_data[art])
#Run the fitting.
if self.parentFn.bootstrap_enable_toggle == False:
res = minimize(self.residual,
param,
args=(scale[self.indx_L:self.indx_R + 1],
data[self.indx_L:self.indx_R + 1],
self.parentFn.def_options))
#Repopulate the parameter object.
for art in self.param:
if self.param[art]['to_show'] == True and self.param[art][
'calc'] == False:
self.param[art]['value'] = res.params[art].value
self.param[art]['stderr'] = res.params[art].stderr
#Extra parameters, which are not fit or inherited.
#self.param['N_FCS']['value'] = np.round(1/self.param['GN0']['value'],4)
#Populate param for the plotting. Would like to use same method as
plot_param = Parameters()
#self.def_param.add('A1', value=1.0, min=0,max=1.0, vary=False)
for art in self.param:
if self.param[art]['to_show'] == True and self.param[art][
'calc'] == False:
plot_param.add(art,
value=float(self.param[art]['value']),
min=float(self.param[art]['minv']),
max=float(self.param[art]['maxv']),
vary=self.param[art]['vary'])
#Display fitted equation.
if self.parentFn.def_options['Diff_eq'] == 5:
self.model_autoNorm = PB.equation_(
plot_param, scale[self.indx_L:self.indx_R + 1],
self.parentFn.def_options)
elif self.parentFn.def_options['Diff_eq'] == 4:
self.model_autoNorm = VD.equation_(
plot_param, scale[self.indx_L:self.indx_R + 1],
self.parentFn.def_options)
elif self.parentFn.def_options['Diff_eq'] == 3:
self.model_autoNorm = GS.equation_(
plot_param, scale[self.indx_L:self.indx_R + 1],
self.parentFn.def_options)
else:
self.model_autoNorm = SE.equation_(
plot_param, scale[self.indx_L:self.indx_R + 1],
self.parentFn.def_options)
self.model_autotime = scale[self.indx_L:self.indx_R + 1]
if self.parentFn.bootstrap_enable_toggle == False:
self.residualVar = res.residual
else:
self.residualVar = self.model_autoNorm - data[self.
indx_L:self.indx_R +
1].astype(np.float64)
output = fit_report(res.params)
if (res.chisqr > self.parentFn.chisqr):
print('CAUTION DATA DID NOT FIT WELL CHI^2 >',
self.parentFn.chisqr, ' ', res.chisqr)
self.goodFit = False
else:
self.goodFit = True
self.fitted = True
self.chisqr = res.chisqr
self.localTime = time.asctime(time.localtime(time.time()))
#Update the displayed options.
if self.parentFn.def_options['Diff_eq'] == 5:
PB.calc_param_fcs(self.parentFn, self)
elif self.parentFn.def_options['Diff_eq'] == 4:
VD.calc_param_fcs(self.parentFn, self)
elif self.parentFn.def_options['Diff_eq'] == 3:
GS.calc_param_fcs(self.parentFn, self)
else:
SE.calc_param_fcs(self.parentFn, self)
def csv_import_method(fit_obj, file_path, feed=None):
if feed == None:
r_obj = csv.reader(open(str(file_path), 'r'))
else:
r_obj = csv.reader(feed.split('\n'), delimiter=',')
line_one = next(r_obj)
if line_one.__len__() > 1:
if float(line_one[1]) == 2:
version = 2
else:
print('version not known:', line_one[1])
else:
version = 1
corrObj1 = corrObject(file_path, fit_obj)
if version == 1:
fit_obj.objIdArr.append(corrObj1)
c = 0
tscale = []
tdata = []
for line in csv.reader(open(file_path, 'r')):
if (c > 0):
tscale.append(line[0])
tdata.append(line[1])
c += 1
corrObj1.autoNorm = np.array(tdata).astype(np.float64).reshape(-1)
corrObj1.autotime = np.array(tscale).astype(np.float64).reshape(-1)
corrObj1.max = np.max(corrObj1.autoNorm)
corrObj1.min = np.min(corrObj1.autoNorm)
corrObj1.calculate_suitability()
corrObj1.name = corrObj1.name + '-CH' + str(0)
corrObj1.ch_type = 0
corrObj1.datalen = len(tdata)
corrObj1.param = copy.deepcopy(fit_obj.def_param)
fit_obj.fill_series_list()
if version >= 2:
numOfCH = float(next(r_obj)[1])
if numOfCH == 1:
fit_obj.objIdArr.append(corrObj1)
corrObj1.type = str(next(r_obj)[1])
corrObj1.ch_type = int(next(r_obj)[1])
corrObj1.name = corrObj1.name + '-CH' + str(corrObj1.ch_type)
corrObj1.parent_name = 'no name'
corrObj1.parent_uqid = '0'
line = next(r_obj)
while line[0][:4] != 'Time':
if line[0] == 'kcount':
corrObj1.kcount = float(line[1])
if line[0] == 'numberNandB':
corrObj1.numberNandB = float(line[1])
if line[0] == 'brightnessNandB':
corrObj1.brightnessNandB = float(line[1])
if line[0] == 'CV':
corrObj1.CV = float(line[1])
if line[0] == 'carpet pos':
carpet = int(line[1])
if line[0] == 'pc':
pc_text = int(line[1])
if line[0] == 'pbc_f0':
corrObj1.pbc_f0 = float(line[1])
if line[0] == 'pbc_tb':
corrObj1.pbc_tb = float(line[1])
if line[0] == 'parent_name':
corrObj1.parent_name = str(line[1])
if line[0] == 'parent_uqid':
corrObj1.parent_uqid = str(line[1])
line = next(r_obj)
if pc_text != False:
corrObj1.name = corrObj1.name + '_pc_m' + str(pc_text)
tscale = []
tdata = []
#null = next(r_obj)
line = next(r_obj)
while line[0] != 'end':
tscale.append(line[0])
tdata.append(line[1])
line = next(r_obj)
corrObj1.autoNorm = np.array(tdata).astype(np.float64).reshape(-1)
corrObj1.autotime = np.array(tscale).astype(np.float64).reshape(-1)
corrObj1.max = np.max(corrObj1.autoNorm)
corrObj1.min = np.min(corrObj1.autoNorm)
corrObj1.tmax = np.max(corrObj1.autotime)
corrObj1.tmin = np.min(corrObj1.autotime)
corrObj1.siblings = None
corrObj1.param = copy.deepcopy(fit_obj.def_param)
elif numOfCH == 2:
corrObj2 = corrObject(file_path, fit_obj)
corrObj3 = corrObject(file_path, fit_obj)
fit_obj.objIdArr.append(corrObj1)
fit_obj.objIdArr.append(corrObj2)
fit_obj.objIdArr.append(corrObj3)
corrObj1.parent_name = 'no name'
corrObj1.parent_uqid = '0'
corrObj2.parent_name = 'no name'
corrObj2.parent_uqid = '0'
corrObj3.parent_name = 'no name'
corrObj3.parent_uqid = '0'
line_type = next(r_obj)
corrObj1.type = str(line_type[1])
corrObj2.type = str(line_type[1])
corrObj3.type = str(line_type[1])
line_ch = next(r_obj)
corrObj1.ch_type = int(line_ch[1])
corrObj2.ch_type = int(line_ch[2])
corrObj3.ch_type = int(line_ch[3])
corrObj1.name = corrObj1.name + '-CH' + str(corrObj1.ch_type)
corrObj2.name = corrObj2.name + '-CH' + str(corrObj2.ch_type)
corrObj3.name = corrObj3.name + '-CH' + str(corrObj3.ch_type)
line = next(r_obj)
while line[0][:4] != 'Time':
if line[0] == 'kcount':
corrObj1.kcount = float(line[1])
corrObj2.kcount = float(line[2])
if line[0] == 'numberNandB':
corrObj1.numberNandB = float(line[1])
corrObj2.numberNandB = float(line[2])
if line[0] == 'brightnessNandB':
corrObj1.brightnessNandB = float(line[1])
corrObj2.brightnessNandB = float(line[2])
if line[0] == 'CV':
corrObj1.CV = float(line[1])
corrObj2.CV = float(line[2])
corrObj3.CV = float(line[3])
if line[0] == 'carpet pos':
corrObj1.carpet_position = int(line[1])
if line[0] == 'pc':
pc_text = int(line[1])
if line[0] == 'pbc_f0':
corrObj1.pbc_f0 = float(line[1])
corrObj2.pbc_f0 = float(line[2])
if line[0] == 'pbc_tb':
corrObj1.pbc_tb = float(line[1])
corrObj2.pbc_tb = float(line[2])
if line[0] == 'parent_name':
corrObj1.parent_name = str(line[1])
corrObj2.parent_name = str(line[1])
corrObj3.parent_name = str(line[1])
if line[0] == 'parent_uqid':
corrObj1.parent_uqid = str(line[1])
corrObj2.parent_uqid = str(line[1])
corrObj3.parent_uqid = str(line[1])
line = next(r_obj)
if pc_text != False:
corrObj1.name = corrObj1.name + '_pc_m' + str(pc_text)
corrObj2.name = corrObj2.name + '_pc_m' + str(pc_text)
corrObj3.name = corrObj3.name + '_pc_m' + str(pc_text)
#null = next(r_obj)
line = next(r_obj)
tscale = []
tdata0 = []
tdata1 = []
tdata2 = []
while line[0] != 'end':
tscale.append(line[0])
tdata0.append(line[1])
tdata1.append(line[2])
tdata2.append(line[3])
line = next(r_obj)
corrObj1.autotime = np.array(tscale).astype(np.float64).reshape(-1)
corrObj2.autotime = np.array(tscale).astype(np.float64).reshape(-1)
corrObj3.autotime = np.array(tscale).astype(np.float64).reshape(-1)
corrObj1.autoNorm = np.array(tdata0).astype(np.float64).reshape(-1)
corrObj2.autoNorm = np.array(tdata1).astype(np.float64).reshape(-1)
corrObj3.autoNorm = np.array(tdata2).astype(np.float64).reshape(-1)
corrObj1.max = np.max(corrObj1.autoNorm)
corrObj1.min = np.min(corrObj1.autoNorm)
corrObj2.max = np.max(corrObj2.autoNorm)
corrObj2.min = np.min(corrObj2.autoNorm)
corrObj3.max = np.max(corrObj3.autoNorm)
corrObj3.min = np.min(corrObj3.autoNorm)
corrObj1.tmax = np.max(corrObj1.autotime)
corrObj1.tmin = np.min(corrObj1.autotime)
corrObj2.tmax = np.max(corrObj2.autotime)
corrObj2.tmin = np.min(corrObj2.autotime)
corrObj3.tmax = np.max(corrObj3.autotime)
corrObj3.tmin = np.min(corrObj3.autotime)
corrObj1.siblings = [corrObj2, corrObj3]
corrObj2.siblings = [corrObj1, corrObj3]
corrObj3.siblings = [corrObj1, corrObj2]
corrObj1.param = copy.deepcopy(fit_obj.def_param)
corrObj2.param = copy.deepcopy(fit_obj.def_param)
corrObj3.param = copy.deepcopy(fit_obj.def_param)
corrObj1.calculate_suitability()
corrObj2.calculate_suitability()
corrObj3.calculate_suitability()
SE.calc_param_fcs(fit_obj, corrObj1)
def fcs_import_method(fit_obj, file_path, feed=None):
if feed == None:
#f.read().decode('UTF-8','replace')
file = open(str(file_path), 'rb').read()
fileo = file.decode('UTF-8', 'replace').replace("\t", "").split('\n')
r_obj = csv.reader(fileo)
else:
r_obj = csv.reader(feed.replace("\t", "").split('\n'))
version = next(r_obj)
print(version)
null = next(r_obj)
name = next(r_obj)[0].split(' = ')[1]
version_num = float(version[0].split("-")[2].split(" ")[2])
if version_num != 3.0:
print("Version of .fcs file:", version_num,
" must be 3.0 to continue. Please email Dominic with file.")
return False
read = True
ind = 0
channelnum = 0
tdata_arr = []
tscale_arr = []
cdata_arr = []
cscale_arr = []
#Main loop which ends once all file is read.
while read == True:
text = next(r_obj)
while True:
if text[0].split(' = ')[0] == 'Name':
name2 = text[0].split(' = ')[1]
#print('text',name2,tscale_arr.__len__())
if text[0].split(' = ')[0] == 'Channel':
channel_str = text[0].split(' = ')[1]
text = next(r_obj)
if text == []:
read = False
break
if text[0].split(' = ')[0] == 'CountRateArray':
break
if read == False or text == []:
break
#text = next(r_obj)
dimensions = text[0].split(' = ')[1]
len_of_seq = int(dimensions.split(' ')[0])
if len_of_seq > 0:
cdata = []
cscale = []
text = next(r_obj)[0].split(" ")
for v in range(0, len_of_seq):
if text.__len__() > 1:
cscale.append(float(text[0]))
cdata.append(float(text[1]))
text = next(r_obj)[0].split(" ")
cdata_arr.append(cdata)
cscale_arr.append(cscale)
print("cscale_arr", cscale.__len__())
#Reads to first correlation array text.
while text[0].split(' = ')[0] != 'CorrelationArray':
text = next(r_obj)
dimensions = text[0].split(' = ')[1]
len_of_seq = int(dimensions.split(' ')[0])
if len_of_seq > 0:
tdata = []
tscale = []
text = next(r_obj)[0].split(" ")
for v in range(0, len_of_seq):
#print(text)
if text.__len__() > 1:
tscale.append(float(text[0]))
tdata.append(float(text[1]))
text = next(r_obj)[0].split(" ")
tdata_arr.append(tdata)
tscale_arr.append(tscale)
#This is where we find-out how many channels. Unhelpfully after the raw data.
while text[0].split(' = ')[0] != 'Channels':
text = next(r_obj)
num_of_channels = int(text[0].split(' = ')[1])
#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:
print('num_of_channels', num_of_channels)
#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.parent_name = '.fcs files'
corrObj1.parent_uqid = '0'
corrObj1.ch_type = 0
if cscale_arr != []:
#Average counts per bin. Apparently they are normalised to time.
unit = cscale_arr[0][-1] / (cscale_arr[0].__len__() - 1)
#And to be in kHz we divide by 1000.
corrObj1.kcount = np.average(
np.array(cdata_arr[0]) / unit) / 1000
corrObj1.param = copy.deepcopy(fit_obj.def_param)
corrObj1.calculate_suitability()
corrObj1.max = np.max(corrObj1.autoNorm)
corrObj1.min = np.min(corrObj1.autoNorm)
corrObj1.tmax = np.max(corrObj1.autotime)
corrObj1.tmin = np.min(corrObj1.autotime)
if fit_obj.def_options['Diff_eq'] == 4:
VD.calc_param_fcs(fit_obj, corrObj1)
elif fit_obj.def_options['Diff_eq'] == 3:
GS.calc_param_fcs(fit_obj, corrObj1)
else:
SE.calc_param_fcs(fit_obj, corrObj1)
fit_obj.objIdArr.append(corrObj1)
if num_of_channels == 1:
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.
if cscale_arr != []:
corrObj2.kcount = np.average(
np.array(cdata_arr[1]) / unit) / 1000
corrObj2.param = copy.deepcopy(fit_obj.def_param)
corrObj1.siblings = [corrObj2]
corrObj2.siblings = [corrObj1]
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)
corrObj1.siblings = [corrObj2, corrObj3]
corrObj2.siblings = [corrObj1, corrObj3]
corrObj3.siblings = [corrObj1, corrObj2]
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 = []
def sin_import_method(fit_obj, file_path, feed=None):
if feed == None:
r_obj = csv.reader(open(str(file_path)), delimiter='\t')
else:
r_obj = csv.reader(feed.split('\n'), delimiter='\t')
tscale = []
tdata = []
tdata2 = []
tdata3 = []
tdata4 = []
int_tscale = []
int_tdata = []
int_tdata2 = []
proceed = False
for line in r_obj:
if proceed == 'correlated':
if line == []:
proceed = False
else:
tscale.append(float(line[0]))
tdata.append(float(line[1]))
if line.__len__() > 2:
tdata2.append(float(line[2]))
if line.__len__() > 3:
tdata3.append(float(line[3]))
if line.__len__() > 4:
tdata4.append(float(line[4]))
if proceed == 'intensity':
if line == []:
proceed = False
elif line.__len__() > 1:
int_tscale.append(float(line[0]))
int_tdata.append(float(line[1]))
if line.__len__() > 2:
int_tdata2.append(float(line[2]))
if (str(line) == "[\'[CorrelationFunction]\']"):
proceed = 'correlated'
elif (str(line) == "[\'[IntensityHistory]\']"):
proceed = 'intensity'
corrObj1 = corrObject(file_path, fit_obj)
corrObj1.siblings = 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.parent_name = '.sin files'
corrObj1.parent_uqid = '0'
corrObj1.ch_type = 0
#Average counts per bin. Apparently they are normalised to time.
unit = int_tscale[-1] / (int_tscale.__len__() - 1)
#And to be in kHz we divide by 1000.
corrObj1.kcount = np.average(np.array(int_tdata)) / 1000
corrObj1.param = copy.deepcopy(fit_obj.def_param)
corrObj1.calculate_suitability()
corrObj1.max = np.max(corrObj1.autoNorm)
corrObj1.min = np.min(corrObj1.autoNorm)
corrObj1.tmax = np.max(corrObj1.autotime)
corrObj1.tmin = np.min(corrObj1.autotime)
if fit_obj.def_options['Diff_eq'] == 4:
VD.calc_param_fcs(fit_obj, corrObj1)
elif fit_obj.def_options['Diff_eq'] == 3:
GS.calc_param_fcs(fit_obj, corrObj1)
else:
SE.calc_param_fcs(fit_obj, corrObj1)
fit_obj.objIdArr.append(corrObj1)
#Basic
if tdata2 != []:
corrObj2 = corrObject(file_path, fit_obj)
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.parent_name = '.sin files'
corrObj2.parent_uqid = '0'
corrObj2.ch_type = 1
#And to be in kHz we divide by 1000.
corrObj2.kcount = np.average(np.array(int_tdata2)) / 1000
corrObj2.param = copy.deepcopy(fit_obj.def_param)
corrObj1.siblings = [corrObj2]
corrObj2.siblings = [corrObj1]
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)
if tdata3 != []:
corrObj3 = corrObject(file_path, fit_obj)
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.parent_name = '.sin 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)
corrObj1.siblings = [corrObj2, corrObj3]
corrObj2.siblings = [corrObj1, corrObj3]
corrObj3.siblings = [corrObj1, corrObj2]
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)
if tdata4 != []:
corrObj4 = corrObject(file_path, fit_obj)
corrObj4.autoNorm = np.array(tdata4).astype(np.float64).reshape(-1)
corrObj4.autotime = np.array(tscale).astype(
np.float64).reshape(-1) * 1000
corrObj4.ch_type = 3
corrObj4.name = corrObj4.name + '-CH10'
corrObj4.parent_name = '.sin 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)