def IgorLoad(self,Source):
from igor import binarywave,igorpy
Waves={}
Variables={}
if Source[-3:] == 'ibw':
Waves[binarywave.load(Source)['wave']['wave_header']['bname']]=binarywave.load(Source)['wave']['wData']
Variables['SampleInterval']=1
elif Source[-3:] == 'pxp':
#tree=igorpy.load(Source).format()
#print tree
#print '#############'
b=igorpy.load(Source)
for i in b:
if isinstance(i, igorpy.Wave):
Waves[str(i.name)]=i.data
elif isinstance(i, igorpy.Variables):
Variables=i.uservar
elif Source[-3:] == 'txt':
b=numpy.loadtxt(Source)
Waves[Source.split("/")[-1].replace('.txt','').replace('.','_')]=b
Variables=None
elif Source[-3:] == 'csv':
b=numpy.loadtxt(Source)
Waves[Source.split("/")[-1].replace('.txt','').replace('.','_')]=b
Variables=None
elif Source[-3:] == 'wcp':
print "not supported yet, but you can import an igor file"
b,c=self.read_block(Source)
for i,j in enumerate(b):
Waves[str(j[0])+str(i)]=numpy.array(j[1])
for i in c:
Variables[i]=c[i]
try:
return Waves,Variables
except UnboundLocalError:
msgBox = QtGui.QMessageBox()
msgBox.setText(
"""
<b>Filtype not Supported</b>
<p>Only txt, ibw and pxp files are supported
""")
msgBox.exec_()
return None, None
def read_single_pxt(reference_path: typing.Union[Path, str], byte_order=None):
"""
Uses igor.igorpy to load a single .PXT or .PXP file
:return:
"""
import igor.igorpy as igor
if isinstance(reference_path, Path):
reference_path = str(reference_path.absolute())
loaded = None
if byte_order is None:
for try_byte_order in ['>', '=', '<']:
try:
loaded = igor.load(reference_path, initial_byte_order=try_byte_order)
break
except Exception: # pylint: disable=broad-except
# bad byte ordering probably
pass
else:
loaded = igor.load(reference_path, initial_byte_order=byte_order)
children = [c for c in loaded.children if isinstance(c, igor.Wave)]
if len(children) > 1:
warnings.warn('Igor PXT file contained {} waves. Ignoring all but first.', len(children))
return wave_to_xarray(children[0])
def read_single_ibw(reference_path: typing.Union[Path, str]):
"""
Currently igorpy does not support this though
Uses igor.igorpy to load an .ibw file
:param reference_path:
:return:
"""
import igor.igorpy as igor
if isinstance(reference_path, Path):
reference_path = str(reference_path.absolute())
return igor.load(reference_path)
def read_experiment(reference_path: typing.Union[Path, str], **kwargs):
"""
Reads a whole experiment and translates all contained waves into xr.Dataset instances as appropriate
:param reference_path:
:return:
"""
import igor.igorpy as igor
if isinstance(reference_path, Path):
reference_path = str(reference_path.absolute())
return igor.load(reference_path, **kwargs)
def extractDebleachedData(filePath):
allData = igor.load(filePath)
bbb = allData.children[0].userstr[b"S_waveNames"]
aaa = bbb.decode("UTF-8")
dataNames = aaa.split(";")[:-1]
waves = list()
for m in sc.arange(len(dataNames)):
waveNum = sc.int32(dataNames[m][1 + str.rfind(dataNames[0], "e"):])
str1 = "waves.append(allData." + dataNames[m] + "_%d.data)" % (
waveNum - 1)
#print(dataNames[m],str1)
exec(str1)
return sc.array(waves)
def extractDebleachedFminData(filePath):
"""
extractDebleachedFminData takes a path to a pxp data file as only argument and extracts all the wave data found there,
provided the data has the suffix F_min
Example:
dataDir="./microcircuitsNetworks/"
fileName= "cort76dp1c.pxp"
waveData, timeStamps=extractDebleachedFminData(dataDir+fileName)
"""
allData = igor.load(filePath)
#dataNames= st.digits.split(allData.children[0].userstr["S_waveNames"], ";")[:-1]
bbb = allData.children[0].userstr[b"S_waveNames"]
aaa = bbb.decode("UTF-8")
dataNames = aaa.split(";")[:-1]
waves = list()
for m in sc.arange(len(dataNames)):
waveNum = sc.int32(dataNames[m][1 + str.rfind(dataNames[0], "e"):])
str1 = "waves.append(allData." + dataNames[m] + "_%dF_min.data)" % (
waveNum - 1)
#print(dataNames[m],str1)
exec(str1)
return sc.array(waves), sc.array(allData.sec.data)
def extractPXPData(fName):
"""
extractPXPData
Example:
allData=extractPXPData("microcircuitsNetworks/cort76dp1c.pxp")
"""
allData = igor.load(fName)
# Dictionary containing the names of recorded variables during the experiment
bbb = allData.children[0].userstr[b"S_waveNames"]
aaa = bbb.decode("UTF-8")
dataNames = aaa.split(";")[:-1]
extractedData1 = list()
extractedData2 = list()
for nam in dataNames:
if len(nam) > 3:
myInd = sc.int32(nam[4:]) - 1
str2 = "w2=allData." + nam + ".data" % (myInd)
exec(str2)
extractedData2.append(w2)
else:
print("Found empty string")
rawData = sc.array(extractedData2)
return rawData
def Load_SFG(self,Parameters) :
FolderPath = Parameters['FolderPath']
FileName = Parameters['FileName']
if FileName.endswith('.ibw') :
d = binarywave.load(FolderPath + '/' + FileName)
y = np.transpose(d['wave']['wData'])
Start = d['wave']['wave_header']['sfB']
Delta = d['wave']['wave_header']['sfA']
x = np.arange(Start[0],Start[0]+y.shape[1]*Delta[0]-Delta[0]/2,Delta[0])
z = np.arange(Start[1],Start[1]+y.shape[0]*Delta[1]-Delta[1]/2,Delta[1])
print('Igor binary data loaded')
elif FileName.endswith('.itx') :
y = np.loadtxt(FolderPath + '/' + FileName,comments =list(string.ascii_uppercase))
y = np.transpose(y)
with open(FolderPath + '/' + FileName) as f:
reader = csv.reader(f, delimiter="\t")
for row in reader:
if 'SetScale/P' in row[0]:
SetScale = row[0]
xScale = re.findall(r' x (.*?),"",', SetScale)
xScale = xScale[0].split(',')
zScale = re.findall(r' y (.*?),"",', SetScale)
zScale = zScale[0].split(',')
Start = [float(xScale[0]),float(zScale[0])]
Delta = [float(xScale[1]),float(zScale[1])]
x = np.arange(Start[0],Start[0]+y.shape[1]*Delta[0]-Delta[0]/2,Delta[0])
z = np.arange(Start[1],Start[1]+y.shape[0]*Delta[1]-Delta[1]/2,Delta[1])
print('Igor text data loaded')
elif FileName.endswith('.pxp') :
DataName = Parameters['DataName']
igor.ENCODING = 'UTF-8'
d = igor.load(FolderPath + '/' + FileName)
for i in range(len(d.children)) :
if 'data' in str(d[i]) and len(d[i].data) < 10000 :
globals()[d[i].name] = np.array(d[i].data)
if len(d[i].axis[0]) > 0 :
Name = d[i].name+'_x'
globals()[Name] = np.array([])
for j in range(len(d[i].axis[0])) :
globals()[Name] = np.append(globals()[Name], d[i].axis[0][-1] + d[i].axis[0][0] * j)
if len(d[i].axis[1]) > 0 :
globals()[d[i].name] = np.transpose(globals()[d[i].name])
Name = d[i].name+'_y'
globals()[Name] = np.array([])
for j in range(len(d[i].axis[1])) :
globals()[Name] = np.append(globals()[Name], d[i].axis[1][-1] + d[i].axis[1][0] * j)
x = eval(DataName+'_x')
y = eval(DataName)
z = eval(DataName+'_y')
z = np.round(z,decimals=1)
elif FileName.endswith('sif') :
FileData = sif_reader.xr_open(FolderPath + '/' + FileName)
y = FileData.values[:,0,:]
x = [i for i in range(len(np.transpose(y)))]
z = [i+1 for i in range(len(y))]
try :
FileData.attrs['WavelengthCalibration0']
FileData.attrs['WavelengthCalibration1']
FileData.attrs['WavelengthCalibration2']
FileData.attrs['WavelengthCalibration3']
except :
print('Warning: Wavelength calibration not found')
else :
c0 = FileData.attrs['WavelengthCalibration0']
c1 = FileData.attrs['WavelengthCalibration1']
c2 = FileData.attrs['WavelengthCalibration2']
c3 = FileData.attrs['WavelengthCalibration3']
for i in x :
x[i] = c0 + c1*i + c2*1**2 + c3*i**3
x = np.array(x)
x = 1e7 / x - 12500
try :
Frame = Parameters['Heating']['Frame']
Temperature = Parameters['Heating']['Temperature']
except :
print('Warning: Temperature data not found')
else :
FitModel = QuadraticModel()
ModelParameters = FitModel.make_params()
FitResults = FitModel.fit(Temperature, ModelParameters,x=Frame)
idx = np.array(z)
z = FitResults.eval(x=idx)
z = np.round(z,1)
Data = df(np.transpose(y),index=x,columns=z)
return Data
def read_block(self, Filter='RecordA'):
"""
Return a Block.
**Arguments**
no arguments
"""
#Valid up to 26 channels
alph = list(string.ascii_uppercase)
Array = {} #each key corresponds to a wave
Var = {} #each key corresponds to a variable
b = igorpy.load(self.filename)
#Igor name channels RecordA, RecordB etc...
#the first 7 characters are thus specific a a given channel
ChannelNames = []
for i in b:
if isinstance(i, igorpy.Wave):
if 'Record' in str(i.name):
ChannelNames.append(str(i.name)[0:7])
Array[str(i.name)] = i.data
elif isinstance(i, igorpy.Variables):
Var = i.uservar
ChannelNames = list(set(ChannelNames))
ChannelNames.sort()
if Array == None and Var == None:
return
Filter = Filter
templist1 = []
templist2 = []
AllWaveNames = []
for i in Array:
AllWaveNames.append(i)
AllWaveNames = sorted(AllWaveNames, key=self.splitgroups)
#for i in Var:
# exec("Analysis."+i+"= Var[i]")
Waves = []
# loop for record number
counter = 0
for i in AllWaveNames:
if Filter in i:
counter += 1
#Listing suffixes
Suffixes = list(AllWaveNames)
for i, j in enumerate(Suffixes):
for k in ChannelNames:
Suffixes[i] = Suffixes[i].replace(k, '')
Suffixes = sorted(set(Suffixes), key=lambda x: Suffixes.index(x))
#Identify date of creation from filename
#TODO, use variable instead
filename = self.filename.split('/')[-1]
blck = Block()
convertiontable = [
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep',
'Oct', 'Nov', 'Dec'
]
Day = int(filename[2:4])
Month = int(convertiontable.index(filename[4:7])) + 1
Year = int(filename[7:11])
date = datetime.date(Year, Month, Day)
blck.datetime = date
for n in Suffixes:
seg = Segment()
for i in AllWaveNames:
for k, j in enumerate(ChannelNames):
if i == j + n:
seg.name = j
anaSig = AnalogSignal()
#ADCMAX = header['ADCMAX']
#VMax = analysisHeader['VMax'][c]
#YG = float(header['YG%d'%c].replace(',','.'))
anaSig.signal = Array[
i] #[alph[c],data[:,header['YO%d'%c]].astype('f4')*Var['VMax']/Var['ADCMAX']/YG]
#Waves.append(signal)
anaSig.sampling_rate = 1000. / Var["SampleInterval"]
anaSig.t_start = 0.
anaSig.name = j
anaSig.channel = k
#anaSig.unit = header['YU%d'%c]
seg._analogsignals.append(anaSig)
blck._segments.append(seg)
return blck
def import_pxp_file(self,
filename=None,
protein_column_title=None,
group_dictionary=None,
units=None,
create_group_dictionary_interactively=False):
""" group_dictionary is a set of keys correspond to lists;
each list contains the column headers in the pxp file for that group."""
##########
if filename:
self.filename = filename
elif not self.filename:
raise Exception(
"No file name provided or set in attributes of object.")
f = igor.load(self.filename)
self.zmin = f.zaxis.data[0]
self.zmax = f.zaxis.data[-1]
self.zstep = f.zaxis.data[1] - f.zaxis.data[0]
##########
if create_group_dictionary_interactively:
group_dictionary = {}
toplevel = True
while toplevel:
print(
"""Interactive Group Definition:\n1. Add A Group To The Dictionary\n2. List Columns In The File\n3. Exit"""
)
answer = input(
"""Please Enter The Integer Of Your Choice - """)
if answer == "1":
secondlevel = True
answer = input("""Enter The Group Key - """)
group_dictionary.update({answer: []})
while secondlevel:
group = input(
"""Enter Column Title Or EXIT To Leave This Group - """
)
if group == "EXIT":
secondlevel = False
elif not hasattr(f, group):
print(
"""\nThat Is Not One Of The Columns, Try Again\n"""
)
else:
group_dictionary[answer].append(group)
elif answer == "2":
key_list = f.__dict__.keys()
key_string = ''
for key in key_list:
key_string += key
a = 25 - len(key)
if a < 1:
a = 1
for i in range(a):
key_string += ' '
key_string += '\n'
print(key_string)
elif answer == "3":
toplevel = False
else:
print("\nNot One Of The Options, Try Again\n")
if group_dictionary:
self.group_dictionary = group_dictionary
if len(self.group_dictionary.keys()) == 0:
self.group_dictionary = {
'headgroups': [
'headgroup1', 'headgroup1_2', 'headgroup1_3', 'headgroup2',
'headgroup2_2', 'headgroup2_3'
],
'tethers': ['bME', 'tetherg', 'tether'],
'tails': ['lipid1', 'methyl1', 'lipid2', 'methyl2'],
'substrate': ['substrate']
}
if list(self.group_dictionary.keys()).count('tails') != 1:
raise Exception(
"""A group named 'tails' (tail distribution) must be included for purposes of centering the bilayer."""
)
return
##########
# Get the protein density
if protein_column_title:
self.protein_column_title = protein_column_title
try:
protein_density = np.array(
eval('f.' + self.protein_column_title + '.data'))
except:
raise Exception(self.protein_column_title + ' was not found in ' +
self.filename +
'. I do need it for relative normalizing.')
return
##########
# Check the z-axis variables and the array lengths
if round((self.zmax - self.zmin) / self.zstep, 8) % 1.0 != 0.0:
raise Exception(
'zmin and zmax are not separated by an integral multiple of zsteps, problem with the file?'
)
return
elif round((((self.zmax - self.zmin) / self.zstep) + 1),
8) != np.shape(protein_density)[0]:
raise Exception(
'The number of points in the first dimension of the density array does not match the z-dimensions supplied.\n This is a weird error for neutron data.'
)
return
##########
# Units
if units:
self.units = units
##########
# Setting the dictionary of various densities in the file
temp_dict = {}
for i in self.group_dictionary.keys():
temp_dict.update({i: 0 * protein_density})
for j in self.group_dictionary[i]:
try:
temp_dict.update(
{i: temp_dict[i] + getattr(getattr(f, j), 'data')})
except:
raise Exception(
j + ' was not found in ' + self.filename +
'. Please check your dictionary of component groups for the correct column headings.'
)
return
self.density_dictionary = temp_dict
##########
# Get the norm of the protein density (this is used for relative normalizing in plots)
self.protein_norm = sum(protein_density) * self.zstep
##########
# Calculate the center of the lipidic distribution
norm = sum(self.density_dictionary['tails']) * self.zstep
self.bilayer_center = sum([
self.density_dictionary['tails'][i] * (self.zmin + i * self.zstep)
for i in range(len(self.density_dictionary['tails']))
]) * self.zstep / norm
def import_pxp_file(self,
filename=None,
data_column_title=None,
msigma_column_title=None,
psigma_column_title=None,
units='A',
include_confidence=True):
if ((not filename) and (not self.filename)):
raise Exception('No filename given to this object or function.')
elif (filename):
self.filename = filename
if (data_column_title):
self.data_column_title = data_column_title
if (msigma_column_title):
self.msigma_column_title = msigma_column_title
if (psigma_column_title):
self.psigma_column_title = psigma_column_title
f = igor.load(self.filename)
# Find the z-axis data
self.zmin = f.zaxis.data[0]
self.zmax = f.zaxis.data[-1]
self.zstep = f.zaxis.data[1] - f.zaxis.data[0]
# Locate protein median data
try:
self.density = np.array(
eval('f.' + self.data_column_title + '.data'))
except:
raise Exception('Data for ' + self.data_column_title +
' was not found in ' + self.filename)
return
# Locate confidence intervals
if include_confidence:
if not (hasattr(f, self.msigma_column_title)
and hasattr(f, self.psigma_column_title)):
raise Exception("Could not find one or both of '" +
self.msigma_column_title + "' and '" +
self.psigma_column_title + "' in '" +
self.filename + "'")
self.msigma = np.array(
eval('f.' + self.msigma_column_title + '.data'))
self.psigma = np.array(
eval('f.' + self.psigma_column_title + '.data'))
# Check the array lengths for inconsistencies
if round((self.zmax - self.zmin) / self.zstep, 8) % 1.0 != 0.0:
raise Exception(
'zmin and zmax are not separated by an integral multiple of equal zsteps, problem with the file?'
)
return
elif round((((self.zmax - self.zmin) / self.zstep) + 1),
8) != np.shape(self.density)[0]:
raise Exception(
'The number of points in the first dimension of the density array does not match the z-dimensions supplied.\n This is a weird error for neutron data.'
)
return
self.units = units
print('\n\n\nUnits in the pxp are assumed to be ' + self.units +
""". Please convert the units if this is incorrect.""")
print(
""" Try 'instance_name'.convert_units(1.0, 'desired_units')\n\n\n"""
)
# Normalize the density provided
norm = sum(self.density) * self.zstep
self.norm = norm
if norm != 1.0:
print(
'\n\n\nThe profile has area = {:}'.format(norm) +
""", changing that to be 1.\n The normalization is stored in 'instance_name'.norm\n\n\n"""
)
self.density = self.density / norm
if include_confidence:
self.msigma = self.msigma / norm
self.psigma = self.psigma / norm
# Calculate the mean and width of the profile
self.mean = sum([
self.density[i] * (self.zmin + i * self.zstep)
for i in range(len(self.density))
]) * self.zstep
square_sum = sum([
self.density[i] * (self.zmin + i * self.zstep - self.mean)**2.0
for i in range(len(self.density))
]) * self.zstep
self.second_moment = np.sqrt(square_sum)
def deconvolve(star, psf):
star_fft = fftpack.fftshift(fftpack.fftn(star))
psf_fft = fftpack.fftshift(fftpack.fftn(psf))
return fftpack.fftshift(
fftpack.ifftn(fftpack.ifftshift(star_fft / psf_fft)))
def fwhm(sigma):
# Computes the FWHM from sigma for a Gaussian
return 2.0 * np.sqrt(2.0 * np.log(2.0)) * sigma
# load data
fName = 'psf_8um_bead.pxp'
igorData = igor.load(fName)
xProfile = igorData.x_profile.data
yProfile = igorData.y_profile.data
zProfile = igorData.z_profile.data
#yProfile = yProfile[200:400]
#yProfile = smoothThroughAveragingNeighbors(yProfile,9)
# set parameters
dX = 7.09814e-08 # in m
dY = 7.09584e-08
dZ = 1e-06
beadsLength = 8.0e-6 # beads are on averge 8 um wide
# x, y and z axes
x = np.linspace(dX, len(xProfile) * dX, len(xProfile))
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Sun May 13 19:30:36 2018 @author: javier """ import igor.igorpy as igor import numpy as np import matplotlib.pyplot as plt import Amperometry as amp filePackage = '../EGFPv EXP8.pxp' recording = igor.load(filePackage) recordingDuration = 100.2 #duration in seconds dt = 1./len(recording.Data.data) Fs = 1./dt #1kHz wave = recording.Data.data/1e-12 datos = wave-wave.mean() time = np.linspace(0, recordingDuration, len(wave), endpoint=True) avgPoints = 20 workingData = movingAvg(datos,avgPoints) #smothen the signal with moving average procedure peakThr = 10 H = getPeaks(workingData,time, peakThr) #get peaks for the filtered signal index = getIndex(time,H[:,0]) #get the index of the time where peaks occured baseline = 0 extremos = getExtremes(index,datos,baseline) #get the init and end of a spike event based on the peak occurence
view7 = grid.add_view(row=2, col=3, col_span=1, bgcolor=(1,1,0,alpha),
border_color=(1,1,0),
margin=10)
##################### Data and Meta-data #####################
import igor.igorpy as igor
igor.ENCODING = 'UTF-8'
datum = {}
exp_path = './data/2015-09-30/'
igo_path = './data/2015-09-30/CA1c2-1.pxp'
img_path = './data/2015-09-30/CA1c2/'
print('Experiment path @ %s' % exp_path)
# igor file (electrophysiology)
print('Loading igor file @ %s' % igo_path)
ig = igor.load(igo_path)
_id_igor = dir(ig)
# imaging tiff
import os
print('loading imaging @ %s' % img_path)
for filename in os.listdir(img_path):
if filename.endswith(".tiff"):
datum[filename.split('_')[0]] = [filename]
# datum is dictionary contains tiff and igor waveform
print('integrating imaging and electrophysiology')
for _id in datum.keys():
if _id in _id_igor:
datum[_id].append(ig[_id])
else:
def read_block(self,Filter= 'RecordA'):
"""
Return a Block.
**Arguments**
no arguments
"""
#Valid up to 26 channels
alph=list(string.ascii_uppercase)
Array={} #each key corresponds to a wave
Var={} #each key corresponds to a variable
b=igorpy.load(self.filename)
#Igor name channels RecordA, RecordB etc...
#the first 7 characters are thus specific a a given channel
ChannelNames=[]
for i in b:
if isinstance(i, igorpy.Wave):
if 'Record' in str(i.name):
ChannelNames.append(str(i.name)[0:7])
Array[str(i.name)]=i.data
elif isinstance(i, igorpy.Variables):
Var=i.uservar
ChannelNames=list(set(ChannelNames))
ChannelNames.sort()
if Array == None and Var == None:
return
Filter=Filter
templist1=[]
templist2=[]
AllWaveNames=[]
for i in Array:
AllWaveNames.append(i)
AllWaveNames=sorted(AllWaveNames, key=self.splitgroups)
#for i in Var:
# exec("Analysis."+i+"= Var[i]")
Waves=[]
# loop for record number
counter=0
for i in AllWaveNames:
if Filter in i:
counter+=1
#Listing suffixes
Suffixes=list(AllWaveNames)
for i,j in enumerate(Suffixes):
for k in ChannelNames:
Suffixes[i]=Suffixes[i].replace(k,'')
Suffixes = sorted(set(Suffixes), key=lambda x: Suffixes.index(x))
#Identify date of creation from filename
#TODO, use variable instead
filename=self.filename.split('/')[-1]
blck=Block()
convertiontable=['Jan','Feb','Mar','Apr','May','Jun','Jul','Aug','Sep','Oct','Nov','Dec']
Day=int(filename[2:4])
Month=int(convertiontable.index(filename[4:7]))+1
Year=int(filename[7:11])
date = datetime.date(Year,Month,Day)
blck.datetime=date
for n in Suffixes:
seg = Segment()
for i in AllWaveNames:
for k,j in enumerate(ChannelNames):
if i == j+n:
seg.name=j
anaSig = AnalogSignal()
#ADCMAX = header['ADCMAX']
#VMax = analysisHeader['VMax'][c]
#YG = float(header['YG%d'%c].replace(',','.'))
anaSig.signal = Array[i] #[alph[c],data[:,header['YO%d'%c]].astype('f4')*Var['VMax']/Var['ADCMAX']/YG]
#Waves.append(signal)
anaSig.sampling_rate = 1000./Var["SampleInterval"]
anaSig.t_start = 0.
anaSig.name = j
anaSig.channel = k
#anaSig.unit = header['YU%d'%c]
seg._analogsignals.append(anaSig)
blck._segments.append(seg)
return blck
