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# -*- coding: utf-8 -*-
#
# This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy.
#
# Copyright (C) 2015 Benjamin Watts, Paul Scherrer Institut
# License: GNU GPL v3
#
# Mantis is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# any later version.
#
# Mantis is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details <http://www.gnu.org/licenses/>.
from __future__ import print_function
import json
import re, numpy, sys
from os.path import splitext, join, dirname, isfile
from collections import OrderedDict
title = 'SDF'
extension = ['*.hdr']
read_types = ['spectrum','image','stack']
write_types = []
def identify(filename):
try:
if isfile(splitext(filename)[0] + '.json'):
print("JSON-file found. No need to fetch again.")
return False
else:
HDR = HDR_FileParser(filename)
return HDR.num_regions > 0 # return true if file contains at least one region
except:
return False
def GetFileStructure(FileName):
HDR = HDR_FileParser(FileName)
if HDR.num_regions<2 and HDR.num_channels<2 and isfile(splitext(FileName)[0] + '.json'): #if json file exists, skip datachoicedialog
return None # exit if only one choice
D = OrderedDict()
for i,R in enumerate(['Region_'+str(r) for r in range(HDR.num_regions)]):
D[R] = OrderedDict()
D[R].definition = 'SDF'
D[R].scan_type = HDR.hdr['ScanDefinition']['Type']
D[R].data_shape = HDR.data_size[i]
D[R].data_axes = [HDR.hdr['ScanDefinition']['Regions'][1]['PAxis']['Name'],HDR.hdr['ScanDefinition']['Regions'][1]['QAxis']['Name'],HDR.hdr['ScanDefinition']['StackAxis']['Name']]
for ch in range(1,HDR.num_channels+1):
D[R][HDR.hdr['Channels'][ch]['Name']] = OrderedDict()
return D
#-----------------------------------------------------------------------
class HDR_FileParser:
"""Parse .hdr file for metadata."""
hdr = []
f = []
def __init__(self, fileName,identify=False):
if not HDR_FileParser.hdr or HDR_FileParser.f != fileName: # prevent class from fetching and parsing the *.hdr file multiple times. Use the class attribute "hdr" instead if available and check if a new file (not "f") is loaded.
# compile some regular expressions
self.MatchReStruct = re.compile(r'[\s\{\}\(\)=";]')
self.MatchReArray = re.compile(r'[,\s\{\(\);]')
self.__file = open(fileName)
# Parse the HDR file
HDR_FileParser.hdr = self.parseStructure()
HDR_FileParser.f = fileName
self.__file.close()
else:
None
self.hdr = HDR_FileParser.hdr
if 'ScanDefinition' in self.hdr:
self.num_regions = int(self.hdr['ScanDefinition']['Regions'][0])
self.num_channels = int(self.hdr['Channels'][0])
self.file_path, self.file_ext = splitext(fileName)
self.data_size = self.parse_DataSize()
self.data_names = self.parseDataNames()
else:
self.num_regions = 0
self.num_channels = 0
#-----------------------------------------------------------------------
def parseStructure(self):
""".hdr files consist of structures and arrays. This routine sorts through the structure parts."""
Structure = {}
BuildWord=''
BeforeEq=True
QuotedWord=False
raw = self.__file.read(1)
while len(raw) > 0:#until we reach the end of the file
matched = self.MatchReStruct.match(raw)
if matched == None:
BuildWord+=raw
elif matched.group() == '"':
QuotedWord= not QuotedWord
elif QuotedWord==True:
BuildWord+=raw
elif matched.group() == '=':
FieldName=BuildWord
BuildWord=''
BeforeEq=False
elif matched.group() == ';':
try: # convert numbers into ints or floats or end up with a string
Structure[FieldName] = int(BuildWord)
except ValueError:
try:
Structure[FieldName] = float(BuildWord)
except ValueError:
Structure[FieldName] = BuildWord
except TypeError:
Structure[FieldName] = BuildWord
BuildWord=''
BeforeEq=True
elif matched.group() == '{':
#Must be after =
BuildWord = self.parseStructure()
elif matched.group() == '}':
#break loop and return dictionary
break
elif matched.group() == '(':
#Must be after =
BuildWord= self.parseArray()
elif matched.group() == ')':
#This should not happen
print(') in structure')
raw = self.__file.read(1)
return Structure
#-----------------------------------------------------------------------
def parseArray(self):
""".hdr files consist of structures and arrays. This rountine sorts through the array parts."""
Array = []
BuildWord=''
raw = self.__file.read(1)
while len(raw) > 0:#until we reach the end of the file
matched = self.MatchReArray.match(raw)
if matched == None:
BuildWord+=raw
elif matched.group() == ',':
if len(BuildWord) > 0:
try: #convert numbers in array into ints or floats
Array.append(int(BuildWord))
except ValueError:
Array.append(float(BuildWord))
BuildWord=''
elif matched.group() == ';':
print('; in array')
elif matched.group() == '{':
Array.append(self.parseStructure())
elif matched.group() == '(':
Array.append(self.parseArray())
elif matched.group() == ')':
if len(BuildWord) > 0:
try: #convert numbers in array into ints or floats
Array.append(int(BuildWord))
except ValueError:
try:
Array.append(float(BuildWord))
except ValueError: # There is an error in the HDF5toSDF conversion script at the SLS/PolLux which appends arrays with a superfluous "}". Obviously, this char cannot be converted to int or float. We therefore just skip it here.
pass
break
raw = self.__file.read(1)
return Array
#-----------------------------------------------------------------------
def parseDataNames(self):
"""Figure out names for the .xsp or .xim files that contain the actual data, then check that the files actually exist, printing warnings if they don't."""
DataNames = []
DataFlag = self.hdr['ScanDefinition']['Flags']
# Only for spectra:
if DataFlag in ['Spectra','Multi-Region Spectra']:
for num_R in range(self.num_regions):
DataNames2 = []
for num_Ch in range(self.num_channels):
DataNames2.append([self.file_path+'_'+str(num_R)+'.xsp'])
DataNames.append(DataNames2)
return DataNames
Alphabet = 'abcdefghijklmnopqrstuvwxyz'
boollst = [self.data_size[0][2] > 1,self.num_regions > 1]
bitfield = sum(val << bool for bool, val in enumerate(boollst[::-1]))
# Different detection channels can occur
# Four cases have to be distinguished.
if bitfield == 3: # multi region stack
DataNames = [[[self.file_path + '_' + Alphabet[num_Ch] + str(num_E).zfill(3) + str(num_R) + '.xim' for num_E in
range(self.data_size[0][2])] for num_Ch in range(self.num_channels)] for num_R in
range(self.num_regions)]
elif bitfield == 2 and not DataFlag in ['Image']: # single region stack excluding line scans!
DataNames = [[[self.file_path + '_' + Alphabet[num_Ch] + str(num_E).zfill(3) + '.xim' for num_E in
range(self.data_size[0][2])] for num_Ch in range(self.num_channels)]]
elif bitfield == 1: # multi region image
DataNames = [[[self.file_path + '_' + Alphabet[num_Ch] + str(num_R) + '.xim'] for num_Ch in range(self.num_channels)] for
num_R in range(self.num_regions)]
else: # single region image
DataNames = [[[self.file_path + '_' + Alphabet[num_Ch] + '.xim'] for num_Ch in range(self.num_channels)]]
#ToDo: File exist check
return DataNames
#-----------------------------------------------------------------------
def parse_DataSize(self):
"""Calculate data array size. This is useful for making sure all of the lists of data are the correct length."""
DataSize = []
for R_num in range(self.num_regions):
DataSize.append([1,1,1])# [PAxis,QAxis,StackAxis] (switch to [X1,X2,E] later)]
DataSize[R_num][0] = int(self.hdr['ScanDefinition']['Regions'][R_num+1]['PAxis']['Points'][0])
if 'QAxis' in self.hdr['ScanDefinition']['Regions'][R_num+1] and 'Points' in self.hdr['ScanDefinition']['Regions'][R_num+1]['QAxis']:
DataSize[R_num][1] = int(self.hdr['ScanDefinition']['Regions'][R_num+1]['QAxis']['Points'][0])
if 'StackAxis' in self.hdr['ScanDefinition'] and 'Points' in self.hdr['ScanDefinition']['StackAxis']:
DataSize[R_num][2] = int(self.hdr['ScanDefinition']['StackAxis']['Points'][0])
if self.hdr['ScanDefinition']['Type'] in ['NEXAFS Point Scan','NEXAFS Line Scan']:
DataSize[R_num] = [DataSize[R_num][1],1,DataSize[R_num][0]]#switch to [X1,X2,E] format
# DataSize[R_num] = [1,DataSize[R_num][1],DataSize[R_num][0]]#also works, but might be problematic for finding number of spatial points
return DataSize
#-----------------------------------------------------------------------
def read(filename, self, selection=None, JSONstatus=None, *args, **kwargs):
HDR = HDR_FileParser(filename)
if JSONstatus:
with open(splitext(filename)[0] + '.json', 'w') as outfile:
json.dump(HDR.hdr, outfile, indent=4, sort_keys=True, ensure_ascii=True)
print("JSON-file written at "+ splitext(filename)[0] + '.json')
allowed_flag =['Image Stack','Image','Multi-Region Image Stack','Multi-Region Image']
allowed_type =['NEXAFS Image Scan','NEXAFS Line Scan','Image Scan', 'Line Scan']
flag = HDR.hdr['ScanDefinition']['Flags']
type = HDR.hdr['ScanDefinition']['Type']
region, channel = selection
if not (flag in allowed_flag and type in allowed_type):
print("Unknown Format")
return
linescan = False
if type in ['NEXAFS Line Scan', 'Line Scan']:
linescan = True
p_axis = HDR.hdr['ScanDefinition']['Regions'][region+1]['PAxis']
q_axis = HDR.hdr['ScanDefinition']['Regions'][region+1]['QAxis']
stack_axis = HDR.hdr['ScanDefinition']['StackAxis']
if linescan: # if line scan
if p_axis['Name'] == "Energy": # vertical
self.ev = numpy.array([float(i) for i in p_axis['Points'][1:] ])
self.y_dist = numpy.array([float(i) for i in q_axis['Points'][1:] ])
self.x_dist = numpy.array([0]) # set x-pos to 0
if q_axis['Name'] == "Energy": # horizontal
self.ev = numpy.array([float(i) for i in q_axis['Points'][1:]])
self.x_dist = numpy.array([float(i) for i in p_axis['Points'][1:]])
self.y_dist = numpy.array([0]) # set y-pos to 0
else: # if image stacks or single images
assert p_axis['Name'] == "Sample X"
self.x_dist = numpy.array([float(i) for i in p_axis['Points'][1:] ])
assert q_axis['Name'] == "Sample Y"
self.y_dist = numpy.array([float(i) for i in q_axis['Points'][1:] ])
assert stack_axis['Name'] == "Energy"
self.ev = numpy.array([float(i) for i in stack_axis['Points'][1:] ])
#print(self.x_dist,self.y_dist,self.ev)
self.n_cols = len(self.x_dist)
self.n_rows = len(self.y_dist)
self.n_ev = len(self.ev)
#print(self.n_cols,self.n_rows,self.n_ev)
msec = float(HDR.hdr['ScanDefinition']['Dwell'])
self.data_dwell = numpy.ones((self.n_ev))*msec
imagestack = numpy.empty((self.n_cols,self.n_rows,self.n_ev), numpy.int32)
if linescan: # if linescan load only first existing image and iterate over each row.
line_img = (numpy.loadtxt(HDR.data_names[region][channel][0], numpy.int32).T)
if q_axis['Name'] == "Energy": # if horizontal, transpose matrix
line_img = line_img.T
for i,row in enumerate(line_img):
imagestack[:, :, i] = row
else: # no linescan
for i in range(len(HDR.data_names[region][channel])):
try:
imagestack[:,:,i] = numpy.loadtxt(HDR.data_names[region][channel][i], numpy.int32).T
except ValueError:
print("Aborted stack or XIMs with inconsistent dimensions.")
#imagestack[:,:,i] = numpy.nan
except IOError:
print("Image file no. "+str(i)+" not found.")
#imagestack[:,:,i] = numpy.nan
self.absdata = numpy.empty((self.n_cols,self.n_rows, self.n_ev))
self.absdata = numpy.reshape(imagestack, (self.n_cols,self.n_rows, self.n_ev), order='F')
self.fill_h5_struct_from_stk()
return
#-----------------------------------------------------------------------
def read_sdf_i0(self, filename):
HDR = HDR_FileParser(filename)
if 'ScanType' in HDR.hdr['ScanDefinition'] and HDR.hdr['ScanDefinition']['ScanType'] == 'Spectra':
Energies = HDR.hdr['ScanDefinition']['Regions'][1]['PAxis']['Points'][1:]
tempimage = numpy.loadtxt(HDR.data_names[0][0][0], numpy.float32)
Data = tempimage[:,1]
elif HDR.hdr['ScanDefinition']['Type'] == 'NEXAFS Line Scan':
Energies = HDR.hdr['ScanDefinition']['Regions'][1]['PAxis']['Points'][1:]
tempimage = numpy.loadtxt(HDR.data_names[0][0][0], numpy.int32)
Data = numpy.mean(tempimage,axis=0)
else:# Image Stack
Energies = HDR.hdr['ScanDefinition']['StackAxis']['Points'][1:]
tempimage = numpy.empty((HDR.data_size[0][0],HDR.data_size[0][1]), numpy.int32)
Data = numpy.empty((HDR.data_size[0][2]), numpy.int32)
for i in range(len(HDR.data_names[0][0])):
tempimage = numpy.loadtxt(HDR.data_names[0][0][i], numpy.int32)
Data[i] = numpy.mean(tempimage)
msec = float(HDR.hdr['ScanDefinition']['Dwell'])#shouldn't this be needed?
self.i0_dwell = msec
self.evi0 = numpy.array([float(i) for i in Energies])
self.i0data = Data
return
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