././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1701535927.1322315 mantis-xray-3.1.15/0000775000175000017500000000000014532660267013220 5ustar00wattswatts././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/LICENSE0000644000175000017500000007733214332463747014241 0ustar00wattswatts GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. 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END OF TERMS AND CONDITIONS ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/MANIFEST.in0000644000175000017500000000033414332463747014756 0ustar00wattswattsinclude *.md include LICENSE include mantis_xray/Mantis_batch_settings.txt include mantis_xray/images/* include mantis_xray/henke.xdr include mantis_xray/*.ui include mantis_xray/*.qss recursive-include mantis_xray *.py ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1701535927.1322315 mantis-xray-3.1.15/PKG-INFO0000644000175000017500000000521714532660267014320 0ustar00wattswattsMetadata-Version: 2.1 Name: mantis-xray Version: 3.1.15 Summary: MANTiS is a Multivariate ANalysis Tool for x-ray Spectromicroscopy Home-page: https://spectromicroscopy.com/ Author: Mirna Lerotic Author-email: mirna@2ndlookconsulting.com Project-URL: Code, https://github.com/mlerotic/spectromicroscopy Project-URL: Documentation, https://docs.spectromicroscopy.com Classifier: Programming Language :: Python :: 3 Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+) Classifier: Operating System :: OS Independent Classifier: Topic :: Scientific/Engineering Requires-Python: >=3 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: PyQt5 Requires-Dist: numpy Requires-Dist: scipy Requires-Dist: matplotlib Requires-Dist: h5py Requires-Dist: Pillow Requires-Dist: lxml Requires-Dist: pyqtgraph<=0.12.4 Requires-Dist: scikit-image Provides-Extra: netcdf Requires-Dist: netcdf4-python; extra == "netcdf" # Spectromicroscopy # [Spectromicroscopy](http://spectromicroscopy.com) combines spectral data with microscopy, where typical datasets consist of a stack of microscopic images taken across an energy range. Due to the data complexity, manual analysis can be time consuming and inefficient, whereas multivariate analysis tools not only reduce the time needed but also can uncover hidden trends in the data. # Mantis # [MANTiS](http://spectromicroscopy.com) is Multivariate ANalysis Tool for Spectromicroscopy developed in Python by [2nd Look Consulting](http://2ndlookconsulting.com). It uses principal component analysis and cluster analysis to classify pixels according to spectral similarity. ## Download ## Mantis package and binaries can be downloaded from [spectromicroscopy.com](http://spectromicroscopy.com). Alternatively, you can install [Python](https://www.python.org/downloads/) and then run the command: `python3 -m pip install mantis-xray` ## Update ## You can upgrade to the latest package release with the command: `pip3 install mantis-xray -U`. It is recommended that you also upgrade the dependencies with: `pip3 install mantis-xray -U --upgrade-strategy "eager"` ## Run ## Installation via pip provides the `mantis-xray` command (alternatively `python3 -m mantis_xray`) to start the Mantis GUI. ## User Guide ## Mantis User Guide can be found on the project Wiki pages [Home](https://github.com/mlerotic/spectromicroscopy/wiki). ## References ## Please use the following reference when quoting Mantis Lerotic M, Mak R, Wirick S, Meirer F, Jacobsen C. MANTiS: a program for the analysis of X-ray spectromicroscopy data. J. Synchrotron Rad. 2014 Sep; 21(5); 1206–1212 [http://dx.doi.org/10.1107/S1600577514013964] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/README.md0000644000175000017500000000335514332463747014505 0ustar00wattswatts# Spectromicroscopy # [Spectromicroscopy](http://spectromicroscopy.com) combines spectral data with microscopy, where typical datasets consist of a stack of microscopic images taken across an energy range. Due to the data complexity, manual analysis can be time consuming and inefficient, whereas multivariate analysis tools not only reduce the time needed but also can uncover hidden trends in the data. # Mantis # [MANTiS](http://spectromicroscopy.com) is Multivariate ANalysis Tool for Spectromicroscopy developed in Python by [2nd Look Consulting](http://2ndlookconsulting.com). It uses principal component analysis and cluster analysis to classify pixels according to spectral similarity. ## Download ## Mantis package and binaries can be downloaded from [spectromicroscopy.com](http://spectromicroscopy.com). Alternatively, you can install [Python](https://www.python.org/downloads/) and then run the command: `python3 -m pip install mantis-xray` ## Update ## You can upgrade to the latest package release with the command: `pip3 install mantis-xray -U`. It is recommended that you also upgrade the dependencies with: `pip3 install mantis-xray -U --upgrade-strategy "eager"` ## Run ## Installation via pip provides the `mantis-xray` command (alternatively `python3 -m mantis_xray`) to start the Mantis GUI. ## User Guide ## Mantis User Guide can be found on the project Wiki pages [Home](https://github.com/mlerotic/spectromicroscopy/wiki). ## References ## Please use the following reference when quoting Mantis Lerotic M, Mak R, Wirick S, Meirer F, Jacobsen C. MANTiS: a program for the analysis of X-ray spectromicroscopy data. J. Synchrotron Rad. 2014 Sep; 21(5); 1206–1212 [http://dx.doi.org/10.1107/S1600577514013964] ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1701535927.128231 mantis-xray-3.1.15/mantis_xray/0000775000175000017500000000000014532660267015556 5ustar00wattswatts././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/Mantis_batch_settings.txt0000644000175000017500000000066214332463747022637 0ustar00wattswattsVERSION: 2 WORK_DIR: D:\Work\Python\Mantis\Mantis_scr\TestBatch OUTPUT_DIR_NAME: MantisBatchResults FILENAME: luhae.hdf5 SAVE_HDF5: 0 ALIGN_STACK: 0 I0_FILE: '' I0_HISTOGRAM: 0 RUN_PCA: 1 N_SPCA: 4 RUN_CLUSTER_ANALYSIS: 1 N_CLUSTERS: 5 THICKNESS_CORRECTION: 1 RUN_SPECTRAL_ANALYSIS: 1 SA_SPECTRUM: SA_USE_CA_SPECTRA: 1 RUN_KEY_ENGS: 0 KE_THRESHOLD: 0.10 SAVE_PNG: 1 SAVE_PDF: 0 SAVE_SVG: 0 ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/Mrc.py0000644000175000017500000053154214332463747016663 0ustar00wattswatts"""Provide methods for reading and writing files in the MRC format. Requires NumPy. This module has been imported successfully when used with the following combinations of Python and NumPy: Python 2.5.6 ; NumPy 1.3.0.dev6083 Python 2.6.6 ; NumPy 1.4.1 Python 2.7.9 ; NumPy 1.8.2 Python 2.7.10 ; NumPy 1.8.0rc1 Python 3.4.2 ; NumPy 1.8.2 . Other combinations for Python versions greater than or equal to 2.5 and NumPy versions greater than or equal to 1.3.0 likely work. Implements the MRC file format as described at http://msg.ucsf.edu/IVE/IVE4_HTML/IM_ref2.html . The Mrc class is likely easiest to use if you want read-only access to an Mrc file or want read-write access and the modifications that you'll make do not affect the size or format of the extended header or image data. The Mrc class does use memory mapping of the file. Depending on the system, that may make it unusable for large files. An example of how to use the Mrc class is this: import numpy import Mrc a = Mrc.bindFile('somefile.mrc') # a is a NumPy array with the image data memory mapped from # somefile.mrc. You can use it directly with any function # that will take a NumPy array. hist = numpy.histogram(a, bins=200) # Print out key information from the header. a.Mrc.info() # Use a.Mrc.hdr to access the MRC header fields. wavelength0_nm = a.Mrc.hdr.wave[0] If you only want a copy of all the highest resolution data set from a MRC file as a NumPy array, you can use load(): import numpy import Mrc a = Mrc.load('somefile.mrc') The Mrc2 class is what you would use if you wanted to create a MRC file from scratch. An example of that is: import numpy import Mrc a = numpy.reshape(numpy.asarray( numpy.linspace(0, 5999, 6000), dtype='i2'), (10,20,30)) m = Mrc.Mrc2('newfile.mrc', mode='w') # If you want the header to use the MRC 2014 format rather than # the Priism format, insert # m.hdr = Mrc.hdrChangeToMrc2014Format(m.hdr) # Set the header size and pixel type fields. You could also use # m.initHdrForArr(a) instead. m.setHdrForShapeType(a.shape, a.dtype) # Set other fields in the header. m.hdr.setSpacing(0.1, 0.1, 0.3) m.hdr.wave[0] = 540 m.hdr.mmm1 = (0.0, 5999.0, 2999.5) m.hdr.setTitle('Written by Mrc2 of Mrc.py') m.writeHeader() m.writeStack(a) m.close() A short cut for writing a NumPy array as an Mrc file is to use save(). If the only fields that you want to set in the header are the basic size and pixel type information, it is very simple to use: import numpy import Mrc a = numpy.reshape(numpy.asarray( numpy.linspace(0, 5999, 6000), dtype='i2'), (10,20,30)) Mrc.save(a, 'newfile.mrc', ifExists='overwrite') Known limitations: 1) Does not support the 4-bit unsigned integer sample format (mode 101). 2) Does not provide any mechanism for accessing the lower resolution versions of the image data allowed by Priism's version of the MRC format. 3) Uses a NumPy structured array type to represent mode 3 (complex values represented by two signed 16-bit integers) image data. Nothing is provided to make that data act more like standard complex arrays in NumPy. Release notes: Version 0.1.1 corrected Mrc2.makeSymmetryInfo() since it didn't use self when accessing the header information. Also changed the logic in getExtHeaderFormat() to match Priism's when working with a file without the extended header type field but with the map field. In that case assume a non-crystallographic extended header if the space group is 0, 1, or 401. Version 0.1.0 as included with Priism made the following changes which could affect compatibility with client code: 1) Added FROM_PRIISM to the module to distinguish this version from other versions of Mrc.py derived from Priithon. 2) Added HdrBase, HdrPriism, Hdr2014, Hdr2014Priism, ManagedTitleArray, and ReorderedArray classes. The combination of HdrBase and HdrPriism replaced a class defined within implement_hdr(). They do not have the type data attribute (2 byte field immediately after mmm1) that class had and change the interpretation of nspg (absorbing what had been in the type data attribute) and blank (split by adding ntst). The title attribute changed from being backed by a a 10a80 element in the structured array to a (10,80)i1 element. Since interactions with the title attribute are mediated through a ManagedTitleArray, assigning strings to the titles should work as before. 3) Mode 0 image data is interpreted as signed 8-bit integers for compatibility with the MRC 2014 standard. 4) Mode 3 image data (complex values stored as two signed 16-bit integers) was treated as one 4-byte floating-point value. Now it's represented by a NumPy structured array with two 16-bit signed integer fields. The first field is called 'real' and the second is called 'imag'. 5) The extInts and extFloats data attributes of the Mrc and Mrc2 classes are now always set. They may be None, depending on the size and format of the extended header. 6) Removed insertExtHdr() from the Mrc class. 7) Removed extHdrSize, extHdrnint, and extHdrnfloat keywords from __init__() for the Mrc class. 8) Changed the initial values in the header when an instance of Mrc2 is created for a new file. 9) Changed the return value of MrcMode2dtype() from the generic Python type equivalent to the sample representation to a NumPy dtype. 10) As a result of the changes for (2), changed the type returned by implement_hdr() and makeHdrArray(). 11) Changed initHdrArrayFrom() to have a return value. """ __author__ = 'Sebastian Haase ' __license__ = 'BSD license - see PRIITHON_LICENSE.txt' __version__ = '0.1.1' import numpy as N import sys import os # Python 3 renamed __builtin__ to builtins. Work around that without using # from __future__ so that this will work with older versions of Python 2. try: import __builtin__ as builtins except ImportError: import builtins import string import tempfile import weakref # Mark this as a Mrc.py originating from Priism in case a client wants to # try to distinguish between this version and versions from elsewhere. FROM_PRIISM = True def bindFile(fn, writable=0): """Return a NumPy array memory mapped from an existing MRC file. The returned NumPy array will have an attribute, Mrc, that is an instance of the Mrc class. You can use that to access the header or extended header of the file. For instance, if x was returned by bindFile(), x.Mrc.hdr.Num is the number of x samples, number of y samples, and number of sections from the file's header. Positional parameters: fn -- Is the name of the MRC file to bind. Keyword parameters: writable -- If True, the returned array will allow the elements of the array to be modified. The Mrc instance packaged with the array will also allow modification of the header and extended header entries. Changes made to the array elements or the header will affect the file to which the array is bound. """ mode = 'r' if writable: mode = 'r+' a = Mrc(fn, mode) return a.data_withMrc(fn) class Mrc: """Provide memory mapped access to existing MRC files. If x is a Mrc instance, x.data has the highest resolution image data from the file, x.hdr has the header fields, and x.e has the extended header as an array of unsigned 8-bit integers. If the extended header size is greater than zero, the extended header format is Priism's format, and the number of integers or floating-point values per section in the extended header is greater than zero, x.extInts has the integer values from the extended header, x.extFloats has the floating-point values from the extended header, and x.extSym is None. If the extended header has symmetry information, x.extInts is None, x.extFloats is None, and x.extSym is an array of 80 character records for the symmetry information. Any other cases will have x.extInts equal to None, x.extFloats equal to None, and x.extSym equal to None. If used to modify a file, the Mrc class does not provide public methods to change the format or size of the image data or change the size of the extended header data and then remap the image data and extended header data into memory. Because of that, it is best to use the Mrc class for read-only access or for read-write access where the modifications do not change the layout or format of the MRC file. The Mrc2 class can handle more general modifications to an existing MRC file. Depending on the system and the handling of memory mapping in Python, it may not be possible to memory map files which are too large (around 1 gigabyte was a frequent barrier with Python 2.5 and earlier). The Mrc2 class which does not use memory mapping could be useful for those files. If the size of the extended header is not a multiple of the size of the data type used to represent one image sample, the memory mapped image data will be misaligned. Use the Mrc2 class for files like that. Version 0.1.0 of Mrc.py as included with Priism removed the insertExtHdr() function from this class. It also changed the conditions for when the extInts and extFloats attributes are set. """ def __init__(self, path, mode='r'): """Initialize the Mrc object. Maps the entire file into memory. Verion 0.1.0 of Mrc.py as included with Priism removed the extHdrSize, extHdrnint, and extHdrnfloat keywords. Positional parameters: path -- Is the file to map into memory. Keyword parameters: mode -- If mode is 'r', requests read-only access to the file. If mode is 'r+', requests read and write access to the file. """ self.path = os.path.abspath(path) self.filename = os.path.basename(path) self.m = N.memmap(path, mode=mode) self.h = self.m[:1024] self.hdr = makeHdrArray(self.h) if hdrIsByteSwapped(self.hdr): self.hdr._array.dtype = self.hdr._array.dtype.newbyteorder() self.isByteSwapped = True else: self.isByteSwapped = False self.data_offset = 1024 + max(0, self.hdr.next) self.d = self.m[self.data_offset:] self.e = self.m[1024:self.data_offset] self.doDataMap() self.numInts = max(0, self.hdr.NumIntegers) self.numFloats = max(0, self.hdr.NumFloats) if self.hdr.next > 0: fmt = getExtHeaderFormat(self.hdr) else: fmt = -1 if fmt == 0: self.doSymMap() elif fmt == 1 and (self.numInts > 0 or self.numFloats > 0): self.doExtHdrMap() else: self.extHdrArray = None self.extInts = None self.extFloats = None self.extSym = None def doExtHdrMap(self, nz=0): """Map a NumPy structured array to the Priism-style extended header. Creates self.extHdrArray, the structured array to represent the extended header. Also generates self.extInts, a view of self.extHdrArray with the integer entries, and self.extFloats, a view of self.extHdrArray with the floating-point entries. Sets self.extSym to None. Keyword parameters: nz -- Is the number of sections of data to include in self.extHdrArray. If nz is zero, the number of sections included will be the maximum of zero and the number of sections from the header (self.hdr.Num[2]). If nz is less than zero, the number of sections will be the maximum possible given the size of the extended header and the number of integer and floating-point values per section. """ if nz == 0: nz = max(0, self.hdr.Num[-1]) maxnz = len(self.e) // ((self.numInts + self.numFloats) * 4) if nz < 0 or nz > maxnz: nz=maxnz byteorder = '=' type_descr = [('int', '%s%di4' % (byteorder, self.numInts)), ('float', '%s%df4' % (byteorder, self.numFloats))] self.extHdrArray = N.recarray(shape=nz, dtype=type_descr, buf=self.e) if self.isByteSwapped: self.extHdrArray = self.extHdrArray.newbyteorder() self.extInts = self.extHdrArray.field('int') self.extFloats = self.extHdrArray.field('float') self.extSym = None def doSymMap(self): """Map a NumPy structured array to the symmetry information. Creates self.extHdrArray, a structured array to represent the extended header. Also generates self.extSym, an array of 80 character strings mapped to as much of the extended header as possible. Sets self.extInts and self.extFloats to None. """ nrec = self.hdr.next // 80 nrem = self.hdr.next - 80 * nrec type_descr = [('records', '(%d,80)i1' % nrec), ('extra', '%di1' % nrem)] self.extHdrArray = N.recarray(shape=1, dtype=type_descr, buf=self.e) self.extSym = ManagedTitleArray(self.extHdrArray.field('records')[0]) self.extInts = None self.extFloats = None def doDataMap(self): """Map a NumPy array to the highest resolution data set in the file. Creates self.data as the mapped array. """ dtype = MrcMode2dtype(self.hdr.PixelType) shape = shapeFromHdr(self.hdr) self.data = self.d.view() self.data.dtype = dtype n0 = self.data.shape[0] n1 = N.prod(shape) if n0 < n1: # The file is smaller then the space needed for the lowest # resolution data set. Truncate the slowest varying dimension. print('** WARNING **: file truncated - shape from header: %s ' 'expected to get %s but got %s' % (str(shape), str(N.prod(shape)), str(n0))) n1 = n1 // shape[0] s0 = n0 // n1 shape = (s0,) + shape[1:] self.data = self.data[:(s0*n1)] elif n0 > n1: # The file is larger than the space needed for the highest # resolution data set. Ignore the excess. self.data = self.data[:n1] self.data.shape = shape if self.isByteSwapped: self.data = self.data.newbyteorder() def setTitle(self, s, i=-1, push=False, truncate=False): """Set a title in the MRC header. Provided for compatibility with previous versions of Mrc.py. In the version, you can use self.hdr.setTitle(). Positional parameters: s -- Is the character string for the title. If s is longer than 80 characters and truncate is False, a ValueError exception will be raised. Since no byte swapping is done for the titles in the header, s should be encoded in ASCII or another format that does not use multibyte characters. Keyword parameters: i -- Is the index of the title to set. If i is less than zero, the last title not in use will be set. If i is less than zero and all the titles are in use and push is False or i is greater than 9, a ValueError exception will be raised. push -- If True, i is less than zero, and all titles are in use, titles will be pushed down before assigning s to the last title. That will discard the first title and title[k] (for k greater than and equal to 0 and less than 9) will be title[k+1] from before the change. The push keyword was added in version 0.1.0 of Mrc.py as included with Priism truncate -- If True, only use the first 80 characters from s. The truncate keyword was added in version 0.1.0 of Mrc.py as included with Priism. """ self.hdr.setTitle(s, i=i, push=push, truncate=truncate) def axisOrderStr(self, onlyLetters=True): """Return a string indicating the ordering of dimensions. x, y, z, w, and t will appear at most once in the string, and at least three of them will be present. The letters that do appear will be present in order from slowest varying to fastest varying. The values for the axis field in the header do not affect the result. Keyword parameters: onlyLetters -- If True, only the letters for the dimensions will appear in the string. If False, the first character of the string will be '[', the last character of the string will be ']', and a letter for a dimension will be preceded by a comma if it is not the first, slowest-varying, dimension. """ return axisOrderStr(self.hdr, onlyLetters) def looksOK(self, verbose=1): """Perform basic tests on file. Currently tests the file size against what is expected from the header information. Keyword parameters: verbose -- If greater than or equal to one, some diagnostic information will be printed. Larger values generate more diagnostic information. Currently, values larger than three have the same effect as a value of three does. Return value: Returns True if all the tests pass. Returns False if one or more fail. """ shape = self.data.shape b = self.data.dtype.itemsize eb = N.prod(shape) * b ab = len(self.d) secb = N.prod(shape[-2:]) * b if self.hdr.sub > 1: nres = self.hdr.sub if self.hdr.zfac > 1: zfac = self.hdr.zfac if self.hdr.ImgSequence != 1: # Treat an invalid image sequence value as if it was # zero. An image sequence value or zero or two has # z as the fastest varying dimension after y and x. if len(shape) >= 3: resChangesNz = True zind = -3 else: resChangesNz = False else: # WZT order has z changing next fastest after # wavelength, y, and x. if len(shape) >= 4: resChangesNz = True zind = -4 else: resChangesNz = False else: resChangesNz = False else: nres = 1 resChangesNz = False elb = 0 resShape = list(shape) for i in range(1, nres): if resChangesNz: resShape[zind] = (resShape[zind] + zfac - 1) // zfac resShape[-1] = resShape[-1] // 2 resShape[-2] = resShape[-2] // 2 elb += N.prod(resShape) elb *= b # Computing the number of sections of data present only makes # sense if there is no additional resolutions present. if ab < eb or elb == 0: displaySectionInfo = True anSecs = ab / float(secb) enSecs = eb / float(secb) else: displaySectionInfo = False etb = eb + elb if verbose >= 3: print('expected total data bytes: %s' % str(etb)) print('expected number of resolutions: %s' % str(nres)) print('expected bytes in highest resolution: %s' % str(eb)) print('expected bytes in other resolutions: %s' % str(elb)) print('data bytes in file: %s' % str(ab)) if displaySectionInfo: print('expected total secs: %s' % str(enSecs)) print('file has total secs: %s' % str(anSecs)) if etb == ab: if verbose >= 2: print('OK') return 1 elif etb < ab: if verbose >= 1: print('* have %s extra bytes in file' % str(ab - etb)) if displaySectionInfo: print('* have %.2f extra hidden sections in file' % (anSecs - enSecs)) return 0 elif eb <= ab: if verbose >= 1: print('* lower resolution data truncated by %s bytes' % str(etb - ab)) return 0 else: if verbose >= 1: print('* highest resolution data truncated by %s bytes' % str(eb - ab)) print('* file missing %.2f sections of highest resolution' % (enSecs - anSecs)) print('PLEASE SET shape to %s sections !!! ' % str(int(anSecs))) return 0 def info(self): """Print useful information from header.""" hdrInfo(self.hdr) def data_withMrc(self, fn): """Return the image data as a NumPy array. The Mrc attribute of the returned array is this Mrc object. Positional parameters: fn -- Is for compatibility with bindFile() and previous versions of Mrc.py; its value is not used. """ class ndarray_inMrcFile(N.ndarray): def __array_finalize__(self, obj): self.Mrc = getattr(obj, 'Mrc', None) data = self.data data.__class__ = ndarray_inMrcFile ddd = weakref.proxy(data) self.data = ddd data.Mrc = self return data def close(self): """Deletes the memory map of the MRC file. Implicitly commits to disk any changes made. """ # As of NumPy 1.9, memmap no longer has a close method. Instead # use del for all versions. if hasattr(self, 'm'): del self.m ########################################################################### ########################################################################### ########################################################################### ########################################################################### def open(path, mode='r'): """Return a Mrc2 object for a MRC file with the given file name. Positional parameters: path -- Is the name of the file to open. May be None to create a temporary file. For more information, look at the documentation for Mrc2.__init__ . Keyword parameters: mode -- Controls how the file is opened. Use 'r' for read-only access, 'r+' for read and write access to an existing file, 'w' for write access which will ignore the contents of the file if it already exists, and 'w+' for read and write access which will ignore the contents of the file if it already exists. For more information, look at the documentation for Mrc2.__init__ . Return value: Returns a Mrc2 object for the file. When mode is 'r' or 'r+', the header and extended header, if it is present, have been read, and the file is positioned at the start of the image data for the first image. When mode is 'w' or 'w+', the header fields have been set to default values, the file is positioned at the start of the header, and a call to setHdrForShapeType() or initHdrForArr() for the object will be necessary before reading or writing image data. """ return Mrc2(path, mode) def load(fn): """Copy the highest resolution data set from a MRC file to memory. Positional parameters: fn -- Is the name of the MRC file from which to read. Return value: Returns a three-dimensional NumPy array with the data from the file. The array is dimensioned (nsections, ny, nx). The ordering of elements in the first dimension is the same as in the file. The array is not mapped to the file so changes to the array will not affect the file and changes to the file will not affect the array. """ m = open(fn) a = m.readStack(m.hdr.Num[2]) return a def save(a, fn, ifExists='ask', zAxisOrder=None, hdr=None, hdrEval='', calcMMM=True, extInts=None, extFloats=None): """Save the contents of a NumPy array as a MRC-formatted file. Positional parameters: a -- Is the NumPy array to save. a must have one to five dimensions. The type of a must be natively supported by the MRC format. Acceptable types include 32-bit floating point, unsigned and signed 16-bit integer, 64-bit complex, and 8-bit signed integer. fn -- Is the name of the file to be written. Keyword parameters: ifExists -- Controls what happens if there is already a file with the given name. ifExists should be 'ask', 'overwrite', or 'raise'. If the first letter of ifExists is 'o', the existing file will be overwritten. If the first ifExists is 'a', execution will be suspended until there is a response to an interactive prompt about overwriting the file. If the prompt is anwered with 'y' or 'Y' followed by a return, the file will be overwritten. Any other answer followed by a return will generate an exception. If the first letter of ifExists is neither 'o' nor 'a', an exception will be generated if the file already exists. zAxisOrder -- Controls how the dimensions besides the last two of the array are translated to the z, wavelength, and time axes of the file. The ordering of the dimensions in zAxisOrder is from slowest varying (the first dimension of a), to fastest varying. When zAxisOrder is None, it is equivalent to 'z' if the array has three dimensions, 'tz' if the array has four dimensions and to 'tzw' in all other cases. Any ' ', '-', '.', or ',' characters in zAxisOrder are treated as delimiters and are stripped out. The remaining characters are converted to lower case. In the case where the array has three dimensions, only the last character in zAxisOrder after the delimiters have been stripped has an effect. It affects the number of z samples (nz), number of wavelengths (nw), number of time points (nt), and image sequence as follows: nz nw nt Priism sequence 'z' a.shape[0] 1 1 ZTW 'w' 1 a.shape[0] 1 WZT 't' 1 1 a.shape[0] ZTW If the array has four dimensions, only the last two characters of zAxisOrder have an effect: nz nw nt Priism sequence 'zw' a.shape[0] a.shape[1] 1 WZT 'zt' not supported by Priism sequence types 'wz' a.shape[1] a.shape[0] 1 ZTW 'wt' 1 a.shape[0] a.shape[1] ZTW 'tz' a.shape[1] 1 a.shape[0] ZTW 'tw' 1 a.shape[1] a.shape[0] WZT If the array has five dimensions, only the last three characters of zAxisOrder have an effect: nz nw nt Priism sequence 'zwt' not supported by Priism sequence types 'ztw' not supported by Priism sequence types 'wtz' a.shape[2] a.shape[0] a.shape[1] ZTW 'wzt' not supported by Priism sequence types 'tzw' a.shape[1] a.shape[2] a.shape[0] WZT 'twz' a.shape[2] a.shape[1] a.shape[0] ZWT hdr -- If not None, should act like an instance of the HdrPriism or Hdr2014Priism classes, i.e. a value returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. The values of all fields in hdr will be copied to the output file except for the number of samples ('Num' field; bytes 1 - 12), the pixel type ('PixelType' field; bytes 13 - 16), and the number of bytes in the extended header ('next' field; bytes 93 - 96). If both hdr and zAxisOrder are not None, the values for the number of wavelengths ('NumWaves' field; bytes 197 - 198), number of time points ('NumTimes' field; bytes 181 - 182), and image sequence ('ImgSequence' field; bytes 183 - 184) will be overwritten by the values in hdr. calcMMM -- If True, the minimum value for each wavelength, the maximum value for each wavelength, and the median value for first wavelength will be calculated and stored in the header. Those calculated values will overwrite the values set by hdr. hdrEval -- If not an empty string or None, hdrEval will be executed with Python's exec in a context where all global variables are accessible and the local variables are those of the calling function augmented with a variable named 'hdr'. That local variable represents the header to be saved to the file. It is a header as returned by makeHdrArray() or implement_hdr(). hdrEval is executed after any changes to the header made due to the zAxisOrder, hdr, calcMMM, extInts, and extFloats parameters. extInts -- If not None, will be used to initialize the integer fields in the extended header. When not None, extInts must be a NumPy array. The size of the last dimension will be used as the number of integers per section. The product of the sizes for the remaining dimensions will be used as the number of sections of data available. When extInts is None and extFloats is not None, there will be no integer entries in the extended header. extFloats -- If not None, will be used to initialize the floating-point fields in the extended header. When not None, extFloats must be a NumPy array. The size of the last dimension will be used as the number of floating-point values per section. The product of the sizes for the remaining dimensions will be used as the number of sections of data available. When extFloats is None and extInts is not None, there will be no floating-point entries in the extended header. If there are a different number of sections of data available from extInts and extFloats, the size of the extended header will be set based on the larger number of sections and the unspecified values will be filled with zeros. """ if os.path.exists(fn): if ifExists[0] == 'o': pass elif ifExists[0] == 'a': try: # First try the Python 2 way for this. answer = raw_input('overwrite?') except NameError: # Now try the Python 3 one; note that input() has a different # meaning in Python 2. answer = input('overwrite?') yes = answer.lower() == 'y' if not yes: raise RuntimeError('not overwriting existing file "%s"' % fn) else: raise RuntimeError('not overwriting existing file "%s"' % fn) m = Mrc2(fn, mode='w') m.initHdrForArr(a, zAxisOrder) if hdr is not None: m.hdr = initHdrArrayFrom(m.hdr, hdr) if calcMMM: wAxis = axisOrderStr(m.hdr).find('w') if wAxis < 0: m.hdr.mmm1 = computeMinMaxMedian(N.real(a)) else: nw = m.hdr.NumWaves m.hdr.mmm1 = computeMinMaxMedian(N.real(a.take((0,), wAxis))) if nw >=2: m.hdr.mm2 = computeMinMax(N.real(a.take((1,), wAxis))) if nw >=3: m.hdr.mm3 = computeMinMax(N.real(a.take((2,), wAxis))) if nw >=4: m.hdr.mm4 = computeMinMax(N.real(a.take((3,), wAxis))) if nw >=5: m.hdr.mm5 = computeMinMax(N.real(a.take((4,), wAxis))) if extInts is not None: numints = extInts.shape[-1] numextsec_int = extInts.size // numints else: numints = 0 numextsec_int = 0 if extFloats is not None: numfloats = extFloats.shape[-1] numextsec_float = extFloats.size // numfloats else: numfloats = 0 numextsec_float = 0 if ((numints > 0 and numextsec_int > 0) or (numfloats > 0 and numextsec_float > 0)): m.makeExtendedHdr(numints, numfloats, nSecs=max(numextsec_int, numextsec_float)) if numints == 1: m.extInts[0:numextsec_int] = N.ravel(extInts) m.extInts[numextsec_int:] = 0 elif numints > 1: m.extInts[0:numextsec_int, 0:numints] = ( N.reshape(extInts, (numextsec_int, numints))) m.extInts[numextsec_int:, 0:numints] = 0 if numfloats == 1: m.extFloats[0:numextsec_float] = N.ravel(extFloats) m.extFloats[numextsec_float:] = 0.0 elif numfloats > 1: m.extFloats[0:numextsec_float, 0:numfloats] = ( N.reshape(extFloats, (numextsec_float, numfloats))) m.extFloats[numextsec_float:, 0:numfloats] = 0.0 if hdrEval: fr = sys._getframe(1) loc = {'hdr':m.hdr} loc.update(fr.f_locals) glo = fr.f_globals exec(hdrEval, glo, loc) m.writeHeader() m.writeExtHeader(seekTo0=True) m.writeStack(a) m.close() def computeMinMaxMedian(array): """Compute statistics for save(). This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: array -- Is a NumPy array. Return value: Returns a tuple with the minimum, maximum, and median of the array. If array is structured, the values returned are for the first component. """ if array.dtype.fields is None: return (N.min(array), N.max(array), N.median(array)) else: return (N.min(array[array.dtype.names[0]]), N.max(array[array.dtype.names[0]]), N.median(array[array.dtype.names[0]])) def computeMinMax(array): """Compute statistics for save(). This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: array -- Is a NumPy array. Return value: Returns a tuple with the minimum and maximum of the array. If array is structured, the values returned are for the first component. """ if array.dtype.fields is None: return (N.min(array), N.max(array)) else: return (N.min(array[array.dtype.names[0]]), N.max(array[array.dtype.names[0]])) ########################################################################### ########################################################################### ########################################################################### ########################################################################### class Mrc2: """Provide access to MRC files. Unlike the Mrc class, does not use a memory map to access the file. Actively manages the header and extended header data. If x is a Mrc2 instance. x.hdr has the header fields, and x.e has the extended header as an array of unsigned 8-bit integers. If the extended header size is greater than zero, the extended header is in Priism's format, and the number of integers or floating-point values per section in the extended header is greater than zero, x.extInts has the integer values from the extended header, x.extFloats has the floating-point values from the extended header, and x.extSym is None. If the extended header has symmetry information, x.extInts is None, x.extFloats is None, and x.extSym is an array of 80 character records for the symmetry information. Any other cases will have x.extInts equal to None, x.extFloats equal to None, and x.extSym equal to None. Some internal state depends on the values in hdr.Num and hdr.PixelType. To modify those fields, the recommended procedure is to do so indirectly with initHdrForArr() or setHdrForShapeType(). If you do modify those fields directly, call _initWhenHdrArraySet() so that the internal state is consistent with your changes. For the fields related to the extended header, hdr.NumIntegers, hdr.NumFloats, and hdr.next, there's no public way to modify those directly while maintaining a consistent internal state for the extended header. Use makeExtendedHdr() or makeSymmetryInfo() to modify those fields. For the image data, provides the functions seekSec(), readSec(), readStack(), writeSec(), and writeStack() to position the file at a given section, read image data, or write image data. Version 0.1.0 of Mrc.py as included with Priism changed the conditions for when the extInts and extFloats attributes are set. """ def __init__(self, path, mode='r'): """Initialize the Mrc2 object. If mode is 'r' or 'r+', reads the header and, if present, the extended header, and positions the file at the start of the image data for the first section. When mode is 'w' or 'w+', the header fields are set to the default values, the file is positioned at the start of the header, and a call to setHdrForShapeType() or initHdrForArr() will be necessary before reading or writing image data. Positional parameters: path -- Is the name of the file to use. If path is None, a temporary file will be generated, and that file will be deleted when the close() method is called. A value of None for path will only work if the mode parameter is set to 'w' or 'w+'. The _path attribute of the created object will be set to path if path is not None; otherwise, it will be set to the name of the temporary file. Allowing None for path was added in version 0.1.0 of Mrc.py as included with Priism. Keyword parameters: mode -- Specifies how the file should be opened. It has similar semantics to the mode parameter for Python's open() except that all modes implicitly include 'b' for working with binary files. Allowed values for mode are: reading writing notes 'r' allowed forbidden path must exist when __init__ called 'r+' allowed allowed path must exist when __init__ called 'w' forbidden allowed path overwritten if it exists 'w+' allowed allowed path overwritten if it exists """ if path is None: self._f, self._path = tempfile.mkstemp() self._f = os.fdopen(self._f, mode + 'b') self._delete_on_close = True else: self._f = builtins.open(path, mode + 'b') self._path = path self._delete_on_close = False self._name = os.path.basename(self._path) self._mode = mode self._hdrSize = 1024 self._dataOffset = self._hdrSize self._fileIsByteSwapped = False if mode in ('r', 'r+'): self._initFromExistingFile() self.seekSec(0) else: self.hdr = makeHdrArray() self.hdr.Num = (0, 0, 0) self.hdr.PixelType = 1 self.hdr.mst = (0, 0, 0) self.hdr.m = (1, 1, 1) self.hdr.d = (1.0, 1.0, 1.0) self.hdr.angle = (90.0, 90.0, 90.0) self.hdr.axis = (1, 2, 3) self.hdr.mmm1 = (0.0, 0.0, 0.0) self.hdr.nspg = 0 self.hdr.next = 0 self.hdr.dvid = 0xc0a0 self.hdr.nblank = 0 self.hdr.ntst = 0 self.hdr.blank = 0 self.hdr.NumIntegers = 0 self.hdr.NumFloats = 0 self.hdr.sub = 1 self.hdr.zfac = 1 self.hdr.mm2 = (0.0, 0.0) self.hdr.mm3 = (0.0, 0.0) self.hdr.mm4 = (0.0, 0.0) self.hdr.ImageType = 0 self.hdr.LensNum = 0 self.hdr.n1 = 0 self.hdr.n2 = 0 self.hdr.v1 = 0 self.hdr.v2 = 0 self.hdr.mm5 = (0.0, 0.0) self.hdr.NumTimes = 1 self.hdr.ImgSequence = 0 self.hdr.tilt = (0.0, 0.0, 0.0) self.hdr.NumWaves = 1 self.hdr.wave = (0, 0, 0, 0, 0) self.hdr.zxy0 = (0.0, 0.0, 0.0) self.hdr.NumTitles = 0 self.hdr.title = ' ' * 80 self._shape = None self._shape2d = None self._dtype = None # scalar data type of pixels self._secByteSize = 0 self.e = N.zeros(0, dtype='u1') self._extHdrArray = None self.extInts = None self.extFloats = None self.extSym = None def initHdrForArr(self, arr, zAxisOrder=None): """Initialize the MRC header from the shape and type of a NumPy array. Positional parameters: arr -- Is the NumPy array whose shape and type are to be used. zAxisOrder -- Controls how the dimensions besides the last two of the array are translated to the z, wavelength, and time axes of the file. The ordering of the dimensions in zAxisOrder is from slowest varying (the first dimension of a), to fastest varying. When zAxisOrder is None, it is equivalent to 'z' if the array has three dimensions, 'tz' if the array has four dimensions and to 'tzw' in all other cases. Any ' ', '-', '.', or ',' characters in zAxisOrder are treated as delimiters and are stripped out. The remaining characters are converted to lower case. The documentation for save() in this module has the details for how the zAxisOrder will set the header values for the image sequence type and the number of samples in z, wavelength, and time. """ if zAxisOrder is None: if arr.ndim ==3: zAxisOrder = 'z' elif arr.ndim ==4: zAxisOrder = 'tz' else: zAxisOrder = 'tzw' else: # remove delimiter characters '-., ' zAxisOrder = zAxisOrder.translate( string.join([chr(i) for i in range(256)], ''), '-., ').lower() mrcmode = dtype2MrcMode(arr.dtype) init_simple(self.hdr, mrcmode, arr.shape, isByteSwapped=self._fileIsByteSwapped) if arr.ndim == 1 or arr.ndim == 2: pass elif arr.ndim == 3: if zAxisOrder[-1] == 'z': self.hdr.ImgSequence = 0 elif zAxisOrder[-1] == 'w': self.hdr.ImgSequence = 1 self.hdr.NumWaves = arr.shape[-3] elif zAxisOrder[-1] == 't': self.hdr.ImgSequence = 0 self.hdr.NumTimes = arr.shape[-3] else: raise ValueError('unsupported axis order') elif arr.ndim == 4: if zAxisOrder[-2:] == 'zt': raise ValueError('unsupported axis order; time varies ' 'faster than z') elif zAxisOrder[-2:] == 'tz': self.hdr.ImgSequence = 0 self.hdr.NumTimes = arr.shape[-4] elif zAxisOrder[-2:] == 'wz': self.hdr.ImgSequence = 0 self.hdr.NumWaves = arr.shape[-4] elif zAxisOrder[-2:] == 'zw': self.hdr.ImgSequence = 1 self.hdr.NumWaves = arr.shape[-3] elif zAxisOrder[-2:] == 'tw': self.hdr.ImgSequence = 1 self.hdr.NumWaves = arr.shape[-3] self.hdr.NumTimes = arr.shape[-4] elif zAxisOrder[-2:] == 'wt': self.hdr.ImgSequence = 0 self.hdr.NumWaves = arr.shape[-4] self.hdr.NumTimes = arr.shape[-3] else: raise ValueError('unsupported axis order') elif arr.ndim == 5: if zAxisOrder[-3:] == 'wtz': self.hdr.ImgSequence = 0 self.hdr.NumWaves = arr.shape[-5] self.hdr.NumTimes = arr.shape[-4] elif zAxisOrder[-3:] == 'tzw': self.hdr.ImgSequence = 1 self.hdr.NumWaves = arr.shape[-3] self.hdr.NumTimes = arr.shape[-5] elif zAxisOrder[-3:] == 'twz': self.hdr.ImgSequence = 2 self.hdr.NumWaves = arr.shape[-4] self.hdr.NumTimes = arr.shape[-5] else: raise ValueError('unsupported axis order') else: raise ValueError('unsupported array ndim') if self.hdr.NumWaves > 5: print('WARNING: more than 5 wavelengths for MRC file') self._initWhenHdrArraySet() def _initFromExistingFile(self): """Initialize the header for __init__ from the contents of the file.""" self.seekHeader() buffer = N.fromfile(self._f, dtype='u1', count=1024) self.hdr = makeHdrArray(buffer, makeWeak=False) if hdrIsByteSwapped(self.hdr): self.hdr._array.dtype = self.hdr._array.dtype.newbyteorder() self._fileIsByteSwapped = True self._extHdrSize = self.hdr.next self._extHdrNumInts = max(0, self.hdr.NumIntegers) self._extHdrNumFloats = max(0, self.hdr.NumFloats) self._extHdrBytesPerSec = ( (self._extHdrNumInts + self._extHdrNumFloats) * 4) self._dataOffset = self._hdrSize + self._extHdrSize if self._extHdrSize > 0: self.e = N.fromfile(self._f, dtype='u1', count=self._extHdrSize) fmt = getExtHeaderFormat(self.hdr) else: self.e = N.zeros(0, dtype='u1') fmt = -1 if fmt == 0: nrec = self._extHdrSize // 80 nrem = self._extHdrSize - 80 * nrec type_descr = [('records', '(%d,80)i1' % nrec), ('extra', '%di1' % nrem)] self.extHdrArray = N.recarray(shape=1, dtype=type_descr, buf=self.e) self.extSym = ManagedTitleArray( self.extHdrArray.field('records')[0]) self.extInts = None self.extFloats = None elif (fmt == 1 and (self._extHdrNumInts > 0 or self._extHdrNumFloats > 0)): nSecs = self._extHdrSize // self._extHdrBytesPerSec byteorder = '=' type_descr = [ ('int', '%s%di4' % (byteorder, self._extHdrNumInts)), ('float', '%s%df4' % (byteorder, self._extHdrNumFloats))] self._extHdrArray = N.recarray(shape=nSecs, dtype=type_descr, buf=self.e) if self._fileIsByteSwapped: self._extHdrArray = self._extHdrArray.newbyteorder() self.extInts = self._extHdrArray.field('int') self.extFloats = self._extHdrArray.field('float') self.extSym = None else: self._extHdrArray = None self.extInts = None self.extFloats = None self.extSym = None self._initWhenHdrArraySet() def _initWhenHdrArraySet(self): """Reset internal attributes based on size and pixel type in header.""" nx, ny, nsecs = self.hdr.Num if nx < 0: nx = 0 if ny < 0: ny = 0 if nsecs < 0: nsecs = 0 self._shape = (nsecs, ny, nx) # todo: wavelenths , times self._shape2d = self._shape[-2:] self._dtype = MrcMode2dtype(self.hdr.PixelType) if self._fileIsByteSwapped: self._dtype = self._dtype.newbyteorder() self._secByteSize = self._dtype.itemsize * N.prod(self._shape2d) def setHdrForShapeType(self, shape, type): """Set the size and pixel type fields in the header. For a file opened in 'w' or 'w+' mode, this and initHdrForArr() are the two ways to make a Mrc2 object ready to read or write image data. As currently implemented, only uses the last two elements of shape and the product of all the remaining elements of shape to set the size fields in the header. It does not modify the fields for the number of time points, number of wavelengths, or image sequence. Positional parameters: shape -- Is a tuple to specify the shape of the data to be stored in the MRC file. The ith element of the tuple is the number of samples for the ith dimension. The 0th dimension is the slowest varying. The fastest varying dimension, usually called x, is the last element in the tuple. Shape should have at least two elements. type -- Is the NumPy dtype or Python type that will be used to represent each pixel value in the file. If the type is not equivalent to one of the pixel formats supported by MRC, an exception will be raised. """ mrcmode = dtype2MrcMode(type) self.hdr.PixelType = mrcmode self.hdr.Num = shape[-1], shape[-2], N.prod(shape[:-2]) self._initWhenHdrArraySet() def makeExtendedHdr(self, numInts, numFloats, nSecs=None): """Create a Priism extended header or remove the extended header. Will remove the extended header if nSecs is zero or both numInts and numFloats are zero. If header is in Priism's format, sets the space group to zero. Also sets the space group to zero if the header does not claim to support the exttyp field and the space group is different than 0, 1, or 401. The entries for a new header will all be zero. If there already was an extended header, the resources for the previous extended header are released, and no attempt is made to copy the previous values to the new header. When a new extended header is created, the integer values can be accessed with self.extInts. The floating point values can be accessed with self.extFloats. Both are NumPy array views. If numInts is greater than one or is zero, the shape for self.extInts will be (nSecs, numInts). If numInts is one, the shape for self.extFloats will be (nSecs,). If numFloats is greater than one or is zero, the shape for self.extFloats will be (nSecs, numFloats). If numFloats is one, the shape for self.extFloats will be (nSecs,). makeExtendedHdr() does not change the contents of the file. To commit changes made to the shape of the extended header, call writeHeader(). To commit changes made to the values in the extended header, call writeExtHeader(). Positional parameters: numInts -- Is the number of integer values to store per section in the extended header. Must be non-negative. numFloats -- Is the number of floating-point values to store per section in the extended header. Must be non-negative. Keyword parameters: nSecs -- If not None, nSecs is the number of sections of storage to allocate for the extended header. If nSecs is None, the number of sections allocated will be the number of sections from the header. """ if numInts < 0 or numFloats < 0: raise ValueError('Number of integers or floating point ' 'values is negative') if numInts > 32767 or numFloats > 32767: raise ValueError('Number of integers or floating point ' 'values is too large to store in header fields') if nSecs is not None and nSecs < 0: raise ValueError('Number of sections is negative') if nSecs is None: if self._shape is None: nSecs = 0 else: nSecs = self._shape[0] bytesPerSec = (numInts + numFloats) * 4 ntot = minExtHdrSize(nSecs, bytesPerSec) if ntot > 2147483647: raise ValueError('Requested extended header size is too ' 'large for the extended header size field') self._extHdrNumInts = self.hdr.NumIntegers = numInts self._extHdrNumFloats = self.hdr.NumFloats = numFloats if hdrHasExtType(self.hdr): self.hdr.exttyp = N.fromstring('AGAR', dtype='i1') else: if (hdrIsInPriismFormat(self.hdr) or (self.hdr.nspg != 0 and self.hdr.nspg != 1 and self.hdr.nspg != 401)): self.hdr.nspg = 0 self._extHdrBytesPerSec = bytesPerSec self._extHdrSize = self.hdr.next = ntot self._dataOffset = self._hdrSize + self._extHdrSize self.e = N.zeros(self._extHdrSize, dtype='u1') if self._extHdrSize > 0 and self._extHdrBytesPerSec > 0: nSecs = self._extHdrSize // self._extHdrBytesPerSec byteorder = '=' type_descr = [ ('int', '%s%di4' % (byteorder, self._extHdrNumInts)), ('float', '%s%df4' % (byteorder, self._extHdrNumFloats))] self._extHdrArray = N.recarray(nSecs, dtype=type_descr, buf=self.e) self.extInts = self._extHdrArray.field('int') self.extFloats = self._extHdrArray.field('float') else: self._extHdrArray = None self.extInts = None self.extFloats = None self.extSym = None def makeSymmetryInfo(self, nbytes, nspg=None): """Create the extended header for symmetry information. If the header is in Priism's format and the space group, after applying the nspg keyword, is zero, will raise a RuntimeError exception since files with zero for the space group are assumed to use Priism-style extended headers. Sets the NumIntegers and NumFloats fields to zero. The new extended header is filled with spaces. If there already was an extended header, the resources for the previous extended header are released, and no attempt is made to copy the previous values to the new header. When a new extended header is created, the symmetry information, as an array of 80 character records, can be accessed with self.extSym. self.extInts and self.extFloats are set to None. makeSymmetryInfo() does not change the contents of the file. To commit changes made to the shape of the extended header, call writeHeader(). To commit changes made to the values in the extended header, call writeExtHeader(). This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: nbytes -- Is the number of bytes to allocate. A ValueError exception will be raised if nbytes is less than zero. A value of zero will remove the extended header. Note that values of nbytes that are not multiples of eight could lead to misalignment of image data if the file is memory mapped with the Mrc class. Keyword parameters: nspg -- If not None, the space group in the header will be set to the specified value. """ if nbytes < 0: raise ValueError('Negative number of bytes requested for ' 'extended header') if nbytes > 2147483647: raise ValueError('Requested number of bytes is too large ' 'for the extended header size field') if nspg is not None: self.hdr.nspg = nspg if hdrIsInPriismFormat(self.hdr) and self.hdr.nspg == 0: raise RuntimeError('Used makeSymmetryInfo() when the space ' 'group is zero') self.hdr.next = nbytes self.hdr.NumIntegers = 0 self.hdr.NumFloats = 0 if hdrHasExtType(self.hdr): self.hdr.exttyp = N.fromstring('MRC0', dtype='i1') self._extHdrSize = nbytes self._extHdrNumInts = 0 self._extHdrNumFloats = 0 self._extHdrBytesPerSec = 0 self._dataOffset = self._hdrSize + self._extHdrSize if self._extHdrSize > 0: self.e = N.empty(self._extHdrSize, dtype='u1') # ASCII for space. self.e[:] = 32 nrec = self._extHdrSize // 80 nrem = self._extHdrSize - 80 * nrec type_descr = [('records', '(%d,80)i1' % nrec), ('extra', '%di1' % nrem)] self._extHdrArray = N.recarray(shape=1, dtype=type_descr, buf=self.e) self.extSym = ManagedTitleArray( self._extHdrArray.field('records')[0]) else: self.e = N.zeros(0, dtype='u1') self._extHdrArray = None self.extSym = None self.extInts = None self.extFloats = None def makeGenericExtendedHdr(self, nbytes, fmt): """Allocate space for an extended header that is not for symmetry information and does not use Priism's format. The bytes in the new extended header are set to zero. If there already was an extended header, the resources for the previous extended header are released, and no attempt is made to copy the previous values to the new header. The new extended header, as an array of unsigned bytes, can be accessed through self.e. Sets self.extInts, self.extFloats, and self.extSym to None. makeGenericExtendedHdr() does not change the contents of the file. To commit the changes made to the shape or format of the extended header, call writeHeader(). To commit changes made to the values in the extended header, call writeExtHeader(). This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: nbytes -- Is the number of bytes to allocate. If nbytes is zero, the extended header will be removed. A ValueError exception will be raised if nbytes is less than zero. Note that values of nbytes that are not multiples of eight could lead to misalignment of image data if the file is memory mapped with the Mrc class. fmt -- Is a four character ASCII string describing the format of the extended header. A ValueError exception will be raised if fmt is 'AGAR', 'MRC0', of 'CCP4'. Use makeExtendedHdr() or makeSymmetryInfo() to create extended headers for those formats. A RuntimeError exception will be raised if the header format does not store a string describing the extended header format. """ if nbytes < 0: raise ValueError('Negative number of bytes requested for ' 'extended header') if nbytes > 2147483647: raise ValueError('Requested number of bytes is too large ' 'for the extended header size field') if fmt == 'AGAR': raise ValueError('Use makeExtendedHdr() to create an extended ' 'with the Priism format') if fmt == 'MRC0' or fmt == 'CCP4': raise ValueError('Use makeSymmetryInfo() to create an extended ' 'header with symmetry information') if len(fmt) != 4: raise ValueError('fmt is not a four character string') if not hdrHasExtType(self.hdr): raise RuntimeError('Header does not store a string code for the ' 'extended header format') self.hdr.exttyp = N.fromstring(fmt, dtype='i1') self.hdr.next = nbytes self._extHdrSize = nbytes self._extHdrNumInts = 0 self._extHdrNumFloats = 0 self._extHdrBytesPerSec = 0 self._dataOffset = self._hdrSize + self._extHdrSize self.e = N.zeros(nbytes, dtype='u1') self.extInts = None self.extFloats = None self.extSym = None def setTitle(self, s, i=-1, push=False, truncate=False): """Set a title in the MRC header. This function was added in version 0.1.0 of Mrc.py as included with Priism. That version also allows calling setTitle() directly on the header: self.hdr.setTitle(). Positional parameters: s -- Is the character string for the title. If s is longer than 80 characters and truncate is False, a ValueError exception will be raised. Since no byte swapping is done for the titles in the header, s should be encoded in ASCII or another format that does not use multibyte characters. Keyword parameters: i -- Is the index of the title to set. If i is less than zero, the last title not in use will be set. If i is less than zero and all the titles are in use and push is False or i is greater than 9, a ValueError exception will be raised. push -- If True, i is less than zero, and all titles are in use, titles will be pushed down before assigning s to the last title. That will discard the first title and title[k] (for k greater than and equal to 0 and less than 9) will be title[k+1] from before the change. truncate -- If True, only use the first 80 characters from s. """ self.hdr.setTitle(s, i=i, push=push, truncate=truncate) def axisOrderStr(self, onlyLetters=True): """Return a string indicating the ordering of dimensions. x, y, z, w, and t will appear at most once in the string, and at least three of them will be present. The letters that do appear will be present in order from slowest varying to fastest varying. The values for the axis field in the header do not affect the result. This function was added in version 0.1.0 of Mrc.py as included with Priism. Keyword parameters: onlyLetters -- If True, only the letters for the dimensions will appear in the string. If False, the first character of the string will be '[', the last character of the string will be ']', and a letter for a dimension will be preceded by a comma if it is not the first, slowest-varying dimension. """ return axisOrderStr(self.hdr, onlyLetters) def info(self): """Print useful information from header.""" hdrInfo(self.hdr) def close(self): """Close the file associated with the Mrc2 object. Delete that file if it was created as a temporary file. """ self._f.close() if self._delete_on_close: os.remove(self._path) def flush(self): """Flush any changes to the Mrc2 object's file to disk.""" self._f.flush() def seekSec(self, i): """Seek to the start of the image data for a given section. Positional parameters: i -- Is the 0-based section index. """ if self._secByteSize == 0: raise ValueError('not inited yet - unknown shape, type') self._f.seek(self._dataOffset + i * self._secByteSize) def seekHeader(self): """Seek to the start of the MRC header.""" self._f.seek(0) def seekExtHeader(self): """Seek to the start of the MRC extended header.""" self._f.seek(self._hdrSize) def readSec(self, i=None): """Read one image from the MRC file. Keyword parameters: i -- If i is None, starts the read at the current position for the file. If i is not None, seeks to the start of section i and reads from that location. Return value: Returns a two-dimensional NumPy array with the image data. The shape of the array is (self.hdr.Num[1], self.hdr.Num[0]). The format for each image element is the same as in the file. """ if i is not None: self.seekSec(i) a = N.fromfile(self._f, self._dtype, N.prod(self._shape2d)) a.shape = self._shape2d return a def writeSec(self, a, i=None): """Write image data to the MRC file. Positional parameters: a -- Is a NumPy array with the data to write. The format for each sample should match, up to byte order, the format for data values in the file. No checks are done on the dimensions of a, so any amount of data can be written. To best ensure compatibility with future versions of Mrc2, a should be two-dimensional with a shape of (self.Hdr.Num[1], self.hdr.Num[0]). Keyword parameters: i -- If i is None, the write starts at the current position for the file. If i is not None, seeks to the start of section i and starts the write at that location. """ if a.dtype.type != self._dtype.type: raise TypeError('type of data, %s, to write does not match ' 'type, %s, in header' % (a.dtype.name, self._dtype.name)) if self._fileIsByteSwapped != isSwappedDtype(a.dtype): v = a.byteswap() else: v = a if i is not None: self.seekSec(i) return v.tofile(self._f) def readStack(self, nz, i=None): """Read nz images from the MRC file. Positional parameters: nz -- Is the number of images to read. Keyword parameters: i -- If i is None, the read starts at the current position for the file. If i is not None, seeks to the start of section i and starts the read there. Return value: Returns a three-dimensional NumPy array with the image data. The shape of the array is (nz, self.hdr.Num[1], self.hdr.Num[0]). The format for each image element is the same as in the file. """ if i is not None: self.seekSec(i) a = N.fromfile(self._f, self._dtype, nz * N.prod(self._shape2d)) a.shape = (nz,) + self._shape2d return a def writeStack(self, a, i=None): """Write image data to the MRC file. Positional parameters: a -- Is a NumPy array with the data to write. The format for each sample should match, up to byte order, the format for data values in the file. No checks are done on the dimensions of a, so any amount of data can be written. To best ensure compatibility with future versions of Mrc2, a should have at least two dimensions and the shape of the last two dimensions should be (self.Hdr.Num[1], self.Hdr.Num[0]). Keyword parameters: i -- If i is None, the write starts at the current position for the file. If i is not None, seeks to the start of section i and starts the write at that location. """ if a.dtype.type != self._dtype.type: raise TypeError('type of data, %s, to write does not match ' 'type, %s, in header' % (a.dtype.name, self._dtype.name)) if self._fileIsByteSwapped != isSwappedDtype(a.dtype): v = a.byteswap() else: v = a if i is not None: self.seekSec(i) return v.tofile(self._f) def writeHeader(self, seekTo0=False): """Write the 1024 byte MRC header to the file. Keyword parameters: seekTo0 -- If True, the file position will be set after writing the header to the start of the first section's image data. """ self.seekHeader() self.hdr._array.tofile(self._f) if seekTo0: self.seekSec(0) def writeExtHeader(self, seekTo0=False): """Write the extended header to the file. Keyword parameters: seekTo0 -- If True, the file position will be set after writing the extended header to the start of the first section's image data. """ self.seekExtHeader() self.e.tofile(self._f) if seekTo0: self.seekSec(0) ########################################################################### ########################################################################### ########################################################################### ########################################################################### class HdrBase(object): """Represents a MRC header without extensions. Only provides access to the parts of the header that are in the original formulation of the MRC format. Those fields are listed below and can be accessed as x.name_of_field where x is an instance of this class. For example, x.Num would access the number of columns, rows, and sections. This class was added in version 0.1.0 of Mrc.py as included with Priism. """ # Use __slots__ so any assignments to unspecified attributes or # properties will give an AttributeError exception. __slots__ = ('_array',) def __init__(self, hdrArray): """Initialize the HdrBase object. Positional parameters: hdrArray -- Is a MRC header as a NumPy structured array with one element. The dtype of the array will have to compatible with whatever subclass of HdrBase is being instantiated. A NumPy structured array with a dtype of numpy.dtype(mrcHdr_dtype) is compatible with HdrBase and HdrPriism. A NumPy structured array with a dtype of numpy.dtype(mrc2014Hdr_dtype) is compatible with HdrBase, Hdr2014 and Hdr2014Priism. """ self._array = hdrArray def _getNum(self): """Is three integers as a NumPy array. The first is the number of columns; i.e. the number of samples in the fastest varying dimension as stored in the file. The second is the number of rows; i.e. the number of samples in the second-fastest varying dimension as stored in the file. Occupies bytes 1 - 12 (numbered from one) in the header. """ return self._array['Num'][0] def _setNum(self, value): self._array['Num'][0] = value Num = property(_getNum, _setNum) def _getPixelType(self): """Is an integer code for the format of the image data. For supported Python types or NumPy dtypes, Mrc.dtype2MrcMode() can compute the code. Codes recognized by this software are (zero through 4 are part of the original formulation of the format): 0: signed (two's complement) 8-bit integer 1: signed (two's complement) 16-bit integer 2: 32-bit IEEE floating-point value 3: real and imaginary parts of a complex value; both as signed (two's complement) 16-bit integers 4: real and imaginary parts of a complex value; both as 32-bit IEEE floating-point values 5: nothing currently standardized; treated as signed (two's complement) 16-bit integer 6: unsigned 16-bit integer 7: nothing currently standardized; treated as signed (two's complement) 32-bit integer 101: nothing currently standardized; treated as unsigned 4-bit integer; if the number of columns is odd, each row has an extra unused 4 bits at the end so rows start on 8-bit boundaries Occupies bytes 13 - 16 (numbered from one) in the header. """ return self._array['PixelType'][0] def _setPixelType(self, value): self._array['PixelType'][0] = value PixelType = property(_getPixelType, _setPixelType) def _getmst(self): """Is three integers as a NumPy array. For crystallographic data, these are the location of the first column, first row, and first section in the unit cell. For non-crystallographic data, these are usually used to describe the dataset's relationship to another, larger, dataset from which it was drawn; these values might then be the indices in that larger dataset for the first sample in this dataset. Occupies bytes 17 - 28 (numbered from one) in the header. """ return self._array['mst'][0] def _setmst(self, value): self._array['mst'][0] = value mst = property(_getmst, _setmst) def _getm(self): """Is three integers as a NumPy array. For crystallographic data, holds the number of samples along the x, y, and z axes, respectively, of the unit cell. For non-crystallographic data, it is common to use either ones for the values in m or to set the values equal to the values in Num. An exception to that is for MRC 2014 files. Then, m[2] is used to store the number of z slices per volume when the space group is 1 or 401. Occupies bytes 29 - 40 (numbered from one) in the header. """ return self._array['m'][0] def _setm(self, value): self._array['m'][0] = value m = property(_getm, _setm) def _getd(self): """Is three floating-point values as a NumPy array. For crystallographic data, they are the dimensions of the unit cell in Angstroms. For non-crystallographic data, the elements of d are the elements of m times the spacing between samples in that dimension so that d[axis[0]] / m[axis[0]] is the spacing between samples in a column, d[axis[1]] / m[axis[1]] is the spacing between samples in a row, and d[axis[2]] / m[axis[2]] is the spacing between sections. By convention, EM data uses Angstroms for the spacing and optical data uses microns. Occupies bytes 41 - 52 (numbered from one) in the header. """ return self._array['d'][0] def _setd(self, value): self._array['d'][0] = value d = property(_getd, _setd) def _getangle(self): """Is three floating-point values as a NumPy array. They are the angles, in degrees, between the axes of the unit cell. For non-crystallographic data, these are all set to 90. Occupies bytes 53 - 64 (numbered from one) in the header. """ return self._array['angle'][0] def _setangle(self, value): self._array['angle'][0] = value angle = property(_getangle, _setangle) def _getaxis(self): """Is three integers as a NumPy array. The first is the axis (1 for x; 2 for y; 3 for z) that corresponds to columns in the file. The second is the axis that corresponds to rows in the file. The third is the axis that corresponds to sections in the file. Occupies bytes 65 - 76 (numbered from one) in the header. """ return self._array['axis'][0] def _setaxis(self, value): self._array['axis'][0] = value axis = property(_getaxis, _setaxis) def _getmmm1(self): """Is three floating-point values as a NumPy array. The first is the minimum density value. The second is the maximum density value. The third is the mean density value. For files using Priism's format, these statistics are for the data from the first wavelength. Occupies bytes 77 - 88 (numbered from one) in the header. """ return self._array['mmm1'][0] def _setmmm1(self, value): self._array['mmm1'][0] = value mmm1 = property(_getmmm1, _setmmm1) def _getnspg(self): """Is an integer to hold the space group for crystallographic data. For non-crystallographic data, the convention is to set the space group to zero, one, or 401. Priism's version of MRC uses zero. Image2000 and MRC 2014 use zero for single images or image sequences, one for cases where the images represent a volume, and 401 for volume stacks. Occupies bytes 89 - 92 (numbered from one) in the header. """ return self._array['nspg'][0] def _setnspg(self, value): self._array['nspg'][0] = value nspg = property(_getnspg, _setnspg) def _getnext(self): """Is the number of bytes in the extended header. Occupies bytes 93 - 96 (numbered from one) in the header. """ return self._array['next'][0] def _setnext(self, value): self._array['next'][0] = value next = property(_getnext, _setnext) def _getNumTitles(self): """Is an integer for the number of titles used. Up to 10 titles can be stored. Occupies bytes 221 - 224 (numbered from one) in the header. """ return self._array['NumTitles'][0] def _setNumTitles(self, value): self._array['NumTitles'][0] = value NumTitles = property(_getNumTitles, _setNumTitles) def _gettitle(self): """Are the titles as a ten element array where each element is an eighty character string. Occupies bytes 225 - 1024 (numbered from one) in the header. """ return ManagedTitleArray(self._array['title'][0]) def _settitle(self, value): a = ManagedTitleArray(self._array['title'][0]) if isStringLike(value): nper = self._array['title'][0].shape[1] if len(value) >= len(a) * nper: for i in range(0, len(a)): a[i] = value[i*80:(i+1)*80] else: for i in range(0, len(a)): a[i] = value elif hasattr(value, '__len__'): if len(value) == len(a): for i in range(0, len(a)): a[i] = value[i] else: raise ValueError('collection assigned to title does not have ' ' %d elements' % len(a)) else: raise TypeError('invalid type for assignment to title') title = property(_gettitle, _settitle) def getSpacing(self): """Return spacing between samples. By convention, the units for the spacing are microns for optical microscope data and Angstroms for electron microscope data. If the axis values in the header are invalid or a value in m in the header is zero, the spacing values are not well defined. Returns a three element NumPy array. The first value is the spacing in the direction set by axis[0], the second value is the spacing in the direction set by axis[1], and the third value is the spacing in the direction set by axis[2]. """ r = N.empty(3, dtype='f4') m = self.m d = self.d ax = self.axis for i in range(0, 3): j = ax[i] - 1 if (j < 0 or j > 2 or m[j] == 0): r[i] = 0.0 else: r[i] = d[j] / m[j] return r def setSpacing(self, d0, d1, d2): """Set spacing between samples. By convention, the units for the spacing are microns for optical microscope data and Angstroms for electron microscope data. If the axis values in the header are invalid or a value in m in the header is zero, the spacing values are not well defined. Positional parameters: d0 -- Is the spacing between samples in the direction set by axis[0]. d1 -- Is the spacing between samples in the direction set by axis[1]. d2 -- Is the spacing between samples in the direction set by axis[2]. """ m = self.m d = self.d ax = self.axis j = ax[0] - 1 if j >= 0 and j < 3: d[j] = d0 * m[j] j = ax[1] - 1 if j >= 0 and j < 3: d[j] = d1 * m[j] j = ax[2] - 1 if j >= 0 and j < 3: d[j] = d2 * m[j] def clearTitles(self): """Set the number of titles to zero and fill the titles with spaces.""" self.NumTitles = 0 self.title = ' ' * 80 def setTitle(self, s, i=-1, push=False, truncate=False): """Set a title in the MRC header. Positional parameters: s -- Is the character string for the title. If s is longer than 80 characters and truncate is False, a ValueError exception will be raised. Since no byte swapping is done for the titles in the header, s should be encoded in ASCII or another format that does not use multibyte characters. Keyword parameters: i -- Is the index of the title to set. If i is less than zero, the last title not in use will be set. If i is less than zero and all the titles are in use and push is False or i is greater than 9, a ValueError exception will be raised. push -- If True, i is less than zero, and all titles are in use, titles will be pushed down before assigning s to the last title. That will discard the first title and title[k] (for k greater than and equal to 0 and less than 9) will be title[k+1] from before the change. truncate -- If True, only use the first 80 characters from s. """ n = max(0, min(self.NumTitles, 10)) b = N.fromstring(s, dtype='i1') if b.shape[0] > 80: if not truncate: raise ValueError('Mrc only support title up to 80 characters') b = b[0:80] elif b.shape[0] < 80: # Pad with spaces to match what is done by Priism libraries. b = N.concatenate((b, 32 * N.ones(80 - b.shape[0], dtype='i1'))) if i < 0: i = n if n == 10 and push: for i in range(0, 9): self.title[i] = self.title[i + 1] i = 9 n = 9 if i > 9: raise ValueError('Mrc only support up to 10 titles (0<=i<10)') if i >= n: self.NumTitles = i + 1 self.title[i] = b class HdrPriism(HdrBase): """Represents a MRC header with the Priism extensions. Provides access to all parts of the header. The fields that are in common between the basic MRC format and Priism are described in HdrBase. The fields that are Priism extensions listed below. Some properties are provided to make it easier to use instances of this class interchangeably with instances of Hdr2014Priism, especially for retrieving values without modifying them. This class was added in version 0.1.0 of Mrc.py as included with Priism. The dynamically declared class it replaced had data attributes with the same names except that it did not have nblank, ntst, rms, and origin and it had a data attribute called type that was the the first two bytes of the nspg attribute in this class (the nspg attribute in that class class was only two bytes and was immediately after the type attribute). The blank attribute in that class was larger, spanning the nblank, ntst, and blank attributes in this class. That class did not have getSpacing(), setSpacing(), clearTitles(), setTitle(), index2zwt() or zwt2index functions. """ # Use __slots__ so any assignments to unspecified attributes or # properties will give an AttributeError exception. __slots__ = () def index2zwt(self, i, over=False): """Convert section index to 3D index in z, wavelength, and time. Positional parameters: i -- Is the section index. If less than zero, it is treated as a displacement from one past the end (i.e. -1 is the last valid section index, and -2 is the next-to-last valid section index). Keyword parameters: over -- If True, a value of i past the end will cause a ValueError exception to be raised. If False, a value of i past the end will lead to a value for the index in the slowest-varying dimension to be past the end. Return value: Returns a three element tuple of the zero-based indices for z, wavelength, and time. """ nw = max(1, self.NumWaves) nt = max(1, self.NumTimes) nz = max(1, self.Num[2] // (nw * nt)) seq = self.ImgSequence if seq < 0 or seq > 2: seq = 0 return index2zwt(i, nz, nw, nt, seq, over=over) def zwt2index(self, z, w, t, over=False): """Convert a 3D index in z, wavelength, and time to a section index. Positional parameters: z -- Is the index in z. If less than zero, it is treated as a displacement from one past the end i.e. -1 is the last valid section index, and -2 is the next-to-last valid section index). w -- Is the wavelength index. If less than zero, it is treated as a displacement from one past the end. t -- Is the time index. If less than zero, it is treated as a displacement from one past the end. Keyword parameters: over -- If True, a ValueError exception will be raised if any of z, w, or t are past the end in their respective dimesions. If False, a value for z, w, or t that is past the end will only result in a ValueError exception if that dimension is not the slowest-varying non-singleton dimension. Return value: Returns a zero-based index for the section. """ nw = max(1, self.NumWaves) nt = max(1, self.NumTimes) nz = max(1, self.Num[2] // (nw * nt)) seq = self.ImgSequence if seq < 0 or seq > 2: seq = 0 return zwt2index(z, w, t, nz, nw, nt, seq, over=over) def _getdvid(self): """Is a 16-bit integer that is a code for the originator of the data. Files from Priism and John Sedat's microscopes put 0xc0a0 (-16224 as a signed integer) in this value. Occupies bytes 97 - 98 (numbered from one) in the header. """ return self._array['dvid'][0] def _setdvid(self, value): self._array['dvid'][0] = value dvid = property(_getdvid, _setdvid) def _getnblank(self): """Is a 16-bit integer that is not currently reserved for any specific purpose. Occupies bytes 99 - 100 (numbered from one) in the header. """ return self._array['nblank'][0] def _setnblank(self, value): self._array['nblank'][0] = value nblank = property(_getnblank, _setnblank) def _getntst(self): """Is an integer used to store the starting time index, i.e. the index in a longer time series for the first time point in this dataset. Occupies bytes 101 - 104 (numbered from one) in the header. """ return self._array['ntst'][0] def _setntst(self, value): self._array['ntst'][0] = value ntst = property(_getntst, _setntst) def _getblank(self): """Is 24 bytes that is not currently reserved for any specific purpose. Priism and this implementation do not perform any byte-swapping on this data. If you store multi-byte quantities here, you will need to handle the byte-swapping. Occupies bytes 105 - 128 (numbered from one) in the header. """ return self._array['blank'][0] def _setblank(self, value): self._array['blank'][0] = value blank = property(_getblank, _setblank) def _getNumIntegers(self): """Is the number of 4-byte integers to store per section in the extended header. Occupies bytes 129 - 130 (numbered from one) in the header. """ return self._array['NumIntegers'][0] def _setNumIntegers(self, value): self._array['NumIntegers'][0] = value NumIntegers = property(_getNumIntegers, _setNumIntegers) def _getNumFloats(self): """Is the number of 4-byte floating-point values to store per section in the extended header. Occupies bytes 131 - 132 (numbered from one) in the header. """ return self._array['NumFloats'][0] def _setNumFloats(self, value): self._array['NumFloats'][0] = value NumFloats = property(_getNumFloats, _setNumFloats) def _getsub(self): """Is the number of different resolutions stored in the dataset. Occupies bytes 133 - 134 (numbered from one) in the header. """ return self._array['sub'][0] def _setsub(self, value): self._array['sub'][0] = value sub = property(_getsub, _setsub) def _getzfac(self): """For multiple resolutions, the number of z samples in a resolution will be the number of z samples in the next higher resolution divided by this integer and rounding up any remainder. Occupies bytes 135 - 136 (numbered from one) in the header. """ return self._array['zfac'][0] def _setzfac(self, value): self._array['zfac'][0] = value zfac = property(_getzfac, _setzfac) def _getmm2(self): """Is two floating-point values as a NumPy array. The first is the minimum density for the second wavelength, and the second is the maximum density for the second wavelength. Occupies bytes 137 - 144 (numbered from one) in the header. """ return self._array['mm2'][0] def _setmm2(self, value): self._array['mm2'][0] = value mm2 = property(_getmm2, _setmm2) def _getmm3(self): """Is two floating-point values as a NumPy array. The first is the minimum density for the third wavelength, and the second is the maximum density for the third wavelength. Occupies bytes 145 - 152 (numbered from one) in the header. """ return self._array['mm3'][0] def _setmm3(self, value): self._array['mm3'][0] = value mm3 = property(_getmm3, _setmm3) def _getmm4(self): """Is two floating-point values as a NumPy array. The first is the minimum density for the fourth wavelength, and the second is the maximum density for the fourth wavelength. Occupies bytes 153 - 160 (numbered from one) in the header. """ return self._array['mm4'][0] def _setmm4(self, value): self._array['mm4'][0] = value mm4 = property(_getmm4, _setmm4) def _getImageType(self): """Is a 16-bit integer which is a code for the type of data in the file. http://msg.ucsf.edu/IVE/IVE4_HTML/IM_ref2.html#ImageTypes describes the types that Priism defines and how the n1, n2, v1, and v2 fields are used for each type. Occupies bytes 161 - 162 (numbered from one) in the header. """ return self._array['ImageType'][0] def _setImageType(self, value): self._array['ImageType'][0] = value ImageType = property(_getImageType, _setImageType) def _getLensNum(self): """For optical data, this 16-bit integer is used to indicate which microscope configuration was used when the data was collected. Each microscope system would have its own table of configurations and corresponding integer codes. Occupies bytes 163 - 164 (numbered from one) in the header. """ return self._array['LensNum'][0] def _setLensNum(self, value): self._array['LensNum'][0] = value LensNum = property(_getLensNum, _setLensNum) def _getn1(self): """Is a 16-bit integer whose interpretation depends on the value for ImageType. Occupies bytes 165 - 166 (numbered from one) in the header. """ return self._array['n1'][0] def _setn1(self, value): self._array['n1'][0] = value n1 = property(_getn1, _setn1) def _getn2(self): """Is a 16-bit integer whose interpretation depends on the value for ImageType. Occupies bytes 167 - 168 (numbered from one) in the header. """ return self._array['n2'][0] def _setn2(self, value): self._array['n2'][0] = value n2 = property(_getn2, _setn2) def _getv1(self): """Is a 16-bit integer which is used to store a floating point value, f, as round_to_nearest(f * 100.0). What f is depends on the value for ImageType. This Python implementation leaves the conversion between f and v1 to the caller. Occupies bytes 169 - 170 (numbered from one) in the header. """ return self._array['v1'][0] def _setv1(self, value): self._array['v1'][0] = value v1 = property(_getv1, _setv1) def _getv2(self): """Is a 16-bit integer which is used to store a floating point value, f, as round_to_nearest(f * 100.0). What f is depends on the value for ImageType. This Python implementation leaves the conversion between f and v2 to the caller. Occupies bytes 171 - 172 (numbered from one) in the header. """ return self._array['v2'][0] def _setv2(self, value): self._array['v2'][0] = value v2 = property(_getv2, _setv2) def _getmm5(self): """Is two floating-point values as a NumPy array. The first is the minimum density for the fifth wavelength, and the second is the maximum density for the fifth wavelength. Occupies bytes 173 - 180 (numbered from one) in the header. """ return self._array['mm5'][0] def _setmm5(self, value): self._array['mm5'][0] = value mm5 = property(_getmm5, _setmm5) def _getNumTimes(self): """Is an integer for the number of time points stored in the file. Occupies bytes 181 - 182 (numbered from one) in the header. """ return self._array['NumTimes'][0] def _setNumTimes(self, value): self._array['NumTimes'][0] = value NumTimes = property(_getNumTimes, _setNumTimes) def _getImgSequence(self): """Is an integer code for how the sections are arranged into z, wavelength, and time points. Three values are undersood by Priism: 0: Z varies fastest, followed by time, followed by wavelength 1: wavelength varies fastest, followed by z, followed by time 2: z varies fastest, followed by wavelength, followed by time Occupies bytes 183 - 184 (numbered from one) in the header. """ return self._array['ImgSequence'][0] def _setImgSequence(self, value): self._array['ImgSequence'][0] = value ImgSequence = property(_getImgSequence, _setImgSequence) def _gettilt(self): """Is three floating-point values as a NumPy array. The three values are a trio of rotation angles in degrees. For image windows, Priism uses those angles to rotate the coordinates of overlayed objects to the coordinates aligned to the image axes. The transformation uses the first angle as rotation about the original +x axis of the objects with a positive angle rotating the +y axis towards the +z axis. The second angle rotates about the +y axis from the first rotation with a positive angle rotating the +z axis towards the +x axis. The third angle rotates about +z axis from the second rotation with a positive angle rotating the +x axis towards the +y axis. Occupies bytes 185 - 196 (numbered from one) in the header. """ return self._array['tilt'][0] def _settilt(self, value): self._array['tilt'][0] = value tilt = property(_gettilt, _settilt) def _getNumWaves(self): """Is an integer for the number of wavelengths stored in the file. The wavelength dimension is handled differently than z or time in that other header values store per-wavelength metadata. That storage allows for information about five or less wavelengths. Because of that limitation and because many Priism applications assume a maximum of five wavelengths, you would normally restrict the number of wavelengths stored to be five or less. Occupies bytes 197 - 198 (numbered from one) in the header. """ return self._array['NumWaves'][0] def _setNumWaves(self, value): self._array['NumWaves'][0] = value NumWaves = property(_getNumWaves, _setNumWaves) def _getwave(self): """Is five 16-bit integers as a NumPy array. They are the wavelengths for the emitted or transmitted light in nanometers rounded to the nearest integer. For a broad passband, you would normally use some measure of the center of the passband as the wavelength to store in the header. Occupies bytes 199 - 208 (numbered from one) in the header. """ return self._array['wave'][0] def _setwave(self, value): self._array['wave'][0] = value wave = property(_getwave, _setwave) def _getzxy0(self): """Is three floating-point values as a NumPy array. The three values specify an origin to use in coordinate transformations. The first value is the z coordinate, the second value is the x coordinate, and the third value is the y coordinate. The units for each are, by convention, the same as used for the spacing between samples: Angstroms for EM data and microns for optical data. Occupies bytes 209 - 220 (numbered from one) in the header. """ return self._array['zxy0'][0] def _setzxy0(self, value): self._array['zxy0'][0] = value zxy0 = property(_getzxy0, _setzxy0) def _getrms(self): """Is part of the MRC 2014 format but is not in the Priism format. Accesses will return -1. Attempts to set this to something other than -1 will generate an AttributeError exception. """ return -1.0 def _setrms(self, value): if value != -1.0: raise AttributeError('Priism MRC header does not store RMS') rms = property(_getrms, _setrms) def _getorigin(self): """Is three floating-point values that are part of the MRC 2014 format. The Priism format zxy0 field has the same purpose but is in a different part of the header and has a different ordering for the coordinates. Reorder and return those values when accessed. When set, reorder the values and set the fields used by the Priism format. """ return ReorderedArray(self._array['zxy0'][0], (1, 2, 0)) def _setorigin(self, value): self._array['zxy0'][0] = (value[2], value[0], value[1]) origin = property(_getorigin, _setorigin, ) class Hdr2014(HdrBase): """Represents a MRC 2014 header without extensions. Only provides access to the parts of the header that are in the MRC 2014 specification. The fields that are part of the original MRC formulation are described in HdrBase. The additions for MRC 2014 are listed below. This class was added in version 0.1.0 of Mrc.py as included with Priism. """ # Use __slots__ so any assignments to unspecified attributes or # properties will give an AttributeError exception. __slots__ = () def index2zwt(self, i, over=False): """Convert section index to 3D index in z, wavelength, and time. Positional parameters: i -- Is the section index. If less than zero, it is treated as a displacement from one past the end (i.e. -1 is the last valid section index, and -2 is the next-to-last valid section index). Keyword parameters: over -- If True, a value of i past the end will cause a ValueError exception to be raised. If False, a value of i past the end will lead to a value for the index in the slowest-varying dimension to be past the end. Return value: Returns a three element tuple of the zero-based indices for z, wavelength, and time. """ if self.nspg == 401: if self.m[2] > 0: nz = self.m[2] nt = max(1, self.Num[2] // nz) else: nz = 1 nt = max(self.Num[2], 1) else: nz = max(self.Num[2], 1) nt = 1 nw = 1 # All the available image sequence types have z varying faster # than time; use the one with wavelength varying fastest since # any overflow will not go into the wavelength dimension. return index2zwt(i, nz, nw, nt, 1, over=over) def zwt2index(self, z, w, t, over=False): """Convert a 3D index in z, wavelength, and time to a section index. Positional parameters: z -- Is the index in z. If less than zero, it is treated as a displacement from one past the end i.e. -1 is the last valid section index, and -2 is the next-to-last valid section index). w -- Is the wavelength index. If less than zero, it is treated as a displacement from one past the end. t -- Is the time index. If less than zero, it is treated as a displacement from one past the end. Keyword parameters: over -- If True, a ValueError exception will be raised if any of z, w, or t are past the end in their respective dimesions. If False, a value for z, w, or t that is past the end will only result in a ValueError exception if that dimension is not the slowest-varying non-singleton dimension. Return value: Returns a zero-based index for the section. """ if self.nspg == 401: if self.m[2] > 0: nz = self.m[2] nt = max(1, self.Num[2] // nz) else: nz = 1 nt = max(self.Num[2], 1) else: nz = max(self.Num[2], 1) nt = 1 nw = 1 return zwt2index(z, w, t, nz, nw, nt, 1, over=over) def _getexttyp(self): """Is four bytes treated a four character string that is a code for the layout of the extended header. This implementation understands three different values, all encoded in ASCII, for this field: 'MRC0', 'CCP4' (treated as a synonym for 'MRC0'), and 'AGAR'. This field was introduced by the MRC 2014 standard and was not part of the earlier Image 2000 standard. Occupies bytes 105 - 108 (numbered from one) in the header. """ return self._array['exttyp'][0] def _setexttyp(self, value): self._array['exttyp'][0] = value exttyp = property(_getexttyp, _setexttyp) def _getnversion(self): """Is a 4-byte integer which stores the version number of the MRC format used by this file. The version number is 10 times the Gregorian year when the specification was issued plus a zero-based (up to 9) version number within a year. Files which use the MRC 2014 format would have 20140 in this field. This field was introduced by the MRC 2014 standard and was not part of the earlier Image 2000 standard. Occupies bytes 109 - 112 (numbered from one) in the header. """ return self._array['nversion'][0] def _setnversion(self, value): self._array['nversion'][0] = value nversion = property(_getnversion, _setnversion) def _getorigin(self): """Is three floating-point values as a NumPy array. The three values specify an origin to use in coordinate transformations. The first value is the x coordinate, the second value is the y coordinate, and the third value is the z coordinate. The units for each are, by convention, the same as used for the spacing between samples: Angstroms for EM data and microns for optical data. Occupies bytes 197 - 208 (numbered from one) in the header. """ return self._array['origin'][0] def _setorigin(self, value): self._array['origin'][0] = value origin = property(_getorigin, _setorigin) def _getmap(self): """Is four bytes treated as a four character string that identifies this file as an MRC file. The MRC 2014 and Image 2000 standards specify that this field should be set to 'MAP ' encoded in ASCII. Occupies bytes 209 - 212 (numbered from one) in the header. """ return self._array['map'][0] def _setmap(self, value): self._array['map'][0] = value map = property(_getmap, _setmap) def _getmachst(self): """Is four bytes that are a code for how floating-point, complex, integer, and character values are stored. In practice, two combinations of values are used: 0x44 and 0x41 in the first two bytes (68 and 65 in decimal; the MRC 2014 documentation has 0x44 in the second byte as well) and unspecified values (typically zero) in the second two bytes for little-endian and 0x11 (17 in decimal) in the first two bytes and unspecified values (typically zero) in the second two bytes for big-endian. Occupies bytes 213 - 216 (numbered from one) in the header. """ return self._array['machst'][0] def _setmachst(self, value): self._array['machst'][0] = value machst = property(_getmachst, _setmachst) def _getrms(self): """Is a floating-point value for the RMS deviation of the densities from the mean density. If the RMS deviation has not been computed, the convention is to put a value less than zero in this field. Occupies bytes 217 - 220 (numbered from one) in the header. """ return self._array['rms'][0] def _setrms(self, value): self._array['rms'][0] = value rms = property(_getrms, _setrms) class Hdr2014Priism(Hdr2014): """Represents a MRC 2014 header with fields from Priism where they do not conflict with the MRC 2014 standard. Provides access to all parts of the header. The fields that are part of the original MRC formulation are described in HdrBase. The fields specific to MRC 2014 are described in Hdr2014. The extensions from Priism are listed below. Some properties are provided to make it easier to use instances of this class interchangeably with instances of HdrPriism, especially for retrieving values without modifying them. This class was added in version 0.1.0 of Mrc.py as included with Priism. """ # Use __slots__ so any assignments to unspecified attributes or # properties will give an AttributeError exception. __slots__ = () def _getextra0(self): """Is 4 bytes that is not currently reserved for any specific purpose. Priism and this implementation do not perform any byte-swapping on this data. If you store multi-byte quantities here, you will need to handle the byte-swapping. Occupies bytes 97 - 100 (numbered from one) in the header. """ return self._array['extra0'][0] def _setextra0(self, value): self._array['extra0'][0] = value extra0 = property(_getextra0, _setextra0) def _getntst(self): """Is an integer used to store the starting time index, i.e. the index in a longer time series for the first time point in this dataset. Occupies bytes 101 - 104 (numbered from one) in the header. """ return self._array['ntst'][0] def _setntst(self, value): self._array['ntst'][0] = value ntst = property(_getntst, _setntst) def _getextra1(self): """Is 16 bytes that is not currently reserved for any specific purpose. Priism and this implementation do not perform any byte-swapping on this data. If you store multi-byte quantities here, you will need to handle the byte-swapping. Occupies bytes 113 - 128 (numbered from one) in the header. """ return self._array['extra1'][0] def _setextra1(self, value): self._array['extra1'][0] = value extra1 = property(_getextra1, _setextra1) def _getNumIntegers(self): """Is the number of 4-byte integers to store per section in the extended header. Occupies bytes 129 - 130 (numbered from one) in the header. """ return self._array['NumIntegers'][0] def _setNumIntegers(self, value): self._array['NumIntegers'][0] = value NumIntegers = property(_getNumIntegers, _setNumIntegers) def _getNumFloats(self): """Is the number of 4-byte floating-point values to store per section in the extended header. Occupies bytes 131 - 132 (numbered from one) in the header. """ return self._array['NumFloats'][0] def _setNumFloats(self, value): self._array['NumFloats'][0] = value NumFloats = property(_getNumFloats, _setNumFloats) def _getsub(self): """Is the number of different resolutions stored in the dataset. Occupies bytes 133 - 134 (numbered from one) in the header. """ return self._array['sub'][0] def _setsub(self, value): self._array['sub'][0] = value sub = property(_getsub, _setsub) def _getzfac(self): """For multiple resolutions, the number of z samples in a resolution will be the number of z samples in the next higher resolution divided by this integer and rounding up any remainder. Occupies bytes 135 - 136 (numbered from one) in the header. """ return self._array['zfac'][0] def _setzfac(self, value): self._array['zfac'][0] = value zfac = property(_getzfac, _setzfac) def _getextra2(self): """Is 24 bytes that is not currently reserved for any specific purpose. Priism and this implementation do not perform any byte-swapping on this data. If you store multi-byte quantities here, you will need to handle the byte-swapping. Occupies bytes 137 - 160 (numbered from one) in the header. """ return self._array['extra2'][0] def _setextra2(self, value): self._array['extra2'][0] = value extra2 = property(_getextra2, _setextra2) def _getImageType(self): """Is a 16-bit integer which is a code for the type of data in the file. http://msg.ucsf.edu/IVE/IVE4_HTML/IM_ref2.html#ImageTypes describes the types that Priism defines and how the n1, n2, v1, and v2 fields are used for each type. Occupies bytes 161 - 162 (numbered from one) in the header. """ return self._array['ImageType'][0] def _setImageType(self, value): self._array['ImageType'][0] = value ImageType = property(_getImageType, _setImageType) def _getLensNum(self): """For optical data, this 16-bit integer is used to indicate which microscope configuration was used when the data was collected. Each microscope system would have its own table of configurations and corresponding integer codes. Occupies bytes 163 - 164 (numbered from one) in the header. """ return self._array['LensNum'][0] def _setLensNum(self, value): self._array['LensNum'][0] = value LensNum = property(_getLensNum, _setLensNum) def _getn1(self): """Is a 16-bit integer whose interpretation depends on the value for ImageType. Occupies bytes 165 - 166 (numbered from one) in the header. """ return self._array['n1'][0] def _setn1(self, value): self._array['n1'][0] = value n1 = property(_getn1, _setn1) def _getn2(self): """Is a 16-bit integer whose interpretation depends on the value for ImageType. Occupies bytes 167 - 168 (numbered from one) in the header. """ return self._array['n2'][0] def _setn2(self, value): self._array['n2'][0] = value n2 = property(_getn2, _setn2) def _getv1(self): """Is a 16-bit integer which is used to store a floating point value, f, as round_to_nearest(f * 100.0). What f is depends on the value for ImageType. This Python implementation leaves the conversion between f and v1 to the caller. Occupies bytes 169 - 170 (numbered from one) in the header. """ return self._array['v1'][0] def _setv1(self, value): self._array['v1'][0] = value v1 = property(_getv1, _setv1) def _getv2(self): """Is a 16-bit integer which is used to store a floating point value, f, as round_to_nearest(f * 100.0). What f is depends on the value for ImageType. This Python implementation leaves the conversion between f and v2 to the caller. Occupies bytes 171 - 172 (numbered from one) in the header. """ return self._array['v2'][0] def _setv2(self, value): self._array['v2'][0] = value v2 = property(_getv2, _setv2) def _getextra3(self): """Is 12 bytes that is not currently reserved for any specific purpose. Priism and this implementation do not perform any byte-swapping on this data. If you store multi-byte quantities here, you will need to handle the byte-swapping. Occupies bytes 173 - 184 (numbered from one) in the header. """ return self._array['extra3'][0] def _setextra3(self, value): self._array['extra3'][0] = value extra3 = property(_getextra3, _setextra3) def _gettilt(self): """Is three floating-point values as a NumPy array. The three values are a trio of rotation angles in degrees. For image windows, Priism uses those angles to rotate the coordinates of overlayed objects to the coordinates aligned to the image axes. The transformation uses the first angle as rotation about the original +x axis of the objects with a positive angle rotating the +y axis towards the +z axis. The second angle rotates about the +y axis from the first rotation with a positive angle rotating the +z axis towards the +x axis. The third angle rotates about +z axis from the second rotation with a positive angle rotating the +x axis towards the +y axis. Occupies bytes 185 - 196 (numbered from one) in the header. """ return self._array['tilt'][0] def _settilt(self, value): self._array['tilt'][0] = value tilt = property(_gettilt, _settilt) def _getmm2(self): """Is not part of the MRC 2014 format with Priism extensions. Is part of the Priism format. On access, will return a NumPy array with two zero values. Any attempt to set to something which is not two zero values will generate an AttributeError exception. """ return N.zeros(2, dtype='f4') def _setmm2(self, value): if value[0] != 0.0 or value[1] != 0.0: raise AttributeError('MRC 2014 header does not store minimum ' 'and maximum values for wavelengths past ' 'the first') mm2 = property(_getmm2, _setmm2) def _getmm3(self): """Is not part of the MRC 2014 format with Priism extensions. Is part of the Priism format. On access, will return a NumPy array with two zero values. Any attempt to set to something which is not two zero values will generate an AttributeError exception. """ return N.zeros(2, dtype='f4') def _setmm3(self, value): if value[0] != 0.0 or value[1] != 0.0: raise AttributeError('MRC 2014 header does not store minimum ' 'and maximum values for wavelengths past ' 'the first') mm3 = property(_getmm3, _setmm3) def _getmm4(self): """Is not part of the MRC 2014 format with Priism extensions. Is part of the Priism format. On access, will return a NumPy array with two zero values. Any attempt to set to something which is not two zero values will generate an AttributeError exception. """ return N.zeros(2, dtype='f4') def _setmm4(self, value): if value[0] != 0.0 or value[1] != 0.0: raise AttributeError('MRC 2014 header does not store minimum ' 'and maximum values for wavelengths past ' 'the first') mm4 = property(_getmm4, _setmm4) def _getmm5(self): """Is not part of the MRC 2014 format with Priism extensions. Is part of the Priism format. On access, will return a NumPy array with two zero values. Any attempt to set to something which is not two zero values will generate an AttributeError exception. """ return N.zeros(2, dtype='f4') def _setmm5(self, value): if value[0] != 0.0 or value[1] != 0.0: raise AttributeError('MRC 2014 header does not store minimum ' 'and maximum values for wavelengths past ' 'the first') mm5 = property(_getmm5, _setmm5) def _getNumTimes(self): """The MRC 2014 format store the number of volumes implicitly and uses a space group of 401 to indicate that multiple volumes may be present. Handle Priism's notion of number of time points with the MRC 2014 mechanism. """ if self._array['nspg'][0] == 401: # The file represents a volume stack. The third component # of m is the number of z slices per volume. if self._array['m'][0][2] > 0: return self._array['Num'][0][2] // self._array['m'][0][2] # The number of z slices per volume is invalid, assume one # slice per volume. return self._array['Num'][0][2] return 1 def _setNumTimes(self, value): if value > 0: if self._array['nspg'][0] == 401: oldnz = self._array['m'][0][2] else: if value > 1: self._array['nspg'][0] = 401 oldnz = self._array['Num'][0][2] newnz = self._array['Num'][0][2] // value # Preserve pixel spacing. if oldnz != 0: self._array['d'][0][2] *= newnz / float(oldnz) else: self._array['d'][0][2] = 0.0 self._array['m'][0][2] = newnz else: raise ValueError('Attempts to set the number of time points to ' 'a nonpositive value') NumTimes = property(_getNumTimes, _setNumTimes) def _getImgSequence(self): """Is not part of the MRC 2014 format with Priism extensions. All MRC 2014 files implicitly have z varying faster than time. Return zero on accesses. Any attempts to set the value to something other than zero will generate an AttributeError exception. """ # This is Priism's code for the ZTW ordering. All of Priism's # defined orderings are the equivalent for this purpose, though, # since the number of wavelengths is one and z varies faster than # time. return 0 def _setImgSequence(self, value): if value != 0: raise AttributeError('The MRC 2014 header does not store the ' 'image sequence code') ImgSequence = property(_getImgSequence, _setImgSequence) def _getNumWaves(self): """Is not part of the MRC 2014 format with Priism extensions. All such files implicitly have a single wavelength. Return one for all accesses. Any attempts to set the value to something other than one will generate an AttributeError exception. """ return 1 def _setNumWaves(self, value): if value != 1: raise AttributeError('MRC 2014 format does not store the ' 'number of wavelengths') NumWaves = property(_getNumWaves, _setNumWaves) def _getwave(self): """The MRC 2014 format with Priism extensions does not store wavelength values. Return a NumPy array with five zeros for any access. Any attempt to set the wavelength values to something other than zeros will generate an AttributeError exception. """ return N.zeros(5, dtype='i2') def _setwave(self, value): if (value[0] != 0 or value[1] != 0 or value[2] != 0 or value[3] != 0 or value[4] != 0): raise AttributeError('MRC 2014 header does not store ' 'wavelength values') wave = property(_getwave, _setwave) def _getzxy0(self): """Is three floating-point values that are part of the Priism format. The MRC 2014 format origin field has the same purpose but is in a different part of the header and has a different ordering for the coordinates. Reorder and return those values when accessed. When set, reorder the values and set the fields used by the MRC 2014 format. """ return ReorderedArray(self._array['origin'][0], (2, 0, 1)) def _setzxy0(self, value): self._array['origin'][0] = (value[1], value[2], value[0]) zxy0 = property(_getzxy0, _setzxy0) ########################################################################### ########################################################################### ########################################################################### ########################################################################### class ManagedTitleArray(object): """Since strings in the header are represented by NumPy arrays of signed one byte integers, provide a more convenient interface so that a caller can work with Python strings. This class represents an array of strings from the header or extended header. This class was added in version 0.1.0 of Mrc.py as included with Priism. """ # Use __slots__ so any assignments to unspecified attributes or # properties will give an AttributeError exception. __slots__ = ('_array',) def __init__(self, array): """Initializes a ManagedTitleArray. Positional parameters: array -- Is a NumPy two-dimensional array of one-byte integers. The first dimension is the number of strings; the second dimension represents the characters in the string. """ self._array = array def __repr__(self): s = 'ManagedTitleArray(' + repr(self._array) + ')' return s def __str__(self): s = '[' for i in range(0, self._array.shape[0] - 1): s = s + "'" + self._array[i].tostring() + "' ," if self._array.shape[0] > 0: s = s + "'" + self._array[-1].tostring() + "']" else: s = s + ']' return s def __len__(self): return self._array.shape[0] def __getitem__(self, key): if isinstance(key, slice): return [self._array[i].tostring() for i in range(*key.indices(self._array.shape[0]))] else: return self._array[key].tostring() def __setitem__(self, key, value): if isinstance(key, slice): if isStringLike(value): for i in range(*key.indices(self._array.shape[0])): self[i] = value else: r = range(*key.indices(self._array.shape[0])) if len(r) != len(value): raise ValueError('Number of elements on right hand side does ' 'not match number on left') j = 0 for i in r: self[i] = value[j] j += 1 else: b = N.fromstring(value, dtype='i1') if b.shape[0] >= self._array.shape[1]: self._array[key][:] = b[0:self._array.shape[1]] else: # Pad with spaces. self._array[key][:] = N.concatenate( (b, 32 * N.ones(self._array.shape[1] - b.shape[0], dtype='i1'))) ########################################################################### ########################################################################### ########################################################################### ########################################################################### class ReorderedArray(object): """Provide an interface that acts like a reordered view for the first dimension of a NumPy array. This class was added in version 0.1.0 of Mrc.py as included with Priism. """ # Use __slots__ so any assignments to unspecified attributes or # properties will give an AttributeError exception. __slots__ = ('_array', '_indices') def __init__(self, array, indices): """Initializes a ReorderedArray. Positional parameters: array -- Is a NumPy array with at least one dimension. indices -- Is an iterable with the new indices (from first to last) to use for first dimension of the array. The length of indices should not exceed the size of the first dimension of array. """ self._array = array self._indices = indices def __repr__(self): s = ('ReorderedArray(' + repr(self._array) + ',' + repr(self._indices) + ')') return s def __str__(self): s = '[' for i in range(0, len(self._indices) - 1): s = s + str(self._array[self._indices[i]]) + ', ' if len(self._indices) > 0: s = s + str(self._array[self._indices[-1]]) + ']' else: s = s + ']' return s def __len__(self): return len(self._indices) def __getitem__(self, key): if isinstance(key, slice): return [self._array[self._indices[i]] for i in range(*key.indices(self._array.shape[0]))] else: return self._array[self._indices[key]] def __setitem__(self, key, value): if isinstance(key, slice): r = range(*key.indices(self._array.shape[0])) if hasattr(value, '__len__'): if len(r) != len(value): raise ValueError('Number of elements on right hand side ' 'does not match number on left') j = 0 for i in r: self[self._indices[i]] = value[j] j += 1 else: for i in r: self[self._indices[i]] = value else: self._array[self._indices[key]] = value ########################################################################### ########################################################################### ########################################################################### ########################################################################### def minExtHdrSize(nSecs, bytesPerSec): """Return the smallest multiple of 1024 capable of holding the extended header data. Positional parameters: nSecs -- Is the number of sections of extended header data. It is assumed to be a non-negative value. bytesPerSec -- Is the number of bytes per section to store. It is assumed to be a non-negative value. """ t = nSecs * bytesPerSec r = t % 1024 if r != 0: t += 1024 - r return t def MrcMode2dtype(mode): """Return a NumPy dtype equivalent to a MRC pixel type code. Raises a RuntimeError exception if the given mode is not known to this implementation. Raises a NotImplementedError is mode is part of the Priism or MRC 2014 specifications but is not handled by this implementation. Version 0.1.0 of Mrc.py as included with Priism changed the return value from a Python type to a NumPy dtype. Positional parameters: mode -- Is the integer code for how sample values are represented, i.e. the value of the 'PixelType' field in the header. """ if mode == 0: dt = N.dtype('i1') elif mode == 1: dt = N.dtype('i2') elif mode == 2: dt = N.dtype('f4') elif mode == 3: dt = N.dtype({'names':['real', 'imag'], 'formats':['i2', 'i2']}) elif mode == 4: dt = N.dtype('c8') elif mode == 5: dt = N.dtype('i2') elif mode == 6: dt = N.dtype('u2') elif mode == 7: dt = N.dtype('i4') elif mode == 101: raise NotImplementedError('Mrc.py does not handle the unsigned ' '4-bit data type') else: raise RuntimeError('Unknown pixel type code, %d' % mode) return dt def dtype2MrcMode(dtype): """Return the MRC sample format code number equivalent to a Python type or NumPy dtype. Positional parameters: dtype -- Is the Python type or NumPy dtype used for each sample. Return value: Returns an integer for the field labeled 'PixelType' (bytes 13 - 16) in the MRC header. If dtype is not equivalent to one of the types supported by the MRC format, a ValueError exception will be raised. """ hastype = hasattr(dtype, 'type') if dtype == N.int8 or (hastype and dtype.type == N.int8): return 0 if dtype == N.int16 or (hastype and dtype.type == N.int16): return 1 if dtype == N.float32 or (hastype and dtype.type == N.float32): return 2 if dtype == N.complex64 or (hastype and dtype.type == N.complex64): return 4 if dtype == N.uint16 or (hastype and dtype.type == N.uint16): return 6 if dtype == N.int32 or (hastype and dtype.type == N.int32): return 7 if (hasattr(dtype, 'fields') and dtype.fields is not None and len(dtype.fields) == 2): fields_ok = 0 for k in dtype.fields: if dtype.fields[k][0] == N.int16: if dtype.fields[k][1] == 0: fields_ok |= 1 elif dtype.fields[k][1] == 2: fields_ok |= 2 if fields_ok == 3: return 3 if hasattr(dtype, 'name'): name = dtype.name else: name = str(dtype) raise ValueError('MRC does not support %s' % name) def shapeFromHdr(hdr, verbose=0): """Return a tuple of array dimensions equivalent to the sizes set in a MRC header. Positional parameters: hdr -- Is the MRC header to use. It is expected to be a header as returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. Non-positive values for the number of wavelengths or time points in the header will be treated as if they were equal to one. Negative values in the header for the number of x samples, number of y samples, or number of sections will be treated as if they were zero. Keyword parameters: verbose -- If true, a string giving the ordering of the dimensions from slowest to fastest will be printed. Each dimension will be represented by a single letter, 'x', 'y', 'z', 'w', or 't', and those letters will be separated by commas. Return value: Returns a tuple of integers which are the size for each dimension, from slowest to fastest varying, specified by the MRC header. """ zOrder = hdr.ImgSequence # , 'Image sequence. 0=ZTW, 1=WZT, 2=ZWT. '), if zOrder < 0 or zOrder > 2: # The value is invalid; use the default, ZTW, instead. zOrder = 0 nt, nw = hdr.NumTimes, hdr.NumWaves nx, ny, nsecs = hdr.Num if nt <= 0: nt=1 if nw <= 0: nw=1 if nx < 0: nx = 0 if ny < 0: ny = 0 if nsecs < 0: nsecs = 0 nz = nsecs // (nt * nw) if nt == nw == 1: shape = (nz, ny, nx) orderLetters = 'zyx' elif nz == 1 == nw: shape = (nt, ny, nx) orderLetters = 'tyx' elif nt == 1 or nw == 1: if zOrder == 0 or zOrder == 2: nn = nt if nt == 1: nn = nw orderLetters = 'wyx' else: orderLetters = 'tyx' shape = (nn, nz, ny, nx) else: # if zOrder == 1: if nt == 1: shape = (nz, nw, ny, nx) orderLetters = 'zwyx' else: shape = (nt, nz, ny, nx) orderLetters = 'tzyx' else: # both nt and nw > 1 if zOrder == 0: shape = (nw, nt, nz, ny, nx) orderLetters = 'wtzyx' elif zOrder == 1: shape = (nt, nz, nw, ny, nx) orderLetters = 'tzwyx' else: # zOrder == 2: shape = (nt, nw, nz, ny, nx) orderLetters = 'twzyx' if verbose: print(','.join(orderLetters)) return shape def implement_hdr(hdrArray, hasMap=False): """Return a HdrPriism or Hdr2014Priism instance to wrap the given NumPy structured array. If h is an object returned by this function, it can be used in statements like h.d = (1,2,3) or h.LensNum = 13 to modify values in the header or in statements like shape = (h.Num[2], h.Num[1], h.Num[0]) or mean = h.mmm1[2] to retrieve values from the header. The documentation for HdrBase and HdrPriism describes the fields available in a HdrPriism instance. The documentation for HdrBase, Hdr2014, and Hdr2014Priism describes the fields available in a Hdr2014Priism instance. The return value also has convenience methods for the spacing between samples and for the titles in the header. Those are described in the documentation for HdrBase. To get the original NumPy structured array from the return value, use h._array Positional parameters: hdrArray -- Is a MRC header as a NumPy structured array with one element. The dtype of the array should be numpy.dtype(mrcHdr_dtype) or numpy.dtype(mrc2014Hdr_dtype). Keyword parameters: hasMap --- If True, return an instance of the Hdr2014Priism class. If False, return an instance of HdrPriism class. The hasMap keyword was added in version 0.1.0 of Mrc.py as included with Priism. Return value: If hasMap is False, the return value is an instance of the HdrPriism class. If hasMap is True, the return value is an instance of the Hdr2014Priism class. Version 0.1.0 of Mrc.py as included with Priism changed the return value from an instance of a class defined within implement_hdr() to an instance of HdrPriism or Hdr2014Priism. """ if hasMap: return Hdr2014Priism(hdrArray) return HdrPriism(hdrArray) def makeHdrArray(buffer=None, makeWeak=True): """Create a NumPy structured array for the header and wrap it for easy access to the header fields. Keyword parameters: buffer -- If not None, the structured array will be overlayed on top of the first 1024 bytes of buffer. Typically, buffer would be a NumPy array of unsigned 8-bit integers read from the start of a MRC file. makeWeak -- Only has an effect if buffer is not None. In that case, the returned object will be marked as a weak reference when makeWeak is True. The makeWeak keyword was added in version 0.1.0 of Mrc.py as included with Priism. Return value: Returns an object representing the header. If the header was not generated from an existing buffer, the header fields have not been initialized. The documentation for the return value of implement_hdr() describes how the returned object may be used. The NumPy structured array embedded as the _array attribute of the returned object either has a NumPy dtype of numpy.dtype(mrcHdr_dtype) or numpy.dtype(mrc2014Hdr_dtype). """ if buffer is not None: h=buffer if hdrHasMapField(buffer): h.dtype = mrc2014Hdr_dtype hasmap = True else: h.dtype = mrcHdr_dtype hasmap = False if makeWeak: h = weakref.proxy(h) else: h = N.recarray(1, mrcHdr_dtype) hasmap = False return implement_hdr(h, hasMap=hasmap) def hdrInfo(hdr): """Print a subset of information from a MRC header. Positional parameters: hdr -- Is a MRC header as returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. """ shape = hdr.Num[::-1] nz = shape[0] numInts = max(0, hdr.NumIntegers) numFloats = max(0, hdr.NumFloats) print('width: %s' % str(shape[2])) print('height: %s' % str(shape[1])) print('# total slices: %s' % str(shape[0])) nt, nw = hdr.NumTimes, hdr.NumWaves if nt <= 0 or nw <= 0: print(' ** ERROR ** : NumTimes or NumWaves is not positive') print('NumTimes: %s' % str(nt)) print('NumWaves: %s' % str(nw)) else: if nt == 1 and nw == 1: print() elif nw == 1: print(' (%d times for %d zsecs)' % (nt, nz//nt)) elif nt == 1: print(' (%d waves in %d zsecs)' % (nw, nz//nw)) else: print(' (%d times for %d waves in %d zsecs)'% (nt, nw, nz // (nw * nt))) if nt != 1 or nw != 1: print('# slice order: %d (0,1,2 = (ZTW or WZT or ZWT)' % hdr.ImgSequence) d = hdr.getSpacing() print('pixel width x (um): %s' % str(d[0])) print('pixel width y (um): %s' % str(d[1])) print('pixel height (um): %s' % str(d[2])) print('# wavelengths: %s' % str(nw)) print(' wavelength 1 (nm): %s' % str(hdr.wave[0])) print(' intensity min/max/mean: %s %s %s' % (str(hdr.mmm1[0]), str(hdr.mmm1[1]), str(hdr.mmm1[2]))) if nw > 1: print(' wavelength 2 (nm): %s' % str(hdr.wave[1])) print(' intensity min/max: %s %s' % (str(hdr.mm2[0]), str(hdr.mm2[1]))) if nw > 2: print(' wavelength 3 (nm): %s' % str(hdr.wave[2])) print(' intensity min/max: %s %s' % (str(hdr.mm3[0]), str(hdr.mm3[1]))) if nw > 3: print(' wavelength 4 (nm): %s' % str(hdr.wave[3])) print(' intensity min/max: %s %s' % (str(hdr.mm4[0]), str(hdr.mm4[1]))) if nw > 4: print(' wavelength 5 (nm): %s' % str(hdr.wave[4])) print(' intensity min/max: %s %s' % (str(hdr.mm5[0]), str(hdr.mm5[1]))) if hdr.LensNum == 12: name = ' (60x)' elif hdr.LensNum == 13: name = ' (100x)' else: name = '(??)' print('lens type: %s %s' % (str(hdr.LensNum), name)) print('origin (um) x/y/z: %s %s %s' % (str(hdr.zxy0[1]), str(hdr.zxy0[2]), str(hdr.zxy0[0]))) if hdr.PixelType == 0: name = '8 bit (signed)' elif hdr.PixelType == 1: name = '16 bit (signed)' elif hdr.PixelType == 2: name = '32 bit (signed real)' elif hdr.PixelType == 3: name = '16 bit (signed complex integer)' elif hdr.PixelType == 4: name = '32 bit (signed complex real)' elif hdr.PixelType == 5: name = '16 bit (signed) IW_EMTOM' elif hdr.PixelType == 6: name = '16 bit (unsigned short)' elif hdr.PixelType == 7: name = '32 bit (signed long)' elif hdr.PixelType == 101: name = 'unsigned 4-bit' else: name = ' ** undefined ** ' print('# pixel data type: %s' % name) if hdr.next > 0: n = numInts + numFloats if n > 0: name = ' (%d secs)' % (hdr.next // (4. * n)) else: name = ' (??? secs)' name2 = ' (%d ints + %d reals per section)' % (numInts, numFloats) else: name = '' name2 = None print('# extended header size: %s %s' % (str(hdr.next), name)) if name2 is not None: print(name2) if hdr.NumTitles < 0: print(' ** ERROR ** : NumTitles less than zero (NumTitles = %s )' % str(hdr.NumTitles)) elif hdr.NumTitles > 0: n = hdr.NumTitles if n > 10: print(' ** ERROR ** : NumTitles larger than 10 (NumTitles = %s )' % hdr.NumTitles) n=10 for i in range(n): print('title %d: %s'%(i, hdr.title[i])) def axisOrderStr(hdr, onlyLetters=True): """Return a string indicating the ordering of dimensions. x, y, z, w, and t will appear at most once in the string, and at least three of them will be present. The letters that do appear will be present in order from slowest varying to fastest varying. The values for the axis field in the header do not affect the result. Positional parameters: hdr -- Is a MRC header as returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. Keyword parameters: onlyLetters -- If True, only the letters for the dimensions will appear in the string. If False, the first character of the string will be '[', the last character of the string will be ']', and a letter for a dimension will be preceded by a comma if it is not the first, slowest- varying dimension. """ # 'Image sequence. 0=ZTW, 1=WZT, 2=ZWT.' Given from fastest to slowest. zOrder = hdr.ImgSequence if zOrder < 0 or zOrder > 2: # The value is invalid; use the ZTW instead. zOrder = 0 nt, nw = max(1, hdr.NumTimes), max(1, hdr.NumWaves) if nt == nw == 1: orderLetters= 'zyx' elif nt == 1: orderLetters= ('wzyx', 'zwyx', 'wzyx')[zOrder] elif nw == 1: orderLetters= ('tzyx', 'tzyx', 'tzyx')[zOrder] else: orderLetters= ('wtzyx', 'tzwyx', 'twzyx')[zOrder] if onlyLetters: return orderLetters else: return '[' + ','.join(orderLetters) + ']' def index2zwt(i, nz, nw, nt, seq, over=False): """Convert section index to 3D index in z, wavelength, and time. This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: i -- Is the section index. If less than zero, it is treated as a displacement from one past the end (i.e. -1 is the last valid section index, and -2 is the next-to-last valid section index). nz -- Is the number of samples in z. Assumed to be positive. nw -- Is the number of wavelengths. Assumed to be positive. nt -- Is the number of time points. Assumed to be positive. seq -- Is the code for the interleaving of z, wavelength, and time. If seq is 0, z varies fastest, followed by time, then followed by wavelength. If seq is 1, wavelength variest fastest, followed by z, then followed by time. If seq is 2, z varies fastest, followed by wavelength, then followed by time. For any other value, a ValueError exception will be raised. Keyword parameters: over -- If True, a value of i past the end will cause a ValueError exception to be raised. If False, a value of i past the end will lead to a value for the index in the slowest-varying dimension to be past the end. Return value: Returns a three element tuple of the zero-based indices for z, wavelength, and time. """ if i < 0: ri = i + nz * nw * nt if ri < 0: raise ValueError('section index, %d, is before first' % i) else: ri = i if over and ri >= nz * nw * nt: raise ValueError('index is greater than or equal to nz * nw * nt') if seq == 0: result = (ri % nz, ri // (nz * nt), (ri // nz) % nt) elif seq == 1: result = ((ri // nw) % nz, ri % nw, ri // (nw * nz)) elif seq == 2: result = (ri % nz, (ri // nz) % nw, ri // (nz * nw)) else: raise ValueError('invalid code, %d, for sequence arrangement' % seq) return result def zwt2index(z, w, t, nz, nw, nt, seq, over=False): """Convert a 3D index in z, wavelength, and time to a section index. This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: z -- Is the index in z. If less than zero, it is treated as a displacement from one past the end i.e. -1 is the last valid section index, and -2 is the next-to-last valid section index). w -- Is the wavelength index. If less than zero, it is treated as a displacement from one past the end. t -- Is the time index. If less than zero, it is treated as a displacement from one past the end. nz -- Is the number of samples in z. Assumed to be positive. nw -- Is the number of wavelengths. Assumed to be positive. nt -- Is the number of time points. Assumed to be positive. Keyword parameters: over -- If True, a ValueError exception will be raised if any of z, w, or t are past the end in their respective dimesions. If False, a value for z, w, or t that is past the end will only result in a ValueError exception if that dimension is not the slowest-varying non-singleton dimension. Return value: Returns a zero-based index for the section. """ if z < 0: rz = z + nz if rz < 0: raise ValueError('%d is before first z index' % z) else: rz = z if w < 0: rw = w + nw if rw < 0: raise ValueError('%d is before first wavelength index' % w) else: rw = w if t < 0: rt = t + nt if rt < 0: raise ValueError('%d is before first time point index' % t) else: rt = t if over and (rz >= nz or rw >= nw or rt >= nt): raise ValueError('z, wavelength, or time indices out of bounds') if seq == 0: if (rz >= nz and (nt > 1 or nw > 1 or t > 0 or w > 0)): raise ValueError('z index past end and z is not slowest-varying ' 'non-singleton dimension') if (rt >= nt and (nw > 1 or w > 0)): raise ValueError('time point index past end and time is not ' 'slowest-varying non-singleton dimension') result = rz + nz * (rt + nt * rw) elif seq == 1: if (rw >= nw and (nz > 1 or nt > 1 or z > 0 or t > 0)): raise ValueError('wavelength index past end and wavelength is not ' 'slowest-varying non-singleton dimension') if (rz >= nz and (nt > 1 or t > 0)): raise ValueError('z index past end and z is not slowest-varying ' 'non-singleton dimension') result = rw + nw * (rz + nz * rt) elif seq == 2: if (rz >= nz and (nw > 1 or nt > 1 or w > 0 or t > 0)): raise ValueError('z index past end and z is not slowest-varying ' 'non-singleton dimension') if (rw >= nw and (nt > 1 or t > 0)): raise ValueError('wavelength index past end and wavelength is not ' 'slowest-varying non-singleton dimension') result = rz + nz * (rw + nw * rt) else: raise ValueError('invalid code, %d, for sequence arrangement' % seq) return result def init_simple(hdr, mode, nxOrShape, ny=None, nz=None, isByteSwapped=None): """Initialize a MRC header for a given size and pixel type code. Positional parameters: hdr -- Is a MRC header as returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. mode -- Is the MRC pixel type code to use. nxOrShape -- If ny or nz is not None, this should be a scalar which is the number of samples in x. If ny and nz are None, it must be an iterable. If it has one element, that will be the number of samples in x; ny and nz will be one. If it has two elements, the last will be the number of samples in x, the second to last will be the number of samples in y, and nz will be one. If has more than two elements, the last will be the number of samples in x, the second to last will be the number of samples in y, and the product of the remaining elements will be the number of samples in z. Keyword parameters: ny -- If not None, this will be the number of samples in y. If not None, nxOrShape should be a scalar. nz -- If not None, this will be the number of samples in z. If not None, nxOrShape should be a scalar. isByteSwapped -- If None, the stamps for byte order in the header will be set to match the byte ordering of hdr. If True, the stamps for byte order in the header will be set to be opposite of the native byte ordering. If False, the stamps for byte order in the header will match the native byte ordering. The isByteSwapped keyword was added in version 0.1.0 of Mrc.py as included with Priism. """ if ny is None and nz is None: if len(nxOrShape) == 2: nz, (ny, nx) = 1, nxOrShape elif len(nxOrShape) == 1: nz, ny, (nx,) = 1, 1, nxOrShape elif len(nxOrShape) == 3: nz, ny, nx = nxOrShape else: ny, nx = nxOrShape[-2:] nz = N.prod(nxOrShape[:-2]) else: if ny is None: ny = 1 if nz is None: nz = 1 nx = nxOrShape hdr.Num = (nx, ny, nz) hdr.PixelType = mode hdr.mst = (0, 0, 0) hdr.m = (1, 1, 1) hdr.d = (1, 1, 1) hdr.angle = (90, 90, 90) hdr.axis = (1, 2, 3) hdr.mmm1 = (0, 100000, 5000) hdr.nspg = 0 hdr.next = 0 hdr.ntst = 0 hdr.NumIntegers = 0 hdr.NumFloats = 0 hdr.sub = 1 hdr.zfac = 1 hdr.ImageType = 0 hdr.LensNum = 0 hdr.n1 = 0 hdr.n2 = 0 hdr.v1 = 0 hdr.v2 = 0 hdr.tilt = (0, 0, 0) hdr.zxy0 = (0, 0, 0) hdr.NumTitles = 0 hdr.title = ' ' * 80 if hdrIsInPriismFormat(hdr): hdr.dvid = 0xc0a0 hdr.nblank = 0 hdr.blank = 0 hdr.mm2 = (0, 10000) hdr.mm3 = (0, 10000) hdr.mm4 = (0, 10000) hdr.mm5 = (0, 10000) hdr.NumTimes = 1 # Zero means that z changes fastest, then time, and then wavelength. # One means that wavelength changes fastest, then z, and then time. # Two means that z changes fastest, then wavelength, and then time. hdr.ImgSequence = 0 hdr.NumWaves = 1 hdr.wave = (999, 0, 0, 0, 0) else: hdr.extra0 = 0 hdr.exttyp = N.fromstring('AGAR', dtype='i1') hdr.nversion = 20140 hdr.extra1 = 0 hdr.extra2 = 0 hdr.extra3 = 0 hdr.map = N.fromstring('MAP ', dtype='i1') if isByteSwapped is None: isByteSwapped = isSwappedDtype(hdr._array.dtype) if isByteSwapped: hdr.machst = (_SYSSWAPST0, _SYSSWAPST1, 0, 0) else: hdr.machst = (_SYSMACHST0, _SYSMACHST1, 0, 0) hdr.rms = -1.0 def initHdrArrayFrom(hdrDest, hdrSrc): """Copy all fields from hdrSrc to hdrDest except the number of samples ('Num' field; bytes 1 - 12), pixel type ('PixelType' field; bytes 13 - 16), and the number of bytes in the extended header ('next' field; bytes 93 - 96. Do not change the byte order for hdrDest. Positional parameters: hdrDest -- Is the header to change. Assumed to be a MRC header as returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. hdrSrc -- Is the header from which to copy. Assumed to be a MRC header as returned by makeHdrArray() or implement_hdr(). If hdrIsInPriismFormat(hdrSrc) is different than hdrIsInPriismFormat(hdrDest), the format of the destination will be changed to match the source. Return value: Returns the header, wrapped with a HdrPriism or Hdr2014Priism instance, as appropriate. The return value was added in version 0.1.0 of Mrc.py as included with Priism. """ srcIsPriism = hdrIsInPriismFormat(hdrSrc) destIsPriism = hdrIsInPriismFormat(hdrDest) if srcIsPriism: if not destIsPriism: if isSwappedDtype(hdrDest._array.dtype): hdrDest._array.dtype = N.dtype(mrcHdr_dtype).newbyteorder() else: hdrDest._array.dtype = N.dtype(mrcHdr_dtype) hdrDest = HdrPriism(hdrDest._array) hdrDest.mst = hdrSrc.mst hdrDest.m = hdrSrc.m hdrDest.d = hdrSrc.d hdrDest.angle = hdrSrc.angle hdrDest.axis = hdrSrc.axis hdrDest.mmm1 = hdrSrc.mmm1 hdrDest.nspg = hdrSrc.nspg hdrDest.next = 0 hdrDest.dvid = hdrSrc.dvid hdrDest.nblank = hdrSrc.nblank hdrDest.ntst = hdrSrc.ntst hdrDest.blank = hdrSrc.blank hdrDest.NumIntegers = 0 hdrDest.NumFloats = 0 hdrDest.sub = hdrSrc.sub hdrDest.zfac = hdrSrc.zfac hdrDest.mm2 = hdrSrc.mm2 hdrDest.mm3 = hdrSrc.mm3 hdrDest.mm4 = hdrSrc.mm4 hdrDest.ImageType = hdrSrc.ImageType hdrDest.LensNum = hdrSrc.LensNum hdrDest.n1 = hdrSrc.n1 hdrDest.n2 = hdrSrc.n2 hdrDest.v1 = hdrSrc.v1 hdrDest.v2 = hdrSrc.v2 hdrDest.mm5 = hdrSrc.mm5 hdrDest.NumTimes = hdrSrc.NumTimes hdrDest.ImgSequence = hdrSrc.ImgSequence hdrDest.tilt = hdrSrc.tilt hdrDest.NumWaves = hdrSrc.NumWaves hdrDest.wave = hdrSrc.wave hdrDest.zxy0 = hdrSrc.zxy0 hdrDest.NumTitles = hdrSrc.NumTitles hdrDest.title = hdrSrc.title else: if destIsPriism: if isSwappedDtype(hdrDest._array.dtype): hdrDest._array.dtype = N.dtype(mrc2014Hdr_dtype).newbyteorder() else: hdrDest._array.dtype = N.dtype(mrc2014Hdr_dtype) hdrDest = Hdr2014Priism(hdrDest._array) hdrDest.mst = hdrSrc.mst hdrDest.m = hdrSrc.m hdrDest.d = hdrSrc.d hdrDest.angle = hdrSrc.angle hdrDest.axis = hdrSrc.axis hdrDest.mmm1 = hdrSrc.mmm1 hdrDest.nspg = hdrSrc.nspg hdrDest.next = 0 hdrDest.extra0 = hdrSrc.extra0 hdrDest.ntst = hdrSrc.ntst hdrDest.exttyp = hdrSrc.exttyp hdrDest.nversion = hdrSrc.nversion hdrDest.extra1 = hdrSrc.extra1 hdrDest.NumIntegers = 0 hdrDest.NumFloats = 0 hdrDest.sub = hdrSrc.sub hdrDest.zfac = hdrSrc.zfac hdrDest.extra2 = hdrSrc.extra2 hdrDest.ImageType = hdrSrc.ImageType hdrDest.LensNum = hdrSrc.LensNum hdrDest.n1 = hdrSrc.n1 hdrDest.n2 = hdrSrc.n2 hdrDest.v1 = hdrSrc.v1 hdrDest.v2 = hdrSrc.v2 hdrDest.extra3 = hdrSrc.extra3 hdrDest.tilt = hdrSrc.tilt hdrDest.origin = hdrSrc.origin hdrDest.map = hdrSrc.map hdrDest.machst = hdrSrc.machst hdrDest.rms = hdrSrc.rms hdrDest.NumTitles = hdrSrc.NumTitles hdrDest.title = hdrSrc.title return hdrDest def setTitle(hdr, s, i=-1, push=False, truncate=False): """Set a title in the MRC header. Provided for compatibility with previous versions of Mrc.py. In this version, you can use hdr.setTitle(). Positional parameters: hdr -- Is the MRC header to modify. It is expected to be a header returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. s -- Is the character string for the title. If s is longer than 80 characters and truncate is False, a ValueError exception will be raised. Since no byte swapping is done for the titles in the header, s should be encoded in ASCII or another format that does not use multibyte characters. Keyword parameters: i -- Is the index of the title to set. If i is less than zero, the last title not in use will be set. If i is less than zero and all the titles are in use and push is False or i is greater than 9, a ValueError exception will be raised. push -- If True, i is less than zero, and all titles are in use, titles will be pushed down before assigning s to the last title. That will discard the first title and title[k] (for k greater than and equal to 0 and less than 9) will be title[k+1] from before the change. The push keyword was added in version 0.1.0 of Mrc.py as included with Priism. truncate -- If True, only use the first 80 characters from s. The truncate keyword was added in version 0.1.0 of Mrc.py as included with Priism. """ hdr.setTitle(s, i=i, push=push, truncate=truncate) def hdrChangeToMrc2014Format(hdr): """Change the header to have the format from MRC 2014. This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: hdr -- Is a MRC header as returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. Return value: Returns the header wrapped with a Hdr2014Priism instance. """ if not hdrIsInPriismFormat(hdr): return hdr nw = max(hdr.NumWaves, 1) nt = max(hdr.NumTimes, 1) if nw > 1: if hdr.ImgSequence == 1 or (hdr.ImgSequence == 2 and nt > 1): print('WARNING: MRC 2014 header does not support multiple ' 'wavelengths; any image data present will be out of sequence') else: print('WARNING: MRC 2014 header does not support multiple wavelengths') dx, dy, dz = hdr.getSpacing() origz, origx, origy = hdr.zxy0 if isSwappedDtype(hdr._array.dtype): hdr._array.dtype = N.dtype(mrc2014Hdr_dtype).newbyteorder() isswapped = True else: hdr._array.dtype = N.dtype(mrc2014Hdr_dtype) isswapped = False hdr = Hdr2014Priism(hdr._array) hdr.map = N.fromstring('MAP ', dtype='i1') hdr.nversion = 20140 if nt > 1: hdr.nspg = 401 hdr.m[2] = max(hdr.Num[2], 0) // nt hdr.setSpacing(dx, dy, dz) if hdr.nspg == 0 or hdr.nspg == 401: hdr.exttyp = N.fromstring('AGAR', dtype='i1') else: hdr.exttyp = N.fromstring('MRC0', dtype='i1') hdr.origin = (origx, origy, origz) if isswapped: hdr.machst = (_SYSSWAPST0, _SYSSWAPST1, 0, 0) else: hdr.machst = (_SYSMACHST0, _SYSMACHST1, 0, 0) hdr.rms = -1.0 return hdr def hdrChangeToPriismFormat(hdr): """Change the header to have the format from Priism. This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: hdr -- Is a MRC header as returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. Return value: Returns the header wrapped with a HdrPriism instance. """ if hdrIsInPriismFormat(hdr): return hdr origx, origy, origz = hdr.origin spg = hdr.npsg if isSwappedDtype(hdr._array.dtype): hdr._array.dtype = N.dtype(mrcHdr_dtype).newbyteorder() else: hdr._array.dtype = N.dtype(mrcHdr_dtype) hdr = HdrPriism(hdr._array) if spg == 1 or spg == 401: hdr.nspg = 0 if spg == 401: hdr.NumTimes = hdr.Num[2] // max(hdr.m[2], 1) hdr.dvid = 0xc0a0 hdr.nblank = 0 hdr.blank = 0 hdr.mm2 = (0.0, 0.0) hdr.mm3 = (0.0, 0.0) hdr.mm4 = (0.0, 0.0) hdr.ImgSequence = 0 hdr.NumWaves = 1 hdr.mm5 = (0.0, 0.0) hdr.wave = (0, 0, 0, 0, 0) hdr.zxy0 = (origz, origx, origy) return hdr def hdrHasMapField(hdr): """Return True if the header has a properly formatted map field. This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: hdr -- Is the header as an array of 1024 unsigned 8-bit integers. """ # The map field is bytes 208 through 211 (numbered from zero). It # has 'MAP ' in ASCII when properly formatted. return (hdr[208] == 77 and hdr[209] == 65 and hdr[210] == 80 and hdr[211] == 32) def hdrIsByteSwapped(hdr): """Return True if the header is swapped from the native byte ordering. This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: hdr -- Is a MRC header as returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. Return value: Returns True if the machine stamp (which is insensitive to whether hdr.dtype has been byte swapped) or the Num field (which is sensitive to whether hdr.dtype has been byte swapped) indicates that the header is not in the machine byte order. Otherwise, returns False. For ease of interpreting the return value, isSwappedDtype(hdr.dtype) should be False. """ if hasattr(hdr, 'machst'): if (hdr.machst[0] == _SYSMACHST0 and hdr.machst[1] >= _SYSMACHST1LO and hdr.machst[1] <= _SYSMACHST1HI): return False elif (hdr.machst[0] == _SYSSWAPST0 and hdr.machst[1] >= _SYSSWAPST1LO and hdr.machst[1] <= _SYSSWAPST1HI): return True # Use the test employed by Priism. Assumes that the actual number of # samples in x and y are both positive and less than 65536. Under those # conditions a byte-swapped header will have both either less than zero or # greater than 65535. nx = hdr.Num[0] ny = hdr.Num[1] return (nx < 0 or nx > 65535) and (ny < 0 or ny > 65535) def hdrIsInPriismFormat(hdr): """Return True if the header uses the format from Priism. This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: hdr -- Is a MRC header as returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. """ return not hasattr(hdr, 'map') def hdrHasExtType(hdr): """Return True if the header claims to handle the extended header type field. This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: hdr -- Is a MRC header as returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. """ has = False if hasattr(hdr, 'nversion'): # Check for a version number that looks reasonable. if (hdr.nversion >= 20140 and hdr.nversion <= 20509 and hasattr(hdr, 'exttyp')): has = True return has def getExtHeaderFormat(hdr): """Return a code for the format of the extended header. This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: hdr -- Is a MRC header as returned by makeHdrArray() or implement_hdr(). If x is an instance of the Mrc or Mrc2 classes, you can use x.hdr for this parameter. Return value: Returns zero if the extended header is to be interpreted as symmetry information. Returns one if the extended header is to be interpreted as the Priism format. Returns -1 if the extended header format is not understood by this implementation. """ if hdrHasExtType(hdr): if (N.array_equal(hdr.exttyp, N.fromstring('MRC0', dtype='i1')) or N.array_equal(hdr.exttyp, N.fromstring('CCP4', dtype='i1'))): fmt = 0 elif N.array_equal(hdr.exttyp, N.fromstring('AGAR', dtype='i1')): fmt = 1 else: fmt = -1 elif hasattr(hdr, 'map'): if hdr.nspg == 0 or hdr.nspg == 1 or hdr.nspg == 401: fmt = 1 else: fmt = 0 else: if hdr.nspg == 0: fmt = 1 else: fmt = 0 return fmt def isStringLike(value): """Return True if value is a bytearray, bytes, str, or unicode. Otherwise return False. This function was added in version 0.1.0 of Mrc.py as included with Priism. """ if isinstance(value, str): return True # bytes is only defined in Python 3 and newer versions of Python 2. try: if isinstance(value, bytes): return True except: pass # unicode is only defined in Python 2. try: if isinstance(value, unicode): return True except: pass # bytearray was introduced in Python 2.6. try: if isinstance(value, bytearray): return True except: pass return False def isSwappedDtype(dtype): """Return True if the given NumPy dtype is not in machine byte order. Will raise a ValueError exception if dtype is a structured type and has components which have different byte orders. This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: dtype -- Is the NumPy dtype to test. """ swappingtype = checkDtypeSwapping(dtype) if swappingtype == -2: raise ValueError('Structured dtype has commponents with different ' 'endianness') return swappingtype < 0 def checkDtypeSwapping(dtype): """Check the byte swapping of the given NumPy dtype. This function was added in version 0.1.0 of Mrc.py as included with Priism. Positional parameters: dtype -- Is the NumPy dtype to check. Return value: The return value will be one of the following: 1 -- The dtype is compatible with the native byte order. It has one or more components that are in the native byte order and all remaining components are not sensitive to the byte order. 0 -- The dtype is compatible with the native byte order. It only has components that are not sensitive to the byte order. -1 -- The dtype requires byte swapping to be compatible with the native byte order. It has one or more components that are not in the native byte order and all remaining components are not sensitive to the byte order. -2 -- The dtype does not have a consistent byte ordering. At least one component is in the native byte order, and at least one component is byte swapped relative to the native byte order. """ if dtype.fields is None: if dtype.byteorder == '=' or dtype.byteorder == _SYSBYTEORDER: rval = 1 elif dtype.byteorder == '|': rval = 0 else: rval = -1 else: rval = 0 for f in dtype.fields: rval_child = checkDtypeSwapping(dtype.fields[f][0]) if rval_child != 0 and rval != rval_child: if rval == 0: rval = rval_child else: rval = -2 break return rval # The second element in each tuple is the name by which that field will be # accessed. If changed, similar changes will be necessary in the # property definitions for the HdrBase or HdrPriism classes. mrcHdrFields = [ ('3i4', 'Num'), ('1i4', 'PixelType'), ('3i4', 'mst'), ('3i4', 'm'), ('3f4', 'd'), ('3f4', 'angle'), ('3i4', 'axis'), ('3f4', 'mmm1'), ('1i4', 'nspg'), ('1i4', 'next'), ('1i2', 'dvid'), ('1i2', 'nblank'), ('1i4', 'ntst'), ('24i1', 'blank'), ('1i2', 'NumIntegers'), ('1i2', 'NumFloats'), ('1i2', 'sub'), ('1i2', 'zfac'), ('2f4', 'mm2'), ('2f4', 'mm3'), ('2f4', 'mm4'), ('1i2', 'ImageType'), ('1i2', 'LensNum'), ('1i2', 'n1'), ('1i2', 'n2'), ('1i2', 'v1'), ('1i2', 'v2'), ('2f4', 'mm5'), ('1i2', 'NumTimes'), ('1i2', 'ImgSequence'), ('3f4', 'tilt'), ('1i2', 'NumWaves'), ('5i2', 'wave'), ('3f4', 'zxy0'), ('1i4', 'NumTitles'), ('(10,80)i1', 'title'), ] mrcHdrNames = [] mrcHdrFormats = [] for ff in mrcHdrFields: mrcHdrFormats.append(ff[0]) mrcHdrNames.append(ff[1]) del ff del mrcHdrFields mrcHdr_dtype = list(zip(mrcHdrNames, mrcHdrFormats)) # This describes the MRC 2014 , http://www.ccpem.ac.uk/mrc_format/mrc2014.php , # format with the addition of fields from Priism that do not conflict with the # MRC 2014 format. The fields that are Priism extensions are ntst, # NumIntegers, NumFloats, sub, zfac, ImageType, LensNum, n1, n2, v1, v2, and # tilt. mrc2014HdrFields = [ ('3i4', 'Num'), ('1i4', 'PixelType'), ('3i4', 'mst'), ('3i4', 'm'), ('3f4', 'd'), ('3f4', 'angle'), ('3i4', 'axis'), ('3f4', 'mmm1'), ('1i4', 'nspg'), ('1i4', 'next'), ('4i1', 'extra0'), ('1i4', 'ntst'), ('4i1', 'exttyp'), ('1i4', 'nversion'), ('16i1', 'extra1'), ('1i2', 'NumIntegers'), ('1i2', 'NumFloats'), ('1i2', 'sub'), ('1i2', 'zfac'), ('24i1', 'extra2'), ('1i2', 'ImageType'), ('1i2', 'LensNum'), ('1i2', 'n1'), ('1i2', 'n2'), ('1i2', 'v1'), ('1i2', 'v2'), ('12i1', 'extra3'), ('3f4', 'tilt'), ('3f4', 'origin'), ('4i1', 'map'), ('4u1', 'machst'), ('1f4', 'rms'), ('1i4', 'NumTitles'), ('(10,80)i1', 'title'), ] mrc2014HdrNames = [] mrc2014HdrFormats = [] for ff in mrc2014HdrFields: mrc2014HdrFormats.append(ff[0]) mrc2014HdrNames.append(ff[1]) del ff del mrc2014HdrFields mrc2014Hdr_dtype = list(zip(mrc2014HdrNames, mrc2014HdrFormats)) # Set character used for NumPy dtypes that corresponds to the native byte # ordering. Also record the values to use in the MRC 2014 machine stamp # field if a file is in the native ordering or if it is byte-swapped relative # to the native ordering. Since the MRC 2014 documentation says that # a machine stamp of 68 and 68 is safe for specifying little-endian, ignore the # least significant 4 bits of the second byte (they specify how characters are # encoded) when testing for a little-endian stamp.. For writing, follow the # CCP4 documentation and use 68 and 65 for a little-endian machine stamp. if sys.byteorder == 'little': _SYSBYTEORDER = '<' _SYSMACHST0 = 68 _SYSMACHST1 = 65 _SYSMACHST1LO = 64 _SYSMACHST1HI = 79 _SYSSWAPST0 = 17 _SYSSWAPST1 = 17 _SYSSWAPST1LO = 17 _SYSSWAPST1HI = 17 else: _SYSBYTEORDER = '>' _SYSMACHST0 = 17 _SYSMACHST1 = 17 _SYSMACHST1LO = 17 _SYSMACHST1HI = 17 _SYSSWAPST0 = 68 _SYSSWAPST1 = 65 _SYSSWAPST1LO = 64 _SYSSWAPST1HI = 79 ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1701535927.128231 mantis-xray-3.1.15/mantis_xray/TomoCS/0000775000175000017500000000000014532660267016722 5ustar00wattswatts././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/TomoCS/__init__.py0000644000175000017500000000000114332463747021022 0ustar00wattswatts ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/TomoCS/_rank_order.py0000644000175000017500000000401714332463747021563 0ustar00wattswatts"""rankorder.py - convert an image of any type to an image of ints whose pixels have an identical rank order compared to the original image Originally part of CellProfiler, code licensed under both GPL and BSD licenses. Website: http://www.cellprofiler.org Copyright (c) 2003-2009 Massachusetts Institute of Technology Copyright (c) 2009-2011 Broad Institute All rights reserved. Original author: Lee Kamentstky """ import numpy def rank_order(image): """Return an image of the same shape where each pixel is the index of the pixel value in the ascending order of the unique values of `image`, aka the rank-order value. Parameters ---------- image: ndarray Returns ------- labels: ndarray of type np.uint32, of shape image.shape New array where each pixel has the rank-order value of the corresponding pixel in `image`. Pixel values are between 0 and n - 1, where n is the number of distinct unique values in `image`. original_values: 1-d ndarray Unique original values of `image` Examples -------- >>> a = np.array([[1, 4, 5], [4, 4, 1], [5, 1, 1]]) >>> a array([[1, 4, 5], [4, 4, 1], [5, 1, 1]]) >>> rank_order(a) (array([[0, 1, 2], [1, 1, 0], [2, 0, 0]], dtype=uint32), array([1, 4, 5])) >>> b = np.array([-1., 2.5, 3.1, 2.5]) >>> rank_order(b) (array([0, 1, 2, 1], dtype=uint32), array([-1. , 2.5, 3.1])) """ flat_image = image.ravel() sort_order = flat_image.argsort().astype(numpy.uint32) flat_image = flat_image[sort_order] sort_rank = numpy.zeros_like(sort_order) is_different = flat_image[:-1] != flat_image[1:] numpy.cumsum(is_different, out=sort_rank[1:]) original_values = numpy.zeros((sort_rank[-1] + 1,), image.dtype) original_values[0] = flat_image[0] original_values[1:] = flat_image[1:][is_different] int_image = numpy.zeros_like(sort_order) int_image[sort_order] = sort_rank return (int_image.reshape(image.shape), original_values) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/TomoCS/forward_backward_tv.py0000644000175000017500000004601014332463747023310 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2015 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # License: GNU GPL v3 # # Originally part of tomotv, code licensed under BSD license. # Website: https://github.com/emmanuelle/tomo-tv # # 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 . from __future__ import absolute_import import numpy as np from scipy import sparse from .tv_denoising import tv_denoise_fista from .projections import back_projection, projection # ------------------ Computing energies --------------------------- def tv_norm(im): """Compute the (isotropic) TV norm of an image""" grad_x1 = np.diff(im, axis=0) grad_x2 = np.diff(im, axis=1) return np.sqrt(grad_x1[:, :-1]**2 + grad_x2[:-1, :]**2).sum() def tv_norm_anisotropic(im): """Compute the anisotropic TV norm of an image""" grad_x1 = np.diff(im, axis=0) grad_x2 = np.diff(im, axis=1) return np.abs(grad_x1[:, :-1]).sum() + np.abs(grad_x2[:-1, :]).sum() # ------------------ Proximal iterators ---------------------------- def fista_tv(y, beta, niter, H, verbose=0, mask=None): """ TV regression using FISTA algorithm (Fast Iterative Shrinkage/Thresholding Algorithm) Parameters ---------- y : ndarray of floats Measures (tomography projection). If H is given, y is a column vector. If H is not given, y is a 2-D array where each line is a projection along a different angle beta : float weight of TV norm niter : number of forward-backward iterations to perform H : sparse matrix tomography design matrix. Should be in csr format. mask : array of bools Returns ------- res : list list of iterates of the reconstructed images energies : list values of the function to be minimized at the different iterations. Its values should be decreasing. Notes ----- This algorithm minimizes iteratively the energy E(x) = 1/2 || H x - y ||^2 + beta TV(x) = f(x) + beta TV(x) by forward - backward iterations: u_n = prox_{gamma beta TV}(x_n - gamma nabla f(x_n))) t_{n+1} = 1/2 * (1 + sqrt(1 + 4 t_n^2)) x_{n+1} = u_n + (t_n - 1)/t_{n+1} * (u_n - u_{n-1}) References ---------- - A. Beck and M. Teboulle (2009). A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM J. Imaging Sci., 2(1):183-202. - Nelly Pustelnik's thesis (in French), http://tel.archives-ouvertes.fr/tel-00559126_v4/ Paragraph 3.3.1-c p. 69 , FISTA """ n_meas, n_pix = H.shape if mask is not None: l = len(mask) else: l = int(np.sqrt(n_pix)) n_angles = n_meas / l Ht = sparse.csr_matrix(H.transpose()) x0 = np.zeros(n_pix)[:, np.newaxis] res, energies = [], [] gamma = .9/ (l * n_angles) x = x0 u_old = np.zeros((l, l)) t_old = 1 for i in range(niter): if verbose: print(i) eps = 1.e-4 err = H * x - y back_proj = Ht * err tmp = x - gamma * back_proj if mask is not None: tmp2d = np.zeros((l, l)) tmp2d[mask] = tmp.ravel() else: tmp2d = tmp.reshape((l, l)) u_n = tv_denoise_fista(tmp2d, weight=beta*gamma, eps=eps) t_new = (1 + np.sqrt(1 + 4 * t_old**2))/2. t_old = t_new x = u_n + (t_old - 1)/t_new * (u_n - u_old) u_old = u_n res.append(x) data_fidelity_err = 1./2 * (err**2).sum() tv_value = beta * tv_norm(x) energy = data_fidelity_err + tv_value energies.append(energy) if mask is not None: x = x[mask][:, np.newaxis] else: x = x.ravel()[:, np.newaxis] return res, energies def ista_tv(y, beta, niter, H=None): """ TV regression using ISTA algorithm (Iterative Shrinkage/Thresholding Algorithm) Parameters ---------- y : ndarray of floats Measures (tomography projection). If H is given, y is a column vector. If H is not given, y is a 2-D array where each line is a projection along a different angle beta : float weight of TV norm niter : number of forward-backward iterations to perform H : sparse matrix or None tomography design matrix. Should be in csr format. If H is none, the projections as well as the back-projection are computed by a direct method, without writing explicitely the design matrix. Returns ------- res : list list of iterates of the reconstructed images energies : list values of the function to be minimized at the different iterations. Its values should be decreasing. Notes ----- This algorithm minimizes iteratively the energy E(x) = 1/2 || H x - y ||^2 + beta TV(x) by simple forward - backward iterations: x_{n + 1} = prox_{gamma beta TV(.)} (x_n - gamma nabla f(x_n)) where f(x) = 1/2 || H x - y ||^2 References ---------- - Proximal Splitting Methods in Signal Processing, P. Combettes and J.-C. Pesquet, Fixed-Point Algorithms for Inverse Problems in Science and Engineering, p. 185 (2011). Algorithm 10.3 with lamba_n = 1. - Nelly Pustelnik's thesis (in French), http://tel.archives-ouvertes.fr/tel-00559126_v4/ Paragraph 3.3.1-c p. 68 , ISTA """ if H is None: method = 'direct' n_angles, l = y.shape n_pix = l ** 2 else: method = 'matrix' n_angles, l = y.shape n_meas, n_pix = H.shape l = int(np.sqrt(n_pix)) n_angles = n_meas / l if method == 'matrix': Ht = sparse.csr_matrix(H.transpose()) x0 = np.zeros((l, l)) res, energies = [], [] # l * n_angles is the Lipschitz constant of Ht H gamma = .9/ (l * n_angles) x = x0 for i in range(niter): eps = 1.e-4 # Forward part if method == 'matrix': x = x.ravel()[:, np.newaxis] err = H * x - y back_proj = Ht * err tmp = x - gamma * back_proj tmp = tmp.reshape((l, l)) else: err = projection(x, n_angles) - y back_proj = back_projection(err) tmp = x - gamma * back_proj # backward: TV prox x = tv_denoise_fista(tmp, weight=beta*gamma, eps=eps) res.append(x) # compute the energy data_fidelity_err = 1./2 * (err**2).sum() tv_value = beta * tv_norm(x) energy = data_fidelity_err + tv_value energies.append(energy) return res, energies def gfb_tv(y, beta, niter, H=None, val_min=0, val_max=1, x0=None, stop_tol=1.e-4, nonnegconst=1): """ TV regression + interval constraint using the generalized forward backward splitting (GFB). Parameters ---------- y : ndarray of floats Measures (tomography projection). If H is given, y is a column vector. If H is not given, y is a 2-D array where each line is a projection along a different angle beta : float weight of TV norm niter : number of forward-backward iterations to perform H : sparse matrix or None tomography design matrix. Should be in csr format. If H is none, the projections as well as the back-projection are computed by a direct method, without writing explicitely the design matrix. val_min, val_max: floats We impose that the image values are in [val_min, val_max] x0 : ndarray of floats, optional (default is None) Initial guess Returns ------- res : list list of iterates of the reconstructed images energies : list values of the function to be minimized at the different iterations. Its values should be decreasing. Notes ----- This algorithm minimizes iteratively the energy E(x) = 1/2 || H x - y ||^2 + beta TV(x) + i_C(x) where TV(.) is the total variation pseudo-norm and i_C is the indicator function of the convex set [val_min, val_max]. The algorithm used the generalized forward-backward scheme z1_{n + 1} = z1_n - x_n + prox_{2 gamma beta TV(.)} (2*x_n - z1_n - gamma nabla f(x_n)) z2_{n+1} = z1_n - x_n + prox_{i_C(.)}(2*x_n - z2_n - gamma nabla f(x_n) where f(x) = 1/2 || H x - y ||^2 This method can in fact be used for other sums of non-smooth functions for which the prox operator is known. References ---------- Hugo Raguet, Jalal M. Fadili and Gabriel Peyre, Generalized Forward-Backward Splitting Algorithm, preprint arXiv:1108.4404v2, 2011. See also http://www.ceremade.dauphine.fr/~peyre/numerical-tour/tours/inverse_9b_gfb/ """ n_angles, l = y.shape n_meas, n_pix = H.shape l = int(np.sqrt(n_pix)) n_angles = n_meas / l Ht = sparse.csr_matrix(H.transpose()) if x0 is None: x0 = np.zeros((l, l)) z_1 = np.zeros((l**2, 1)) z_2 = np.zeros((l**2, 1)) res, energies = [], [] # l * n_angles is the Lipschitz constant of Ht H gamma = 2 * .9/ (l * n_angles) x = x0 energy = np.inf for i in range(niter): eps = 1.e-4 # Forward part x = x.ravel()[:, np.newaxis] err = H * x - y back_proj = Ht * err # backward: TV and i_c proxs # TV part tmp_z_1 = 2 * x - z_1 - gamma * back_proj tmp_z_1 = tmp_z_1.reshape((l, l)) z_1 = z_1 + tv_denoise_fista(tmp_z_1, weight=2 * beta * gamma, eps=eps).ravel()[:, np.newaxis] - x # Projection on the interval tmp_z_2 = 2 * x - z_2 - gamma * back_proj if nonnegconst == 1: tmp_z_2[tmp_z_2 < val_min] = val_min #print 'Non negative constraint imposed.' tmp_z_2[tmp_z_2 > val_max] = val_max z_2 = z_2 - x + tmp_z_2 # update x: average of z_i x = (0.5 * (z_1 + z_2)).reshape(l, l) res.append(x) # compute the energy data_fidelity_err = 1./2 * (err**2).sum() tv_value = beta * tv_norm(x) energy = data_fidelity_err + tv_value energies.append(energy) # stop criterion if i>2 and np.abs(energy - energies[-2]) < stop_tol*energies[1]: break return res, energies def gfb_tv_weng(y, beta, niter, H=None, val_min=0, val_max=1, x0=None, stop_tol=1.e-4, xb=None, xa=None, beta2=None, nonnegconst=1): """ TV regression + interval constraint using the generalized forward backward splitting (GFB) with adjacent energies TV regularization Parameters ---------- y : ndarray of floats Measures (tomography projection). If H is given, y is a column vector. If H is not given, y is a 2-D array where each line is a projection along a different angle beta : float weight of TV norm niter : number of forward-backward iterations to perform H : sparse matrix or None tomography design matrix. Should be in csr format. If H is none, the projections as well as the back-projection are computed by a direct method, without writing explicitely the design matrix. val_min, val_max: floats We impose that the image values are in [val_min, val_max] x0 : ndarray of floats, optional (default is None) Initial guess at energy Em xb : ndarray of floats, optional (default is None) Initial guess at adjacent energy Em-1 xa : ndarray of floats, optional (default is None) Initial guess at adjacent energy Em+1 Returns ------- res : list list of iterates of the reconstructed images CSenergies : list values of the function to be minimized at the different iterations. Its values should be decreasing. Notes ----- This algorithm minimizes iteratively the CSenergy E(x) = 1/2 || H x - y ||^2 + beta TV(x) + +beta2 TV(x,xa,xb) + i_C(x) where TV(.) is the total variation pseudo-norm and i_C is the indicator function of the convex set [val_min, val_max]. The algorithm used the generalized forward-backward scheme z1_{n + 1} = z1_n - x_n + prox_{2 gamma beta TV(.)} (2*x_n - z1_n - gamma nabla f(x_n)) z2_{n+1} = z1_n - x_n + prox_{i_C(.)}(2*x_n - z2_n - gamma nabla f(x_n)) z3_{n + 1} = z1_n - x_n + 0.1 (xa+xb) + prox_{2 gamma beta2 TV(.)} (2*x_n - z1_n - gamma nabla f(x_n)) where f(x) = 1/2 || H x - y ||^2 This method can in fact be used for other sums of non-smooth functions for which the prox operator is known. References ---------- Hugo Raguet, Jalal M. Fadili and Gabriel Peyre, Generalized Forward-Backward Splitting Algorithm, preprint arXiv:1108.4404v2, 2011. See also http://www.ceremade.dauphine.fr/~peyre/numerical-tour/tours/inverse_9b_gfb/ """ n_angles, l = y.shape n_meas, n_pix = H.shape l = int(np.sqrt(n_pix)) n_angles = n_meas / l Ht = sparse.csr_matrix(H.transpose()) if x0 is None: x0 = np.zeros((l, l)) z_1 = np.zeros((l**2, 1)) z_2 = np.zeros((l**2, 1)) z_3 = np.zeros((l**2, 1)) res, energies = [], [] xb = xb.ravel()[:, np.newaxis] xa = xa.ravel()[:, np.newaxis] if beta2 == None: #beta2=beta beta2=0 # l * n_angles is the Lipschitz constant of Ht H gamma = 2 * .9/ (l * n_angles) x = x0 energy = np.inf for i in range(niter): eps = 1.e-4 # Forward part x = x.ravel()[:, np.newaxis] err = H * x - y back_proj = Ht * err # backward: TV and i_c proxs # TV part tmp_z_1 = 2 * x - z_1 - gamma * back_proj tmp_z_1 = tmp_z_1.reshape((l, l)) z_1 = z_1 + tv_denoise_fista(tmp_z_1, weight=2 * beta * gamma, eps=eps).ravel()[:, np.newaxis] - x # Projection on the interval tmp_z_2 = 2 * x - z_2 - gamma * back_proj if nonnegconst: tmp_z_2[tmp_z_2 < val_min] = val_min tmp_z_2[tmp_z_2 > val_max] = val_max z_2 = z_2 - x + tmp_z_2 if beta2 != 0: #Use images taken at energy before and after the current one tmp_z_3 = 2 * x - z_3 - gamma * back_proj tmp_z_3 = (tmp_z_3+(xa+xb)*.1).reshape((l, l)) z_3 = z_3 + tv_denoise_fista(tmp_z_3, weight=2 * beta2 * gamma, eps=eps).ravel()[:, np.newaxis] - x # update x: average of z_i x = (0.3333 * (z_1 + z_2 + z_3)).reshape(l, l) else: x = (0.5 * (z_1 + z_2)).reshape(l, l) res.append(x) # compute the energy data_fidelity_err = 1./2 * (err**2).sum() tv_value = beta * tv_norm(x) energy = data_fidelity_err + tv_value energies.append(energy) # stop criterion if i>2 and np.abs(energy - energies[-2]) < stop_tol*energies[1]: break return res, energies def gfb_tv_local(y, beta, niter, mask_pix, mask_reg, H=None, val_min=0, val_max=1, x0=None): """ TV regression + interval constraint using the generalized forward backward splitting (GFB), in local tomography mode. Parameters ---------- y : ndarray of floats Measures (tomography projection). If H is given, y is a column vector. If H is not given, y is a 2-D array where each line is a projection along a different angle beta : float weight of TV norm niter : number of forward-backward iterations to perform mask_pix: ndarray of bools Domain where pixels are reconstructed (typically, the disk inside a square). mask_reg: ndarray of bools Domain where the spatial regularization is performed H : sparse matrix or None tomography design matrix. Should be in csr format. If H is none, the projections as well as the back-projection are computed by a direct method, without writing explicitely the design matrix. val_min, val_max: floats We impose that the image values are in [val_min, val_max] x0 : ndarray of floats, optional (default is None) Initial guess Returns ------- res : list list of iterates of the reconstructed images energies : list values of the function to be minimized at the different iterations. Its values should be decreasing. Notes ----- This algorithm minimizes iteratively the energy E(x) = 1/2 || H x - y ||^2 + beta TV(x) + i_C(x) where TV(.) is the total variation pseudo-norm and i_C is the indicator function of the convex set [val_min, val_max]. The algorithm used the generalized forward-backward scheme z1_{n + 1} = z1_n - x_n + prox_{2 gamma beta TV(.)} (2*x_n - z1_n - gamma nabla f(x_n)) z2_{n+1} = z1_n - x_n + prox_{i_C(.)}(2*x_n - z2_n - gamma nabla f(x_n) where f(x) = 1/2 || H x - y ||^2 This method can in fact be used for other sums of non-smooth functions for which the prox operator is known. References ---------- Hugo Raguet, Jalal M. Fadili and Gabriel Peyre, Generalized Forward-Backward Splitting Algorithm, preprint arXiv:1108.4404v2, 2011. See also http://www.ceremade.dauphine.fr/~peyre/numerical-tour/tours/inverse_9b_gfb/ """ mask_reg = mask_reg[mask_pix] n_meas, n_pix = H.shape l = len(mask_pix) n_angles = n_meas / l Ht = sparse.csr_matrix(H.transpose()) z_1 = np.zeros((n_pix, 1)) z_2 = np.zeros((n_pix, 1)) res, energies = [], [] # l * n_angles is the Lipschitz constant of Ht H gamma = 2 * .5/ (l * n_angles) x0 = np.zeros(n_pix)[:, np.newaxis] x = x0 for i in range(niter): eps = 1.e-4 # Forward part err = H * x - y back_proj = Ht * err grad_descent = x - gamma * back_proj # backward: TV and i_c proxs # TV part tmp_z_1 = 2 * x - z_1 - gamma * back_proj tmp_z_1_2d = np.zeros((l, l)) tmp_z_1_2d[mask_pix] = tmp_z_1.ravel() z_1 = z_1 + tv_denoise_fista(tmp_z_1_2d, weight=2 * beta * gamma, eps=eps)[mask_pix][:, np.newaxis] - x # Projection on the interval tmp_z_2 = 2 * x - z_2 - gamma * back_proj tmp_z_2[tmp_z_2 < val_min] = val_min tmp_z_2[tmp_z_2 > val_max] = val_max z_2 = z_2 - x + tmp_z_2 # update x: average of z_i x = (0.5 * (z_1 + z_2)) x[~mask_reg] = grad_descent[~mask_reg] tmp = np.zeros((l, l)) tmp[mask_pix] = x.ravel() res.append(tmp) return res, energies ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1677150153.0 mantis-xray-3.1.15/mantis_xray/TomoCS/projections.py0000664000175000017500000003233114375643711021635 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2015 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # License: GNU GPL v3 # # Originally part of tomotv, code licensed under BSD license. # Website: https://github.com/emmanuelle/tomo-tv # # 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 . from __future__ import absolute_import import numpy as np from scipy import sparse from scipy import ndimage from scipy import fftpack from ._rank_order import rank_order # --------------- Tomo projection operator -------------------- def build_projection_operator(l_x, n_dir=None, angles=None, l_det=None, subpix=1, offset=0, pixels_mask=None): """ Compute the tomography design matrix. Parameters ---------- l_x : int linear size of image array n_dir : int, default l_x number of angles at which projections are acquired. n_dir projection angles are regularly spaced between 0 and 180. l_det : int, default is l_x number of pixels in the detector. If l_det is not specified, we suppose that l_det = l_x. subpix : int, default 1 number of linear subdivisions used to compute the projection of one image pixel onto a detector pixel. For example, if subpix=2, one image pixel is divided into 2x2 subpixels that are projected onto the detector, and the value of the projections is computed from these 4 projections. offset : int, default 0 width of the strip of image pixels not covered by the detector. offset > 0 means that the image is acquired in local tomography (aka ROI) mode, with the image larger than the detector. If the linear size of the array is l_x, the size of the detector is l_x - 2 offset. pixels_mask : 1-d ndarray of size l_x**2 mask of pixels to keep in the matrix (useful if one wishes to remove pixels inside or outside of a circle, for example) Returns ------- p : sparse matrix of shape (n_dir l_x, l_x**2), in csr format Tomography design matrix. The csr (compressed sparse row) allows for efficient subsequent matrix multiplication. The dtype of the elements is float32, in order to save memory. Notes ----- The returned matrix is sparse, but may nevertheless require a lot of memory for large l_x. For example, with l_x=512 and n_dir=512, the operator takes around 3 Gb of memory. The memory cost is of the order of l_x^2 x n_dir x 8 in bytes. For a given angle, the center of the pixels are rotated by the angle, and projected onto the detector. The value of the data pixel is added to the two pixels of the detector in between which the projection is located, with weights determined by a linear interpolation. Using subpix > 1 slows down the computation of the operator, because a histogram in 2-D has to be computed in order to group the projections of subpixels corresponding to a single image pixel. (this should be accelerated by a Cython function... to be written) Examples -------- >>> # Image with 256 pixels, 128 directions >>> op = build_projection_operator(256, n_dir=128) >>> # Image with 128 pixels (to be reconstructed), 256 detector pixels >>> # subpix = 2 is used for a good precision of the projection of the >>> # coarse image pixels >>> op = build_projection_operator(128, n_dir=256, l_det=256, subpix=2) >>> # Image with 256 pixels, that is twice the size of the detector that >>> # has 128 pixels. >>> op = build_projection_operator(256, n_dir=256, l_det=128, offset=64) >>> # Image with 256 pixels, that is twice the size of the detector that >>> # has 256 pixels. We use subpixels for better precision. >>> op = build_projection_operator(256, n_dir=256, l_det=256, offset=64) >>> # Using a mask: projection operator only for pixels inside a >>> # central circle >>> l_x = 128 >>> X, Y = np.ogrid[:l_x, :l_x] >>> mask = (X - l_x/2)**2 + (Y - l_x/2)**2 < (l_x/2)**2 >>> op = build_projection_operator(l_x, pixels_mask=mask) >>> op.shape (16384, 12849) """ if l_det is None: l_det = l_x X, Y = _generate_center_coordinates(subpix*l_x) X *= 1./subpix Y *= 1./subpix Xbig, Ybig = _generate_center_coordinates(l_det) Xbig *= (l_x - 2*offset) / float(l_det) orig = Xbig.min() labels = None if subpix > 1: # Block-group subpixels Xlab, Ylab = np.mgrid[:subpix * l_x, :subpix * l_x] labels = (l_x * (Xlab / subpix) + Ylab / subpix).ravel() if n_dir is None: n_dir = l_x if angles is None: angles = np.linspace(0, np.pi, n_dir, endpoint=False) else: n_dir = len(angles) weights, data_inds, detector_inds = [], [], [] # Indices for data pixels. For each data, one data pixel # will contribute to the value of two detector pixels. for i, angle in enumerate(angles): # rotate data pixels centers Xrot = np.cos(angle) * X - np.sin(angle) * Y # compute linear interpolation weights inds, dat_inds, w = _weights_fast(Xrot, dx=(l_x - 2*offset)/float(l_det), orig=orig, labels=labels) # crop projections outside the detector mask = np.logical_and(inds >= 0, inds < l_det) weights.append(w[mask]) detector_inds.append((inds[mask] + i * l_det).astype(np.int32)) data_inds.append(dat_inds[mask]) weights = np.concatenate(weights) weights /= subpix**2 detector_inds = np.concatenate(detector_inds) data_inds = np.concatenate(data_inds) if pixels_mask is not None: if pixels_mask.ndim > 1: pixels_mask = pixels_mask.ravel() mask = pixels_mask[data_inds] data_inds = data_inds[mask] data_inds = rank_order(data_inds)[0] detector_inds = detector_inds[mask] weights = weights[mask] proj_operator = sparse.coo_matrix((weights, (detector_inds, data_inds))) return sparse.csr_matrix(proj_operator) def _weights_fast(x, dx=1, orig=0, ravel=True, labels=None): """ Compute linear interpolation weights for projection array `x` and regularly spaced detector pixels separated by `dx` and starting at `orig`. """ if ravel: x = np.ravel(x) floor_x = np.floor((x - orig) / dx).astype(np.int32) alpha = ((x - orig - floor_x * dx) / dx).astype(float32) inds = np.hstack((floor_x, floor_x + 1)) weights = np.hstack((1 - alpha, alpha)) data_inds = np.arange(x.size, dtype=np.int32) data_inds = np.hstack((data_inds, data_inds)) if labels is not None: data_inds = np.hstack((labels, labels)) order = np.argsort(inds) inds, data_inds, weights = inds[order], data_inds[order], weights[order] steps = np.nonzero(np.diff(inds) > 0)[0] + 1 steps = np.concatenate(([0], steps)) inds_s, data_inds_s, weights_s = [], [], [] for i in range(len(steps) - 1): d, w = data_inds[steps[i]:steps[i+1]], \ weights[steps[i]:steps[i+1]] count = np.bincount(d, weights=w) mask = count>0 w = count[mask] weights_s.append(w) datind = np.arange(len(mask))[mask] data_inds_s.append(datind) detind = inds[steps[i]]*np.ones(mask.sum()) inds_s.append(detind) #stop inds = np.concatenate(inds_s) data_inds = np.concatenate(data_inds_s) weights = np.concatenate(weights_s) return inds, data_inds, weights def _weights(x, dx=1, orig=0, ravel=True, labels=None): """ Compute linear interpolation weights for projection array `x` and regularly spaced detector pixels separated by `dx` and starting at `orig`. """ if ravel: x = np.ravel(x) floor_x = np.floor((x - orig) / dx).astype(np.int32) alpha = ((x - orig - floor_x * dx) / dx).astype(float32) inds = np.hstack((floor_x, floor_x + 1)) weights = np.hstack((1 - alpha, alpha)) data_inds = np.arange(x.size, dtype=np.int32) data_inds = np.hstack((data_inds, data_inds)) if labels is not None: data_inds = np.hstack((labels, labels)) w = np.histogram2d(data_inds, inds, bins=(np.arange(data_inds.max()+1.5), np.arange(inds.max()+1.5)), weights=weights)[0] data_inds, inds = np.argwhere(w>0).T weights = w[w>0] return inds, data_inds, weights def _weights_nn(x, dx=1, orig=0, ravel=True): """ Nearest-neighbour interpolation """ if ravel: x = np.ravel(x) floor_x = np.floor(x - orig) return floor_x.astype(float32) def _generate_center_coordinates(l_x): """ Compute the coordinates of pixels centers for an image of linear size l_x """ l_x = float(l_x) X, Y = np.mgrid[:l_x, :l_x] center = l_x / 2. X += 0.5 - center Y += 0.5 - center return X, Y # ----------------- Direct projection method ------------------------- # (without computing explicitely the design matrix) def back_projection(projections): """ Back-projection (without filtering) Parameters ---------- projections: ndarray of floats, of shape n_dir x l_x Each line of projections is the projection of a data image acquired at a different angle. The projections angles are supposed to be regularly spaced between 0 and 180. Returns ------- recons: ndarray of shape l_x x l_x Reconstructed array Notes ----- A linear interpolation is used when rotating the back-projection. This function uses ``scipy.ndimage.rotate`` for the rotation. """ n_dir, l_x = projections.shape recons = np.zeros((l_x, l_x), dtype=float) angles = np.linspace(0, 180, n_dir, endpoint=False) for angle, line in zip(angles, projections): # BP: repeat the detector line along the direction of the beam tmp = np.tile(line[:, np.newaxis], (1, l_x)) # Rotate the back-projection of the detector line, and add # it to the reconstructed image recons += ndimage.rotate(tmp, -angle, order=1, \ reshape=False) return recons def projection(im, n_dir=None, interpolation='nearest'): """ Tomography projection of an image along n_dir directions. Parameters ---------- im : ndarray of square shape l_x x l_x Image to be projected n_dir : int Number of projection angles. Projection angles are regularly spaced between 0 and 180. interpolation : str, {'interpolation', 'nearest'} Interpolation method used during the projection. Default is 'nearest'. Returns ------- projections: ndarray of shape n_dir x l_x Array of projections. Notes ----- The centers of the data pixels are projected onto the detector, then the contribution of a data pixel to detector pixels is computed by nearest neighbor or linear interpolation. The function ``np.bincount`` is used to compute the projection, with weights corresponding to the values of data pixels, multiplied by interpolation weights in the case of linear interpolation. """ l_x = len(im) if n_dir is None: n_dir = l_x im = im.ravel() projections = np.empty((n_dir, l_x)) X, Y = _generate_center_coordinates(l_x) angles = np.linspace(0, np.pi, n_dir, endpoint=False) for i, angle in enumerate(angles): Xrot = np.cos(angle) * X - np.sin(angle) * Y if interpolation == 'nearest': inds = _weights_nn(Xrot, dx=1, orig=X.min()) mask = inds>= 0 w = im[mask] elif interpolation == 'linear': inds, _, w = _weights(Xrot, dx=1, orig=X.min()) w[:l_x**2] *= im w[l_x**2:] *= im mask = inds >= 0 w = w[mask] projections[i] = np.bincount(inds[mask].astype(np.int), \ weights=w)[:l_x] return projections # -----------------Filtered back-projection---------------------- def filter_projections(proj_set, reg=False): """ Ramp filter used in the filtered back projection. We use zero padding. Parameters ---------- proj_set: 2-d ndarray each line is one projection (1 line of the detector) to be filtered Returns ------- res: 2-d ndarray filtered projections Notes ----- We use zero padding. However, we do not use any filtering (hanning, etc.) in the FFT yet. """ nb_angles, l_x = proj_set.shape # Assum l is even for now ramp = 1./l_x * np.hstack((np.arange(l_x), np.arange(l_x, 0, -1))) return fftpack.ifft(ramp * fftpack.fft(proj_set, 2*l_x, axis=1), axis=1)[:,:l_x] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/TomoCS/sirt.py0000644000175000017500000002350414332463747020261 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2016 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # 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 . from __future__ import division import os import numpy as np #from skimage.morphology._greyreconstruct import reconstruction_loop M_PI = 3.14159265358979323846264338327 #---------------------------------------------------------------------- def preprocessing(ry, rz, num_pixels, center, gridx, gridy) : for i in range(ry): gridx[i] = -ry/2.+i for i in range(rz): gridy[i] = -rz/2.+i mov = float(num_pixels)/2.0-center if (mov-np.ceil(mov) < 1e-2): mov += 1e-2 return mov, gridx, gridy #---------------------------------------------------------------------- def calc_quadrant(theta_p) : if ((theta_p >= 0 and theta_p < M_PI/2) or (theta_p >= M_PI and theta_p < 3*M_PI/2)) : quadrant = True else : quadrant = False return quadrant #---------------------------------------------------------------------- def calc_coords(ry, rz, xi, yi, sin_p, cos_p, gridx, gridy, coordx, coordy): srcx = xi*cos_p-yi*sin_p srcy = xi*sin_p+yi*cos_p detx = -xi*cos_p-yi*sin_p dety = -xi*sin_p+yi*cos_p slope = (srcy-dety)/(srcx-detx) islope = 1/slope for n in range(ry+1): coordy[n] = slope*(gridx[n]-srcx)+srcy for n in range(rz+1) : coordx[n] = islope*(gridy[n]-srcy)+srcx return coordx, coordy #---------------------------------------------------------------------- def trim_coords( ry, rz, coordx, coordy, gridx, gridy, ax, ay, bx, by): asize = 0 bsize = 0 for n in range(rz+1): if (coordx[n] > gridx[0]) : if (coordx[n] < gridx[ry]) : ax[asize] = coordx[n] ay[asize] = gridy[n] asize +=1 for n in range(ry+1): if (coordy[n] > gridy[0]) : if (coordy[n] < gridy[rz]) : bx[bsize] = gridx[n] by[bsize] = coordy[n] bsize +=1 return asize, ax, ay, bsize, bx, by #---------------------------------------------------------------------- def sort_intersections( ind_condition, asize, ax, ay, bsize, bx, by, coorx, coory) : i=0 j=0 k=0 while (ix1) indy = i2-(i2>x2) indi[n] = indy+(indx*rz) return indi, dist #---------------------------------------------------------------------- def calc_simdata( p, s, c, ry, rz, num_slices, num_pixels, csize, indi, dist, model, simdata): index_model = s*ry*rz index_data = c+s*num_pixels+p*num_slices*num_pixels for n in range(csize-1): simdata[index_data] += model[indi[n]+index_model]*dist[n] #simdata[index_data] += model[p,s,c]*dist[n] return simdata #---------------------------------------------------------------------- # Calculate sirt # tomo : ndarray # 3D tomographic data. # theta : array # Projection angles in radian. # recon : ndarray, optional # Initial values of the reconstruction object. # num_gridx, num_gridy : int, optional # Number of pixels along x- and y-axes in the reconstruction grid. # num_iter : int, optional # Number of algorithm iterations performed. def calculate_sirt(tomo, theta, num_iter): dx, dy, dz = tomo.shape print ('Calculate SIRT reconstruction') center = np.ones(dy, dtype='float32') * dz / 2. ngridx = dz ngridy = dz #tomo = -np.log(tomo) #recon = 1e-6 * np.ones((dy, ngridx, ngridy), dtype='float32') recon = 1e-6 * np.ones((dy*ngridx*ngridy), dtype='float32') print ('tomoshape', tomo.shape) data = tomo gridx = np.zeros((ngridx+1), dtype='float32') gridy = np.zeros((ngridy+1), dtype='float32') coordx = np.zeros((ngridy+1), dtype='float32') coordy = np.zeros((ngridx+1), dtype='float32') ax = np.zeros((ngridx+ngridy), dtype='float32') ay = np.zeros((ngridx+ngridy), dtype='float32') bx = np.zeros((ngridx+ngridy), dtype='float32') by = np.zeros((ngridx+ngridy), dtype='float32') coorx = np.zeros((ngridx+ngridy), dtype='float32') coory = np.zeros((ngridx+ngridy), dtype='float32') dist = np.zeros((ngridx+ngridy), dtype='float32') indi = np.zeros((ngridx+ngridy), dtype='int') for i in range(num_iter): print ('Iteration ', i) simdata = np.zeros((dx*dy*dz), dtype='float32') #For each slice for s in range(dy): print ('Slice', s) mov, gridx, gridy = preprocessing(ngridx, ngridy, dz, center[s], gridx, gridy) sum_dist = np.zeros((ngridx*ngridy), dtype='float32') update = np.zeros((ngridx*ngridy), dtype='float32') # For each projection angle for p in range(dx): # Calculate the sin and cos values # of the projection angle and find # at which quadrant on the cartesian grid. theta_p = np.fmod(theta[p], 2*M_PI) quadrant = calc_quadrant(theta_p) sin_p = np.sin(theta_p) cos_p = np.cos(theta_p) # For each detector pixel for d in range(dz): # Calculate coordinates xi = -1e6 yi = -(dz-1)/2.0+d+mov coordx, coordy = calc_coords( ngridx, ngridy, xi, yi, sin_p, cos_p, gridx, gridy, coordx, coordy) # Merge the (coordx, gridy) and (gridx, coordy) asize, ax, ay, bsize, bx, by = trim_coords( ngridx, ngridy, coordx, coordy, gridx, gridy, ax, ay, bx, by) # Sort the array of intersection points (ax, ay) and # (bx, by). The new sorted intersection points are # stored in (coorx, coory). Total number of points # are csize. csize = sort_intersections( quadrant, asize, ax, ay, bsize, bx, by, coorx, coory) # Calculate the distances (dist) between the # intersection points (coorx, coory). Find the # indices of the pixels on the reconstruction grid. indi, dist = calc_dist( ngridx, ngridy, csize, coorx, coory, indi, dist) # Calculate simdata simdata = calc_simdata(p, s, d, ngridx, ngridy, dy, dz, csize, indi, dist, recon, simdata) # Calculate dist*dist sum_dist2 = 0.0 for n in range(csize-1): sum_dist2 += dist[n]*dist[n] sum_dist[indi[n]] += dist[n] # Update if (sum_dist2 != 0.0) : ind_data = d+s*dz+p*dy*dz upd = (data[p,s,d]-simdata[ind_data])/sum_dist2 for n in range(csize-1): update[indi[n]] += upd*dist[n] m = 0 for n in range(ngridx*ngridy): if (sum_dist[n] != 0.0) : ind_recon = s*ngridx*ngridy recon[m+ind_recon] += update[m]/sum_dist[n] m+=1 recon = np.reshape(recon,(dy, ngridx, ngridy), order='C') print ('SIRT Reconstruction Done') return recon ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1677150153.0 mantis-xray-3.1.15/mantis_xray/TomoCS/tv_denoising.py0000664000175000017500000001374114375643711021772 0ustar00wattswattsfrom __future__ import print_function # # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2015 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # License: GNU GPL v3 # # Originally part of tomotv, code licensed under BSD license. # Website: https://github.com/emmanuelle/tomo-tv # # 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 . import numpy as np def div(grad): """ Compute divergence of image gradient """ res = np.zeros(grad.shape[1:]) for d in range(grad.shape[0]): this_grad = np.rollaxis(grad[d], d) this_res = np.rollaxis(res, d) this_res[:-1] += this_grad[:-1] this_res[1:-1] -= this_grad[:-2] this_res[-1] -= this_grad[-2] return res def gradient(img): """ Compute gradient of an image Parameters =========== img: ndarray N-dimensional image Returns ======= gradient: ndarray Gradient of the image: the i-th component along the first axis is the gradient along the i-th axis of the original array img """ shape = [img.ndim, ] + list(img.shape) gradient = np.zeros(shape, dtype=img.dtype) # 'Clever' code to have a view of the gradient with dimension i stop # at -1 slice_all = [0, slice(None, -1),] for d in range(img.ndim): gradient[tuple(slice_all)] = np.diff(img, axis=d) slice_all[0] = d + 1 slice_all.insert(1, slice(None)) return gradient def _projector_on_dual(grad): """ modifies in place the gradient to project it on the L2 unit ball """ norm = np.maximum(np.sqrt(np.sum(grad**2, 0)), 1.) for grad_comp in grad: grad_comp /= norm return grad def dual_gap(im, new, gap, weight): """ dual gap of total variation denoising see "Total variation regularization for fMRI-based prediction of behavior", by Michel et al. (2011) for a derivation of the dual gap """ im_norm = (im**2).sum() gx, gy = np.zeros_like(new), np.zeros_like(new) gx[:-1] = np.diff(new, axis=0) gy[:, :-1] = np.diff(new, axis=1) if im.ndim == 3: gz = np.zeros_like(new) gz[..., :-1] = np.diff(new, axis=2) tv_new = 2 * weight * np.sqrt(gx**2 + gy**2 + gz**2).sum() else: tv_new = 2 * weight * np.sqrt(gx**2 + gy**2).sum() dual_gap = (gap**2).sum() + tv_new - im_norm + (new**2).sum() return 0.5 / im_norm * dual_gap def tv_denoise_fista(im, weight=50, eps=5.e-5, n_iter_max=200, check_gap_frequency=3): """ Perform total-variation denoising on 2-d and 3-d images Find the argmin `res` of 1/2 * ||im - res||^2 + weight * TV(res), where TV is the isotropic l1 norm of the gradient. Parameters ---------- im: ndarray of floats (2-d or 3-d) input data to be denoised. `im` can be of any numeric type, but it is cast into an ndarray of floats for the computation of the denoised image. weight: float, optional denoising weight. The greater ``weight``, the more denoising (at the expense of fidelity to ``input``) eps: float, optional precision required. The distance to the exact solution is computed by the dual gap of the optimization problem and rescaled by the l2 norm of the image (for contrast invariance). n_iter_max: int, optional maximal number of iterations used for the optimization. Returns ------- out: ndarray denoised array Notes ----- The principle of total variation denoising is explained in http://en.wikipedia.org/wiki/Total_variation_denoising The principle of total variation denoising is to minimize the total variation of the image, which can be roughly described as the integral of the norm of the image gradient. Total variation denoising tends to produce "cartoon-like" images, that is, piecewise-constant images. This function implements the FISTA (Fast Iterative Shrinkage Thresholding Algorithm) algorithm of Beck et Teboulle, adapted to total variation denoising in "Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems" (2009). """ if not im.dtype.kind == 'f': im = im.astype(float) shape = [im.ndim, ] + list(im.shape) grad_im = np.zeros(shape) grad_aux = np.zeros(shape) t = 1. i = 0 while i < n_iter_max: error = weight * div(grad_aux) - im grad_tmp = gradient(error) grad_tmp *= 1./ (8 * weight) grad_aux += grad_tmp grad_tmp = _projector_on_dual(grad_aux) t_new = 1. / 2 * (1 + np.sqrt(1 + 4 * t**2)) t_factor = (t - 1) / t_new grad_aux = (1 + t_factor) * grad_tmp - t_factor * grad_im grad_im = grad_tmp t = t_new if (i % check_gap_frequency) == 0: gap = weight * div(grad_im) new = im - gap dgap = dual_gap(im, new, gap, weight) if dgap < eps: break i += 1 return new if __name__ == '__main__': from scipy.misc import face import matplotlib.pyplot as plt from time import time l = face().astype(float) # normalize image between 0 and 1 l /= l.max() l += 0.1 * l.std() * np.random.randn(*l.shape) t0 = time() res = tv_denoise_fista(l, weight=0.05, eps=5.e-5) t1 = time() print(t1 - t0) plt.figure() plt.subplot(121) plt.imshow(l, cmap='gray') plt.subplot(122) plt.imshow(res, cmap='gray') plt.show() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1677150153.0 mantis-xray-3.1.15/mantis_xray/TomoCS/util.py0000664000175000017500000000564514375643711020263 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2015 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # License: GNU GPL v3 # # Originally part of tomotv, code licensed under BSD license. # Website: https://github.com/emmanuelle/tomo-tv # # 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 . import numpy as np from scipy import ndimage def generate_synthetic_data(l_x=128, seed=None, crop=True, n_pts=25): """ Generate synthetic binary data looking like phase separation Parameters ---------- l_x: int, default 128 Linear size of the returned image seed: int, default 0 seed with which to initialize the random number generator. crop: bool, default True If True, non-zero data are found only within a central circle of radius l_x / 2 n_pts: int, default 25 number of seeds used to generate the structures. The larger n_pts, the finer will be the structures. Returns ------- res: ndarray of float32, of shape lxl Output binary image Examples -------- >>> im = generate_synthetic_data(l_x=256, seed=2, n_pts=25) >>> # Finer structures >>> im = generate_synthetic_data(l_x=256, n_pts=100) """ if seed is None: seed = 0 # Fix the seed for reproducible results rs = np.random.RandomState(seed) x, y = np.ogrid[:l_x, :l_x] mask = np.zeros((l_x, l_x)) points = l_x * rs.rand(2, n_pts) mask[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1 mask = ndimage.gaussian_filter(mask, sigma=l_x / (4. * np.sqrt(n_pts))) # Limit the non-zero data to a central circle if crop: mask_outer = (x - l_x / 2) ** 2 + (y - l_x / 2) ** 2 < (l_x / 2) ** 2 mask = np.logical_and(mask > mask.mean(), mask_outer) else: mask = mask > mask.mean() return mask.astype(float32) def tv_l0_norm(im): """Compute the (isotropic) TV norm of an image""" grad_x1 = np.diff(im, axis=0) grad_x2 = np.diff(im, axis=1) return (grad_x1[:, :-1]**2 + grad_x2[:-1, :]**2 > 0).mean() def compute_sparsity(im): l_x = len(im) X, Y = np.ogrid[:l_x, :l_x] mask = ((X - l_x/2)**2 + (Y - l_x/2)**2 <= (l_x/2)**2) grad1 = ndimage.morphological_gradient(im, footprint=np.ones((3, 3))) grad2 = ndimage.morphological_gradient(im, footprint=ndimage.generate_binary_structure(2, 1)) return (grad1[mask] > 0).mean(), (grad2[mask] > 0).mean() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1701535738.0 mantis-xray-3.1.15/mantis_xray/__init__.py0000664000175000017500000000002714532657772017675 0ustar00wattswatts__version__ = '3.1.15' ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/__main__.py0000644000175000017500000000142114332463747017646 0ustar00wattswattsimport sys, getopt def main(args=None): """The main routine.""" if args is None: args = sys.argv[1:] # Do argument parsing here (eg. with argparse) and anything else # you want your project to do. Return values are exit codes. try: options, extraParams = getopt.getopt(args, '', ['batch', 'nnma']) except: print('Error - wrong command line option used. Available options are --batch and --nnma') return batch_mode = False for opt, arg in options: if opt in '--batch': batch_mode = True if batch_mode: from . import mantis mantis.main() else: from . import mantis_qt mantis_qt.main() # Open the GUI if __name__ == "__main__": sys.exit(main()) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1678690458.0 mantis-xray-3.1.15/mantis_xray/analyze.py0000775000175000017500000027434714403544232017605 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2011 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # 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 . from __future__ import division import os import copy import numpy as np import scipy.interpolate import scipy.spatial import scipy.ndimage from scipy.cluster.vq import kmeans2, whiten from scipy import optimize import scipy.signal import scipy as sp mmult = sp.dot import warnings warnings.simplefilter('ignore', DeprecationWarning) #----------------------------------------------------------------------------- def erf(x): # save the sign of x sign = 1 if x < 0: sign = -1 x = abs(x) # constants a1 = 0.254829592 a2 = -0.284496736 a3 = 1.421413741 a4 = -1.453152027 a5 = 1.061405429 p = 0.3275911 # A&S formula 7.1.26 t = 1.0/(1.0 + p*x) y = 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*np.math.exp(-x*x) return sign*y # erf(-x) = -erf(x) def stepfunc(p, x): #JS Nexafs book - step function # P - position of the inflection point # H - step height # G - FWHM width of the step # E - independent variable, energy (x) P = p[0] H = p[1] G = p[2] c = 1.665 y = H*(0.5+0.5*erf((x-P)/(G/c))) return y def gaussian(p, x): A = p[0] mu = p[1] sigma = p[2] #offset = p[3] y = A * np.exp(-((x-mu)**2)/(2*sigma**2))#+offset return y def model(p, nsteps, npeaks, x): offset = p[0] pg = nsteps*3+1 istepfitparams = p[1:pg] y = np.zeros((x.size)) if nsteps > 0: for i in range(x.size): y[i] = stepfunc(istepfitparams, x[i]) for i in range(npeaks): pp = [p[pg+i*3],p[pg+1+i*3],p[pg+2+i*3]] y = y + gaussian(pp,x) y = y+offset return y def model_error(p, nsteps, npeaks, x, y): # err = np.zeros(x.size) # for i in range(x.size): # err[i] = (y[i]-model(p, x[i])) # return err err = y-model(p, nsteps, npeaks, x) return err #---------------------------------------------------------------------- class Cfitparams: def __init__(self): self.base = 0.0 self.stepfitparams = np.zeros((8)) self.gauss_fp_a = np.zeros((12)) self.gauss_fp_m = np.zeros((12)) self.gauss_fp_s = np.zeros((12)) #---------------------------------------------------------------------- class analyze: def __init__(self, stkdata): self.stack = stkdata self.pca_calculated = 0 self.clusters_calculated = 0 self.target_spectra = 0 self.tspectrum_loaded = 0 self.n_target_spectra = 0 self.tspec_names = [] self.xrayfitsp_loaded = 0 self.xrayfitspectra = 0 self.n_xrayfitsp = 0 self.xfspec_names = [] self.xfitpars = [] self.pcaimages4D = [] self.eigenvals4D = [] self.eigenvecs4D = [] self.variance4D = [] self.pcaimagebounds4D = [] self.target_svd_maps4D = [] self.original_svd_maps4D = [] self.target_pcafit_maps4D = [] self.original_fit_maps4D = [] self.target_pcafit_coeffs4D = [] self.target_pcafit_spectra4D = [] #---------------------------------------------------------------------- # Calculate pca def delete_data(self): self.target_spectra = 0 self.tspectrum_loaded = 0 self.n_target_spectra = 0 self.tspec_names = [] self.pcaimages = 0 self.pcaimagebounds = 0 self.eigenvals = 0 self.eigenvecs = 0 self.cluster_distances = 0 self.clustersizes = 0 self.cluster_indices = 0 self.clusterspectra = 0 #---------------------------------------------------------------------- # Calculate pca def calculate_pca(self): #covariance matrix n_pix = self.stack.n_cols*self.stack.n_rows od = self.stack.od #normalize od spectra - not used in pca_gui.pro #norms = np.apply_along_axis(np.linalg.norm, 1, od) odn = np.zeros((n_pix, self.stack.n_ev)) for i in range(n_pix): odn[i,:] = od[i,:]/np.linalg.norm(od[i,:]) covmatrix = np.dot(od.T,od) self.pcaimages = np.zeros((self.stack.n_cols, self.stack.n_rows, self.stack.n_ev)) self.pcaimagebounds = np.zeros((self.stack.n_ev)) try: self.eigenvals, self.eigenvecs = np.linalg.eigh(covmatrix) #sort the eigenvals and eigenvecs perm = np.argsort(-np.abs(self.eigenvals)) self.eigenvals = self.eigenvals[perm] self.eigenvecs = self.eigenvecs[:,perm] self.pcaimages = np.dot(od,self.eigenvecs) #calculate eigenimages self.pcaimages = np.reshape(self.pcaimages, (self.stack.n_cols, self.stack.n_rows, self.stack.n_ev), order='F') #Find bounds for displaying color-tables for i in range(self.stack.n_ev): min_val = np.amin(self.pcaimages[:,:,i]) max_val = np.amax(self.pcaimages[:,:,i]) self.pcaimagebounds[i] = np.amax((np.abs(min_val), np.abs(max_val))) #calculate variance captured by the pca components self.variance = self.eigenvals.copy() totalvar = self.variance.sum() self.variance = self.variance/totalvar #Scree plot - find an elbow in the curve - between 1 and 20 components maxpoints = min(25, self.stack.n_ev-1) #Find a line between first (x1, y1) and last point (x2, y2) and calculate distances: y2 = np.log(self.eigenvals[maxpoints]) x2 = maxpoints y1 = np.log(self.eigenvals[0]) x1 = 0 #Calculate distances between all the points and the line x1 and x2 are points on the line and x0 are eigenvals distance = np.zeros((maxpoints)) for i in range(maxpoints): y0 = np.log(self.eigenvals[i]) x0=i distance[i] = np.abs((x2-x1)*(y1-y0)-(x1-x0)*(y2-y1))/np.math.sqrt((x2-x1)**2+(y2-y1)**2) #Point with the largest distance is the "elbow" sigpca = np.argmax(distance) self.numsigpca = sigpca + 1 except: print( "pca not converging") self.pca_calculated = 1 if self.n_target_spectra > 1: self.fit_target_spectra() return #---------------------------------------------------------------------- # Calculate pca def calculate_pca_4D(self): #covariance matrix n_pix = self.stack.n_cols*self.stack.n_rows self.pcaimages4D = [] self.eigenvals4D = [] self.eigenvecs4D = [] self.variance4D = [] self.pcaimagebounds4D = [] for jth in range(self.stack.n_theta): od3d = self.stack.od4D[:,:,:,jth] od = od3d.copy() od = np.reshape(od, (n_pix, self.stack.n_ev), order='F') #normalize od spectra - not used in pca_gui.pro #norms = np.apply_along_axis(np.linalg.norm, 1, od) odn = np.zeros((n_pix, self.stack.n_ev)) for i in range(n_pix): odn[i,:] = od[i,:]/np.linalg.norm(od[i,:]) covmatrix = np.dot(od.T,od) self.pcaimages = np.zeros((self.stack.n_cols, self.stack.n_rows, self.stack.n_ev)) self.pcaimagebounds = np.zeros((self.stack.n_ev)) try: self.eigenvals, self.eigenvecs = np.linalg.eigh(covmatrix) #sort the eigenvals and eigenvecs perm = np.argsort(-np.abs(self.eigenvals)) self.eigenvals = self.eigenvals[perm] self.eigenvecs = self.eigenvecs[:,perm] self.pcaimages = np.dot(od,self.eigenvecs) #calculate eigenimages self.pcaimages = np.reshape(self.pcaimages, (self.stack.n_cols, self.stack.n_rows, self.stack.n_ev), order='F') #Find bounds for displaying color-tables for i in range(self.stack.n_ev): min_val = np.amin(self.pcaimages[:,:,i]) max_val = np.amax(self.pcaimages[:,:,i]) self.pcaimagebounds[i] = np.amax((np.abs(min_val), np.abs(max_val))) #calculate variance captured by the pca components self.variance = self.eigenvals.copy() totalvar = self.variance.sum() self.variance = self.variance/totalvar #Scree plot - find an elbow in the curve - between 1 and 20 components maxpoints = min(25, self.stack.n_ev-1) #Find a line between first (x1, y1) and last point (x2, y2) and calculate distances: y2 = np.log(self.eigenvals[maxpoints]) x2 = maxpoints y1 = np.log(self.eigenvals[0]) x1 = 0 #Calculate distances between all the points and the line x1 and x2 are points on the line and x0 are eigenvals distance = np.zeros((maxpoints)) for i in range(maxpoints): y0 = np.log(self.eigenvals[i]) x0=i distance[i] = np.abs((x2-x1)*(y1-y0)-(x1-x0)*(y2-y1))/np.math.sqrt((x2-x1)**2+(y2-y1)**2) #Point with the largest distance is the "elbow" sigpca = np.argmax(distance) self.numsigpca = sigpca + 1 except: print ("pca not converging") self.pca_calculated = 1 if self.n_target_spectra > 1: self.fit_target_spectra() self.pcaimages4D.append(self.pcaimages) self.eigenvals4D.append(self.eigenvals) self.eigenvecs4D.append(self.eigenvecs) self.variance4D.append(self.variance) self.pcaimagebounds4D.append(self.pcaimagebounds) return #---------------------------------------------------------------------- # Move PC up def move_pc_up(self, ipc): if ipc == 0: return if len(self.pcaimages4D) == 0: temp = self.pcaimages.copy() self.pcaimages[:,:, ipc] = temp[:,:, ipc-1] self.pcaimages[:,:, ipc-1] = temp[:,:, ipc] temp = self.pcaimagebounds.copy() self.pcaimagebounds[ipc] = temp[ipc-1] self.pcaimagebounds[ipc-1] = temp[ipc] temp = self.eigenvals.copy() self.eigenvals[ipc] = temp[ipc-1] self.eigenvals[ipc-1] = temp[ipc] temp = self.eigenvecs.copy() self.eigenvecs[:, ipc] = temp[:, ipc-1] self.eigenvecs[:, ipc-1] = temp[:, ipc] temp = self.variance.copy() self.variance[ipc] = temp[ipc-1] self.variance[ipc-1] = temp[ipc] else: for jth in range(self.stack.n_theta): temp = self.pcaimages4D[jth].copy() self.pcaimages4D[jth][:,:, ipc] = temp[:,:, ipc-1] self.pcaimages4D[jth][:,:, ipc-1] = temp[:,:, ipc] temp = self.pcaimagebounds4D[jth].copy() self.pcaimagebounds4D[jth][ipc] = temp[ipc-1] self.pcaimagebounds4D[jth][ipc-1] = temp[ipc] temp = self.eigenvals4D[jth].copy() self.eigenvals4D[jth][ipc] = temp[ipc-1] self.eigenvals4D[jth][ipc-1] = temp[ipc] temp = self.eigenvecs4D[jth].copy() self.eigenvecs4D[jth][:, ipc] = temp[:, ipc-1] self.eigenvecs4D[jth][:, ipc-1] = temp[:, ipc] temp = self.variance4D[jth].copy() self.variance4D[jth][ipc] = temp[ipc-1] self.variance4D[jth][ipc-1] = temp[ipc] if self.n_target_spectra > 1: self.fit_target_spectra() if len(self.target_svd_maps4D) > 0: self.calculate_targetmaps_4D() #---------------------------------------------------------------------- # Find clusters def calculate_clusters(self, nclusters, remove1stpca = 0, sigmasplit = 0, pcscalingfactor = 0.0): #Reduced data matrix od_reduced(n_pixels,n_significant_components) #od_reduced = np.zeros((self.stack.n_cols, self.stack.n_rows, self.numsigpca)) self.nclusters = nclusters npixels = self.stack.n_cols * self.stack.n_rows inverse_n_pixels = 1./float(npixels) inverse_n_pixels_less_one = 1./float(npixels-1) dc_offsets = np.zeros((self.numsigpca)) #rms_deviations = np.zeros((self.numsigpca)) od_reduced = np.zeros((self.stack.n_cols, self.stack.n_rows,self.numsigpca)) for i in range(self.numsigpca): eimage = self.pcaimages[:,:,i] dc_offsets[i] = np.sum(eimage)*inverse_n_pixels # Since we're looking at deviations from an average, # we divide by (N-1). #rms_deviations[i] = np.sqrt(np.sum((eimage-dc_offsets[i])**2)*inverse_n_pixels_less_one) # The straightforward thing is to do # d_reduced[i,0:(n_pixels-1)] = eimage # However, things work much better if we subtract the # DC offsets from each eigenimage. One could also divide # by rms_deviations, but that seems to overweight # the sensitivity to weaker components too much. rms_gamma = pcscalingfactor od_reduced[:,:,i] = (eimage-dc_offsets[i]) *(self.eigenvals[0]/self.eigenvals[i])**rms_gamma if remove1stpca == 0 : #od_reduced = od_reduced[:,:,0:self.numsigpca] od_reduced = np.reshape(od_reduced, (npixels,self.numsigpca), order='F') else: od_reduced = od_reduced[:,:,1:self.numsigpca] od_reduced = np.reshape(od_reduced, (npixels,self.numsigpca-1), order='F') indx = np.zeros(npixels) clustercentroids, indx = kmeans2(od_reduced, nclusters, iter=200, minit = 'points' ) #calculate cluster distances self.cluster_distances = np.zeros((self.stack.n_cols*self.stack.n_rows)) for i in range(npixels): clind = indx[i] self.cluster_distances[i] = scipy.spatial.distance.euclidean(od_reduced[i,:],clustercentroids[clind,:]) self.cluster_distances = np.reshape(self.cluster_distances, (self.stack.n_cols, self.stack.n_rows), order='F') indx = np.reshape(indx, (self.stack.n_cols, self.stack.n_rows), order='F') self.clustersizes = np.zeros((nclusters,), dtype=int) for i in range(nclusters): clind = np.where(indx == i) self.clustersizes[i] = indx[clind].shape[0] #sort the data with the cluster with the most members first count_indices = np.argsort(self.clustersizes) count_indices = count_indices[::-1] self.cluster_indices = np.zeros((self.stack.n_cols, self.stack.n_rows), dtype=int) self.clusterspectra = np.zeros((nclusters, self.stack.n_ev)) for i in range(nclusters): clind = np.where(indx == count_indices[i]) self.cluster_indices[clind] = i self.clustersizes[i] = self.cluster_indices[clind].shape[0] for ie in range(self.stack.n_ev): thiseng_od = self.stack.od3d[:,:,ie] self.clusterspectra[i,ie] = np.sum(thiseng_od[clind])/self.clustersizes[i] #Calculate SSE Sum of Squared errors indx = np.reshape(self.cluster_indices, (npixels), order='F') self.sse = np.zeros((npixels)) for i in range(npixels): clind = indx[i] self.sse[i] = np.sum(np.square(self.stack.od[i,:]-self.clusterspectra[clind,:])) self.sse = np.reshape(self.sse, (self.stack.n_cols, self.stack.n_rows), order='F') if (sigmasplit ==1): #Check the validity of cluster analysis and if needed add another cluster new_cluster_indices = self.cluster_indices.copy() new_nclusters = nclusters recalc_clusters = False for i in range(nclusters): clind = np.where(self.cluster_indices == i) cl_sse_mean = np.mean(self.sse[clind]) cl_see_std = np.std(self.sse[clind]) sigma9 = cl_sse_mean+9*cl_see_std maxsse = np.max(self.sse[clind]) if (maxsse > sigma9): recalc_clusters = True sse_helper = np.zeros((self.stack.n_cols, self.stack.n_rows), dtype=int) sse_helper[clind] = self.sse[clind] newcluster_ind = np.where(sse_helper > sigma9) new_cluster_indices[newcluster_ind] = new_nclusters new_nclusters += 1 if recalc_clusters == True: nclusters = new_nclusters self.cluster_indices = new_cluster_indices self.clusterspectra = np.zeros((nclusters, self.stack.n_ev)) self.clustersizes = np.zeros((nclusters,), dtype=int) for i in range(nclusters): clind = np.where(self.cluster_indices == i) self.clustersizes[i] = self.cluster_indices[clind].shape[0] if self.clustersizes[i]>0: for ie in range(self.stack.n_ev): thiseng_od = self.stack.od3d[:,:,ie] self.clusterspectra[i,ie] = np.sum(thiseng_od[clind])/self.clustersizes[i] #Calculate SSE Sum of Squared errors indx = np.reshape(self.cluster_indices, (npixels), order='F') self.sse = np.zeros((npixels)) for i in range(npixels): clind = indx[i] self.sse[i] = np.sqrt(np.sum(np.square(self.stack.od[i,:]-self.clusterspectra[clind,:]))) self.sse = np.reshape(self.sse, (self.stack.n_cols, self.stack.n_rows), order='F') self.cluster_distances = self.sse self.clusters_calculated = 1 return int(nclusters) #---------------------------------------------------------------------- # Find clusters def calculate_clusters_4D(self, nclusters, remove1stpca = 0, sigmasplit = 0, pcscalingfactor = 0.0): #Reduced data matrix od_reduced(n_pixels,n_significant_components) #od_reduced = np.zeros((self.stack.n_cols, self.stack.n_rows, self.numsigpca)) self.nclusters = nclusters npixels = self.stack.n_cols * self.stack.n_rows inverse_n_pixels = 1./float(npixels) inverse_n_pixels_less_one = 1./float(npixels-1) dc_offsets = np.zeros((self.numsigpca)) #rms_deviations = np.zeros((self.numsigpca)) od_reduced = np.zeros((self.stack.n_cols, self.stack.n_rows,self.numsigpca)) for i in range(self.numsigpca): eimage = self.pcaimages[:,:,i] dc_offsets[i] = np.sum(eimage)*inverse_n_pixels # Since we're looking at deviations from an average, # we divide by (N-1). #rms_deviations[i] = np.sqrt(np.sum((eimage-dc_offsets[i])**2)*inverse_n_pixels_less_one) # The straightforward thing is to do # d_reduced[i,0:(n_pixels-1)] = eimage # However, things work much better if we subtract the # DC offsets from each eigenimage. One could also divide # by rms_deviations, but that seems to overweight # the sensitivity to weaker components too much. rms_gamma = pcscalingfactor od_reduced[:,:,i] = (eimage-dc_offsets[i]) *(self.eigenvals[0]/self.eigenvals[i])**rms_gamma if remove1stpca == 0 : #od_reduced = od_reduced[:,:,0:self.numsigpca] od_reduced = np.reshape(od_reduced, (npixels,self.numsigpca), order='F') else: od_reduced = od_reduced[:,:,1:self.numsigpca] od_reduced = np.reshape(od_reduced, (npixels,self.numsigpca-1), order='F') indx = np.zeros(npixels) clustercentroids, indx = kmeans2(od_reduced, nclusters, iter=200, minit = 'points' ) #calculate cluster distances self.cluster_distances = np.zeros((self.stack.n_cols*self.stack.n_rows)) for i in range(npixels): clind = indx[i] self.cluster_distances[i] = scipy.spatial.distance.euclidean(od_reduced[i,:],clustercentroids[clind,:]) self.cluster_distances = np.reshape(self.cluster_distances, (self.stack.n_cols, self.stack.n_rows), order='F') indx = np.reshape(indx, (self.stack.n_cols, self.stack.n_rows), order='F') self.clustersizes = np.zeros((nclusters,), dtype=int) for i in range(nclusters): clind = np.where(indx == i) self.clustersizes[i] = indx[clind].shape[0] #sort the data with the cluster with the most members first count_indices = np.argsort(self.clustersizes) count_indices = count_indices[::-1] self.cluster_indices = np.zeros((self.stack.n_cols, self.stack.n_rows), dtype=int) self.clusterspectra = np.zeros((nclusters, self.stack.n_ev)) for i in range(nclusters): clind = np.where(indx == count_indices[i]) self.cluster_indices[clind] = i self.clustersizes[i] = self.cluster_indices[clind].shape[0] for ie in range(self.stack.n_ev): thiseng_od = self.stack.od3d[:,:,ie] self.clusterspectra[i,ie] = np.sum(thiseng_od[clind])/self.clustersizes[i] #Calculate SSE Sum of Squared errors indx = np.reshape(self.cluster_indices, (npixels), order='F') self.sse = np.zeros((npixels)) for i in range(npixels): clind = indx[i] self.sse[i] = np.sum(np.square(self.stack.od[i,:]-self.clusterspectra[clind,:])) self.sse = np.reshape(self.sse, (self.stack.n_cols, self.stack.n_rows), order='F') if (sigmasplit ==1): #Check the validity of cluster analysis and if needed add another cluster new_cluster_indices = self.cluster_indices.copy() new_nclusters = nclusters recalc_clusters = False for i in range(nclusters): clind = np.where(self.cluster_indices == i) cl_sse_mean = np.mean(self.sse[clind]) cl_see_std = np.std(self.sse[clind]) sigma9 = cl_sse_mean+9*cl_see_std maxsse = np.max(self.sse[clind]) if (maxsse > sigma9): recalc_clusters = True sse_helper = np.zeros((self.stack.n_cols, self.stack.n_rows), dtype=int) sse_helper[clind] = self.sse[clind] newcluster_ind = np.where(sse_helper > sigma9) new_cluster_indices[newcluster_ind] = new_nclusters new_nclusters += 1 if recalc_clusters == True: nclusters = new_nclusters self.cluster_indices = new_cluster_indices self.clusterspectra = np.zeros((nclusters, self.stack.n_ev)) self.clustersizes = np.zeros((nclusters,), dtype=int) for i in range(nclusters): clind = np.where(self.cluster_indices == i) self.clustersizes[i] = self.cluster_indices[clind].shape[0] if self.clustersizes[i]>0: for ie in range(self.stack.n_ev): thiseng_od = self.stack.od3d[:,:,ie] self.clusterspectra[i,ie] = np.sum(thiseng_od[clind])/self.clustersizes[i] #Calculate SSE Sum of Squared errors indx = np.reshape(self.cluster_indices, (npixels), order='F') self.sse = np.zeros((npixels)) for i in range(npixels): clind = indx[i] self.sse[i] = np.sqrt(np.sum(np.square(self.stack.od[i,:]-self.clusterspectra[clind,:]))) self.sse = np.reshape(self.sse, (self.stack.n_cols, self.stack.n_rows), order='F') self.cluster_distances = self.sse self.clusters_calculated = 1 return int(nclusters) #---------------------------------------------------------------------- # Find clusters def calculate_clusters_kmeansangle(self, nclusters, remove1stpca = 0, sigmasplit = 0, cosinemeasure = False): cosinemeasure = True self.nclusters = nclusters npixels = self.stack.n_cols * self.stack.n_rows inverse_n_pixels = 1./float(npixels) inverse_n_pixels_less_one = 1./float(npixels-1) dc_offsets = np.zeros((self.numsigpca)) #rms_deviations = np.zeros((self.numsigpca)) od_reduced = np.zeros((self.stack.n_cols, self.stack.n_rows,self.numsigpca)) for i in range(self.numsigpca): eimage = self.pcaimages[:,:,i] dc_offsets[i] = np.sum(eimage)*inverse_n_pixels # Since we're looking at deviations from an average, # we divide by (N-1). #rms_deviations[i] = np.sqrt(np.sum((eimage-dc_offsets[i])**2)*inverse_n_pixels_less_one) # The straightforward thing is to do # d_reduced[i,0:(n_pixels-1)] = eimage # However, things work much better if we subtract the # DC offsets from each eigenimage. One could also divide # by rms_deviations, but that seems to overweight # the sensitivity to weaker components too much. rms_gamma = 0.0 od_reduced[:,:,i] = (eimage-dc_offsets[i]) *(self.eigenvals[0]/self.eigenvals[i])**rms_gamma if remove1stpca == 0 : #od_reduced = od_reduced[:,:,0:self.numsigpca] od_reduced = np.reshape(od_reduced, (npixels,self.numsigpca), order='F') nsigpca = self.numsigpca else: od_reduced = od_reduced[:,:,1:self.numsigpca] od_reduced = np.reshape(od_reduced, (npixels,self.numsigpca-1), order='F') nsigpca = self.numsigpca-1 n_iterations = 5 # When "Angle distance measure" is used we d_reduced is normalized # so that all spectra have norm equal to 1 (this amounts to # projection of all the pixels to unit sphere in principal # component space. # For angle distance measure we will only include part of the # pixels if cutoff is set. Pixels with lowest optical density are # not included in the calculation since they will be uniformly # distributed over the unit sphere and might obstract finding of # the clusters. angle_cutoff_value = 0 if cosinemeasure: od_reduced_old = od_reduced.copy() for i in range(npixels): od_reduced[i,:] = od_reduced[i,:]/np.linalg.norm(od_reduced[i,:]) if angle_cutoff_value > 0: d_integrated = np.zeros((npixels)) d_integrated = np.apply_along_axis(np.sum, self.od, 0) included_pixels = np.where(d_integrated > angle_cutoff_value) od_reduced_all_pixels = od_reduced.copy() od_reduced = od_reduced[:, included_pixels] # Initial 'learning rate'. LearningRates = 0.3-0.2*np.arange(n_iterations+1)/float(n_iterations-1) # Normal random cluster weights. cluster_weights = np.zeros((nsigpca, nclusters)) randomindices = np.random.uniform(0, npixels-1,size=nclusters) for i in range(nclusters): cluster_weights[:,i] = od_reduced[randomindices[i], :] if cosinemeasure: for i in range(nclusters): cluster_weights[:, i] = cluster_weights[:, i]/np.linalg.norm(cluster_weights[:,i]) Metric = np.zeros((nclusters)) # Start by picking a percentage of the pixels at random, # and using them to start finding our cluster centers n_random_pixels = int(0.50*float(npixels)) random_sample_indices = float(npixels-1)*np.random.uniform(0,1,(n_random_pixels,)) # Make sure we don't do any pixels twice ursi, uindices = np.unique(random_sample_indices, return_index = True) random_sample_indices = random_sample_indices[uindices] n_random_pixels = len(random_sample_indices) this_learning_rate = LearningRates[0] for i_sample in range(n_random_pixels): Sample = random_sample_indices[i_sample] Vector = np.tile(od_reduced[Sample,:], (nclusters,1)).T - cluster_weights #Calculate distances if cosinemeasure == False: for i_cluster in range(nclusters): Metric[i_cluster] = np.sqrt(np.dot(Vector[:, i_cluster].T, Vector[:, i_cluster])) else: #Use angle between vectors instead euclidean distance for i_cluster in range(nclusters): Metric[i_cluster] = scipy.spatial.distance.cosine(od_reduced[ Sample, :], cluster_weights[:, i_cluster].T) MinIndex = np.argmin(Metric) cluster_weights[:,MinIndex] = this_learning_rate* Vector[:,MinIndex] + cluster_weights[:,MinIndex] if cosinemeasure: cluster_weights[:, MinIndex] = cluster_weights[:, MinIndex]/np.linalg.norm(cluster_weights[:,MinIndex]) # Random ordering of sample indices. random_ordered = np.arange(npixels) np.random.shuffle(random_ordered) self.cluster_distances = np.zeros((npixels)) Tempcluster_indices = np.zeros((npixels)) cluster_histogram = np.zeros((nclusters)) cluster_indices = np.zeros((npixels)) New_RMSDistanceIterations = np.zeros((n_iterations)) for i_iteration in range(n_iterations): this_max_distance = 0.0 this_learning_rate = LearningRates[i_iteration] for Sample in range(npixels): # In our case the data array is # d_reduced(n_significant_components,n_pixels), and we have # WorkCol(1,n_clusters). Calculate # Vector(n_significant_components,n_clusters) by multiplying # all the components for this pixel by n_clusters values of # 1 to pick them off, and then subtracting from the result # the current guess of the weights (the cluster centers). Vector = np.tile(od_reduced[random_ordered[Sample],:], (nclusters,1)).T - cluster_weights #Calculate distances if cosinemeasure == False: for i_cluster in range(nclusters): Metric[i_cluster] = np.sqrt(np.dot(Vector[:, i_cluster].T, Vector[:, i_cluster].T)) else: #Use angle between vectors instead euclidean distance for i_cluster in range(nclusters): Metric[i_cluster] = scipy.spatial.distance.cosine(od_reduced[random_ordered[Sample], :], cluster_weights[:, i_cluster].T) MinIndex = np.argmin(Metric) MinMetric = Metric[MinIndex] this_max_distance = max([this_max_distance, MinMetric ]) cluster_weights[:,MinIndex] = this_learning_rate* Vector[:,MinIndex] + cluster_weights[:,MinIndex] if cosinemeasure: cluster_weights[:, MinIndex] = cluster_weights[:, MinIndex]/np.linalg.norm(cluster_weights[:,MinIndex]) self.cluster_distances[random_ordered[Sample]] = MinMetric if (i_iteration == (n_iterations-1)) : Tempcluster_indices[random_ordered[Sample]] = MinIndex cluster_histogram[MinIndex] = cluster_histogram[MinIndex]+1 # Since we're talking about distances from the cluster # center, which is in some ways an average of pixel positions, # we use (npixels-1) in the denominator. New_RMSDistanceIterations[i_iteration] = np.sqrt(np.sum(self.cluster_distances**2)/float(npixels-1)) # Next we sort the data with the cluster with the most members first count_indices = np.argsort(cluster_histogram) count_indices = count_indices[::-1] cluster_histogram = cluster_histogram[count_indices] self.cluster_indices = np.zeros((npixels), dtype=int) ClustersFound = 0 for i_cluster in range(nclusters): i_temp_cluster = count_indices[i_cluster] these_pixels = np.where(Tempcluster_indices == i_temp_cluster)[0] if len(these_pixels) > 0: cluster_indices[these_pixels] = i_cluster ClustersFound = ClustersFound + 1 # Next we sort the cluster_weights with the cluster with the most # members first temp_weights = cluster_weights.copy() for i_cluster in range(ClustersFound): cluster_weights[0:nsigpca, i_cluster] = temp_weights[0:nsigpca, count_indices[i_cluster]] cluster_histogram = cluster_histogram[0:ClustersFound] cluster_weights = cluster_weights[:, 0:ClustersFound] # # Recalculate the cluster centers to be equal to the average of the # # pixel weights. For angle measure will be done later. # for i_cen in range(nclusters): # cluster_members = np.where(cluster_indices == i_cen) # n_mem = len(cluster_members[0]) # if len(cluster_members[0])> 0: # WorkRow2=np.ones((n_mem)) # cluster_weights[:,i_cen]=np.dot(od_reduced[:,cluster_members],WorkRow2)/n_mem self.cluster_distances = np.reshape(self.cluster_distances, (self.stack.n_cols, self.stack.n_rows), order='F') self.cluster_indices = cluster_indices self.clustersizes = cluster_histogram self.clusterspectra = np.zeros((nclusters, self.stack.n_ev)) self.sse = np.zeros((npixels)) for i in range(nclusters): clind = np.where(self.cluster_indices == count_indices[i]) self.clustersizes[i] = self.cluster_indices[clind].shape[0] for ie in range(self.stack.n_ev): thiseng_od = self.stack.od[:,ie] self.clusterspectra[i,ie] = np.sum(thiseng_od[clind])/self.clustersizes[i] #Calculate SSE Sum of Squared errors for i in range(npixels): clind = self.cluster_indices[i] self.sse[i] = np.sum(np.square(self.stack.od[i,:]-self.clusterspectra[clind,:])) self.sse = np.reshape(self.sse, (self.stack.n_cols, self.stack.n_rows), order='F') if (sigmasplit ==1): #Check the validity of cluster analysis and if needed add another cluster new_cluster_indices = self.cluster_indices.copy() new_nclusters = nclusters recalc_clusters = False for i in range(nclusters): clind = np.where(self.cluster_indices == i) cl_sse_mean = np.mean(self.sse[clind]) cl_see_std = np.std(self.sse[clind]) sigma9 = cl_sse_mean+9*cl_see_std maxsse = np.max(self.sse[clind]) if (maxsse > sigma9): recalc_clusters = True sse_helper = np.zeros((self.stack.n_cols, self.stack.n_rows), dtype=int) sse_helper[clind] = self.sse[clind] newcluster_ind = np.where(sse_helper > sigma9) new_cluster_indices[newcluster_ind] = new_nclusters new_nclusters += 1 if recalc_clusters == True: nclusters = new_nclusters self.cluster_indices = new_cluster_indices self.clusterspectra = np.zeros((nclusters, self.stack.n_ev)) self.clustersizes = np.zeros((nclusters,), dtype=int) for i in range(nclusters): clind = np.where(self.cluster_indices == i) self.clustersizes[i] = self.cluster_indices[clind].shape[0] if self.clustersizes[i]>0: for ie in range(self.stack.n_ev): thiseng_od = self.stack.od3d[:,:,ie] self.clusterspectra[i,ie] = np.sum(thiseng_od[clind])/self.clustersizes[i] #Calculate SSE Sum of Squared errors indx = np.reshape(self.cluster_indices, (npixels), order='F') self.sse = np.zeros((npixels)) for i in range(npixels): clind = indx[i] self.sse[i] = np.sqrt(np.sum(np.square(self.stack.od[i,:]-self.clusterspectra[clind,:]))) self.sse = np.reshape(self.sse, (self.stack.n_cols, self.stack.n_rows), order='F') self.cluster_indices = np.reshape(self.cluster_indices, (self.stack.n_cols, self.stack.n_rows), order='F') self.cluster_distances = self.sse self.clusters_calculated = 1 return int(nclusters) #---------------------------------------------------------------------- # Find clusters using EM clustering def calculate_clusters_em(self, nclusters): #Reduced data matrix od_reduced(n_pixels,n_significant_components) #od_reduced = np.zeros((self.stack.n_cols, self.stack.n_rows, self.numsigpca)) npixels = self.stack.n_cols * self.stack.n_rows inverse_n_pixels = 1./float(npixels) od_reduced = self.pcaimages[:,:,0:self.numsigpca] od_reduced = np.reshape(od_reduced, (npixels,self.numsigpca), order='F') #kmeans(obs,k_or_guess,iter=20,thresh=1e-5) self.indx = np.zeros(npixels) res, self.indx = kmeans2(od_reduced,5) self.indx = np.reshape(self.indx, (self.stack.n_cols, self.stack.n_rows), order='F') #----------------------------------------------------------------------------- # Spectral analysis # This routine reads in a mu spectrum in units of inverse microns. # The spectrum is interpolated onto the energy range of the stack, # and loaded into the matrix target_spectra(pca_gui_par.n_targets,n_ev). # If there is a PCA calculation done, we find the fits to the # target spectra from the components. def read_target_spectrum(self, filename = '', flat = False): # Load spectrum from a file spectrum_evdata = 0 spectrum_data = 0 spectrum_common_name = ' ' if flat == False: fn = os.path.basename(str(filename)) basename, extension = os.path.splitext(fn) if extension == '.csv': spectrum_evdata, spectrum_data, spectrum_common_name = self.stack.read_csv(filename) elif extension == '.xas': spectrum_evdata, spectrum_data, spectrum_common_name = self.stack.read_xas(filename) elif extension == '.txt': spectrum_evdata, spectrum_data, spectrum_common_name = self.stack.read_txt(filename) # Map this spectrum onto our energy range - interpolate to ev ftspec = scipy.interpolate.interp1d(spectrum_evdata, spectrum_data, kind='cubic', bounds_error=False) target_spectrum = np.reshape(ftspec(self.stack.ev), (1,self.stack.n_ev)) #fix the edges if needed if self.stack.ev[0]spectrum_evdata[-1]: indx = np.where(self.stack.ev>spectrum_evdata[-1]) target_spectrum[0,indx] = spectrum_data[-1] else: target_spectrum = np.ones((1,self.stack.n_ev)) spectrum_common_name = 'Flat' if self.tspectrum_loaded == 0: self.target_spectra = target_spectrum self.tspectrum_loaded = 1 self.n_target_spectra += 1 else: self.target_spectra = np.vstack((self.target_spectra,target_spectrum)) self.n_target_spectra += 1 self.tspec_names.append(spectrum_common_name) self.fit_target_spectra() self.calc_svd_maps() #----------------------------------------------------------------------------- def add_cluster_target_spectra(self): # Load spectrum from a file or cluster spectra for i in range(self.nclusters): target_spectrum = self.clusterspectra[i,:] if self.tspectrum_loaded == 0: self.target_spectra = target_spectrum self.tspectrum_loaded = 1 self.n_target_spectra += 1 else: self.target_spectra = np.vstack((self.target_spectra,target_spectrum)) self.n_target_spectra += 1 self.tspec_names.append('Cluster '+str(i+1)) self.fit_target_spectra() self.calc_svd_maps() #----------------------------------------------------------------------------- def remove_spectrum(self, i_spec): if self.n_target_spectra > 1: self.target_spectra = np.delete(self.target_spectra, i_spec, axis=0) del self.tspec_names[i_spec] self.n_target_spectra -= 1 self.fit_target_spectra() self.calc_svd_maps() else: self.target_spectra = [] self.tspec_names = [] self.tspectrum_loaded = 0 self.n_target_spectra = 0 self.target_svd_maps4D = [] self.original_svd_maps4D = [] self.target_pcafit_maps4D = [] self.original_fit_maps4D = [] self.target_pcafit_coeffs4D = [] self.target_pcafit_spectra4D = [] #----------------------------------------------------------------------------- def move_spectrum(self, old_position, new_position): temp_target_spectra = self.target_spectra.copy() temp_target_spectra[old_position,:] = self.target_spectra[new_position,:] temp_target_spectra[new_position,:] = self.target_spectra[old_position,:] self.target_spectra = temp_target_spectra temp_tspec_name = self.tspec_names[new_position] self.tspec_names[new_position] = self.tspec_names[old_position] self.tspec_names[old_position] = temp_tspec_name self.fit_target_spectra() self.calc_svd_maps() #----------------------------------------------------------------------------- # This routine calculates: # - the transformation matrix T as target_spectrumfit_coeffs, and # the fits to the target spectra as target_fittedspectra # - the inverse of T # - the eigenvector matrix C(S_abstract,N) by transposing the matrix # CT(N,S_abstract)=evecs(N,S_abstract) # - the target maps t(P,S_targets) as targetfit_maps def fit_target_spectra(self): # We want to find T(S_physical,S_abstract) which is # CT(N,S_abstract)##mu(S_physical,N). But mu(S_physical,N) is # known here as target_spectra(S_physical,N), and # CT(N,S_abstract) is just a limited version of evecs(N,S). # We will call T(S_physical,S_abstract) by the name # target_spectrumfit_coeffs(S_physical,S_abstract). if self.pca_calculated == 0: return CT = self.eigenvecs[:,0:self.numsigpca] self.target_pcafit_coeffs = np.dot(self.target_spectra, CT ) # Now we get the target spectra as approximated from our # components with # mu(S_physical,N)=C(S_abstract,N)##T(S_physical,S_abstract). self.target_pcafit_spectra = np.dot(self.target_pcafit_coeffs, CT.T) # To get the maps, we need to find R(P,Sbar_abstract) # from ct(N,Sbar_abstract)##d(P,N), and we also # need to invert the transformation matrix. Start by # finding the singular value decomposition of the # transformation matrix. U, s, V = np.linalg.svd(self.target_pcafit_coeffs, full_matrices=False) # This gives T^{-1}(Sbar_abstract,S_physical) t_inverse = np.dot(np.dot(V.T, np.linalg.inv(np.diag(s))), U.T) # This is R(P,Sbar_abstract)=CT(N,Sbar_abstract)##D(P,N) r_matrix = np.dot(self.stack.od, CT) # and this gives us the maps as # t(P,S_physical) = T^{-1}(Sbar_abstract,S_physical)##R(P,Sbar_abstract) # but in fact it is t(P,n_targets)! self.target_pcafit_maps = np.dot(r_matrix, t_inverse) self.target_pcafit_maps = np.reshape(self.target_pcafit_maps, (self.stack.n_cols, self.stack.n_rows, self.n_target_spectra), order='F') self.original_fit_maps = self.target_pcafit_maps.copy() #Find fit errors self.target_rms = (self.target_spectra-self.target_pcafit_spectra)**2 self.target_rms = np.sqrt(np.sum(self.target_rms, axis=1)/self.stack.n_ev) return #----------------------------------------------------------------------------- # This routine calculates the SVD composition maps # 1. The optical density is calculated from the stack, and the # data matrix D of dimensions (pixels,energies) is formed # 2. mu_inverse is calculated using SVD # 3. svd_maps is calculated from multiplying mu_inverse ## d def calc_svd_maps(self, usefittedspectra = False): if usefittedspectra: U, s, V = np.linalg.svd(self.target_pcafit_spectra, full_matrices=False) else: U, s, V = np.linalg.svd(self.target_spectra, full_matrices=False) mu_inverse = t_inverse = np.dot(np.dot(V.T, np.linalg.inv(np.diag(s))), U.T) self.target_svd_maps = np.dot(self.stack.od, mu_inverse) self.target_svd_maps = np.reshape(self.target_svd_maps, (self.stack.n_cols, self.stack.n_rows, self.n_target_spectra), order='F') self.original_svd_maps = self.target_svd_maps.copy() #---------------------------------------------------------------------- # Calculate composition maps for 4D data def calculate_targetmaps_4D(self): n_pix = self.stack.n_cols*self.stack.n_rows self.target_svd_maps4D = [] self.original_svd_maps4D = [] self.target_pcafit_maps4D = [] self.original_fit_maps4D = [] self.target_pcafit_coeffs4D = [] self.target_pcafit_spectra4D = [] tempod = self.stack.od.copy() for jth in range(self.stack.n_theta): od3d = self.stack.od4D[:,:,:,jth] od = od3d.copy() self.stack.od = np.reshape(od, (n_pix, self.stack.n_ev), order='F') if len(self.eigenvecs4D) > 0: self.eigenvecs = self.eigenvecs4D[jth] self.fit_target_spectra() self.calc_svd_maps() self.target_svd_maps4D.append(self.target_svd_maps) self.original_svd_maps4D.append(self.original_svd_maps) if len(self.eigenvecs4D) > 0: self.target_pcafit_maps4D.append(self.target_pcafit_maps) self.original_fit_maps4D.append(self.original_fit_maps) self.target_pcafit_coeffs4D.append(self.target_pcafit_coeffs) self.target_pcafit_spectra4D.append(self.target_pcafit_spectra) self.stack.od = tempod #----------------------------------------------------------------------------- # Apply threshold on SVD or PCA maps def svd_map_threshold(self, cutoff1, cutoff2 = None, svd = False, pca = False): if svd: self.target_svd_maps = self.original_svd_maps.copy() self.target_svd_maps.clip(min=cutoff1, out=self.target_svd_maps) if cutoff2 != None: self.target_svd_maps.clip(max=cutoff2, out=self.target_svd_maps) if len(self.target_svd_maps4D) > 0: self.target_svd_maps4D = copy.deepcopy(self.original_svd_maps4D) if cutoff2 != None: maxclip = cutoff2 else: maxclip = np.amax(self.target_svd_maps4D) self.target_svd_maps4D = np.clip(self.target_svd_maps4D, cutoff1, maxclip) if pca: self.target_pcafit_maps = self.original_fit_maps.copy() self.target_pcafit_maps.clip(min=cutoff1, out=self.target_pcafit_maps) if cutoff2 != None: self.target_pcafit_maps.clip(max=cutoff2, out=self.target_pcafit_maps) if len(self.target_pcafit_maps) > 0: self.target_pcafit_maps4D = copy.deepcopy(self.original_fit_maps4D) if cutoff2 != None: maxclip = cutoff2 else: maxclip = np.amax(self.target_pcafit_maps4D) self.target_pcafit_maps4D = np.clip(self.target_pcafit_maps4D, cutoff1, maxclip) #----------------------------------------------------------------------------- # Find key energies by finding peaks and valleys in significant pca spectra def calc_key_engs(self, threshold): key_engs = [] for i in range(self.numsigpca): pcaspectrum = self.eigenvecs[:,i] pmax,pmin = self.find_peaks(pcaspectrum, threshold, x = self.stack.ev) for i in range(len(pmin)): key_engs.append(pmin[i][0]) for i in range(len(pmax)): key_engs.append(pmax[i][0]) key_engs = np.array(key_engs) #Sort the energies and remove double entries key_engs = np.unique(key_engs) return key_engs #----------------------------------------------------------------------------- #Peakfinder def find_peaks(self, v, delta, x = None): """ Converted from MATLAB script at http://billauer.co.il/peakdet.html by endolith https://gist.github.com/250860 Returns two arrays function [maxtab, mintab]=peakdet(v, delta, x) %PEAKDET Detect peaks in a vector % [MAXTAB, MINTAB] = PEAKDET(V, DELTA) finds the local % maxima and minima ("peaks") in the vector V. % MAXTAB and MINTAB consists of two columns. Column 1 % contains indices in V, and column 2 the found values. % % With [MAXTAB, MINTAB] = PEAKDET(V, DELTA, X) the indices % in MAXTAB and MINTAB are replaced with the corresponding % X-values. % % A point is considered a maximum peak if it has the maximal % value, and was preceded (to the left) by a value lower by % DELTA. % Eli Billauer, 3.4.05 (Explicitly not copyrighted). % This function is released to the public domain; Any use is allowed. """ maxtab = [] mintab = [] if x is None: x = np.arange(len(v)) v = np.asarray(v) if len(v) != len(x): print ('Input vectors v and x must have same length') return -1 if not np.isscalar(delta): print ('Input argument delta must be a scalar') return -1 if delta <= 0: print ('Input argument delta must be positive') return -1 mn, mx = np.Inf, -np.Inf mnpos, mxpos = np.NaN, np.NaN lookformax = True for i in np.arange(len(v)): this = v[i] if this > mx: mx = this mxpos = x[i] if this < mn: mn = this mnpos = x[i] if lookformax: if this < mx-delta: maxtab.append((mxpos, mx)) mn = this mnpos = x[i] lookformax = False else: if this > mn+delta: mintab.append((mnpos, mn)) mx = this mxpos = x[i] lookformax = True return np.array(maxtab), np.array(mintab) #---------------------------------------------------------------------- def load_xraypeakfit_spectrum(self, filename): # Load spectrum from a file spectrum_evdata = 0 spectrum_data = 0 spectrum_common_name = ' ' spectrum_evdata, spectrum_data, spectrum_common_name = self.stack.read_csv(filename) if self.stack.n_ev > 0: # Map this spectrum onto our energy range - interpolate to ev ftspec = scipy.interpolate.interp1d(spectrum_evdata, spectrum_data, kind='cubic', bounds_error=False) xfit_spectrum = np.reshape(ftspec(self.stack.ev), (1,self.stack.n_ev)) else: self.stack.ev = spectrum_evdata self.stack.n_ev = len(self.stack.ev) xfit_spectrum = np.reshape(spectrum_data, (1,self.stack.n_ev)) #fix the edges if needed if self.stack.ev[0]spectrum_evdata[-1]: indx = np.where(self.stack.ev>spectrum_evdata[-1]) xfit_spectrum[0,indx] = spectrum_data[-1] if self.xrayfitsp_loaded == 0: self.xrayfitspectra = xfit_spectrum self.xrayfitsp_loaded = 1 self.n_xrayfitsp = 1 else: self.xrayfitspectra = np.vstack((self.xrayfitspectra,xfit_spectrum)) self.n_xrayfitsp += 1 if spectrum_common_name == ' ': spectrum_common_name = 'Spectrum %d' % (self.n_xrayfitsp) self.xfspec_names.append(spectrum_common_name) self.xfitpars.append(Cfitparams()) #Find peaks: self.init_fit_params(self.n_xrayfitsp-1) #---------------------------------------------------------------------- #Load spectra from cluster analysis def load_xraypeakfit_clusterspectrum(self, i_cluster): xfit_spectrum = self.clusterspectra[i_cluster,:].copy() xfit_spectrum = np.reshape(xfit_spectrum, (1,self.stack.n_ev)) spectrum_common_name = 'Cluster '+str(i_cluster+1) if self.xrayfitsp_loaded == 0: self.xrayfitspectra = xfit_spectrum.copy() self.xrayfitsp_loaded = 1 self.n_xrayfitsp = 1 else: self.xrayfitspectra = np.vstack((self.xrayfitspectra, xfit_spectrum)) self.n_xrayfitsp += 1 self.xfspec_names.append(spectrum_common_name) self.xfitpars.append(Cfitparams()) #Find peaks: self.init_fit_params(self.n_xrayfitsp-1) #---------------------------------------------------------------------- def init_fit_params(self, index): pmax,pmin = self.find_peaks(self.xrayfitspectra[index], 0.03, x = self.stack.ev) fp = self.xfitpars[index] peakengs = [] if len(pmax) > 0: for i in range(12): if i < len(pmax): peakengs.append(pmax[i][0]) else: peakengs.append(0) else: delta = int(self.stack.n_ev/13) for i in range(12): peakengs.append(self.stack.ev[delta*i]) fp.stepfitparams = [peakengs[0], 0.5, 3.0, peakengs[1], 0.5, 3.0] for i in range(12): fp.gauss_fp_a[i] = 1.0 fp.gauss_fp_m[i] = peakengs[i] fp.gauss_fp_s[i] = 0.5 fp.base = np.mean(self.xrayfitspectra[index][0:5]) self.set_init_fit_params(index, fp.base, fp.stepfitparams, fp.gauss_fp_a, fp.gauss_fp_m, fp.gauss_fp_s) return fp.base, fp.stepfitparams, fp.gauss_fp_a, fp.gauss_fp_m, fp.gauss_fp_s #---------------------------------------------------------------------- def set_init_fit_params(self, index, base, stepfitparams, peak_a, peak_m, peak_s): self.xfitpars[index].base = base self.xfitpars[index].stepfitparams = stepfitparams for i in range(12): self.xfitpars[index].gauss_fp_a[i] = peak_a[i] self.xfitpars[index].gauss_fp_m[i] = peak_m[i] self.xfitpars[index].gauss_fp_s[i] = peak_s[i] return #---------------------------------------------------------------------- def fit_spectrum(self, i_spec, nsteps, npeaks): xfit_spectrum = self.xrayfitspectra[i_spec] fp = self.xfitpars[i_spec] p = [] self.nsteps = nsteps self.npeaks = npeaks p.append(fp.base) for i in range(nsteps*3): p.append(fp.stepfitparams[i]) for i in range(npeaks): p.append(fp.gauss_fp_a[i]) p.append(fp.gauss_fp_m[i]) p.append(fp.gauss_fp_s[i]) #p2, success = optimize.leastsq(model_error, p[:], args=(nsteps, npeaks, np.array(self.stack.ev).astype(np.float64), np.array(xfit_spectrum).astype(np.float64))) bounds=[] #base can be a negative number bounds.append((fp.base-0.5,fp.base+0.5)) for i in range(1,len(p)): bmin = 0 bmax = None bounds.append((bmin,bmax)) p2, success = leastsqbound(model_error, p[:], bounds, args=(nsteps, npeaks, np.array(self.stack.ev).astype(np.float64), np.array(xfit_spectrum).astype(np.float64))) fp.stepfitparams = np.zeros((8)) fp.gauss_fp_a = np.zeros((12)) fp.gauss_fp_m = np.zeros((12)) fp.gauss_fp_s = np.zeros((12)) fp.base = p2[0] fp.stepfitparams[0:nsteps*3] = p2[1:nsteps*3+1] for i in range(npeaks): fp.gauss_fp_a[i] = p2[nsteps*3+1+i*3] fp.gauss_fp_m[i] = p2[nsteps*3+2+i*3] fp.gauss_fp_s[i] = p2[nsteps*3+3+i*3] y = model(p2, nsteps, npeaks, self.stack.ev) separate_y = [] #Add base y1 = np.ones((self.stack.ev.size))*fp.base separate_y.append(y1) #Add step for i in range(nsteps): y1 = np.zeros((self.stack.ev.size)) istepfitparams = p2[3*i+1:3*i+4] for i in range(self.stack.ev.size): y1[i] = stepfunc(istepfitparams, self.stack.ev[i]) separate_y.append(y1) #Add peaks pg = nsteps*3+1 for i in range(npeaks): pp = [p2[pg+i*3],p2[pg+1+i*3],p2[pg+2+i*3]] y1 = gaussian(pp,self.stack.ev) separate_y.append(y1) return y, separate_y #---------------------------------------------------------------------- # Calculate Fast Independent Component Analysis; FASTICA uses Hyvarinen's # fixed-point algorithm # A. Hyvarinen. Fast and Robust Fixed-Point Algorithms for Independent # Component Analysis. IEEE Transactions on Neural Networks 10(3):626-634, 1999. def calculate_fastica(self, mixedsig, numOfIC): mixedsig = mixedsig.transpose() print ('msig', mixedsig.shape) # Remove the mean and check the data mixedmean = np.mean(mixedsig, axis=1) mixedsig = mixedsig - mixedmean[:,np.newaxis] Dim = mixedsig.shape[0] NumOfSampl = mixedsig.shape[1] print ('Dim, NumOfSampl',Dim,NumOfSampl) # Default values for optional parameters verbose = True # Default values for 'pcamat' parameters firstEig = 1 lastEig = Dim interactivePCA = 'off' # Default values for 'fpica' parameters approach = 'defl' g = 'pow3' finetune = 'off' a1 = 1 a2 = 1 myy = 1 stabilization = 'off' epsilon = 0.0001 maxNumIterations = 1000 maxFinetune = 5 initState = 'rand' guess = 0 sampleSize = 1 displayMode = 'off' displayInterval = 1 # Parameters for fastICA b_verbose = True # print information about data if b_verbose: print ('Number of signals:', Dim) print ('Number of samples: ', NumOfSampl) # Check if the data has been entered the wrong way, # but warn only... it may be on purpose if (Dim > NumOfSampl): if b_verbose: print ('Warning: ') print ('The signal matrix may be oriented in the wrong way.') print ('In that case transpose the matrix.') # Calculating PCA # We already have the PCA data if b_verbose: print ('Values for PCA calculations supplied.\n') print ('PCA calculations not needed.\n') # PCA was already calculated: D = np.identity(self.numsigpca)*self.eigenvals[0:self.numsigpca] E = self.eigenvecs[:,0:self.numsigpca] # Calculate the whitening # Calculate the whitening and dewhitening matrices (these handle # dimensionality simultaneously). whiteningMatrix = mmult(np.linalg.inv (np.sqrt(D)), E.transpose()) dewhiteningMatrix = mmult(E, np.sqrt(D)) print ('wd=', whiteningMatrix.shape, dewhiteningMatrix.shape) # Project to the eigenvectors of the covariance matrix. # Whiten the samples and reduce dimension simultaneously. if b_verbose: print ('Whitening...') whitesig = np.dot(whiteningMatrix,mixedsig) print ('whitesig', whitesig.shape) # Just some security... if np.sum(np.imag(whitesig)) != 0: print ('Whitened vectors have imaginary values.') # Calculating the ICA # Check some parameters # The dimension of the data may have been reduced during PCA calculations. # The original dimension is calculated from the data by default, and the # number of IC is by default set to equal that dimension. Dim = whitesig.shape[0] # The number of IC's must be less or equal to the dimension of data if numOfIC > Dim: numOfIC = Dim # Show warning only if verbose = 'on' and user supplied a value for 'numOfIC' if b_verbose: print( 'Warning: estimating only ,', numOfIC, ' independent components' ) print( '(Cannot estimate more independent components than dimension of data)') # Calculate the ICA with fixed point algorithm. A, W = self.calc_fpica (whitesig, whiteningMatrix, dewhiteningMatrix, approach, numOfIC, g, finetune, a1, a2, myy, stabilization, epsilon, maxNumIterations, maxFinetune, initState, guess, sampleSize, displayMode, displayInterval, verbose) print ('A,W', A.shape, W.shape) # Check for valid return if W.any(): # Add the mean back in. if b_verbose: print ('Adding the mean back to the data.') icasig = mmult(W, mixedsig) + mmult(mmult(W, mixedmean), np.ones((1, NumOfSampl))) else: icasig = [] return icasig #---------------------------------------------------------------------- def getSamples(self, max, percentage): Samples = (np.random.random((max,)) < percentage).nonzero() return Samples #---------------------------------------------------------------------- # Fixed point ICA # This function is adapted from Hyvarinen's fixed point algorithm Matlab version # [A, W] = fpica(whitesig, whiteningMatrix, dewhiteningMatrix, approach, # numOfIC, g, finetune, a1, a2, mu, stabilization, epsilon, # maxNumIterations, maxFinetune, initState, guess, sampleSize, # displayMode, displayInterval, verbose); # # Perform independent component analysis using Hyvarinen's fixed point # algorithm. Outputs an estimate of the mixing matrix A and its inverse W. # # whitesig :the whitened data as row vectors # whiteningMatrix :whitening matrix # dewhiteningMatrix :dewhitening matrix # approach [ 'symm' | 'defl' ] :the approach used (deflation or symmetric) # numOfIC [ 0 - Dim of whitesig ] :number of independent components estimated # g [ 'pow3' | 'tanh' | :the nonlinearity used # 'gaus' | 'skew' ] # finetune [same as g + 'off'] :the nonlinearity used in finetuning. # a1 :parameter for tuning 'tanh' # a2 :parameter for tuning 'gaus' # mu :step size in stabilized algorithm # stabilization [ 'on' | 'off' ] :if mu < 1 then automatically on # epsilon :stopping criterion # maxNumIterations :maximum number of iterations # maxFinetune :maximum number of iterations for finetuning # initState [ 'rand' | 'guess' ] :initial guess or random initial state. See below # guess :initial guess for A. Ignored if initState = 'rand' # sampleSize [ 0 - 1 ] :percentage of the samples used in one iteration # displayMode [ 'signals' | 'basis' | :plot running estimate # 'filters' | 'off' ] # displayInterval :number of iterations we take between plots # verbose [ 'on' | 'off' ] :report progress in text format def calc_fpica(self, X, whiteningMatrix, dewhiteningMatrix, approach, numOfIC, g, finetune, a1, a2, myy, stabilization, epsilon, maxNumIterations, maxFinetune, initState, guess, sampleSize, displayMode, displayInterval, b_verbose): vectorSize = X.shape[0] numSamples = X.shape[1] # Checking the value for approach if approach == 'symm': approachMode = 1 elif approach == 'defl': approachMode = 2 else: print ('Illegal value for parameter approach:', approach) return if b_verbose: print ('Used approach:', approach) #Checking the value for numOfIC if vectorSize < numOfIC: print ('Must have numOfIC <= Dimension!') return # Checking the sampleSize if sampleSize > 1: sampleSize = 1 if b_verbose: print ('Warning: Setting sampleSize to 1.\n') elif sampleSize < 1: if (sampleSize * numSamples) < 1000: sampleSize = np.min(1000/numSamples, 1) if b_verbose: print ('Warning: Setting ampleSize to ',sampleSize,' samples=', np.floor(sampleSize * numSamples)) print ('sample size', sampleSize) if b_verbose and (sampleSize < 1): print ('Using about ',sampleSize*100,' of the samples in random order in every step.') # Checking the value for nonlinearity. if g == 'pow3': gOrig = 10 elif g =='tanh': gOrig = 20 elif g == 'gauss': gOrig = 30 elif g == 'skew': gOrig = 40 else: print ('Illegal value for parameter g: ', g) if sampleSize != 1: gOrig = gOrig + 2 if myy != 1: gOrig = gOrig + 1 if b_verbose: print ('Used nonlinearity: ', g) finetuningEnabled = 1 if finetune == 'pow3': gFine = 10 + 1 elif finetune == 'tanh': gFine = 20 + 1 elif finetune == 'gauss': gFine = 30 + 1 elif finetune == 'skew': gFine = 40 + 1 elif finetune == 'off': if myy != 1: gFine = gOrig else : gFine = gOrig + 1 finetuningEnabled = 0 else: print ('Illegal value for parameter finetune :', finetune) return if b_verbose and finetuningEnabled: print ('Finetuning enabled, nonlinearity: ', finetune) if stabilization == 'on': stabilizationEnabled = 1 elif stabilization == 'off': if myy != 1: stabilizationEnabled = 1 else: stabilizationEnabled = 0 else: print ('Illegal value for parameter stabilization: ', stabilization) if b_verbose and stabilizationEnabled: print ('Using stabilized algorithm.') # Some other parameters myyOrig = myy # When we start fine-tuning we'll set myy = myyK * myy myyK = 0.01 # How many times do we try for convergence until we give up. failureLimit = 5 usedNlinearity = gOrig stroke = 0 notFine = 1 long = 0 # Checking the value for initial state. if initState == 'rand': initialStateMode = 0; elif initState == 'guess': if guess.shape[0] != whiteningMatrix.shape[1]: initialStateMode = 0 if b_verbose: print ('Warning: size of initial guess is incorrect. Using random initial guess.') else: initialStateMode = 1 if guess.shape[0] < numOfIC: if b_verbose: print ('Warning: initial guess only for first ',guess.shape[0],' components. Using random initial guess for others.') guess[:, guess.shape[0] + 1:numOfIC] = np.random.uniform(-0.5,0.5,(vectorSize,numOfIC-guess.shape[0])) elif guess.shape[0]>numOfIC: guess=guess[:,1:numOfIC] print ('Warning: Initial guess too large. The excess column are dropped.') if b_verbose: print( 'Using initial guess.') else: print ('Illegal value for parameter initState:', initState) return # Checking the value for display mode. if (displayMode =='off') or (displayMode == 'none'): usedDisplay = 0 elif (displayMode =='on') or (displayMode == 'signals'): usedDisplay = 1 if (b_verbose and (numSamples > 10000)): print ('Warning: Data vectors are very long. Plotting may take long time.') if (b_verbose and (numOfIC > 25)): print ('Warning: There are too many signals to plot. Plot may not look good.') elif (displayMode =='basis'): usedDisplay = 2 if (b_verbose and (numOfIC > 25)): print( 'Warning: There are too many signals to plot. Plot may not look good.') elif (displayMode =='filters'): usedDisplay = 3 if (b_verbose and (vectorSize > 25)): print ('Warning: There are too many signals to plot. Plot may not look good.') else: print( 'Illegal value for parameter displayMode:', displayMode) return # The displayInterval can't be less than 1... if displayInterval < 1: displayInterval = 1 # Start ICA calculation if b_verbose: print ('Starting ICA calculation...') # SYMMETRIC APPROACH if approachMode == 1: print ('Symmetric approach under construction') return # DEFLATION APPROACH elif approachMode == 2: B = np.zeros((numOfIC, numOfIC)) # The search for a basis vector is repeated numOfIC times. round = 0 numFailures = 0 while (round < numOfIC): myy = myyOrig usedNlinearity = gOrig stroke = 0 notFine = 1 long = 0 endFinetuning = 0 # Show the progress... if b_verbose: print ('IC :', round) # Take a random initial vector of length 1 and orthogonalize it # with respect to the other vectors. if initialStateMode == 0: w = np.random.standard_normal((vectorSize,)) elif initialStateMode == 1: w=mmult(whiteningMatrix,guess[:,round]) w = w - mmult(mmult(B, B.T), w) norm = sp.sqrt((w*w).sum()) w = w / norm wOld = np.zeros(w.shape) wOld2 = np.zeros(w.shape) # This is the actual fixed-point iteration loop. # for i = 1 : maxNumIterations + 1 i = 1 gabba = 1 while (i <= (maxNumIterations + gabba)): if (usedDisplay > 0): print ('display') #Project the vector into the space orthogonal to the space # spanned by the earlier found basis vectors. Note that we can do # the projection with matrix B, since the zero entries do not # contribute to the projection. w = w - mmult(mmult(B, B.T), w) norm = sp.sqrt((w*w).sum()) w = w / norm if notFine: if i == (maxNumIterations + 1): if b_verbose: print ('Component number',round,' did not converge in ',maxNumIterations, 'iterations.') round = round - 1 numFailures = numFailures + 1 if numFailures > failureLimit: if b_verbose: print ('Too many failures to converge ', numFailures,' Giving up.') if round == 0: A=[] W=[] return break else: if i >= endFinetuning: #So the algorithm will stop on the next test... wOld = w.copy() # Show the progress... if b_verbose: print( '.') # Test for termination condition. Note that the algorithm has # converged if the direction of w and wOld is the same, this # is why we test the two cases. normm = sp.sqrt(((w - wOld)*(w - wOld)).sum()) normp = sp.sqrt(((w + wOld)*(w + wOld)).sum()) conv = min((normm), normp) if (conv < epsilon): if finetuningEnabled and notFine: if b_verbose: print ('Initial convergence, fine-tuning: ') notFine = 0 gabba = maxFinetune wOld = np.zeros(w.shape) wOld2 = np.zeros(w.shape) usedNlinearity = gFine myy = myyK * myyOrig endFinetuning = maxFinetune + i else: numFailures = 0 # Save the vector B[:, round] = w.copy() # Calculate the de-whitened vector. A = np.dot(dewhiteningMatrix, w) # Calculate ICA filter. W = np.dot(w.transpose(), whiteningMatrix) # Show the progress... if b_verbose: print ('computed ( ',i,' steps ) ') break elif stabilizationEnabled: if (not stroke) and (np.linalg.norm(w - wOld2) < epsilon or np.linalg.norm(w + wOld2) < epsilon): stroke = myy if b_verbose: print ('Stroke!') myy = .5*myy if np.mod(usedNlinearity,2) == 0: usedNlinearity = usedNlinearity + 1 elif stroke: myy = stroke stroke = 0 if (myy == 1) and (np.mod(usedNlinearity,2) != 0): usedNlinearity = usedNlinearity - 1 elif (notFine) and (not long) and (i > maxNumIterations / 2): if b_verbose: print( 'Taking long (reducing step size) ') long = 1 myy = .5*myy if np.mod(usedNlinearity,2) == 0: usedNlinearity = usedNlinearity + 1 wOld2 = wOld wOld = w # pow3 if usedNlinearity == 10: u = mmult(X.T, w) w = mmult(X, u*u*u)/numSamples - 3.*w elif usedNlinearity == 11: u = mmult(X.T, w) EXGpow3 = mmult(X, u*u*u)/numSamples Beta = mmult(w.T, EXGpow3) w = w - myy * (EXGpow3 - Beta*w)/(3-Beta) elif usedNlinearity == 12: Xsub = self._get_rsamples(X) u = mmult(Xsub.T, w) w = mmult(Xsub, u*u*u)/Xsub.shape[1] - 3.*w elif usedNlinearity == 13: Xsub=X[:,self.getSamples(numSamples, sampleSize)] u = mmult(Xsub.T, w) EXGpow3 = mmult(Xsub, u*u*u)/Xsub.shape[1] Beta = mmult(w.T, EXGpow3) w = w - myy * (EXGpow3 - Beta*w)/(3-Beta) # tanh elif usedNlinearity == 20: u = mmult(X.T, w) tang = sp.tanh(a1 * u) temp = mmult((1. - tang*tang).sum(axis=0), w) w = (mmult(X, tang) - a1*temp)/numSamples elif usedNlinearity == 21: u = mmult(X.T, w) tang = sp.tanh(a1 * u) Beta = mmult(u.T, tang) temp = (1. - tang*tang).sum(axis=0) w = w-myy*((mmult(X, tang)-Beta*w)/(a1*temp-Beta)) elif usedNlinearity == 22: Xsub=X[:,self.getSamples(numSamples, sampleSize)] u = mmult(Xsub.T, w) tang = sp.tanh(a1 * u) temp = mmult((1. - tang*tang).sum(axis=0), w) w = (mmult(Xsub, tang) - a1*temp)/Xsub.shape[1] elif usedNlinearity == 23: Xsub=X[:,self.getSamples(numSamples, sampleSize)] u = mmult(Xsub.T, w) tang = sp.tanh(a1 * u) Beta = mmult(u.T, tang) w = w - myy * ((mmult(Xsub, tang)-Beta*w) / (a1*(1. - tang*tang).sum(axis=0) - Beta)) # gauss elif usedNlinearity == 30: # This has been split for performance reasons. u = mmult(X.T, w) u2 = u*u ex = sp.exp(-a2*u2*0.5) gauss = u*ex dgauss = (1. - a2 *u2)*ex w = (mmult(X, gauss)-mmult(dgauss.sum(axis=0), w))/numSamples elif usedNlinearity == 31: u = mmult(X.T, w) u2 = u*u ex = sp.exp(-a2*u2*0.5) gauss = u*ex dgauss = (1. - a2 *u2)*ex Beta = mmult(u.T, gauss) w = w - myy*((mmult(X, gauss)-Beta*w)/ (dgauss.sum(axis=0)-Beta)) elif usedNlinearity == 32: Xsub=X[:,self.getSamples(numSamples, sampleSize)] u = mmult(Xsub.T, w) u2 = u*u ex = sp.exp(-a2*u2*0.5) gauss = u*ex dgauss = (1. - a2 *u2)*ex w = (mmult(Xsub, gauss)- mmult(dgauss.sum(axis=0), w))/Xsub.shape[1] elif usedNlinearity == 33: Xsub=X[:,self.getSamples(numSamples, sampleSize)] u = mmult(Xsub.T, w) u2 = u*u ex = sp.exp(-a2*u2*0.5) gauss = u*ex dgauss = (1. - a2 *u2)*ex Beta = mmult(u.T, gauss) w = w - myy*((mmult(Xsub, gauss)-Beta*w)/ (dgauss.sum(axis=0)-Beta)) # skew elif usedNlinearity == 40: u = mmult(X.T, w) w = mmult(X, u*u)/numSamples elif usedNlinearity == 41: u = mmult(X.T, w) EXGskew = mmult(X, u*u) / numSamples Beta = mmult(w.T, EXGskew) w = w - myy * (EXGskew - mmult(Beta, w))/(-Beta) elif usedNlinearity == 42: Xsub=X[:,self.getSamples(numSamples, sampleSize)] u = mmult(Xsub.T, w) w = mmult(Xsub, u*u)/Xsub.shape[1] elif usedNlinearity == 43: Xsub=X[:,self.getSamples(numSamples, sampleSize)] u = mmult(Xsub.T, w) EXGskew = mmult(Xsub, u*u) / Xsub.shape[1] Beta = mmult(w.T, EXGskew) w = w - myy * (EXGskew - Beta*w)/(-Beta) else: print ('Code for desired nonlinearity not found!') return # Normalize the new w. norm = sp.sqrt((w*w).sum()) w = w / norm i = i + 1 round = round + 1 if b_verbose: print ('Done.') # In the end let's check the data for some security if A.imag.any(): if b_verbose: print ('Warning: removing the imaginary part from the result.') A = A.real W = W.imag return A, W """ Constrained multivariate Levenberg-Marquardt optimization """ from scipy.optimize import leastsq def internal2external_grad(xi,bounds): """ Calculate the internal to external gradiant Calculates the partial of external over internal """ ge = np.empty_like(xi) for i,(v,bound) in enumerate(zip(xi,bounds)): a = bound[0] # minimum b = bound[1] # maximum if a == None and b == None: # No constraints ge[i] = 1.0 elif b == None: # only min ge[i] = v/np.sqrt(v**2+1) elif a == None: # only max ge[i] = -v/np.sqrt(v**2+1) else: # both min and max ge[i] = (b-a)*np.cos(v)/2. return ge def i2e_cov_x(xi,bounds,cov_x): grad = internal2external_grad(xi,bounds) grad = np.atleast_2d(grad) return np.dot(grad.T,grad)*cov_x def internal2external(xi,bounds): """ Convert a series of internal variables to external variables""" xe = np.empty_like(xi) for i,(v,bound) in enumerate(zip(xi,bounds)): a = bound[0] # minimum b = bound[1] # maximum if a == None and b == None: # No constraints xe[i] = v elif b == None: # only min xe[i] = a-1.+np.sqrt(v**2.+1.) elif a == None: # only max xe[i] = b+1.-np.sqrt(v**2.+1.) else: # both min and max xe[i] = a+((b-a)/2.)*( np.sin(v)+1.) return xe def external2internal(xe,bounds): """ Convert a series of external variables to internal variables""" xi = np.empty_like(xe) for i,(v,bound) in enumerate(zip(xe,bounds)): a = bound[0] # minimum b = bound[1] # maximum if a == None and b == None: # No constraints xi[i] = v elif b == None: # only min xi[i] = np.sqrt( (v-a+1.)**2.-1 ) elif a == None: # only max xi[i] = np.sqrt( (b-v+1.)**2.-1 ) else: # both min and max xi[i] = np.arcsin( (2.*(v-a)/(b-a))-1.) return xi def err(p,bounds,efunc,args): pe = internal2external(p,bounds) # convert to external variables return efunc(pe,*args) def calc_cov_x(infodic,p): """ Calculate cov_x from fjac, ipvt and p as is done in leastsq """ fjac = infodic['fjac'] ipvt = infodic['ipvt'] n = len(p) # adapted from leastsq function in scipy/optimize/minpack.py perm = np.take(np.eye(n),ipvt-1,0) r = np.triu(np.transpose(fjac)[:n,:]) R = np.dot(r,perm) #try: cov_x = np.linalg.inv(np.dot(np.transpose(R),R)) #except LinAlgError: # cov_x = None return cov_x def leastsqbound(func,x0,bounds,args=(),**kw): """ Constrained multivariant Levenberg-Marquard optimization Minimize the sum of squares of a given function using the Levenberg-Marquard algorithm. Contraints on parameters are inforced using variable transformations as described in the MINUIT User's Guide by Fred James and Matthias Winkler. Parameters: * func functions to call for optimization. * x0 Starting estimate for the minimization. * bounds (min,max) pair for each element of x, defining the bounds on that parameter. Use None for one of min or max when there is no bound in that direction. * args Any extra arguments to func are places in this tuple. Returns: (x,{cov_x,infodict,mesg},ier) Return is described in the scipy.optimize.leastsq function. x and con_v are corrected to take into account the parameter transformation, infodic is not corrected. Additional keyword arguments are passed directly to the scipy.optimize.leastsq algorithm. """ # check for full output if "full_output" in kw and kw["full_output"]: full=True else: full=False # convert x0 to internal variables i0 = external2internal(x0,bounds) # perfrom unconstrained optimization using internal variables r = leastsq(err,i0,args=(bounds,func,args),**kw) # unpack return convert to external variables and return if full: xi,cov_xi,infodic,mesg,ier = r xe = internal2external(xi,bounds) cov_xe = i2e_cov_x(xi,bounds,cov_xi) # XXX correct infodic 'fjac','ipvt', and 'qtf' return xe,cov_xe,infodic,mesg,ier else: xi,ier = r xe = internal2external(xi,bounds) return xe,ier ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1678706378.0 mantis-xray-3.1.15/mantis_xray/data_stack.py0000775000175000017500000010511314403603312020213 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2011 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # 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 . from __future__ import division from __future__ import print_function import numpy as np import scipy as sp import scipy.interpolate import scipy.ndimage import h5py import datetime import os from .file_plugins import file_stk from .file_plugins import file_sdf from .file_plugins import file_xrm from .file_plugins import file_ncb from .file_plugins import file_dataexch_hdf5 from . import data_struct # ---------------------------------------------------------------------- class data: def __init__(self, data_struct): self.data_struct = data_struct self.i0_dwell = None self.i0data = np.zeros(1) self.n_ev = 0 self.n_theta = 0 self.shifts = [] self.stack4D = None # ---------------------------------------------------------------------- def new_data(self): self.n_cols = 0 self.n_rows = 0 self.n_ev = 0 self.x_dist = 0 self.y_dist = 0 self.x_start = 0 self.x_stop = 0 self.y_start = 0 self.y_stop = 0 self.x_pxsize = 0 self.y_pxsize = 0 self.squarepx = True self.i0_dwell = None self.ev = 0 self.absdata = 0 self.i0data = np.zeros(1) self.evi0 = 0 self.od = 0 self.od3d = 0 self.xshifts = 0 self.yshifts = 0 self.shifts = [] self.stack4D = None self.n_theta = 0 self.theta = 0 self.od4D = 0 self.data_struct.spectromicroscopy.normalization.white_spectrum = None self.data_struct.spectromicroscopy.normalization.white_spectrum_energy = None self.data_struct.spectromicroscopy.normalization.white_spectrum_energy_units = None self.data_struct.spectromicroscopy.optical_density = None # ---------------------------------------------------------------------- def read_stk_i0(self, filename, extension): if extension == '.xas': file_stk.read_stk_i0_xas(self, filename) elif extension == '.csv': file_stk.read_stk_i0_csv(self, filename) self.calculate_optical_density() self.fill_h5_struct_normalization() # ---------------------------------------------------------------------- def read_sdf_i0(self, filename): file_sdf.read_sdf_i0(self, filename) self.calculate_optical_density() self.fill_h5_struct_normalization() # ---------------------------------------------------------------------- def read_xrm_ReferenceImages(self, filenames): self.calculate_optical_density_from_refimgs(filenames) self.fill_h5_struct_normalization() # ---------------------------------------------------------------------- def read_h54D(self, filename): file_dataexch_hdf5.read(filename, self) if self.data_struct.spectromicroscopy.normalization.white_spectrum is not None: self.calculate_optical_density() self.fill_h5_struct_normalization() self.scale_bar() # ---------------------------------------------------------------------- def read_ncb4D(self, filenames): self.new_data() file_ncb.read_ncb4D(self, filenames) now = datetime.datetime.now() self.data_struct.implements = 'information:exchange:spectromicroscopy' self.data_struct.version = '1.0' self.data_struct.information.file_creation_datetime = now.strftime("%Y-%m-%dT%H:%M") self.data_struct.information.comment = 'Converted in Mantis' self.data_struct.exchange.data = self.stack4D self.data_struct.exchange.data_signal = 1 self.data_struct.exchange.data_axes = 'x:y:energy:theta' self.data_struct.exchange.theta = np.array(self.theta) self.data_struct.exchange.theta_units = 'degrees' self.data_struct.exchange.x = self.x_dist self.data_struct.exchange.y = self.y_dist self.scale_bar() # ---------------------------------------------------------------------- def read_ncb4Denergy(self, filename): f = open(str(filename), 'rU') elist = [] for line in f: if line.startswith('*'): if 'Common name' in line: spectrum_common_name = line.split(':')[-1].strip() else: e, = [float(x) for x in line.split()] elist.append(e) self.ev = np.array(elist) f.close() self.n_ev = self.ev.size self.data_struct.exchange.energy = self.ev self.data_struct.exchange.energy_units = 'ev' # ---------------------------------------------------------------------- def read_dpt(self, filename): self.new_data() n_rows = 11 n_cols = 8 imgstack = np.zeros((n_rows, n_cols)) f = open(str(filename), 'r') elist = [] for line in f: if line.startswith("*"): pass else: x = line.split(',') e = float(x[0]) x = x[1:] data = [] for i in range(len(x)): data.append(float(x[i])) elist.append(e) data = np.array(data) data = np.reshape(data, (n_rows, n_cols), order='F') imgstack = np.dstack((imgstack, data)) imgstack = imgstack[:, :, 1:] f.close() self.n_cols = imgstack.shape[0] self.n_rows = imgstack.shape[1] self.n_ev = imgstack.shape[2] pixelsize = 1 # Since we do not have a scanning microscope we fill the x_dist and y_dist from pixel_size self.x_dist = np.arange(float(self.n_cols)) * pixelsize self.y_dist = np.arange(float(self.n_rows)) * pixelsize self.ev = np.array(elist) msec = np.ones((self.n_ev)) self.data_dwell = msec self.absdata = imgstack # Check if the energies are consecutive, if they are not sort the data sort = 0 for i in range(self.n_ev - 1): if self.ev[i] > self.ev[i + 1]: sort = 1 break if sort == 1: sortind = np.argsort(self.ev) self.ev = self.ev[sortind] self.absdata = self.absdata[:, :, sortind] # self.original_n_cols = imgstack.shape[0] # self.original_n_rows = imgstack.shape[1] # self.original_n_ev = imgstack.shape[2] # self.original_ev = self.ev.copy() # self.original_absdata = self.absdata.copy() self.fill_h5_struct_from_stk() self.scale_bar() # Fix the normalization self.evi0 = self.ev.copy() self.i0data = np.ones(self.n_ev) self.i0_dwell = self.data_dwell self.fill_h5_struct_normalization() # Optical density does not have to be calculated - use raw data self.od3d = self.absdata.copy() self.od = np.reshape(self.od3d, (n_rows * n_cols, self.n_ev), order='F') # ---------------------------------------------------------------------- def fill_h5_struct_from_stk(self): now = datetime.datetime.now() self.data_struct.implements = 'information:exchange:spectromicroscopy' self.data_struct.version = '1.0' self.data_struct.information.file_creation_datetime = now.strftime("%Y-%m-%dT%H:%M") self.data_struct.information.comment = 'Converted in Mantis' self.data_struct.exchange.data = self.absdata self.data_struct.exchange.data_signal = 1 self.data_struct.exchange.data_axes = 'x:y:energy' self.data_struct.exchange.energy = self.ev self.data_struct.exchange.energy_units = 'ev' self.data_struct.exchange.x = self.x_dist self.data_struct.exchange.y = self.y_dist # ---------------------------------------------------------------------- def fill_h5_struct_normalization(self): self.data_struct.spectromicroscopy.normalization.white_spectrum = self.i0data self.data_struct.spectromicroscopy.normalization.white_spectrum_energy = self.evi0 self.data_struct.spectromicroscopy.normalization.white_spectrum_energy_units = 'eV' if self.stack4D is None: self.data_struct.spectromicroscopy.optical_density = self.od else: self.data_struct.spectromicroscopy.optical_density = self.od4D # ---------------------------------------------------------------------- def calc_histogram(self): # calculate average flux for each pixel self.averageflux = np.nanmean(self.absdata, axis=2) self.histogram = self.averageflux return # ---------------------------------------------------------------------- def i0_from_histogram(self, i0_indices): self.evi0hist = self.ev.copy() # i0_indices = np.where((fluxmin<=self.averageflux)&(self.averageflux<=fluxmax)) self.evi0 = self.ev.copy() self.i0_dwell = self.data_dwell if self.stack4D is None: self.i0datahist = np.zeros((self.n_ev)) self.i0data = self.i0datahist if np.any(i0_indices): #invnumel = 1. / self.averageflux[i0_indices].shape[0] for ie in range(self.n_ev): thiseng_abs = self.absdata[:, :, ie] #self.i0datahist[ie] = np.sum(thiseng_abs[i0_indices]) * invnumel finite_vals = thiseng_abs[i0_indices][np.isfinite(thiseng_abs[i0_indices])] if len(finite_vals)>0: self.i0datahist[ie] = np.nanmean(finite_vals) else: self.i0datahist[ie] = self.i0datahist[ie-1] #If this fails on the first image then the data is probably completely empty anyway self.calculate_optical_density() else: self.i0datahist = np.zeros((self.n_ev, self.n_theta)) self.i0data = self.i0datahist self.od4D = np.zeros((self.n_cols, self.n_rows, self.n_ev, self.n_theta)) if np.any(i0_indices): invnumel = 1. / self.averageflux[i0_indices].shape[0] else: return for i in range(self.n_theta): for ie in range(self.n_ev): thiseng_abs = self.stack4D[:, :, ie, i] self.i0datahist[ie, i] = np.sum(thiseng_abs[i0_indices]) * invnumel self.calculate_optical_density_4D() self.fill_h5_struct_normalization() return # ---------------------------------------------------------------------- def UsePreNormalizedData(self): self.evi0 = self.ev.copy() self.i0data = np.ones(self.n_ev) self.i0_dwell = self.data_dwell self.od = np.empty((self.n_cols, self.n_rows, self.n_ev)) for i in range(self.n_ev): self.od[:, :, i] = self.absdata[:, :, i] self.od3d = self.od.copy() n_pixels = self.n_cols * self.n_rows # Optical density matrix is rearranged into n_pixelsxn_ev self.od = np.reshape(self.od, (n_pixels, self.n_ev), order='F') if self.stack4D is not None: self.od4D = self.stack4D.copy() self.fill_h5_struct_normalization() return # ---------------------------------------------------------------------- def set_i0(self, i0data, evdata): self.evi0 = evdata self.i0data = i0data self.i0_dwell = self.data_dwell self.calculate_optical_density() self.fill_h5_struct_normalization() return # ---------------------------------------------------------------------- def reset_i0(self): self.i0_dwell = None self.i0data = 0 self.evi0 = 0 self.od = 0 self.od3d = 0 self.data_struct.spectromicroscopy.normalization.white_spectrum = None self.data_struct.spectromicroscopy.normalization.white_spectrum_energy = None self.data_struct.spectromicroscopy.normalization.white_spectrum_energy_units = None self.data_struct.spectromicroscopy.optical_density = None # ---------------------------------------------------------------------- # Normalize the data: calculate optical density matrix D def calculate_optical_density(self): if self.stack4D is not None: self.calculate_optical_density_4D() return n_pixels = self.n_cols * self.n_rows self.od = np.empty((self.n_cols, self.n_rows, self.n_ev)) # little hack to deal with rounding errors self.evi0[self.evi0.size - 1] += 0.001 self.evi0[0] -= 0.001 if len(self.evi0) > 3: # >3 is needed to avoid boundary error! fi0int = scipy.interpolate.interp1d(self.evi0.astype(np.double), self.i0data.astype(np.double), kind='cubic', bounds_error=False, fill_value=0.0) elif len(self.evi0) > 1: # use linear interpolation when there are fewer points fi0int = scipy.interpolate.interp1d(self.evi0.astype(np.double), self.i0data.astype(np.double), bounds_error=False, fill_value=0.0) else: # use constant value when only a single value is available fi0int = lambda x: self.i0data.astype(np.double) i0 = fi0int(self.ev) if (self.data_dwell is not None) and (self.i0_dwell is not None): i0 = i0 * (self.data_dwell / self.i0_dwell) # zero out all negative values in the image stack negative_indices = np.where(self.absdata <= 0) if negative_indices: self.absdata[negative_indices] = 0.01 for i in range(self.n_ev): self.od[:, :, i] = - np.log(self.absdata[:, :, i] / i0[i]) # clean up the result nan_indices = np.where(np.isfinite(self.od) == False) if nan_indices: self.od[nan_indices] = 0 self.od3d = self.od.copy() # Optical density matrix is rearranged into n_pixelsxn_ev self.od = np.reshape(self.od, (n_pixels, self.n_ev), order='F') return # ---------------------------------------------------------------------- # Normalize the data: calculate optical density matrix D def calculate_optical_density_4D(self): n_pixels = self.n_cols * self.n_rows self.od4D = np.zeros((self.n_cols, self.n_rows, self.n_ev, self.n_theta)) # little hack to deal with rounding errors self.evi0[self.evi0.size - 1] += 0.001 self.i0data = np.array(self.i0data) i0dims = self.i0data.shape for ith in range(self.n_theta): self.od = np.empty((self.n_cols, self.n_rows, self.n_ev)) if len(i0dims) == 2: self.i0data = self.i0datahist[:, ith] if len(self.evi0) > 2: fi0int = scipy.interpolate.interp1d(self.evi0, self.i0data, kind='cubic', bounds_error=False, fill_value=0.0) else: fi0int = scipy.interpolate.interp1d(self.evi0, self.i0data, bounds_error=False, fill_value=0.0) i0 = fi0int(self.ev) if (self.data_dwell is not None) and (self.i0_dwell is not None): i0 = i0 * (self.data_dwell / self.i0_dwell) # zero out all negative values in the image stack negative_indices = np.where(self.stack4D <= 0) if negative_indices: self.stack4D[negative_indices] = 0.01 for i in range(self.n_ev): self.od[:, :, i] = - np.log(self.stack4D[:, :, i, ith] / i0[i]) # clean up the result nan_indices = np.where(np.isfinite(self.od) == False) if nan_indices: self.od[nan_indices] = 0 self.od4D[:, :, :, ith] = self.od[:, :, :] self.od3d = self.od.copy() # Optical density matrix is rearranged into n_pixelsxn_ev self.od = np.reshape(self.od, (n_pixels, self.n_ev), order='F') return # ---------------------------------------------------------------------- # Normalize the data: calculate optical density matrix D def calculate_optical_density_from_refimgs(self, files): n_pixels = self.n_cols * self.n_rows self.od = np.empty((self.n_cols, self.n_rows, self.n_ev)) # zero out all negative values in the image stack negative_indices = np.where(self.absdata <= 0) if negative_indices: self.absdata[negative_indices] = 0.01 # Load reference images refimgs = np.empty((self.n_cols, self.n_rows, self.n_ev)) refimgs_ev = [] for i in range(len(files)): ncols, nrows, iev, imgdata = file_xrm.read_xrm_fileinfo(files[i], readimgdata=True) refimgs[:, :, i] = np.reshape(imgdata, (ncols, nrows), order='F') refimgs_ev.append(iev) # Check if the energies are consecutive, if they are not sort the data consec = 0 for i in range(len(refimgs_ev) - 1): if refimgs_ev[i] > refimgs_ev[i + 1]: consec = 1 break if consec == 1: sortind = np.argsort(refimgs_ev) refimgs_ev = refimgs_ev[sortind] refimgs = refimgs[:, :, refimgs_ev] for i in range(self.n_ev): if self.ev[i] != refimgs_ev[i]: print('Error, wrong reference image energy') return self.od[:, :, i] = - np.log(self.absdata[:, :, i] / refimgs[:, :, i]) # clean up the result nan_indices = np.where(np.isfinite(self.od) == False) if nan_indices: self.od[nan_indices] = 0 self.od3d = self.od.copy() # Optical density matrix is rearranged into n_pixelsxn_ev self.od = np.reshape(self.od, (n_pixels, self.n_ev), order='F') self.evi0 = refimgs_ev self.i0data = np.ones((self.n_ev)) self.i0_dwell = self.data_dwell return # ---------------------------------------------------------------------- def scale_bar(self): self.x_start = np.min(self.x_dist) self.x_stop = np.max(self.x_dist) self.x_pxsize = np.round(np.abs(self.x_stop - self.x_start) / (self.n_cols - 1), 5) # um per px in y direction, "-1" because stop-start is 1 px shorter than n_rows self.y_start = np.min(self.y_dist) self.y_stop = np.max(self.y_dist) self.y_pxsize = np.round(np.abs(self.y_stop - self.y_start) / (self.n_rows - 1), 5) # um per px in y direction, "-1" because stop-start is 1 px shorter than n_rows if self.x_pxsize == self.y_pxsize: self.squarepx = True else: self.squarepx = False bar_microns = 0.2 * np.abs(self.x_stop - self.x_start) if bar_microns >= 10.: bar_microns = 10. * int(0.5 + 0.1 * int(0.5 + bar_microns)) bar_string = str(int(0.01 + bar_microns)).strip() elif bar_microns >= 1.: bar_microns = float(int(0.5 + bar_microns)) if bar_microns == 1.: bar_string = '1' else: bar_string = str(int(0.01 + bar_microns)).strip() else: bar_microns = np.maximum(0.1 * int(0.5 + 10 * bar_microns), 0.1) bar_string = str(bar_microns).strip() self.scale_bar_string = bar_string self.scale_bar_pixels_x = int(0.5 + float(self.n_cols) * float(bar_microns) / float(abs(self.x_stop - self.x_start))) self.scale_bar_pixels_y = int(0.01 * self.n_rows) if self.scale_bar_pixels_y < 2: self.scale_bar_pixels_y = 2 # ---------------------------------------------------------------------- def write_xas(self, filename, evdata, data): f = open(filename, 'w') print('********************* X-ray Absorption Data ********************', file=f) print('*', file=f) print('* Formula: ', file=f) print('* Common name: ', file=f) print('* Edge: ', file=f) print('* Acquisition mode: ', file=f) print('* Source and purity: ', file=f) print('* Comments: Stack list ROI ""', file=f) print('* Delta eV: ', file=f) print('* Min eV: ', file=f) print('* Max eV: ', file=f) print('* Y axis: ', file=f) print('* Contact person: ', file=f) print('* Write date: ', file=f) print('* Journal: ', file=f) print('* Authors: ', file=f) print('* Title: ', file=f) print('* Volume: ', file=f) print('* Issue number: ', file=f) print('* Year: ', file=f) print('* Pages: ', file=f) print('* Booktitle: ', file=f) print('* Editors: ', file=f) print('* Publisher: ', file=f) print('* Address: ', file=f) print('*--------------------------------------------------------------', file=f) for ie in range(self.n_ev): print('\t {0:06.2f}, {1:06f}'.format(evdata[ie], data[ie]), file=f) f.close() return # ---------------------------------------------------------------------- def write_csv(self, filename, evdata, data, cname=''): f = open(filename, 'w') print('********************* X-ray Absorption Data ********************', file=f) print('*', file=f) print('* Formula: ', file=f) print('* Common name: {0}'.format(cname), file=f) print('* Edge: ', file=f) print('* Acquisition mode: ', file=f) print('* Source and purity: ', file=f) print('* Comments: Stack list ROI ""', file=f) print('* Delta eV: ', file=f) print('* Min eV: ', file=f) print('* Max eV: ', file=f) print('* Y axis: ', file=f) print('* Contact person: ', file=f) print('* Write date: ', file=f) print('* Journal: ', file=f) print('* Authors: ', file=f) print('* Title: ', file=f) print('* Volume: ', file=f) print('* Issue number: ', file=f) print('* Year: ', file=f) print('* Pages: ', file=f) print('* Booktitle: ', file=f) print('* Editors: ', file=f) print('* Publisher: ', file=f) print('* Address: ', file=f) print('*--------------------------------------------------------------', file=f) for ie in range(self.n_ev): print('{0:06.2f}, {1:012g}'.format(evdata[ie], data[ie]), file=f) f.close() return # ---------------------------------------------------------------------- # Read x-ray absorption spectrum def read_xas(self, filename): spectrum_common_name = ' ' f = open(str(filename), 'rU') elist = [] ilist = [] for line in f: if line.startswith('*'): if 'Common name' in line: spectrum_common_name = line.split(':')[-1].strip() else: e, i = [float(x) for x in line.split()] elist.append(e) ilist.append(i) spectrum_evdata = np.array(elist) spectrum_data = np.array(ilist) f.close() if spectrum_evdata[-1] < spectrum_evdata[0]: spectrum_evdata = spectrum_evdata[::-1] spectrum_data = spectrum_data[::-1] if spectrum_common_name == ' ': spectrum_common_name = os.path.splitext(os.path.basename(str(filename)))[0] return spectrum_evdata, spectrum_data, spectrum_common_name # ---------------------------------------------------------------------- # Read x-ray absorption spectrum def read_txt(self, filename): spectrum_common_name = os.path.splitext(os.path.basename(str(filename)))[0] f = open(str(filename), 'rU') elist = [] ilist = [] for line in f: if line.startswith('%'): pass else: e, i = [float(x) for x in line.split()] elist.append(e) ilist.append(i) spectrum_evdata = np.array(elist) spectrum_data = np.array(ilist) f.close() if spectrum_evdata[-1] < spectrum_evdata[0]: spectrum_evdata = spectrum_evdata[::-1] spectrum_data = spectrum_data[::-1] return spectrum_evdata, spectrum_data, spectrum_common_name # ---------------------------------------------------------------------- # Read x-ray absorption spectrum def read_csv(self, filename): spectrum_common_name = ' ' f = open(str(filename), 'rU') elist = [] ilist = [] # Check the first character of the line and skip if not a number allowedchars = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '-', '.'] for line in f: if line.startswith('*'): if 'Common name' in line: spectrum_common_name = line.split(':')[-1].strip() elif line[0] not in allowedchars: continue else: e, i = [float(x) for x in line.split(',')] elist.append(e) ilist.append(i) spectrum_evdata = np.array(elist) spectrum_data = np.array(ilist) f.close() if spectrum_evdata[-1] < spectrum_evdata[0]: spectrum_evdata = spectrum_evdata[::-1] spectrum_data = spectrum_data[::-1] if spectrum_common_name == ' ': spectrum_common_name = os.path.splitext(os.path.basename(str(filename)))[0] return spectrum_evdata, spectrum_data, spectrum_common_name # ---------------------------------------------------------------------- # Register images using Fourier Shift Theorem # EdgeEnhancement: 0 = no edge enhacement; 1 = sobel; 2 = prewitt def register_images(self, ref_image, image2, have_ref_img_fft=False, edge_enhancement=0): if have_ref_img_fft == False: if edge_enhancement == 1: self.ref_fft = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(scipy.ndimage.filters.sobel(ref_image)))) elif edge_enhancement == 2: self.ref_fft = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(scipy.ndimage.filters.prewitt(ref_image)))) else: self.ref_fft = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(ref_image))) if edge_enhancement == 1: img2_fft = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(scipy.ndimage.filters.sobel(image2)))) if edge_enhancement == 2: img2_fft = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(scipy.ndimage.filters.prewitt(image2)))) else: img2_fft = np.fft.fftshift(np.fft.fft2(np.fft.fftshift(image2))) fr = (self.ref_fft * img2_fft.conjugate()) / (np.abs(self.ref_fft) * np.abs(img2_fft)) fr = np.fft.fftshift(np.fft.ifft2(np.fft.fftshift(fr))) fr = np.abs(fr) shape = ref_image.shape xc, yc = np.unravel_index(np.argmax(fr), shape) # Limit the search to 1 pixel border if xc == 0: xc = 1 if xc == shape[0] - 1: xc = shape[0] - 2 if yc == 0: yc = 1 if yc == shape[1] - 1: yc = shape[1] - 2 # Use peak fit to find the shifts xpts = [xc - 1, xc, xc + 1] ypts = fr[xpts, yc] xf, fit = self.peak_fit(xpts, ypts) xpts = [yc - 1, yc, yc + 1] ypts = fr[xc, xpts] yf, fit = self.peak_fit(xpts, ypts) xshift = xf - float(shape[0]) / 2.0 yshift = yf - float(shape[1]) / 2.0 return xshift, yshift, fr # ---------------------------------------------------------------------- # Apply image registration def apply_image_registration(self, image, xshift, yshift): shape = image.shape nx = shape[0] ny = shape[1] outofboundariesval = np.sum(image) / float(nx * ny) shifted_img = scipy.ndimage.interpolation.shift(image, [xshift, yshift], mode='constant', cval=outofboundariesval) return shifted_img # ---------------------------------------------------------------------- # Apply image registration def crop_registed_images(self, images, min_xshift, max_xshift, min_yshift, max_yshift): # if the image is moved to the right (positive) we need to crop the left side xleft = int(np.ceil(max_xshift)) if xleft < 0: xleft = 0 # if the image is moved to the left (negative) we need to crop the right side xright = int(np.floor(self.n_cols + min_xshift)) if xright > (self.n_cols): xright = int(self.n_cols) ybottom = int(np.ceil(max_yshift)) if ybottom < 0: ybottom = 0 ytop = int(np.floor(self.n_rows + min_yshift)) if ytop > (self.n_rows): ytop = int(self.n_rows) if self.stack4D is not None: cropped_stack = images[xleft:xright, ybottom:ytop, :, :] else: cropped_stack = images[xleft:xright, ybottom:ytop, :] return cropped_stack, xleft, xright, ybottom, ytop # ---------------------------------------------------------------------- # Quadratic peak fit: Fits the 3 data pairs to y=a+bx+cx^2, returning fit=[a,b,c]' # and xpeak at position of inflection' def peak_fit(self, x, y): y1y0 = y[1] - y[0] y2y0 = y[2] - y[0] x1x0 = float(x[1] - x[0]) x2x0 = float(x[2] - x[0]) x1x0sq = float(x[1] * x[1] - x[0] * x[0]) x2x0sq = float(x[2] * x[2] - x[0] * x[0]) c_num = y2y0 * x1x0 - y1y0 * x2x0 c_denom = x2x0sq * x1x0 - x1x0sq * x2x0 if c_denom == 0: print('Divide by zero error') return c = c_num / float(c_denom) if x1x0 == 0: print('Divide by zero error') return b = (y1y0 - c * x1x0sq) / float(x1x0) a = y[0] - b * x[0] - c * x[0] * x[0] fit = [a, b, c] if c == 0: xpeak = 0. print('Cannot find xpeak') return else: # Constrain the fit to be within these three points. xpeak = -b / (2.0 * c) if xpeak < x[0]: xpeak = float(x[0]) if xpeak > x[2]: xpeak = float(x[2]) return xpeak, fit # ----------------------------------------------------------------------------- # Despike image using Enhanced Lee Filter def despike(self, image, leefilt_percent=50.0): fimg = self.lee_filter(image) leefilt_max = np.amax(fimg) threshold = (1. + 0.01 * leefilt_percent) * leefilt_max datadim = np.int32(image.shape) ncols = datadim[0].copy() nrows = datadim[1].copy() spikes = np.where(image > threshold) n_spikes = fimg[spikes].shape[0] result_img = image.copy() if n_spikes > 0: xsp = spikes[0] ysp = spikes[1] for i in range(n_spikes): ix = xsp[i] iy = ysp[i] print(ix, iy) if ix == 0: ix1 = 1 ix2 = 2 elif ix == (ncols - 1): ix1 = ncols - 2 ix2 = ncols - 3 else: ix1 = ix - 1 ix2 = ix + 1 if iy == 0: iy1 = 1 iy2 = 2 elif iy == (nrows - 1): iy1 = nrows - 2 iy2 = nrows - 3 else: iy1 = iy - 1 iy2 = iy + 1 print(result_img[ix, iy]) result_img[ix, iy] = 0.25 * (image[ix1, iy] + image[ix2, iy] + image[ix, iy1] + image[ix, iy2]) print(result_img[ix, iy]) return result_img # ----------------------------------------------------------------------------- # Lee filter def lee_filter(self, image): nbox = 5 # The size of the filter box is 2N+1. The default value is 5. sig = 5.0 # Estimate of the standard deviation. The default is 5. delta = int((nbox - 1) / 2) # width of window datadim = np.int32(image.shape) n_cols = datadim[0].copy() n_rows = datadim[1].copy() Imean = np.zeros((n_cols, n_rows)) scipy.ndimage.filters.uniform_filter(image, size=nbox, output=Imean) Imean2 = Imean ** 2 # variance z = np.empty((n_cols, n_rows)) for l in range(delta, n_cols - delta): for s in range(delta, n_rows - delta): z[l, s] = np.sum((image[l - delta:l + delta, s - delta:s + delta] - Imean[l, s]) ** 2) z = z / float(nbox ** 2 - 1.0) z = (z + Imean2) / float(1.0 + sig ** 2) - Imean2 ind = np.where(z < 0) n_ind = z[ind].shape[0] if n_ind > 0: z[ind] = 0 lf_image = Imean + (image - Imean) * (z / (Imean2 * sig ** 2 + z)) return lf_image ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/data_struct.py0000755000175000017500000001531514332463747020455 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2011 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # 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 . # This module is used to pass data between routines and is based on # https://confluence.aps.anl.gov/display/NX/Data+Exchange+Basics class Descr(object): def __get__(self, instance, owner): # Check if the value has been set if (not hasattr(self, "_value")): return None # print "Getting value: %s" % self._value return self._value def __set__(self, instance, value): # print "Setting to %s" % value self._value = value def __delete__(self, instance): del (self._value) ### Information HDF5 Group # ---------------------------------------------------------------------- class ids(object): proposal = Descr() activity = Descr() esaf = Descr() # ---------------------------------------------------------------------- class experimenter(object): name = Descr() role = Descr() affiliation = Descr() address = Descr() phone = Descr() email = Descr() facility_user_id = Descr() # ---------------------------------------------------------------------- class sample(object): name = Descr() description = Descr() # preparation_date [string - ISO 8601 format] preparation_datetime = Descr() # chemical_formula [string - abbreviated CIF format] chemical_formula = Descr() environment = Descr() temperature = Descr() temperature_units = Descr() pressure = Descr() pressure_units = Descr() # ---------------------------------------------------------------------- class objective(object): manufacturer = Descr() model = Descr() comment = Descr() magnification = Descr() # ---------------------------------------------------------------------- class scintillator(object): name = Descr() type = Descr() comment = Descr() scintillating_thickness = Descr() scintillating_thickness_units = Descr() substrate_thickness = Descr() substrate_thickness_units = Descr() # ---------------------------------------------------------------------- class facility(object): name = Descr() beamline = Descr() # ---------------------------------------------------------------------- class accelerator(object): ring_current = Descr() ring_current_units = Descr() primary_beam_energy = Descr() primary_beam_energy_units = Descr() monostripe = Descr() # ---------------------------------------------------------------------- class pixel_size(object): horizontal = Descr() horizontal_units = Descr() vertical = Descr() vertical_units = Descr() # ---------------------------------------------------------------------- class dimensions(object): horizontal = Descr() vertical = Descr() # ---------------------------------------------------------------------- class binning(object): horizontal = Descr() vertical = Descr() # ---------------------------------------------------------------------- class axis_directions(object): horizontal = Descr() vertical = Descr() # ---------------------------------------------------------------------- class roi(object): x1 = Descr() y1 = Descr() x2 = Descr() y2 = Descr() # ---------------------------------------------------------------------- class detector(object): manufacturer = Descr() model = Descr() serial_number = Descr() bit_depth = Descr() operating_temperature = Descr() operating_temperature_units = Descr() exposure_time = Descr() exposure_time_units = Descr() frame_rate = Descr() pixel_size = pixel_size() dimensions = dimensions() binning = binning() axis_directions = axis_directions() roi = roi() # ---------------------------------------------------------------------- class information(object): title = Descr() comment = Descr() file_creation_datetime = Descr() ids = ids() experimenter = experimenter() sample = sample() objective = objective() scintillator = scintillator() facility = facility() accelerator = accelerator() detector = detector() # Exchange HDF5 group # ---------------------------------------------------------------------- class exchange(object): title = Descr() comment = Descr() data_collection_datetime = Descr() # n-dimensional dataset data = Descr() data_signal = Descr() data_description = Descr() data_units = Descr() data_axes = Descr() data_detector = Descr() # These are described in data attribute axes 'x:y:z' but can be arbitrary x = Descr() x_units = Descr() y = Descr() y_units = Descr() z = Descr() z_units = Descr() energy = Descr() energy_units = Descr() theta = Descr() theta_units = Descr() white_data = Descr() white_data_units = Descr() dark_data = Descr() dark_data_units = Descr() rotation = Descr() # Spectromicroscopy HDF5 Group # ---------------------------------------------------------------------- class normalization(object): white_spectrum = Descr() white_spectrum_units = Descr() white_spectrum_energy = Descr() white_spectrum_energy_units = Descr() # ---------------------------------------------------------------------- class spectromicroscopy(object): positions = Descr() positions_units = Descr() positions_names = Descr() normalization = normalization() optical_density = Descr() data_dwell = Descr() i0_dwell = Descr() xshifts = Descr() yshifts = Descr() # HDF5 Root Group # ---------------------------------------------------------------------- class h5(object): # implements [string] comma separated string that tells the user which entries file contains implements = Descr() version = Descr() information = information() exchange = exchange() spectromicroscopy = spectromicroscopy() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/dialogalign.ui0000644000175000017500000000376214332463747020377 0ustar00wattswatts Dialog 0 0 400 100 0 0 Alignment Routine Selector Ubuntu 8 Choose an image registration tool: 0 40 Ubuntu 8 FFT cross-correlation 0 40 Ubuntu 8 Manual alignment ././@PaxHeader0000000000000000000000000000003300000000000010211 xustar0027 mtime=1701535927.128231 mantis-xray-3.1.15/mantis_xray/file_plugins/0000775000175000017500000000000014532660267020236 5ustar00wattswatts././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1701535457.0 mantis-xray-3.1.15/mantis_xray/file_plugins/__init__.py0000644000175000017500000002132314532657341022345 0ustar00wattswatts# -*- coding: utf-8 -*- # # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2015 Benjamin Watts, Paul Scherrer Institute # 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 . """ The file_plugins system is exposed to general code through the functions defined here in __init__.py: identify(filename) : Returns an instance of the plugin that claims to deal with the file at the URL 'filename'. GetFileStructure(filename) : Returns a structure describing the internal organisation of the file, indicating sets of data available to choose from. load(filename,stack_object,..) : Loads data from the URL 'filename' into the object (data_stack type) 'stack_object'. The plugin used can be stated or determined automatically, using 'identify'. Further functions for writing files via the plugins need to be written yet. To access the system, you should import the module ('import file_plugins') and then access the above functions as attributes of the module (e.g. 'file_plugins.load('data.hdf5',data_stk)' ). Each file plugin should be included here in the 'file_plugins' directory. Each plugin should define the following: title : A short string naming the plugin. extension : A list of strings indicating the file extensions that the plugin handles (e.g. ['*.txt']). read_types : A list of strings indicating the data types that the plugin will read (e.g. ['spectrum','image','stack']). write_types : A list of strings indicating the data types that the plugin will write (e.g. ['spectrum','image','stack']). identify(filename) : Returns boolean indicating if the plugin can read the file at URL 'filename'. GetFileStructure(filename) : Returns a structure describing the internal organisation of the file, indicating sets of data available to choose from. read(filename,stack_object,..) : Loads data from the URL 'filename' into the object (data_stack type) 'stack_object'. """ from __future__ import print_function import pkgutil, importlib, os, sys import numpy from .. import data_stack verbose = True # These variables declare the options that each plugin can claim the ability to handle actions = ['read','write'] data_types = ['spectrum','image','stack','results'] # Go through the directory and try to load each plugin plugins = [] for m in pkgutil.iter_modules(path=__path__): if verbose: print("Loading file plugin:", m[1], ".", end=' ') try: spec = importlib.util.spec_from_file_location(m.name, os.path.join(__path__[0],m.name+'.py')) module = importlib.util.module_from_spec(spec) sys.modules[m.name] = module spec.loader.exec_module(module) # check if there is a read() function in plugin if 'read' in dir(module): plugins.append(module) if verbose: print("("+plugins[-1].title+") Success!") else: if verbose: print('Not a valid plugin - skipping.') except ImportError as e: if verbose: print("prerequisites not satisfied:", e) # if getattr(sys, 'frozen', False): # module_names = ['file_dataexch_hdf5', 'file_ncb', 'file_nexus_hdf5', 'file_sdf', 'file_stk', 'file_tif', 'file_xrm'] # for m in module_names: # if verbose: print "Loading file plugin:", m, "...", # try: # # # details = imp.find_module(m) # # check if there is a read() function in plugin # if 'read' in dir(imp.load_module(m,*details)): # plugins.append(imp.load_module(m,*details)) # if verbose: print "("+plugins[-1].title+") Success!" # else: # if verbose: print 'Not a valid plugin - skipping.' # # except ImportError as e: # if verbose: print "prerequisites not satisfied:", e # Go through set of plugins and assemble lists of supported file types for each action and data type supported_filters = dict([a,dict([t,[]] for t in data_types)] for a in actions) supported_plugins = dict([a,dict([t,[]] for t in data_types)] for a in actions) filter_list = dict([a,dict([t,[]] for t in data_types)] for a in actions) for P in plugins: for action in actions: for data_type in data_types: if data_type in getattr(P,action+'_types'): filter_list[action][data_type].append(P.title+' ('+' '.join(P.extension)+')') supported_plugins[action][data_type].append(P) for ext in P.extension: if ext not in supported_filters[action][data_type]: supported_filters[action][data_type].append(ext) for data_type in data_types: filter_list['read'][data_type] = ['Supported Formats ('+' '.join(supported_filters['read'][data_type])+')']+filter_list['read'][data_type] filter_list['read'][data_type].append('All files (*.*)') def load(filename, stack_object=None, plugin=None, selection=None, json=None): """ Pass the load command over to the appropriate plugin so that it can import data from the named file. """ if plugin is None: plugin = identify(filename) if plugin is None: return None else: print("load", filename, "with the", plugin.title, "plugin.") if selection is None: plugin.read(filename, stack_object, selection, json) elif len(selection) == 1: plugin.read(filename, stack_object, selection[0], json) else: plugin.read(filename,stack_object,selection[0],json) temp_stack = data_stack.data(stack_object.data_struct) full_stack = stack_object.absdata.copy() for s in selection[1:]: plugin.read(filename,temp_stack,s) if full_stack.shape[1] > temp_stack.absdata.shape[1]: temp_stack.absdata = numpy.pad(temp_stack.absdata,((0,0),(0,full_stack.shape[1]-temp_stack.absdata.shape[1]),(0,0)), mode='constant',constant_values=0) elif full_stack.shape[1] < temp_stack.absdata.shape[1]: full_stack = numpy.pad(full_stack,((0,0),(0,temp_stack.absdata.shape[1]-full_stack.shape[1]),(0,0)), mode='constant',constant_values=0) full_stack = numpy.vstack((full_stack,temp_stack.absdata)) stack_object.absdata = full_stack stack_object.x_dist = numpy.arange(full_stack.shape[0]) stack_object.y_dist = numpy.arange(full_stack.shape[1]) stack_object.n_cols = len(stack_object.x_dist) stack_object.n_rows = len(stack_object.y_dist) return def save(filename, data_object, data_type, plugin=None): """ Pass the save command over to the appropriate plugin so that it can write data to the named file. """ print("save", filename, "with the", plugin.title, "plugin.") plugin.write(filename, data_object, data_type) def GetFileStructure(filename, plugin=None): """ Use the plugin to skim-read the file and return the structure of the data. Returns None if there is only a single data array (i.e. no choices to be made). """ if plugin is None: plugin = identify(filename) if plugin is None: return None else: print("get info from", filename, "with the", plugin.title, "plugin.") FileInfo = plugin.GetFileStructure(filename) #if FileInfo is not None: #print len(FileInfo), len(FileInfo[next(iter(FileInfo))]) #print FileInfo return FileInfo def identify(filename): """ Cycle through plugins until finding one that claims to understand the file format. First it tries those claiming corresponding file extensions, followed by all other plugins until an appropriate plugin is found. """ print("Identifying file:", filename, "...", end=' ') ext = os.path.splitext(filename)[1] flag = [True]*len(plugins) for i,P in enumerate(plugins): if '*'+ext in P.extension: if P.identify(filename): return P elif flag[i] == True: #if plugin returns False, e.g. dataexch_hdf5 does not match, try the next plugin and find the same extension flag[i] = False continue else: break print("Error! unknown file type.") return None ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1677150153.0 mantis-xray-3.1.15/mantis_xray/file_plugins/file_bim.py0000664000175000017500000001444214375643711022363 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2015 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # 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 . from __future__ import division import numpy as np import os #---------------------------------------------------------------------- def read_bim(self, filename): f = open(str(filename),'rb') data = np.fromfile(f, np.uint32, 6) nmotpos = data[0] ndatatype = data[1] ndate = data[2] naxisnames = data[3] self.n_cols = data[4] self.n_rows = data[5] self.n_ev = 1 angles = np.fromfile(f, np.float64, 1) pixelsize = np.fromfile(f, np.float32, 1) self.x_dist = np.arange(float(self.n_cols))*pixelsize self.y_dist = np.arange(float(self.n_rows))*pixelsize data = np.fromfile(f, np.uint32, 2) hbin = data[0] vbin = data[1] energy = np.fromfile(f, np.float64, 1) motpos = np.fromfile(f, np.float32, nmotpos) axisnames = np.fromfile(f, np.uint8, naxisnames) exposuretime = np.fromfile(f, np.float32, 1) nimages = np.fromfile(f, np.uint32, 1) data = np.fromfile(f, np.uint8, ndatatype) data = np.fromfile(f, np.uint8, ndate) npix = self.n_cols*self.n_rows imagestack = np.fromfile(f, np.float32, npix) self.absdata = np.reshape(imagestack, (self.n_cols, self.n_rows, self.n_ev), order='C') fn = os.path.basename(str(filename)) fnlist = fn.split('_') ind = fnlist.index('eV') self.ev = [float(fnlist[ind-1])] self.data_dwell = np.zeros((self.n_ev))+exposuretime f.close() return #---------------------------------------------------------------------- def read_bim_info(filename): f = open(str(filename),'rb') data = np.fromfile(f, np.uint32, 6) n_cols = data[4] n_rows = data[5] f.close() fn = os.path.basename(str(filename)) fnlist = fn.split('_') ind = fnlist.index('eV') ev = float(fnlist[ind-1]) return n_cols, n_rows, ev #---------------------------------------------------------------------- def read_bim_list(self, filelist, filepath, ds): #Fill the common stack data file1 = os.path.join(filepath, filelist[0]) f = open(str(file1),'rb') data = np.fromfile(f, np.uint32, 6) nmotpos = data[0] ndatatype = data[1] ndate = data[2] naxisnames = data[3] ncols = data[4] nrows = data[5] npix = ncols*nrows nev = len(filelist) absdata = np.zeros((ncols, nrows, nev)) ev = np.zeros((nev)) angles = np.fromfile(f, np.float64, 1) pixelsize = np.fromfile(f, np.float32, 1) x_dist = np.arange(float(ncols))*pixelsize y_dist = np.arange(float(nrows))*pixelsize data = np.fromfile(f, np.uint32, 2) hbin = data[0] vbin = data[1] energy = np.fromfile(f, np.float64, 1) motpos = np.fromfile(f, np.float32, nmotpos) axisnames = np.fromfile(f, np.uint8, naxisnames) exposuretime = np.fromfile(f, np.float32, 1) nimages = np.fromfile(f, np.uint32, 1) data = np.fromfile(f, np.uint8, ndatatype) data = np.fromfile(f, np.uint8, ndate) dwell_msec = exposuretime f.close() #Read the image data for i in range(len(filelist)): fn = filelist[i] filename = os.path.join(filepath, fn) f = open(str(filename),'rb') data = np.fromfile(f, np.uint32, 6) angles = np.fromfile(f, np.float64, 1) pixelsize = np.fromfile(f, np.float32, 1) data = np.fromfile(f, np.uint32, 2) energy = np.fromfile(f, np.float64, 1) motpos = np.fromfile(f, np.float32, nmotpos) axisnames = np.fromfile(f, np.uint8, naxisnames) exposuretime = np.fromfile(f, np.float32, 1) nimages = np.fromfile(f, np.uint32, 1) data = np.fromfile(f, np.uint8, ndatatype) data = np.fromfile(f, np.uint8, ndate) imagestack = np.fromfile(f, np.float32, npix) absdata[:,:,i] = np.reshape(imagestack, (ncols, nrows), order='C') fn = os.path.basename(str(filename)) fnlist = fn.split('_') ind = fnlist.index('eV') ev[i] = float(fnlist[ind-1]) f.close() if ev[-1]. import numpy import os title = 'text table' extension = ['*.csv','*.txt','*.xas'] read_types = ['spectrum','stack'] write_types = ['spectrum','stack'] def identify(filename): try: with open(filename, 'tr') as check_file: # try open file in text mode and read a single line check_file.readline() return True except: return False def GetFileStructure(FileName): return None #---------------------------------------------------------------------- def read(filename, self, selection=None, *args, **kwargs): with open(str(filename),'r') as f: Line = f.readline().split() if Line == ['#X', '#Y', '#Wave', '#Intensity']: read_stack(filename, self, **kwargs) else: read_spectrum(filename, self, **kwargs) return def read_stack(filename, self, selection=None, *args, **kwargs): """ This reads the text version of an SPC hyperspectral map from a Raman measurement. """ f = open(str(filename),'r') xlist = [] ylist = [] wlist = [] ilist = [] for line in f: if line.startswith(("*","%","#")): pass else: x, y, w, i = [float (x) for x in line.split()] xlist.append(x) ylist.append(y) wlist.append(w) ilist.append(i) self.x_dist = numpy.unique(xlist) self.y_dist = numpy.unique(ylist) self.ev = numpy.unique(wlist) self.n_cols = len(self.x_dist) self.n_rows = len(self.y_dist) self.n_ev = len(self.ev) self.data_dwell = numpy.ones((self.n_ev))*1.0 self.absdata = numpy.empty((self.n_cols,self.n_rows, self.n_ev)) self.absdata = numpy.flip(numpy.transpose(numpy.reshape(ilist, (self.n_rows, self.n_cols, self.n_ev), order='C'), axes=[1,0,2]), axis=2) self.fill_h5_struct_from_stk() return def read_spectrum(filename, self, selection=None, *args, **kwargs): f = open(str(filename),'r') elist = [] ilist = [] for line in f: if line.startswith(("*","%","#")): pass else: e, i = [float (x) for x in line.split(',')] elist.append(e) ilist.append(i) self.evi0 = numpy.array(elist) self.i0data = numpy.array(ilist) f.close() self.i0_dwell = None return #---------------------------------------------------------------------- def write(filename, data_object, data_type): """Switchyard for writing different types of data.""" if data_type in ['spectrum']: write_spectrum(filename, data_object.absdata, data_object.ev) elif data_type in ['stack']: write_spectrum(filename, numpy.average(data_object.absdata,axis=(0,1)), energies=data_object.ev, title='I0 Spectrum') #---------------------------------------------------------------------- def write_spectrum(filename, data, energies, title=None ): if title is None: title = 'Spectrum' with open(filename, 'w') as f: print('********************* '+title+' ********************', file=f) print('*', file=f) print('* ev, intensity', file=f) for ie in range(len(energies)): print('{0:06.2f}, {1:06f}'.format(energies[ie], data[ie]), file=f) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/file_plugins/file_dataexch_hdf5.py0000755000175000017500000025200514332463747024305 0ustar00wattswatts# -*- coding: utf-8 -*- # # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2011 Mirna Lerotic - 2nd Look, Nicholas Schwarz, Benjamin Watts # http://2ndlookconsulting.com # 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 . # File format details can be found on # https://confluence.aps.anl.gov/display/NX/Data+Exchange+Basics from __future__ import division from __future__ import print_function import os import numpy as np import h5py from mantis_xray import data_struct verbose = 0 title = 'Exchange' extension = ['*.hdf','*.hdf5'] read_types = ['spectrum','image','stack'] write_types = ['spectrum','image','stack','results'] def identify(filename): try: # Open HDF5 file aps_format = False # Open HDF5 file f = h5py.File(filename, 'r') #Check is exchange is in the file if 'exchange' in f: aps_format = True f.close() return aps_format except: return False def read_old( filepath, data_stk): data_stk.read_h5(filepath) return def GetFileStructure(FileName): return None #---------------------------------------------------------------------- def read(filename, stack_object, selection=None, *args, **kwargs): """ToDo: .attrs returns a list in py2 and a set in py3! .attrs and .keys() need to be wrapped in list()""" have4d = 0 #new_stack = data_stack.data(data_struct) # Open HDF5 file f = h5py.File(filename, 'r') # Read basic definitions ds = f['implements'] data_struct.implements = ds[...] ds = f['version'] data_struct.version = ds[...] stack_object.have_dimscale = 0 #Information HDF5 group if 'information' in f: informationGrp = f['information'] if 'title' in informationGrp: title = informationGrp['title'] data_struct.information.title = title[...] if 'comment' in informationGrp: com = informationGrp['comment'] data_struct.information.comment = com[...] if 'file_creation_datetime' in informationGrp: fcdt = informationGrp['file_creation_datetime'] data_struct.information.file_creation_datetime = fcdt[...] # /ids if 'ids' in informationGrp: idsGrp = informationGrp['ids'] if 'proposal' in idsGrp: prop = idsGrp['proposal'] data_struct.information.ids.proposal = prop[...] if 'acitivity' in idsGrp: act = idsGrp['activity'] data_struct.information.ids.activity = act[...] if 'esaf' in idsGrp: esaf = idsGrp['esaf'] data_struct.information.ids.esaf = esaf[...] # /experimenter if 'experimenter' in informationGrp: experimenterGrp = informationGrp['experimenter'] if 'name' in experimenterGrp: name = experimenterGrp['name'] data_struct.information.experimenter.name = name[...] if 'role' in experimenterGrp: role = experimenterGrp['role'] data_struct.information.experimenter.role = role[...] if 'affiliation' in experimenterGrp: affiliation = experimenterGrp['affiliation'] data_struct.information.experimenter.affiliation = affiliation[...] if 'address' in experimenterGrp: address = experimenterGrp['address'] data_struct.information.experimenter.address = address[...] if 'phone' in experimenterGrp: phone = experimenterGrp['phone'] data_struct.information.experimenter.phone = phone[...] if 'email' in experimenterGrp: email = experimenterGrp['email'] data_struct.information.experimenter.email = email[...] if 'facility_user_id' in experimenterGrp: facility_user_id = experimenterGrp['facility_user_id'] data_struct.information.experimenter.facility_user_id = facility_user_id[...] # /sample if 'sample' in informationGrp: sampleGrp = informationGrp['sample'] if 'name' in sampleGrp: name = sampleGrp['name'] data_struct.information.sample.name = name[...] if 'description' in sampleGrp: description = sampleGrp['description'] data_struct.information.sample.description = description[...] if 'preparation_datetime' in sampleGrp: preparation_datetime = sampleGrp['preparation_datetime'] data_struct.information.sample.preparation_datetime = preparation_datetime[...] if 'chemical_formula' in sampleGrp: cf = sampleGrp['chemical_formula'] data_struct.information.sample.chemical_formula = cf[...] if 'environment' in sampleGrp: env = sampleGrp['environment'] data_struct.information.sample.environment = env[...] if 'temperature' in sampleGrp: temp = sampleGrp['temperature'] data_struct.information.sample.temperature = temp[...] data_struct.information.sample.temperature_units = temp.attrs['units'] if 'pressure' in sampleGrp: press = sampleGrp['pressure'] data_struct.information.sample.pressure = press[...] data_struct.information.sample.pressure_units = press.attrs['units'] # /objective if 'objective' in informationGrp: objectiveGrp = informationGrp['objective'] if 'manufacturer' in objectiveGrp: man = objectiveGrp['manufacturer'] data_struct.information.objective.manufacturer = man[...] if 'model' in objectiveGrp: model = objectiveGrp['model'] data_struct.information.objective.model = model[...] if 'comment' in objectiveGrp: com = objectiveGrp['comment'] data_struct.information.objective.comment = com[...] if 'magnification' in objectiveGrp: mag = objectiveGrp['magnification'] data_struct.information.objective.magnification = mag[...] # /scintillator if 'scintillator' in informationGrp: scintGrp = informationGrp['scintillator'] if 'name' in scintGrp: name = scintGrp['name'] data_struct.information.scintillator.name = name[...] if 'type' in scintGrp: type = scintGrp['type'] data_struct.information.scintillator.type = type[...] if 'comment' in scintGrp: com = scintGrp['comment'] data_struct.information.scintillator.comment = com[...] if 'scintillating_thickness' in scintGrp: sct = scintGrp['scintillating_thickness'] data_struct.information.scintillator.scintillating_thickness = sct[...] data_struct.information.scintillator.scintillating_thickness_units = sct.attrs['units'] if 'substrate_thickness' in scintGrp: subt = scintGrp['substrate_thickness'] data_struct.information.scintillator.substrate_thickness = subt[...] data_struct.information.scintillator.substrate_thickness_units = subt.attrs['units'] # /facility if 'facility' in informationGrp: facilityGrp = informationGrp['facility'] if 'name' in facilityGrp: name = facilityGrp['name'] data_struct.information.facility.name = name[...] if 'beamline' in facilityGrp: bl = facilityGrp['beamline'] data_struct.information.facility.beamline = bl[...] # /accelerator if 'accelerator' in informationGrp: acceleratorGrp = informationGrp['accelerator'] if 'ring_current' in acceleratorGrp: rc = acceleratorGrp['ring_current'] data_struct.information.accelerator.ring_current = rc[...] data_struct.information.accelerator.ring_current_units = rc.attrs['units'] if 'primary_beam_energy' in acceleratorGrp: pbe = acceleratorGrp['primary_beam_energy'] data_struct.information.accelerator.primary_beam_energy = pbe[...] data_struct.information.accelerator.primary_beam_energy_units = pbe.attrs['units'] if 'monostripe' in acceleratorGrp: ms = acceleratorGrp['monostripe'] data_struct.information.accelerator.monostripe = ms[...] # /detector if 'detector' in informationGrp: detectorGrp = informationGrp['detector'] if 'manufacturer' in detectorGrp: manf = detectorGrp['manufacturer'] data_struct.information.detector.manufacturer = manf[...] if 'model' in detectorGrp: model = detectorGrp['model'] data_struct.information.detector.model = model[...] if 'serial_number' in detectorGrp: sn = detectorGrp['serial_number'] data_struct.information.detector.serial_number = sn[...] if 'bit_depth' in detectorGrp: bd = detectorGrp['bit_depth'] data_struct.information.detector.bit_depth = bd[...] if 'operating_temperature' in detectorGrp: ot = detectorGrp['operating_temperature'] data_struct.information.detector.operating_temperature = ot[...] data_struct.information.detector.operating_temperature_units = ot.attrs['units'] if 'exposure_time' in detectorGrp: et = detectorGrp['exposure_time'] data_struct.information.detector.exposure_time = et[...] data_struct.information.detector.exposure_time_units = et.attrs['units'] if 'frame_rate' in detectorGrp: fr = detectorGrp['frame_rate'] data_struct.information.detector.frame_rate = fr[...] if 'pixel_size' in detectorGrp: psGrp = detectorGrp['pixel_size'] hor = psGrp['horizontal'] data_struct.information.detector.pixel_size.horizontal = hor[...] data_struct.information.detector.pixel_size.horizontal_units = hor.attrs['units'] ver = psGrp['vertical'] data_struct.information.detector.pixel_size.vertical = ver[...] data_struct.information.detector.pixel_size.vertical_units = ver.attrs['units'] if 'dimensions' in detectorGrp: dimGrp = detectorGrp['dimenstions'] h = dimGrp['horizontal'] data_struct.information.detector.dimensions.horizontal = h[...] v = dimGrp['vertical'] data_struct.information.detector.dimensions.vertical = v[...] if 'binning' in detectorGrp: binningGrp = detectorGrp['binning'] h = binningGrp['horizontal'] data_struct.information.detector.binning.horizontal = h[...] v = binningGrp['vertical'] data_struct.information.detector.binning.vertical = v[...] if 'axis_directions' in detectorGrp: axisdirGrp = detectorGrp['axis_directions'] h = axisdirGrp['horizontal'] data_struct.information.detector.axis_directions.horizontal = h[...] v = axisdirGrp['vertical'] data_struct.information.detector.axis_directions.vertical = v[...] if 'roi' in detectorGrp: roiGrp = detectorGrp['roi'] x1 = roiGrp['x1'] data_struct.information.detector.roi.x1 = x1[...] y1 = roiGrp['y1'] data_struct.information.detector.roi.y1 = y1[...] x2 = roiGrp['x2'] data_struct.information.detector.roi.x2 = x2[...] y2 = roiGrp['x2'] data_struct.information.detector.roi.y2 = y2[...] #Exchange HDF5 Group if 'exchange' in f: exchangeGrp = f['exchange'] if 'title' in exchangeGrp: title = exchangeGrp['title'] data_struct.exchange.title = title[...] if 'comment' in exchangeGrp: com = exchangeGrp['comment'] data_struct.exchange.comment = com[...] if 'data_collection_datetime' in exchangeGrp: dcdt = exchangeGrp['data_collection_datetime'] data_struct.exchange.data_collection_datetime = dcdt[...] if data_struct.version != '1.0': #load old version data # /exchange/detector # detectorGrp = exchangeGrp['detector'] # dsdata = detectorGrp['data'] # data_struct.exchange.data = dsdata[...] #detectorGrp = exchangeGrp['detector'] dsdata = exchangeGrp['data'] data_struct.exchange.data = dsdata[...] if 'axes' in dsdata.attrs: data_struct.exchange.data_axes = dsdata.attrs['axes'] if 'x' in exchangeGrp: dsx = exchangeGrp['x'] data_struct.exchange.x = dsx[...] data_struct.have_dimscale = 1 if 'y' in exchangeGrp: dsy = exchangeGrp['y'] data_struct.exchange.y = dsy[...] # # if 'x' in detectorGrp: # dsx = detectorGrp['x'] # data_struct.exchange.x = dsx[...] # stack_object.have_dimscale = 1 # # if 'y' in detectorGrp: # dsy = detectorGrp['y'] # data_struct.exchange.y = dsy[...] dseng = exchangeGrp['energy'] data_struct.exchange.energy = dseng[...] data_struct.exchange.energy_units = dseng.attrs['units'] else: # Version 1.0 data = exchangeGrp['data'] data_struct.exchange.data = data[...] if 'signal' in data.attrs: data_struct.exchange.data_signal = data.attrs['signal'] if 'description' in data.attrs: data_struct.exchange.data_description = data.attrs['description'] if 'units' in data.attrs: data_struct.exchange.data_units = data.attrs['units'] if 'detector' in data.attrs: data_struct.exchange.data_detector = data.attrs['detector'] if 'axes' in data.attrs: data_struct.exchange.data_axes = data.attrs['axes'] axes_list = data_struct.exchange.data_axes.split(':') #Axes list can be arbitrary but for spectromicroscopy it is always x:y:z for i in axes_list: ax = exchangeGrp[i] if i == 'x': data_struct.exchange.x = ax[...] if 'units' in ax.attrs: data_struct.exchange.x_units = ax.attrs['units'] stack_object.have_dimscale = 1 if i == 'y': data_struct.exchange.y = ax[...] if 'units' in ax.attrs: data_struct.exchange.y_units = ax.attrs['units'] if i == 'z': data_struct.exchange.z = ax[...] if 'units' in ax.attrs: data_struct.exchange.z_units = ax.attrs['units'] energy = exchangeGrp['energy'] data_struct.exchange.energy = energy[...] data_struct.exchange.energy_units= energy.attrs['units'] if 'theta' in exchangeGrp: th = exchangeGrp['theta'] data_struct.exchange.theta = th[...] data_struct.exchange.theta_units = th.attrs['units'] have4d = 1 if 'white_data' in exchangeGrp: wd = exchangeGrp['white_data'] data_struct.exchange.white_data = wd[...] data_struct.exchange.white_data_units = wd.attrs['units'] if 'dark_data' in exchangeGrp: dd = exchangeGrp['dark_data'] data_struct.exchange.dark_data = dd[...] data_struct.exchange.dark_data_units = dd.attrs['units'] if 'rotation' in exchangeGrp: rot = exchangeGrp['rotation'] data_struct.exchange.rotation = rot[...] # Spectromicroscopy HDF5 group if 'spectromicroscopy' in f: spectromicroscopyGrp = f['spectromicroscopy'] if 'positions' in spectromicroscopyGrp: pos = spectromicroscopyGrp['positions'] data_struct.spectromicroscopy.positions = pos[...] data_struct.spectromicroscopy.positions_units = pos.attrs['units'] data_struct.spectromicroscopy.positions_names = pos.attrs['names'] # if 'optical_density' in spectromicroscopyGrp: # od = spectromicroscopyGrp['optical_density'] # stack_object.data_struct.spectromicroscopy.optical_density = od[...] if 'normalization' in spectromicroscopyGrp: normGrp = spectromicroscopyGrp['normalization'] ws = normGrp['white_spectrum'] #Check if white_spectrum is an array wspectrum = ws[...] try: data_struct.spectromicroscopy.normalization.white_spectrum = ws[...] if 'units' in ws.attrs: data_struct.spectromicroscopy.normalization.white_spectrum_units = ws.attrs['units'] wse = normGrp['white_spectrum_energy'] data_struct.spectromicroscopy.normalization.white_spectrum_energy = wse[...] if 'units' in ws.attrs: data_struct.spectromicroscopy.normalization.white_spectrum_energy_units = wse.attrs['units'] except: pass # Close f.close() if have4d == 0: stack_object.absdata = data_struct.exchange.data else: stack_object.stack4D = data_struct.exchange.data stack_object.theta = data_struct.exchange.theta stack_object.n_theta = len(stack_object.theta) stack_object.absdata = stack_object.stack4D[:,:,:,0] datadim = np.int32(stack_object.absdata.shape) stack_object.n_cols = datadim[0].copy() stack_object.n_rows = datadim[1].copy() stack_object.ev = data_struct.exchange.energy stack_object.n_ev = np.int32(stack_object.ev.shape[0]).copy() npixels = stack_object.n_cols*stack_object.n_rows*stack_object.n_ev #Check if the energies are consecutive, if they are not sort the data consec = 0 for i in range(stack_object.n_ev - 1): if stack_object.ev[i] > stack_object.ev[i+1]: consec = 1 break if (consec == 1) and (have4d == 0): if verbose == 1: print("sort the energy data") sortind = np.argsort(stack_object.ev) stack_object.ev = stack_object.ev[sortind] stack_object.absdata = stack_object.absdata[:,:,sortind] #Save sorted energies: stack_object.data_struct.exchange.data = stack_object.absdata stack_object.data_struct.exchange.energy = stack_object.ev if stack_object.have_dimscale == 1: stack_object.x_dist = data_struct.exchange.x stack_object.y_dist = data_struct.exchange.y else: stack_object.x_dist = range(stack_object.n_cols) stack_object.y_dist = range(stack_object.n_rows) stack_object.i0data = data_struct.spectromicroscopy.normalization.white_spectrum stack_object.evi0 = data_struct.spectromicroscopy.normalization.white_spectrum_energy stack_object.data_dwell = np.ones((stack_object.n_ev)) stack_object.i0_dwell = np.ones((stack_object.n_ev)) if stack_object.data_struct.spectromicroscopy.normalization.white_spectrum is not None: stack_object.calculate_optical_density() stack_object.fill_h5_struct_normalization() if verbose == 1: print('filename ', filename) #print 'File creation date ', data_struct.file_creation_datetime print('Data array shape: ', stack_object.absdata.shape) print('n columns ', stack_object.n_cols) print('n_rows ', stack_object.n_rows) print('n_ev ', stack_object.n_ev) print('ev array ', stack_object.ev) print('x dist ', stack_object.x_dist) print('y_dist ', stack_object.y_dist) #print 'type ', type #print 'i0 data ', stack_object.i0data #print 'evi0 ', stack_object.evi0 return #---------------------------------------------------------------------- def write(filename, data_object, data_type): """Switchyard for writing different types of data.""" if data_type in ['stack']: write_h5(filename, data_object.data_struct) #---------------------------------------------------------------------- def write_h5(filename, data_struct): # Open HDF5 file f = h5py.File(filename, 'w') # This is an implementation of version 1.0 data_struct.implements = 'information:exchange:spectromicroscopy' data_struct.version = '1.0' # HDF5 Root Group ds = f.create_dataset('implements', data = data_struct.implements) ds = f.create_dataset('version', data = data_struct.version) #Information HDF5 group if 'information' in dir(data_struct): informationGrp = f.create_group('information') if data_struct.information.title is not None: ds = informationGrp.create_dataset('title', data = data_struct.information.title) if data_struct.information.comment is not None: ds = informationGrp.create_dataset('comment', data = str(data_struct.information.comment)) if data_struct.information.file_creation_datetime is not None: ds = informationGrp.create_dataset('file_creation_datetime', data = str(data_struct.information.file_creation_datetime)) # /ids idsGrp = informationGrp.create_group('ids') have_ids = 0 if data_struct.information.ids.proposal is not None: ds = idsGrp.create_dataset('proposal', data = data_struct.information.ids.proposal) have_ids = 1 if data_struct.information.ids.activity is not None: ds = idsGrp.create_dataset('activity', data = data_struct.information.ids.activity) have_ids = 1 if data_struct.information.ids.esaf is not None: ds = idsGrp.create_dataset('esaf', data = data_struct.information.ids.esaf) have_ids = 1 if have_ids == 0: del informationGrp['ids'] # /experimenter experimenterGrp = informationGrp.create_group('experimenter') have_exp = 0 if data_struct.information.experimenter.name is not None: ds = experimenterGrp.create_dataset('name', data = str(data_struct.information.experimenter.name)) have_exp = 1 if data_struct.information.experimenter.role is not None: ds = experimenterGrp.create_dataset('role', data = data_struct.information.experimenter.role) have_exp = 1 if data_struct.information.experimenter.affiliation is not None: ds = experimenterGrp.create_dataset('affiliation', data = data_struct.information.experimenter.affiliation) have_exp = 1 if data_struct.information.experimenter.address is not None: ds = experimenterGrp.create_dataset('address', data = data_struct.information.experimenter.address) have_exp = 1 if data_struct.information.experimenter.phone is not None: ds = experimenterGrp.create_dataset('phone', data = data_struct.information.experimenter.phone) have_exp = 1 if data_struct.information.experimenter.email is not None: ds = experimenterGrp.create_dataset('email', data = data_struct.information.experimenter.email) have_exp = 1 if data_struct.information.experimenter.facility_user_id is not None: ds = experimenterGrp.create_dataset('facility_user_id', data = data_struct.information.experimenter.facility_user_id) have_exp = 1 if have_exp == 0: del informationGrp['experimenter'] # /sample sampleGrp = informationGrp.create_group('sample') have_samp = 0 if data_struct.information.sample.name is not None: ds = sampleGrp.create_dataset('name', data = str(data_struct.information.sample.name)) have_samp = 1 if data_struct.information.sample.description is not None: ds = sampleGrp.create_dataset('description', data = data_struct.information.sample.description) have_samp = 1 if data_struct.information.sample.preparation_datetime is not None: ds = sampleGrp.create_dataset('preparation_datetime', data = data_struct.information.sample.preparation_datetime) have_samp = 1 if data_struct.information.sample.chemical_formula is not None: ds = sampleGrp.create_dataset('chemical_formula', data = data_struct.information.chemical_formula) have_samp = 1 if data_struct.information.sample.environment is not None: ds = sampleGrp.create_dataset('environment', data = data_struct.information.sample.environment) have_samp = 1 if data_struct.information.sample.temperature is not None: ds = sampleGrp.create_dataset('temperature', data = data_struct.information.sample.temperature) ds.attrs['units'] = data_struct.information.sample.temperature_units have_samp = 1 if data_struct.information.sample.pressure is not None: ds = sampleGrp.create_dataset('pressure', data = data_struct.information.sample.pressure) ds.attrs['units'] = data_struct.information.sample.pressure_units have_samp = 1 if have_samp == 0: del informationGrp['sample'] # /objective objectiveGrp = informationGrp.create_group('objective') have_obj = 0 if data_struct.information.objective.manufacturer is not None: ds = objectiveGrp.create_dataset('manufacturer', data = data_struct.information.objective.manufacturer) have_obj = 1 if data_struct.information.objective.model is not None: ds = objectiveGrp.create_dataset('model', data = data_struct.information.objective.model) have_obj = 1 if data_struct.information.objective.comment is not None: ds = objectiveGrp.create_dataset('comment', data = data_struct.information.objective.comment) have_obj = 1 if data_struct.information.objective.magnification is not None: ds = objectiveGrp.create_dataset('magnification', data = data_struct.information.objective.magnification) have_obj = 1 if have_obj == 0: del informationGrp['objective'] # /scintillator scintGrp = informationGrp.create_group('scintillator') have_scint = 0 if data_struct.information.scintillator.name is not None: ds = scintGrp.create_dataset('name', data = data_struct.information.scintillator.name) have_scint = 1 if data_struct.information.scintillator.type is not None: ds = scintGrp.create_dataset('type', data = data_struct.information.scintillator.type) have_scint = 1 if data_struct.information.scintillator.comment is not None: ds = scintGrp.create_dataset('comment', data = data_struct.information.scintillator.comment) have_scint = 1 if data_struct.information.scintillator.scintillating_thickness is not None: ds = scintGrp.create_dataset('scintillating_thickness', data = data_struct.information.scintillator.scintillating_thickness) ds.attrs['units'] = data_struct.information.scintillator.scintillating_thickness_units have_scint = 1 if data_struct.information.scintillator.substrate_thickness is not None: ds = scintGrp.create_dataset('substrate_thickness', data = data_struct.information.scintillator.substrate_thickness) ds.attrs['units'] = data_struct.information.scintillator.substrate_thickness_units have_scint = 1 if have_scint == 0: del informationGrp['scintillator'] # /facility facilityGrp = informationGrp.create_group('facility') have_fac = 0 if data_struct.information.facility.name is not None: ds = facilityGrp.create_dataset('name', data = data_struct.information.facility.name) have_fac = 1 if data_struct.information.facility.beamline is not None: ds = facilityGrp.create_dataset('beamline', data = data_struct.information.facility.beamline) have_fac = 1 if have_fac == 0: del informationGrp['facility'] # /accelerator acceleratorGrp = informationGrp.create_group('accelerator') have_ac = 0 if data_struct.information.accelerator.ring_current is not None: ds = acceleratorGrp.create_dataset('ring_current', data = data_struct.information.accelerator.ring_current) ds.attrs['units'] = data_struct.information.accelerator.ring_current_units have_ac = 1 if data_struct.information.accelerator.primary_beam_energy is not None: ds = acceleratorGrp.create_dataset('primary_beam_energy', data = data_struct.information.accelerator.primary_beam_energy) ds.attrs['units'] = data_struct.information.accelerator.primary_beam_energy_units have_ac = 1 if data_struct.information.accelerator.monostripe is not None: ds = acceleratorGrp.create_dataset('monostripe', data = data_struct.information.accelerator.monostripe) have_ac = 1 if have_ac == 0: del informationGrp['accelerator'] # /detector detectorGrp = informationGrp.create_group('detector') have_detc = 0 if data_struct.information.detector.manufacturer is not None: ds = detectorGrp.create_dataset('manufacturer', data = data_struct.information.detector.manufacturer) have_detc = 1 if data_struct.information.detector.model is not None: ds = detectorGrp.create_dataset('model', data = data_struct.information.detector.model) have_detc = 1 if data_struct.information.detector.serial_number is not None: ds = detectorGrp.create_dataset('serial_number', data = data_struct.information.detector.serial_number) have_detc = 1 if data_struct.information.detector.bit_depth is not None: ds = detectorGrp.create_dataset('bit_depth', data = data_struct.information.detector.bit_depth) have_detc = 1 if data_struct.information.detector.operating_temperature is not None: ds = detectorGrp.create_dataset('operating_temperature', data = data_struct.information.detector.operating_temperature) ds.attrs['units'] = data_struct.information.detector.operating_temperature_units have_detc = 1 if data_struct.information.detector.exposure_time is not None: ds = detectorGrp.create_dataset('exposure_time', data = data_struct.information.detector.exposure_time) ds.attrs['units'] = data_struct.information.detector.exposure_time_units have_detc = 1 if data_struct.information.detector.frame_rate is not None: ds = detectorGrp.create_dataset('frame_rate', data = data_struct.information.detector.frame_rate) have_detc = 1 if data_struct.information.detector.pixel_size.horizontal is not None: psGrp = detectorGrp.create_group('pixel_size') ds = psGrp.create_dataset('horizontal', data = data_struct.information.detector.pixel_size.horizontal) ds.attrs['units'] = data_struct.information.detector.pixel_size.horizontal_units ds = psGrp.create_dataset('vertical', data = data_struct.information.detector.pixel_size.vertical) ds.attrs['units'] = data_struct.information.detector.pixel_size.vertical_units have_detc = 1 if data_struct.information.detector.dimensions.horizontal is not None: dimGrp = detectorGrp.create_group('dimensions') ds = dimGrp.create_dataset('horizontal', data = data_struct.information.detector.dimensions.horizontal) ds = dimGrp.create_dataset('vertical', data = data_struct.information.detector.dimensions.vertical) have_detc = 1 if data_struct.information.detector.binning.horizontal is not None: binningGrp = detectorGrp.create_group('binning') ds = binningGrp.create_dataset('horizontal', data = data_struct.information.detector.binning.horizontal) ds = binningGrp.create_dataset('vertical', data = data_struct.information.detector.binning.vertical) have_detc = 1 if data_struct.information.detector.axis_directions.horizontal is not None: axisdirGrp = detectorGrp.create_group('axis_directions') ds = axisdirGrp.create_dataset('horizontal', data = data_struct.information.detector.axis_directions.horizontal) ds = axisdirGrp.create_dataset('vertical', data = data_struct.information.detector.axis_directions.vertical) have_detc = 1 if data_struct.information.detector.roi.x1 is not None: roiGrp = detectorGrp.create_group('roi') ds = roiGrp.create_dataset('x1', data = data_struct.information.detector.roi.x1) ds = roiGrp.create_dataset('y1', data = data_struct.information.detector.roi.y1) ds = roiGrp.create_dataset('x2', data = data_struct.information.detector.roi.x2) ds = roiGrp.create_dataset('y2', data = data_struct.information.detector.roi.y2) have_detc = 1 if have_detc == 0: del informationGrp['detector'] # exchange definition # exchange HDF5 group # /exchange if 'exchange' in dir(data_struct): exchangeGrp = f.create_group("exchange") if data_struct.exchange.title is not None: ds = exchangeGrp.create_dataset('title', data = data_struct.exchange.title) if data_struct.exchange.comment is not None: ds = exchangeGrp.create_dataset('comment', data = data_struct.exchange.comment) if data_struct.exchange.data_collection_datetime is not None: ds = exchangeGrp.create_dataset('data_collection_datetime', data = data_struct.exchange.data_collection_datetime) # /exchange/data ds_data = exchangeGrp.create_dataset('data', data = data_struct.exchange.data) if data_struct.exchange.data_signal is not None: ds_data.attrs['signal'] = data_struct.exchange.data_signal if data_struct.exchange.data_description is not None: ds_data.attrs['description'] = data_struct.exchange.data_description if data_struct.exchange.data_units is not None: ds_data.attrs['units'] = data_struct.exchange.data_units if data_struct.exchange.data_detector is not None: ds_data.attrs['detector'] = data_struct.exchange.data_detector if data_struct.exchange.data_axes is not None: ds_data.attrs['axes'] = data_struct.exchange.data_axes if data_struct.exchange.x is not None: ds = exchangeGrp.create_dataset('x', data = data_struct.exchange.x) if data_struct.exchange.x_units is not None: ds.attrs['units'] = data_struct.exchange.x_units if data_struct.exchange.y is not None: ds = exchangeGrp.create_dataset('y', data = data_struct.exchange.y) if data_struct.exchange.y_units is not None: ds.attrs['units'] = data_struct.exchange.y_units if data_struct.exchange.z is not None: ds = exchangeGrp.create_dataset('z', data = data_struct.exchange.z) if data_struct.exchange.z_units is not None: ds.attrs['units'] = data_struct.exchange.z_units if data_struct.exchange.energy is not None: ds = exchangeGrp.create_dataset('energy', data = data_struct.exchange.energy) if data_struct.exchange.energy_units is not None: ds.attrs['units'] = data_struct.exchange.energy_units if data_struct.exchange.theta is not None: ds = exchangeGrp.create_dataset('theta', data = data_struct.exchange.theta) ds.attrs['units'] = data_struct.exchange.theta_units # /exchange/white_data if data_struct.exchange.white_data is not None: ds = exchangeGrp.create_dataset('white_data', data = data_struct.exchange.white_data) ds.attrs['units'] = data_struct.exchange.white_data_units # /exchange/dark_data if data_struct.exchange.dark_data is not None: ds = exchangeGrp.create_dataset('dark_data', data = data_struct.exchange.dark_data) ds.attrs['units'] = data_struct.exchange.dark_data_units if data_struct.exchange.rotation is not None: ds = exchangeGrp.create_dataset('rotation', data = data_struct.exchange.rotation) # Spectromicroscopy HDF5 group if 'spectromicroscopy' in dir(data_struct): spectromicroscopyGrp = f.create_group('spectromicroscopy') if data_struct.spectromicroscopy.positions is not None: ds = spectromicroscopyGrp.create_dataset('positions', data = data_struct.spectromicroscopy.positions) if data_struct.spectromicroscopy.positions_units is not None: ds.attrs['units'] = data_struct.spectromicroscopy.positions_units if data_struct.spectromicroscopy.positions_names is not None: ds.attrs['names'] = data_struct.spectromicroscopy.positions_names if data_struct.spectromicroscopy.xshifts is not None: ds = spectromicroscopyGrp.create_dataset('xshifts', data = data_struct.spectromicroscopy.xshifts) if data_struct.spectromicroscopy.yshifts is not None: ds = spectromicroscopyGrp.create_dataset('yshifts', data = data_struct.spectromicroscopy.yshifts) if data_struct.spectromicroscopy.optical_density is not None: ds = spectromicroscopyGrp.create_dataset('optical_density', data = data_struct.spectromicroscopy.optical_density) # /spectromicroscopy/normalization normalizationGrp = spectromicroscopyGrp.create_group('normalization') if data_struct.spectromicroscopy.normalization.white_spectrum is not None: ds = normalizationGrp.create_dataset('white_spectrum', data = data_struct.spectromicroscopy.normalization.white_spectrum) if data_struct.spectromicroscopy.normalization.white_spectrum_units is not None: ds.attrs['units'] = data_struct.spectromicroscopy.normalization.white_spectrum_units ds = normalizationGrp.create_dataset('white_spectrum_energy', data = data_struct.spectromicroscopy.normalization.white_spectrum_energy) if data_struct.spectromicroscopy.normalization.white_spectrum_energy_units is not None: ds.attrs['units'] = data_struct.spectromicroscopy.normalization.white_spectrum_energy_units else: del spectromicroscopyGrp['normalization'] # Close f.close() #---------------------------------------------------------------------- def write_results_h5(filename, data_struct, anlz): test_file = 0 #Check if file exists try: # Open HDF5 file f = h5py.File(filename, 'r') test_file = 1 f.close() except: pass #Try to save new hdf5 file if test_file == 0: #try: write_h5(filename, data_struct) #except: # print 'Error: Could not open nor create HDF5 file ', filename # return -1 # Open HDF5 file f = h5py.File(filename, 'r+') # Read basic definitions ds = f['implements'] implements = ds[...] if 'Mantis' in f: mantisGrp = f['Mantis'] else: del f['implements'] implements = implements+':Mantis' ds = f.create_dataset('implements', data = implements) mantisGrp = f.create_group('Mantis') if anlz.pca_calculated == 1: if 'pca' in mantisGrp: del mantisGrp['pca'] pcaGrp = mantisGrp.create_group('pca') else: pcaGrp = mantisGrp.create_group('pca') ds_data = pcaGrp.create_dataset('pca_images', data = anlz.pcaimages) ds_data = pcaGrp.create_dataset('pca_eigenvectors', data = anlz.eigenvecs) ds_data = pcaGrp.create_dataset('pca_eigenvalues', data = anlz.eigenvals) if anlz.clusters_calculated == 1: if 'cluster_analysis' in mantisGrp: del mantisGrp['cluster_analysis'] caGrp = mantisGrp.create_group('cluster_analysis') else: caGrp = mantisGrp.create_group('cluster_analysis') ds_data = caGrp.create_dataset('cluster_indices', data = anlz.cluster_indices) ds_data = caGrp.create_dataset('cluster_spectra', data = anlz.clusterspectra) ds_data = caGrp.create_dataset('cluster_distances', data = anlz.cluster_distances) if anlz.tspectrum_loaded == 1: if 'spectral_maps' in mantisGrp: del mantisGrp['spectral_maps'] caGrp = mantisGrp.create_group('spectral_maps') else: caGrp = mantisGrp.create_group('spectral_maps') ds_data = caGrp.create_dataset('raw_maps', data = anlz.target_svd_maps) ds_data = caGrp.create_dataset('fitted_maps', data = anlz.target_pcafit_maps) ds_data = caGrp.create_dataset('raw_spectra', data = anlz.target_spectra) ds_data = caGrp.create_dataset('fitted_spectra', data = anlz.target_pcafit_spectra) f.close() return 0 #---------------------------------------------------------------------- class h5: def __init__(self): pass #---------------------------------------------------------------------- def check_h5_format(self, filename): aps_format = False # Open HDF5 file f = h5py.File(filename, 'r') #Check is exchange is in the file #Exchange HDF5 Group if 'exchange' in f: aps_format = True f.close() return aps_format #---------------------------------------------------------------------- def read_h5(self, filename):#, data_struct): have4d = 0 # Open HDF5 file f = h5py.File(filename, 'r') # Read basic definitions ds = f['implements'] data_struct.implements = ds[...] ds = f['version'] data_struct.version = ds[...] self.have_dimscale = 0 #Information HDF5 group if 'information' in f: informationGrp = f['information'] if 'title' in informationGrp: title = informationGrp['title'] data_struct.information.title = title[...] if 'comment' in informationGrp: com = informationGrp['comment'] data_struct.information.comment = com[...] if 'file_creation_datetime' in informationGrp: fcdt = informationGrp['file_creation_datetime'] data_struct.information.file_creation_datetime = fcdt[...] # /ids if 'ids' in informationGrp: idsGrp = informationGrp['ids'] if 'proposal' in idsGrp: prop = idsGrp['proposal'] data_struct.information.ids.proposal = prop[...] if 'acitivity' in idsGrp: act = idsGrp['activity'] data_struct.information.ids.activity = act[...] if 'esaf' in idsGrp: esaf = idsGrp['esaf'] data_struct.information.ids.esaf = esaf[...] # /experimenter if 'experimenter' in informationGrp: experimenterGrp = informationGrp['experimenter'] if 'name' in experimenterGrp: name = experimenterGrp['name'] data_struct.information.experimenter.name = name[...] if 'role' in experimenterGrp: role = experimenterGrp['role'] data_struct.information.experimenter.role = role[...] if 'affiliation' in experimenterGrp: affiliation = experimenterGrp['affiliation'] data_struct.information.experimenter.affiliation = affiliation[...] if 'address' in experimenterGrp: address = experimenterGrp['address'] data_struct.information.experimenter.address = address[...] if 'phone' in experimenterGrp: phone = experimenterGrp['phone'] data_struct.information.experimenter.phone = phone[...] if 'email' in experimenterGrp: email = experimenterGrp['email'] data_struct.information.experimenter.email = email[...] if 'facility_user_id' in experimenterGrp: facility_user_id = experimenterGrp['facility_user_id'] data_struct.information.experimenter.facility_user_id = facility_user_id[...] # /sample if 'sample' in informationGrp: sampleGrp = informationGrp['sample'] if 'name' in sampleGrp: name = sampleGrp['name'] data_struct.information.sample.name = name[...] if 'description' in sampleGrp: description = sampleGrp['description'] data_struct.information.sample.description = description[...] if 'preparation_datetime' in sampleGrp: preparation_datetime = sampleGrp['preparation_datetime'] data_struct.information.sample.preparation_datetime = preparation_datetime[...] if 'chemical_formula' in sampleGrp: cf = sampleGrp['chemical_formula'] data_struct.information.sample.chemical_formula = cf[...] if 'environment' in sampleGrp: env = sampleGrp['environment'] data_struct.information.sample.environment = env[...] if 'temperature' in sampleGrp: temp = sampleGrp['temperature'] data_struct.information.sample.temperature = temp[...] data_struct.information.sample.temperature_units = temp.attrs['units'] if 'pressure' in sampleGrp: press = sampleGrp['pressure'] data_struct.information.sample.pressure = press[...] data_struct.information.sample.pressure_units = press.attrs['units'] # /objective if 'objective' in informationGrp: objectiveGrp = informationGrp['objective'] if 'manufacturer' in objectiveGrp: man = objectiveGrp['manufacturer'] data_struct.information.objective.manufacturer = man[...] if 'model' in objectiveGrp: model = objectiveGrp['model'] data_struct.information.objective.model = model[...] if 'comment' in objectiveGrp: com = objectiveGrp['comment'] data_struct.information.objective.comment = com[...] if 'magnification' in objectiveGrp: mag = objectiveGrp['magnification'] data_struct.information.objective.magnification = mag[...] # /scintillator if 'scintillator' in informationGrp: scintGrp = informationGrp['scintillator'] if 'name' in scintGrp: name = scintGrp['name'] data_struct.information.scintillator.name = name[...] if 'type' in scintGrp: type = scintGrp['type'] data_struct.information.scintillator.type = type[...] if 'comment' in scintGrp: com = scintGrp['comment'] data_struct.information.scintillator.comment = com[...] if 'scintillating_thickness' in scintGrp: sct = scintGrp['scintillating_thickness'] data_struct.information.scintillator.scintillating_thickness = sct[...] data_struct.information.scintillator.scintillating_thickness_units = sct.attrs['units'] if 'substrate_thickness' in scintGrp: subt = scintGrp['substrate_thickness'] data_struct.information.scintillator.substrate_thickness = subt[...] data_struct.information.scintillator.substrate_thickness_units = subt.attrs['units'] # /facility if 'facility' in informationGrp: facilityGrp = informationGrp['facility'] if 'name' in facilityGrp: name = facilityGrp['name'] data_struct.information.facility.name = name[...] if 'beamline' in facilityGrp: bl = facilityGrp['beamline'] data_struct.information.facility.beamline = bl[...] # /accelerator if 'accelerator' in informationGrp: acceleratorGrp = informationGrp['accelerator'] if 'ring_current' in acceleratorGrp: rc = acceleratorGrp['ring_current'] data_struct.information.accelerator.ring_current = rc[...] data_struct.information.accelerator.ring_current_units = rc.attrs['units'] if 'primary_beam_energy' in acceleratorGrp: pbe = acceleratorGrp['primary_beam_energy'] data_struct.information.accelerator.primary_beam_energy = pbe[...] data_struct.information.accelerator.primary_beam_energy_units = pbe.attrs['units'] if 'monostripe' in acceleratorGrp: ms = acceleratorGrp['monostripe'] data_struct.information.accelerator.monostripe = ms[...] # /detector if 'detector' in informationGrp: detectorGrp = informationGrp['detector'] if 'manufacturer' in detectorGrp: manf = detectorGrp['manufacturer'] data_struct.information.detector.manufacturer = manf[...] if 'model' in detectorGrp: model = detectorGrp['model'] data_struct.information.detector.model = model[...] if 'serial_number' in detectorGrp: sn = detectorGrp['serial_number'] data_struct.information.detector.serial_number = sn[...] if 'bit_depth' in detectorGrp: bd = detectorGrp['bit_depth'] data_struct.information.detector.bit_depth = bd[...] if 'operating_temperature' in detectorGrp: ot = detectorGrp['operating_temperature'] data_struct.information.detector.operating_temperature = ot[...] data_struct.information.detector.operating_temperature_units = ot.attrs['units'] if 'exposure_time' in detectorGrp: et = detectorGrp['exposure_time'] data_struct.information.detector.exposure_time = et[...] data_struct.information.detector.exposure_time_units = et.attrs['units'] if 'frame_rate' in detectorGrp: fr = detectorGrp['frame_rate'] data_struct.information.detector.frame_rate = fr[...] if 'pixel_size' in detectorGrp: psGrp = detectorGrp['pixel_size'] hor = psGrp['horizontal'] data_struct.information.detector.pixel_size.horizontal = hor[...] data_struct.information.detector.pixel_size.horizontal_units = hor.attrs['units'] ver = psGrp['vertical'] data_struct.information.detector.pixel_size.vertical = ver[...] data_struct.information.detector.pixel_size.vertical_units = ver.attrs['units'] if 'dimensions' in detectorGrp: dimGrp = detectorGrp['dimenstions'] h = dimGrp['horizontal'] data_struct.information.detector.dimensions.horizontal = h[...] v = dimGrp['vertical'] data_struct.information.detector.dimensions.vertical = v[...] if 'binning' in detectorGrp: binningGrp = detectorGrp['binning'] h = binningGrp['horizontal'] data_struct.information.detector.binning.horizontal = h[...] v = binningGrp['vertical'] data_struct.information.detector.binning.vertical = v[...] if 'axis_directions' in detectorGrp: axisdirGrp = detectorGrp['axis_directions'] h = axisdirGrp['horizontal'] data_struct.information.detector.axis_directions.horizontal = h[...] v = axisdirGrp['vertical'] data_struct.information.detector.axis_directions.vertical = v[...] if 'roi' in detectorGrp: roiGrp = detectorGrp['roi'] x1 = roiGrp['x1'] data_struct.information.detector.roi.x1 = x1[...] y1 = roiGrp['y1'] data_struct.information.detector.roi.y1 = y1[...] x2 = roiGrp['x2'] data_struct.information.detector.roi.x2 = x2[...] y2 = roiGrp['x2'] data_struct.information.detector.roi.y2 = y2[...] #Exchange HDF5 Group if 'exchange' in f: exchangeGrp = f['exchange'] if 'title' in exchangeGrp: title = exchangeGrp['title'] data_struct.exchange.title = title[...] if 'comment' in exchangeGrp: com = exchangeGrp['comment'] data_struct.exchange.comment = com[...] if 'data_collection_datetime' in exchangeGrp: dcdt = exchangeGrp['data_collection_datetime'] data_struct.exchange.data_collection_datetime = dcdt[...] if data_struct.version != '1.0': #load old version data # /exchange/detector # detectorGrp = exchangeGrp['detector'] # dsdata = detectorGrp['data'] # data_struct.exchange.data = dsdata[...] #detectorGrp = exchangeGrp['detector'] dsdata = exchangeGrp['data'] data_struct.exchange.data = dsdata[...] if 'axes' in dsdata.attrs: data_struct.exchange.data_axes = dsdata.attrs['axes'] # # if 'x' in detectorGrp: # dsx = detectorGrp['x'] # data_struct.exchange.x = dsx[...] # self.have_dimscale = 1 # # if 'y' in detectorGrp: # dsy = detectorGrp['y'] # data_struct.exchange.y = dsy[...] dseng = exchangeGrp['energy'] data_struct.exchange.energy = dseng[...] #data_struct.exchange.energy_units = dseng.attrs['units'] else: # Version 1.0 data = exchangeGrp['data'] data_struct.exchange.data = data[...] if 'signal' in data.attrs: data_struct.exchange.data_signal = data.attrs['signal'] if 'description' in data.attrs: data_struct.exchange.data_description = data.attrs['description'] if 'units' in data.attrs: data_struct.exchange.data_units = data.attrs['units'] if 'detector' in data.attrs: data_struct.exchange.data_detector = data.attrs['detector'] if 'axes' in data.attrs: data_struct.exchange.data_axes = data.attrs['axes'] axes_list = data_struct.exchange.data_axes.split(':') #Axes list can be arbitrary but for spectromicroscopy it is always x:y:z for i in axes_list: ax = exchangeGrp[i] if i == 'x': data_struct.exchange.x = ax[...] if 'units' in ax.attrs: data_struct.exchange.x_units = ax.attrs['units'] self.have_dimscale = 1 if i == 'y': data_struct.exchange.y = ax[...] if 'units' in ax.attrs: data_struct.exchange.y_units = ax.attrs['units'] if i == 'z': data_struct.exchange.z = ax[...] if 'units' in ax.attrs: data_struct.exchange.z_units = ax.attrs['units'] energy = exchangeGrp['energy'] data_struct.exchange.energy = energy[...] data_struct.exchange.energy_units= energy.attrs['units'] if 'theta' in exchangeGrp: th = exchangeGrp['theta'] data_struct.exchange.theta = th[...] data_struct.exchange.theta_units = th.attrs['units'] have4d = 1 if 'white_data' in exchangeGrp: wd = exchangeGrp['white_data'] data_struct.exchange.white_data = wd[...] data_struct.exchange.white_data_units = wd.attrs['units'] if 'dark_data' in exchangeGrp: dd = exchangeGrp['dark_data'] data_struct.exchange.dark_data = dd[...] data_struct.exchange.dark_data_units = dd.attrs['units'] if 'rotation' in exchangeGrp: rot = exchangeGrp['rotation'] data_struct.exchange.rotation = rot[...] # Spectromicroscopy HDF5 group if 'spectromicroscopy' in f: spectromicroscopyGrp = f['spectromicroscopy'] if 'positions' in spectromicroscopyGrp: pos = spectromicroscopyGrp['positions'] data_struct.spectromicroscopy.positions = pos[...] data_struct.spectromicroscopy.positions_units = pos.attrs['units'] data_struct.spectromicroscopy.positions_names = pos.attrs['names'] # if 'optical_density' in spectromicroscopyGrp: # od = spectromicroscopyGrp['optical_density'] # self.data_struct.spectromicroscopy.optical_density = od[...] if 'normalization' in spectromicroscopyGrp: normGrp = spectromicroscopyGrp['normalization'] ws = normGrp['white_spectrum'] #Check if white_spectrum is an array wspectrum = ws[...] try: data_struct.spectromicroscopy.normalization.white_spectrum = ws[...] if 'units' in ws.attrs: data_struct.spectromicroscopy.normalization.white_spectrum_units = ws.attrs['units'] wse = normGrp['white_spectrum_energy'] data_struct.spectromicroscopy.normalization.white_spectrum_energy = wse[...] if 'units' in ws.attrs: data_struct.spectromicroscopy.normalization.white_spectrum_energy_units = wse.attrs['units'] except: pass # Close f.close() if have4d == 0: self.absdata = data_struct.exchange.data else: self.stack4D = data_struct.exchange.data self.theta = data_struct.exchange.theta self.n_theta = len(self.theta) self.absdata = self.stack4D[:,:,:,0] datadim = np.int32(self.absdata.shape) self.n_cols = datadim[0].copy() self.n_rows = datadim[1].copy() self.ev = data_struct.exchange.energy self.n_ev = np.int32(self.ev.shape[0]).copy() npixels = self.n_cols*self.n_rows*self.n_ev #Check if the energies are consecutive, if they are not sort the data consec = 0 for i in range(self.n_ev - 1): if self.ev[i] > self.ev[i+1]: consec = 1 break if consec == 1: if verbose == 1: print("sort the energy data") sortind = np.argsort(self.ev) self.ev = self.ev[sortind] self.absdata = self.absdata[:,:,sortind] #Save sorted energies: self.data_struct.exchange.data = self.absdata self.data_struct.exchange.energy = self.ev if self.have_dimscale == 1: self.x_dist = data_struct.exchange.x self.y_dist = data_struct.exchange.y else: self.x_dist = range(self.n_cols) self.y_dist = range(self.n_rows) self.i0data = data_struct.spectromicroscopy.normalization.white_spectrum self.evi0 = data_struct.spectromicroscopy.normalization.white_spectrum_energy self.data_dwell = np.ones((self.n_ev)) self.i0_dwell = np.ones((self.n_ev)) if verbose == 1: print('filename ', filename) #print 'File creation date ', data_struct.file_creation_datetime print('Data array shape: ', self.absdata.shape) print('n columns ', self.n_cols) print('n_rows ', self.n_rows) print('n_ev ', self.n_ev) print('ev array ', self.ev) print('x dist ', self.x_dist) print('y_dist ', self.y_dist) #print 'type ', type #print 'i0 data ', self.i0data #print 'evi0 ', self.evi0 return #---------------------------------------------------------------------- def write_h5(self, filename, data_struct): # Open HDF5 file f = h5py.File(filename, 'w') # This is an implementation of version 1.0 data_struct.implements = 'information:exchange:spectromicroscopy' data_struct.version = '1.0' # HDF5 Root Group ds = f.create_dataset('implements', data = data_struct.implements) ds = f.create_dataset('version', data = data_struct.version) #Information HDF5 group informationGrp = f.create_group('information') if data_struct.information.title is not None: ds = informationGrp.create_dataset('title', data = data_struct.information.title) if data_struct.information.comment is not None: ds = informationGrp.create_dataset('comment', data = str(data_struct.information.comment)) if data_struct.information.file_creation_datetime is not None: ds = informationGrp.create_dataset('file_creation_datetime', data = str(data_struct.information.file_creation_datetime)) # /ids idsGrp = informationGrp.create_group('ids') have_ids = 0 if data_struct.information.ids.proposal is not None: ds = idsGrp.create_dataset('proposal', data = data_struct.information.ids.proposal) have_ids = 1 if data_struct.information.ids.activity is not None: ds = idsGrp.create_dataset('activity', data = data_struct.information.ids.activity) have_ids = 1 if data_struct.information.ids.esaf is not None: ds = idsGrp.create_dataset('esaf', data = data_struct.information.ids.esaf) have_ids = 1 if have_ids == 0: del informationGrp['ids'] # /experimenter experimenterGrp = informationGrp.create_group('experimenter') have_exp = 0 if data_struct.information.experimenter.name is not None: ds = experimenterGrp.create_dataset('name', data = str(data_struct.information.experimenter.name)) have_exp = 1 if data_struct.information.experimenter.role is not None: ds = experimenterGrp.create_dataset('role', data = data_struct.information.experimenter.role) have_exp = 1 if data_struct.information.experimenter.affiliation is not None: ds = experimenterGrp.create_dataset('affiliation', data = data_struct.information.experimenter.affiliation) have_exp = 1 if data_struct.information.experimenter.address is not None: ds = experimenterGrp.create_dataset('address', data = data_struct.information.experimenter.address) have_exp = 1 if data_struct.information.experimenter.phone is not None: ds = experimenterGrp.create_dataset('phone', data = data_struct.information.experimenter.phone) have_exp = 1 if data_struct.information.experimenter.email is not None: ds = experimenterGrp.create_dataset('email', data = data_struct.information.experimenter.email) have_exp = 1 if data_struct.information.experimenter.facility_user_id is not None: ds = experimenterGrp.create_dataset('facility_user_id', data = data_struct.information.experimenter.facility_user_id) have_exp = 1 if have_exp == 0: del informationGrp['experimenter'] # /sample sampleGrp = informationGrp.create_group('sample') have_samp = 0 if data_struct.information.sample.name is not None: ds = sampleGrp.create_dataset('name', data = str(data_struct.information.sample.name)) have_samp = 1 if data_struct.information.sample.description is not None: ds = sampleGrp.create_dataset('description', data = data_struct.information.sample.description) have_samp = 1 if data_struct.information.sample.preparation_datetime is not None: ds = sampleGrp.create_dataset('preparation_datetime', data = data_struct.information.sample.preparation_datetime) have_samp = 1 if data_struct.information.sample.chemical_formula is not None: ds = sampleGrp.create_dataset('chemical_formula', data = data_struct.information.chemical_formula) have_samp = 1 if data_struct.information.sample.environment is not None: ds = sampleGrp.create_dataset('environment', data = data_struct.information.sample.environment) have_samp = 1 if data_struct.information.sample.temperature is not None: ds = sampleGrp.create_dataset('temperature', data = data_struct.information.sample.temperature) ds.attrs['units'] = data_struct.information.sample.temperature_units have_samp = 1 if data_struct.information.sample.pressure is not None: ds = sampleGrp.create_dataset('pressure', data = data_struct.information.sample.pressure) ds.attrs['units'] = data_struct.information.sample.pressure_units have_samp = 1 if have_samp == 0: del informationGrp['sample'] # /objective objectiveGrp = informationGrp.create_group('objective') have_obj = 0 if data_struct.information.objective.manufacturer is not None: ds = objectiveGrp.create_dataset('manufacturer', data = data_struct.information.objective.manufacturer) have_obj = 1 if data_struct.information.objective.model is not None: ds = objectiveGrp.create_dataset('model', data = data_struct.information.objective.model) have_obj = 1 if data_struct.information.objective.comment is not None: ds = objectiveGrp.create_dataset('comment', data = data_struct.information.objective.comment) have_obj = 1 if data_struct.information.objective.magnification is not None: ds = objectiveGrp.create_dataset('magnification', data = data_struct.information.objective.magnification) have_obj = 1 if have_obj == 0: del informationGrp['objective'] # /scintillator scintGrp = informationGrp.create_group('scintillator') have_scint = 0 if data_struct.information.scintillator.name is not None: ds = scintGrp.create_dataset('name', data = data_struct.information.scintillator.name) have_scint = 1 if data_struct.information.scintillator.type is not None: ds = scintGrp.create_dataset('type', data = data_struct.information.scintillator.type) have_scint = 1 if data_struct.information.scintillator.comment is not None: ds = scintGrp.create_dataset('comment', data = data_struct.information.scintillator.comment) have_scint = 1 if data_struct.information.scintillator.scintillating_thickness is not None: ds = scintGrp.create_dataset('scintillating_thickness', data = data_struct.information.scintillator.scintillating_thickness) ds.attrs['units'] = data_struct.information.scintillator.scintillating_thickness_units have_scint = 1 if data_struct.information.scintillator.substrate_thickness is not None: ds = scintGrp.create_dataset('substrate_thickness', data = data_struct.information.scintillator.substrate_thickness) ds.attrs['units'] = data_struct.information.scintillator.substrate_thickness_units have_scint = 1 if have_scint == 0: del informationGrp['scintillator'] # /facility facilityGrp = informationGrp.create_group('facility') have_fac = 0 if data_struct.information.facility.name is not None: ds = facilityGrp.create_dataset('name', data = data_struct.information.facility.name) have_fac = 1 if data_struct.information.facility.beamline is not None: ds = facilityGrp.create_dataset('beamline', data = data_struct.information.facility.beamline) have_fac = 1 if have_fac == 0: del informationGrp['facility'] # /accelerator acceleratorGrp = informationGrp.create_group('accelerator') have_ac = 0 if data_struct.information.accelerator.ring_current is not None: ds = acceleratorGrp.create_dataset('ring_current', data = data_struct.information.accelerator.ring_current) ds.attrs['units'] = data_struct.information.accelerator.ring_current_units have_ac = 1 if data_struct.information.accelerator.primary_beam_energy is not None: ds = acceleratorGrp.create_dataset('primary_beam_energy', data = data_struct.information.accelerator.primary_beam_energy) ds.attrs['units'] = data_struct.information.accelerator.primary_beam_energy_units have_ac = 1 if data_struct.information.accelerator.monostripe is not None: ds = acceleratorGrp.create_dataset('monostripe', data = data_struct.information.accelerator.monostripe) have_ac = 1 if have_ac == 0: del informationGrp['accelerator'] # /detector detectorGrp = informationGrp.create_group('detector') have_detc = 0 if data_struct.information.detector.manufacturer is not None: ds = detectorGrp.create_dataset('manufacturer', data = data_struct.information.detector.manufacturer) have_detc = 1 if data_struct.information.detector.model is not None: ds = detectorGrp.create_dataset('model', data = data_struct.information.detector.model) have_detc = 1 if data_struct.information.detector.serial_number is not None: ds = detectorGrp.create_dataset('serial_number', data = data_struct.information.detector.serial_number) have_detc = 1 if data_struct.information.detector.bit_depth is not None: ds = detectorGrp.create_dataset('bit_depth', data = data_struct.information.detector.bit_depth) have_detc = 1 if data_struct.information.detector.operating_temperature is not None: ds = detectorGrp.create_dataset('operating_temperature', data = data_struct.information.detector.operating_temperature) ds.attrs['units'] = data_struct.information.detector.operating_temperature_units have_detc = 1 if data_struct.information.detector.exposure_time is not None: ds = detectorGrp.create_dataset('exposure_time', data = data_struct.information.detector.exposure_time) ds.attrs['units'] = data_struct.information.detector.exposure_time_units have_detc = 1 if data_struct.information.detector.frame_rate is not None: ds = detectorGrp.create_dataset('frame_rate', data = data_struct.information.detector.frame_rate) have_detc = 1 if data_struct.information.detector.pixel_size.horizontal is not None: psGrp = detectorGrp.create_group('pixel_size') ds = psGrp.create_dataset('horizontal', data = data_struct.information.detector.pixel_size.horizontal) ds.attrs['units'] = data_struct.information.detector.pixel_size.horizontal_units ds = psGrp.create_dataset('vertical', data = data_struct.information.detector.pixel_size.vertical) ds.attrs['units'] = data_struct.information.detector.pixel_size.vertical_units have_detc = 1 if data_struct.information.detector.dimensions.horizontal is not None: dimGrp = detectorGrp.create_group('dimensions') ds = dimGrp.create_dataset('horizontal', data = data_struct.information.detector.dimensions.horizontal) ds = dimGrp.create_dataset('vertical', data = data_struct.information.detector.dimensions.vertical) have_detc = 1 if data_struct.information.detector.binning.horizontal is not None: binningGrp = detectorGrp.create_group('binning') ds = binningGrp.create_dataset('horizontal', data = data_struct.information.detector.binning.horizontal) ds = binningGrp.create_dataset('vertical', data = data_struct.information.detector.binning.vertical) have_detc = 1 if data_struct.information.detector.axis_directions.horizontal is not None: axisdirGrp = detectorGrp.create_group('axis_directions') ds = axisdirGrp.create_dataset('horizontal', data = data_struct.information.detector.axis_directions.horizontal) ds = axisdirGrp.create_dataset('vertical', data = data_struct.information.detector.axis_directions.vertical) have_detc = 1 if data_struct.information.detector.roi.x1 is not None: roiGrp = detectorGrp.create_group('roi') ds = roiGrp.create_dataset('x1', data = data_struct.information.detector.roi.x1) ds = roiGrp.create_dataset('y1', data = data_struct.information.detector.roi.y1) ds = roiGrp.create_dataset('x2', data = data_struct.information.detector.roi.x2) ds = roiGrp.create_dataset('y2', data = data_struct.information.detector.roi.y2) have_detc = 1 if have_detc == 0: del informationGrp['detector'] # exchange definition # exchange HDF5 group # /exchange exchangeGrp = f.create_group("exchange") if data_struct.exchange.title is not None: ds = exchangeGrp.create_dataset('title', data = data_struct.exchange.title) if data_struct.exchange.comment is not None: ds = exchangeGrp.create_dataset('comment', data = data_struct.exchange.comment) if data_struct.exchange.data_collection_datetime is not None: ds = exchangeGrp.create_dataset('data_collection_datetime', data = data_struct.exchange.data_collection_datetime) # /exchange/data ds_data = exchangeGrp.create_dataset('data', data = data_struct.exchange.data) if data_struct.exchange.data_signal is not None: ds_data.attrs['signal'] = data_struct.exchange.data_signal if data_struct.exchange.data_description is not None: ds_data.attrs['description'] = data_struct.exchange.data_description if data_struct.exchange.data_units is not None: ds_data.attrs['units'] = data_struct.exchange.data_units if data_struct.exchange.data_detector is not None: ds_data.attrs['detector'] = data_struct.exchange.data_detector if data_struct.exchange.data_axes is not None: ds_data.attrs['axes'] = data_struct.exchange.data_axes if data_struct.exchange.x is not None: ds = exchangeGrp.create_dataset('x', data = data_struct.exchange.x) if data_struct.exchange.x_units is not None: ds.attrs['units'] = data_struct.exchange.x_units if data_struct.exchange.y is not None: ds = exchangeGrp.create_dataset('y', data = data_struct.exchange.y) if data_struct.exchange.y_units is not None: ds.attrs['units'] = data_struct.exchange.y_units if data_struct.exchange.z is not None: ds = exchangeGrp.create_dataset('z', data = data_struct.exchange.z) if data_struct.exchange.z_units is not None: ds.attrs['units'] = data_struct.exchange.z_units if data_struct.exchange.energy is not None: ds = exchangeGrp.create_dataset('energy', data = data_struct.exchange.energy) ds.attrs['units'] = data_struct.exchange.energy_units if data_struct.exchange.theta is not None: ds = exchangeGrp.create_dataset('theta', data = data_struct.exchange.theta) ds.attrs['units'] = data_struct.exchange.theta_units # /exchange/white_data if data_struct.exchange.white_data is not None: ds = exchangeGrp.create_dataset('white_data', data = data_struct.exchange.white_data) ds.attrs['units'] = data_struct.exchange.white_data_units # /exchange/dark_data if data_struct.exchange.dark_data is not None: ds = exchangeGrp.create_dataset('dark_data', data = data_struct.exchange.dark_data) ds.attrs['units'] = data_struct.exchange.dark_data_units if data_struct.exchange.rotation is not None: ds = exchangeGrp.create_dataset('rotation', data = data_struct.exchange.rotation) # Spectromicroscopy HDF5 group spectromicroscopyGrp = f.create_group('spectromicroscopy') if data_struct.spectromicroscopy.positions is not None: ds = spectromicroscopyGrp.create_dataset('positions', data = data_struct.spectromicroscopy.positions) if data_struct.spectromicroscopy.positions_units is not None: ds.attrs['units'] = data_struct.spectromicroscopy.positions_units if data_struct.spectromicroscopy.positions_names is not None: ds.attrs['names'] = data_struct.spectromicroscopy.positions_names if data_struct.spectromicroscopy.xshifts is not None: ds = spectromicroscopyGrp.create_dataset('xshifts', data = data_struct.spectromicroscopy.xshifts) if data_struct.spectromicroscopy.yshifts is not None: ds = spectromicroscopyGrp.create_dataset('yshifts', data = data_struct.spectromicroscopy.yshifts) if self.data_struct.spectromicroscopy.optical_density is not None: ds = spectromicroscopyGrp.create_dataset('optical_density', data = self.data_struct.spectromicroscopy.optical_density) # /spectromicroscopy/normalization normalizationGrp = spectromicroscopyGrp.create_group('normalization') if data_struct.spectromicroscopy.normalization.white_spectrum is not None: ds = normalizationGrp.create_dataset('white_spectrum', data = data_struct.spectromicroscopy.normalization.white_spectrum) if data_struct.spectromicroscopy.normalization.white_spectrum_units is not None: ds.attrs['units'] = data_struct.spectromicroscopy.normalization.white_spectrum_units ds = normalizationGrp.create_dataset('white_spectrum_energy', data = data_struct.spectromicroscopy.normalization.white_spectrum_energy) if data_struct.spectromicroscopy.normalization.white_spectrum_energy_units is not None: ds.attrs['units'] = data_struct.spectromicroscopy.normalization.white_spectrum_energy_units else: del spectromicroscopyGrp['normalization'] # Close f.close() #---------------------------------------------------------------------- def write_results_h5(self, filename, data_struct, anlz): test_file = 0 #Check if file exists try: # Open HDF5 file f = h5py.File(filename, 'r') test_file = 1 f.close() except: pass #Try to save new hdf5 file if test_file == 0: #try: self.write_h5(filename, data_struct) #except: # print 'Error: Could not open nor create HDF5 file ', filename # return -1 # Open HDF5 file f = h5py.File(filename, 'r+') # Read basic definitions ds = f['implements'] implements = ds[...] if 'Mantis' in f: mantisGrp = f['Mantis'] else: del f['implements'] implements = implements+':Mantis' ds = f.create_dataset('implements', data = implements) mantisGrp = f.create_group('Mantis') if anlz.pca_calculated == 1: if 'pca' in mantisGrp: del mantisGrp['pca'] pcaGrp = mantisGrp.create_group('pca') else: pcaGrp = mantisGrp.create_group('pca') ds_data = pcaGrp.create_dataset('pca_images', data = anlz.pcaimages) ds_data = pcaGrp.create_dataset('pca_eigenvectors', data = anlz.eigenvecs) ds_data = pcaGrp.create_dataset('pca_eigenvalues', data = anlz.eigenvals) if anlz.clusters_calculated == 1: if 'cluster_analysis' in mantisGrp: del mantisGrp['cluster_analysis'] caGrp = mantisGrp.create_group('cluster_analysis') else: caGrp = mantisGrp.create_group('cluster_analysis') ds_data = caGrp.create_dataset('cluster_indices', data = anlz.cluster_indices) ds_data = caGrp.create_dataset('cluster_spectra', data = anlz.clusterspectra) ds_data = caGrp.create_dataset('cluster_distances', data = anlz.cluster_distances) if anlz.tspectrum_loaded == 1: if 'spectral_maps' in mantisGrp: del mantisGrp['spectral_maps'] caGrp = mantisGrp.create_group('spectral_maps') else: caGrp = mantisGrp.create_group('spectral_maps') ds_data = caGrp.create_dataset('raw_maps', data = anlz.target_svd_maps) ds_data = caGrp.create_dataset('fitted_maps', data = anlz.target_pcafit_maps) ds_data = caGrp.create_dataset('raw_spectra', data = anlz.target_spectra) ds_data = caGrp.create_dataset('fitted_spectra', data = anlz.target_pcafit_spectra) f.close() return 0 ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/file_plugins/file_json.py0000644000175000017500000002403514332463747022564 0ustar00wattswatts# -*- 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 . from __future__ import print_function import json import re, numpy, sys from os.path import splitext, isfile from collections import OrderedDict title = 'JSON' extension = ['*.json', '*.hdr'] read_types = ['spectrum','image','stack'] write_types = [] def identify(filename): try: if isfile(splitext(filename)[0] + '.json'): JS = JS_FileLoader(splitext(filename)[0] + '.json') return JS.num_regions>0 # return true if file contains at least one region else: return False except: return False def GetFileStructure(FileName): JS = JS_FileLoader(FileName) if JS.num_regions<2 and JS.num_channels<2 and isfile(splitext(FileName)[0] + '.json'): #if json file exists, skip datachoicedialog print("JSON-file found.") return None # exit if only one choice D = OrderedDict() for i,R in enumerate(['Region_'+str(r) for r in range(JS.num_regions)]): D[R] = OrderedDict() D[R].definition = 'JSON' D[R].scan_type = JS.js['ScanDefinition']['Type'] D[R].data_shape = JS.data_size[i] D[R].data_axes = [JS.js['ScanDefinition']['Regions'][1]['PAxis']['Name'],JS.js['ScanDefinition']['Regions'][1]['QAxis']['Name'],JS.js['ScanDefinition']['StackAxis']['Name']] for ch in range(1,JS.num_channels+1): D[R][JS.js['Channels'][ch]['Name']] = OrderedDict() return D #---------------------------------------------------------------------- class JS_FileLoader: """Parse .hdr file for metadata.""" js = [] f = [] def __init__(self, fileName,identify=False): fileName = (splitext(fileName)[0] + '.json') if not JS_FileLoader.js or JS_FileLoader.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 is loaded. # Parse the JSON file with open(fileName) as f: JS_FileLoader.js = json.load(f) JS_FileLoader.f = fileName else: None self.js = JS_FileLoader.js if 'ScanDefinition' in self.js: self.num_regions = int(self.js['ScanDefinition']['Regions'][0]) self.num_channels = int(self.js['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 parseDataNames(self): #ToDo: Assigning Scan types by DataFlags is prone to errors. Better detect automatically via (HDR.hdr['ScanDefinition']['Regions'][0]) etc. """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 = []#Regions DataNames2 = []#Channels DataNames3 = []#Energies Alphabet = 'abcdefghijklmnopqrstuvwxyz' DataFlag = self.js['ScanDefinition']['Flags'] 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) elif DataFlag == 'Image': DataNames2 = [] for num_Ch in range(self.num_channels): DataNames2.append([self.file_path+'_'+Alphabet[num_Ch]+'.xim']) DataNames.append(DataNames2) elif DataFlag in ['Multi-Region Image']: for num_R in range(self.num_regions): DataNames2 = [] for num_Ch in range(self.num_channels): DataNames2.append([self.file_path + '_' + Alphabet[num_Ch] + str(num_R) + '.xim']) DataNames.append(DataNames2) elif DataFlag in ['Multi-Region Image Stack']: for num_Ch in range(self.num_channels): DataNames2 = [] for num_R in range(self.num_regions): DataNames3 = [] for num_E in range(self.data_size[0][2]): DataNames3.append( self.file_path + '_' + Alphabet[num_Ch] + str(num_E).zfill(3) + str(num_R) + '.xim') DataNames2.append(DataNames3) DataNames = [DataNames2] # print(DataNames[0]) elif DataFlag == 'Image Stack': DataNames2 = [] for num_Ch in range(self.num_channels): DataNames3 = [] for num_E in range(self.data_size[0][2]): DataNames3.append(self.file_path+'_'+Alphabet[num_Ch]+str(num_E).zfill(3)+'.xim') DataNames2.append(DataNames3) DataNames = [DataNames2] else: print("WARNING! Unknown flag:", DataFlag) #for num_R in range(len(DataNames)):#Check that names correspond to existing files #for num_Ch in range(len(DataNames[num_R])): #for num_E in range(len(DataNames[num_R][num_Ch])): #if exists(DataNames[num_R][num_Ch][num_E]) == False: #print "WARNING! Data file doesn't exist:", DataNames[num_R][num_Ch][num_E] 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.js['ScanDefinition']['Regions'][R_num+1]['PAxis']['Points'][0]) if 'QAxis' in self.js['ScanDefinition']['Regions'][R_num+1] and 'Points' in self.js['ScanDefinition']['Regions'][R_num+1]['QAxis']: DataSize[R_num][1] = int(self.js['ScanDefinition']['Regions'][R_num+1]['QAxis']['Points'][0]) if 'StackAxis' in self.js['ScanDefinition'] and 'Points' in self.js['ScanDefinition']['StackAxis']: DataSize[R_num][2] = int(self.js['ScanDefinition']['StackAxis']['Points'][0]) if self.js['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, *args, **kwargs): JS = JS_FileLoader(filename) allowed = ['Image Stack', 'Image', 'Multi-Region Image Stack'] if JS.js['ScanDefinition']['Flags'] in allowed: self.x_dist = numpy.array([float(i) for i in JS.js['ScanDefinition']['Regions'][selection[0]+1]['PAxis']['Points'][1:] ]) self.y_dist = numpy.array([float(i) for i in JS.js['ScanDefinition']['Regions'][selection[0]+1]['QAxis']['Points'][1:] ]) self.ev = numpy.array([float(i) for i in JS.js['ScanDefinition']['StackAxis']['Points'][1:] ]) self.n_cols = len(self.x_dist) self.n_rows = len(self.y_dist) self.n_ev = len(self.ev) msec = float(JS.js['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) for i in range(len(JS.data_names[selection[1]][selection[0]])): try: imagestack[:,:,i] = numpy.loadtxt(JS.data_names[selection[1]][selection[0]][i], numpy.int32).T except ValueError: print("Aborted stack or XIMs with inconsistent dimensions.") imagestack[:,:,i] = numpy.nan except IOError: print("Image file 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() else: print("Unknown Format") return #---------------------------------------------------------------------- def read_js_i0(self, filename, *args, **kwargs): JS = JS_FileLoader(filename) if 'ScanType' in JS.js['ScanDefinition'] and JS.js['ScanDefinition']['ScanType'] == 'Spectra': Energies = JS.js['ScanDefinition']['Regions'][1]['PAxis']['Points'][1:] tempimage = numpy.loadtxt(JS.data_names[0][0][0], numpy.float32) Data = tempimage[:,1] elif JS.js['ScanDefinition']['Type'] == 'NEXAFS Line Scan': Energies = JS.js['ScanDefinition']['Regions'][1]['PAxis']['Points'][1:] tempimage = numpy.loadtxt(JS.data_names[0][0][0], numpy.int32) Data = numpy.mean(tempimage,axis=0) else:# Image Stack Energies = JS.js['ScanDefinition']['StackAxis']['Points'][1:] tempimage = numpy.empty((JS.data_size[0][0],JS.data_size[0][1]), numpy.int32) Data = numpy.empty((JS.data_size[0][2]), numpy.int32) for i in range(len(JS.data_names[0][0])): tempimage = numpy.loadtxt(JS.data_names[0][0][i], numpy.int32) Data[i] = numpy.mean(tempimage) msec = float(JS.js['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././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1677150153.0 mantis-xray-3.1.15/mantis_xray/file_plugins/file_ncb.py0000664000175000017500000005146014375643711022357 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2011 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # 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 . from __future__ import division from __future__ import print_function import numpy as np import os import re verbose = 0 title = 'Ncb' extension = ['*.ncb'] read_types = ['image', 'stack'] write_types = ['image', 'stack', 'results'] def identify(filename): try: basename, extension = os.path.splitext(filename) dat_fn = basename + '.dat' f = open(str(dat_fn), 'r') f.close() return True except: return False def GetFileStructure(FileName): return None # ---------------------------------------------------------------------- def read(filename, self, selection=None, *args, **kwargs): basename, extension = os.path.splitext(filename) dat_fn = basename + '.dat' f = open(str(dat_fn), 'r') lines = f.readlines() temp = lines[0].split() self.n_cols = int(temp[0]) self.n_rows = int(temp[1]) scale = float(temp[2]) # print self.n_cols, self.n_rows, scale temp = lines[1].split() x_start = float(temp[0]) x_stop = float(temp[1]) temp = lines[2].split() y_start = float(temp[0]) y_stop = float(temp[1]) self.n_ev = int(lines[3]) # print self.n_ev self.ev = np.zeros((self.n_ev)) self.data_dwell = np.zeros((self.n_ev)) filename_list = [] for i in range(self.n_ev): self.ev[i] = float(lines[4 + i]) # print self.ev for i in range(self.n_ev): self.ev[i] = float(lines[4 + i]) try: for i in range(self.n_ev): temp = lines[4 + self.n_ev + i].split() filename_list.append(temp[0]) self.data_dwell[i] = float(temp[2]) except: self.data_dwell = np.ones((self.n_ev)) f.close() if scale < 0: dataformat = float32 else: dataformat = np.int16 # print 'data format', dataformat f = open(str(filename), 'rb') big_array = np.fromfile(f, dataformat, self.n_cols * self.n_rows * self.n_ev) f.close() if scale > 0 and scale != 1: image_stack = big_array.astype(float) / scale print("data rescaled by ", 1. / scale) else: image_stack = big_array.astype(float) if (x_start > x_stop): image_stack = image_stack[::-1, :, :] t = x_start x_start = x_stop x_stop = t print('x data reversed') if (y_start > y_stop): image_stack = image_stack[:, ::-1, :] t = y_start y_start = y_stop y_stop = t print('y data reversed') xstep = (x_stop - x_start) / (self.n_cols - 1) self.x_dist = np.arange(x_start, x_stop + xstep, xstep) ystep = (y_stop - y_start) / (self.n_rows - 1) self.y_dist = np.arange(y_start, y_stop + ystep, ystep) self.absdata = np.empty((self.n_cols, self.n_rows, self.n_ev)) self.absdata = np.reshape(image_stack, (self.n_cols, self.n_rows, self.n_ev), order='F') self.fill_h5_struct_from_stk() return # ---------------------------------------------------------------------- def read_ncb4D(self, filenames): if verbose: print('First energy stack:', filenames[0]) data_files = natural_sort(filenames) neng = len(data_files) if verbose: print('Number of energies ', neng) self.stack4D = [] theta = [] for i in range(neng): # Read the data thisstack, thistheta = read_ncb_data(self, data_files[i]) # Check if we have negative angles, if yes convert to 0-360deg for ith in range(len(thistheta)): if thistheta[ith] < 0: thistheta[ith] = thistheta[ith] + 360 self.stack4D.append(thisstack) theta.append(thistheta) dims = thisstack.shape self.stack4D = np.array(self.stack4D) self.stack4D = np.transpose(self.stack4D, axes=(1, 2, 0, 3)) self.theta = theta[0] self.n_theta = len(theta) self.absdata = self.stack4D[:, :, :, 0] self.data_dwell = np.ones((neng)) self.fill_h5_struct_from_stk() return # ---------------------------------------------------------------------- def read_ncb_data(self, filename): basename, extension = os.path.splitext(filename) dat_fn = basename + '.dat' f = open(str(dat_fn), 'r') lines = f.readlines() temp = lines[0].split() self.n_cols = int(temp[0]) self.n_rows = int(temp[1]) scale = float(temp[2]) if verbose: print('self.n_cols, self.n_rows, scale', self.n_cols, self.n_rows, scale) temp = lines[1].split() x_start = float(temp[0]) x_stop = float(temp[1]) temp = lines[2].split() y_start = float(temp[0]) y_stop = float(temp[1]) self.n_theta = int(lines[3]) angles = np.zeros((self.n_theta)) self.data_dwell = np.zeros((self.n_theta)) filename_list = [] for i in range(self.n_theta): angles[i] = float(lines[4 + i]) for i in range(self.n_theta): angles[i] = float(lines[4 + i]) try: for i in range(self.n_theta): temp = lines[4 + self.n_ev + i].split() filename_list.append(temp[0]) self.data_dwell[i] = float(temp[2]) except: self.data_dwell = np.ones((self.n_theta)) f.close() if scale < 0: dataformat = float32 else: dataformat = np.int16 f = open(str(filename), 'rb') big_array = np.fromfile(f, dataformat, self.n_cols * self.n_rows * self.n_theta) f.close() if scale > 0 and scale != 1: image_stack = big_array.astype(float) / scale if verbose: print("data rescaled by ", 1. / scale) else: image_stack = big_array.astype(float) if (x_start > x_stop): image_stack = image_stack[::-1, :, :] t = x_start x_start = x_stop x_stop = t if verbose: print('x data reversed') if (y_start > y_stop): image_stack = image_stack[:, ::-1, :] t = y_start y_start = y_stop y_stop = t if verbose: print('y data reversed') xstep = (x_stop - x_start) / (self.n_cols - 1) self.x_dist = np.arange(x_start, x_stop + xstep, xstep) ystep = (y_stop - y_start) / (self.n_rows - 1) self.y_dist = np.arange(y_start, y_stop + ystep, ystep) image_stack = np.reshape(image_stack, (self.n_cols, self.n_rows, self.n_theta), order='F') return image_stack, angles # ---------------------------------------------------------------------- # This procedure writes a whole stack (3d (E,x,y) array) to a binary file # with associated *.dat file to track paramaters def write(filename, stack, data_type): # ,norm): # # def operate_on_Narray(A, B, function): # try: # return [operate_on_Narray(a, b, function) for a, b in zip(A, B)] # except TypeError as e: # # Not iterable # return function(A, B) # import matplotlib.pyplot as plt # def export_figure_matplotlib(name,arr, dpi=100, resize_fact=1, plt_show=False): # fig = plt.figure(frameon=False) # fig.set_size_inches(arr.shape[1] / dpi, arr.shape[0] / dpi) # ax = plt.Axes(fig, [0., 0., 1., 1.]) # ax.set_axis_off() # fig.add_axes(ax) # ax.imshow(arr, cmap='gray', interpolation='none') # if plt_show: # plt.show() # else: # plt.savefig(name+'.tif', dpi=(dpi * resize_fact)) # plt.close() print('Writing .ncb stack:', filename) basename, extension = os.path.splitext(filename) dat_fn = basename + '.dat' filename = basename + '.ncb' image_stack = np.transpose(stack.absdata, axes=(1, 0, 2)) '''Experimental OD filtering and edge substraction''' # if norm: # # image_stack = np.transpose(stack.od3d, axes=(1,0,2)) # #image_stack = stack.od3d.copy() # OD_filter = np.amax(image_stack, 2) # #returning the ODmask as tif # mask = np.where((OD_filter > 2.5), 0, np.ones(np.shape(image_stack[:,:,0]))) # export_figure_matplotlib(basename+"od_mask",mask) # # for i in range(np.size(image_stack, 2)): # image_stack[:,:,i] = np.where((OD_filter > 2.5),0, image_stack[:,:,i]) # #image_stack = np.where(image_stack > 2,0, image_stack) #OD > 2 filter # e0 = 0 # num = 5 # ##pre-edge # ave = image_stack[:,:,e0] # for i in range(num-1): # ave = ave + image_stack[:,:,e0+i+1] # ave = ave / num # # #export pre-edge_average for contour plot. # pre = np.transpose(stack.absdata, axes=(1,0,2))[:,:,e0] # for i in range(num-1): # pre = pre + np.transpose(stack.absdata, axes=(1,0,2))[:,:,e0+i+1] # pre = pre / num # export_figure_matplotlib(basename,pre) # # for i in range(np.size(image_stack, 2)): # image_stack[:,:,i] = image_stack[:,:,i]-ave # # #post-edge # ave = image_stack[:,:,-1] # for i in range(num-1): # ave = ave + image_stack[:,:,e0-i-2] # ave = (ave / num) + 0.0001 # #print(ave) # for i in range(np.size(image_stack, 2)): # image_stack[:,:,i] = image_stack[:,:,i] - operate_on_Narray(ave, image_stack[:,:,i], lambda a, b: a/((1+ np.exp(-b+0.5*a)**(25*1/a)))) image_stack = np.reshape(image_stack, (stack.n_cols * stack.n_rows * stack.n_ev), order='F') tmax = np.amax(image_stack) tmin = np.amin(image_stack) test = np.amax([abs(tmin), abs(tmax)]) scale = 1. if test > 3e4 or test < 1e3: scale = 10. ** (3 - np.fix(np.math.log10(test))) # Save image stack to a binary .dat file f = open(str(filename), 'wb') if scale != 1.0: print('Scaling the data by ', scale) saveddata = image_stack * scale saveddata.astype(np.int16).tofile(f) else: scale = -1.0 image_stack.astype(float32).tofile(f) f.close() # print 'imagedims', image_stack.shape f = open(str(dat_fn), 'w') print('\t%d\t%d\t%.6f' % (stack.n_rows, stack.n_cols, scale), file=f) x_start = stack.y_dist[0] x_stop = stack.y_dist[-1] if x_start != 0.: x_stop = x_stop - x_start x_start = 0. print('\t%.6f\t%.6f' % (x_start, x_stop), file=f) y_start = stack.x_dist[0] y_stop = stack.x_dist[-1] if y_start != 0.: y_stop = y_stop - y_start y_start = 0. print('\t%.6f\t%.6f' % (y_start, y_stop), file=f) print('\t%d' % (stack.n_ev), file=f) for i in range(stack.n_ev): print('\t%.6f' % (stack.ev[i]), file=f) for i in range(stack.n_ev): thisstr = 'image' + str(i + 1) print('%s\t%.6f\t%.6f' % (thisstr, stack.ev[i], stack.data_dwell[i]), file=f) f.close() return # ---------------------------------------------------------------------- def natural_sort(l): convert = lambda text: int(text) if text.isdigit() else text.lower() alphanum_key = lambda key: [convert(c) for c in re.split('([0-9]+)', key)] return sorted(l, key=alphanum_key) # ---------------------------------------------------------------------- class Cncb: def __init__(self): pass # ---------------------------------------------------------------------- def read_ncb(self, filename): print('Reading stack:', filename) basename, extension = os.path.splitext(filename) dat_fn = basename + '.dat' f = open(str(dat_fn), 'r') lines = f.readlines() temp = lines[0].split() self.n_cols = int(temp[0]) self.n_rows = int(temp[1]) scale = float(temp[2]) # print self.n_cols, self.n_rows, scale temp = lines[1].split() x_start = float(temp[0]) x_stop = float(temp[1]) temp = lines[2].split() y_start = float(temp[0]) y_stop = float(temp[1]) self.n_ev = int(lines[3]) # print self.n_ev self.ev = np.zeros((self.n_ev)) self.data_dwell = np.zeros((self.n_ev)) filename_list = [] for i in range(self.n_ev): self.ev[i] = float(lines[4 + i]) # print self.ev for i in range(self.n_ev): self.ev[i] = float(lines[4 + i]) try: for i in range(self.n_ev): temp = lines[4 + self.n_ev + i].split() filename_list.append(temp[0]) self.data_dwell[i] = float(temp[2]) except: self.data_dwell = np.ones((self.n_ev)) f.close() if scale < 0: dataformat = float32 else: dataformat = np.int16 # print 'data format', dataformat f = open(str(filename), 'rb') big_array = np.fromfile(f, dataformat, self.n_cols * self.n_rows * self.n_ev) f.close() if scale > 0 and scale != 1: image_stack = big_array.astype(float) / scale print("data rescaled by ", 1. / scale) else: image_stack = big_array.astype(float) if (x_start > x_stop): image_stack = image_stack[::-1, :, :] t = x_start x_start = x_stop x_stop = t print('x data reversed') if (y_start > y_stop): image_stack = image_stack[:, ::-1, :] t = y_start y_start = y_stop y_stop = t print('y data reversed') xstep = (x_stop - x_start) / (self.n_cols - 1) self.x_dist = np.arange(x_start, x_stop + xstep, xstep) ystep = (y_stop - y_start) / (self.n_rows - 1) self.y_dist = np.arange(y_start, y_stop + ystep, ystep) self.absdata = np.empty((self.n_cols, self.n_rows, self.n_ev)) self.absdata = np.reshape(image_stack, (self.n_cols, self.n_rows, self.n_ev), order='F') return # ---------------------------------------------------------------------- # This procedure writes a whole stack (3d (E,x,y) array) to a binary file # with associated *.dat file to track paramaters def write_ncb(self, filename, data_struct): print('Writing .ncb stack:', filename) basename, extension = os.path.splitext(filename) dat_fn = basename + '.dat' image_stack = np.transpose(self.absdata, axes=(1, 0, 2)) # image_stack = self.absdata.copy() image_stack = np.reshape(image_stack, (self.n_cols * self.n_rows * self.n_ev), order='F') tmax = np.amax(image_stack) tmin = np.amin(image_stack) test = np.amax([abs(tmin), abs(tmax)]) scale = 1. if test > 3e4 or test < 1e3: scale = 10. ** (3 - np.fix(np.math.log10(test))) # Save image stack to a binary .dat file f = open(str(filename), 'wb') if scale != 1.0: print('Scaling the data by ', scale) saveddata = image_stack * scale saveddata.astype(np.int16).tofile(f) else: scale = -1.0 image_stack.astype(float32).tofile(f) f.close() # print 'imagedims', image_stack.shape f = open(str(dat_fn), 'w') print('\t%d\t%d\t%.6f' % (self.n_rows, self.n_cols, scale), file=f) # print('\t%d\t%d\t%.6f' %(self.n_cols, self.n_rows, scale) x_start = self.y_dist[0] x_stop = self.y_dist[-1] if x_start != 0.: x_stop = x_stop - x_start x_start = 0. print('\t%.6f\t%.6f' % (x_start, x_stop), file=f) y_start = self.x_dist[0] y_stop = self.x_dist[-1] if y_start != 0.: y_stop = y_stop - y_start y_start = 0. print('\t%.6f\t%.6f' % (y_start, y_stop), file=f) print('\t%d' % (self.n_ev), file=f) for i in range(self.n_ev): print('\t%.6f' % (self.ev[i]), file=f) for i in range(self.n_ev): thisstr = 'image' + str(i + 1) print('%s\t%.6f\t%.6f' % (thisstr, self.ev[i], self.data_dwell[i]), file=f) f.close() return # ---------------------------------------------------------------------- def read_ncb_data(self, filename): # print 'Reading stack:', filename basename, extension = os.path.splitext(filename) dat_fn = basename + '.dat' f = open(str(dat_fn), 'r') lines = f.readlines() temp = lines[0].split() self.n_cols = int(temp[0]) self.n_rows = int(temp[1]) scale = float(temp[2]) if verbose: print('self.n_cols, self.n_rows, scale', self.n_cols, self.n_rows, scale) temp = lines[1].split() x_start = float(temp[0]) x_stop = float(temp[1]) temp = lines[2].split() y_start = float(temp[0]) y_stop = float(temp[1]) self.n_theta = int(lines[3]) # print self.n_ev angles = np.zeros((self.n_theta)) self.data_dwell = np.zeros((self.n_theta)) filename_list = [] for i in range(self.n_theta): angles[i] = float(lines[4 + i]) # print self.ev for i in range(self.n_theta): angles[i] = float(lines[4 + i]) try: for i in range(self.n_theta): temp = lines[4 + self.n_ev + i].split() filename_list.append(temp[0]) self.data_dwell[i] = float(temp[2]) except: self.data_dwell = np.ones((self.n_theta)) f.close() if scale < 0: dataformat = float32 else: dataformat = np.int16 # print 'data format', dataformat f = open(str(filename), 'rb') big_array = np.fromfile(f, dataformat, self.n_cols * self.n_rows * self.n_theta) f.close() if scale > 0 and scale != 1: image_stack = big_array.astype(float) / scale if verbose: print("data rescaled by ", 1. / scale) else: image_stack = big_array.astype(float) if (x_start > x_stop): image_stack = image_stack[::-1, :, :] t = x_start x_start = x_stop x_stop = t if verbose: print('x data reversed') if (y_start > y_stop): image_stack = image_stack[:, ::-1, :] t = y_start y_start = y_stop y_stop = t if verbose: print('y data reversed') xstep = (x_stop - x_start) / (self.n_cols - 1) self.x_dist = np.arange(x_start, x_stop + xstep, xstep) ystep = (y_stop - y_start) / (self.n_rows - 1) self.y_dist = np.arange(y_start, y_stop + ystep, ystep) image_stack = np.reshape(image_stack, (self.n_cols, self.n_rows, self.n_theta), order='F') return image_stack, angles # ---------------------------------------------------------------------- def read_ncb4D(self, filenames): if verbose: print('First energy stack:', filenames[0]) data_files = natural_sort(filenames) neng = len(data_files) if verbose: print('Number of energies ', neng) self.stack4D = [] theta = [] for i in range(neng): # Read the data thisstack, thistheta = self.read_ncb_data(data_files[i]) # Check if we have negative angles, if yes convert to 0-360deg for ith in range(len(thistheta)): if thistheta[ith] < 0: thistheta[ith] = thistheta[ith] + 360 self.stack4D.append(thisstack) theta.append(thistheta) dims = thisstack.shape self.stack4D = np.array(self.stack4D) self.stack4D = np.transpose(self.stack4D, axes=(1, 2, 0, 3)) self.theta = theta[0] self.n_theta = len(theta) self.absdata = self.stack4D[:, :, :, 0] self.data_dwell = np.ones((neng)) return ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/file_plugins/file_nexus_hdf5.py0000755000175000017500000001501714332463747023666 0ustar00wattswatts# -*- coding: utf-8 -*- # # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2015 Benjamin Watts, Paul Scherrer Institute # 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 . from __future__ import print_function import sys, os, numpy, h5py from collections import OrderedDict #from PyQt5 import QtGui title = 'NXstxm' extension = ['*.hdf','*.hdf5','*.nxs'] read_types = ['spectrum','image','stack'] write_types = []#'spectrum','image','stack'] def perhaps_decode(value): try: return value.decode("utf-8") # h5py <3.0 except AttributeError: return value # h5py >=3.0 def identify(filename): try: # Open HDF5 file f = h5py.File(filename, 'r') # Count valid entries n_regions = 0 for entry in f: if 'NX_class' in list(f[entry].attrs) and f[entry].attrs['NX_class'] in [b'NXentry', u'NXentry']: if 'definition' in list(f[entry]) and f[entry]['definition'][0] in [b'NXstxm', u'NXstxm']: n_regions += 1 f.close() return n_regions>0 # return true if file contains at least one NXstxm entry except: return False def read(FileName,stack_object,selection=(0,0), *args, **kwargs): """Todo: Add support for single images!""" D = GetFileStructure(FileName) entry = list(D.keys())[selection[0]] detector = list(D[entry].keys())[selection[1]] #[counter0, ...] F = h5py.File(FileName, 'r') if 'energy' in list(F[entry][detector]): stack_object.ev = numpy.array(F[entry][detector]['energy']) elif 'photon_energy' in list(F[entry][detector]): stack_object.ev = numpy.array(F[entry][detector]['photon_energy']) else: print("Can't find photon energy!") stack_object.x_dist = numpy.array(F[entry][detector]['sample_x']) stack_object.y_dist = numpy.array(F[entry][detector]['sample_y']) stack_object.data_dwell = numpy.array(F[entry][detector]['count_time']) stack_object.n_cols = len(stack_object.x_dist) stack_object.n_rows = len(stack_object.y_dist) stack_object.n_ev = len(stack_object.ev) if 'axes' in list(F[entry][detector].attrs): # Specification correct axes_list = [item.decode('UTF-8') for item in F[entry][detector].attrs['axes']] if 'line_position' in axes_list: axes_order = [axes_list.index('line_position'),axes_list.index('line_position'),axes_list.index('energy')] #linescan elif 'energy' in axes_list: axes_order = [axes_list.index('sample_x'),axes_list.index('sample_y'),axes_list.index('energy')] #stack else: axes_order = [axes_list.index('sample_x'),axes_list.index('sample_y')] #image else: # Old version from before the specification was finalised if 'energy' in list(F[entry][detector]): try: energy_axis = F[entry][detector]['energy'].attrs['axis'] except: print("Only stacks are supported!") elif 'photon_energy' in list(F[entry][detector]): energy_axis = F[entry][detector]['photon_energy'].attrs['axis'] else: print("Can't find photon energy!") axes_order = [F[entry][detector]['sample_x'].attrs['axis']-1,F[entry][detector]['sample_y'].attrs['axis']-1,energy_axis-1] signal_name = 'data' if 'signal' in list(F[entry][detector].attrs): signal_name = perhaps_decode(F[entry][detector].attrs['signal']) if axes_order[0] == axes_order[1]: #i.e. if linescan temp = numpy.transpose(numpy.array(F[entry][detector][signal_name]),axes=axes_order[1:]) stack_object.absdata = numpy.tile(temp,(temp.shape[0],1,1)) elif len(axes_order)<3: #i.e. if image stack_object.absdata = numpy.expand_dims(numpy.transpose(numpy.array(F[entry][detector][signal_name]),axes=axes_order),2) else: stack_object.absdata = numpy.transpose(numpy.array(F[entry][detector][signal_name]),axes=axes_order) F.close() stack_object.fill_h5_struct_from_stk() def GetFileStructure(FileName): """ToDo: Currently, the file will be opened two times. Maybe a solution like in the sdf-plugin would be better.""" F = h5py.File(FileName, 'r') D = OrderedDict() for entry in F: if 'NX_class' in list(F[entry].attrs) and F[entry].attrs['NX_class'] in [b'NXentry', u'NXentry']: D[entry] = OrderedDict() D[entry].norm_data = OrderedDict() D[entry].definition = None D[entry].scan_type = None D[entry].data_shape = None D[entry].data_axes = None for data in F[entry]: if 'NX_class' in list(F[entry][data].attrs) and F[entry][data].attrs['NX_class'] in [b'NXdata', u'NXdata']: D[entry][data] = OrderedDict() #print "should collect more info in each NXdata group" elif 'NX_class' in list(F[entry][data].attrs) and F[entry][data].attrs['NX_class'] in [b'NXmonitor', u'NXmonitor']: D[entry].norm_data[data] = OrderedDict() if len(D[entry].norm_data) == 0: D[entry].norm_data = None if 'definition' in list(F[entry]): D[entry].definition = F[entry]['definition'][0].decode("utf-8") if len(D[entry].keys()) > 0: channel_zero = list(D[entry].keys())[0] if 'stxm_scan_type' in list(F[entry][channel_zero]): D[entry].scan_type = F[entry][channel_zero]['stxm_scan_type'][0].decode("utf-8") signal_name = 'data' if 'signal' in list(F[entry][channel_zero].attrs): signal_name = perhaps_decode(F[entry][channel_zero].attrs['signal']) if signal_name in list(F[entry][channel_zero]): D[entry].data_shape = F[entry][channel_zero][signal_name].shape if 'axes' in list(F[entry][channel_zero].attrs): D[entry].data_axes = [perhaps_decode(item) for item in F[entry][channel_zero].attrs['axes']] F.close() if len(D) == 0: return None else: return D ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/file_plugins/file_sdf.py0000644000175000017500000003244514332463747022373 0ustar00wattswatts# -*- 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 . 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('[\s\{\}\(\)=";]') self.MatchReArray = re.compile('[,\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): #ToDo: Assigning Scan types by DataFlags is prone to errors. Better detect automatically via (HDR.hdr['ScanDefinition']['Regions'][0]) etc. """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 = []#Regions DataNames2 = []#Channels DataNames3 = []#Energies Alphabet = 'abcdefghijklmnopqrstuvwxyz' DataFlag = self.hdr['ScanDefinition']['Flags'] 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) elif DataFlag == 'Image': DataNames2 = [] for num_Ch in range(self.num_channels): DataNames2.append([self.file_path+'_'+Alphabet[num_Ch]+'.xim']) DataNames.append(DataNames2) elif DataFlag in ['Multi-Region Image']: for num_R in range(self.num_regions): DataNames2 = [] for num_Ch in range(self.num_channels): DataNames2.append([self.file_path+'_'+Alphabet[num_Ch]+str(num_R)+'.xim']) DataNames.append(DataNames2) elif DataFlag in ['Multi-Region Image Stack']: for num_Ch in range(self.num_channels): DataNames2 = [] for num_R in range(self.num_regions): DataNames3 = [] for num_E in range(self.data_size[0][2]): DataNames3.append(self.file_path + '_' + Alphabet[num_Ch] + str(num_E).zfill(3)+str(num_R)+ '.xim') DataNames2.append(DataNames3) DataNames = [DataNames2] elif DataFlag == 'Image Stack': DataNames2 = [] for num_Ch in range(self.num_channels): DataNames3 = [] for num_E in range(self.data_size[0][2]): DataNames3.append(self.file_path+'_'+Alphabet[num_Ch]+str(num_E).zfill(3)+'.xim') DataNames2.append(DataNames3) DataNames = [DataNames2] else: print("WARNING! Unknown flag:", DataFlag) #for num_R in range(len(DataNames)):#Check that names correspond to existing files #for num_Ch in range(len(DataNames[num_R])): #for num_E in range(len(DataNames[num_R][num_Ch])): #if exists(DataNames[num_R][num_Ch][num_E]) == False: #print "WARNING! Data file doesn't exist:", DataNames[num_R][num_Ch][num_E] 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): 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 =['Image Stack','Image','Multi-Region Image Stack'] if HDR.hdr['ScanDefinition']['Flags'] in allowed: self.x_dist = numpy.array([float(i) for i in HDR.hdr['ScanDefinition']['Regions'][selection[0]+1]['PAxis']['Points'][1:] ]) self.y_dist = numpy.array([float(i) for i in HDR.hdr['ScanDefinition']['Regions'][selection[0]+1]['QAxis']['Points'][1:] ]) self.ev = numpy.array([float(i) for i in HDR.hdr['ScanDefinition']['StackAxis']['Points'][1:] ]) self.n_cols = len(self.x_dist) self.n_rows = len(self.y_dist) self.n_ev = len(self.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) for i in range(len(HDR.data_names[selection[1]][selection[0]])): try: imagestack[:,:,i] = numpy.loadtxt(HDR.data_names[selection[1]][selection[0]][i], numpy.int32).T except ValueError: print("Aborted stack or XIMs with inconsistent dimensions.") imagestack[:,:,i] = numpy.nan except IOError: print("Image file 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() else: print("Unknown Format") 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././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/file_plugins/file_sm_netcdf.py0000644000175000017500000002243214332463747023554 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2011 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # 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 . from __future__ import division import os.path import numpy as np import scipy as scy import netCDF4 from mantis_xray import data_struct #---------------------------------------------------------------------- def read_sm_header(filename): try: print (filename) f = netCDF4.Dataset(filename, 'r', format='NETCDF4') ncols = len(f.dimensions['n_fast_pixels']) nrows = len(f.dimensions['n_slow_pixels']) ev = getattr(f,'start_ev') x_center_um = getattr(f,'x_center_um') y_center_um = getattr(f,'y_center_um') f.close() return 1, ncols, nrows, ev except: return -1, 0,0,0 #---------------------------------------------------------------------- def read_sm_list(self, filelist, filepath, ds): #Fill the common stack data file1 = os.path.join(filepath, filelist[0]) fcdf = netCDF4.Dataset(file1, 'r', format='NETCDF4') ncols = len(fcdf.dimensions['n_fast_pixels']) nrows = len(fcdf.dimensions['n_slow_pixels']) npix = ncols*nrows nev = len(filelist) absdata = np.zeros((ncols, nrows, nev)) ev = np.zeros((nev)) #fdevicen = netCDF4.chartostring(getattr(fcdf,'fast_device_name')) #sdevicen = netCDF4.chartostring(getattr(fcdf,'slow_device_name')) fdevicenl = getattr(fcdf,'fast_device_name') sdevicenl = getattr(fcdf,'slow_device_name') fdevicen = chr(fdevicenl[0]) + chr(fdevicenl[1]) + chr(fdevicenl[2]) + chr(fdevicenl[3]) sdevicen = chr(sdevicenl[0]) + chr(sdevicenl[1]) + chr(sdevicenl[2]) + chr(sdevicenl[3]) x_center_um = -1 y_center_um = -1 if fdevicen == 'XPZT': x_center_um = getattr(fcdf,'x_center_um') if sdevicen == 'YPZT': y_center_um = getattr(fcdf,'y_center_um') if (x_center_um == -1) or (y_center_um == -1): print ('Error in stack build: Not piezo scan') return x_pixel_um = getattr(fcdf,'fast_pixel_um') y_pixel_um = getattr(fcdf,'slow_pixel_um') x_direction = getattr(fcdf,'fast_direction') y_direction = getattr(fcdf,'slow_direction') dwell_msec = getattr(fcdf,'dwell_msec') x_dist = (np.arange(npix)-np.fix(npix/2.0)) * x_direction * x_pixel_um + x_center_um y_dist = (np.arange(npix)-np.fix(npix/2.0)) * y_direction * y_pixel_um + y_center_um ds.implements = 'information:exchange:spectromicroscopy' ds.version = '1.0' ds.information.file_creation_datetime = ' '#netCDF4.chartostring(getattr(fcdf,'systime')) ds.information.comment = ' '#netCDF4.chartostring(getattr(fcdf,'comments1')) ds.information.experimenter.name = ' '#netCDF4.chartostring(getattr(fcdf,'scientist')) ds.information.sample.name = ' '#netCDF4.chartostring(getattr(fcdf,'sample')) fcdf.close() #Read the image data for i in range(len(filelist)): fn = filelist[i] filename = os.path.join(filepath, fn) fcdf = netCDF4.Dataset(filename, 'r', format='NETCDF4') image = fcdf.variables['image_data'][:] absdata[:,:,i] = image[:,:,0] eng = getattr(fcdf,'start_ev') ev[i] = eng fcdf.close() if ev[-1]. from __future__ import division import numpy as np import scipy.interpolate import os title = 'STK' extension = ['*.stk'] read_types = ['spectrum','image','stack'] write_types = [] def identify(filename): try: #Binary file - the only way to check if the file is ok to check the extension bn, extension = os.path.splitext(filename) if extension.lower() == '.stk': return True else: return False except: return False def read_old( filepath, data_stk): data_stk.read_h5(filepath) return def GetFileStructure(FileName): return None #---------------------------------------------------------------------- def read(filename, self, selection=None, *args, **kwargs): f = open(str(filename),'rb') data = np.fromfile(f, np.int32, 3) data.byteswap(True) self.n_cols = data[0] self.n_rows = data[1] self.n_ev = data[2] #print 'self.n_cols, self.n_rows, self.n_ev', self.n_cols, self.n_rows, self.n_ev self.x_dist = np.fromfile(f, np.float32, self.n_cols) self.x_dist.byteswap(True) self.y_dist = np.fromfile(f, np.float32, self.n_rows) self.y_dist.byteswap(True) self.ev = np.fromfile(f, np.float32, self.n_ev) self.ev.byteswap(True) msec = np.fromfile(f, np.float32, self.n_ev) msec.byteswap(True) self.data_dwell = msec imagestack = np.fromfile(f, np.float32, self.n_cols*self.n_rows*self.n_ev) imagestack.byteswap(True) self.absdata = np.empty((self.n_cols, self.n_rows, self.n_ev)) self.absdata = np.reshape(imagestack, (self.n_cols, self.n_rows, self.n_ev), order='F') f.close() self.fill_h5_struct_from_stk() return #---------------------------------------------------------------------- def read_stk_i0_xas(self, filename): f = open(str(filename),'r') elist = [] ilist = [] for line in f: if line.startswith("*"): pass else: e, i = [float (x) for x in line.split()] elist.append(e) ilist.append(i) self.evi0 = np.array(elist) self.i0data = np.array(ilist) f.close() self.i0_dwell = None return #---------------------------------------------------------------------- def read_stk_i0_csv(self, filename): f = open(str(filename),'r') elist = [] ilist = [] for line in f: if line.startswith("*"): pass else: e, i = [float (x) for x in line.split(',')] elist.append(e) ilist.append(i) self.evi0 = np.array(elist) self.i0data = np.array(ilist) f.close() self.i0_dwell = None return #---------------------------------------------------------------------- class x1astk: def __init__(self): pass #---------------------------------------------------------------------- def read_stk(self, filename): f = open(str(filename),'rb') data = np.fromfile(f, np.int32, 3) data.byteswap(True) self.n_cols = data[0] self.n_rows = data[1] self.n_ev = data[2] #print 'self.n_cols, self.n_rows, self.n_ev', self.n_cols, self.n_rows, self.n_ev self.x_dist = np.fromfile(f, np.float32, self.n_cols) self.x_dist.byteswap(True) self.y_dist = np.fromfile(f, np.float32, self.n_rows) self.y_dist.byteswap(True) self.ev = np.fromfile(f, np.float32, self.n_ev) self.ev.byteswap(True) msec = np.fromfile(f, np.float32, self.n_ev) msec.byteswap(True) self.data_dwell = msec imagestack = np.fromfile(f, np.float32, self.n_cols*self.n_rows*self.n_ev) imagestack.byteswap(True) self.absdata = np.empty((self.n_cols, self.n_rows, self.n_ev)) self.absdata = np.reshape(imagestack, (self.n_cols, self.n_rows, self.n_ev), order='F') f.close() # self.original_n_cols = self.n_cols.copy() # self.original_n_rows = self.n_rows.copy() # self.original_n_ev = self.n_ev.copy() # self.original_ev = self.ev.copy() # self.original_absdata = self.absdata.copy() return #---------------------------------------------------------------------- def read_stk_i0_xas(self, filename): f = open(str(filename),'r') elist = [] ilist = [] for line in f: if line.startswith("*"): pass else: e, i = [float (x) for x in line.split()] elist.append(e) ilist.append(i) self.evi0 = np.array(elist) self.i0data = np.array(ilist) f.close() self.i0_dwell = None return #---------------------------------------------------------------------- def read_stk_i0_csv(self, filename): f = open(str(filename),'r') elist = [] ilist = [] for line in f: print(line) if line.startswith("*"): pass else: e, i = [float (x) for x in line.split(',')] elist.append(e) ilist.append(i) self.evi0 = np.array(elist) self.i0data = np.array(ilist) f.close() self.i0_dwell = None return ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1677150153.0 mantis-xray-3.1.15/mantis_xray/file_plugins/file_tif.py0000664000175000017500000000730314375643711022374 0ustar00wattswattsfrom __future__ import print_function # # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2011 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # 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 . import numpy as np from PIL import Image import os title = 'Tiff' extension = ['*.tif','*.tiff'] read_types = ['image','stack'] write_types = ['image','stack','results'] def identify(filename): try: # Open Tiff file img = Image.open(filename) return True except: return False def GetFileStructure(FileName): return None #---------------------------------------------------------------------- def read(filename, self, selection=None, *args, **kwargs): img = Image.open(filename) imgstack = [] i = 0 while True: try: img.seek(i) i += 1 imgstack.append(np.array((img))) except EOFError: break imgstack = np.array((imgstack)) imgstack = np.transpose(imgstack, axes=(1,2,0)) self.n_cols = imgstack.shape[0] self.n_rows = imgstack.shape[1] self.n_ev = imgstack.shape[2] print('File {0} dims: [{1},{2},{3}]'.format(filename, self.n_cols, self.n_rows, self.n_ev)) pixelsize = 1 #Since we do not have a scanning microscope we fill the x_dist and y_dist from pixel_size self.x_dist = np.arange(float(self.n_cols))*pixelsize self.y_dist = np.arange(float(self.n_rows))*pixelsize #Read energies from file basename, extension = os.path.splitext(filename) engfilename = basename+'.txt' f = open(str(engfilename),'r') elist = [] for line in f: if line.startswith("*"): pass else: e = float(line) elist.append(e) self.ev = np.array(elist) f.close() msec = np.ones((self.n_ev)) self.data_dwell = msec self.absdata = imgstack #Check if the energies are consecutive, if they are not sort the data sort = 0 for i in range(self.n_ev - 1): if self.ev[i] > self.ev[i+1]: sort = 1 break if sort == 1: sortind = np.argsort(self.ev) self.ev = self.ev[sortind] self.absdata = self.absdata[:,:,sortind] self.fill_h5_struct_from_stk() #---------------------------------------------------------------------- def write(filename, data_object, data_type): """Switchyard for writing different types of data.""" if data_type in ['stack']: write_tif(filename, data_object.absdata, data_object.ev) #---------------------------------------------------------------------- def write_tif(filename, data, energies = []): dims = data.shape for i in range(dims[2]): basename, extension = os.path.splitext(filename) thisfn = basename + '_' + str(i+1) + extension img1 = Image.fromarray(data[:,:,i]) img1.save(thisfn) if len(energies) > 0: thisfn = basename + '.txt' f = open(thisfn, 'w') for i in range(len(energies)): print('%.6f' %(energies[i]), file=f) f.close() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1677150153.0 mantis-xray-3.1.15/mantis_xray/file_plugins/file_xrm.py0000664000175000017500000040650714375643711022431 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2011 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # 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 . import numpy as np import scipy as scy import struct import os from mantis_xray import data_struct as ds title = 'XRM' extension = ['*.xrm','*.txrm'] read_types = ['spectrum','image','stack'] write_types = [] def identify(filename): try: xrm_format = False if isOleFile(filename): ole = OleFileIO(filename) if ole.exists('ImageInfo/ImageWidth'): xrm_format = True ole.close() else: xrm_format = False return xrm_format except: return False def GetFileStructure(FileName): return None #---------------------------------------------------------------------- def read(filename, self, selection=None, *args, **kwargs): basename, extension = os.path.splitext(filename) if extension == '.xrm': read_xrm(filename, self) elif extension == '.txrm': read_txrm(filename, self) self.fill_h5_struct_from_stk() return #---------------------------------------------------------------------- def read_xrm_fileinfo(filename, readimgdata = False): if not isOleFile(filename): print ("File not valid OLE type.") return # Open OLE file: ole = OleFileIO(filename) verbose = False if ole.exists('ImageInfo/ImagesTaken'): stream = ole.openstream('ImageInfo/ImagesTaken') data = stream.read() nev = struct.unpack(' 1: print ('This file has more than one image and cannot be used to make a stack.') return 0, 0, 0 if ole.exists('ImageInfo/ImageWidth'): stream = ole.openstream('ImageInfo/ImageWidth') data = stream.read() nev = struct.unpack('0: this_ev = float(this_ev) else: # In this case energy is stored like this: _0250.52_eV_ this_ev = str_list[i-1].strip() this_ev = float(this_ev) if verbose: print ("Successfully read energy value from file name.", this_ev) eV = this_ev break except: eV = eV[0] print ('Using energy stored in the file.', eV) ole.close() if readimgdata == False: return n_cols, n_rows, eV else: return n_cols, n_rows, eV, imgdata #---------------------------------------------------------------------- def read_xrm_list(self, filelist, filepath, ds): #Fill the common stack data file1 = os.path.join(filepath, filelist[0]) if not isOleFile(file1): print ("File not valid OLE type.") return # Open OLE file: ole = OleFileIO(file1) verbose = False if ole.exists('Version'): stream = ole.openstream('Version') data = stream.read() version = struct.unpack('0: this_ev = float(this_ev) else: # In this case energy is stored like this: _0250.52_eV_ this_ev = str_list[i-1] this_ev = float(this_ev) if verbose: print ("Successfully read energy value from file name.", this_ev) eV = this_ev break except: eV = eV[0] print ('Using energy stored in the file.', eV) ev[j] = this_ev if ev[-1]512 bytes # - getsect: added conformity checks # - DEBUG_MODE constant to activate debug display # 2007-09-04 v0.13 PL: - improved/translated (lots of) comments # - updated license # - converted tabs to 4 spaces # 2007-11-19 v0.14 PL: - added OleFileIO._raise_defect() to adapt sensitivity # - improved _unicode() to use Python 2.x unicode support # - fixed bug in _OleDirectoryEntry # 2007-11-25 v0.15 PL: - added safety checks to detect FAT loops # - fixed _OleStream which didn't check stream size # - added/improved many docstrings and comments # - moved helper functions _unicode and _clsid out of # OleFileIO class # - improved OleFileIO._find() to add Unix path syntax # - OleFileIO._find() is now case-insensitive # - added get_type() and get_rootentry_name() # - rewritten loaddirectory and _OleDirectoryEntry # 2007-11-27 v0.16 PL: - added _OleDirectoryEntry.kids_dict # - added detection of duplicate filenames in storages # - added detection of duplicate references to streams # - added get_size() and exists() to _OleDirectoryEntry # - added isOleFile to check header before parsing # - added __all__ list to control public keywords in pydoc # 2007-12-04 v0.17 PL: - added _load_direntry to fix a bug in loaddirectory # - improved _unicode(), added workarounds for Python <2.3 # - added set_debug_mode and -d option to set debug mode # - fixed bugs in OleFileIO.open and _OleDirectoryEntry # - added safety check in main for large or binary # properties # - allow size>0 for storages for some implementations # 2007-12-05 v0.18 PL: - fixed several bugs in handling of FAT, MiniFAT and # streams # - added option '-c' in main to check all streams # 2009-12-10 v0.19 PL: - bugfix for 32 bit arrays on 64 bits platforms # (thanks to Ben G. and Martijn for reporting the bug) # 2009-12-11 v0.20 PL: - bugfix in OleFileIO.open when filename is not plain str # 2010-01-22 v0.21 PL: - added support for big-endian CPUs such as PowerPC Macs # 2012-02-16 v0.22 PL: - fixed bug in getproperties, patch by chuckleberryfinn # (https://bitbucket.org/decalage/olefileio_pl/issue/7) # - added close method to OleFileIO (fixed issue #2) # 2012-07-25 v0.23 PL: - added support for file-like objects (patch by mete0r_kr) # 2013-05-05 v0.24 PL: - getproperties: added conversion from filetime to python # datetime # - main: displays properties with date format # - new class OleMetadata to parse standard properties # - added get_metadata method # 2013-05-07 v0.24 PL: - a few improvements in OleMetadata # 2013-05-24 v0.25 PL: - getproperties: option to not convert some timestamps # - OleMetaData: total_edit_time is now a number of seconds, # not a timestamp # - getproperties: added support for VT_BOOL, VT_INT, V_UINT # - getproperties: filter out null chars from strings # - getproperties: raise non-fatal defects instead of # exceptions when properties cannot be parsed properly # 2013-05-27 PL: - getproperties: improved exception handling # - _raise_defect: added option to set exception type # - all non-fatal issues are now recorded, and displayed # when run as a script # 2013-07-11 v0.26 PL: - added methods to get modification and creation times # of a directory entry or a storage/stream # - fixed parsing of direntry timestamps # 2013-07-24 PL: - new options in listdir to list storages and/or streams # 2014-02-04 v0.30 PL: - upgraded code to support Python 3.x by Martin Panter # - several fixes for Python 2.6 (xrange, MAGIC) # - reused i32 from Pillow's _binary # 2014-07-18 v0.31 - preliminary support for 4K sectors # 2014-07-27 v0.31 PL: - a few improvements in OleFileIO.open (header parsing) # - Fixed loadfat for large files with 4K sectors (issue #3) # 2014-07-30 v0.32 PL: - added write_sect to write sectors to disk # - added write_mode option to OleFileIO.__init__ and open # 2014-07-31 PL: - fixed padding in write_sect for Python 3, added checks # - added write_stream to write a stream to disk # 2014-09-26 v0.40 PL: - renamed OleFileIO_PL to olefile # 2014-11-09 NE: - added support for Jython (Niko Ehrenfeuchter) # 2014-11-13 v0.41 PL: - improved isOleFile and OleFileIO.open to support OLE # data in a string buffer and file-like objects. # 2014-11-21 PL: - updated comments according to Pillow's commits # 2015-01-24 v0.42 PL: - changed the default path name encoding from Latin-1 # to UTF-8 on Python 2.x (Unicode on Python 3.x) # - added path_encoding option to override the default # - fixed a bug in _list when a storage is empty #----------------------------------------------------------------------------- # TODO (for version 1.0): # + get rid of print statements, to simplify Python 2.x and 3.x support # + add is_stream and is_storage # + remove leading and trailing slashes where a path is used # + add functions path_list2str and path_str2list # + fix how all the methods handle unicode str and/or bytes as arguments # + add path attrib to _OleDirEntry, set it once and for all in init or # append_kids (then listdir/_list can be simplified) # - TESTS with Linux, MacOSX, Python 1.5.2, various files, PIL, ... # - add underscore to each private method, to avoid their display in # pydoc/epydoc documentation - Remove it for classes to be documented # - replace all raised exceptions with _raise_defect (at least in OleFileIO) # - merge code from _OleStream and OleFileIO.getsect to read sectors # (maybe add a class for FAT and MiniFAT ?) # - add method to check all streams (follow sectors chains without storing all # stream in memory, and report anomalies) # - use _OleDirectoryEntry.kids_dict to improve _find and _list ? # - fix Unicode names handling (find some way to stay compatible with Py1.5.2) # => if possible avoid converting names to Latin-1 # - review DIFAT code: fix handling of DIFSECT blocks in FAT (not stop) # - rewrite OleFileIO.getproperties # - improve docstrings to show more sample uses # - see also original notes and FIXME below # - remove all obsolete FIXMEs # - OleMetadata: fix version attrib according to # http://msdn.microsoft.com/en-us/library/dd945671%28v=office.12%29.aspx # IDEAS: # - in OleFileIO._open and _OleStream, use size=None instead of 0x7FFFFFFF for # streams with unknown size # - use arrays of int instead of long integers for FAT/MiniFAT, to improve # performance and reduce memory usage ? (possible issue with values >2^31) # - provide tests with unittest (may need write support to create samples) # - move all debug code (and maybe dump methods) to a separate module, with # a class which inherits OleFileIO ? # - fix docstrings to follow epydoc format # - add support for big endian byte order ? # - create a simple OLE explorer with wxPython # FUTURE EVOLUTIONS to add write support: # see issue #6 on Bitbucket: # https://bitbucket.org/decalage/olefileio_pl/issue/6/improve-olefileio_pl-to-write-ole-files #----------------------------------------------------------------------------- # NOTES from PIL 1.1.6: # History: # 1997-01-20 fl Created # 1997-01-22 fl Fixed 64-bit portability quirk # 2003-09-09 fl Fixed typo in OleFileIO.loadfat (noted by Daniel Haertle) # 2004-02-29 fl Changed long hex constants to signed integers # # Notes: # FIXME: sort out sign problem (eliminate long hex constants) # FIXME: change filename to use "a/b/c" instead of ["a", "b", "c"] # FIXME: provide a glob mechanism function (using fnmatchcase) # # Literature: # # "FlashPix Format Specification, Appendix A", Kodak and Microsoft, # September 1996. # # Quotes: # # "If this document and functionality of the Software conflict, # the actual functionality of the Software represents the correct # functionality" -- Microsoft, in the OLE format specification #------------------------------------------------------------------------------ import io import sys import struct, array, os.path, datetime #=== COMPATIBILITY WORKAROUNDS ================================================ #[PL] Define explicitly the public API to avoid private objects in pydoc: #TODO: add more # __all__ = ['OleFileIO', 'isOleFile', 'MAGIC'] # For Python 3.x, need to redefine long as int: if str is not bytes: long = int # Need to make sure we use xrange both on Python 2 and 3.x: try: # on Python 2 we need xrange: iterrange = xrange except: # no xrange, for Python 3 it was renamed as range: iterrange = range #[PL] workaround to fix an issue with array item size on 64 bits systems: if array.array('L').itemsize == 4: # on 32 bits platforms, long integers in an array are 32 bits: UINT32 = 'L' elif array.array('I').itemsize == 4: # on 64 bits platforms, integers in an array are 32 bits: UINT32 = 'I' elif array.array('i').itemsize == 4: # On 64 bit Jython, signed integers ('i') are the only way to store our 32 # bit values in an array in a *somewhat* reasonable way, as the otherwise # perfectly suited 'H' (unsigned int, 32 bits) results in a completely # unusable behaviour. This is most likely caused by the fact that Java # doesn't have unsigned values, and thus Jython's "array" implementation, # which is based on "jarray", doesn't have them either. # NOTE: to trick Jython into converting the values it would normally # interpret as "signed" into "unsigned", a binary-and operation with # 0xFFFFFFFF can be used. This way it is possible to use the same comparing # operations on all platforms / implementations. The corresponding code # lines are flagged with a 'JYTHON-WORKAROUND' tag below. UINT32 = 'i' else: raise ValueError('Need to fix a bug with 32 bit arrays, please contact author...') #[PL] These workarounds were inspired from the Path module # (see http://www.jorendorff.com/articles/python/path/) #TODO: test with old Python versions # Pre-2.3 workaround for basestring. try: basestring except NameError: try: # is Unicode supported (Python >2.0 or >1.6 ?) basestring = (str, unicode) except NameError: basestring = str #[PL] Experimental setting: if True, OLE filenames will be kept in Unicode # if False (default PIL behaviour), all filenames are converted to Latin-1. KEEP_UNICODE_NAMES = True if sys.version_info[0] < 3: # On Python 2.x, the default encoding for path names is UTF-8: DEFAULT_PATH_ENCODING = 'utf-8' else: # On Python 3.x, the default encoding for path names is Unicode (None): DEFAULT_PATH_ENCODING = None #=== DEBUGGING =============================================================== #TODO: replace this by proper logging #[PL] DEBUG display mode: False by default, use set_debug_mode() or "-d" on # command line to change it. DEBUG_MODE = False def debug_print(msg): print(msg) def debug_pass(msg): pass debug = debug_pass def set_debug_mode(debug_mode): """ Set debug mode on or off, to control display of debugging messages. :param mode: True or False """ global DEBUG_MODE, debug DEBUG_MODE = debug_mode if debug_mode: debug = debug_print else: debug = debug_pass #=== CONSTANTS =============================================================== # magic bytes that should be at the beginning of every OLE file: MAGIC = b'\xD0\xCF\x11\xE0\xA1\xB1\x1A\xE1' #[PL]: added constants for Sector IDs (from AAF specifications) MAXREGSECT = 0xFFFFFFFA # (-6) maximum SECT DIFSECT = 0xFFFFFFFC # (-4) denotes a DIFAT sector in a FAT FATSECT = 0xFFFFFFFD # (-3) denotes a FAT sector in a FAT ENDOFCHAIN = 0xFFFFFFFE # (-2) end of a virtual stream chain FREESECT = 0xFFFFFFFF # (-1) unallocated sector #[PL]: added constants for Directory Entry IDs (from AAF specifications) MAXREGSID = 0xFFFFFFFA # (-6) maximum directory entry ID NOSTREAM = 0xFFFFFFFF # (-1) unallocated directory entry #[PL] object types in storage (from AAF specifications) STGTY_EMPTY = 0 # empty directory entry (according to OpenOffice.org doc) STGTY_STORAGE = 1 # element is a storage object STGTY_STREAM = 2 # element is a stream object STGTY_LOCKBYTES = 3 # element is an ILockBytes object STGTY_PROPERTY = 4 # element is an IPropertyStorage object STGTY_ROOT = 5 # element is a root storage # # -------------------------------------------------------------------- # property types VT_EMPTY=0; VT_NULL=1; VT_I2=2; VT_I4=3; VT_R4=4; VT_R8=5; VT_CY=6; VT_DATE=7; VT_BSTR=8; VT_DISPATCH=9; VT_ERROR=10; VT_BOOL=11; VT_VARIANT=12; VT_UNKNOWN=13; VT_DECIMAL=14; VT_I1=16; VT_UI1=17; VT_UI2=18; VT_UI4=19; VT_I8=20; VT_UI8=21; VT_INT=22; VT_UINT=23; VT_VOID=24; VT_HRESULT=25; VT_PTR=26; VT_SAFEARRAY=27; VT_CARRAY=28; VT_USERDEFINED=29; VT_LPSTR=30; VT_LPWSTR=31; VT_FILETIME=64; VT_BLOB=65; VT_STREAM=66; VT_STORAGE=67; VT_STREAMED_OBJECT=68; VT_STORED_OBJECT=69; VT_BLOB_OBJECT=70; VT_CF=71; VT_CLSID=72; VT_VECTOR=0x1000; # map property id to name (for debugging purposes) VT = {} for keyword, var in list(vars().items()): if keyword[:3] == "VT_": VT[var] = keyword # # -------------------------------------------------------------------- # Some common document types (root.clsid fields) WORD_CLSID = "00020900-0000-0000-C000-000000000046" #TODO: check Excel, PPT, ... #[PL]: Defect levels to classify parsing errors - see OleFileIO._raise_defect() DEFECT_UNSURE = 10 # a case which looks weird, but not sure it's a defect DEFECT_POTENTIAL = 20 # a potential defect DEFECT_INCORRECT = 30 # an error according to specifications, but parsing # can go on DEFECT_FATAL = 40 # an error which cannot be ignored, parsing is # impossible # Minimal size of an empty OLE file, with 512-bytes sectors = 1536 bytes # (this is used in isOleFile and OleFile.open) MINIMAL_OLEFILE_SIZE = 1536 #[PL] add useful constants to __all__: # for key in list(vars().keys()): # if key.startswith('STGTY_') or key.startswith('DEFECT_'): # __all__.append(key) #=== FUNCTIONS =============================================================== def isOleFile (filename): """ Test if a file is an OLE container (according to the magic bytes in its header). :param filename: string-like or file-like object, OLE file to parse - if filename is a string smaller than 1536 bytes, it is the path of the file to open. (bytes or unicode string) - if filename is a string longer than 1535 bytes, it is parsed as the content of an OLE file in memory. (bytes type only) - if filename is a file-like object (with read and seek methods), it is parsed as-is. :returns: True if OLE, False otherwise. """ # check if filename is a string-like or file-like object: if hasattr(filename, 'read'): # file-like object: use it directly header = filename.read(len(MAGIC)) # just in case, seek back to start of file: filename.seek(0) elif isinstance(filename, bytes) and len(filename) >= MINIMAL_OLEFILE_SIZE: # filename is a bytes string containing the OLE file to be parsed: header = filename[:len(MAGIC)] else: # string-like object: filename of file on disk header = open(filename, 'rb').read(len(MAGIC)) if header == MAGIC: return True else: return False if bytes is str: # version for Python 2.x def i8(c): return ord(c) else: # version for Python 3.x def i8(c): return c if c.__class__ is int else c[0] #TODO: replace i16 and i32 with more readable struct.unpack equivalent? def i16(c, o = 0): """ Converts a 2-bytes (16 bits) string to an integer. :param c: string containing bytes to convert :param o: offset of bytes to convert in string """ return i8(c[o]) | (i8(c[o+1])<<8) def i32(c, o = 0): """ Converts a 4-bytes (32 bits) string to an integer. :param c: string containing bytes to convert :param o: offset of bytes to convert in string """ ## return int(ord(c[o])+(ord(c[o+1])<<8)+(ord(c[o+2])<<16)+(ord(c[o+3])<<24)) ## # [PL]: added int() because "<<" gives long int since Python 2.4 # copied from Pillow's _binary: return i8(c[o]) | (i8(c[o+1])<<8) | (i8(c[o+2])<<16) | (i8(c[o+3])<<24) def _clsid(clsid): """ Converts a CLSID to a human-readable string. :param clsid: string of length 16. """ assert len(clsid) == 16 # if clsid is only made of null bytes, return an empty string: # (PL: why not simply return the string with zeroes?) if not clsid.strip(b"\0"): return "" return (("%08X-%04X-%04X-%02X%02X-" + "%02X" * 6) % ((i32(clsid, 0), i16(clsid, 4), i16(clsid, 6)) + tuple(map(i8, clsid[8:16])))) def filetime2datetime(filetime): """ convert FILETIME (64 bits int) to Python datetime.datetime """ # TODO: manage exception when microseconds is too large # inspired from http://code.activestate.com/recipes/511425-filetime-to-datetime/ _FILETIME_null_date = datetime.datetime(1601, 1, 1, 0, 0, 0) #debug('timedelta days=%d' % (filetime//(10*1000000*3600*24))) return _FILETIME_null_date + datetime.timedelta(microseconds=filetime//10) #=== CLASSES ================================================================== class OleMetadata: """ class to parse and store metadata from standard properties of OLE files. Available attributes: codepage, title, subject, author, keywords, comments, template, last_saved_by, revision_number, total_edit_time, last_printed, create_time, last_saved_time, num_pages, num_words, num_chars, thumbnail, creating_application, security, codepage_doc, category, presentation_target, bytes, lines, paragraphs, slides, notes, hidden_slides, mm_clips, scale_crop, heading_pairs, titles_of_parts, manager, company, links_dirty, chars_with_spaces, unused, shared_doc, link_base, hlinks, hlinks_changed, version, dig_sig, content_type, content_status, language, doc_version Note: an attribute is set to None when not present in the properties of the OLE file. References for SummaryInformation stream: - http://msdn.microsoft.com/en-us/library/dd942545.aspx - http://msdn.microsoft.com/en-us/library/dd925819%28v=office.12%29.aspx - http://msdn.microsoft.com/en-us/library/windows/desktop/aa380376%28v=vs.85%29.aspx - http://msdn.microsoft.com/en-us/library/aa372045.aspx - http://sedna-soft.de/summary-information-stream/ - http://poi.apache.org/apidocs/org/apache/poi/hpsf/SummaryInformation.html References for DocumentSummaryInformation stream: - http://msdn.microsoft.com/en-us/library/dd945671%28v=office.12%29.aspx - http://msdn.microsoft.com/en-us/library/windows/desktop/aa380374%28v=vs.85%29.aspx - http://poi.apache.org/apidocs/org/apache/poi/hpsf/DocumentSummaryInformation.html new in version 0.25 """ # attribute names for SummaryInformation stream properties: # (ordered by property id, starting at 1) SUMMARY_ATTRIBS = ['codepage', 'title', 'subject', 'author', 'keywords', 'comments', 'template', 'last_saved_by', 'revision_number', 'total_edit_time', 'last_printed', 'create_time', 'last_saved_time', 'num_pages', 'num_words', 'num_chars', 'thumbnail', 'creating_application', 'security'] # attribute names for DocumentSummaryInformation stream properties: # (ordered by property id, starting at 1) DOCSUM_ATTRIBS = ['codepage_doc', 'category', 'presentation_target', 'bytes', 'lines', 'paragraphs', 'slides', 'notes', 'hidden_slides', 'mm_clips', 'scale_crop', 'heading_pairs', 'titles_of_parts', 'manager', 'company', 'links_dirty', 'chars_with_spaces', 'unused', 'shared_doc', 'link_base', 'hlinks', 'hlinks_changed', 'version', 'dig_sig', 'content_type', 'content_status', 'language', 'doc_version'] def __init__(self): """ Constructor for OleMetadata All attributes are set to None by default """ # properties from SummaryInformation stream self.codepage = None self.title = None self.subject = None self.author = None self.keywords = None self.comments = None self.template = None self.last_saved_by = None self.revision_number = None self.total_edit_time = None self.last_printed = None self.create_time = None self.last_saved_time = None self.num_pages = None self.num_words = None self.num_chars = None self.thumbnail = None self.creating_application = None self.security = None # properties from DocumentSummaryInformation stream self.codepage_doc = None self.category = None self.presentation_target = None self.bytes = None self.lines = None self.paragraphs = None self.slides = None self.notes = None self.hidden_slides = None self.mm_clips = None self.scale_crop = None self.heading_pairs = None self.titles_of_parts = None self.manager = None self.company = None self.links_dirty = None self.chars_with_spaces = None self.unused = None self.shared_doc = None self.link_base = None self.hlinks = None self.hlinks_changed = None self.version = None self.dig_sig = None self.content_type = None self.content_status = None self.language = None self.doc_version = None def parse_properties(self, olefile): """ Parse standard properties of an OLE file, from the streams "\x05SummaryInformation" and "\x05DocumentSummaryInformation", if present. Properties are converted to strings, integers or python datetime objects. If a property is not present, its value is set to None. """ # first set all attributes to None: for attrib in (self.SUMMARY_ATTRIBS + self.DOCSUM_ATTRIBS): setattr(self, attrib, None) if olefile.exists("\x05SummaryInformation"): # get properties from the stream: # (converting timestamps to python datetime, except total_edit_time, # which is property #10) props = olefile.getproperties("\x05SummaryInformation", convert_time=True, no_conversion=[10]) # store them into this object's attributes: for i in range(len(self.SUMMARY_ATTRIBS)): # ids for standards properties start at 0x01, until 0x13 value = props.get(i+1, None) setattr(self, self.SUMMARY_ATTRIBS[i], value) if olefile.exists("\x05DocumentSummaryInformation"): # get properties from the stream: props = olefile.getproperties("\x05DocumentSummaryInformation", convert_time=True) # store them into this object's attributes: for i in range(len(self.DOCSUM_ATTRIBS)): # ids for standards properties start at 0x01, until 0x13 value = props.get(i+1, None) setattr(self, self.DOCSUM_ATTRIBS[i], value) def dump(self): """ Dump all metadata, for debugging purposes. """ print('Properties from SummaryInformation stream:') for prop in self.SUMMARY_ATTRIBS: value = getattr(self, prop) print('- %s: %s' % (prop, repr(value))) print('Properties from DocumentSummaryInformation stream:') for prop in self.DOCSUM_ATTRIBS: value = getattr(self, prop) print('- %s: %s' % (prop, repr(value))) #--- _OleStream --------------------------------------------------------------- class _OleStream(io.BytesIO): """ OLE2 Stream Returns a read-only file object which can be used to read the contents of a OLE stream (instance of the BytesIO class). To open a stream, use the openstream method in the OleFile class. This function can be used with either ordinary streams, or ministreams, depending on the offset, sectorsize, and fat table arguments. Attributes: - size: actual size of data stream, after it was opened. """ # FIXME: should store the list of sects obtained by following # the fat chain, and load new sectors on demand instead of # loading it all in one go. def __init__(self, fp, sect, size, offset, sectorsize, fat, filesize): """ Constructor for _OleStream class. :param fp: file object, the OLE container or the MiniFAT stream :param sect: sector index of first sector in the stream :param size: total size of the stream :param offset: offset in bytes for the first FAT or MiniFAT sector :param sectorsize: size of one sector :param fat: array/list of sector indexes (FAT or MiniFAT) :param filesize: size of OLE file (for debugging) :returns: a BytesIO instance containing the OLE stream """ debug('_OleStream.__init__:') debug(' sect=%d (%X), size=%d, offset=%d, sectorsize=%d, len(fat)=%d, fp=%s' %(sect,sect,size,offset,sectorsize,len(fat), repr(fp))) #[PL] To detect malformed documents with FAT loops, we compute the # expected number of sectors in the stream: unknown_size = False if size==0x7FFFFFFF: # this is the case when called from OleFileIO._open(), and stream # size is not known in advance (for example when reading the # Directory stream). Then we can only guess maximum size: size = len(fat)*sectorsize # and we keep a record that size was unknown: unknown_size = True debug(' stream with UNKNOWN SIZE') nb_sectors = (size + (sectorsize-1)) // sectorsize debug('nb_sectors = %d' % nb_sectors) # This number should (at least) be less than the total number of # sectors in the given FAT: if nb_sectors > len(fat): raise IOError('malformed OLE document, stream too large') # optimization(?): data is first a list of strings, and join() is called # at the end to concatenate all in one string. # (this may not be really useful with recent Python versions) data = [] # if size is zero, then first sector index should be ENDOFCHAIN: if size == 0 and sect != ENDOFCHAIN: debug('size == 0 and sect != ENDOFCHAIN:') raise IOError('incorrect OLE sector index for empty stream') #[PL] A fixed-length for loop is used instead of an undefined while # loop to avoid DoS attacks: for i in range(nb_sectors): # Sector index may be ENDOFCHAIN, but only if size was unknown if sect == ENDOFCHAIN: if unknown_size: break else: # else this means that the stream is smaller than declared: debug('sect=ENDOFCHAIN before expected size') raise IOError('incomplete OLE stream') # sector index should be within FAT: if sect<0 or sect>=len(fat): debug('sect=%d (%X) / len(fat)=%d' % (sect, sect, len(fat))) debug('i=%d / nb_sectors=%d' %(i, nb_sectors)) ## tmp_data = b"".join(data) ## f = open('test_debug.bin', 'wb') ## f.write(tmp_data) ## f.close() ## debug('data read so far: %d bytes' % len(tmp_data)) raise IOError('incorrect OLE FAT, sector index out of range') #TODO: merge this code with OleFileIO.getsect() ? #TODO: check if this works with 4K sectors: try: fp.seek(offset + sectorsize * sect) except: debug('sect=%d, seek=%d, filesize=%d' % (sect, offset+sectorsize*sect, filesize)) raise IOError('OLE sector index out of range') sector_data = fp.read(sectorsize) # [PL] check if there was enough data: # Note: if sector is the last of the file, sometimes it is not a # complete sector (of 512 or 4K), so we may read less than # sectorsize. if len(sector_data)!=sectorsize and sect!=(len(fat)-1): debug('sect=%d / len(fat)=%d, seek=%d / filesize=%d, len read=%d' % (sect, len(fat), offset+sectorsize*sect, filesize, len(sector_data))) debug('seek+len(read)=%d' % (offset+sectorsize*sect+len(sector_data))) raise IOError('incomplete OLE sector') data.append(sector_data) # jump to next sector in the FAT: try: sect = fat[sect] & 0xFFFFFFFF # JYTHON-WORKAROUND except IndexError: # [PL] if pointer is out of the FAT an exception is raised raise IOError('incorrect OLE FAT, sector index out of range') #[PL] Last sector should be a "end of chain" marker: if sect != ENDOFCHAIN: raise IOError('incorrect last sector index in OLE stream') data = b"".join(data) # Data is truncated to the actual stream size: if len(data) >= size: data = data[:size] # actual stream size is stored for future use: self.size = size elif unknown_size: # actual stream size was not known, now we know the size of read # data: self.size = len(data) else: # read data is less than expected: debug('len(data)=%d, size=%d' % (len(data), size)) raise IOError('OLE stream size is less than declared') # when all data is read in memory, BytesIO constructor is called io.BytesIO.__init__(self, data) # Then the _OleStream object can be used as a read-only file object. #--- _OleDirectoryEntry ------------------------------------------------------- class _OleDirectoryEntry: """ OLE2 Directory Entry """ #[PL] parsing code moved from OleFileIO.loaddirectory # struct to parse directory entries: # <: little-endian byte order, standard sizes # (note: this should guarantee that Q returns a 64 bits int) # 64s: string containing entry name in unicode (max 31 chars) + null char # H: uint16, number of bytes used in name buffer, including null = (len+1)*2 # B: uint8, dir entry type (between 0 and 5) # B: uint8, color: 0=black, 1=red # I: uint32, index of left child node in the red-black tree, NOSTREAM if none # I: uint32, index of right child node in the red-black tree, NOSTREAM if none # I: uint32, index of child root node if it is a storage, else NOSTREAM # 16s: CLSID, unique identifier (only used if it is a storage) # I: uint32, user flags # Q (was 8s): uint64, creation timestamp or zero # Q (was 8s): uint64, modification timestamp or zero # I: uint32, SID of first sector if stream or ministream, SID of 1st sector # of stream containing ministreams if root entry, 0 otherwise # I: uint32, total stream size in bytes if stream (low 32 bits), 0 otherwise # I: uint32, total stream size in bytes if stream (high 32 bits), 0 otherwise STRUCT_DIRENTRY = '<64sHBBIII16sIQQIII' # size of a directory entry: 128 bytes DIRENTRY_SIZE = 128 assert struct.calcsize(STRUCT_DIRENTRY) == DIRENTRY_SIZE def __init__(self, entry, sid, olefile): """ Constructor for an _OleDirectoryEntry object. Parses a 128-bytes entry from the OLE Directory stream. :param entry : string (must be 128 bytes long) :param sid : index of this directory entry in the OLE file directory :param olefile: OleFileIO containing this directory entry """ self.sid = sid # ref to olefile is stored for future use self.olefile = olefile # kids is a list of children entries, if this entry is a storage: # (list of _OleDirectoryEntry objects) self.kids = [] # kids_dict is a dictionary of children entries, indexed by their # name in lowercase: used to quickly find an entry, and to detect # duplicates self.kids_dict = {} # flag used to detect if the entry is referenced more than once in # directory: self.used = False # decode DirEntry ( name, namelength, self.entry_type, self.color, self.sid_left, self.sid_right, self.sid_child, clsid, self.dwUserFlags, self.createTime, self.modifyTime, self.isectStart, sizeLow, sizeHigh ) = struct.unpack(_OleDirectoryEntry.STRUCT_DIRENTRY, entry) if self.entry_type not in [STGTY_ROOT, STGTY_STORAGE, STGTY_STREAM, STGTY_EMPTY]: olefile._raise_defect(DEFECT_INCORRECT, 'unhandled OLE storage type') # only first directory entry can (and should) be root: if self.entry_type == STGTY_ROOT and sid != 0: olefile._raise_defect(DEFECT_INCORRECT, 'duplicate OLE root entry') if sid == 0 and self.entry_type != STGTY_ROOT: olefile._raise_defect(DEFECT_INCORRECT, 'incorrect OLE root entry') #debug (struct.unpack(fmt_entry, entry[:len_entry])) # name should be at most 31 unicode characters + null character, # so 64 bytes in total (31*2 + 2): if namelength>64: olefile._raise_defect(DEFECT_INCORRECT, 'incorrect DirEntry name length') # if exception not raised, namelength is set to the maximum value: namelength = 64 # only characters without ending null char are kept: name = name[:(namelength-2)] #TODO: check if the name is actually followed by a null unicode character ([MS-CFB] 2.6.1) #TODO: check if the name does not contain forbidden characters: # [MS-CFB] 2.6.1: "The following characters are illegal and MUST NOT be part of the name: '/', '\', ':', '!'." # name is converted from UTF-16LE to the path encoding specified in the OleFileIO: self.name = olefile._decode_utf16_str(name) debug('DirEntry SID=%d: %s' % (self.sid, repr(self.name))) debug(' - type: %d' % self.entry_type) debug(' - sect: %d' % self.isectStart) debug(' - SID left: %d, right: %d, child: %d' % (self.sid_left, self.sid_right, self.sid_child)) # sizeHigh is only used for 4K sectors, it should be zero for 512 bytes # sectors, BUT apparently some implementations set it as 0xFFFFFFFF, 1 # or some other value so it cannot be raised as a defect in general: if olefile.sectorsize == 512: if sizeHigh != 0 and sizeHigh != 0xFFFFFFFF: debug('sectorsize=%d, sizeLow=%d, sizeHigh=%d (%X)' % (olefile.sectorsize, sizeLow, sizeHigh, sizeHigh)) olefile._raise_defect(DEFECT_UNSURE, 'incorrect OLE stream size') self.size = sizeLow else: self.size = sizeLow + (long(sizeHigh)<<32) debug(' - size: %d (sizeLow=%d, sizeHigh=%d)' % (self.size, sizeLow, sizeHigh)) self.clsid = _clsid(clsid) # a storage should have a null size, BUT some implementations such as # Word 8 for Mac seem to allow non-null values => Potential defect: if self.entry_type == STGTY_STORAGE and self.size != 0: olefile._raise_defect(DEFECT_POTENTIAL, 'OLE storage with size>0') # check if stream is not already referenced elsewhere: if self.entry_type in (STGTY_ROOT, STGTY_STREAM) and self.size>0: if self.size < olefile.minisectorcutoff \ and self.entry_type==STGTY_STREAM: # only streams can be in MiniFAT # ministream object minifat = True else: minifat = False olefile._check_duplicate_stream(self.isectStart, minifat) def build_storage_tree(self): """ Read and build the red-black tree attached to this _OleDirectoryEntry object, if it is a storage. Note that this method builds a tree of all subentries, so it should only be called for the root object once. """ debug('build_storage_tree: SID=%d - %s - sid_child=%d' % (self.sid, repr(self.name), self.sid_child)) if self.sid_child != NOSTREAM: # if child SID is not NOSTREAM, then this entry is a storage. # Let's walk through the tree of children to fill the kids list: self.append_kids(self.sid_child) # Note from OpenOffice documentation: the safest way is to # recreate the tree because some implementations may store broken # red-black trees... # in the OLE file, entries are sorted on (length, name). # for convenience, we sort them on name instead: # (see rich comparison methods in this class) self.kids.sort() def append_kids(self, child_sid): """ Walk through red-black tree of children of this directory entry to add all of them to the kids list. (recursive method) :param child_sid : index of child directory entry to use, or None when called first time for the root. (only used during recursion) """ #[PL] this method was added to use simple recursion instead of a complex # algorithm. # if this is not a storage or a leaf of the tree, nothing to do: if child_sid == NOSTREAM: return # check if child SID is in the proper range: if child_sid<0 or child_sid>=len(self.olefile.direntries): self.olefile._raise_defect(DEFECT_FATAL, 'OLE DirEntry index out of range') # get child direntry: child = self.olefile._load_direntry(child_sid) #direntries[child_sid] debug('append_kids: child_sid=%d - %s - sid_left=%d, sid_right=%d, sid_child=%d' % (child.sid, repr(child.name), child.sid_left, child.sid_right, child.sid_child)) # the directory entries are organized as a red-black tree. # (cf. Wikipedia for details) # First walk through left side of the tree: self.append_kids(child.sid_left) # Check if its name is not already used (case-insensitive): name_lower = child.name.lower() if name_lower in self.kids_dict: self.olefile._raise_defect(DEFECT_INCORRECT, "Duplicate filename in OLE storage") # Then the child_sid _OleDirectoryEntry object is appended to the # kids list and dictionary: self.kids.append(child) self.kids_dict[name_lower] = child # Check if kid was not already referenced in a storage: if child.used: self.olefile._raise_defect(DEFECT_INCORRECT, 'OLE Entry referenced more than once') child.used = True # Finally walk through right side of the tree: self.append_kids(child.sid_right) # Afterwards build kid's own tree if it's also a storage: child.build_storage_tree() def __eq__(self, other): "Compare entries by name" return self.name == other.name def __lt__(self, other): "Compare entries by name" return self.name < other.name def __ne__(self, other): return not self.__eq__(other) def __le__(self, other): return self.__eq__(other) or self.__lt__(other) # Reflected __lt__() and __le__() will be used for __gt__() and __ge__() #TODO: replace by the same function as MS implementation ? # (order by name length first, then case-insensitive order) def dump(self, tab = 0): "Dump this entry, and all its subentries (for debug purposes only)" TYPES = ["(invalid)", "(storage)", "(stream)", "(lockbytes)", "(property)", "(root)"] #print(" "*tab + repr(self.name), TYPES[self.entry_type], end=' ') #if self.entry_type in (STGTY_STREAM, STGTY_ROOT): # print(self.size, "bytes", end=' ') #print() #if self.entry_type in (STGTY_STORAGE, STGTY_ROOT) and self.clsid: # print(" "*tab + "{%s}" % self.clsid) for kid in self.kids: kid.dump(tab + 2) def getmtime(self): """ Return modification time of a directory entry. :returns: None if modification time is null, a python datetime object otherwise (UTC timezone) new in version 0.26 """ if self.modifyTime == 0: return None return filetime2datetime(self.modifyTime) def getctime(self): """ Return creation time of a directory entry. :returns: None if modification time is null, a python datetime object otherwise (UTC timezone) new in version 0.26 """ if self.createTime == 0: return None return filetime2datetime(self.createTime) #--- OleFileIO ---------------------------------------------------------------- class OleFileIO: """ OLE container object This class encapsulates the interface to an OLE 2 structured storage file. Use the listdir and openstream methods to access the contents of this file. Object names are given as a list of strings, one for each subentry level. The root entry should be omitted. For example, the following code extracts all image streams from a Microsoft Image Composer file:: ole = OleFileIO("fan.mic") for entry in ole.listdir(): if entry[1:2] == "Image": fin = ole.openstream(entry) fout = open(entry[0:1], "wb") while True: s = fin.read(8192) if not s: break fout.write(s) You can use the viewer application provided with the Python Imaging Library to view the resulting files (which happens to be standard TIFF files). """ def __init__(self, filename=None, raise_defects=DEFECT_FATAL, write_mode=False, debug=False, path_encoding=DEFAULT_PATH_ENCODING): """ Constructor for the OleFileIO class. :param filename: file to open. - if filename is a string smaller than 1536 bytes, it is the path of the file to open. (bytes or unicode string) - if filename is a string longer than 1535 bytes, it is parsed as the content of an OLE file in memory. (bytes type only) - if filename is a file-like object (with read, seek and tell methods), it is parsed as-is. :param raise_defects: minimal level for defects to be raised as exceptions. (use DEFECT_FATAL for a typical application, DEFECT_INCORRECT for a security-oriented application, see source code for details) :param write_mode: bool, if True the file is opened in read/write mode instead of read-only by default. :param debug: bool, set debug mode :param path_encoding: None or str, name of the codec to use for path names (streams and storages), or None for Unicode. Unicode by default on Python 3+, UTF-8 on Python 2.x. (new in olefile 0.42, was hardcoded to Latin-1 until olefile v0.41) """ set_debug_mode(debug) # minimal level for defects to be raised as exceptions: self._raise_defects_level = raise_defects # list of defects/issues not raised as exceptions: # tuples of (exception type, message) self.parsing_issues = [] self.write_mode = write_mode self.path_encoding = path_encoding self._filesize = None self.fp = None if filename: self.open(filename, write_mode=write_mode) def _raise_defect(self, defect_level, message, exception_type=IOError): """ This method should be called for any defect found during file parsing. It may raise an IOError exception according to the minimal level chosen for the OleFileIO object. :param defect_level: defect level, possible values are: - DEFECT_UNSURE : a case which looks weird, but not sure it's a defect - DEFECT_POTENTIAL : a potential defect - DEFECT_INCORRECT : an error according to specifications, but parsing can go on - DEFECT_FATAL : an error which cannot be ignored, parsing is impossible :param message: string describing the defect, used with raised exception. :param exception_type: exception class to be raised, IOError by default """ # added by [PL] if defect_level >= self._raise_defects_level: raise exception_type(message) else: # just record the issue, no exception raised: self.parsing_issues.append((exception_type, message)) def _decode_utf16_str(self, utf16_str, errors='replace'): """ Decode a string encoded in UTF-16 LE format, as found in the OLE directory or in property streams. Return a string encoded according to the path_encoding specified for the OleFileIO object. :param utf16_str: bytes string encoded in UTF-16 LE format :param errors: str, see python documentation for str.decode() :return: str, encoded according to path_encoding """ unicode_str = utf16_str.decode('UTF-16LE', errors) if self.path_encoding: # an encoding has been specified for path names: return unicode_str.encode(self.path_encoding, errors) else: # path_encoding=None, return the Unicode string as-is: return unicode_str def open(self, filename, write_mode=False): """ Open an OLE2 file in read-only or read/write mode. Read and parse the header, FAT and directory. :param filename: string-like or file-like object, OLE file to parse - if filename is a string smaller than 1536 bytes, it is the path of the file to open. (bytes or unicode string) - if filename is a string longer than 1535 bytes, it is parsed as the content of an OLE file in memory. (bytes type only) - if filename is a file-like object (with read, seek and tell methods), it is parsed as-is. :param write_mode: bool, if True the file is opened in read/write mode instead of read-only by default. (ignored if filename is not a path) """ self.write_mode = write_mode #[PL] check if filename is a string-like or file-like object: # (it is better to check for a read() method) if hasattr(filename, 'read'): #TODO: also check seek and tell methods? # file-like object: use it directly self.fp = filename elif isinstance(filename, bytes) and len(filename) >= MINIMAL_OLEFILE_SIZE: # filename is a bytes string containing the OLE file to be parsed: # convert it to BytesIO self.fp = io.BytesIO(filename) else: # string-like object: filename of file on disk if self.write_mode: # open file in mode 'read with update, binary' # According to https://docs.python.org/2/library/functions.html#open # 'w' would truncate the file, 'a' may only append on some Unixes mode = 'r+b' else: # read-only mode by default mode = 'rb' self.fp = open(filename, mode) # obtain the filesize by using seek and tell, which should work on most # file-like objects: #TODO: do it above, using getsize with filename when possible? #TODO: fix code to fail with clear exception when filesize cannot be obtained filesize=0 self.fp.seek(0, os.SEEK_END) try: filesize = self.fp.tell() finally: self.fp.seek(0) self._filesize = filesize # lists of streams in FAT and MiniFAT, to detect duplicate references # (list of indexes of first sectors of each stream) self._used_streams_fat = [] self._used_streams_minifat = [] header = self.fp.read(512) if len(header) != 512 or header[:8] != MAGIC: self._raise_defect(DEFECT_FATAL, "not an OLE2 structured storage file") # [PL] header structure according to AAF specifications: ##Header ##struct StructuredStorageHeader { // [offset from start (bytes), length (bytes)] ##BYTE _abSig[8]; // [00H,08] {0xd0, 0xcf, 0x11, 0xe0, 0xa1, 0xb1, ## // 0x1a, 0xe1} for current version ##CLSID _clsid; // [08H,16] reserved must be zero (WriteClassStg/ ## // GetClassFile uses root directory class id) ##USHORT _uMinorVersion; // [18H,02] minor version of the format: 33 is ## // written by reference implementation ##USHORT _uDllVersion; // [1AH,02] major version of the dll/format: 3 for ## // 512-byte sectors, 4 for 4 KB sectors ##USHORT _uByteOrder; // [1CH,02] 0xFFFE: indicates Intel byte-ordering ##USHORT _uSectorShift; // [1EH,02] size of sectors in power-of-two; ## // typically 9 indicating 512-byte sectors ##USHORT _uMiniSectorShift; // [20H,02] size of mini-sectors in power-of-two; ## // typically 6 indicating 64-byte mini-sectors ##USHORT _usReserved; // [22H,02] reserved, must be zero ##ULONG _ulReserved1; // [24H,04] reserved, must be zero ##FSINDEX _csectDir; // [28H,04] must be zero for 512-byte sectors, ## // number of SECTs in directory chain for 4 KB ## // sectors ##FSINDEX _csectFat; // [2CH,04] number of SECTs in the FAT chain ##SECT _sectDirStart; // [30H,04] first SECT in the directory chain ##DFSIGNATURE _signature; // [34H,04] signature used for transactions; must ## // be zero. The reference implementation ## // does not support transactions ##ULONG _ulMiniSectorCutoff; // [38H,04] maximum size for a mini stream; ## // typically 4096 bytes ##SECT _sectMiniFatStart; // [3CH,04] first SECT in the MiniFAT chain ##FSINDEX _csectMiniFat; // [40H,04] number of SECTs in the MiniFAT chain ##SECT _sectDifStart; // [44H,04] first SECT in the DIFAT chain ##FSINDEX _csectDif; // [48H,04] number of SECTs in the DIFAT chain ##SECT _sectFat[109]; // [4CH,436] the SECTs of first 109 FAT sectors ##}; # [PL] header decoding: # '<' indicates little-endian byte ordering for Intel (cf. struct module help) fmt_header = '<8s16sHHHHHHLLLLLLLLLL' header_size = struct.calcsize(fmt_header) debug( "fmt_header size = %d, +FAT = %d" % (header_size, header_size + 109*4) ) header1 = header[:header_size] ( self.Sig, self.clsid, self.MinorVersion, self.DllVersion, self.ByteOrder, self.SectorShift, self.MiniSectorShift, self.Reserved, self.Reserved1, self.csectDir, self.csectFat, self.sectDirStart, self.signature, self.MiniSectorCutoff, self.MiniFatStart, self.csectMiniFat, self.sectDifStart, self.csectDif ) = struct.unpack(fmt_header, header1) debug( struct.unpack(fmt_header, header1)) if self.Sig != MAGIC: # OLE signature should always be present self._raise_defect(DEFECT_FATAL, "incorrect OLE signature") if self.clsid != bytearray(16): # according to AAF specs, CLSID should always be zero self._raise_defect(DEFECT_INCORRECT, "incorrect CLSID in OLE header") debug( "MinorVersion = %d" % self.MinorVersion ) debug( "DllVersion = %d" % self.DllVersion ) if self.DllVersion not in [3, 4]: # version 3: usual format, 512 bytes per sector # version 4: large format, 4K per sector self._raise_defect(DEFECT_INCORRECT, "incorrect DllVersion in OLE header") debug( "ByteOrder = %X" % self.ByteOrder ) if self.ByteOrder != 0xFFFE: # For now only common little-endian documents are handled correctly self._raise_defect(DEFECT_FATAL, "incorrect ByteOrder in OLE header") # TODO: add big-endian support for documents created on Mac ? # But according to [MS-CFB] ? v20140502, ByteOrder MUST be 0xFFFE. self.SectorSize = 2**self.SectorShift debug( "SectorSize = %d" % self.SectorSize ) if self.SectorSize not in [512, 4096]: self._raise_defect(DEFECT_INCORRECT, "incorrect SectorSize in OLE header") if (self.DllVersion==3 and self.SectorSize!=512) \ or (self.DllVersion==4 and self.SectorSize!=4096): self._raise_defect(DEFECT_INCORRECT, "SectorSize does not match DllVersion in OLE header") self.MiniSectorSize = 2**self.MiniSectorShift debug( "MiniSectorSize = %d" % self.MiniSectorSize ) if self.MiniSectorSize not in [64]: self._raise_defect(DEFECT_INCORRECT, "incorrect MiniSectorSize in OLE header") if self.Reserved != 0 or self.Reserved1 != 0: self._raise_defect(DEFECT_INCORRECT, "incorrect OLE header (non-null reserved bytes)") debug( "csectDir = %d" % self.csectDir ) # Number of directory sectors (only allowed if DllVersion != 3) if self.SectorSize==512 and self.csectDir!=0: self._raise_defect(DEFECT_INCORRECT, "incorrect csectDir in OLE header") debug( "csectFat = %d" % self.csectFat ) # csectFat = number of FAT sectors in the file debug( "sectDirStart = %X" % self.sectDirStart ) # sectDirStart = 1st sector containing the directory debug( "signature = %d" % self.signature ) # Signature should be zero, BUT some implementations do not follow this # rule => only a potential defect: # (according to MS-CFB, may be != 0 for applications supporting file # transactions) if self.signature != 0: self._raise_defect(DEFECT_POTENTIAL, "incorrect OLE header (signature>0)") debug( "MiniSectorCutoff = %d" % self.MiniSectorCutoff ) # MS-CFB: This integer field MUST be set to 0x00001000. This field # specifies the maximum size of a user-defined data stream allocated # from the mini FAT and mini stream, and that cutoff is 4096 bytes. # Any user-defined data stream larger than or equal to this cutoff size # must be allocated as normal sectors from the FAT. if self.MiniSectorCutoff != 0x1000: self._raise_defect(DEFECT_INCORRECT, "incorrect MiniSectorCutoff in OLE header") debug( "MiniFatStart = %X" % self.MiniFatStart ) debug( "csectMiniFat = %d" % self.csectMiniFat ) debug( "sectDifStart = %X" % self.sectDifStart ) debug( "csectDif = %d" % self.csectDif ) # calculate the number of sectors in the file # (-1 because header doesn't count) self.nb_sect = ( (filesize + self.SectorSize-1) // self.SectorSize) - 1 debug( "Number of sectors in the file: %d" % self.nb_sect ) #TODO: change this test, because an OLE file MAY contain other data # after the last sector. # file clsid self.clsid = _clsid(header[8:24]) #TODO: remove redundant attributes, and fix the code which uses them? self.sectorsize = self.SectorSize #1 << i16(header, 30) self.minisectorsize = self.MiniSectorSize #1 << i16(header, 32) self.minisectorcutoff = self.MiniSectorCutoff # i32(header, 56) # check known streams for duplicate references (these are always in FAT, # never in MiniFAT): self._check_duplicate_stream(self.sectDirStart) # check MiniFAT only if it is not empty: if self.csectMiniFat: self._check_duplicate_stream(self.MiniFatStart) # check DIFAT only if it is not empty: if self.csectDif: self._check_duplicate_stream(self.sectDifStart) # Load file allocation tables self.loadfat(header) # Load direcory. This sets both the direntries list (ordered by sid) # and the root (ordered by hierarchy) members. self.loaddirectory(self.sectDirStart)#i32(header, 48)) self.ministream = None self.minifatsect = self.MiniFatStart #i32(header, 60) def close(self): """ close the OLE file, to release the file object """ self.fp.close() def _check_duplicate_stream(self, first_sect, minifat=False): """ Checks if a stream has not been already referenced elsewhere. This method should only be called once for each known stream, and only if stream size is not null. :param first_sect: int, index of first sector of the stream in FAT :param minifat: bool, if True, stream is located in the MiniFAT, else in the FAT """ if minifat: debug('_check_duplicate_stream: sect=%d in MiniFAT' % first_sect) used_streams = self._used_streams_minifat else: debug('_check_duplicate_stream: sect=%d in FAT' % first_sect) # some values can be safely ignored (not a real stream): if first_sect in (DIFSECT,FATSECT,ENDOFCHAIN,FREESECT): return used_streams = self._used_streams_fat #TODO: would it be more efficient using a dict or hash values, instead # of a list of long ? if first_sect in used_streams: self._raise_defect(DEFECT_INCORRECT, 'Stream referenced twice') else: used_streams.append(first_sect) def dumpfat(self, fat, firstindex=0): "Displays a part of FAT in human-readable form for debugging purpose" # [PL] added only for debug if not DEBUG_MODE: return # dictionary to convert special FAT values in human-readable strings VPL = 8 # values per line (8+1 * 8+1 = 81) fatnames = { FREESECT: "..free..", ENDOFCHAIN: "[ END. ]", FATSECT: "FATSECT ", DIFSECT: "DIFSECT " } nbsect = len(fat) nlines = (nbsect+VPL-1)//VPL #print("index", end=" ") #for i in range(VPL): # print("%8X" % i, end=" ") #print() for l in range(nlines): index = l*VPL #print("%8X:" % (firstindex+index), end=" ") for i in range(index, index+VPL): if i>=nbsect: break sect = fat[i] aux = sect & 0xFFFFFFFF # JYTHON-WORKAROUND if aux in fatnames: name = fatnames[aux] else: if sect == i+1: name = " --->" else: name = "%8X" % sect #print(name, end=" ") #print() def dumpsect(self, sector, firstindex=0): "Displays a sector in a human-readable form, for debugging purpose." if not DEBUG_MODE: return VPL=8 # number of values per line (8+1 * 8+1 = 81) tab = array.array(UINT32, sector) if sys.byteorder == 'big': tab.byteswap() nbsect = len(tab) nlines = (nbsect+VPL-1)//VPL #print("index", end=" ") # for i in range(VPL): # print("%8X" % i, end=" ") # print() for l in range(nlines): index = l*VPL #print("%8X:" % (firstindex+index), end=" ") for i in range(index, index+VPL): if i>=nbsect: break sect = tab[i] name = "%8X" % sect # print(name, end=" ") # print() def sect2array(self, sect): """ convert a sector to an array of 32 bits unsigned integers, swapping bytes on big endian CPUs such as PowerPC (old Macs) """ a = array.array(UINT32, sect) # if CPU is big endian, swap bytes: if sys.byteorder == 'big': a.byteswap() return a def loadfat_sect(self, sect): """ Adds the indexes of the given sector to the FAT :param sect: string containing the first FAT sector, or array of long integers :returns: index of last FAT sector. """ # a FAT sector is an array of ulong integers. if isinstance(sect, array.array): # if sect is already an array it is directly used fat1 = sect else: # if it's a raw sector, it is parsed in an array fat1 = self.sect2array(sect) self.dumpsect(sect) # The FAT is a sector chain starting at the first index of itself. for isect in fat1: isect = isect & 0xFFFFFFFF # JYTHON-WORKAROUND debug("isect = %X" % isect) if isect == ENDOFCHAIN or isect == FREESECT: # the end of the sector chain has been reached debug("found end of sector chain") break # read the FAT sector s = self.getsect(isect) # parse it as an array of 32 bits integers, and add it to the # global FAT array nextfat = self.sect2array(s) self.fat = self.fat + nextfat return isect def loadfat(self, header): """ Load the FAT table. """ # The 1st sector of the file contains sector numbers for the first 109 # FAT sectors, right after the header which is 76 bytes long. # (always 109, whatever the sector size: 512 bytes = 76+4*109) # Additional sectors are described by DIF blocks sect = header[76:512] debug( "len(sect)=%d, so %d integers" % (len(sect), len(sect)//4) ) #fat = [] # [PL] FAT is an array of 32 bits unsigned ints, it's more effective # to use an array than a list in Python. # It's initialized as empty first: self.fat = array.array(UINT32) self.loadfat_sect(sect) #self.dumpfat(self.fat) ## for i in range(0, len(sect), 4): ## ix = i32(sect, i) ## #[PL] if ix == -2 or ix == -1: # ix == 0xFFFFFFFE or ix == 0xFFFFFFFF: ## if ix == 0xFFFFFFFE or ix == 0xFFFFFFFF: ## break ## s = self.getsect(ix) ## #fat = fat + [i32(s, i) for i in range(0, len(s), 4)] ## fat = fat + array.array(UINT32, s) if self.csectDif != 0: # [PL] There's a DIFAT because file is larger than 6.8MB # some checks just in case: if self.csectFat <= 109: # there must be at least 109 blocks in header and the rest in # DIFAT, so number of sectors must be >109. self._raise_defect(DEFECT_INCORRECT, 'incorrect DIFAT, not enough sectors') if self.sectDifStart >= self.nb_sect: # initial DIFAT block index must be valid self._raise_defect(DEFECT_FATAL, 'incorrect DIFAT, first index out of range') debug( "DIFAT analysis..." ) # We compute the necessary number of DIFAT sectors : # Number of pointers per DIFAT sector = (sectorsize/4)-1 # (-1 because the last pointer is the next DIFAT sector number) nb_difat_sectors = (self.sectorsize//4)-1 # (if 512 bytes: each DIFAT sector = 127 pointers + 1 towards next DIFAT sector) nb_difat = (self.csectFat-109 + nb_difat_sectors-1)//nb_difat_sectors debug( "nb_difat = %d" % nb_difat ) if self.csectDif != nb_difat: raise IOError('incorrect DIFAT') isect_difat = self.sectDifStart for i in iterrange(nb_difat): debug( "DIFAT block %d, sector %X" % (i, isect_difat) ) #TODO: check if corresponding FAT SID = DIFSECT sector_difat = self.getsect(isect_difat) difat = self.sect2array(sector_difat) self.dumpsect(sector_difat) self.loadfat_sect(difat[:nb_difat_sectors]) # last DIFAT pointer is next DIFAT sector: isect_difat = difat[nb_difat_sectors] debug( "next DIFAT sector: %X" % isect_difat ) # checks: if isect_difat not in [ENDOFCHAIN, FREESECT]: # last DIFAT pointer value must be ENDOFCHAIN or FREESECT raise IOError('incorrect end of DIFAT') ## if len(self.fat) != self.csectFat: ## # FAT should contain csectFat blocks ## print("FAT length: %d instead of %d" % (len(self.fat), self.csectFat)) ## raise IOError('incorrect DIFAT') # since FAT is read from fixed-size sectors, it may contain more values # than the actual number of sectors in the file. # Keep only the relevant sector indexes: if len(self.fat) > self.nb_sect: debug('len(fat)=%d, shrunk to nb_sect=%d' % (len(self.fat), self.nb_sect)) self.fat = self.fat[:self.nb_sect] debug('\nFAT:') self.dumpfat(self.fat) def loadminifat(self): """ Load the MiniFAT table. """ # MiniFAT is stored in a standard sub-stream, pointed to by a header # field. # NOTE: there are two sizes to take into account for this stream: # 1) Stream size is calculated according to the number of sectors # declared in the OLE header. This allocated stream may be more than # needed to store the actual sector indexes. # (self.csectMiniFat is the number of sectors of size self.SectorSize) stream_size = self.csectMiniFat * self.SectorSize # 2) Actually used size is calculated by dividing the MiniStream size # (given by root entry size) by the size of mini sectors, *4 for # 32 bits indexes: nb_minisectors = (self.root.size + self.MiniSectorSize-1) // self.MiniSectorSize used_size = nb_minisectors * 4 debug('loadminifat(): minifatsect=%d, nb FAT sectors=%d, used_size=%d, stream_size=%d, nb MiniSectors=%d' % (self.minifatsect, self.csectMiniFat, used_size, stream_size, nb_minisectors)) if used_size > stream_size: # This is not really a problem, but may indicate a wrong implementation: self._raise_defect(DEFECT_INCORRECT, 'OLE MiniStream is larger than MiniFAT') # In any case, first read stream_size: s = self._open(self.minifatsect, stream_size, force_FAT=True).read() #[PL] Old code replaced by an array: #self.minifat = [i32(s, i) for i in range(0, len(s), 4)] self.minifat = self.sect2array(s) # Then shrink the array to used size, to avoid indexes out of MiniStream: debug('MiniFAT shrunk from %d to %d sectors' % (len(self.minifat), nb_minisectors)) self.minifat = self.minifat[:nb_minisectors] debug('loadminifat(): len=%d' % len(self.minifat)) debug('\nMiniFAT:') self.dumpfat(self.minifat) def getsect(self, sect): """ Read given sector from file on disk. :param sect: int, sector index :returns: a string containing the sector data. """ # From [MS-CFB]: A sector number can be converted into a byte offset # into the file by using the following formula: # (sector number + 1) x Sector Size. # This implies that sector #0 of the file begins at byte offset Sector # Size, not at 0. # [PL] the original code in PIL was wrong when sectors are 4KB instead of # 512 bytes: #self.fp.seek(512 + self.sectorsize * sect) #[PL]: added safety checks: #print("getsect(%X)" % sect) try: self.fp.seek(self.sectorsize * (sect+1)) except: debug('getsect(): sect=%X, seek=%d, filesize=%d' % (sect, self.sectorsize*(sect+1), self._filesize)) self._raise_defect(DEFECT_FATAL, 'OLE sector index out of range') sector = self.fp.read(self.sectorsize) if len(sector) != self.sectorsize: debug('getsect(): sect=%X, read=%d, sectorsize=%d' % (sect, len(sector), self.sectorsize)) self._raise_defect(DEFECT_FATAL, 'incomplete OLE sector') return sector def write_sect(self, sect, data, padding=b'\x00'): """ Write given sector to file on disk. :param sect: int, sector index :param data: bytes, sector data :param padding: single byte, padding character if data < sector size """ if not isinstance(data, bytes): raise TypeError("write_sect: data must be a bytes string") if not isinstance(padding, bytes) or len(padding)!=1: raise TypeError("write_sect: padding must be a bytes string of 1 char") #TODO: we could allow padding=None for no padding at all try: self.fp.seek(self.sectorsize * (sect+1)) except: debug('write_sect(): sect=%X, seek=%d, filesize=%d' % (sect, self.sectorsize*(sect+1), self._filesize)) self._raise_defect(DEFECT_FATAL, 'OLE sector index out of range') if len(data) < self.sectorsize: # add padding data += padding * (self.sectorsize - len(data)) elif len(data) < self.sectorsize: raise ValueError("Data is larger than sector size") self.fp.write(data) def loaddirectory(self, sect): """ Load the directory. :param sect: sector index of directory stream. """ # The directory is stored in a standard # substream, independent of its size. # open directory stream as a read-only file: # (stream size is not known in advance) self.directory_fp = self._open(sect) #[PL] to detect malformed documents and avoid DoS attacks, the maximum # number of directory entries can be calculated: max_entries = self.directory_fp.size // 128 debug('loaddirectory: size=%d, max_entries=%d' % (self.directory_fp.size, max_entries)) # Create list of directory entries #self.direntries = [] # We start with a list of "None" object self.direntries = [None] * max_entries ## for sid in iterrange(max_entries): ## entry = fp.read(128) ## if not entry: ## break ## self.direntries.append(_OleDirectoryEntry(entry, sid, self)) # load root entry: root_entry = self._load_direntry(0) # Root entry is the first entry: self.root = self.direntries[0] # read and build all storage trees, starting from the root: self.root.build_storage_tree() def _load_direntry (self, sid): """ Load a directory entry from the directory. This method should only be called once for each storage/stream when loading the directory. :param sid: index of storage/stream in the directory. :returns: a _OleDirectoryEntry object :exception IOError: if the entry has always been referenced. """ # check if SID is OK: if sid<0 or sid>=len(self.direntries): self._raise_defect(DEFECT_FATAL, "OLE directory index out of range") # check if entry was already referenced: if self.direntries[sid] is not None: self._raise_defect(DEFECT_INCORRECT, "double reference for OLE stream/storage") # if exception not raised, return the object return self.direntries[sid] self.directory_fp.seek(sid * 128) entry = self.directory_fp.read(128) self.direntries[sid] = _OleDirectoryEntry(entry, sid, self) return self.direntries[sid] def dumpdirectory(self): """ Dump directory (for debugging only) """ self.root.dump() def _open(self, start, size = 0x7FFFFFFF, force_FAT=False): """ Open a stream, either in FAT or MiniFAT according to its size. (openstream helper) :param start: index of first sector :param size: size of stream (or nothing if size is unknown) :param force_FAT: if False (default), stream will be opened in FAT or MiniFAT according to size. If True, it will always be opened in FAT. """ debug('OleFileIO.open(): sect=%d, size=%d, force_FAT=%s' % (start, size, str(force_FAT))) # stream size is compared to the MiniSectorCutoff threshold: if size < self.minisectorcutoff and not force_FAT: # ministream object if not self.ministream: # load MiniFAT if it wasn't already done: self.loadminifat() # The first sector index of the miniFAT stream is stored in the # root directory entry: size_ministream = self.root.size debug('Opening MiniStream: sect=%d, size=%d' % (self.root.isectStart, size_ministream)) self.ministream = self._open(self.root.isectStart, size_ministream, force_FAT=True) return _OleStream(fp=self.ministream, sect=start, size=size, offset=0, sectorsize=self.minisectorsize, fat=self.minifat, filesize=self.ministream.size) else: # standard stream return _OleStream(fp=self.fp, sect=start, size=size, offset=self.sectorsize, sectorsize=self.sectorsize, fat=self.fat, filesize=self._filesize) def _list(self, files, prefix, node, streams=True, storages=False): """ listdir helper :param files: list of files to fill in :param prefix: current location in storage tree (list of names) :param node: current node (_OleDirectoryEntry object) :param streams: bool, include streams if True (True by default) - new in v0.26 :param storages: bool, include storages if True (False by default) - new in v0.26 (note: the root storage is never included) """ prefix = prefix + [node.name] for entry in node.kids: if entry.entry_type == STGTY_STORAGE: # this is a storage if storages: # add it to the list files.append(prefix[1:] + [entry.name]) # check its kids self._list(files, prefix, entry, streams, storages) elif entry.entry_type == STGTY_STREAM: # this is a stream if streams: # add it to the list files.append(prefix[1:] + [entry.name]) else: self._raise_defect(DEFECT_INCORRECT, 'The directory tree contains an entry which is not a stream nor a storage.') def listdir(self, streams=True, storages=False): """ Return a list of streams and/or storages stored in this file :param streams: bool, include streams if True (True by default) - new in v0.26 :param storages: bool, include storages if True (False by default) - new in v0.26 (note: the root storage is never included) :returns: list of stream and/or storage paths """ files = [] self._list(files, [], self.root, streams, storages) return files def _find(self, filename): """ Returns directory entry of given filename. (openstream helper) Note: this method is case-insensitive. :param filename: path of stream in storage tree (except root entry), either: - a string using Unix path syntax, for example: 'storage_1/storage_1.2/stream' - or a list of storage filenames, path to the desired stream/storage. Example: ['storage_1', 'storage_1.2', 'stream'] :returns: sid of requested filename :exception IOError: if file not found """ # if filename is a string instead of a list, split it on slashes to # convert to a list: if isinstance(filename, basestring): filename = filename.split('/') # walk across storage tree, following given path: node = self.root for name in filename: for kid in node.kids: if kid.name.lower() == name.lower(): break else: raise IOError("file not found") node = kid return node.sid def openstream(self, filename): """ Open a stream as a read-only file object (BytesIO). Note: filename is case-insensitive. :param filename: path of stream in storage tree (except root entry), either: - a string using Unix path syntax, for example: 'storage_1/storage_1.2/stream' - or a list of storage filenames, path to the desired stream/storage. Example: ['storage_1', 'storage_1.2', 'stream'] :returns: file object (read-only) :exception IOError: if filename not found, or if this is not a stream. """ sid = self._find(filename) entry = self.direntries[sid] if entry.entry_type != STGTY_STREAM: raise IOError("this file is not a stream") return self._open(entry.isectStart, entry.size) def write_stream(self, stream_name, data): """ Write a stream to disk. For now, it is only possible to replace an existing stream by data of the same size. :param stream_name: path of stream in storage tree (except root entry), either: - a string using Unix path syntax, for example: 'storage_1/storage_1.2/stream' - or a list of storage filenames, path to the desired stream/storage. Example: ['storage_1', 'storage_1.2', 'stream'] :param data: bytes, data to be written, must be the same size as the original stream. """ if not isinstance(data, bytes): raise TypeError("write_stream: data must be a bytes string") sid = self._find(stream_name) entry = self.direntries[sid] if entry.entry_type != STGTY_STREAM: raise IOError("this is not a stream") size = entry.size if size != len(data): raise ValueError("write_stream: data must be the same size as the existing stream") if size < self.minisectorcutoff: raise NotImplementedError("Writing a stream in MiniFAT is not implemented yet") sect = entry.isectStart # number of sectors to write nb_sectors = (size + (self.sectorsize-1)) // self.sectorsize debug('nb_sectors = %d' % nb_sectors) for i in range(nb_sectors): ## try: ## self.fp.seek(offset + self.sectorsize * sect) ## except: ## debug('sect=%d, seek=%d' % ## (sect, offset+self.sectorsize*sect)) ## raise IOError('OLE sector index out of range') # extract one sector from data, the last one being smaller: if i<(nb_sectors-1): data_sector = data [i*self.sectorsize : (i+1)*self.sectorsize] #TODO: comment this if it works assert(len(data_sector)==self.sectorsize) else: data_sector = data [i*self.sectorsize:] #TODO: comment this if it works debug('write_stream: size=%d sectorsize=%d data_sector=%d size%%sectorsize=%d' % (size, self.sectorsize, len(data_sector), size % self.sectorsize)) assert(len(data_sector) % self.sectorsize==size % self.sectorsize) self.write_sect(sect, data_sector) ## self.fp.write(data_sector) # jump to next sector in the FAT: try: sect = self.fat[sect] except IndexError: # [PL] if pointer is out of the FAT an exception is raised raise IOError('incorrect OLE FAT, sector index out of range') #[PL] Last sector should be a "end of chain" marker: if sect != ENDOFCHAIN: raise IOError('incorrect last sector index in OLE stream') def get_type(self, filename): """ Test if given filename exists as a stream or a storage in the OLE container, and return its type. :param filename: path of stream in storage tree. (see openstream for syntax) :returns: False if object does not exist, its entry type (>0) otherwise: - STGTY_STREAM: a stream - STGTY_STORAGE: a storage - STGTY_ROOT: the root entry """ try: sid = self._find(filename) entry = self.direntries[sid] return entry.entry_type except: return False def getmtime(self, filename): """ Return modification time of a stream/storage. :param filename: path of stream/storage in storage tree. (see openstream for syntax) :returns: None if modification time is null, a python datetime object otherwise (UTC timezone) new in version 0.26 """ sid = self._find(filename) entry = self.direntries[sid] return entry.getmtime() def getctime(self, filename): """ Return creation time of a stream/storage. :param filename: path of stream/storage in storage tree. (see openstream for syntax) :returns: None if creation time is null, a python datetime object otherwise (UTC timezone) new in version 0.26 """ sid = self._find(filename) entry = self.direntries[sid] return entry.getctime() def exists(self, filename): """ Test if given filename exists as a stream or a storage in the OLE container. Note: filename is case-insensitive. :param filename: path of stream in storage tree. (see openstream for syntax) :returns: True if object exist, else False. """ try: sid = self._find(filename) return True except: return False def get_size(self, filename): """ Return size of a stream in the OLE container, in bytes. :param filename: path of stream in storage tree (see openstream for syntax) :returns: size in bytes (long integer) :exception IOError: if file not found :exception TypeError: if this is not a stream. """ sid = self._find(filename) entry = self.direntries[sid] if entry.entry_type != STGTY_STREAM: #TODO: Should it return zero instead of raising an exception ? raise TypeError('object is not an OLE stream') return entry.size def get_rootentry_name(self): """ Return root entry name. Should usually be 'Root Entry' or 'R' in most implementations. """ return self.root.name def getproperties(self, filename, convert_time=False, no_conversion=None): """ Return properties described in substream. :param filename: path of stream in storage tree (see openstream for syntax) :param convert_time: bool, if True timestamps will be converted to Python datetime :param no_conversion: None or list of int, timestamps not to be converted (for example total editing time is not a real timestamp) :returns: a dictionary of values indexed by id (integer) """ #REFERENCE: [MS-OLEPS] https://msdn.microsoft.com/en-us/library/dd942421.aspx # make sure no_conversion is a list, just to simplify code below: if no_conversion == None: no_conversion = [] # stream path as a string to report exceptions: streampath = filename if not isinstance(streampath, str): streampath = '/'.join(streampath) fp = self.openstream(filename) data = {} try: # header s = fp.read(28) clsid = _clsid(s[8:24]) # format id s = fp.read(20) fmtid = _clsid(s[:16]) fp.seek(i32(s, 16)) # get section s = b"****" + fp.read(i32(fp.read(4))-4) # number of properties: num_props = i32(s, 4) except BaseException as exc: # catch exception while parsing property header, and only raise # a DEFECT_INCORRECT then return an empty dict, because this is not # a fatal error when parsing the whole file msg = 'Error while parsing properties header in stream %s: %s' % ( repr(streampath), exc) self._raise_defect(DEFECT_INCORRECT, msg, type(exc)) return data for i in range(num_props): try: id = 0 # just in case of an exception id = i32(s, 8+i*8) offset = i32(s, 12+i*8) type = i32(s, offset) debug ('property id=%d: type=%d offset=%X' % (id, type, offset)) # test for common types first (should perhaps use # a dictionary instead?) if type == VT_I2: # 16-bit signed integer value = i16(s, offset+4) if value >= 32768: value = value - 65536 elif type == VT_UI2: # 2-byte unsigned integer value = i16(s, offset+4) elif type in (VT_I4, VT_INT, VT_ERROR): # VT_I4: 32-bit signed integer # VT_ERROR: HRESULT, similar to 32-bit signed integer, # see http://msdn.microsoft.com/en-us/library/cc230330.aspx value = i32(s, offset+4) elif type in (VT_UI4, VT_UINT): # 4-byte unsigned integer value = i32(s, offset+4) # FIXME elif type in (VT_BSTR, VT_LPSTR): # CodePageString, see http://msdn.microsoft.com/en-us/library/dd942354.aspx # size is a 32 bits integer, including the null terminator, and # possibly trailing or embedded null chars #TODO: if codepage is unicode, the string should be converted as such count = i32(s, offset+4) value = s[offset+8:offset+8+count-1] # remove all null chars: value = value.replace(b'\x00', b'') elif type == VT_BLOB: # binary large object (BLOB) # see http://msdn.microsoft.com/en-us/library/dd942282.aspx count = i32(s, offset+4) value = s[offset+8:offset+8+count] elif type == VT_LPWSTR: # UnicodeString # see http://msdn.microsoft.com/en-us/library/dd942313.aspx # "the string should NOT contain embedded or additional trailing # null characters." count = i32(s, offset+4) value = self._decode_utf16_str(s[offset+8:offset+8+count*2]) elif type == VT_FILETIME: value = long(i32(s, offset+4)) + (long(i32(s, offset+8))<<32) # FILETIME is a 64-bit int: "number of 100ns periods # since Jan 1,1601". if convert_time and id not in no_conversion: debug('Converting property #%d to python datetime, value=%d=%fs' %(id, value, float(value)/10000000)) # convert FILETIME to Python datetime.datetime # inspired from http://code.activestate.com/recipes/511425-filetime-to-datetime/ _FILETIME_null_date = datetime.datetime(1601, 1, 1, 0, 0, 0) debug('timedelta days=%d' % (value//(10*1000000*3600*24))) value = _FILETIME_null_date + datetime.timedelta(microseconds=value//10) else: # legacy code kept for backward compatibility: returns a # number of seconds since Jan 1,1601 value = value // 10000000 # seconds elif type == VT_UI1: # 1-byte unsigned integer value = i8(s[offset+4]) elif type == VT_CLSID: value = _clsid(s[offset+4:offset+20]) elif type == VT_CF: # PropertyIdentifier or ClipboardData?? # see http://msdn.microsoft.com/en-us/library/dd941945.aspx count = i32(s, offset+4) value = s[offset+8:offset+8+count] elif type == VT_BOOL: # VARIANT_BOOL, 16 bits bool, 0x0000=Fals, 0xFFFF=True # see http://msdn.microsoft.com/en-us/library/cc237864.aspx value = bool(i16(s, offset+4)) else: value = None # everything else yields "None" debug ('property id=%d: type=%d not implemented in parser yet' % (id, type)) # missing: VT_EMPTY, VT_NULL, VT_R4, VT_R8, VT_CY, VT_DATE, # VT_DECIMAL, VT_I1, VT_I8, VT_UI8, # see http://msdn.microsoft.com/en-us/library/dd942033.aspx # FIXME: add support for VT_VECTOR # VT_VECTOR is a 32 uint giving the number of items, followed by # the items in sequence. The VT_VECTOR value is combined with the # type of items, e.g. VT_VECTOR|VT_BSTR # see http://msdn.microsoft.com/en-us/library/dd942011.aspx #print("%08x" % id, repr(value), end=" ") #print("(%s)" % VT[i32(s, offset) & 0xFFF]) data[id] = value except BaseException as exc: # catch exception while parsing each property, and only raise # a DEFECT_INCORRECT, because parsing can go on msg = 'Error while parsing property id %d in stream %s: %s' % ( id, repr(streampath), exc) self._raise_defect(DEFECT_INCORRECT, msg, type(exc)) return data def get_metadata(self): """ Parse standard properties streams, return an OleMetadata object containing all the available metadata. (also stored in the metadata attribute of the OleFileIO object) new in version 0.25 """ self.metadata = OleMetadata() self.metadata.parse_properties(self) return self.metadata # # -------------------------------------------------------------------- # This script can be used to dump the directory of any OLE2 structured # storage file. if __name__ == "__main__": import sys # [PL] display quick usage info if launched from command-line if len(sys.argv) <= 1: # print('olefile version %s %s - %s' % (__version__, __date__, __author__)) # print( # """ # Launched from the command line, this script parses OLE files and prints info. # # Usage: olefile.py [-d] [-c] [file2 ...] # # Options: # -d : debug mode (displays a lot of debug information, for developers only) # -c : check all streams (for debugging purposes) # # For more information, see http://www.decalage.info/olefile # """) sys.exit() check_streams = False for filename in sys.argv[1:]: ## try: # OPTIONS: if filename == '-d': # option to switch debug mode on: set_debug_mode(True) continue if filename == '-c': # option to switch check streams mode on: check_streams = True continue ole = OleFileIO(filename)#, raise_defects=DEFECT_INCORRECT) print("-" * 68) print(filename) print("-" * 68) ole.dumpdirectory() for streamname in ole.listdir(): if streamname[-1][0] == "\005": print(streamname, ": properties") props = ole.getproperties(streamname, convert_time=True) props = sorted(props.items()) for k, v in props: #[PL]: avoid to display too large or binary values: if isinstance(v, (basestring, bytes)): if len(v) > 50: v = v[:50] if isinstance(v, bytes): # quick and dirty binary check: for c in (1,2,3,4,5,6,7,11,12,14,15,16,17,18,19,20, 21,22,23,24,25,26,27,28,29,30,31): if c in bytearray(v): v = '(binary data)' break print(" ", k, v) if check_streams: # Read all streams to check if there are errors: #print('\nChecking streams...') for streamname in ole.listdir(): # print name using repr() to convert binary chars to \xNN: #print('-', repr('/'.join(streamname)),'-', end=' ') st_type = ole.get_type(streamname) if st_type == STGTY_STREAM: print('size %d' % ole.get_size(streamname)) # just try to read stream in memory: ole.openstream(streamname) # else: # print('NOT a stream : type=%d' % st_type) # print() ## for streamname in ole.listdir(): ## # print name using repr() to convert binary chars to \xNN: ## print('-', repr('/'.join(streamname)),'-', end=' ') ## print(ole.getmtime(streamname)) ## print() print('Modification/Creation times of all directory entries:') for entry in ole.direntries: if entry is not None: print('- %s: mtime=%s ctime=%s' % (entry.name, entry.getmtime(), entry.getctime())) print() # parse and display metadata: meta = ole.get_metadata() meta.dump() print() #[PL] Test a few new methods: root = ole.get_rootentry_name() print('Root entry name: "%s"' % root) if ole.exists('worddocument'): print("This is a Word document.") print("type of stream 'WordDocument':", ole.get_type('worddocument')) print("size :", ole.get_size('worddocument')) if ole.exists('macros/vba'): print("This document may contain VBA macros.") # print parsing issues: print('\nNon-fatal issues raised during parsing:') if ole.parsing_issues: for exctype, msg in ole.parsing_issues: print('- %s: %s' % (exctype.__name__, msg)) else: print('None') ## except IOError as v: ## print("***", "cannot read", file, "-", v) ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/helpers.py0000644000175000017500000000240614332463747017574 0ustar00wattswattsdef resource_path(relative_path): import os, sys """ Get absolute path to resource, works for dev and for PyInstaller """ base_path = getattr(sys, '_MEIPASS', os.path.dirname(__file__)) return os.path.join(base_path, relative_path) def check_for_updates(current_version): import urllib.request, re # Scrape the version string from the PyPI:mantis-xray RSS feed try: with urllib.request.urlopen("https://pypi.org/rss/project/mantis_xray/releases.xml") as init_file: pypi_rss = init_file.read() #print(pypi_rss) pypi_list = re.findall(r"(?:\\s*)([\d\.]+)(?:\s*\)", pypi_rss.decode())[0] print("Latest package on PyPI is version {0}".format(pypi_list)) except: pass # Scrape version string from the code in the github repository try: with urllib.request.urlopen("https://raw.githubusercontent.com/mlerotic/spectromicroscopy/master/mantis_xray/__init__.py") as init_file: github_init = init_file.read() #print(github_init) github_latest = re.search(r"(?:__version__*\s=*\s)['|\"]+([\d\.]+)", github_init.decode()).group(1) print("Current development code is version {0}".format(github_latest)) except: pass ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/henke.py0000644000175000017500000006723114332463747017233 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2011 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # 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 . ''' ! This is from file COMPOUND.DAT for HENKE. Entries are: ! compound formula density (g/cc) water H2O 1.0 protein H48.6C32.9N8.9O8.9S0.6 1.35 lipid H62.5C31.5O6.3 1.0 nucleosome H42.1C31.9N10.3O13.9P1.6S0.3 1.5 dna H35.5C30.8N11.7O18.9P3.1 1.7 helium He 0.1663e-3 chromatin H49.95C24.64N8.66O15.57P1.07S0.03 1.527 air N78.08O20.95Ar0.93 1.2047e-3 pmma C5H8O2 1.18 nitride Si3N4 3.44 graphite C 2.26 nickel Ni 8.876 beryl Be 1.845 copper Cu 8.96 quartz SiO2 2.20 aluminum Al 2.70 gold Au 19.3 ice H2O 0.92 carbon C 1.0 polystyrene C8H8 1.06 silicon Si 2.33 germanium Ge 5.323 ''' from __future__ import division import numpy as np import scipy.interpolate from xdrlib import * import string #----------------------------------------------------------------------------- class henke: def __init__(self): self.compound_name = [ 'water' , 'protein', 'lipid', 'nucleosome', 'dna', 'helium', 'chromatin', 'air', 'pmma', 'nitride', 'graphite', 'nickel', 'beryl', 'copper', 'quartz', 'aluminum', 'gold', 'ice', 'carbon', 'polystyrene', 'silicon', 'germanium'] self.compound_forumula = ['H2O', 'H48.6C32.9N8.9O8.9S0.6', 'H62.5C31.5O6.3', 'H42.1C31.9N10.3O13.9P1.6S0.3', 'H35.5C30.8N11.7O18.9P3.1', 'He' , 'H49.95C24.64N8.66O15.57P1.07S0.03', 'N78.08O20.95Ar0.93', 'C5H8O2', 'Si3N4', 'C', 'Ni', 'Be', 'Cu', 'SiO2', 'Al', 'Au', 'H2O', 'C', 'C8H8', 'Si', 'Ge'] self.compound_density = [ 1.0, 1.35, 1.0, 1.5, 1.7, 1.66E-04, 1.527, 1.20E-03, 1.18, 3.44, 2.26, 8.876, 1.845, 8.96, 2.2, 2.7, 19.3, 0.92, 1, 1.06, 2.33, 5.323 ] #----------------------------------------------------------------------------- def compound(self, compound_string, density): # Type in a chemical formula and the density. The routine # then figures out the atomic weight in g/mole of "molecules" # and how many of what Z atoms you have. z_array = [] if compound_string in self.compound_name: compound_string = self.compound_forumula[self.compound_name.index(compound_string)] if compound_string in self.compound_forumula: z_array = self.zcompound(compound_string, z_array) atwt = self.zatwt(z_array) return z_array, atwt #----------------------------------------------------------------------------- def zcompound(self, compound_string, z_array, paren_multiplier=False): verbose = False if verbose: print ('compound_string', compound_string) if paren_multiplier == False: z_array = np.zeros(92) paren_multiplier=1. max_z_index=93 last_char_index = len(compound_string) - 1 # If we don't start off with a parenthesis, all we have to do # is strip off the first element and process it. We then # call the routine over again to handle the next part of # the string... if (compound_string[0] != '(') : # Look to see if the string has an element # like "C" or like "He". first_char=compound_string[0] if len(compound_string) > 1: second_char = compound_string[1] else: second_char = '' this_element_name = first_char if ((second_char >= 'a') and (second_char <= 'z')) : this_element_name = this_element_name + second_char num_start_index = 2 else: this_element_name = this_element_name + ' ' num_start_index = 1 if verbose: print ('this_element_name:',this_element_name,', num_start_index:', num_start_index) this_z=0 if this_element_name == 'H ': this_z=1 elif this_element_name == 'He': this_z=2 elif this_element_name == 'Li': this_z=3 elif this_element_name == 'Be': this_z=4 elif this_element_name == 'B ': this_z=5 elif this_element_name == 'C ': this_z=6 elif this_element_name == 'N ': this_z=7 elif this_element_name == 'O ': this_z=8 elif this_element_name == 'F ': this_z=9 elif this_element_name == 'Ne': this_z=10 elif this_element_name == 'Na': this_z=11 elif this_element_name == 'Mg': this_z=12 elif this_element_name == 'Al': this_z=13 elif this_element_name == 'Si': this_z=14 elif this_element_name == 'P ': this_z=15 elif this_element_name == 'S ': this_z=16 elif this_element_name == 'Cl': this_z=17 elif this_element_name == 'Ar': this_z=18 elif this_element_name == 'K ': this_z=19 elif this_element_name == 'Ca': this_z=20 elif this_element_name == 'Sc': this_z=21 elif this_element_name == 'Ti': this_z=22 elif this_element_name == 'V ': this_z=23 elif this_element_name == 'Cr': this_z=24 elif this_element_name == 'Mn': this_z=25 elif this_element_name == 'Fe': this_z=26 elif this_element_name == 'Co': this_z=27 elif this_element_name == 'Ni': this_z=28 elif this_element_name == 'Cu': this_z=29 elif this_element_name == 'Zn': this_z=30 elif this_element_name == 'Ga': this_z=31 elif this_element_name == 'Ge': this_z=32 elif this_element_name == 'As': this_z=33 elif this_element_name == 'Se': this_z=34 elif this_element_name == 'Br': this_z=35 elif this_element_name == 'Kr': this_z=36 elif this_element_name == 'Rb': this_z=37 elif this_element_name == 'Sr': this_z=38 elif this_element_name == 'Y ': this_z=39 elif this_element_name == 'Zr': this_z=40 elif this_element_name == 'Nb': this_z=41 elif this_element_name == 'Mo': this_z=42 elif this_element_name == 'Tc': this_z=43 elif this_element_name == 'Ru': this_z=44 elif this_element_name == 'Rh': this_z=45 elif this_element_name == 'Pd': this_z=46 elif this_element_name == 'Ag': this_z=47 elif this_element_name == 'Cd': this_z=48 elif this_element_name == 'In': this_z=49 elif this_element_name == 'Sn': this_z=50 elif this_element_name == 'Sb': this_z=51 elif this_element_name == 'Te': this_z=52 elif this_element_name == 'I ': this_z=53 elif this_element_name == 'Xe': this_z=54 elif this_element_name == 'Cs': this_z=55 elif this_element_name == 'Ba': this_z=56 elif this_element_name == 'La': this_z=57 elif this_element_name == 'Ce': this_z=58 elif this_element_name == 'Pr': this_z=59 elif this_element_name == 'Nd': this_z=60 elif this_element_name == 'Pm': this_z=61 elif this_element_name == 'Sm': this_z=62 elif this_element_name == 'Eu': this_z=63 elif this_element_name == 'Gd': this_z=64 elif this_element_name == 'Tb': this_z=65 elif this_element_name == 'Dy': this_z=66 elif this_element_name == 'Ho': this_z=67 elif this_element_name == 'Er': this_z=68 elif this_element_name == 'Tm': this_z=69 elif this_element_name == 'Yb': this_z=70 elif this_element_name == 'Lu': this_z=71 elif this_element_name == 'Hf': this_z=72 elif this_element_name == 'Ta': this_z=73 elif this_element_name == 'W ': this_z=74 elif this_element_name == 'Re': this_z=75 elif this_element_name == 'Os': this_z=76 elif this_element_name == 'Ir': this_z=77 elif this_element_name == 'Pt': this_z=78 elif this_element_name == 'Au': this_z=79 elif this_element_name == 'Hg': this_z=80 elif this_element_name == 'Tl': this_z=81 elif this_element_name == 'Pb': this_z=82 elif this_element_name == 'Bi': this_z=83 elif this_element_name == 'Po': this_z=84 elif this_element_name == 'At': this_z=85 elif this_element_name == 'Rn': this_z=86 elif this_element_name == 'Fr': this_z=87 elif this_element_name == 'Ra': this_z=88 elif this_element_name == 'Ac': this_z=89 elif this_element_name == 'Th': this_z=90 elif this_element_name == 'Pa': this_z=91 elif this_element_name == 'U ': this_z=92 else: this_z=0 if (this_z == 0) : print ('zcompound is confused: ',compound_string) compound_string='' z_array = 0 return # Find the next element or parenthesis, as # anything before it must be a number. postnum_index = num_start_index if len(compound_string) > num_start_index+1 : test_char = compound_string[postnum_index] else : test_char = '' while ( ((test_char == '0') or (test_char == '1') or \ (test_char == '2') or (test_char == '2') or \ (test_char == '3') or (test_char == '4') or \ (test_char == '5') or (test_char == '6') or \ (test_char == '7') or (test_char == '8') or \ (test_char == '9') or (test_char == '.')) and \ (postnum_index <= last_char_index) ) : postnum_index=postnum_index+1 if (postnum_index <= last_char_index) : test_char=compound_string[postnum_index] else: test_char='' # is there more? if (num_start_index != postnum_index) : number_string=compound_string[num_start_index:postnum_index] num_multiplier=1. if verbose: print( 'Trying to interpret ',number_string,' as a number.') if (len(number_string) != 0) : num_multiplier = float(number_string) else: num_multiplier=1. # We've handled this element, so pop it into the # matrix and continue. if (this_z <= max_z_index) : z_array[this_z-1] = z_array[this_z-1] + num_multiplier else: print ('zcompound: z_array smaller than ',max_z_index) z_array = 0 return # And deal with what's left remaining_string=compound_string[postnum_index:last_char_index+1] if len(remaining_string) > 0: z_array = self.zcompound(remaining_string,z_array,paren_multiplier=True) return z_array #----------------------------------------------------------------------------- def zatwt(self, z_array): maxz=z_array.size atwt=0. for i in range(maxz): if (z_array[i] != 0.): if i+1 == 1: this_atwt=1.00794 elif i+1 == 2: this_atwt=4.0026 elif i+1 == 3: this_atwt=6.941 elif i+1 == 4: this_atwt=9.01218 elif i+1 == 5: this_atwt=10.81 elif i+1 == 6: this_atwt=12.011 elif i+1 == 7: this_atwt=14.0067 elif i+1 == 8: this_atwt=15.9994 elif i+1 == 9: this_atwt=18.9984 elif i+1 == 10: this_atwt=21.179 elif i+1 == 11: this_atwt=22.98977 elif i+1 == 12: this_atwt=24.305 elif i+1 == 13: this_atwt=26.98154 elif i+1 == 14: this_atwt=28.0855 elif i+1 == 15: this_atwt=30.97376 elif i+1 == 16: this_atwt=32.06 elif i+1 == 17: this_atwt=35.453 elif i+1 == 18: this_atwt=39.948 elif i+1 == 19: this_atwt=39.0983 elif i+1 == 20: this_atwt=40.08 elif i+1 == 21: this_atwt=44.9559 elif i+1 == 22: this_atwt=47.88 elif i+1 == 23: this_atwt=50.9415 elif i+1 == 24: this_atwt=51.996 elif i+1 == 25: this_atwt=54.9380 elif i+1 == 26: this_atwt=55.847 elif i+1 == 27: this_atwt=58.9332 elif i+1 == 28: this_atwt=58.69 elif i+1 == 29: this_atwt=63.546 elif i+1 == 30: this_atwt=65.38 elif i+1 == 31: this_atwt=69.72 elif i+1 == 32: this_atwt=72.59 elif i+1 == 33: this_atwt=74.9216 elif i+1 == 34: this_atwt=78.96 elif i+1 == 35: this_atwt=79.904 elif i+1 == 36: this_atwt=83.80 elif i+1 == 37: this_atwt=85.4678 elif i+1 == 38: this_atwt=87.62 elif i+1 == 39: this_atwt=88.9059 elif i+1 == 40: this_atwt=91.22 elif i+1 == 41: this_atwt=92.9064 elif i+1 == 42: this_atwt=95.94 elif i+1 == 43: this_atwt=98. elif i+1 == 44: this_atwt=101.07 elif i+1 == 45: this_atwt=102.9055 elif i+1 == 46: this_atwt=106.42 elif i+1 == 47: this_atwt=107.8682 elif i+1 == 48: this_atwt=112.41 elif i+1 == 49: this_atwt=114.82 elif i+1 == 50: this_atwt=118.69 elif i+1 == 51: this_atwt=121.75 elif i+1 == 52: this_atwt=127.60 elif i+1 == 53: this_atwt=126.9054 elif i+1 == 54: this_atwt=131.29 elif i+1 == 55: this_atwt=132.9054 elif i+1 == 56: this_atwt=137.33 elif i+1 == 57: this_atwt=138.9055 elif i+1 == 58: this_atwt=140.12 elif i+1 == 59: this_atwt=140.9077 elif i+1 == 60: this_atwt=144.24 elif i+1 == 61: this_atwt=145. elif i+1 == 62: this_atwt=150.36 elif i+1 == 63: this_atwt=151.96 elif i+1 == 64: this_atwt=157.25 elif i+1 == 65: this_atwt=158.9254 elif i+1 == 66: this_atwt=162.5 elif i+1 == 67: this_atwt=164.9304 elif i+1 == 68: this_atwt=167.26 elif i+1 == 69: this_atwt=168.9342 elif i+1 == 70: this_atwt=173.04 elif i+1 == 71: this_atwt=174.967 elif i+1 == 72: this_atwt=178.49 elif i+1 == 73: this_atwt=180.9479 elif i+1 == 74: this_atwt=183.85 elif i+1 == 75: this_atwt=186.207 elif i+1 == 76: this_atwt=190.2 elif i+1 == 77: this_atwt=192.22 elif i+1 == 78: this_atwt=195.08 elif i+1 == 79: this_atwt=196.9665 elif i+1 == 80: this_atwt=200.59 elif i+1 == 81: this_atwt=204.383 elif i+1 == 82: this_atwt=207.2 elif i+1 == 83: this_atwt=208.9804 elif i+1 == 84: this_atwt=209. elif i+1 == 85: this_atwt=210. elif i+1 == 86: this_atwt=222. elif i+1 == 87: this_atwt=223. elif i+1 == 88: this_atwt=226.0254 elif i+1 == 89: this_atwt=227.0278 elif i+1 == 90: this_atwt=232.0381 elif i+1 == 91: this_atwt=231.0359 elif i+1 == 92: this_atwt=238.0289 else: this_atwt=0. atwt=atwt+z_array[i]*this_atwt return atwt #----------------------------------------------------------------------------- def extra(self, ielement = -1): energies, f1, f2, n_extra, energies_extra, f1_extra, f2_extra = self.read(ielement, all = False) if n_extra != 0: energies_all=np.concatenate((energies,energies_extra), axis=0) f1_all=np.concatenate((f1,f1_extra), axis=0) f2_all=np.concatenate((f2,f2_extra), axis=0) sort_order=energies_all.argsort() energies_all=energies_all[sort_order] f1_all=f1_all[sort_order] f2_all=f2_all[sort_order] else: energies_all=energies f1_all=f1 f2_all=f2 return energies, f1, f2, energies_extra, f1_extra, f2_extra #----------------------------------------------------------------------------- def read(self, ielement = -1, all = True): # If we don't specifiy element return all energies if ielement == -1 : all = True verbose = False expected_pos = 0 filename = 'henke.xdr' file = open(str(filename),'rb') # try: # file = open(str(filename),'rb') # except: # print ('Could not open file ', filename) # return -1 if verbose: print ('File: ', filename) buf = file.read() u = Unpacker(buf) if all: n_elements = u.unpack_int() n_energies = u.unpack_int() if verbose: print ('n_energies: ', n_energies) print ('n_elements: ', n_elements) expected_pos = expected_pos+2*4 print ('Actual, expected file position before reading energies: ' ,u.get_position(), expected_pos) energies = u.unpack_farray(n_energies, u.unpack_float) energies = np.array(energies) if verbose: print ('energies: ', energies) f1 = np.zeros((n_elements, n_energies)) f2 = np.zeros((n_elements, n_energies)) this_f1 = np.zeros((n_energies)) this_f2 = np.zeros((n_energies)) if verbose: expected_pos = expected_pos+4*n_energies print ('Actual, expected file position before reading elements: ',u.get_position(), expected_pos) for i_element in range(n_elements): this_f1 = u.unpack_farray(n_energies, u.unpack_float) this_f2 = u.unpack_farray(n_energies, u.unpack_float) f1[i_element, :] = this_f1 f2[i_element, :] = this_f2 if verbose: expected_pos =expected_pos+n_elements*n_energies*2*4 print ('Actual, expected file position before reading n_extra_energies: ', u.get_position(), expected_pos) n_extra_energies = u.unpack_int() if verbose: print ('n_extra_energies: ', n_extra_energies) if verbose: expected_pos = expected_pos+4 print ('Actual, expected file position before reading extras: ',u.get_position(), expected_pos) n_extra = np.zeros((n_elements), dtype = np.int) extra_energies = np.zeros((n_elements, n_extra_energies)) extra_f1 = np.zeros((n_elements, n_extra_energies)) extra_f2 = np.zeros((n_elements, n_extra_energies)) this_n_extra = 0 this_extra_energies = np.zeros((n_extra_energies)) this_extra_f1 = np.zeros((n_extra_energies)) this_extra_f2 = np.zeros((n_extra_energies)) for i_element in range(n_elements): this_n_extra = u.unpack_int() this_extra_energies = u.unpack_farray(n_extra_energies, u.unpack_float) this_extra_f1 = u.unpack_farray(n_extra_energies, u.unpack_float) this_extra_f2 = u.unpack_farray(n_extra_energies, u.unpack_float) n_extra[i_element] = this_n_extra extra_energies[i_element, :] = this_extra_energies extra_f1[i_element, :] = this_extra_f1 extra_f2[i_element, :] = this_extra_f2 else: n_elements = u.unpack_int() n_energies = u.unpack_int() energies = u.unpack_farray(n_energies, u.unpack_float) energies = np.array(energies) if verbose: print ('energies: ', energies) byte_offset = 4+4+4*n_energies + 8*ielement*n_energies u.set_position(byte_offset) f1 = u.unpack_farray(n_energies, u.unpack_float) f2 = u.unpack_farray(n_energies, u.unpack_float) byte_offset = 4+4+4*n_energies + 8*n_elements*n_energies u.set_position(byte_offset) n_extra_energies = u.unpack_int() if verbose: print ('n_extra_energies ', n_extra_energies) # Now we have the above plus i_element times the quantity: # (2 for n_extra, and n_extra_energies each of three floats) byte_offset = long(4)+long(4)+long(4)*n_energies + long(8)*n_elements*n_energies + long(4) + ielement*(4+12*n_extra_energies) u.set_position(byte_offset) n_extra = u.unpack_int() this_extra_energies = u.unpack_farray(n_extra_energies, u.unpack_float) this_extra_f1 = u.unpack_farray(n_extra_energies, u.unpack_float) this_extra_f2 = u.unpack_farray(n_extra_energies, u.unpack_float) extra_energies = this_extra_energies[0:n_extra] extra_f1 = this_extra_f1[0:n_extra] extra_f2 = this_extra_f2[0:n_extra] file.close() return energies, f1, f2, n_extra, extra_energies, extra_f1, extra_f2 #----------------------------------------------------------------------------- def array(self, compound_name, density, graze_mrad = 0): z_array, atwt = self.compound(compound_name,density) maxz = 92 first_time = 1 for i in range(maxz): if (z_array[i] != 0.): energies, this_f1, this_f2, n_extra, extra_energies, extra_f1, extra_f2 = self.read(ielement=i) if (first_time == 1) : f1 = z_array[i]*this_f1 f2 = z_array[i]*this_f2 first_time = 0 else: f1 = f1+z_array[i]*this_f1 f2 = f2+z_array[i]*this_f2 num_energies = len(energies) AVOGADRO=6.02204531e23 HC_ANGSTROMS=12398.52 RE=2.817938070e-13 # in cm if (atwt != 0.0) : molecules_per_cc = density * AVOGADRO / atwt else: molecules_per_cc = 0.0 wavelength_angstroms = HC_ANGSTROMS/energies # This constant has wavelength in angstroms and then # they are converted to centimeters. constant = RE * (1.0e-16 * wavelength_angstroms * wavelength_angstroms) * molecules_per_cc / (2.0 * np.math.pi) delta = constant * f1 beta = constant * f2 # Alpha is in inverse meters squared alpha = 1.e4 * density * AVOGADRO * RE / (2.*np.math.pi*atwt) #alpha = alpha[0] if (graze_mrad == 0.): reflect=np.ones((num_energies)) else: theta = 1.0e-3 * graze_mrad sinth = np.sin(theta) sinth2 = sinth * sinth coscot = np.cos(theta) coscot = coscot * coscot / sinth alpha = 2.0 * delta - delta * delta + beta * beta gamma = 2.0 * (1.0 - delta) * beta rhosq = 0.5 * (sinth2 - alpha + np.sqrt((sinth2 - alpha)*(sinth2-alpha) + gamma*gamma) ) rho = np.sqrt(rhosq) i_sigma = (4.0 * rhosq * (sinth - rho) * (sinth - rho) + \ gamma * gamma) / \ (4.0 * rhosq * (sinth + rho) * (sinth + rho) + \ gamma * gamma) piosig = (4.0 * rhosq * (rho - coscot) * (rho - coscot) + \ gamma * gamma) / \ (4.0 * rhosq * (rho + coscot) * (rho + coscot) + \ gamma * gamma) reflect= 50.0 * i_sigma * (1 + piosig) denom = energies*4.*np.math.pi*beta zeroes = np.where(denom == 0.) nonzeroes = np.where(denom != 0.) denom[zeroes] = 1e-8 inverse_mu = np.array((len(energies))) inverse_mu = 1.239852/denom if (len(zeroes) > 0) : inverse_mu[zeroes] = np.inf return energies, f1, f2, delta, beta, graze_mrad, reflect, inverse_mu, atwt, alpha #----------------------------------------------------------------------------- def dose_calc(self, stack, i_composition, od_spectrum, i0_signal, dosecalc_detector_eff): pix_nm_squared = 1.e6*(stack.x_dist[1]-stack.x_dist[0])*(stack.y_dist[1]-stack.y_dist[0]) dose = 0. #rho doesn't matter here rho = 1. henke_energies, f1, f2, delta, beta, graze_mrad, reflect, inverse_mu, atwt, alpha = self.array(i_composition,rho) func_f2_array = scipy.interpolate.interp1d(henke_energies, f2, bounds_error=False, fill_value=0.0) f2_array = func_f2_array(stack.ev) # This is the scaling of f2_array as described in pca_gui_man.tex i_max_ev = np.argmax(stack.ev) if stack.data_dwell == None: stack.data_dwell = 1. if (od_spectrum[i_max_ev] != 0.) : f2_array = f2_array[0,i_max_ev]*(stack.ev/stack.ev[i_max_ev])*(od_spectrum/od_spectrum[i_max_ev]) # See the documentation file pca_gui_man.tex. Is i0_signal # normalized to 1 sec? Does not seem to be the case! Might # have to change it at some point. dose = 6.74e5*(1./(dosecalc_detector_eff* pix_nm_squared*atwt))*np.sum(i0_signal*1e-3*stack.data_dwell *f2_array) return dose ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/henke.xdr0000644000175000017500000137140014332463747017375 0ustar00wattswatts\A A"SA%7A'A*A-]dA0+kA3A5A8#A;خA>AAAEAHL0AK7AN,AR,AUAYA\A` AcAgiAk($AnArAvAzA~ffAAuAA}AA rAF AAA0A:AArA|A|PAAAYAAΥA7AqAAT,ABA>AE9AYAyAǧAA)AA ATA,AbAcAkAffA0A DAAAB )B!|B@Bh>B B ңBBaBB?BBBq[B B!^B$(>B&B)B,@B/ B1ބB4B7kB:B=B@BCBGBJ:BMOBP&BT3BWB[VB^BbDBeBiBmlBqDBu+By"4B})BBBBB'B_BBB>BBBu2BBvFBfB BFB$BByBL"B*=B9B KB BdB6B_!Bɔ{B B'mBӅB'BkB3B=B0!B`ByB}qBaHBTBYC7 CICeCCC C ,CtCƨC"NCCCpbCC C#^C%C(lC+%`C-=C0C3C6}C9q'CBDAVwDDvDG{DJDN'DQ|DT!DXQD[7D__mDbZDf1DjcDDn-DrDuDy{D} D HDD>DffD DФDD`DDD~fDDo D DHD&DDRD>D\D)D{DDHDD\DqDRD8RD}qDRD0DןD{DާDAD=D=DiD@D'DD&fE3EEEE %E ]E EE3FA3FEfFHFfFKFNfFR&FUFY3F\F`FcfFgcFk!Fn3FrfFvFz3F~ŚFr3F3FF3FFB3FF3F,F3FfFnF3FxF 3F3FUF FFfFlfFO3F>F8F@FSFtfFǡFfF$3FyF3FNF3F\fF3FF`<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<?e?????)?J8?e?}V???????R??.???n?~R?n?\>?G?1? ??????t??V?7?+?b?ם???t?S?2v? ?E?ρ???o~?Oa?.? ??̣?2??k?L0?-? ?t??)??y)?\?A ?%F? ??[??? ?k{?R?:?"? 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pHYs  oIDATxcdp\3˃LHDsi ,D&I,h.Y,D% ܻz4 pmÃ{DiX?6\[$%. from wx.lib.embeddedimage import PyEmbeddedImage logo_2l_32 = PyEmbeddedImage( "iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAABHNCSVQICAgIfAhkiAAAB3BJ" "REFUWIXt131s1dUdx/H373d+D+194PbhXkprWwqUAYoFS9oiUtCpZSSoqKDoNjXOzOmejZnL" "lpmSxTi3Zckyt2xZZsKcmWBmZnRqNnlQEYVJ5UEKtK5QLL1tb3tvb++9v9/9PZ790cT9YUxm" "dLo/PH+f5Ly+n/M5fxxFUQWf5lI/1dM/A3wG+L8AtF13t3zgR9vlpwYYz1o89tooxgUXytvv" "uOMTh6hWICmmB0kJE3Pva2ytSsmWFT2y50vf+EQwSnXXVpmsrWFarWJx/17WTwwxT9U4EImx" "t2EZiXkN3NK9lB//8H7lfwL4ybwF8i0EB1tXU160hmBqmMX9e9mUOQPA05rG6ZZV3Nzdxh8e" "feRjRyj3R6plk2YCzE69vAe1pgnrX4foOPYCNUJnpuVixPlBWkyTet/H6V7NQ3979mPBKG92" "XiknzwzRVy4CkA18fmOYaEuuYNvp3Wiage+7rFBmX+xRGVLrhyRicTJtS/nFS3//SBBVKVlc" "0t7JTaKCft9B9z0uDwK++e4b5F2XiFWiXWpkJZxxHNqlRiIW5/D0JOde2c+3utd9pLIq/0y2" "yKZIHL9U5GmnwLkwYGm8ipJtU6uoTMmQk6HLBZrBAqmyPygz7Niz+toYQXXIqvwcKiyb4LqN" "bH/8jx8qEdUNfPxSkVzg84xT5KK6Bq75fA/bjAhJY7Yba0UFlu/zcGmKWChxkTRF45SuUtG2" "VfPGjQ4nkj7imRf4wbzmD5WI+pBRyUGvzJ7QY8CoZFV1Cn00DcAlUmGbEWF/UCbnO9j1CmfX" "G+itURZWRLnrWUHtkxmCXEDhazrPb/U4nJjh7khC3ndVz38FUebd9LAsvPVX1OI0XQs62H5y" "H63CeG+DJyVPOwXeTpQ5cU0FALUX6ZQGFBpfKnHpaBSAfSmXY58LCKRPzW6Xxs5WzizO0lF3" "Nb/96RMfeC3qsq7V1PR8m4pVmzmdG+FxO89Jr4wn/zPAcODy+mUG9953Mx1fThBmoziGS+7e" "CE9eE7BTzNA4GlA4XsSI63g3xLHWeQDsH3qOdbct+sA01MG0TeDaKOUCj5YnadYreMTK8Wun" "wDuBS3/o8menxPIrklTH5jAnJWi/RaVtc4zMMZf0lMXRmENmfojSqNPcmiTSqDE+lKUyAk0X" "V1BSCiz7whz53Qfveh9Ek66Dlx6g4+BTtG+5lY4DB1g/keZl3+HB4iQAl84ziTVJDhWfQUUj" "xCdZZ9DQZRIYLqWM4HzHXJY0FBkdHAMgeVEUJy9JD5YQIoqZDPj9ozven0AwdgrtwBOsBYzR" "McTUFK3C4DYjxgOxWupQ6MwK+p/LMTFuMVMqU7R9irbPTNojPwR3PrIWc1GR0beKRFMGycVR" "ps865M65aLrG0O7ZQcLqFSzcfJ9csG7Te0mIawcO9G4OPOaqOvXpNIYEpESoKkUZouEzZUao" "7/N5Y08BWwezQaVcDDn0uzy1C03u2norduMgDWs0ysNR3j2SI/AFuSELPapjTQYsXX0BlTUZ" "pjNgR5YTWbym1xo8sF30xlK99aFkh1eiKFSSqkpU1QiBg26Zp1yL+y/fwJWByvJJj9HXXI7u" "KZAeg6o9ZVrvqcGo95l00ySiBvHWgFiDTnbIJn3SxKj7Cn7DRibeyRNPjNO6ZhytdBQ7K4k0" "XtYrblON3rcTCe5ZtIyZqSl2uEUmCKlSFP7kFmlUBZtWdiLTaerLLmuFSUtBcPzYDFsNk2Or" "Fdw5ORQtJAhnY50e8xl53aGq6+fksjnss4fRGtcx43Uxcdxmbv04VY0jZA73Ic5FE703p+po" "vXgFC4bfpUsYjIYej9l5NCQb9QipoTOYlgVSEioKUaBC8Xl5OZwe85g4bROda2AmoDyjMLTP" "YuD1Jdzw9e/gLFgF8zsoDx3BSZ+COUsZ6ddw0wM4QRKRrG3uvSUMiJ09h3AdTCFYppkU/DJr" "VZ2fVVtEbZX5qoYqZr9x50OPXY5F6sE22q+vwxElju8qkDnlgwJHHp8hseZ6mtu6OZWTqJEo" "estKzNpm8q/uIEQjkmrDls2I5GVf7F05+CY1nocqBLqiYAU+Z5E8v15nyfdb+UdkmhfHi1SV" "BNWh5KXQZWeTw7VfbacqZRLUTdLUGcXO+xzfmWdRj05maiPnqAehEwazpZdeiDIziVTAcRS8" "QhaxZXqk95BdYDBwaBA6ehhiA7+0p1FuTbFl00ZKre+gd5q8YJTZM1Hi1WiZJbfHqWxyyYvz" "CFXFqFCI1Al8R0FBI9v3JoGjosbmg6YjgxDvzBFyB3fRfef3iK3cQFmJI3514SW93ZZLIQzY" "5ZYYdm36ZMCRCpdld9dQiIyA7mImoGaxwYSQ1K5RSPd5yIiLkVBRNUkYSnwXhveXiOuVrLuz" "AW96H9lXnse3BIpeTeHgX4hVF4m13UiWSgJfQdxk+71CShqERouqc1b69PsuW5OVDHRKLM/G" "ygeUCxK3KMmP+ChKiPQ1Jk46jJ9w0EyNIISx4y4ndhaYv6EC4jbROkFto4vMvEKx70UiyhFi" "K64mM1OPX3YIp8f4NwT0a5f7dbOBAAAAAElFTkSuQmCC") getlogo_2l_32Data = logo_2l_32.GetData getlogo_2l_32Image = logo_2l_32.GetImage getlogo_2l_32Bitmap = logo_2l_32.GetBitmap getlogo_2l_32Icon = logo_2l_32.GetIcon Mantis_logo_about = PyEmbeddedImage( "iVBORw0KGgoAAAANSUhEUgAAAZAAAAE+CAYAAACjqUZSAAAACXBIWXMAAC4jAAAuIwF4pT92" "AAAKT2lDQ1BQaG90b3Nob3AgSUNDIHByb2ZpbGUAAHjanVNnVFPpFj333vRCS4iAlEtvUhUI" "IFJCi4AUkSYqIQkQSoghodkVUcERRUUEG8igiAOOjoCMFVEsDIoK2AfkIaKOg6OIisr74Xuj" "a9a89+bN/rXXPues852zzwfACAyWSDNRNYAMqUIeEeCDx8TG4eQuQIEKJHAAEAizZCFz/SMB" "APh+PDwrIsAHvgABeNMLCADATZvAMByH/w/qQplcAYCEAcB0kThLCIAUAEB6jkKmAEBGAYCd" "mCZTAKAEAGDLY2LjAFAtAGAnf+bTAICd+Jl7AQBblCEVAaCRACATZYhEAGg7AKzPVopFAFgw" "ABRmS8Q5ANgtADBJV2ZIALC3AMDOEAuyAAgMADBRiIUpAAR7AGDIIyN4AISZABRG8lc88Suu" "EOcqAAB4mbI8uSQ5RYFbCC1xB1dXLh4ozkkXKxQ2YQJhmkAuwnmZGTKBNA/g88wAAKCRFRHg" "g/P9eM4Ors7ONo62Dl8t6r8G/yJiYuP+5c+rcEAAAOF0ftH+LC+zGoA7BoBt/qIl7gRoXgug" "dfeLZrIPQLUAoOnaV/Nw+H48PEWhkLnZ2eXk5NhKxEJbYcpXff5nwl/AV/1s+X48/Pf14L7i" "JIEyXYFHBPjgwsz0TKUcz5IJhGLc5o9H/LcL//wd0yLESWK5WCoU41EScY5EmozzMqUiiUKS" 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"pxZNPTr1ri6qa6UbobtEd79up+6Ynr5egJ5Mb6feeb3n+hx9L/1U/W36p/VHDFgGswwkBtsM" "zhg8xTVxbzwdL8fb8VFDXcNAQ6VhlWGX4YSRudE8o9VGjUYPjGnGXOMk423GbcajJgYmISZL" "TepN7ppSTbmmKaY7TDtMx83MzaLN1pk1mz0x1zLnm+eb15vft2BaeFostqi2uGVJsuRaplnu" "trxuhVo5WaVYVVpds0atna0l1rutu6cRp7lOk06rntZnw7Dxtsm2qbcZsOXYBtuutm22fWFn" "Yhdnt8Wuw+6TvZN9un2N/T0HDYfZDqsdWh1+c7RyFDpWOt6azpzuP33F9JbpL2dYzxDP2DPj" "thPLKcRpnVOb00dnF2e5c4PziIuJS4LLLpc+Lpsbxt3IveRKdPVxXeF60vWdm7Obwu2o26/u" "Nu5p7ofcn8w0nymeWTNz0MPIQ+BR5dE/C5+VMGvfrH5PQ0+BZ7XnIy9jL5FXrdewt6V3qvdh" "7xc+9j5yn+M+4zw33jLeWV/MN8C3yLfLT8Nvnl+F30N/I/9k/3r/0QCngCUBZwOJgUGBWwL7" "+Hp8Ib+OPzrbZfay2e1BjKC5QRVBj4KtguXBrSFoyOyQrSH355jOkc5pDoVQfujW0Adh5mGL" "w34MJ4WHhVeGP45wiFga0TGXNXfR3ENz30T6RJZE3ptnMU85ry1KNSo+qi5qPNo3ujS6P8Yu" "ZlnM1VidWElsSxw5LiquNm5svt/87fOH4p3iC+N7F5gvyF1weaHOwvSFpxapLhIsOpZATIhO" "OJTwQRAqqBaMJfITdyWOCnnCHcJnIi/RNtGI2ENcKh5O8kgqTXqS7JG8NXkkxTOlLOW5hCep" "kLxMDUzdmzqeFpp2IG0yPTq9MYOSkZBxQqohTZO2Z+pn5mZ2y6xlhbL+xW6Lty8elQfJa7OQ" "rAVZLQq2QqboVFoo1yoHsmdlV2a/zYnKOZarnivN7cyzytuQN5zvn//tEsIS4ZK2pYZLVy0d" "WOa9rGo5sjxxedsK4xUFK4ZWBqw8uIq2Km3VT6vtV5eufr0mek1rgV7ByoLBtQFr6wtVCuWF" "fevc1+1dT1gvWd+1YfqGnRs+FYmKrhTbF5cVf9go3HjlG4dvyr+Z3JS0qavEuWTPZtJm6ebe" "LZ5bDpaql+aXDm4N2dq0Dd9WtO319kXbL5fNKNu7g7ZDuaO/PLi8ZafJzs07P1SkVPRU+lQ2" "7tLdtWHX+G7R7ht7vPY07NXbW7z3/T7JvttVAVVN1WbVZftJ+7P3P66Jqun4lvttXa1ObXHt" "xwPSA/0HIw6217nU1R3SPVRSj9Yr60cOxx++/p3vdy0NNg1VjZzG4iNwRHnk6fcJ3/ceDTra" "dox7rOEH0x92HWcdL2pCmvKaRptTmvtbYlu6T8w+0dbq3nr8R9sfD5w0PFl5SvNUyWna6YLT" "k2fyz4ydlZ19fi753GDborZ752PO32oPb++6EHTh0kX/i+c7vDvOXPK4dPKy2+UTV7hXmq86" "X23qdOo8/pPTT8e7nLuarrlca7nuer21e2b36RueN87d9L158Rb/1tWeOT3dvfN6b/fF9/Xf" "Ft1+cif9zsu72Xcn7q28T7xf9EDtQdlD3YfVP1v+3Njv3H9qwHeg89HcR/cGhYPP/pH1jw9D" "BY+Zj8uGDYbrnjg+OTniP3L96fynQ89kzyaeF/6i/suuFxYvfvjV69fO0ZjRoZfyl5O/bXyl" "/erA6xmv28bCxh6+yXgzMV70VvvtwXfcdx3vo98PT+R8IH8o/2j5sfVT0Kf7kxmTk/8EA5jz" "/GMzLdsAAAAgY0hSTQAAeiUAAICDAAD5/wAAgOkAAHUwAADqYAAAOpgAABdvkl/FRgAAAH5J" "REFUeNpi/P//PwMuoKyojFsSClgIKUhvnsGgpG2MVa48yJSBiYFCwIjuBWKc3bnuNNwFLOQ4" "GxlQ7AUWbM6eWZuB09k4Y4EUZ5MUjdjAvatnKTMA2YtkGXD3/l1Gkg1AdjZZYYAtZkgyANnZ" "WA14dv8W+XmBmPSPywWAAQBMZTCs9912YAAAAABJRU5ErkJggg==") getslData = sl.GetData getslImage = sl.GetImage getslBitmap = sl.GetBitmap getslIcon = sl.GetIcon ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/mantis.py0000644000175000017500000006464514332463747017442 0ustar00wattswatts# # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2013 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # 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 . import sys import os import numpy as np import matplotlib from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas #from matplotlib.backends.backend_qt5agg import FigureCanvas from matplotlib.figure import Figure from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib import colorbar #Internal imports from . import data_struct from . import data_stack from . import analyze from . import nnma from . import henke from . import tomo_reconstruction from . import file_plugins from .file_plugins import file_xrm from .file_plugins import file_bim from .file_plugins import file_dataexch_hdf5 from .file_plugins import file_ncb from .file_plugins import file_json from .file_plugins import file_tif from .file_plugins import file_stk from .file_plugins import file_csv PlotH = 4.0 PlotW = PlotH*1.61803 #---------------------------------------------------------------------- def save_keyeng(key_engs, odir, filename, stk, anlz, png, pdf, svg): SaveFileName = os.path.join(odir,filename) matplotlib.rcParams['pdf.fonttype'] = 42 fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() odtotal = stk.od3d.sum(axis=0) odtotal = odtotal.sum(axis=0)/(stk.n_rows*stk.n_cols) odtotal /= odtotal.max()/0.7 specplot = axes.plot(stk.ev,odtotal) for i in range(len(key_engs)): axes.axvline(x=key_engs[i], color = 'g', alpha=0.5) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') if png == 1: ext = 'png' fileName_img = SaveFileName+"_keyengs."+ext fig.savefig(fileName_img, pad_inches = 0.0) if pdf == 1: ext = 'pdf' fileName_img = SaveFileName+"_keyengs."+ext fig.savefig(fileName_img, pad_inches = 0.0) if svg == 1: ext = 'svg' fileName_img = SaveFileName+"_keyengs."+ext fig.savefig(fileName_img, pad_inches = 0.0) #Save text file with list of energies textfilepath = SaveFileName+'_keyenergies.csv' f = open(textfilepath, 'w') print('********************* Key Energies ********************', file=f) for i in range(len(key_engs)): print('%.6f' %(key_engs[i]), file=f) f.close() return #---------------------------------------------------------------------- def save_spa(odir, filename, stk, anlz, png, pdf, svg): SaveFileName = os.path.join(odir,filename) matplotlib.rcParams['pdf.fonttype'] = 42 colors=['#FF0000','#000000','#FFFFFF'] spanclrmap=matplotlib.colors.LinearSegmentedColormap.from_list('spancm',colors) for i in range (anlz.n_target_spectra): #Save composition maps if anlz.pca_calculated == 0: tsmapimage = anlz.target_svd_maps[:,:,i] else: tsmapimage = anlz.target_pcafit_maps[:,:,i] fig = matplotlib.figure.Figure(figsize =(PlotH, PlotH)) canvas = FigureCanvas(fig) fig.clf() axes = fig.gca() divider = make_axes_locatable(axes) ax_cb = divider.new_horizontal(size="3%", pad=0.03) fig.add_axes(ax_cb) axes.set_position([0.03,0.03,0.8,0.94]) min_val = np.min(tsmapimage) max_val = np.max(tsmapimage) bound = np.max((np.abs(min_val), np.abs(max_val))) im = axes.imshow(tsmapimage, cmap=spanclrmap, vmin = -bound, vmax = bound) cbar = axes.figure.colorbar(im, orientation='vertical',cax=ax_cb) axes.axis("off") if png == 1: ext = 'png' fileName_img = SaveFileName+"_TSmap_" +str(i+1)+"."+ext fig.savefig(fileName_img, pad_inches = 0.0) if pdf == 1: ext = 'pdf' fileName_img = SaveFileName+"_TSmap_" +str(i+1)+"."+ext fig.savefig(fileName_img, pad_inches = 0.0) if svg == 1: ext = 'svg' fileName_img = SaveFileName+"_TSmap_" +str(i+1)+"."+ext fig.savefig(fileName_img, pad_inches = 0.0) #Save spectra for i in range (anlz.n_target_spectra): tspectrum = anlz.target_spectra[i, :] fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() line1 = axes.plot(stk.ev,tspectrum, color='black', label = 'Raw data') if anlz.pca_calculated == 1: tspectrumfit = anlz.target_pcafit_spectra[i, :] diff = np.abs(tspectrum-tspectrumfit) line2 = axes.plot(stk.ev,tspectrumfit, color='green', label = 'Fit') line3 = axes.plot(stk.ev,diff, color='grey', label = 'Abs(Raw-Fit)') fontP = matplotlib.font_manager.FontProperties() fontP.set_size('small') axes.legend(loc=4, prop = fontP) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') if png == 1: ext = 'png' fileName_spec = SaveFileName+"_Tspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) if pdf == 1: ext = 'pdf' fileName_spec = SaveFileName+"_Tspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) if svg == 1: ext = 'svg' fileName_spec = SaveFileName+"_Tspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) fileName_spec = SaveFileName+"_Tspectrum_" +str(i+1)+".csv" cname = "Tspectrum_" +str(i+1) stk.write_csv(fileName_spec, stk.ev, tspectrum, cname = cname) return #---------------------------------------------------------------------- def MakeColorTable(): maxclcolors = 11 colors_i = np.linspace(0, maxclcolors, maxclcolors+1) colors=['#0000FF','#FF0000','#DFE32D','#36F200','#B366FF', '#FF470A','#33FFFF','#006600','#CCCC99','#993300', '#000000'] clusterclrmap1=matplotlib.colors.LinearSegmentedColormap.from_list('clustercm',colors) bnorm1 = matplotlib.colors.BoundaryNorm(colors_i, clusterclrmap1.N) colors_i = np.linspace(0,maxclcolors+2,maxclcolors+3) #use black color for clusters > maxclcolors, the other 2 colors are for background colors2=['#0000FF','#FF0000','#DFE32D','#36F200','#B366FF', '#FF470A','#33FFFF','#006600','#CCCC99','#993300', '#000000','#FFFFFF','#EEEEEE'] clusterclrmap2=matplotlib.colors.LinearSegmentedColormap.from_list('clustercm2',colors2) bnorm2 = matplotlib.colors.BoundaryNorm(colors_i, clusterclrmap2.N) return clusterclrmap1, bnorm1, clusterclrmap2, bnorm2 #---------------------------------------------------------------------- #If png_pdg = 1 save png, if =2 save pdf, if =3 save svg def SaveScatt(SaveFileName, stk, anlz, png_pdf = 1): colors=['#0000FF','#FF0000','#DFE32D','#36F200','#B366FF', '#FF470A','#33FFFF','#006600','#CCCC99','#993300', '#000000'] od_reduced = anlz.pcaimages[:,:,0:anlz.numsigpca] od_reduced = np.reshape(od_reduced, (stk.n_cols*stk.n_rows,anlz.numsigpca), order='F') clindices = anlz.cluster_indices clindices = np.reshape(clindices, (stk.n_cols*stk.n_rows), order='F') if png_pdf == 1: ext = 'png' elif png_pdf == 2: ext = 'pdf' elif png_pdf == 3: ext = 'svg' nplots = 0 for ip in range(anlz.numsigpca): for jp in range(anlz.numsigpca): if jp >= (ip+1): nplots = nplots+1 nplotsrows = np.ceil(nplots/2) plotsize = 2.5 if nplots > 1 : fig = matplotlib.figure.Figure(figsize =(6.0,plotsize*nplotsrows)) fig.subplots_adjust(wspace = 0.4, hspace = 0.4) else: fig = matplotlib.figure.Figure(figsize =(3.0,2.5)) fig.subplots_adjust(bottom = 0.2, left = 0.2) canvas = FigureCanvas(fig) #axes = fig.gca() matplotlib.rcParams['font.size'] = 6 pplot = 1 for ip in range(anlz.numsigpca): for jp in range(anlz.numsigpca): if jp >= (ip+1): x_comp = od_reduced[:,ip] y_comp = od_reduced[:,jp] if nplots > 1 : axes = fig.add_subplot(nplotsrows,2, pplot) else: axes = fig.add_subplot(1,1,1) pplot = pplot+1 for i in range(anlz.nclusters): thiscluster = np.where(clindices == i) axes.plot(x_comp[thiscluster], y_comp[thiscluster],'.',color=colors[i],alpha=0.5) axes.set_xlabel('Component '+str(ip+1)) axes.set_ylabel('Component '+str(jp+1)) fileName_sct = SaveFileName+"_CAscatterplots."+ext matplotlib.rcParams['pdf.fonttype'] = 42 fig.savefig(fileName_sct) #---------------------------------------------------------------------- def save_ca(odir, filename, stk, anlz, png, pdf, svg): clusterclrmap1, bnorm1, clusterclrmap2, bnorm2 = MakeColorTable() maxclcolors = 11 colors=['#0000FF','#FF0000','#DFE32D','#36F200','#B366FF', '#FF470A','#33FFFF','#006600','#CCCC99','#993300', '#000000'] SaveFileName = os.path.join(odir,filename) matplotlib.rcParams['pdf.fonttype'] = 42 #Save image with composite cluster indices fig = matplotlib.figure.Figure(figsize = (float(stk.n_rows)/30, float(stk.n_cols)/30)) canvas = FigureCanvas(fig) fig.clf() fig.add_axes((0.0,0.0,1.0,1.0)) axes = fig.gca() im = axes.imshow(anlz.cluster_indices, cmap=clusterclrmap1, norm=bnorm1) axes.axis("off") if png == 1: ext = 'png' fileName_caimg = SaveFileName+"_CAcimg."+ext fig.savefig(fileName_caimg, dpi=300, pad_inches = 0.0) if pdf == 1: ext = 'pdf' fileName_caimg = SaveFileName+"_CAcimg."+ext fig.savefig(fileName_caimg, dpi=300, pad_inches = 0.0) if svg == 1: ext = 'svg' fileName_caimg = SaveFileName+"_CAcimg."+ext fig.savefig(fileName_caimg, dpi=300, pad_inches = 0.0) #Save individual cluster images for i in range (anlz.nclusters): indvclusterimage = np.zeros((anlz.stack.n_cols, anlz.stack.n_rows))+20. ind = np.where(anlz.cluster_indices == i) colorcl = min(i,9) indvclusterimage[ind] = colorcl fig = matplotlib.figure.Figure(figsize =(float(stk.n_rows)/30, float(stk.n_cols)/30)) canvas = FigureCanvas(fig) fig.add_axes((0.0,0.0,1.0,1.0)) axes = fig.gca() im = axes.imshow(indvclusterimage, cmap=clusterclrmap2, norm=bnorm2) axes.axis("off") if png == 1: ext = 'png' fileName_img = SaveFileName+"_CAimg_" +str(i+1)+"."+ext fig.savefig(fileName_img, dpi=300, pad_inches = 0.0) if pdf == 1: ext = 'pdf' fileName_img = SaveFileName+"_CAimg_" +str(i+1)+"."+ext fig.savefig(fileName_img, dpi=300, pad_inches = 0.0) if svg == 1: ext = 'svg' fileName_img = SaveFileName+"_CAimg_" +str(i+1)+"."+ext fig.savefig(fileName_img, dpi=300, pad_inches = 0.0) for i in range (anlz.nclusters): clusterspectrum = anlz.clusterspectra[i, ] fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() if i >= maxclcolors: clcolor = colors[maxclcolors-1] else: clcolor = colors[i] specplot = axes.plot(anlz.stack.ev,clusterspectrum, color = clcolor) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') if png == 1: ext = 'png' fileName_spec = SaveFileName+"_CAspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) if pdf == 1: ext = 'pdf' fileName_spec = SaveFileName+"_CAspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) if svg == 1: ext = 'svg' fileName_spec = SaveFileName+"_CAspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) #Save all spectra in one plot fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() for i in range (anlz.nclusters): clusterspectrum = anlz.clusterspectra[i-1, ]/np.amax(anlz.clusterspectra[i-1, ]) if i >= maxclcolors: clcolor = colors[maxclcolors-1] else: clcolor = colors[i] specplot = axes.plot(anlz.stack.ev,clusterspectrum, color = clcolor) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') if png == 1: ext = 'png' fileName_spec = SaveFileName+"_CAspectra"+"."+ext fig.savefig(fileName_spec) if pdf == 1: ext = 'pdf' fileName_spec = SaveFileName+"_CAspectra"+"."+ext fig.savefig(fileName_spec) if svg == 1: ext = 'svg' fileName_spec = SaveFileName+"_CAspectra"+"."+ext fig.savefig(fileName_spec) for i in range (anlz.nclusters): clusterspectrum = anlz.clusterspectra[i, ] fileName_spec = SaveFileName+"_CAspectrum_" +str(i+1)+".csv" cname = "CAspectrum_" +str(i+1) stk.write_csv(fileName_spec, anlz.stack.ev, clusterspectrum, cname=cname) if png: SaveScatt(SaveFileName, stk, anlz, png_pdf = 1) if pdf: SaveScatt(SaveFileName, stk, anlz, png_pdf = 2) if svg: SaveScatt(SaveFileName, stk, anlz, png_pdf = 3) return #---------------------------------------------------------------------- def save_pca(odir, filename, stk, anlz, png, pdf, svg): SaveFileName = os.path.join(odir,filename) matplotlib.rcParams['pdf.fonttype'] = 42 #Save evals evalmax = np.min([stk.n_ev, 40]) pcaevals = anlz.eigenvals[0:evalmax] fig = Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() evalsplot = axes.semilogy(np.arange(1,evalmax+1), pcaevals,'b.') axes.set_xlabel('Principal Component') axes.set_ylabel('Log(Eigenvalue)') if png == 1: ext = 'png' fileName_evals = SaveFileName+"_PCAevals."+ext fig.savefig(fileName_evals, pad_inches = 0.0) if pdf == 1: ext = 'pdf' fileName_evals = SaveFileName+"_PCAevals."+ext fig.savefig(fileName_evals, pad_inches = 0.0) if svg == 1: ext = 'svg' fileName_evals = SaveFileName+"_PCAevals."+ext fig.savefig(fileName_evals, pad_inches = 0.0) for i in range(10): pcaimage = anlz.pcaimages[:,:,i] fig = Figure(figsize =(PlotH*1.15, PlotH)) canvas = FigureCanvas(fig) axes = fig.gca() divider = make_axes_locatable(axes) ax_cb = divider.new_horizontal(size="3%", pad=0.03) fig.add_axes(ax_cb) axes.set_position([0.03,0.03,0.8,0.94]) bound = anlz.pcaimagebounds[i] im = axes.imshow(pcaimage, cmap=matplotlib.cm.get_cmap("seismic_r"), vmin = -bound, vmax = bound) cbar = colorbar(im, orientation='vertical', cax=ax_cb) axes.axis("off") if png == 1: ext = 'png' fileName_img = SaveFileName+"_PCA_" +str(i+1)+"."+ext fig.savefig(fileName_img, pad_inches = 0.0) if pdf == 1: ext = 'pdf' fileName_img = SaveFileName+"_PCA_" +str(i+1)+"."+ext fig.savefig(fileName_img, pad_inches = 0.0) if svg == 1: ext = 'svg' fileName_img = SaveFileName+"_PCA_" +str(i+1)+"."+ext fig.savefig(fileName_img, pad_inches = 0.0) for i in range(10): pcaspectrum = anlz.eigenvecs[:,i] fig = Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() specplot = axes.plot(stk.ev, pcaspectrum) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') if png == 1: ext = 'png' fileName_spec = SaveFileName+"_PCAspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) if pdf == 1: ext = 'pdf' fileName_spec = SaveFileName+"_PCAspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) if svg == 1: ext = 'svg' fileName_spec = SaveFileName+"_PCAspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) for i in range (10): pcaspectrum = anlz.eigenvecs[:,i] fileName_spec = SaveFileName+"_PCAspectrum_" +str(i+1)+".csv" cname = "PCAspectrum_" +str(i+1) stk.write_csv(fileName_spec, stk.ev, pcaspectrum, cname=cname) return #---------------------------------------------------------------------- def batch_mode(): verbose = 1 settingsfile = 'Mantis_batch_settings.txt' version = '2.0.5' wdir = '' outdir = 'MantisResults' filename = '' save_hdf5 = 0 align_stack = 0 i0_file = '' i0_histogram = 0 run_pca = 0 n_spca = 4 run_ca = 0 nclusters = 5 ca_thickness = 0 run_sa = 0 sa_spectra = [] sa_use_clspectra = 0 run_keyengs = 0 kengs_thresh = 0.10 save_png = 1 save_pdf = 0 save_svg = 0 try: f = open(settingsfile, 'rt') for line in f: if ':' in line : slist = line.split(':') tag = slist[0] value = ':'.join(slist[1:]) if tag == 'VERSION': version = float(value) elif tag == 'WORK_DIR' : wdir = value.strip() elif tag == 'OUTPUT_DIR_NAME' : outdir = value.strip() elif tag == 'FILENAME' : filename = value.strip() elif tag == 'ALIGN_STACK' : align_stack = value.strip() elif tag == 'I0_FILE' : i0_file = value.strip() elif tag == 'I0_HISTOGRAM' : i0_histogram = int(value) elif tag == 'SAVE_HDF5' : save_hdf5 = int(value) elif tag == 'RUN_PCA' : run_pca = int(value) elif tag == 'N_SPCA' : n_spca = int(value) elif tag == 'RUN_CLUSTER_ANALYSIS' : run_ca = int(value) elif tag == 'N_CLUSTERS' : nclusters = int(value) elif tag == 'THICKNESS_CORRECTION' : ca_thickness = int(value) elif tag == 'RUN_SPECTRAL_ANALYSIS' : run_sa = int(value) elif tag == 'SA_SPECTRUM' : spname = value.strip() if len(spname) > 0 : sa_spectra.append(spname) elif tag == 'SA_USE_CA_SPECTRA' : sa_use_clspectra = int(value) elif tag == 'RUN_KEY_ENGS' : run_keyengs = int(value) elif tag == 'KE_THRESHOLD' : kengs_thresh = float(value) elif tag == 'SAVE_PNG' : save_png = int(value) elif tag == 'SAVE_PDF' : save_pdf = int(value) elif tag == 'SAVE_SVG' : save_svg = int(value) f.close() except: print('Error: Could not read in Mantis_batch_settings.txt.') return wdir = os.path.normpath(wdir) if verbose: print('Version: ', version) print('Working directory: ', wdir) if not os.path.exists(wdir): print('Error - Directory ', wdir, ' does not exist. Please specify working directory.') return outdir = os.path.join(wdir, outdir) if not os.path.exists(outdir): os.makedirs(outdir) if not os.path.exists(outdir): print('Error: Did not find and could not create a new output directory.') return if save_png == 1: print("Save .png images") if save_pdf == 1: print("Save .pdf images") datastruct = data_struct.h5() stk = data_stack.data(datastruct) anlz = analyze.analyze(stk) print('Reading file:', filename) basename, extension = os.path.splitext(filename) filepath = os.path.join(wdir, filename) supported_filters = file_plugins.supported_filters filter_list = file_plugins.filter_list action = 'read' data_type = 'stack' print (filter_list[action][data_type]) plugin = file_plugins.identify(filepath) file_plugins.load(filepath, stack_object=stk, plugin=plugin, selection=None, json=None) if align_stack: print('Aligning the stack') xshifts = np.zeros((stk.n_ev)) yshifts = np.zeros((stk.n_ev)) referenceimage = stk.absdata[:,:,0].copy() for i in range(stk.n_ev): img2 = stk.absdata[:,:,i] if i==0: xshift, yshift, ccorr = stk.register_images(referenceimage, img2, have_ref_img_fft = False) else: xshift, yshift, ccorr = stk.register_images(referenceimage, img2, have_ref_img_fft = True) # #Limit the shifts to MAXSHIFT chosen by the user # if (self.maxshift > 0): # if (abs(xshift) > self.maxshift): # xshift = npy.sign(xshift)*self.maxshift # if (abs(yshift) > self.maxshift): # yshift = npy.sign(yshift)*self.maxshift xshifts[i] = xshift yshifts[i] = yshift #Apply shifts for i in range(stk.n_ev): img = stk.absdata[:,:,i] if (abs(xshifts[i])>0.02) or (abs(yshifts[i])>0.02): shifted_img = stk.apply_image_registration(img, xshifts[i], yshifts[i]) stk.absdata[:,:,i] = shifted_img if datastruct.spectromicroscopy.normalization.white_spectrum is not None: print("I0 loaded") else: print("Loading I0") if i0_histogram == 1: print('Getting I0 from the histogram') stk.calc_histogram() averagefluxmax = np.max(stk.histogram) histmin = 0.98*averagefluxmax histmax = averagefluxmax stk.i0_from_histogram(histmin, histmax) elif len(i0_file) > 0: print('Reading I0 from file:', i0_file) i0basename, i0extension = os.path.splitext(i0_file) i0filepath = os.path.join(wdir, i0_file) stk.read_stk_i0(i0filepath, i0extension) else: print("Please either set I0_HISTOGRAM to 1 to calculate I0 or specify I0 file.") return if datastruct.spectromicroscopy.normalization.white_spectrum is None: print('Error: I0 not loaded') return if save_hdf5 == 1: fnameh5 = os.path.join(wdir,basename+'_MantisBatch.hdf5') stk.write_h5(fnameh5, data_struct) print('Saving data to HDF5 file:', fnameh5) pca_calculated = 0 if run_pca == 1: print("Running PCA Analysis") anlz.calculate_pca() print("Chosen number of significant components:", n_spca) print("Suggested number of significant components:", anlz.numsigpca) pca_calculated = 1 anlz.numsigpca = n_spca save_pca(outdir, filename, stk, anlz, save_png, save_pdf, save_svg) ca_calculated = 0 if run_ca == 1: if pca_calculated == 0: anlz.calculate_pca() print("Running Cluster Analysis") print("Number of clusters", nclusters) if ca_thickness == 1: print("Thickness correction enabled") nclusters = anlz.calculate_clusters(nclusters, ca_thickness) ca_calculated = 1 save_ca(outdir, filename, stk, anlz, save_png, save_pdf, save_svg) if run_sa == 1: print("Running Spectral Analysis") if len(sa_spectra) > 0: print("Loading spectra:", sa_spectra) for i in range(len(sa_spectra)): sppath = os.path.join(wdir, sa_spectra[i]) anlz.read_target_spectrum(filename=sppath) if sa_use_clspectra == 1: if ca_calculated == 1: print("Loading cluster spectra") anlz.add_cluster_target_spectra() else: print("Please set RUN_CLUSTER_ANALYSIS to 1 to calculate cluster spectra.") if anlz.n_target_spectra > 1: save_spa(outdir, filename, stk, anlz, save_png, save_pdf, save_svg) if run_keyengs == 1: if pca_calculated == 0: anlz.calculate_pca() print("Finding key energies") print("Threshold for finding key energies:", kengs_thresh) key_engs= anlz.calc_key_engs(kengs_thresh) save_keyeng(key_engs, outdir, filename, stk, anlz, save_png, save_pdf, save_svg) if (save_hdf5 == 1) and (pca_calculated == 1) : fnameh5 = os.path.join(wdir,basename+'_MantisBatch.hdf5') stk.write_results_h5(fnameh5, data_struct, anlz) print("Finished doing Mantis analysis") return """ ------------------------------------------------------------------------------------------------""" def main(): verbose = True print('Running Mantis in batch mode.') batch_mode() sys.exit() if __name__ == '__main__': main() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1682867577.0 mantis-xray-3.1.15/mantis_xray/mantis_qt.py0000664000175000017500000232316614423502571020134 0ustar00wattswatts# coding: utf8 # # This file is part of Mantis, a Multivariate ANalysis Tool for Spectromicroscopy. # # Copyright (C) 2013 Mirna Lerotic, 2nd Look # http://2ndlookconsulting.com # 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 . from __future__ import division from __future__ import print_function from __future__ import absolute_import import sys import os import time import copy import numpy as np import getopt from PyQt5 import QtCore, QtGui, QtWidgets, uic from PyQt5.QtCore import Qt, QCoreApplication, pyqtSignal, pyqtSlot QtWidgets.QApplication.setAttribute(QtCore.Qt.AA_EnableHighDpiScaling, True) QtWidgets.QApplication.setAttribute(QtCore.Qt.AA_UseHighDpiPixmaps, True) from PIL import Image from scipy import ndimage from scipy.stats import linregress from scipy.interpolate import interp1d # from skimage.feature import register_translation ## deprecated from skimage.registration import phase_cross_correlation from queue import SimpleQueue, Empty import threading from matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas from matplotlib.backends.backend_qt5agg import ( NavigationToolbar2QT as NavigationToolbar) from matplotlib.figure import Figure import matplotlib from mpl_toolkits.axes_grid1 import make_axes_locatable from matplotlib.widgets import LassoSelector matplotlib.interactive( True ) matplotlib.rcParams['svg.fonttype'] = 'none' from matplotlib import cm import pyqtgraph as pg import pyqtgraph.exporters from lxml import etree #Internal imports from . import data_struct from . import data_stack from . import analyze from . import nnma from . import henke from . import tomo_reconstruction from .helpers import resource_path from . import file_plugins from .file_plugins import file_xrm from .file_plugins import file_bim from .file_plugins import file_dataexch_hdf5 from .file_plugins import file_ncb from .file_plugins import file_json from .file_plugins import file_tif from .file_plugins import file_stk from .file_plugins import file_csv from .__init__ import __version__ as version welcome_string = "Welcome to MANTiS {0}".format(version) print("="*len(welcome_string)) print(welcome_string) print("="*len(welcome_string)) from .helpers import check_for_updates check_for_updates(version) print("\nPlease report issues to https://github.com/mlerotic/spectromicroscopy/issues \n") ## Global Stylesheet qsspath = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'stylesheet_global.qss') if sys.platform == 'win32': Winsizex = 1000 Winsizey = 800 else: Winsizex = 1250 Winsizey = 900 PlotH = 4.0 PlotW = PlotH*1.61803 ImgDpi = 40 verbose = False showtomotab = 1 showmaptab = 1 def rebin(a, shape): sh = shape[0],a.shape[0]//shape[0],shape[1],a.shape[1]//shape[1] return a.reshape(sh).mean(-1).mean(1) #---------------------------------------------------------------------- class common: def __init__(self): self.stack_loaded = 0 self.stack_4d = 0 self.i0_loaded = 0 self.pca_calculated = 0 self.pca4D_calculated = 0 self.cluster_calculated = 0 self.spec_anl_calculated = 0 self.spec_anl4D_calculated = 0 self.ica_calculated = 0 self.xpf_loaded = 0 self.white_scale_bar = 0 self.path = '' self.filename = '' self.font = '' """ ------------------------------------------------------------------------------------------------""" class PageTomo(QtWidgets.QWidget): def __init__(self, common, data_struct, stack, anlz): super(PageTomo, self).__init__() self.initUI(common, data_struct, stack, anlz) #---------------------------------------------------------------------- def initUI(self, common, data_struct, stack, anlz): self.data_struct = data_struct self.stack = stack self.com = common self.anlz = anlz self.tr = tomo_reconstruction.Ctomo(self.stack) self.theta = [] self.algonames = ['Compressed Sensing', 'SIRT'] self.algo = 0 self.fulltomorecdata = [] self.ncomponents = 0 self.tomo_calculated = 0 self.full_tomo_calculated = 0 self.energiesloaded = 0 self.datanames = [] self.haveROI = 0 self.ROIarray = [] self.ROIvol = [] self.select1 = 0 self.icomp = 0 self.islice = 0 self.maxIters = 10 self.beta = 0.5 self.engpar = 0.001 self.useengreg = 0 self.samplethick = 0 self.nonnegconst = 1 self.nprocessors = 6 #panel 1 sizer1 = QtWidgets.QGroupBox('Tomo Data') vbox1 = QtWidgets.QVBoxLayout() self.button_spcomp = QtWidgets.QPushButton('Load Tomo Data for Spectral Components') self.button_spcomp.clicked.connect( self.OnLoadTomoComponents) self.button_spcomp.setEnabled(False) vbox1.addWidget(self.button_spcomp) self.button_engdata = QtWidgets.QPushButton('Load Tomo Data for each Energy') self.button_engdata.clicked.connect( self.OnLoadTomoEng) self.button_engdata.setEnabled(False) vbox1.addWidget(self.button_engdata) self.button_expdata = QtWidgets.QPushButton('Export Tomo Data as .mrc') self.button_expdata.clicked.connect( self.OnExportData) self.button_expdata.setEnabled(False) vbox1.addWidget(self.button_expdata) line = QtWidgets.QFrame() line.setFrameShape(QtWidgets.QFrame.HLine) line.setFrameShadow(QtWidgets.QFrame.Sunken) vbox1.addStretch(1) vbox1.addWidget(line) vbox1.addStretch(1) self.button_loadmrc = QtWidgets.QPushButton('Load Single Tomo Dataset') self.button_loadmrc.clicked.connect( self.OnLoadSingleMrc) vbox1.addWidget(self.button_loadmrc) sizer1.setLayout(vbox1) #panel 2 sizer2 = QtWidgets.QGroupBox('Tomo Reconstruction') vbox2 = QtWidgets.QVBoxLayout() self.button_calc1 = QtWidgets.QPushButton( 'Calculate One Dataset') self.button_calc1.clicked.connect( self.OnCalcTomo1) self.button_calc1.setEnabled(False) vbox2.addWidget(self.button_calc1) hbox21 = QtWidgets.QHBoxLayout() tc1 = QtWidgets.QLabel(self) hbox21.addWidget(tc1) tc1.setText('Choose Tomo Dataset:') self.combonames = QtWidgets.QComboBox(self) self.combonames.activated[int].connect(self.OnSelect1Comp) hbox21.addWidget(self.combonames) vbox2.addLayout(hbox21) hbox22 = QtWidgets.QHBoxLayout() tc2 = QtWidgets.QLabel(self) hbox22.addWidget(tc2) tc2.setText('Binning: ') self.combobin = QtWidgets.QComboBox(self) self.combobin.addItems(['1','2','4','8']) hbox22.addWidget(self.combobin) vbox2.addLayout(hbox22) self.button_calcall = QtWidgets.QPushButton( 'Calculate All Datasets') self.button_calcall.clicked.connect( self.OnCalcTomoFull) self.button_calcall.setEnabled(False) vbox2.addWidget(self.button_calcall) self.button_save = QtWidgets.QPushButton( 'Save as .mrc') self.button_save.clicked.connect( self.OnSave) self.button_save.setEnabled(False) vbox2.addWidget(self.button_save) self.button_saveall = QtWidgets.QPushButton( 'Save All as .mrc') self.button_saveall.clicked.connect( self.OnSaveAll) self.button_saveall.setEnabled(False) vbox2.addWidget(self.button_saveall) line = QtWidgets.QFrame() line.setFrameShape(QtWidgets.QFrame.HLine) line.setFrameShadow(QtWidgets.QFrame.Sunken) vbox2.addStretch(1) vbox2.addWidget(line) vbox2.addStretch(1) # hbox21 = QtWidgets.QHBoxLayout() # tc1 = QtWidgets.QLabel(self) # hbox21.addWidget(tc1) # tc1.setText('Choose Tomo Dataset: ') self.comboalgos = QtWidgets.QComboBox(self) self.comboalgos.activated[int].connect(self.OnSelectAlgo) self.comboalgos.addItems(self.algonames) vbox2.addWidget(self.comboalgos) hbox22 = QtWidgets.QHBoxLayout() text1 = QtWidgets.QLabel(self) text1.setText('Number of iterations') hbox22.addWidget(text1) hbox22.addStretch(1) self.ntc_niterations = QtWidgets.QLineEdit(self) self.ntc_niterations.setFixedWidth(65) self.ntc_niterations.setValidator(QtGui.QIntValidator(1, 99999, self)) self.ntc_niterations.setAlignment(QtCore.Qt.AlignRight) self.ntc_niterations.setText(str(self.maxIters)) hbox22.addWidget(self.ntc_niterations) vbox2.addLayout(hbox22) hbox23 = QtWidgets.QHBoxLayout() self.tc_par = QtWidgets.QLabel(self) self.tc_par.setText("CS Parameter Beta") hbox23.addWidget(self.tc_par) self.ntc_beta = QtWidgets.QLineEdit(self) self.ntc_beta.setFixedWidth(65) self.ntc_beta.setValidator(QtGui.QDoubleValidator(0, 99999, 2, self)) self.ntc_beta.setAlignment(QtCore.Qt.AlignRight) hbox23.addStretch(1) self.ntc_beta.setText(str(self.beta)) hbox23.addWidget(self.ntc_beta) vbox2.addLayout(hbox23) hbox25 = QtWidgets.QHBoxLayout() self.cb_ereg = QtWidgets.QCheckBox('CS Energy Reg Parameter', self) self.cb_ereg.setChecked(False) self.cb_ereg.stateChanged.connect(self.OnCBEngReg) hbox25.addWidget(self.cb_ereg) self.ntc_ereg = QtWidgets.QLineEdit(self) self.ntc_ereg.setFixedWidth(65) self.ntc_ereg.setValidator(QtGui.QDoubleValidator(0, 99999, 2, self)) self.ntc_ereg.setAlignment(QtCore.Qt.AlignRight) hbox25.addStretch(1) self.ntc_ereg.setText(str(self.engpar)) self.ntc_ereg.setEnabled(False) hbox25.addWidget(self.ntc_ereg) vbox2.addLayout(hbox25) hbox24 = QtWidgets.QHBoxLayout() text1 = QtWidgets.QLabel(self) text1.setText('Sample Thickness') hbox24.addWidget(text1) hbox24.addStretch(1) self.ntc_samplethick = QtWidgets.QLineEdit(self) self.ntc_samplethick.setFixedWidth(65) self.ntc_samplethick.setValidator(QtGui.QDoubleValidator(0, 99999, 2, self)) self.ntc_samplethick.setAlignment(QtCore.Qt.AlignRight) self.ntc_samplethick.setText(str(self.samplethick)) hbox24.addWidget(self.ntc_samplethick) vbox2.addLayout(hbox24) self.cb_nneg = QtWidgets.QCheckBox('Non-Negativity Constraints', self) self.cb_nneg.setChecked(True) self.cb_nneg.stateChanged.connect(self.OnNonNegConst) vbox2.addWidget(self.cb_nneg) line = QtWidgets.QFrame() line.setFrameShape(QtWidgets.QFrame.HLine) line.setFrameShadow(QtWidgets.QFrame.Sunken) vbox2.addStretch(1) vbox2.addWidget(line) vbox2.addStretch(1) vbox2.addWidget(QtWidgets.QLabel('Multiprocessing:')) hbox26 = QtWidgets.QHBoxLayout() text1 = QtWidgets.QLabel(self) text1.setText('Number of processors') hbox26.addWidget(text1) hbox26.addStretch(1) self.ntc_processors = QtWidgets.QLineEdit(self) self.ntc_processors.setFixedWidth(65) self.ntc_processors.setValidator(QtGui.QIntValidator(1, 1000, self)) self.ntc_processors.setAlignment(QtCore.Qt.AlignRight) self.ntc_processors.setText(str(self.nprocessors)) hbox26.addWidget(self.ntc_processors) vbox2.addLayout(hbox26) sizer2.setLayout(vbox2) #panel 3 sizer3 = QtWidgets.QGroupBox('ROI') vbox3 = QtWidgets.QVBoxLayout() self.button_roi = QtWidgets.QPushButton( 'Select ROI') self.button_roi.clicked.connect(self.OnSelectROI) self.button_roi.setEnabled(False) vbox3.addWidget(self.button_roi) self.button_roihist = QtWidgets.QPushButton( 'Histogram ROI selection...') self.button_roihist.clicked.connect(self.OnROIHistogram) self.button_roihist.setEnabled(False) vbox3.addWidget(self.button_roihist) self.button_roispec = QtWidgets.QPushButton( 'Show ROI Spectrum') self.button_roispec.clicked.connect(self.OnShowROISpec) self.button_roispec.setEnabled(False) vbox3.addWidget(self.button_roispec) self.button_roidel = QtWidgets.QPushButton( 'Reset ROI') self.button_roidel.clicked.connect(self.OnResetROI) self.button_roidel.setEnabled(False) vbox3.addWidget(self.button_roidel) self.button_saveroi = QtWidgets.QPushButton( 'Save ROI as .mrc') self.button_saveroi.clicked.connect(self.OnSaveROI) self.button_saveroi.setEnabled(False) vbox3.addWidget(self.button_saveroi) self.button_loadroi = QtWidgets.QPushButton( 'Load ROI from .mrc') self.button_loadroi.clicked.connect(self.OnLoadROI) self.button_loadroi.setEnabled(False) vbox3.addWidget(self.button_loadroi) sizer3.setLayout(vbox3) #panel 5 vbox5 = QtWidgets.QVBoxLayout() vbox5.addStretch(1) self.tc_imagecomp = QtWidgets.QLabel(self) self.tc_imagecomp.setText("Dataset: ") vbox5.addWidget(self.tc_imagecomp) gridsizer5 = QtWidgets.QGridLayout() gridsizer5.setSpacing(5) frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.absimgfig = Figure((PlotH*0.9, PlotH*0.9)) self.AbsImagePanel = FigureCanvas(self.absimgfig) self.AbsImagePanel.setParent(self) self.cid1 = self.AbsImagePanel.mpl_connect('button_press_event', self.OnPointImage) fbox.addWidget(self.AbsImagePanel) frame.setLayout(fbox) gridsizer5.addWidget(frame, 1, 1, QtCore .Qt. AlignLeft) self.slider_slice = QtWidgets.QScrollBar(QtCore.Qt.Vertical) self.slider_slice.setFocusPolicy(QtCore.Qt.StrongFocus) self.slider_slice.valueChanged[int].connect(self.OnScrollSlice) self.slider_slice.setRange(0, 100) gridsizer5.addWidget(self.slider_slice, 1, 0, QtCore .Qt. AlignLeft) self.slider_comp = QtWidgets.QScrollBar(QtCore.Qt.Horizontal) self.slider_comp.setFocusPolicy(QtCore.Qt.StrongFocus) self.slider_comp.valueChanged[int].connect(self.OnScrollComp) self.slider_comp.setRange(0, 100) self.slider_comp.setEnabled(True) self.tc_comp = QtWidgets.QLabel(self) self.tc_comp.setText("Component: ") hbox51 = QtWidgets.QHBoxLayout() hbox51.addWidget(self.tc_comp) hbox51.addWidget(self.slider_comp) gridsizer5.addLayout(hbox51, 0, 1) frame2 = QtWidgets.QFrame() frame2.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox2 = QtWidgets.QHBoxLayout() self.absimgfig2 = Figure((PlotH*0.9, PlotH*0.9)) self.AbsImagePanel2 = FigureCanvas(self.absimgfig2) self.AbsImagePanel2.setParent(self) fbox2.addWidget(self.AbsImagePanel2) frame2.setLayout(fbox2) gridsizer5.addWidget(frame2, 2, 1, QtCore .Qt. AlignLeft) frame3 = QtWidgets.QFrame() frame3.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox3 = QtWidgets.QHBoxLayout() self.absimgfig3 = Figure((PlotH*0.9, PlotH*0.9)) self.AbsImagePanel3 = FigureCanvas(self.absimgfig3) self.AbsImagePanel3.setParent(self) fbox3.addWidget(self.AbsImagePanel3) frame3.setLayout(fbox3) gridsizer5.addWidget(frame3, 2, 2, QtCore .Qt. AlignLeft) gridsizer5.addWidget(sizer3, 1, 2, QtCore .Qt. AlignCenter) vbox5.addLayout(gridsizer5) vbox5.addStretch(1) vboxtop = QtWidgets.QVBoxLayout() hboxtop = QtWidgets.QHBoxLayout() vboxt1 = QtWidgets.QVBoxLayout() vboxt1.addStretch (1) vboxt1.addWidget(sizer1) vboxt1.addStretch (1) vboxt1.addWidget(sizer2) vboxt1.addStretch (1) #vboxt1.addWidget(sizer3) #vboxt1.addStretch (1) hboxtop.addStretch (5) hboxtop.addLayout(vboxt1) hboxtop.addStretch (5) hboxtop.addLayout(vbox5) hboxtop.addStretch (5) vboxtop.addStretch (5) vboxtop.addLayout(hboxtop) vboxtop.addStretch (9) vboxtop.setContentsMargins(20,20,20,20) self.setLayout(vboxtop) #---------------------------------------------------------------------- def OnLoadTomoEng(self, event): self.fulltomorecdata = [] self.tomo_calculated = 0 self.full_tomo_calculated = 0 self.NewStackClear() self.button_save.setEnabled(False) self.tc_comp.setText('Component: ') self.slider_comp.setEnabled(False) self.tomodata = self.stack.od4D self.theta = self.stack.theta self.n_cols = self.stack.n_cols self.n_rows = self.stack.n_rows self.datanames = [] for i in range(self.stack.n_ev): self.datanames.append(str(self.stack.ev[i])) self.ncomponents = self.stack.n_ev self.combonames.clear() self.combonames.addItems(self.datanames) self.tc_imagecomp.setText("Dataset: Energies") self.button_calcall.setEnabled(True) self.button_calc1.setEnabled(True) self.button_roi.setEnabled(False) self.energiesloaded = 1 self.button_expdata.setEnabled(True) #---------------------------------------------------------------------- def OnLoadTomoComponents(self, event): self.fulltomorecdata = [] self.tomo_calculated = 0 self.full_tomo_calculated = 0 self.NewStackClear() self.button_save.setEnabled(False) self.button_save.setEnabled(False) self.tc_comp.setText('Component: ') self.slider_comp.setEnabled(False) self.ncomponents = self.anlz.n_target_spectra self.tomodata = np.zeros((self.stack.n_cols, self.stack.n_rows, self.ncomponents, self.stack.n_theta)) for i in range (self.stack.n_theta): if self.window().page4.showraw == True: self.tomodata[:,:,:,i] = self.anlz.target_svd_maps4D[i] else: self.tomodata[:,:,:,i] = self.anlz.target_pcafit_maps[i] self.theta = self.stack.theta self.n_cols = self.stack.n_cols self.n_rows = self.stack.n_rows self.datanames = [] for i in range(self.ncomponents): self.datanames.append(str(self.anlz.tspec_names[i])) self.combonames.clear() self.combonames.addItems(self.datanames) self.tc_imagecomp.setText("Dataset: Spectral Components") self.button_calcall.setEnabled(True) self.button_calc1.setEnabled(True) self.button_roi.setEnabled(False) self.energiesloaded = 0 self.button_expdata.setEnabled(True) #---------------------------------------------------------------------- def OnLoadSingleMrc(self, event): wildcard = "Supported 4D formats (*.mrc *.ali *.ncb);;Mrc files (*.mrc *.ali);;NCB files (*.ncb);;" OpenFileName, _filter = QtWidgets.QFileDialog.getOpenFileName(self, 'Load Tomo Dataset', '', wildcard, None, QtWidgets.QFileDialog.DontUseNativeDialog) OpenFileName = str(OpenFileName) if OpenFileName == '': return basename, extension = os.path.splitext(OpenFileName) if extension == '.mrc': data = tomo_reconstruction.load_mrc(OpenFileName) dims = data.shape #Read energies from file wildcard = "Angle files (*.*);;" OpenFileName2, _filter = QtWidgets.QFileDialog.getOpenFileName(self, 'Load Angle data', '', wildcard, None, QtWidgets.QFileDialog.DontUseNativeDialog) OpenFileName2 = str(OpenFileName2) if OpenFileName2 == '': return f = open(str(OpenFileName2),'r') tlist = [] for line in f: if line.startswith("*"): pass else: t = line if t.strip() == '': continue tlist.append(float(t)) self.theta = np.array(tlist) f.close() ntheta = len(self.theta) if ntheta != dims[2]: data = np.swapaxes(data, 0, 2) print('angle num:', ntheta) dims = data.shape print('Data shape', dims) self.n_cols = dims[0] self.n_rows = dims[1] self.tomodata = np.zeros((dims[0], dims[1], 1, dims[2])) self.tomodata[:,:,0,:] = data elif extension == '.ncb' : data, thetalist = file_ncb.read_ncb_data(self, OpenFileName) dims = data.shape self.theta = np.array(thetalist) ntheta = len(self.theta) if ntheta != dims[2]: data = np.swapaxes(data, 0, 2) dims = data.shape self.n_cols = dims[0] self.n_rows = dims[1] self.tomodata = np.zeros((dims[0], dims[1], 1, dims[2])) self.tomodata[:,:,0,:] = data self.fulltomorecdata = [] self.tomo_calculated = 0 self.full_tomo_calculated = 0 self.NewStackClear() self.button_save.setEnabled(False) self.tc_comp.setText('Component: ') self.slider_comp.setEnabled(False) basename = os.path.basename(str(OpenFileName)) self.datanames = [] self.datanames.append(str(basename)) self.ncomponents = 1 self.combonames.clear() #self.combonames.addItems(self.datanames) self.tc_imagecomp.setText("Dataset:" + OpenFileName) self.button_calcall.setEnabled(False) self.button_calc1.setEnabled(True) self.button_roi.setEnabled(False) self.energiesloaded = 0 self.button_expdata.setEnabled(True) #---------------------------------------------------------------------- def OnCalcTomoFull(self, event): QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) value = self.ntc_niterations.text() self.maxIters = int(value) value = self.ntc_beta.text() self.beta = float(value) value = self.ntc_samplethick.text() self.samplethick = int(value) self.nprocessors = int(self.ntc_processors.text()) self.fulltomorecdata = [] binningfactor = int (self.combobin.currentText()) dims = self.tomodata[:,:,0,:].shape if (self.useengreg == 1) and (self.algo == 0): value = self.ntc_ereg.text() beta2 = float(value) print('Calculate initial reconstructions') initrecs = [] for i in range(self.ncomponents): print('Progress ',i+1,' / ',self.ncomponents) if binningfactor > 1: shape = (int(dims[0]/binningfactor), int(dims[1]/binningfactor)) projdata = np.zeros((shape[0], shape[1], dims[2])) #print 'Binning factor:', binningfactor #print 'Binned data dims', shape for j in range(dims[2]): projdata[:,:,j] = rebin(self.tomodata[0:shape[0]*binningfactor,0:shape[1]*binningfactor,i,j], shape) else: projdata = self.tomodata[:,:,i,:] self.tr.calc_tomo(projdata, self.theta, self.maxIters, self.beta, 0, algorithm = self.algo, nonnegconst = self.nonnegconst, nprocessors = self.nprocessors) initrecs.append(np.swapaxes(np.array(self.tr.tomorec.copy()), 0, 1)) for i in range(self.ncomponents): print('Progress ',i+1,' / ',self.ncomponents) if binningfactor > 1: shape = (int(dims[0]/binningfactor), int(dims[1]/binningfactor)) projdata = np.zeros((shape[0], shape[1], dims[2])) #print 'Binning factor:', binningfactor #print 'Binned data dims', shape for j in range(dims[2]): projdata[:,:,j] = rebin(self.tomodata[0:shape[0]*binningfactor,0:shape[1]*binningfactor,i,j], shape) else: projdata = self.tomodata[:,:,i,:] self.tr.calc_tomo(projdata, self.theta, self.maxIters, self.beta, self.samplethick, algorithm = 2, x0=initrecs, comp = i, beta2=beta2, nonnegconst = self.nonnegconst, nprocessors = self.nprocessors) self.fulltomorecdata.append(self.tr.tomorec.copy()) else: for i in range(self.ncomponents): print('Progress ',i+1,' / ',self.ncomponents) if binningfactor > 1: shape = (int(dims[0]/binningfactor), int(dims[1]/binningfactor)) projdata = np.zeros((shape[0], shape[1], dims[2])) #print 'Binning factor:', binningfactor #print 'Binned data dims', shape for j in range(dims[2]): projdata[:,:,j] = rebin(self.tomodata[0:shape[0]*binningfactor,0:shape[1]*binningfactor,i,j], shape) else: projdata = self.tomodata[:,:,i,:] self.tr.calc_tomo(projdata, self.theta, self.maxIters, self.beta, self.samplethick, algorithm = self.algo, nonnegconst = self.nonnegconst, nprocessors = self.nprocessors) self.fulltomorecdata.append(self.tr.tomorec.copy()) self.tr.tomorec = self.fulltomorecdata[self.icomp] self.full_tomo_calculated = 1 self.tomo_calculated = 1 dims = self.tr.tomorec.shape self.nslices = dims[2] self.islice = int(dims[2]/2) self.slider_slice.setRange(0, dims[2]-1) self.ROIvol = [[]]* dims[2] self.ROIarray = np.zeros((dims[0], dims[1], dims[2])) self.tc_comp.setText('Component: '+self.datanames[self.icomp]) self.slider_slice.setValue(self.islice) self.slider_comp.setRange(0, self.ncomponents-1) self.slider_comp.setEnabled(True) self.slider_comp.setValue(self.icomp) self.button_save.setEnabled(True) self.button_saveall.setEnabled(True) self.button_roi.setEnabled(True) self.button_roihist.setEnabled(True) self.button_loadroi.setEnabled(True) self.ShowImage() QtWidgets.QApplication.restoreOverrideCursor() #---------------------------------------------------------------------- def OnCalcTomo1(self, event): QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) value = self.ntc_niterations.text() self.maxIters = int(value) value = self.ntc_beta.text() self.beta = float(value) value = self.ntc_samplethick.text() self.samplethick = int(value) self.nprocessors = int(self.ntc_processors.text()) dims = self.tomodata[:,:,self.select1,:].shape print('Data dims = ', dims) projdata = self.tomodata[:,:,self.select1,:] binningfactor = int (self.combobin.currentText()) if binningfactor > 1: binneddata = [] shape = (int(dims[0]/binningfactor), int(dims[1]/binningfactor)) projdata = np.zeros((shape[0], shape[1], dims[2])) print('Binning factor:', binningfactor) print('Binned data dims', shape) for i in range(dims[2]): projdata[:,:,i] = rebin(self.tomodata[0:shape[0]*binningfactor,0:shape[1]*binningfactor,self.select1,i], shape) self.tr.calc_tomo(projdata, self.theta, self.maxIters, self.beta, self.samplethick, algorithm = self.algo, nonnegconst=self.nonnegconst, nprocessors = self.nprocessors) self.tomo_calculated = 1 self.full_tomo_calculated = 0 dims = self.tr.tomorec.shape self.nslices = dims[2] self.ROIvol = [[]]* dims[2] self.ROIarray = np.zeros((dims[0], dims[1], dims[2])) self.button_roispec.setEnabled(False) self.islice = int(dims[2]/2) self.slider_slice.setValue(self.islice) self.slider_slice.setRange(0, dims[2]-1) self.tc_comp.setText('Component: '+self.datanames[self.select1]) self.slider_comp.setRange(0, 0) self.button_roi.setEnabled(True) self.button_save.setEnabled(True) self.button_roispec.setEnabled(False) self.button_roi.setEnabled(True) self.button_loadroi.setEnabled(True) self.button_roihist.setEnabled(True) self.ShowImage() QtWidgets.QApplication.restoreOverrideCursor() #---------------------------------------------------------------------- def OnSelect1Comp(self, value): item = value self.select1 = item #---------------------------------------------------------------------- def OnSelectAlgo(self, value): item = value self.algo = item # 0 - CS if self.algo == 0: self.tc_par.setEnabled(True) self.ntc_beta.setEnabled(True) self.cb_ereg.setEnabled(True) self.ntc_ereg.setEnabled(True) else: self.tc_par.setEnabled(False) self.ntc_beta.setEnabled(False) self.cb_ereg.setEnabled(False) self.ntc_ereg.setEnabled(False) #---------------------------------------------------------------------- def OnCBEngReg(self, state): if state == QtCore.Qt.Checked: self.useengreg = 1 self.ntc_ereg.setEnabled(True) else: self.useengreg = 0 self.ntc_ereg.setEnabled(False) #---------------------------------------------------------------------- def OnNonNegConst(self, state): if state == QtCore.Qt.Checked: self.nonnegconst = 1 else: self.nonnegconst = 0 #---------------------------------------------------------------------- def OnScrollSlice(self, value): self.islice = value self.ShowImage() #---------------------------------------------------------------------- def OnScrollComp(self, value): self.icomp = value if self.full_tomo_calculated == 0: return self.tr.tomorec = self.fulltomorecdata[self.icomp] self.tc_comp.setText('Component: '+self.datanames[self.icomp]) self.ShowImage() #---------------------------------------------------------------------- def OnPointImage(self, evt): if self.energiesloaded == 0 or self.full_tomo_calculated == 0: return x = evt.xdata y = evt.ydata if (x == None) or (y == None): return ix = int(np.floor(x)) iy = self.n_rows-1-int(np.floor(y)) if ix<0 : ix=0 if ix>self.n_cols-1 : ix=self.n_cols-1 if iy<0 : iy=0 if iy>self.n_rows-1 : iy=self.n_rows-1 spectrum = [] for i in range(self.ncomponents): spectrum.append(self.fulltomorecdata[i][ix,iy,self.islice]) title = 'Point [{0:d}, {0:d}, {0:d}]'.format(ix,iy,self.islice) plot = PlotFrame(self, self.stack.ev, spectrum, title=title) plot.show() #---------------------------------------------------------------------- def OnExportData(self, event): wildcard = "Mrc files (*.mrc);;" SaveFileName, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save Tomo Reconstructions', '', wildcard) SaveFileName = str(SaveFileName) if SaveFileName == '': return basename, extension = os.path.splitext(SaveFileName) for i in range(self.ncomponents): data = self.tomodata[:,:,i,:] savefn = basename + '_TiltS_'+self.datanames[i]+extension self.tr.save_mrc(savefn, data) savefn2 = basename + '_Angles.txt' f = open(str(savefn2),'wt') for i in range(len(self.theta)): print(self.theta[i], file=f) f.close() #---------------------------------------------------------------------- def OnSave(self, event): wildcard = "Mrc files (*.mrc);;" SaveFileName, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save Tomo Reconstructions', '', wildcard) SaveFileName = str(SaveFileName) if SaveFileName == '': return data = self.tr.tomorec self.tr.save_mrc(SaveFileName, data.T) #---------------------------------------------------------------------- def OnSaveAll(self, event): wildcard = "Mrc files (*.mrc);;" SaveFileName, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save Tomo Reconstructions', '', wildcard) SaveFileName = str(SaveFileName) if SaveFileName == '': return basename, extension = os.path.splitext(SaveFileName) for i in range(self.ncomponents): data = self.fulltomorecdata[i] savefn = basename + '_'+self.datanames[i]+extension self.tr.save_mrc(savefn, data.T) #---------------------------------------------------------------------- def OnSaveROI(self, event): wildcard = "Mrc files (*.mrc);;" SaveFileName, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save ROI Selection', '', wildcard) SaveFileName = str(SaveFileName) if SaveFileName == '': return data = self.ROIarray self.tr.save_mrc(SaveFileName, data) #---------------------------------------------------------------------- def OnLoadROI(self, event): wildcard = "Mrc files (*.mrc);;" OpenFileName, _filter = QtWidgets.QFileDialog.getOpenFileName(self, 'Load ROI Selection', '', wildcard) OpenFileName = str(OpenFileName) if OpenFileName == '': return self.ROIarray = tomo_reconstruction.load_mrc(OpenFileName) for i in range(self.nslices): ROIpix = np.ma.array(self.ROIarray[:,:,i]) ROIpix_masked = np.ma.masked_values(ROIpix, 0) self.ROIvol[i] = ROIpix_masked self.haveROI = 1 if self.full_tomo_calculated == 1: self.button_roispec.setEnabled(True) self.button_roidel.setEnabled(True) self.button_saveroi.setEnabled(True) self.ShowImage() #---------------------------------------------------------------------- def OnSelectLasso(self,verts): # self.lasso.disconnect_events() # path = matplotlib.path.Path(verts) # self.ind = np.nonzero(path.contains_points(self.xys))[0] # print 'Selected '+str(len(self.ind))+' points' # # indices = path.contains_points(self.xys) # # mask = np.array([ path.contains_point((j,i)) for i in range(self.stack.n_rows) for j in range(self.stack.n_cols)]).reshape(self.stack.n_cols,self.stack.n_rows) # # self.ROIvol[:,:,self.islice] = mask # # print np.sum(self.ROIvol) path = matplotlib.path.Path(verts) #find pixels inside the polygon ROIpix = np.zeros((self.n_cols,self.n_rows)) for i in range(self.n_cols): for j in range(self.n_rows): Pinside = path.contains_point((i,j)) if Pinside == True: ROIpix[i, self.n_rows-1-j] = 255 ROIpix = np.ma.array(ROIpix) ROIpix_masked = np.ma.masked_values(ROIpix, 0) self.ROIvol[self.islice] = ROIpix_masked self.ROIarray[:,:,self.islice] = ROIpix[:] self.haveROI = 1 self.ShowImage() #---------------------------------------------------------------------- def OnSelectROI(self, event): self.AbsImagePanel.mpl_disconnect(self.cid1) if self.full_tomo_calculated == 1 and self.energiesloaded == 1: self.button_roispec.setEnabled(True) self.button_roidel.setEnabled(True) self.button_roihist.setEnabled(True) self.button_saveroi.setEnabled(True) lineprops = dict(color='red', linestyle='-', linewidth = 1, alpha=1) self.lasso = LassoSelector(self.axes, onselect=self.OnSelectLasso, useblit=False, props=lineprops) #---------------------------------------------------------------------- def OnROIHistogram(self, event): #self.window().Hide() image = self.tr.tomorec[:,:,self.islice].copy() histogram = ROIHistogram(self, image, self.n_cols, self.n_rows) histogram.show() #---------------------------------------------------------------------- def OnResetROI(self, event): self.button_roispec.setEnabled(False) self.button_roidel.setEnabled(False) self.button_roihist.setEnabled(True) self.button_saveroi.setEnabled(False) self.ROIvol = [[]]*self.nslices self.haveROI = 0 self.cid1 = self.AbsImagePanel.mpl_connect('button_press_event', self.OnPointImage) self.ShowImage() #---------------------------------------------------------------------- def CalcROISpectrum(self): ROIspectrum = np.zeros((self.stack.n_ev)) for ie in range(self.stack.n_ev): indices = np.where(self.ROIarray == 255) numroipix = self.ROIarray[indices].shape[0] if numroipix > 0: roivoxels = self.fulltomorecdata[ie] ROIspectrum[ie] = np.sum(roivoxels[indices])/numroipix return ROIspectrum #---------------------------------------------------------------------- def OnShowROISpec(self): spectrum = self.CalcROISpectrum() title = 'ROI spectrum' plot = PlotFrame(self, self.stack.ev, spectrum, title=title) plot.show() #---------------------------------------------------------------------- def ShowImage(self): if self.tomo_calculated == 0: return image = self.tr.tomorec[:,:,self.islice].copy() fig = self.absimgfig fig.clf() fig.add_axes(((0.0,0.0,1.0,1.0))) axes = fig.gca() fig.patch.set_alpha(1.0) im = axes.imshow(np.rot90(image), cmap=matplotlib.cm.get_cmap("gray")) if self.haveROI == 1: if len(self.ROIvol[self.islice]) > 0: im_red = axes.imshow(np.rot90(self.ROIvol[self.islice]), cmap=matplotlib.cm.get_cmap("autumn")) # if self.window().page1.show_scale_bar == 1: # #Show Scale Bar # if self.com.white_scale_bar == 1: # sbcolor = 'white' # else: # sbcolor = 'black' # startx = int(self.stk.n_cols*0.05) # starty = self.stk.n_rows-int(self.stk.n_rows*0.05)-self.stk.scale_bar_pixels_y # um_string = ' $\mathrm{\mu m}$' # microns = '$'+self.stk.scale_bar_string+' $'+um_string # axes.text(self.stk.scale_bar_pixels_x+startx+1,starty+1, microns, horizontalalignment='left', verticalalignment='center', # color = sbcolor, fontsize=14) # #Matplotlib has flipped scales so I'm using rows instead of cols! # p = matplotlib.patches.Rectangle((startx,starty), self.stk.scale_bar_pixels_x, self.stk.scale_bar_pixels_y, # color = sbcolor, fill = True) # axes.add_patch(p) axes.axis("off") self.AbsImagePanel.draw() self.axes = axes self.xys = np.dstack(np.meshgrid(np.arange(self.n_cols), np.arange(self.n_rows))).reshape(-1,2) #Show orthogonal slices dims = self.tr.tomorec.shape image2 = self.tr.tomorec[:,int(dims[1]/2),:] fig = self.absimgfig2 fig.clf() fig.add_axes(((0.0,0.0,1.0,1.0))) axes2 = fig.gca() fig.patch.set_alpha(1.0) im = axes2.imshow(np.rot90(image2), cmap=matplotlib.cm.get_cmap("gray")) axes2.axis("off") self.AbsImagePanel2.draw() image3 = self.tr.tomorec[int(dims[0]/2),:,:].T fig = self.absimgfig3 fig.clf() fig.add_axes(((0.0,0.0,1.0,1.0))) axes3 = fig.gca() fig.patch.set_alpha(1.0) im = axes3.imshow(np.rot90(image3), cmap=matplotlib.cm.get_cmap("gray")) axes3.axis("off") self.AbsImagePanel3.draw() #---------------------------------------------------------------------- def MakeHistogramROI(self, histmin, histmax): for i in range(self.nslices): hist_indices = np.where((histmin 0: self.loadSpectrum() self.ShowFitParams() #---------------------------------------------------------------------- def OnSpectraListClick(self): item = self.tc_speclist.currentRow() sel = item self.i_spec = sel+1 if self.com.xpf_loaded == 1: self.loadSpectrum() self.slider_spec.setValue(self.i_spec) #---------------------------------------------------------------------- def OnFitSpectrum(self, event): QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.SetFitParams() fitted_spectrum, separate_peaks = self.anlz.fit_spectrum(self.i_spec-1, self.nsteps[self.i_spec-1], self.npeaks[self.i_spec-1]) self.fits[self.i_spec-1] = fitted_spectrum self.fits_sep[self.i_spec-1] = separate_peaks self.spectrumfitted[self.i_spec-1] = 1 self.loadSpectrum() QtWidgets.QApplication.restoreOverrideCursor() self.updatewidgets() #---------------------------------------------------------------------- def OnSave(self, event): #Save images wildcard = "Portable Network Graphics (*.png);;Adobe PDF Files (*.pdf);; SVG (*.svg)" fileName = QtWidgets.QFileDialog.getSaveFileName(self, 'Save Fit Plot', '', wildcard) fileName = str(fileName) if fileName == '': return path, ext = os.path.splitext(fileName) ext = ext[1:].lower() if ext != 'png' and ext != 'pdf' and ext != 'svg': error_message = ( 'Only the PNG, PDF and SVG image formats are supported.\n' 'A file extension of `png\' or `pdf\' must be used.') QtWidgets.QMessageBox.warning(self, 'Error', 'Could not save file: %s' % error_message) return try: matplotlib.rcParams['pdf.fonttype'] = 42 fig = self.Specfig fig.savefig(fileName) except IOError as e: if e.strerror: err = e.strerror else: err = e QtGui.QMessageBox.warning(self, 'Error', 'Could not save file: %s' % err) #Save text file with fit info textfilepath = path+'_'+self.anlz.xfspec_names[self.i_spec-1]+'_fitinfo.txt' f = open(textfilepath, 'w') print('********************* Fit Results ********************', file=f) print('\n', file=f) print('Base:\t\t'+'{0:04.3f}'.format(self.anlz.xfitpars[self.i_spec-1].base), file=f) print('\n', file=f) text = 'Step Inflection Point [eV]:\t' for i in range(self.nsteps[self.i_spec-1]): text = text + '{0:04.3f}'.format(self.anlz.xfitpars[self.i_spec-1].stepfitparams[i*3]) + ', ' print(text, file=f) text = 'Step Height:\t' for i in range(self.nsteps[self.i_spec-1]): text = text + '{0:04.3f}'.format(self.anlz.xfitpars[self.i_spec-1].stepfitparams[i*3+1]) + ', ' print(text, file=f) text = 'Step FWHM:\t' for i in range(self.nsteps[self.i_spec-1]): text = text + '{0:04.3f}'.format(self.anlz.xfitpars[self.i_spec-1].stepfitparams[i*3+2]) + ', ' print(text, file=f) print('\n', file=f) text = 'Peak Positions:\t' for i in range(self.npeaks[self.i_spec-1]): text = text + '{0:04.3f}'.format(self.anlz.xfitpars[self.i_spec-1].gauss_fp_m[i]) + ', ' print(text, file=f) text = 'Peak Sigma:\t' for i in range(self.npeaks[self.i_spec-1]): text = text + '{0:04.3f}'.format(self.anlz.xfitpars[self.i_spec-1].gauss_fp_s[i]) + ', ' print(text, file=f) text = 'Peak Amplitude:\t' for i in range(self.npeaks[self.i_spec-1]): text = text + '{0:04.3f}'.format(self.anlz.xfitpars[self.i_spec-1].gauss_fp_a[i]) + ', ' print(text, file=f) f.close() return #---------------------------------------------------------------------- def OnShowFitParams(self, event): fitparams = [self.anlz.xfitpars[self.i_spec-1].base, self.anlz.xfitpars[self.i_spec-1].stepfitparams, self.anlz.xfitpars[self.i_spec-1].gauss_fp_a, self.anlz.xfitpars[self.i_spec-1].gauss_fp_m, self.anlz.xfitpars[self.i_spec-1].gauss_fp_s] fitparswin = FitParams(self.window(), 'Fit Parameters', fitparams, True, False) fitparswin.show() #---------------------------------------------------------------------- def OnNstepsspin(self, value): num = value self.nsteps[self.i_spec-1] = num #---------------------------------------------------------------------- def OnNgaussspin(self, value): num = value self.npeaks[self.i_spec-1] = num #---------------------------------------------------------------------- def OnShowEngs(self, state): if state == QtCore.Qt.Checked: self.showevlines = 1 else: self.showevlines = 0 if self.anlz.n_xrayfitsp > 0: self.loadSpectrum() #---------------------------------------------------------------------- def updatewidgets(self): self.button_fitspec.setEnabled(True) for i in range(12): self.tc_peakid[i].setText('') peaknames = [] if len(self.npeaks) > 0: for i in range(self.npeaks[self.i_spec-1]): i_eng=(np.abs(self.peak_engs-self.anlz.xfitpars[self.i_spec-1].gauss_fp_m[i])).argmin() diff = np.abs(self.peak_engs[i_eng]-self.anlz.xfitpars[self.i_spec-1].gauss_fp_m[i]) if np.abs(diff) < 0.5: peaknames.append(self.peak_names[i_eng] + '\t({0:01.1f})'.format(diff)) else: peaknames.append('unknown') if self.spectrumfitted[self.i_spec-1] == 1: self.button_save.setEnabled(True) for i in range(self.npeaks[self.i_spec-1]): text = '\t{0:04.3f}'.format(self.anlz.xfitpars[self.i_spec-1].gauss_fp_m[i]) self.tc_peakid[i].setText(text + '\t' + peaknames[i]) self.tc_peakid[i].setStyleSheet('color: rgb({0:}, {1}, {2})'.format(int(255*self.plotcolors[i][0]), int(255*self.plotcolors[i][1]), int(255*self.plotcolors[i][2]))) else: self.button_save.setEnabled(False) self.ShowFitParams() #---------------------------------------------------------------------- def loadSpectrum(self): spectrum = self.anlz.xrayfitspectra[self.i_spec-1, :] fig = self.Specfig fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() line1 = axes.plot(self.stk.ev,spectrum, color='black') if self.spectrumfitted[self.i_spec-1] == 1: offset = self.fits_sep[self.i_spec-1][0] line3 = axes.plot(self.stk.ev, offset, color = 'blue') for i in range(1, 1+self.nsteps[self.i_spec-1]): y = self.fits_sep[self.i_spec-1][i]+offset line3 = axes.plot(self.stk.ev, y, color = 'green') for i in range(0, self.npeaks[self.i_spec-1]): y = self.fits_sep[self.i_spec-1][i+self.nsteps[self.i_spec-1]+1]+offset line3 = axes.plot(self.stk.ev, y, color = self.plotcolors[i]) line2 = axes.plot(self.stk.ev,self.fits[self.i_spec-1], color='red') lines = axes.get_lines() self.colors = [] for i, line in enumerate(lines): self.colors.append(line.get_color()) if self.showevlines: peak_engs = self.window().page5.peak_engs for i in range(len(peak_engs)): if (peak_engs[i]>self.stk.ev[0]) and (peak_engs[i] self.stk.ev[self.stk.n_ev-1]: sel_ev = self.stk.n_ev-1 else: indx = np.abs(self.stk.ev - x).argmin() sel_ev = indx self.i_eng=(np.abs(self.peak_engs-self.stk.ev[sel_ev])).argmin() self.ShowImage() self.ShowODPlot() self.slider_eng.setValue(self.i_eng) #---------------------------------------------------------------------- def ShowODPlot(self): if (self.spectrum_loaded == 0) and (self.com.i0_loaded == 0): return if (self.com.i0_loaded == 0): spectrum = self.anlz.xrayfitspectra[self.i_spectrum-1, :] self.tc_1.setText("X-ray Spectrum: " + self.anlz.xfspec_names[self.i_spectrum-1]) else: if (self.i_spectrum == 1): spectrum = self.stk.od3d.sum(axis=0) spectrum = spectrum.sum(axis=0)/(self.stk.n_rows*self.stk.n_cols) self.tc_1.setText("X-ray Spectrum: Average OD") else: spectrum = self.anlz.xrayfitspectra[self.i_spectrum-2, :] self.tc_1.setText("X-ray Spectrum: " + self.anlz.xfspec_names[self.i_spectrum-2]) fig = self.kespecfig fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() specplot = axes.plot(self.stk.ev,spectrum) for i in range(len(self.peak_engs)): if (self.peak_engs[i]>self.stk.ev[0]) and (self.peak_engs[i]self.stk.ev[0]) and (self.peak_engs[self.i_eng] 0: self.anlz.target_pcafit_maps = self.anlz.target_pcafit_maps4D[self.itheta] self.anlz.original_fit_maps = self.anlz.original_fit_maps4D[self.itheta] self.anlz.target_pcafit_coeffs = self.anlz.target_pcafit_coeffs4D[self.itheta] self.anlz.target_pcafit_spectra = self.anlz.target_pcafit_spectra4D[self.itheta] self.slider_theta.setVisible(True) self.tc_imagetheta.setVisible(True) self.slider_theta.setRange(0, self.stk.n_theta-1) self.slider_theta.setValue(self.itheta) self.tc_imagetheta.setText("4D Data Angle: "+str(self.stk.theta[self.itheta])) self.button_calc4d.setEnabled(True) self.ShowSpectraList() self.loadTSpectrum() self.loadTargetMap() QtWidgets.QApplication.restoreOverrideCursor() except: QtWidgets.QApplication.restoreOverrideCursor() QtGui.QMessageBox.warning(self, 'Error', 'Could not calculate 4D spectra.') self.window().refresh_widgets() #---------------------------------------------------------------------- def Save(self, filename, path, spec_png = True, spec_pdf = False, spec_svg = False, spec_csv = False, img_png = True, img_pdf = False, img_svg = False, img_tif = False): self.SaveFileName = os.path.join(path,filename) try: if img_png: self.SaveMaps(imgformat=1) if img_pdf: self.SaveMaps(imgformat=2) if img_svg: self.SaveMaps(imgformat=3) if img_tif: self.SaveMaps(imgformat=0, savetif=True) if spec_png: self.SaveSpectra(imgformat=1) if spec_pdf: self.SaveSpectra(imgformat=2) if spec_pdf: self.SaveSpectra(imgformat=3) if spec_csv: self.SaveSpectra(imgformat=0, savecsv = True) except IOError as e: if e.strerror: err = e.strerror else: err = e QtGui.QMessageBox.warning(self, 'Error', 'Could not save file: %s' % err) #---------------------------------------------------------------------- def SaveSpectra(self, imgformat=1, savecsv = False): from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas matplotlib.rcParams['pdf.fonttype'] = 42 colors=['#FF0000','#000000','#FFFFFF'] spanclrmap=matplotlib.colors.LinearSegmentedColormap.from_list('spancm',colors) if savecsv: for i in range (self.anlz.n_target_spectra): #Save spectra tspectrum = self.anlz.target_spectra[i, :] fileName_spec = self.SaveFileName+"_Tspectrum_" +str(i+1)+".csv" cname = 'Tspectrum_' +str(i+1) self.stk.write_csv(fileName_spec, self.stk.ev, tspectrum, cname = cname) if imgformat == 0: return if imgformat == 1: ext = 'png' elif imgformat == 2: ext = 'pdf' elif imgformat == 3: ext = 'svg' suffix = "." + ext for i in range (self.anlz.n_target_spectra): #Save spectra tspectrum = self.anlz.target_spectra[i, :] fig = matplotlib.figure.Figure(figsize =(PlotW*1.21, PlotH*0.48)) canvas = FigureCanvas(fig) fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() line1 = axes.plot(self.stk.ev,tspectrum, color='black', label = 'Raw data') if self.com.pca_calculated == 1: tspectrumfit = self.anlz.target_pcafit_spectra[i, :] diff = np.abs(tspectrum-tspectrumfit) line2 = axes.plot(self.stk.ev,tspectrumfit, color='green', label = 'Fit') line3 = axes.plot(self.stk.ev,diff, color='grey', label = 'Abs(Raw-Fit)') fontP = matplotlib.font_manager.FontProperties() fontP.set_size('small') axes.legend(loc=4, prop = fontP) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') fileName_spec = self.SaveFileName+"_Tspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) #Save combined: fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() for i in range (self.anlz.n_target_spectra): #Save spectra tspectrum = self.anlz.target_spectra[i, :] line1 = axes.plot(self.stk.ev,tspectrum, color='black', label = 'Raw data') if self.com.pca_calculated == 1: tspectrumfit = self.anlz.target_pcafit_spectra[i, :] diff = np.abs(tspectrum-tspectrumfit) line2 = axes.plot(self.stk.ev,tspectrumfit, color='green', label = 'Fit') line3 = axes.plot(self.stk.ev,diff, color='grey', label = 'Abs(Raw-Fit)') fontP = matplotlib.font_manager.FontProperties() fontP.set_size('small') axes.legend(loc=4, prop = fontP) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') fileName_spec = self.SaveFileName+"_Tspectra_composite"+"."+ext fig.savefig(fileName_spec) #---------------------------------------------------------------------- def SaveMaps(self, imgformat=1, savetif = False): from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas matplotlib.rcParams['pdf.fonttype'] = 42 colors=['#FF0000','#000000','#FFFFFF'] spanclrmap=matplotlib.colors.LinearSegmentedColormap.from_list('spancm',colors) if imgformat > 0 : if imgformat == 1: ext = 'png' elif imgformat == 2: ext = 'pdf' elif imgformat == 3: ext = 'svg' suffix = "." + ext for i in range (self.anlz.n_target_spectra): #Save composition maps if self.showraw == True: tsmapimage = self.anlz.target_svd_maps[:,:,i] else: tsmapimage = self.anlz.target_pcafit_maps[:,:,i] fig = matplotlib.figure.Figure(figsize =(PlotH, PlotH)) canvas = FigureCanvas(fig) fig.clf() axes = fig.gca() divider = make_axes_locatable(axes) ax_cb = divider.new_horizontal(size="3%", pad=0.03) fig.add_axes(ax_cb) axes.set_position([0.03,0.03,0.8,0.94]) min_val = np.min(tsmapimage) max_val = np.max(tsmapimage) bound = np.max((np.abs(min_val), np.abs(max_val))) if self.show_scale_bar == 1: um_string = ' $\mathrm{\mu m}$' microns = '$'+self.stk.scale_bar_string+' $'+um_string axes.text(self.stk.scale_bar_pixels_x+10,self.stk.n_cols-9, microns, horizontalalignment='left', verticalalignment='center', color = 'white', fontsize=14) #Matplotlib has flipped scales so I'm using rows instead of cols! p = matplotlib.patches.Rectangle((5,self.stk.n_cols-10), self.stk.scale_bar_pixels_x, self.stk.scale_bar_pixels_y, color = 'white', fill = True) axes.add_patch(p) im = axes.imshow(np.rot90(tsmapimage), cmap=spanclrmap, vmin = -bound, vmax = bound) cbar = axes.figure.colorbar(im, orientation='vertical',cax=ax_cb) axes.axis("off") fileName_img = self.SaveFileName+"_TSmap_" +str(i+1)+"."+ext fig.savefig(fileName_img, pad_inches = 0.0) else: for i in range (self.anlz.n_target_spectra): #Save composition maps if self.showraw == True: tsmapimage = self.anlz.target_svd_maps[:,:,i] else: tsmapimage = self.anlz.target_pcafit_maps[:,:,i] fileName_img = self.SaveFileName+"_TSmap_" +str(i+1)+".tif" img1 = Image.fromarray(tsmapimage) img1.save(fileName_img) #---------------------------------------------------------------------- def OnEditSpectraListClick(self): item = self.tc_speclist.currentItem() self.tc_speclist.editItem(item) self.anlz.tspec_names[self.i_tspec-1] = item.data() self.loadTSpectrum() #---------------------------------------------------------------------- def OnSpectraListClick(self): item = self.tc_speclist.currentRow() sel = item self.i_tspec = sel+1 if self.com.spec_anl_calculated == 1: self.loadTSpectrum() self.loadTargetMap() self.slider_tspec.setValue(self.i_tspec) #---------------------------------------------------------------------- def OnTSScroll(self, value): sel = value self.i_tspec = sel self.tc_speclist.setCurrentRow(self.i_tspec-1) if self.com.spec_anl_calculated == 1: self.loadTSpectrum() self.loadTargetMap() #---------------------------------------------------------------------- def OnScrollTheta(self, value): if self.com.spec_anl4D_calculated == 0: return self.itheta = value self.anlz.target_svd_maps = self.anlz.target_svd_maps4D[self.itheta] self.anlz.original_svd_maps = self.anlz.original_svd_maps4D[self.itheta] if len(self.anlz.eigenvecs4D) > 0: self.anlz.target_pcafit_maps = self.anlz.target_pcafit_maps4D[self.itheta] self.anlz.original_fit_maps = self.anlz.original_fit_maps4D[self.itheta] self.anlz.target_pcafit_coeffs = self.anlz.target_pcafit_coeffs4D[self.itheta] self.anlz.target_pcafit_spectra = self.anlz.target_pcafit_spectra4D[self.itheta] self.tc_imagetheta.setText("4D Data Angle: "+'{0:5.2f}\t'.format(self.stk.theta[self.itheta])) self.loadTSpectrum() self.loadTargetMap() self.window().page0.itheta = self.itheta self.window().page0.slider_theta.setValue(self.itheta) self.window().page1.itheta = self.itheta self.window().page1.slider_theta.setValue(self.itheta) #---------------------------------------------------------------------- def OnRBRawFit(self, enabled): state = enabled if state: self.showraw = True else: self.showraw = False if self.com.spec_anl_calculated == 1: self.loadTSpectrum() self.loadTargetMap() #---------------------------------------------------------------------- def OnShowScale(self, state): if state == QtCore.Qt.Checked: self.show_scale_bar = 1 else: self.show_scale_bar = 0 if self.com.spec_anl_calculated == 1: self.loadTSpectrum() self.loadTargetMap() #---------------------------------------------------------------------- def OnRemoveSpectrum(self, event): QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.anlz.remove_spectrum(self.i_tspec-1) self.com.spec_anl_calculated = 1 self.i_tspec = self.i_tspec-1 if self.i_tspec<0: self.i_tspec=0 self.slider_tspec.setMaximum(self.anlz.n_target_spectra) self.slider_tspec.setValue(self.i_tspec) if self.anlz.tspectrum_loaded == 1: if self.com.spec_anl4D_calculated == 1: self.anlz.calculate_targetmaps_4D() self.loadTSpectrum() self.loadTargetMap() self.ShowSpectraList() else: self.com.spec_anl_calculated = 0 self.com.spec_anl4D_calculated = 0 self.ClearWidgets() QtWidgets.QApplication.restoreOverrideCursor() #---------------------------------------------------------------------- def OnMoveSpectrumDown(self, event): if self.i_tspec < self.anlz.n_target_spectra: self.anlz.move_spectrum(self.i_tspec-1, self.i_tspec) self.i_tspec += 1 self.slider_tspec.setValue(self.i_tspec) if self.com.spec_anl4D_calculated == 1: self.anlz.calculate_targetmaps_4D() self.loadTSpectrum() self.loadTargetMap() self.ShowSpectraList() #---------------------------------------------------------------------- def OnMoveSpectrumUp(self, event): if self.i_tspec > 1: self.anlz.move_spectrum(self.i_tspec-1, self.i_tspec-2) if self.com.spec_anl4D_calculated == 1: self.anlz.calculate_targetmaps_4D() self.i_tspec -= 1 self.slider_tspec.setValue(self.i_tspec) self.loadTSpectrum() self.loadTargetMap() self.ShowSpectraList() #---------------------------------------------------------------------- def ClearWidgets(self): fig = self.mapfig fig.clf() self.MapPanel.draw() fig = self.TSpecfig fig.clf() self.TSpectrumPanel.draw() self.tc_tspec.setText("Target Spectrum: ") self.tc_speclist.clear() self.tc_spfitlist.clear() self.com.spec_anl_calculated = 0 self.com.spec_anl4D_calculated = 0 self.i_tspec = 1 self.showraw = True self.rb_raw.setChecked(True) self.slider_tspec.setValue(self.i_tspec) self.button_calc4d.setEnabled(False) self.button_calc4d.setVisible(False) self.textctrl_sp1.setText('Common Name: \n') self.textctrl_sp2.setText('RMS Error: ') self.itheta = 0 self.slider_theta.setVisible(False) self.tc_imagetheta.setVisible(False) self.window().refresh_widgets() #---------------------------------------------------------------------- def ShowFitWeights(self): self.tc_spfitlist.clear() norm_factor = 100./np.sum(np.absolute(self.anlz.target_pcafit_coeffs[self.i_tspec-1, :])) for i in range(self.anlz.numsigpca): textitem = '{0}: {1:5.2f} %'.format(i+1, norm_factor *abs(self.anlz.target_pcafit_coeffs[self.i_tspec-1, i])) self.tc_spfitlist.addItem(textitem) self.tc_spfitlist.setCurrentRow(0) #---------------------------------------------------------------------- def ShowSpectraList(self): self.tc_speclist.clear() for i in range(self.anlz.n_target_spectra): self.tc_speclist.addItem(self.anlz.tspec_names[i]) #---------------------------------------------------------------------- def loadTargetMap(self): if self.showraw == True: tsmapimage = self.anlz.target_svd_maps[:,:,self.i_tspec-1] else: tsmapimage = self.anlz.target_pcafit_maps[:,:,self.i_tspec-1] colors=['#FF0000','#000000','#FFFFFF'] spanclrmap=matplotlib.colors.LinearSegmentedColormap.from_list('spancm',colors) fig = self.mapfig fig.clf() axes = fig.gca() divider = make_axes_locatable(axes) ax_cb = divider.new_horizontal(size="3%", pad=0.03) fig.add_axes(ax_cb) axes.set_position([0.03,0.03,0.8,0.94]) min_val = np.min(tsmapimage) max_val = np.max(tsmapimage) bound = np.max((np.abs(min_val), np.abs(max_val))) if self.show_scale_bar == 1: um_string = ' $\mathrm{\mu m}$' microns = '$'+self.stk.scale_bar_string+' $'+um_string axes.text(self.stk.scale_bar_pixels_x+10,self.stk.n_cols-9, microns, horizontalalignment='left', verticalalignment='center', color = 'white', fontsize=14) #Matplotlib has flipped scales so I'm using rows instead of cols! p = matplotlib.patches.Rectangle((5,self.stk.n_cols-10), self.stk.scale_bar_pixels_x, self.stk.scale_bar_pixels_y, color = 'white', fill = True) axes.add_patch(p) im = axes.imshow(np.rot90(tsmapimage), cmap=spanclrmap, vmin = -bound, vmax = bound) cbar = axes.figure.colorbar(im, orientation='vertical',cax=ax_cb) axes.axis("off") self.MapPanel.draw() #---------------------------------------------------------------------- def loadTSpectrum(self): if self.anlz.tspectrum_loaded == 0: return tspectrum = self.anlz.target_spectra[self.i_tspec-1, :] fig = self.TSpecfig fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() line1 = axes.plot(self.stk.ev,tspectrum, color='black', label = 'Raw data') if self.com.pca_calculated == 1: tspectrumfit = self.anlz.target_pcafit_spectra[self.i_tspec-1, :] diff = np.abs(tspectrum-tspectrumfit) line2 = axes.plot(self.stk.ev,tspectrumfit, color='green', label = 'Fit') line3 = axes.plot(self.stk.ev,diff, color='grey', label = 'Abs(Raw-Fit)') fontP = matplotlib.font_manager.FontProperties() fontP.set_size('small') axes.legend(loc=4, prop = fontP) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') self.TSpectrumPanel.draw() self.tc_tspec.setText("Target Spectrum: " + self.anlz.tspec_names[self.i_tspec-1]) self.textctrl_sp1.setText('Common Name: '+ self.anlz.tspec_names[self.i_tspec-1]) if self.com.pca_calculated == 1: self.textctrl_sp2.setText('RMS Error: '+ str('{0:7.5f}').format(self.anlz.target_rms[self.i_tspec-1])) self.ShowFitWeights() #---------------------------------------------------------------------- class ShowCompositeRBGmap(QtWidgets.QDialog): def __init__(self, parent, common, analz): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.resize(630, 400) self.setWindowTitle('Composite RBG Map') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) self.com = common self.anlz = analz self.show_info = 0 self.n_cols = self.anlz.stack.n_cols self.n_rows = self.anlz.stack.n_rows self.rgbimage = np.zeros((self.n_cols, self.n_rows, 3), dtype=float) self.minr = 0 self.maxr = 100 self.weightr = 100 self.ming = 0 self.maxg = 100 self.weightg = 100 self.minb = 0 self.maxb = 100 self.weightb = 100 self.r_spec = 0 self.g_spec = 1 self.b_spec = 2 sizer1 = QtWidgets.QGroupBox('Red spectrum') fgs1 = QtWidgets.QGridLayout() r = QtWidgets.QLabel(self) r.setText('Red') rl = QtWidgets.QLabel(self) rl.setText('Limits') rw = QtWidgets.QLabel(self) rw.setText('Weight') self.combor = QtWidgets.QComboBox(self) self.combor.addItems(self.anlz.tspec_names) self.combor.activated[int].connect(self.OnSelectR) #self.combor.SetToolTip(wx.ToolTip("select spectrum from dropdown-list")) self.combor.setCurrentIndex(self.r_spec) hbox12 = QtWidgets.QHBoxLayout() self.tcrmin = QtWidgets.QSpinBox() self.tcrmin.setRange(0,100) self.tcrmin.setValue(0) self.tcrmin.valueChanged[int].connect(self.OnLimitMinR) self.tcrmax = QtWidgets.QSpinBox() self.tcrmax.setRange(0,100) self.tcrmax.setValue(100) self.tcrmax.valueChanged[int].connect(self.OnLimitMaxR) hbox12.addWidget(self.tcrmin) hbox12.addWidget(self.tcrmax) self.tcrweight = QtWidgets.QSpinBox() self.tcrweight.setRange(0,100) self.tcrweight.setValue(100) self.tcrweight.valueChanged[int].connect(self.OnWeightR) fgs1.addWidget(r, 0, 0) fgs1.addWidget(self.combor, 0, 1) fgs1.addWidget(rl, 1, 0) fgs1.addLayout(hbox12, 1, 1) fgs1.addWidget(rw, 2, 0) fgs1.addWidget(self.tcrweight, 2, 1) sizer1.setLayout(fgs1) sizer2 = QtWidgets.QGroupBox('Green spectrum') fgs2 = QtWidgets.QGridLayout() g = QtWidgets.QLabel(self) g.setText('Green') gl = QtWidgets.QLabel(self) gl.setText('Limits') gw = QtWidgets.QLabel(self) gw.setText('Weight') self.combog = QtWidgets.QComboBox(self) self.combog.addItems(self.anlz.tspec_names) self.combog.activated[int].connect(self.OnSelectG) #self.combor.SetToolTip(wx.ToolTip("select spectrum from dropdown-list")) self.combog.setCurrentIndex(self.g_spec) hbox12 = QtWidgets.QHBoxLayout() self.tcgmin = QtWidgets.QSpinBox() self.tcgmin.setRange(0,100) self.tcgmin.setValue(0) self.tcgmin.valueChanged[int].connect(self.OnLimitMinG) self.tcgmax = QtWidgets.QSpinBox() self.tcgmax.setRange(0,100) self.tcgmax.setValue(100) self.tcgmax.valueChanged[int].connect(self.OnLimitMaxG) hbox12.addWidget(self.tcgmin) hbox12.addWidget(self.tcgmax) self.tcgweight = QtWidgets.QSpinBox() self.tcgweight.setRange(0,100) self.tcgweight.setValue(100) self.tcgweight.valueChanged[int].connect(self.OnWeightG) fgs2.addWidget(g, 0, 0) fgs2.addWidget(self.combog, 0, 1) fgs2.addWidget(gl, 1, 0) fgs2.addLayout(hbox12, 1, 1) fgs2.addWidget(gw, 2, 0) fgs2.addWidget(self.tcgweight, 2, 1) sizer2.setLayout(fgs2) sizer3 = QtWidgets.QGroupBox('Blue spectrum') fgs3 = QtWidgets.QGridLayout() b = QtWidgets.QLabel(self) b.setText('Blue') bl = QtWidgets.QLabel(self) bl.setText('Limits') bw = QtWidgets.QLabel(self) bw.setText('Weight') self.combob = QtWidgets.QComboBox(self) self.combob.addItems(self.anlz.tspec_names) self.combob.activated[int].connect(self.OnSelectB) #self.combor.SetToolTip(wx.ToolTip("select spectrum from dropdown-list")) self.combob.setCurrentIndex(self.b_spec) hbox12 = QtWidgets.QHBoxLayout() self.tcbmin = QtWidgets.QSpinBox() self.tcbmin.setRange(0,100) self.tcbmin.setValue(0) self.tcbmin.valueChanged[int].connect(self.OnLimitMinB) self.tcbmax = QtWidgets.QSpinBox() self.tcbmax.setRange(0,100) self.tcbmax.setValue(100) self.tcbmax.valueChanged[int].connect(self.OnLimitMaxB) hbox12.addWidget(self.tcbmin) hbox12.addWidget(self.tcbmax) self.tcbweight = QtWidgets.QSpinBox() self.tcbweight.setRange(0,100) self.tcbweight.setValue(100) self.tcbweight.valueChanged[int].connect(self.OnWeightB) fgs3.addWidget(b, 0, 0) fgs3.addWidget(self.combob, 0, 1) fgs3.addWidget(bl, 1, 0) fgs3.addLayout(hbox12, 1, 1) fgs3.addWidget(bw, 2, 0) fgs3.addWidget(self.tcbweight, 2, 1) sizer3.setLayout(fgs3) vbox = QtWidgets.QVBoxLayout() hbox1 = QtWidgets.QHBoxLayout() vbox1 = QtWidgets.QVBoxLayout() vbox1.addWidget(sizer1) vbox1.addWidget(sizer2) vbox1.addWidget(sizer3) self.show_info_cb = QtWidgets.QCheckBox( 'Show Info on the Image', self) self.show_info_cb.stateChanged.connect(self.OnShowInfo) vbox1.addWidget(self.show_info_cb) hbox1.addLayout(vbox1) frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.RGBImagefig = Figure((PlotH, PlotH)) self.RGBImagePanel = FigureCanvas(self.RGBImagefig) fbox.addWidget(self.RGBImagePanel) frame.setLayout(fbox) hbox1.addWidget(frame) vbox.addLayout(hbox1) hbox2 = QtWidgets.QHBoxLayout() button_save = QtWidgets.QPushButton('Save image') button_save.clicked.connect( self.OnSave) hbox2.addWidget(button_save) button_close = QtWidgets.QPushButton('Dismiss') button_close.clicked.connect( self.close) hbox2.addWidget(button_close) vbox.addLayout(hbox2) self.setLayout(vbox) self.CalcR() self.CalcG() self.CalcB() self.draw_image() #---------------------------------------------------------------------- def OnSelectR(self, value): item = value self.r_spec = item self.CalcR() self.draw_image() #---------------------------------------------------------------------- def CalcR(self): if self.parent.page4.showraw == True: tsmap = self.anlz.target_svd_maps[:,:,self.r_spec].copy() else: tsmap = self.anlz.target_pcafit_maps[:,:,self.r_spec].copy() uscale_min = tsmap.min() uscale_max = tsmap.max() scale_min = uscale_min + (uscale_max-uscale_min)*float(self.minr)/100. scale_max = uscale_min + (uscale_max-uscale_min)*float(self.maxr)/100. if scale_min >= scale_max: tsmap = np.zeros((self.n_cols, self.n_rows), dtype=float) else: tsmap = tsmap.clip(min=scale_min, max=scale_max) tsmap = (tsmap -scale_min) / (scale_max - scale_min) indices = np.where(tsmap < 0) tsmap[indices] = 0.0 indices = np.where(tsmap > 1) tsmap[indices] = 1.0 self.rgbimage[:,:,0] = tsmap*float(self.weightr)/100. #---------------------------------------------------------------------- def OnLimitMinR(self, value): self.minr = value #print 'self.minr=', self.minr self.CalcR() self.draw_image() #---------------------------------------------------------------------- def OnLimitMaxR(self, value): self.maxr = value #print 'self.maxr=', self.maxr self.CalcR() self.draw_image() #---------------------------------------------------------------------- def OnWeightR(self, value): self.weightr = value #print 'self.weightr=', self.weightr self.CalcR() self.draw_image() #---------------------------------------------------------------------- def OnSelectG(self, value): item = value self.g_spec = item self.CalcG() self.draw_image() #---------------------------------------------------------------------- def CalcG(self): if self.parent.page4.showraw == True: tsmap = self.anlz.target_svd_maps[:,:,self.g_spec].copy() else: tsmap = self.anlz.target_pcafit_maps[:,:,self.g_spec].copy() uscale_min = tsmap.min() uscale_max = tsmap.max() scale_min = uscale_min + (uscale_max-uscale_min)*float(self.ming)/100. scale_max = uscale_min + (uscale_max-uscale_min)*float(self.maxg)/100. if scale_min >= scale_max: tsmap = np.zeros((self.n_cols, self.n_rows), dtype=float) else: tsmap = tsmap.clip(min=scale_min, max=scale_max) tsmap = (tsmap - scale_min) / (scale_max - scale_min) indices = np.where(tsmap < 0) tsmap[indices] = 0.0 indices = np.where(tsmap > 1) tsmap[indices] = 1.0 self.rgbimage[:,:,1] = tsmap*float(self.weightg)/100. #---------------------------------------------------------------------- def OnLimitMinG(self, value): self.ming = value #print 'self.ming=', self.ming self.CalcG() self.draw_image() #---------------------------------------------------------------------- def OnLimitMaxG(self, value): self.maxg = value #print 'self.maxg=', self.maxg self.CalcG() self.draw_image() #---------------------------------------------------------------------- def OnWeightG(self, value): self.weightg = value #print 'self.weightg=', self.weightg self.CalcG() self.draw_image() #---------------------------------------------------------------------- def OnSelectB(self, value): item = value self.b_spec = item self.CalcB() self.draw_image() #---------------------------------------------------------------------- def CalcB(self): if self.parent.page4.showraw == True: tsmap = self.anlz.target_svd_maps[:,:,self.b_spec].copy() else: tsmap = self.anlz.target_pcafit_maps[:,:,self.b_spec].copy() uscale_min = tsmap.min() uscale_max = tsmap.max() scale_min = uscale_min + (uscale_max-uscale_min)*float(self.minb)/100. scale_max = uscale_min + (uscale_max-uscale_min)*float(self.maxb)/100. if scale_min >= scale_max: tsmap = np.zeros((self.n_cols, self.n_rows), dtype=float) else: tsmap = tsmap.clip(min=scale_min, max=scale_max) tsmap = (tsmap - scale_min) / (scale_max - scale_min) indices = np.where(tsmap < 0) tsmap[indices] = 0.0 indices = np.where(tsmap > 1) tsmap[indices] = 1.0 self.rgbimage[:,:,2] = tsmap*float(self.weightb)/100. #---------------------------------------------------------------------- def OnLimitMinB(self, value): self.minb = value #print 'self.minb=', self.minb self.CalcB() self.draw_image() #---------------------------------------------------------------------- def OnLimitMaxB(self, value): self.maxb = value #print 'self.maxb=', self.maxb self.CalcB() self.draw_image() #---------------------------------------------------------------------- def OnWeightB(self, value): self.weightb = value #print 'self.weightb=', self.weightb self.CalcB() self.draw_image() #---------------------------------------------------------------------- def OnShowInfo(self, state): if state == QtCore.Qt.Checked: self.show_info = 1 else: self.show_info = 0 self.draw_image() #---------------------------------------------------------------------- def draw_image(self): fig = self.RGBImagefig fig.clf() fig.add_axes(((0.0,0.0,1.0,1.0))) axes = fig.gca() fig.patch.set_alpha(1.0) im = axes.imshow(np.rot90(self.rgbimage)) axes.axis("off") if self.show_info == 1: startx = int(self.n_rows*0.02) starty = self.n_cols-int(self.n_cols*0.15) info = 'R:%s [%d] \nG:%s [%d] \nB:%s [%d]' % (self.anlz.tspec_names[self.r_spec], self.weightr, self.anlz.tspec_names[self.g_spec], self.weightg, self.anlz.tspec_names[self.b_spec], self.weightb) axes.text(+startx+1,starty+1, info, horizontalalignment='left', verticalalignment='center', color = 'white', fontsize=8) self.RGBImagePanel.draw() #---------------------------------------------------------------------- def OnSave(self, evt): wildcard = "Portable Network Graphics (*.png);;Adobe PDF Files (*.pdf);;" SaveFileName, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save Plot', '', wildcard) SaveFileName = str(SaveFileName) if SaveFileName == '': return path, ext = os.path.splitext(SaveFileName) ext = ext[1:].lower() if ext != 'png' and ext != 'pdf': error_message = ( 'Only the PNG and PDF image formats are supported.\n' 'A file extension of `png\' or `pdf\' must be used.') QtGui.QMessageBox.warning(self, 'Error', 'Could not save file: %s' % error_message) return matplotlib.rcParams['pdf.fonttype'] = 42 fig = self.RGBImagefig fig.savefig(SaveFileName, pad_inches = 0.0) #---------------------------------------------------------------------- class ShowMapHistogram(QtWidgets.QDialog): def __init__(self, parent, common, analz): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.limit = 1 self.histmax = None self.resize(600, 500) self.setWindowTitle('Histogram') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) self.com = common self.anlz = analz vbox = QtWidgets.QVBoxLayout() frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.histfig = Figure((6.0, 4.2)) self.HistogramPanel = FigureCanvas(self.histfig) self.HistogramPanel.setParent(self) self.HistogramPanel.mpl_connect('button_press_event', self.OnClick) fbox.addWidget(self.HistogramPanel) frame.setLayout(fbox) vbox.addWidget(frame) vbox1 = QtWidgets.QVBoxLayout() sizer1 = QtWidgets.QGroupBox('Histogram Cutoff') st = QtWidgets.QLabel(self) st.setText('Select a cutoff values on the histogram. All the values outside the defined limits will be set to cutoff limit value.') vbox1.addWidget(st) hbox1 = QtWidgets.QHBoxLayout() self.rb_min = QtWidgets.QRadioButton( 'Lower Limit', self) self.rb_max = QtWidgets.QRadioButton('Upper Limit',self) self.rb_min.setChecked(True) self.rb_min.toggled.connect(self.OnRb_limit) hbox1.addWidget(self.rb_min) hbox1.addWidget(self.rb_max) hbox1.addStretch (1) vbox1.addLayout(hbox1) self.tl_cutmin = QtWidgets.QLabel(self) self.tl_cutmin.setText('Lower Cutoff Value: ') vbox1.addWidget(self.tl_cutmin) self.tl_cutmax = QtWidgets.QLabel(self) self.tl_cutmax.setText('Upper Cutoff Value: ') vbox1.addWidget(self.tl_cutmax) sizer1.setLayout(vbox1) vbox.addWidget(sizer1) hbox2 = QtWidgets.QHBoxLayout() self.button_ok = QtWidgets.QPushButton('Accept') self.button_ok.clicked.connect(self.OnAccept) self.button_ok.setEnabled(False) hbox2.addWidget(self.button_ok) button_cancel = QtWidgets.QPushButton('Cancel') button_cancel.clicked.connect(self.close) hbox2.addWidget(button_cancel) vbox.addLayout(hbox2) self.setLayout(vbox) if len(self.anlz.original_svd_maps4D) == 0: self.draw_histogram() else: self.draw_histogram4D() #---------------------------------------------------------------------- def draw_histogram(self): fig = self.histfig fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) self.axes = fig.gca() #target_svd_maps;target_pcafit_maps if self.parent.page4.showraw == True: self.histogram = self.anlz.original_svd_maps else: self.histogram = self.anlz.original_fit_maps histdata = np.reshape(self.histogram, (self.anlz.stack.n_cols*self.anlz.stack.n_rows*self.anlz.n_target_spectra), order='F') self.n, self.bins, patches = self.axes.hist(histdata, 200, normed=1, facecolor='green', alpha=0.75) self.axes.set_xlabel('Thickness per Pixel in Spectral Maps') self.axes.set_ylabel('Percentage of Pixels') self.HistogramPanel.draw() #---------------------------------------------------------------------- def draw_histogram4D(self): fig = self.histfig fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) self.axes = fig.gca() #target_svd_maps;target_pcafit_maps if self.parent.page4.showraw == True: self.histogram = self.anlz.original_svd_maps4D else: self.histogram = self.anlz.original_fit_maps4D histdata = np.reshape(self.histogram, (self.anlz.stack.n_cols*self.anlz.stack.n_rows*self.anlz.n_target_spectra*self.anlz.stack.n_theta), order='F') self.n, self.bins, patches = self.axes.hist(histdata, 200, normed=1, facecolor='green', alpha=0.75) self.axes.set_xlabel('Thickness per Pixel in Spectral Maps') self.axes.set_ylabel('Percentage of Pixels') self.HistogramPanel.draw() #---------------------------------------------------------------------- def OnClick(self, evt): x1 = evt.xdata if x1 == None: return if self.limit == 1: self.tl_cutmin.setText('Lower Cutoff Value: '+str(x1)) self.histmin = x1 else: self.tl_cutmax.setText('Upper Cutoff Value: '+str(x1)) self.histmax = x1 self.button_ok.setEnabled(True) #---------------------------------------------------------------------- def OnRb_limit(self, enabled): state = enabled if state: self.limit = 1 else: self.limit = 2 #---------------------------------------------------------------------- def OnAccept(self, evt): if self.parent.page4.showraw == True: self.anlz.svd_map_threshold(self.histmin, self.histmax, svd=True) else: self.anlz.svd_map_threshold(self.histmin, self.histmax, pca=True) self.parent.page4.loadTargetMap() self.close() #---------------------------------------------------------------------- class SaveWinP4(QtWidgets.QDialog): def __init__(self, parent): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.resize(400, 300) self.setWindowTitle('Save') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) self.com = self.parent.common path, ext = os.path.splitext(self.com.filename) ext = ext[1:].lower() suffix = "." + ext path, fn = os.path.split(self.com.filename) filename = fn[:-len(suffix)] self.path = self.com.path self.filename = filename vboxtop = QtWidgets.QVBoxLayout() vboxtop.setContentsMargins(20,20,20,20) gridtop = QtWidgets.QGridLayout() gridtop.setVerticalSpacing(20) fontb = QtGui.QFont() fontb.setBold(True) st1 = QtWidgets.QLabel(self) st1.setText('Save') st1.setFont(fontb) st2 = QtWidgets.QLabel(self) st2.setText('.pdf') st2.setFont(fontb) st3 = QtWidgets.QLabel(self) st3.setText('.png') st3.setFont(fontb) st4 = QtWidgets.QLabel(self) st4.setText('.svg') st4.setFont(fontb) st5 = QtWidgets.QLabel(self) st5.setText('.csv') st5.setFont(fontb) st8 = QtWidgets.QLabel(self) st8.setText('.tif (data)') st8.setFont(fontb) st6 = QtWidgets.QLabel(self) st6.setText('_spectrum') self.cb11 = QtWidgets.QCheckBox('', self) self.cb11.setChecked(True) self.cb12 = QtWidgets.QCheckBox('', self) self.cb13 = QtWidgets.QCheckBox('', self) self.cb14 = QtWidgets.QCheckBox('', self) st7 = QtWidgets.QLabel(self) st7.setText('_images') self.cb21 = QtWidgets.QCheckBox('', self) self.cb21.setChecked(True) self.cb22 = QtWidgets.QCheckBox('', self) self.cb23 = QtWidgets.QCheckBox('', self) self.cb24 = QtWidgets.QCheckBox('', self) gridtop.addWidget(st1, 0, 0) gridtop.addWidget(st2, 0, 1) gridtop.addWidget(st3, 0, 2) gridtop.addWidget(st4, 0, 3) gridtop.addWidget(st5, 0, 4) gridtop.addWidget(st8, 0, 5) gridtop.addWidget(st6, 1, 0) gridtop.addWidget(self.cb11, 1, 1) gridtop.addWidget(self.cb12, 1, 2) gridtop.addWidget(self.cb13, 1, 3) gridtop.addWidget(self.cb14, 1, 4) gridtop.addWidget(st7, 2, 0) gridtop.addWidget(self.cb21, 2, 1) gridtop.addWidget(self.cb22, 2, 2) gridtop.addWidget(self.cb23, 2, 3) gridtop.addWidget(self.cb24, 2, 5) vboxtop.addStretch(1) vboxtop.addLayout(gridtop) vboxtop.addStretch(2) hbox0 = QtWidgets.QHBoxLayout() stf = QtWidgets.QLabel(self) stf.setText('Filename:\t') self.tc_savefn = QtWidgets.QLineEdit(self) self.tc_savefn.setText(self.filename) hbox0.addWidget(stf) hbox0.addWidget(self.tc_savefn) hbox1 = QtWidgets.QHBoxLayout() stp = QtWidgets.QLabel(self) stp.setText('Path: \t') self.tc_savepath = QtWidgets.QLineEdit(self) self.tc_savepath.setReadOnly(True) self.tc_savepath.setText(self.path) self.tc_savepath.setMinimumWidth(100) hbox1.addWidget(stp) hbox1.addWidget(self.tc_savepath) button_path = QtWidgets.QPushButton('Browse...') button_path.clicked.connect(self.OnBrowseDir) hbox1.addWidget(button_path) hbox2 = QtWidgets.QHBoxLayout() button_save = QtWidgets.QPushButton('Save') button_save.clicked.connect(self.OnSave) hbox2.addWidget(button_save) button_cancel = QtWidgets.QPushButton('Cancel') button_cancel.clicked.connect(self.close) hbox2.addWidget(button_cancel) vboxtop.addLayout(hbox0) vboxtop.addLayout(hbox1) vboxtop.addStretch(1) vboxtop.addLayout(hbox2) self.setLayout(vboxtop) #---------------------------------------------------------------------- def OnBrowseDir(self, evt): directory = QtWidgets.QFileDialog.getExistingDirectory(self, "Choose a directory", self.path, QtWidgets.QFileDialog.ShowDirsOnly|QtWidgets.QFileDialog.ReadOnly) if directory == '': return directory = str(directory) self.com.path = directory self.path = directory self.tc_savepath.setText(self.path) #---------------------------------------------------------------------- def OnSave(self, evt): self.filename = str(self.tc_savefn.text()) sp_pdf = self.cb11.isChecked() sp_png = self.cb12.isChecked() sp_svg = self.cb13.isChecked() sp_csv = self.cb14.isChecked() im_pdf = self.cb21.isChecked() im_png = self.cb22.isChecked() im_svg = self.cb23.isChecked() im_tif = self.cb24.isChecked() self.close() self.parent.page4.Save(self.filename, self.path, spec_png = sp_png, spec_pdf = sp_pdf, spec_svg = sp_svg, spec_csv = sp_csv, img_png = im_png, img_pdf = im_pdf, img_svg = im_svg, img_tif = im_tif) """ ------------------------------------------------------------------------------------------------""" class PageCluster(QtWidgets.QWidget): def __init__(self, common, data_struct, stack, anlz): super(PageCluster, self).__init__() self.initUI(common, data_struct, stack, anlz) #---------------------------------------------------------------------- def initUI(self, common, data_struct, stack, anlz): self.data_struct = data_struct self.stk = stack self.com = common self.anlz = anlz self.selcluster = 1 self.numclusters = 0 self.init_nclusters = 5 self.wo_1st_pca = 0 self.sigma_split = 0 self.showallspectra = 0 self.pcscalingfactor = 0.0 self.MakeColorTable() #panel 1 sizer1 = QtWidgets.QGroupBox('Cluster analysis') vbox1 = QtWidgets.QVBoxLayout() self.button_calcca = QtWidgets.QPushButton('Calculate Clusters') self.button_calcca.clicked.connect( self.OnCalcClusters) self.button_calcca.setEnabled(False) vbox1.addWidget(self.button_calcca) self.button_scatterplots = QtWidgets.QPushButton('Show scatter plots...') self.button_scatterplots.clicked.connect( self.OnShowScatterplots) self.button_scatterplots.setEnabled(False) self.button_savecluster = QtWidgets.QPushButton('Save CA Results...') self.button_savecluster.clicked.connect( self.OnSave) self.button_savecluster.setEnabled(False) vbox1.addStretch(1) hbox11 = QtWidgets.QHBoxLayout() text1 = QtWidgets.QLabel(self) text1.setText('Number of clusters') hbox11.addWidget(text1) self.nclusterspin = QtWidgets.QSpinBox() self.nclusterspin.setRange(2,20) self.nclusterspin.setValue(self.init_nclusters) self.nclusterspin.valueChanged[int].connect(self.OnNClusterspin) hbox11.addWidget(text1) hbox11.addWidget(self.nclusterspin) vbox1.addLayout(hbox11) hbox12 = QtWidgets.QHBoxLayout() text1a = QtWidgets.QLabel(self) text1a.setText("Number of clusters found") hbox12.addWidget(text1a) self.ntc_clusters_found = QtWidgets.QLabel(self) self.ntc_clusters_found.setText(str(self.numclusters)) hbox12.addWidget(self.ntc_clusters_found) vbox1.addLayout(hbox12) hbox13 = QtWidgets.QHBoxLayout() self.remove1stpcacb = QtWidgets.QCheckBox('Reduce thickness effects', self) self.remove1stpcacb.stateChanged.connect(self.OnRemove1stpca) hbox13.addWidget(self.remove1stpcacb) vbox1.addLayout(hbox13) hbox14 = QtWidgets.QHBoxLayout() self.cb_splitclusters = QtWidgets.QCheckBox('Divide clusters with large Sigma', self) self.cb_splitclusters.stateChanged.connect(self.OnSplitClusters) hbox14.addWidget(self.cb_splitclusters) vbox1.addLayout(hbox14) hbox14a = QtWidgets.QHBoxLayout() tc1 = QtWidgets.QLabel(self) tc1.setText("PC scaling factor") hbox14a.addWidget(tc1) self.ntc_pcscaling = QtWidgets.QLineEdit(self) self.ntc_pcscaling.setFixedWidth(65) self.ntc_pcscaling.setValidator(QtGui.QDoubleValidator(0, 99999, 2, self)) self.ntc_pcscaling.setAlignment(QtCore.Qt.AlignRight) self.ntc_pcscaling.setText(str(self.pcscalingfactor)) hbox14a.addWidget(self.ntc_pcscaling) hbox14a.addStretch(1) vbox1.addLayout(hbox14a) line = QtWidgets.QFrame() line.setFrameShape(QtWidgets.QFrame.HLine) line.setFrameShadow(QtWidgets.QFrame.Sunken) vbox1.addStretch(1) vbox1.addWidget(line) vbox1.addStretch(1) vbox1.addWidget(self.button_scatterplots) vbox1.addWidget(self.button_savecluster) sizer1.setLayout(vbox1) #panel 2 vbox2 = QtWidgets.QVBoxLayout() tc_clustercomp = QtWidgets.QLabel(self) tc_clustercomp.setText("Composite cluster image") vbox2.addWidget(tc_clustercomp) frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.clusterimgfig = Figure((PlotH, PlotH)) self.ClusterImagePan = FigureCanvas(self.clusterimgfig) self.ClusterImagePan.mpl_connect('button_press_event', self.OnPointClusterImage) self.ClusterImagePan.setParent(self) fbox.addWidget(self.ClusterImagePan) frame.setLayout(fbox) vbox2.addWidget(frame) #panel 3 vbox3 = QtWidgets.QVBoxLayout() fgs = QtWidgets.QGridLayout() self.tc_cluster = QtWidgets.QLabel(self) self.tc_cluster.setText("Cluster ") fgs.addWidget(self.tc_cluster, 0, 0, QtCore .Qt. AlignLeft) frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() #self.clusterindvimgfig = Figure((PlotH*0.73, PlotH*0.73)) self.clusterindvimgfig = Figure((PlotH, PlotH)) self.ClusterIndvImagePan = FigureCanvas(self.clusterindvimgfig) self.ClusterIndvImagePan.setParent(self) fbox.addWidget(self.ClusterIndvImagePan) frame.setLayout(fbox) fgs.addWidget(frame, 1, 0, QtCore .Qt. AlignLeft) self.slidershow = QtWidgets.QScrollBar(QtCore.Qt.Vertical) self.slidershow.setFocusPolicy(QtCore.Qt.StrongFocus) self.slidershow.setEnabled(False) self.slidershow.valueChanged[int].connect(self.OnClusterScroll) self.slidershow.setRange(1, 20) fgs.addWidget(self.slidershow, 1, 1, QtCore .Qt. AlignLeft) text3 = QtWidgets.QLabel(self) text3.setText('Cluster Error Map') fgs.addWidget(text3, 0, 2, QtCore .Qt. AlignLeft) frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.clusterdistmapfig = Figure((PlotH, PlotH)) self.ClusterDistMapPan = FigureCanvas(self.clusterdistmapfig) self.ClusterDistMapPan.setParent(self) fbox.addWidget(self.ClusterDistMapPan) frame.setLayout(fbox) fgs.addWidget(frame, 1, 2, QtCore .Qt. AlignLeft) vbox3.addLayout(fgs) #panel 4 vbox4 = QtWidgets.QVBoxLayout() self.tc_clustersp = QtWidgets.QLabel(self) self.tc_clustersp.setText("Cluster spectrum") vbox4.addWidget(self.tc_clustersp) frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.clusterspecfig = Figure((PlotW, PlotH)) self.ClusterSpecPan = FigureCanvas(self.clusterspecfig) self.ClusterSpecPan.setParent(self) fbox.addWidget(self.ClusterSpecPan) frame.setLayout(fbox) vbox4.addWidget(frame) #panel 5 sizer5 = QtWidgets.QGroupBox('Display') vbox5 = QtWidgets.QVBoxLayout() hbox51 = QtWidgets.QHBoxLayout() self.showallspectracb = QtWidgets.QCheckBox('Show all spectra', self) self.showallspectracb.stateChanged.connect(self.OnShowallspectra) hbox51.addWidget(self.showallspectracb) vbox5.addLayout(hbox51) sizer5.setLayout(vbox5) vboxtop = QtWidgets.QVBoxLayout() hboxtopL = QtWidgets.QHBoxLayout() vboxtopL = QtWidgets.QVBoxLayout() vboxtopL.addWidget(sizer1) vboxtopL.addWidget(sizer5) hboxtopL.addLayout(vboxtopL) hboxtopL.addStretch(1) gridsizertop = QtWidgets.QGridLayout() gridsizertop.setContentsMargins(15,0,0,0) gridsizertop.addLayout(hboxtopL, 0, 0, QtCore .Qt. AlignLeft) gridsizertop.addLayout(vbox2, 1, 0, QtCore .Qt. AlignLeft) gridsizertop.addLayout(vbox3, 0, 1, QtCore .Qt. AlignLeft) gridsizertop.addLayout(vbox4, 1, 1, QtCore .Qt. AlignLeft) vboxtop.addStretch(1) vboxtop.addLayout(gridsizertop) vboxtop.addStretch(1) self.setLayout(vboxtop) #---------------------------------------------------------------------- def MakeColorTable(self): self.maxclcolors = 11 colors_i = np.linspace(0,self.maxclcolors,self.maxclcolors+1) # self.colors=['#0000FF','#FF0000','#DFE32D','#36F200','#B366FF', # '#FF470A','#33FFFF','#006600','#CCCC99','#993300', # '#000000'] # self.colors=['#D98619','#ED2024','#98CC31','#861F78','#007FFF', # '#6FDBDB','#5C3F32','#FF6EC7','#CCCC99','#993300', # '#000000'] self.colors=['#007FFF','#ED2024','#98CC31','#861F78','#D98619', '#6FDBDB','#5C3F32','#FF6EC7','#CCCC99','#993300', '#000000'] self.clusterclrmap1=matplotlib.colors.LinearSegmentedColormap.from_list('clustercm',self.colors) self.bnorm1 = matplotlib.colors.BoundaryNorm(colors_i, self.clusterclrmap1.N) colors_i = np.linspace(0,self.maxclcolors+2,self.maxclcolors+3) #use black color for clusters > maxclcolors, the other 2 colors are for background # colors2=['#0000FF','#FF0000','#DFE32D','#36F200','#B366FF', # '#FF470A','#33FFFF','#006600','#CCCC99','#993300', # '#000000','#FFFFFF','#EEEEEE'] # colors2=['#D98619','#ED2024','#98CC31','#861F78','#007FFF', # '#6FDBDB','#5C3F32','#FF6EC7','#CCCC99','#993300', # '#000000','#FFFFFF','#EEEEEE'] colors2=['#007FFF','#ED2024','#98CC31','#861F78','#D98619', '#6FDBDB','#5C3F32','#FF6EC7','#CCCC99','#993300', '#000000','#FFFFFF','#EEEEEE'] self.clusterclrmap2=matplotlib.colors.LinearSegmentedColormap.from_list('clustercm2',colors2) self.bnorm2 = matplotlib.colors.BoundaryNorm(colors_i, self.clusterclrmap2.N) #---------------------------------------------------------------------- def OnCalcClusters(self, event): QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.calcclusters = False try: value = self.ntc_pcscaling.text() #try: self.pcscalingfactor = float(value) # except: # self.pcscalingfactor = 0.0 # self.ntc_pcscaling.setText('0.0') self.CalcClusters() self.calcclusters = True self.selcluster = 1 self.slidershow.setValue(self.selcluster) self.slidershow.setMaximum(self.numclusters) self.showClusterImage() self.showClusterSpectrum() self.showIndvClusterImage() self.showClusterDistanceMap() self.com.cluster_calculated = 1 QtWidgets.QApplication.restoreOverrideCursor() except Exception as e: self.com.cluster_calculated = 0 QtWidgets.QApplication.restoreOverrideCursor() print(e) self.window().refresh_widgets() #---------------------------------------------------------------------- def OnNClusterspin(self, value): num = value self.init_nclusters = num #---------------------------------------------------------------------- def OnClusterScroll(self, value): sel = value self.selcluster = sel if self.com.cluster_calculated == 1: self.showClusterSpectrum() self.showIndvClusterImage() #---------------------------------------------------------------------- def OnClusterSpinUp(self, event): if (self.com.cluster_calculated == 1) and (self.selcluster > 1): self.selcluster = self.selcluster - 1 self.slidershow.setValue(self.selcluster) self.showClusterSpectrum() self.showIndvClusterImage() #---------------------------------------------------------------------- def OnClusterSpinDown(self, event): if (self.com.cluster_calculated == 1) and (self.selcluster < self.numclusters): self.selcluster = self.selcluster + 1 self.slidershow.setValue(self.selcluster) self.showClusterSpectrum() self.showIndvClusterImage() #---------------------------------------------------------------------- def OnPointClusterImage(self, evt): x = evt.xdata y = evt.ydata if (x == None) or (y == None): return if self.com.cluster_calculated == 1: try: self.ix = int(np.floor(x)) self.iy = self.stk.n_rows-1-int(np.floor(y)) if self.ix<0 : self.ix=0 if self.ix>self.stk.n_cols-1 : self.ix=self.stk.n_cols-1 if self.iy<0 : self.iy=0 if self.iy>self.stk.n_rows-1 : self.iy=self.stk.n_rows-1 self.selcluster = self.anlz.cluster_indices[self.ix,self.iy] + 1 self.slidershow.setValue(self.selcluster) self.showClusterSpectrum() self.showIndvClusterImage() except: pass #---------------------------------------------------------------------- def CalcClusters(self): nclusters = self.anlz.calculate_clusters(self.init_nclusters, self.wo_1st_pca, self.sigma_split, pcscalingfactor = self.pcscalingfactor) #nclusters = self.anlz.calculate_clusters_kmeansangle(self.init_nclusters, self.wo_1st_pca, self.sigma_split) self.numclusters = nclusters self.ntc_clusters_found.setText(str(self.numclusters)) #---------------------------------------------------------------------- #Show composite cluster image def showClusterImage(self): self.clusterimage = self.anlz.cluster_indices #print self.selpca fig = self.clusterimgfig fig.clf() fig.add_axes(((0.0,0.0,1.0,1.0))) axes = fig.gca() im = axes.imshow(np.rot90(self.clusterimage), cmap=self.clusterclrmap1, norm=self.bnorm1) axes.axis("off") #cbar = axes.figure.colorbar(im) self.ClusterImagePan.draw() #---------------------------------------------------------------------- #Show composite cluster image def showIndvClusterImage(self): indvclusterimage = self.anlz.cluster_indices.copy() indvclusterimage[indvclusterimage!=self.selcluster-1] = 20. fig = self.clusterindvimgfig fig.clf() fig.add_axes(((0.0,0.0,1.0,1.0))) axes = fig.gca() im = axes.imshow(np.rot90(indvclusterimage), cmap=self.clusterclrmap2, norm=self.bnorm2) axes.axis("off") self.ClusterIndvImagePan.draw() self.tc_cluster.setText("Cluster " + str(self.selcluster)) #---------------------------------------------------------------------- def showClusterSpectrum(self): clusterspectrum = self.anlz.clusterspectra[self.selcluster-1, ] fig = self.clusterspecfig fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() if self.showallspectra == 0: if self.selcluster >= self.maxclcolors: clcolor = self.colors[self.maxclcolors-1] else: clcolor = self.colors[self.selcluster-1] specplot = axes.plot(self.anlz.stack.ev,clusterspectrum, color = clcolor) self.tc_clustersp.setText("Cluster " + str(self.selcluster)+ " spectrum" ) else: #Show all spectra for i in range(1, self.numclusters+1): if i >= self.maxclcolors: clcolor = self.colors[self.maxclcolors-1] else: clcolor = self.colors[i-1] clusterspectrum = self.anlz.clusterspectra[i-1, ]/np.amax(self.anlz.clusterspectra[i-1, ]) specplot = axes.plot(self.anlz.stack.ev,clusterspectrum, color = clcolor) self.tc_clustersp.setText(" Normalized Cluster spectra" ) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') self.ClusterSpecPan.draw() #---------------------------------------------------------------------- def showClusterDistanceMap(self): mapimage = self.anlz.cluster_distances #print self.selpca fig = self.clusterdistmapfig fig.clf() fig.add_axes((0.02,0.02,0.96,0.96)) axes = fig.gca() # divider = make_axes_locatable(axes) # axcb = divider.new_horizontal(size="3%", pad=0.03) # fig.add_axes(axcb) # axes.set_position([0.03,0.03,0.8,0.94]) im = axes.imshow(np.rot90(mapimage), cmap=matplotlib.cm.get_cmap('gray')) #cbar = axes.figure.colorbar(im, orientation='vertical',cax=axcb) axes.axis("off") self.ClusterDistMapPan.draw() #---------------------------------------------------------------------- def OnRemove1stpca(self, state): if state == QtCore.Qt.Checked: self.wo_1st_pca = 1 else: self.wo_1st_pca = 0 #---------------------------------------------------------------------- def OnSplitClusters(self, state): if state == QtCore.Qt.Checked: self.sigma_split = 1 else: self.sigma_split = 0 #---------------------------------------------------------------------- def OnShowallspectra(self, state): if state == QtCore.Qt.Checked: self.showallspectra = 1 else: self.showallspectra = 0 if self.com.cluster_calculated == 1: self.showClusterSpectrum() #---------------------------------------------------------------------- def OnSave(self, event): savewin = SaveWinP3(self.window()) savewin.show() #---------------------------------------------------------------------- def Save(self, filename, path, spec_png = True, spec_pdf = False, spec_svg = False, spec_csv = False, img_png = True, img_pdf = False, img_svg = False, img_tif = False, indimgs_png = True, indimgs_pdf = False, indimgs_svg = False, indimgs_tif = False, scatt_png = True, scatt_pdf = False, scatt_svg = False): self.SaveFileName = os.path.join(path,filename) from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas matplotlib.rcParams['pdf.fonttype'] = 42 try: if img_png: ext = 'png' fig = matplotlib.figure.Figure(figsize = (float(self.stk.n_rows)/10, float(self.stk.n_cols)/10)) canvas = FigureCanvas(fig) fig.clf() fig.add_axes((0.0,0.0,1.0,1.0)) axes = fig.gca() im = axes.imshow(np.rot90(self.clusterimage), cmap=self.clusterclrmap1, norm=self.bnorm1) axes.axis("off") fileName_caimg = self.SaveFileName+"_CAcimg."+ext fig.savefig(fileName_caimg, dpi=ImgDpi, pad_inches = 0.0) if img_pdf: ext = 'pdf' suffix = "." + ext fig = matplotlib.figure.Figure(figsize = (float(self.stk.n_rows)/30, float(self.stk.n_cols)/30)) canvas = FigureCanvas(fig) fig.clf() fig.add_axes((0.0,0.0,1.0,1.0)) axes = fig.gca() im = axes.imshow(np.rot90(self.clusterimage), cmap=self.clusterclrmap1, norm=self.bnorm1) axes.axis("off") fileName_caimg = self.SaveFileName+"_CAcimg."+ext fig.savefig(fileName_caimg, dpi=300, pad_inches = 0.0) if img_svg: ext = 'svg' suffix = "." + ext fig = matplotlib.figure.Figure(figsize = (float(self.stk.n_rows)/30, float(self.stk.n_cols)/30)) canvas = FigureCanvas(fig) fig.clf() fig.add_axes((0.0,0.0,1.0,1.0)) axes = fig.gca() im = axes.imshow(np.rot90(self.clusterimage), cmap=self.clusterclrmap1, norm=self.bnorm1) axes.axis("off") fileName_caimg = self.SaveFileName+"_CAcimg."+ext fig.savefig(fileName_caimg, dpi=300, pad_inches = 0.0) if img_tif: fileName_caimg = self.SaveFileName+"_CAcimg.tif" img1 = Image.fromarray(self.clusterimage) #ToDo: Recently throws an error. Possible conflict in module PIL. "Cannot handle this data type: (1, 1), = self.maxclcolors: clcolor = self.colors[self.maxclcolors-1] else: clcolor = self.colors[i] specplot = axes.plot(self.anlz.stack.ev,clusterspectrum, color = clcolor) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') fileName_spec = self.SaveFileName+"_CAspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) #Save all spectra in one plot fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() for i in range(1, self.numclusters+1): clusterspectrum = self.anlz.clusterspectra[i-1, ]/np.amax(self.anlz.clusterspectra[i-1, ]) if i >= self.maxclcolors: clcolor = self.colors[self.maxclcolors-1] else: clcolor = self.colors[i-1] specplot = axes.plot(self.anlz.stack.ev,clusterspectrum, color = clcolor) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') fileName_spec = self.SaveFileName+"_CAspectra"+"."+ext fig.savefig(fileName_spec) if spec_csv: for i in range (self.numclusters): clusterspectrum = self.anlz.clusterspectra[i, ] fileName_spec = self.SaveFileName+"_CAspectrum_" +str(i+1)+".csv" cname = 'CAspectrum_' +str(i+1) self.stk.write_csv(fileName_spec, self.anlz.stack.ev, clusterspectrum, cname=cname) ext = 'pdf' suffix = "." + ext if indimgs_pdf: for i in range (self.numclusters): indvclusterimage = np.zeros((self.anlz.stack.n_cols, self.anlz.stack.n_rows))+20. ind = np.where(self.anlz.cluster_indices == i) colorcl = min(i,9) indvclusterimage[ind] = colorcl fig = matplotlib.figure.Figure(figsize =(float(self.stk.n_rows)/30, float(self.stk.n_cols)/30)) canvas = FigureCanvas(fig) fig.add_axes((0.0,0.0,1.0,1.0)) axes = fig.gca() im = axes.imshow(np.rot90(indvclusterimage), cmap=self.clusterclrmap2, norm=self.bnorm2) axes.axis("off") fileName_img = self.SaveFileName+"_CAimg_" +str(i+1)+"."+ext fig.savefig(fileName_img, dpi=300, pad_inches = 0.0) if spec_pdf: for i in range (self.numclusters): clusterspectrum = self.anlz.clusterspectra[i, ] fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() if i >= self.maxclcolors: clcolor = self.colors[self.maxclcolors-1] else: clcolor = self.colors[i] specplot = axes.plot(self.anlz.stack.ev,clusterspectrum, color = clcolor) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') fileName_spec = self.SaveFileName+"_CAspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) #Save all spectra in one plot fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() for i in range(1, self.numclusters+1): clusterspectrum = self.anlz.clusterspectra[i-1, ]/np.amax(self.anlz.clusterspectra[i-1, ]) if i >= self.maxclcolors: clcolor = self.colors[self.maxclcolors-1] else: clcolor = self.colors[i-1] specplot = axes.plot(self.anlz.stack.ev,clusterspectrum, color = clcolor) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') fileName_spec = self.SaveFileName+"_CAspectra"+"."+ext fig.savefig(fileName_spec) ext = 'svg' suffix = "." + ext if indimgs_svg: for i in range (self.numclusters): indvclusterimage = np.zeros((self.anlz.stack.n_cols, self.anlz.stack.n_rows))+20. ind = np.where(self.anlz.cluster_indices == i) colorcl = min(i,9) indvclusterimage[ind] = colorcl fig = matplotlib.figure.Figure(figsize =(float(self.stk.n_rows)/30, float(self.stk.n_cols)/30)) canvas = FigureCanvas(fig) fig.add_axes((0.0,0.0,1.0,1.0)) axes = fig.gca() im = axes.imshow(np.rot90(indvclusterimage), cmap=self.clusterclrmap2, norm=self.bnorm2) axes.axis("off") fileName_img = self.SaveFileName+"_CAimg_" +str(i+1)+"."+ext fig.savefig(fileName_img, dpi=300, pad_inches = 0.0) if spec_svg: for i in range (self.numclusters): clusterspectrum = self.anlz.clusterspectra[i, ] fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() if i >= self.maxclcolors: clcolor = self.colors[self.maxclcolors-1] else: clcolor = self.colors[i] specplot = axes.plot(self.anlz.stack.ev,clusterspectrum, color = clcolor) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') fileName_spec = self.SaveFileName+"_CAspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) #Save all spectra in one plot fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() for i in range(1, self.numclusters+1): clusterspectrum = self.anlz.clusterspectra[i-1, ]/np.amax(self.anlz.clusterspectra[i-1, ]) if i >= self.maxclcolors: clcolor = self.colors[self.maxclcolors-1] else: clcolor = self.colors[i-1] specplot = axes.plot(self.anlz.stack.ev,clusterspectrum, color = clcolor) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') fileName_spec = self.SaveFileName+"_CAspectra"+"."+ext fig.savefig(fileName_spec) if indimgs_tif: for i in range (self.numclusters): indvclusterimage = np.zeros((self.anlz.stack.n_cols, self.anlz.stack.n_rows))+20. ind = np.where(self.anlz.cluster_indices == i) colorcl = min(i,9) indvclusterimage[ind] = colorcl fileName_img = self.SaveFileName+"_CAimg_" +str(i+1)+".tif" img1 = Image.fromarray(indvclusterimage) img1.save(fileName_img) if scatt_png: self.SaveScatt(png_pdf = 1) if scatt_pdf: self.SaveScatt(png_pdf = 2) if scatt_svg: self.SaveScatt(png_pdf = 3) except IOError as e: if e.strerror: err = e.strerror else: err = e print(e) QtGui.QMessageBox.warning(self, 'Error', 'Could not save file: %s' % err) #---------------------------------------------------------------------- #If png_pdg = 1 save png, if =2 save pdf, if =3 save svg def SaveScatt(self, png_pdf = 1): od_reduced = self.anlz.pcaimages[:,:,0:self.anlz.numsigpca] od_reduced = np.reshape(od_reduced, (self.stk.n_cols*self.stk.n_rows,self.anlz.numsigpca), order='F') clindices = self.anlz.cluster_indices clindices = np.reshape(clindices, (self.stk.n_cols*self.stk.n_rows), order='F') path, ext = os.path.splitext(self.SaveFileName) ext = ext[1:].lower() if png_pdf == 1: ext = 'png' elif png_pdf == 2: ext = 'pdf' elif png_pdf == 3: ext = 'svg' try: QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) suffix = "." + ext nplots = 0 for ip in range(self.anlz.numsigpca): for jp in range(self.anlz.numsigpca): if jp >= (ip+1): nplots = nplots+1 nplotsrows = np.ceil(nplots/2) plotsize = 2.5 from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas if nplots > 1 : fig = matplotlib.figure.Figure(figsize =(6.0,plotsize*nplotsrows)) fig.subplots_adjust(wspace = 0.4, hspace = 0.4) else: fig = matplotlib.figure.Figure(figsize =(3.0,2.5)) fig.subplots_adjust(bottom = 0.2, left = 0.2) canvas = FigureCanvas(fig) #axes = fig.gca() matplotlib.rcParams['font.size'] = 6 pplot = 1 for ip in range(self.anlz.numsigpca): for jp in range(self.anlz.numsigpca): if jp >= (ip+1): x_comp = od_reduced[:,ip] y_comp = od_reduced[:,jp] if nplots > 1 : axes = fig.add_subplot(int(nplotsrows),int(2), int(pplot)) else: axes = fig.add_subplot(1,1,1) pplot = pplot+1 for i in range(self.numclusters): thiscluster = np.where(clindices == i) axes.plot(x_comp[thiscluster], y_comp[thiscluster],'.',color=self.colors[i],alpha=0.5) axes.set_xlabel('Component '+str(ip+1)) axes.set_ylabel('Component '+str(jp+1)) fileName_sct = self.SaveFileName+"_CAscatterplot_" +str(i+1)+"."+ext matplotlib.rcParams['pdf.fonttype'] = 42 fig.savefig(fileName_sct) QtWidgets.QApplication.restoreOverrideCursor() except IOError as e: QtWidgets.QApplication.restoreOverrideCursor() if e.strerror: err = e.strerror else: err = e QtGui.QMessageBox.warning(self, 'Error', 'Could not save file: %s' % err) #---------------------------------------------------------------------- def OnShowScatterplots(self, evt): scattplwin = Scatterplots(self.window(), self.com, self.anlz) scattplwin.show() #---------------------------------------------------------------------- class Scatterplots(QtWidgets.QDialog): def __init__(self, parent, common, analz): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.resize(600, 470) self.setWindowTitle('Scatter plots') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) self.colors = self.parent.page3.colors self.com = common self.anlz = analz self.numsigpca = self.anlz.numsigpca self.ncols = self.anlz.stack.n_cols self.nrows = self.anlz.stack.n_rows self.od_reduced = self.anlz.pcaimages[:,:,0:self.numsigpca] self.od_reduced = np.reshape(self.od_reduced, (self.ncols*self.nrows,self.numsigpca), order='F') self.clindices = self.anlz.cluster_indices self.clindices = np.reshape(self.clindices, (self.ncols*self.nrows), order='F') self.numclusters = self.parent.page3.numclusters self.pca_y = 1 self.pca_x = 1 vbox = QtWidgets.QVBoxLayout() grid1 = QtWidgets.QGridLayout() frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.scattplfig = Figure((5.0, 4.8)) self.ScatterPPanel = FigureCanvas(self.scattplfig) self.ScatterPPanel.setParent(self) fbox.addWidget(self.ScatterPPanel) frame.setLayout(fbox) self.slidershow_y = QtWidgets.QSlider(QtCore.Qt.Vertical) self.slidershow_y.setFocusPolicy(QtCore.Qt.StrongFocus) self.slidershow_y.setRange(1, self.numsigpca) self.slidershow_y.setValue(self.pca_y) self.slidershow_y.valueChanged[int].connect(self.OnSliderScroll_y) grid1.addWidget(self.slidershow_y, 0, 0) grid1.addWidget(frame, 0, 1) self.slidershow_x = QtWidgets.QSlider(QtCore.Qt.Horizontal) self.slidershow_x.setFocusPolicy(QtCore.Qt.StrongFocus) self.slidershow_x.setRange(1, self.numsigpca) self.slidershow_x.setValue(self.pca_x) self.slidershow_x.valueChanged[int].connect(self.OnSliderScroll_x) #grid1.addWidget(wx.StaticText(panel, -1, '')) grid1.addWidget(self.slidershow_x, 1, 1) hbox = QtWidgets.QVBoxLayout() button_close = QtWidgets.QPushButton('Close') button_close.clicked.connect( self.close) hbox.addStretch(1) hbox.addWidget(button_close) vbox.addLayout(grid1) vbox.addLayout(hbox) self.setLayout(vbox) self.draw_scatterplot() #---------------------------------------------------------------------- def OnSliderScroll_x(self, value): self.pca_x = value self.draw_scatterplot() #---------------------------------------------------------------------- def OnSliderScroll_y(self, value): self.pca_y = value self.draw_scatterplot() #---------------------------------------------------------------------- def draw_scatterplot(self): x_comp = self.od_reduced[:,self.pca_x-1] y_comp = self.od_reduced[:,self.pca_y-1] fig = self.scattplfig fig.clf() axes = fig.gca() for i in range(self.numclusters): thiscluster = np.where(self.clindices == i) axes.plot(x_comp[thiscluster], y_comp[thiscluster],'.',color=self.colors[i],alpha=0.5) axes.set_xlabel('Component '+str(self.pca_x)) axes.set_ylabel('Component '+str(self.pca_y)) self.ScatterPPanel.draw() #---------------------------------------------------------------------- class SaveWinP3(QtWidgets.QDialog): def __init__(self, parent): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.resize(400, 300) self.setWindowTitle('Save') pal = QtGui.QPalette() self.setAutoFillBackground(True) #pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) self.com = self.parent.common path, ext = os.path.splitext(self.com.filename) ext = ext[1:].lower() suffix = "." + ext path, fn = os.path.split(self.com.filename) filename = fn[:-len(suffix)] self.path = self.com.path self.filename = filename vboxtop = QtWidgets.QVBoxLayout() vboxtop.setContentsMargins(20,20,20,20) gridtop = QtWidgets.QGridLayout() gridtop.setVerticalSpacing(20) fontb = QtGui.QFont() fontb.setBold(True) st1 = QtWidgets.QLabel(self) st1.setText('Save') st1.setFont(fontb) st2 = QtWidgets.QLabel(self) st2.setText('.pdf') st2.setFont(fontb) st3 = QtWidgets.QLabel(self) st3.setText('.png') st3.setFont(fontb) st4 = QtWidgets.QLabel(self) st4.setText('.svg') st4.setFont(fontb) st5 = QtWidgets.QLabel(self) st5.setText('.csv') st5.setFont(fontb) st10 = QtWidgets.QLabel(self) st10.setText('.tif (data)') st10.setFont(fontb) st6 = QtWidgets.QLabel(self) st6.setText('_spectrum') self.cb11 = QtWidgets.QCheckBox('', self) self.cb11.setChecked(True) self.cb12 = QtWidgets.QCheckBox('', self) self.cb13 = QtWidgets.QCheckBox('', self) self.cb14 = QtWidgets.QCheckBox('', self) st7 = QtWidgets.QLabel(self) st7.setText('_composite_images') self.cb21 = QtWidgets.QCheckBox('', self) self.cb21.setChecked(True) self.cb22 = QtWidgets.QCheckBox('', self) self.cb23 = QtWidgets.QCheckBox('', self) self.cb24 = QtWidgets.QCheckBox('', self) st8 = QtWidgets.QLabel(self) st8.setText('_individual_images') self.cb31 = QtWidgets.QCheckBox('', self) self.cb31.setChecked(True) self.cb32 = QtWidgets.QCheckBox('', self) self.cb33 = QtWidgets.QCheckBox('', self) self.cb34 = QtWidgets.QCheckBox('', self) st9 = QtWidgets.QLabel(self) st9.setText('_scatter_plots') self.cb41 = QtWidgets.QCheckBox('', self) self.cb41.setChecked(True) self.cb42 = QtWidgets.QCheckBox('', self) self.cb43 = QtWidgets.QCheckBox('', self) gridtop.addWidget(st1, 0, 0) gridtop.addWidget(st2, 0, 1) gridtop.addWidget(st3, 0, 2) gridtop.addWidget(st4, 0, 3) gridtop.addWidget(st5, 0, 4) gridtop.addWidget(st10, 0, 5) gridtop.addWidget(st6, 1, 0) gridtop.addWidget(self.cb11, 1, 1) gridtop.addWidget(self.cb12, 1, 2) gridtop.addWidget(self.cb13, 1, 3) gridtop.addWidget(self.cb14, 1, 4) gridtop.addWidget(st7, 2, 0) gridtop.addWidget(self.cb21, 2, 1) gridtop.addWidget(self.cb22, 2, 2) gridtop.addWidget(self.cb23, 2, 3) gridtop.addWidget(self.cb24, 2, 5) gridtop.addWidget(st8, 3, 0) gridtop.addWidget(self.cb31, 3, 1) gridtop.addWidget(self.cb32, 3, 2) gridtop.addWidget(self.cb33, 3, 3) gridtop.addWidget(self.cb34, 3, 5) gridtop.addWidget(st9, 4, 0) gridtop.addWidget(self.cb41, 4, 1) gridtop.addWidget(self.cb42, 4, 2) gridtop.addWidget(self.cb43, 4, 3) vboxtop.addStretch(1) vboxtop.addLayout(gridtop) vboxtop.addStretch(2) hbox0 = QtWidgets.QHBoxLayout() stf = QtWidgets.QLabel(self) stf.setText('Filename:\t') self.tc_savefn = QtWidgets.QLineEdit(self) self.tc_savefn.setText(self.filename) hbox0.addWidget(stf) hbox0.addWidget(self.tc_savefn) hbox1 = QtWidgets.QHBoxLayout() stp = QtWidgets.QLabel(self) stp.setText('Path: \t') self.tc_savepath = QtWidgets.QLineEdit(self) self.tc_savepath.setReadOnly(True) self.tc_savepath.setText(self.path) self.tc_savepath.setMinimumWidth(100) hbox1.addWidget(stp) hbox1.addWidget(self.tc_savepath) button_path = QtWidgets.QPushButton('Browse...') button_path.clicked.connect(self.OnBrowseDir) hbox1.addWidget(button_path) hbox2 = QtWidgets.QHBoxLayout() button_save = QtWidgets.QPushButton('Save') button_save.clicked.connect(self.OnSave) hbox2.addWidget(button_save) button_cancel = QtWidgets.QPushButton('Cancel') button_cancel.clicked.connect(self.close) hbox2.addWidget(button_cancel) vboxtop.addLayout(hbox0) vboxtop.addLayout(hbox1) vboxtop.addStretch(1) vboxtop.addLayout(hbox2) self.setLayout(vboxtop) #---------------------------------------------------------------------- def OnBrowseDir(self, evt): directory = QtWidgets.QFileDialog.getExistingDirectory(self, "Choose a directory", self.path, QtWidgets.QFileDialog.ShowDirsOnly|QtWidgets.QFileDialog.ReadOnly) if directory == '': return directory = str(directory) self.com.path = directory self.path = directory self.tc_savepath.setText(self.path) #---------------------------------------------------------------------- def OnSave(self, evt): self.filename = str(self.tc_savefn.text()) sp_pdf = self.cb11.isChecked() sp_png = self.cb12.isChecked() sp_svg = self.cb13.isChecked() sp_csv = self.cb14.isChecked() im_pdf = self.cb21.isChecked() im_png = self.cb22.isChecked() im_svg = self.cb23.isChecked() im_tif = self.cb24.isChecked() indim_pdf = self.cb31.isChecked() indim_png = self.cb32.isChecked() indim_svg = self.cb33.isChecked() indim_tif = self.cb34.isChecked() scatt_pdf = self.cb41.isChecked() scatt_png = self.cb42.isChecked() scatt_svg = self.cb43.isChecked() self.close() self.parent.page3.Save(self.filename, self.path, spec_png = sp_png, spec_pdf = sp_pdf, spec_svg = sp_svg, spec_csv = sp_csv, img_png = im_png, img_pdf = im_pdf, img_svg = im_svg, img_tif = im_tif, indimgs_png = indim_png, indimgs_pdf = indim_pdf, indimgs_svg = indim_svg, indimgs_tif = indim_tif, scatt_png = scatt_png, scatt_pdf = scatt_pdf, scatt_svg = scatt_svg) """ ------------------------------------------------------------------------------------------------""" class PagePCA(QtWidgets.QWidget): def __init__(self, common, data_struct, stack, anlz): super(PagePCA, self).__init__() self.initUI(common, data_struct, stack, anlz) #---------------------------------------------------------------------- def initUI(self, common, data_struct, stack, anlz): self.com = common self.data_struct = data_struct self.stk = stack self.anlz = anlz self.selpca = 1 self.numsigpca = 2 self.itheta = 0 #panel 1 vbox1 = QtWidgets.QVBoxLayout() self.tc_PCAcomp = QtWidgets.QLabel(self) self.tc_PCAcomp.setText("PCA component ") vbox1.addWidget(self.tc_PCAcomp) hbox11 = QtWidgets.QHBoxLayout() frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.pcaimgfig = Figure((PlotH*1.10, PlotH)) self.PCAImagePan = FigureCanvas(self.pcaimgfig) self.PCAImagePan.setParent(self) fbox.addWidget(self.PCAImagePan) frame.setLayout(fbox) hbox11.addWidget(frame) self.slidershow = QtWidgets.QScrollBar(QtCore.Qt.Vertical) self.slidershow.setFocusPolicy(QtCore.Qt.StrongFocus) self.slidershow.setRange(1,20) self.slidershow.setEnabled(False) self.slidershow.valueChanged[int].connect(self.OnPCAScroll) hbox11.addWidget(self.slidershow) vbox1.addLayout(hbox11) self.slider_theta = QtWidgets.QScrollBar(QtCore.Qt.Horizontal) self.slider_theta.setFocusPolicy(QtCore.Qt.StrongFocus) self.slider_theta.valueChanged[int].connect(self.OnScrollTheta) self.slider_theta.setRange(0, 100) self.slider_theta.setMinimumWidth(250) self.slider_theta.setVisible(False) self.tc_imagetheta = QtWidgets.QLabel(self) self.tc_imagetheta.setText("4D Data Angle: ") self.tc_imagetheta.setVisible(False) hbox51 = QtWidgets.QHBoxLayout() hbox51.addWidget(self.tc_imagetheta) hbox51.addStretch(1) hbox51.addWidget(self.slider_theta) hbox51.addStretch(1) vbox1.addLayout(hbox51) #panel 2 vbox2 = QtWidgets.QVBoxLayout() vbox2.setContentsMargins(20,20,20,20) sizer2 = QtWidgets.QGroupBox('PCA') vbox21 = QtWidgets.QVBoxLayout() self.button_calcpca = QtWidgets.QPushButton('Calculate PCA') self.button_calcpca.clicked.connect( self.OnCalcPCA) self.button_calcpca.setEnabled(False) vbox21.addWidget(self.button_calcpca) self.button_savepca = QtWidgets.QPushButton('Save PCA Results...') self.button_savepca.clicked.connect( self.OnSave) self.button_savepca.setEnabled(False) vbox21.addWidget(self.button_savepca) hbox21 = QtWidgets.QHBoxLayout() text1 = QtWidgets.QLabel(self) text1.setText('Number of significant components') self.npcaspin = QtWidgets.QSpinBox() self.npcaspin.setRange(1,20) self.npcaspin.valueChanged[int].connect(self.OnNPCAspin) hbox21.addWidget(text1) hbox21.addWidget(self.npcaspin) vbox21.addLayout(hbox21) hbox22 = QtWidgets.QHBoxLayout() text2 = QtWidgets.QLabel(self) text2.setText( 'Cumulative variance') self.vartc = QtWidgets.QLabel(self) self.vartc.setText('0%') hbox22.addWidget(text2) hbox22.addWidget(self.vartc) vbox21.addLayout(hbox22) self.button_movepcup = QtWidgets.QPushButton('Move PC up') self.button_movepcup.clicked.connect( self.OnMovePCUP) self.button_movepcup.setEnabled(False) vbox21.addWidget(self.button_movepcup) # line = QtWidgets.QFrame() # line.setFrameShape(QtWidgets.QFrame.HLine) # line.setFrameShadow(QtWidgets.QFrame.Sunken) # vbox21.addWidget(line) self.button_calcpca4D = QtWidgets.QPushButton('Calculate PCA for all angles') self.button_calcpca4D.clicked.connect( self.OnCalcPCA4D) self.button_calcpca4D.setEnabled(False) self.button_calcpca4D.setVisible(False) vbox21.addWidget(self.button_calcpca4D) sizer2.setLayout(vbox21) vbox2.addStretch(1) vbox2.addWidget(sizer2) vbox2.addStretch(3) #panel 3 vbox3 = QtWidgets.QVBoxLayout() self.text_pcaspec = QtWidgets.QLabel(self) self.text_pcaspec.setText("PCA spectrum ") vbox3.addWidget(self.text_pcaspec) frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.pcaspecfig = Figure((PlotW, PlotH)) self.PCASpecPan = FigureCanvas(self.pcaspecfig) self.PCASpecPan.setParent(self) fbox.addWidget(self.PCASpecPan) frame.setLayout(fbox) vbox3.addWidget(frame) #panel 4 vbox4 = QtWidgets.QVBoxLayout() text4 = QtWidgets.QLabel(self) text4.setText("PCA eigenvalues ") vbox4.addWidget(text4) frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.pcaevalsfig = Figure((PlotW, PlotH*0.75)) self.PCAEvalsPan = FigureCanvas(self.pcaevalsfig) self.PCAEvalsPan.setParent(self) self.PCAEvalsPan.mpl_connect('button_press_event', self.OnPointEvalsImage) fbox.addWidget(self.PCAEvalsPan) frame.setLayout(fbox) vbox4.addWidget(frame) vboxtop = QtWidgets.QVBoxLayout() gridsizertop = QtWidgets.QGridLayout() gridsizertop.addLayout(vbox2, 0, 0, QtCore .Qt. AlignLeft) gridsizertop.addLayout(vbox4, 0, 1) gridsizertop.addLayout(vbox1, 1, 0, QtCore .Qt. AlignLeft) gridsizertop.addLayout(vbox3, 1, 1) vboxtop.addStretch(1) vboxtop.addLayout(gridsizertop) vboxtop.addStretch(1) self.setLayout(vboxtop) #---------------------------------------------------------------------- def OnCalcPCA(self, event): QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.calcpca = False self.selpca = 1 self.numsigpca = 2 self.slidershow.setValue(self.selpca) scrollmax = np.min([self.stk.n_ev, 20]) self.slidershow.setMaximum(scrollmax) try: self.CalcPCA() self.calcpca = True self.loadPCAImage() self.loadPCASpectrum() self.showEvals() self.com.pca_calculated = 1 QtWidgets.QApplication.restoreOverrideCursor() except: self.com.pca_calculated = 0 QtWidgets.QApplication.restoreOverrideCursor() QtGui.QMessageBox.warning(self, 'Error', 'PCA not calculated.') self.window().refresh_widgets() #---------------------------------------------------------------------- def OnCalcPCA4D(self, event): QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.calcpca = False self.selpca = 1 self.numsigpca = 2 self.slidershow.setValue(self.selpca) scrollmax = np.min([self.stk.n_ev, 20]) self.slidershow.setMaximum(scrollmax) try: self.CalcPCA4D() self.calcpca = True self.loadPCAImage() self.loadPCASpectrum() self.showEvals() self.com.pca_calculated = 1 self.com.pca4D_calculated = 1 self.slider_theta.setVisible(True) self.tc_imagetheta.setVisible(True) self.slider_theta.setRange(0, self.stk.n_theta-1) self.slider_theta.setValue(self.itheta) self.tc_imagetheta.setText("4D Data Angle: "+str(self.stk.theta[self.itheta])) self.anlz.pcaimages = self.anlz.pcaimages4D[self.itheta] self.anlz.eigenvals = self.anlz.eigenvals4D[self.itheta] self.anlz.eigenvecs = self.anlz.eigenvecs4D[self.itheta] self.anlz.variance = self.anlz.variance4D[self.itheta] self.anlz.pcaimagebounds = self.anlz.pcaimagebounds4D[self.itheta] QtWidgets.QApplication.restoreOverrideCursor() except: self.com.pca_calculated = 0 QtWidgets.QApplication.restoreOverrideCursor() QtGui.QMessageBox.warning(self, 'Error', 'PCA not calculated.') self.window().refresh_widgets() #---------------------------------------------------------------------- def OnNPCAspin(self, value): num = value self.numsigpca = num if self.com.pca_calculated == 1: self.anlz.numsigpca = self.numsigpca # cumulative variance var = self.anlz.variance[:self.numsigpca].sum() self.vartc.setText(str(var.round(decimals=2)*100)+'%') #---------------------------------------------------------------------- def OnMovePCUP(self): thiscomponent = self.selpca-1 if thiscomponent == 0: return self.anlz.move_pc_up(thiscomponent) self.selpca = self.selpca-1 self.loadPCAImage() self.loadPCASpectrum() self.showEvals() self.slidershow.setValue(self.selpca) #---------------------------------------------------------------------- def CalcPCA(self): self.anlz.calculate_pca() #Scree plot criterion self.numsigpca = self.anlz.numsigpca self.npcaspin.setValue(self.numsigpca) # cumulative variance var = self.anlz.variance[:self.numsigpca].sum() self.vartc.setText(str(var.round(decimals=2)*100)+'%') #---------------------------------------------------------------------- def CalcPCA4D(self): if self.com.stack_4d == 1: self.anlz.calculate_pca_4D() else: return #Scree plot criterion self.numsigpca = self.anlz.numsigpca self.npcaspin.setValue(self.numsigpca) # cumulative variance var = self.anlz.variance[:self.numsigpca].sum() self.vartc.setText(str(var.round(decimals=2)*100)+'%') if self.com.spec_anl4D_calculated == 1: self.anlz.calculate_targetmaps_4D() #---------------------------------------------------------------------- def OnPCAScroll(self, value): self.sel = value self.selpca = self.sel if self.calcpca == True: self.loadPCAImage() self.loadPCASpectrum() #---------------------------------------------------------------------- def OnScrollTheta(self, value): if self.com.pca4D_calculated == 0: return self.itheta = value self.anlz.pcaimages = self.anlz.pcaimages4D[self.itheta] self.anlz.eigenvals = self.anlz.eigenvals4D[self.itheta] self.anlz.eigenvecs = self.anlz.eigenvecs4D[self.itheta] self.anlz.variance = self.anlz.variance4D[self.itheta] self.anlz.pcaimagebounds = self.anlz.pcaimagebounds4D[self.itheta] self.tc_imagetheta.setText("4D Data Angle: "+'{0:5.2f}\t'.format(self.stk.theta[self.itheta])) var = self.anlz.variance[:self.numsigpca].sum() self.vartc.setText(str(var.round(decimals=2)*100)+'%') self.loadPCAImage() self.loadPCASpectrum() self.showEvals() self.window().page0.itheta = self.itheta self.window().page0.slider_theta.setValue(self.itheta) self.window().page1.itheta = self.itheta self.window().page1.slider_theta.setValue(self.itheta) #---------------------------------------------------------------------- def OnPointEvalsImage(self, evt): x = evt.xdata y = evt.ydata if self.com.pca_calculated == 1: #Find the closest point to the point clicked on the plot self.selpca = int(np.round(x)) if self.selpca < 1: self.selpca = 1 self.loadPCAImage() self.loadPCASpectrum() #---------------------------------------------------------------------- def OnSave(self, event): savewin = SaveWinP2(self.window()) savewin.show() #---------------------------------------------------------------------- def Save(self, filename, path, spec_png = True, spec_pdf = False, spec_svg = False, spec_csv = False, img_png = True, img_pdf = False, img_svg = False, img_tif = False, evals_png = True, evals_pdf = False, evals_svg = False): self.SaveFileName = os.path.join(path,filename) try: matplotlib.rcParams['pdf.fonttype'] = 42 if evals_png: ext = 'png' suffix = "." + ext fileName_evals = self.SaveFileName+"_PCAevals."+ext fig = self.pcaevalsfig fig.savefig(fileName_evals) if evals_pdf: ext = 'pdf' suffix = "." + ext fileName_evals = self.SaveFileName+"_PCAevals."+ext fig = self.pcaevalsfig fig.savefig(fileName_evals) if evals_svg: ext = 'svg' suffix = "." + ext fileName_evals = self.SaveFileName+"_PCAevals."+ext fig = self.pcaevalsfig fig.savefig(fileName_evals) from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas matplotlib.rcParams['pdf.fonttype'] = 42 ext = 'png' suffix = "." + ext if img_png: for i in range (self.numsigpca): self.pcaimage = self.anlz.pcaimages[:,:,i] fig = matplotlib.figure.Figure(figsize =(PlotH*1.15, PlotH)) canvas = FigureCanvas(fig) axes = fig.gca() divider = make_axes_locatable(axes) ax_cb = divider.new_horizontal(size="3%", pad=0.03) fig.add_axes(ax_cb) axes.set_position([0.03,0.03,0.8,0.94]) bound = self.anlz.pcaimagebounds[i] im = axes.imshow(np.rot90(self.pcaimage), cmap=matplotlib.cm.get_cmap("seismic_r"), vmin = -bound, vmax = bound) cbar = axes.figure.colorbar(im, orientation='vertical',cax=ax_cb) axes.axis("off") fileName_img = self.SaveFileName+"_PCA_" +str(i+1)+"."+ext fig.savefig(fileName_img, pad_inches = 0.0) if spec_png: for i in range (self.numsigpca): pcaspectrum = self.anlz.eigenvecs[:,i] fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() specplot = axes.plot(self.stk.ev, pcaspectrum) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') fileName_spec = self.SaveFileName+"_PCAspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) if spec_csv: for i in range (self.numsigpca): pcaspectrum = self.anlz.eigenvecs[:,i] fileName_spec = self.SaveFileName+"_PCAspectrum_" +str(i+1)+".csv" cname = "PCAspectrum_" +str(i+1) self.stk.write_csv(fileName_spec, self.stk.ev, pcaspectrum, cname = cname) ext = 'pdf' suffix = "." + ext if img_pdf: for i in range (self.numsigpca): self.pcaimage = self.anlz.pcaimages[:,:,i] fig = matplotlib.figure.Figure(figsize =(PlotH*1.15, PlotH)) canvas = FigureCanvas(fig) axes = fig.gca() divider = make_axes_locatable(axes) ax_cb = divider.new_horizontal(size="3%", pad=0.03) fig.add_axes(ax_cb) axes.set_position([0.03,0.03,0.8,0.94]) bound = self.anlz.pcaimagebounds[i] im = axes.imshow(np.rot90(self.pcaimage), cmap=matplotlib.cm.get_cmap("seismic_r"), vmin = -bound, vmax = bound) cbar = axes.figure.colorbar(im, orientation='vertical',cax=ax_cb) axes.axis("off") fileName_img = self.SaveFileName+"_PCA_" +str(i+1)+"."+ext fig.savefig(fileName_img, pad_inches = 0.0) if spec_pdf: for i in range (self.numsigpca): self.pcaspectrum = self.anlz.eigenvecs[:,i] fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() specplot = axes.plot(self.stk.ev,self.pcaspectrum) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') fileName_spec = self.SaveFileName+"_PCAspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) ext = 'svg' suffix = "." + ext if img_svg: for i in range (self.numsigpca): self.pcaimage = self.anlz.pcaimages[:,:,i] fig = matplotlib.figure.Figure(figsize =(PlotH*1.15, PlotH)) canvas = FigureCanvas(fig) axes = fig.gca() divider = make_axes_locatable(axes) ax_cb = divider.new_horizontal(size="3%", pad=0.03) fig.add_axes(ax_cb) axes.set_position([0.03,0.03,0.8,0.94]) bound = self.anlz.pcaimagebounds[i] im = axes.imshow(np.rot90(self.pcaimage), cmap=matplotlib.cm.get_cmap("seismic_r"), vmin = -bound, vmax = bound) cbar = axes.figure.colorbar(im, orientation='vertical',cax=ax_cb) axes.axis("off") fileName_img = self.SaveFileName+"_PCA_" +str(i+1)+"."+ext fig.savefig(fileName_img, pad_inches = 0.0) if spec_svg: for i in range (self.numsigpca): self.pcaspectrum = self.anlz.eigenvecs[:,i] fig = matplotlib.figure.Figure(figsize =(PlotW, PlotH)) canvas = FigureCanvas(fig) fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() specplot = axes.plot(self.stk.ev,self.pcaspectrum) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') fileName_spec = self.SaveFileName+"_PCAspectrum_" +str(i+1)+"."+ext fig.savefig(fileName_spec) if img_tif: for i in range (self.numsigpca): self.pcaimage = self.anlz.pcaimages[:,:,i] fileName_img = self.SaveFileName+"_PCA_" +str(i+1)+".tif" img1 = Image.fromarray(self.pcaimage) img1.save(fileName_img) except IOError as e: if e.strerror: err = e.strerror else: err = e print(err) QtGui.QMessageBox.warning(self, 'Error', 'Could not save file: %s' % err) #---------------------------------------------------------------------- def showEvals(self): evalmax = np.min([self.stk.n_ev, 40]) self.pcaevals = self.anlz.eigenvals[0:evalmax] fig = self.pcaevalsfig fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() evalsplot = axes.semilogy(np.arange(1,evalmax+1), self.pcaevals,'b.') axes.set_xlabel('Principal Component') axes.set_ylabel('Log(Eigenvalue)') self.PCAEvalsPan.draw() #---------------------------------------------------------------------- def loadPCAImage(self): self.tc_PCAcomp.setText("PCA component " + str(self.selpca)) self.text_pcaspec.setText("PCA spectrum "+ str(self.selpca)) self.pcaimage = self.anlz.pcaimages[:,:,self.selpca-1] fig = self.pcaimgfig fig.clf() axes = fig.gca() divider = make_axes_locatable(axes) ax_cb = divider.new_horizontal(size="3%", pad=0.03) fig.add_axes(ax_cb) axes.set_position([0.03,0.03,0.8,0.94]) bound = self.anlz.pcaimagebounds[self.selpca-1] im = axes.imshow(np.rot90(self.pcaimage), cmap=matplotlib.cm.get_cmap("seismic_r"), vmin = -bound, vmax = bound) cbar = axes.figure.colorbar(im, orientation='vertical',cax=ax_cb) axes.axis("off") self.PCAImagePan.draw() #---------------------------------------------------------------------- def loadPCASpectrum(self): self.pcaspectrum = self.anlz.eigenvecs[:,self.selpca-1] fig = self.pcaspecfig fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() specplot = axes.plot(self.stk.ev,self.pcaspectrum) axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Optical Density') self.PCASpecPan.draw() #---------------------------------------------------------------------- class SaveWinP2(QtWidgets.QDialog): def __init__(self, parent): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.resize(400, 300) self.setWindowTitle('Save') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) self.com = self.parent.common path, ext = os.path.splitext(self.com.filename) ext = ext[1:].lower() suffix = "." + ext path, fn = os.path.split(self.com.filename) filename = fn[:-len(suffix)] self.path = self.com.path self.filename = filename vboxtop = QtWidgets.QVBoxLayout() vboxtop.setContentsMargins(20,20,20,20) gridtop = QtWidgets.QGridLayout() gridtop.setVerticalSpacing(20) fontb = QtGui.QFont() fontb.setBold(True) st1 = QtWidgets.QLabel(self) st1.setText('Save') st1.setFont(fontb) st2 = QtWidgets.QLabel(self) st2.setText('.pdf') st2.setFont(fontb) st3 = QtWidgets.QLabel(self) st3.setText('.png') st3.setFont(fontb) st4 = QtWidgets.QLabel(self) st4.setText('.svg') st4.setFont(fontb) st9 = QtWidgets.QLabel(self) st9.setText('.tif (data)') st9.setFont(fontb) st5 = QtWidgets.QLabel(self) st5.setText('.csv') st5.setFont(fontb) st6 = QtWidgets.QLabel(self) st6.setText('_spectrum') self.cb11 = QtWidgets.QCheckBox('', self) self.cb11.setChecked(True) self.cb12 = QtWidgets.QCheckBox('', self) self.cb13 = QtWidgets.QCheckBox('', self) self.cb14 = QtWidgets.QCheckBox('', self) st7 = QtWidgets.QLabel(self) st7.setText('_image') self.cb21 = QtWidgets.QCheckBox('', self) self.cb21.setChecked(True) self.cb22 = QtWidgets.QCheckBox('', self) self.cb23 = QtWidgets.QCheckBox('', self) self.cb24 = QtWidgets.QCheckBox('', self) st8 = QtWidgets.QLabel(self) st8.setText('_eigenvals') self.cb31 = QtWidgets.QCheckBox('', self) self.cb32 = QtWidgets.QCheckBox('', self) self.cb33 = QtWidgets.QCheckBox('', self) gridtop.addWidget(st1, 0, 0) gridtop.addWidget(st2, 0, 1) gridtop.addWidget(st3, 0, 2) gridtop.addWidget(st4, 0, 3) gridtop.addWidget(st9, 0, 5) gridtop.addWidget(st5, 0, 4) gridtop.addWidget(st6, 1, 0) gridtop.addWidget(self.cb11, 1, 1) gridtop.addWidget(self.cb12, 1, 2) gridtop.addWidget(self.cb13, 1, 3) gridtop.addWidget(self.cb14, 1, 4) gridtop.addWidget(st7, 2, 0) gridtop.addWidget(self.cb21, 2, 1) gridtop.addWidget(self.cb22, 2, 2) gridtop.addWidget(self.cb23, 2, 3) gridtop.addWidget(self.cb24, 2, 5) gridtop.addWidget(st8, 3, 0) gridtop.addWidget(self.cb31, 3, 1) gridtop.addWidget(self.cb32, 3, 2) gridtop.addWidget(self.cb33, 3, 3) vboxtop.addStretch(1) vboxtop.addLayout(gridtop) vboxtop.addStretch(2) hbox0 = QtWidgets.QHBoxLayout() stf = QtWidgets.QLabel(self) stf.setText('Filename:\t') self.tc_savefn = QtWidgets.QLineEdit(self) self.tc_savefn.setText(self.filename) hbox0.addWidget(stf) hbox0.addWidget(self.tc_savefn) hbox1 = QtWidgets.QHBoxLayout() stp = QtWidgets.QLabel(self) stp.setText('Path: \t') self.tc_savepath = QtWidgets.QLineEdit(self) self.tc_savepath.setReadOnly(True) self.tc_savepath.setText(self.path) self.tc_savepath.setMinimumWidth(100) hbox1.addWidget(stp) hbox1.addWidget(self.tc_savepath) button_path = QtWidgets.QPushButton('Browse...') button_path.clicked.connect(self.OnBrowseDir) hbox1.addWidget(button_path) hbox2 = QtWidgets.QHBoxLayout() button_save = QtWidgets.QPushButton('Save') button_save.clicked.connect(self.OnSave) hbox2.addWidget(button_save) button_cancel = QtWidgets.QPushButton('Cancel') button_cancel.clicked.connect(self.close) hbox2.addWidget(button_cancel) vboxtop.addLayout(hbox0) vboxtop.addLayout(hbox1) vboxtop.addStretch(1) vboxtop.addLayout(hbox2) self.setLayout(vboxtop) #---------------------------------------------------------------------- def OnBrowseDir(self, evt): directory = QtWidgets.QFileDialog.getExistingDirectory(self, "Choose a directory", self.path, QtWidgets.QFileDialog.ShowDirsOnly|QtWidgets.QFileDialog.ReadOnly) if directory == '': return directory = str(directory) self.com.path = directory self.path = directory self.tc_savepath.setText(self.path) #---------------------------------------------------------------------- def OnSave(self, evt): self.filename = str(self.tc_savefn.text()) sp_pdf = self.cb11.isChecked() sp_png = self.cb12.isChecked() sp_svg = self.cb13.isChecked() sp_csv = self.cb14.isChecked() im_pdf = self.cb21.isChecked() im_png = self.cb22.isChecked() im_svg = self.cb23.isChecked() im_tif = self.cb24.isChecked() ev_pdf = self.cb31.isChecked() ev_png = self.cb32.isChecked() ev_svg = self.cb33.isChecked() self.close() self.parent.page2.Save(self.filename, self.path, spec_png = sp_png, spec_pdf = sp_pdf, spec_svg = sp_svg, spec_csv = sp_csv, img_png = im_png, img_pdf = im_pdf, img_svg = im_svg, img_tif = im_tif, evals_png = ev_png, evals_pdf = ev_pdf, evals_svg = ev_svg) """ ------------------------------------------------------------------------------------------------""" class PageStack(QtWidgets.QWidget): def __init__(self, common, data_struct, stack): super(PageStack, self).__init__() self.initUI(common, data_struct, stack) #---------------------------------------------------------------------- def initUI(self, common, data_struct, stack): self.data_struct = data_struct self.stk = stack self.com = common self.filename = " " self.ix = 0 self.iy = 0 self.iev = 50 self.itheta = 0 self.showflux = True self.show_colorbar = 0 self.dispbrightness_min = 0 self.dispbrightness_max = 100 self.displaygamma = 10.0 self.defaultdisplay = 1.0 self.brightness_min = 0.0 self.brightness_max = 1.0 self.gamma = 1.0 self.colortable = "gray" self.addroi = 0 self.showROImask = 0 self.line = None self.ROIpix = None self.show_scale_bar = 1 self.white_scale_bar = 0 self.movie_playing = 0 #panel 1 sizer1 = QtWidgets.QGroupBox('Preprocess') vbox1 = QtWidgets.QVBoxLayout() vbox1.setSpacing(0) self.button_align = QtWidgets.QPushButton('Align stack...') self.button_align.clicked.connect(self.OnAlignImgsDialog) self.button_align.setEnabled(False) vbox1.addWidget(self.button_align) # self.button_align2 = QtWidgets.QPushButton('Align stack v2...') # self.button_align2.clicked.connect( self.OnAlignImgs2) # self.button_align2.setEnabled(False) # vbox1.addWidget(self.button_align2) self.button_multicrop = QtWidgets.QPushButton('Crop stack 3D/4D...') self.button_multicrop.clicked.connect( self.OnMultiCrop) self.button_multicrop.setEnabled(False) vbox1.addWidget(self.button_multicrop) # self.button_limitev = QtWidgets.QPushButton('Limit energy range...') # self.button_limitev.clicked.connect( self.OnLimitEv) # self.button_limitev.setEnabled(False) # vbox1.addWidget(self.button_limitev) # self.button_subregion = QtWidgets.QPushButton('Clip to subregion...') # self.button_subregion.clicked.connect(self.OnCliptoSubregion) # self.button_subregion.setEnabled(False) # vbox1.addWidget(self.button_subregion) self.button_artefacts = QtWidgets.QPushButton('Artefacts && Leveling') self.button_artefacts.clicked.connect( self.OnArtefacts) self.button_artefacts.setEnabled(False) vbox1.addWidget(self.button_artefacts) self.button_darksig = QtWidgets.QPushButton('Dark signal subtraction...') self.button_darksig.clicked.connect(self.OnDarkSignal) self.button_darksig.setEnabled(False) vbox1.addWidget(self.button_darksig) self.button_savestack = QtWidgets.QPushButton('Save processed stack') self.button_savestack.clicked.connect(self.OnSaveStack) self.button_savestack.setEnabled(False) vbox1.addWidget(self.button_savestack) #self.cb_normthick = QtWidgets.QCheckBox('Prenormalize thickness and make OD', self) #self.cb_normthick.setChecked(False) #vbox1.addWidget(self.cb_normthick) sizer1.setLayout(vbox1) #panel 1B sizer1b = QtWidgets.QGroupBox('Normalize') vbox1b = QtWidgets.QVBoxLayout() vbox1b.setSpacing(0) self.button_i0histogram = QtWidgets.QPushButton('Select I0...') self.button_i0histogram.clicked.connect( self.OnI0histogram) self.button_i0histogram.setEnabled(False) vbox1b.addWidget(self.button_i0histogram) self.button_i0ffile = QtWidgets.QPushButton('I0 from file...') self.button_i0ffile.clicked.connect(self.OnI0FFile) self.button_i0ffile.setEnabled(False) vbox1b.addWidget(self.button_i0ffile) self.button_showi0 = QtWidgets.QPushButton('Show I0...') self.button_showi0.clicked.connect( self.OnShowI0) self.button_showi0.setEnabled(False) vbox1b.addWidget(self.button_showi0) self.button_prenorm = QtWidgets.QPushButton('Use pre-normalized data') self.button_prenorm.clicked.connect(self.OnPreNormalizedData) self.button_prenorm.setEnabled(False) vbox1b.addWidget(self.button_prenorm) self.button_refimgs = QtWidgets.QPushButton('Load Reference Images') self.button_refimgs.clicked.connect(self.OnRefImgs) self.button_refimgs.setEnabled(False) vbox1b.addWidget(self.button_refimgs) self.button_reseti0 = QtWidgets.QPushButton('Reset I0') self.button_reseti0.clicked.connect(self.OnResetI0) self.button_reseti0.setEnabled(False) vbox1b.addWidget(self.button_reseti0) self.button_saveod = QtWidgets.QPushButton('Save OD data') self.button_saveod.clicked.connect(self.OnSaveOD) self.button_saveod.setEnabled(False) vbox1b.addWidget(self.button_saveod) sizer1b.setLayout(vbox1b) #panel 2 sizer2 = QtWidgets.QGroupBox('Display') vbox2 = QtWidgets.QVBoxLayout() vbox2.setSpacing(0) hbox20 = QtWidgets.QHBoxLayout() sizer21 = QtWidgets.QGroupBox('File') vbox21 = QtWidgets.QVBoxLayout() self.textctrl = QtWidgets.QLabel(self) vbox21.addWidget(self.textctrl) self.textctrl.setText('File name') sizer21.setLayout(vbox21) hbox20.addWidget(sizer21) hbox20.addSpacing(15) vbox22 = QtWidgets.QVBoxLayout() self.button_slideshow = QtWidgets.QPushButton('Play stack movie') self.button_slideshow.setMaximumSize (150 , 150) self.button_slideshow.clicked.connect( self.OnSlideshow) self.button_slideshow.setEnabled(False) vbox22.addWidget(self.button_slideshow) self.button_save = QtWidgets.QPushButton( 'Save images...') self.button_save.setMaximumSize (150 , 150) self.button_save.clicked.connect( self.OnSave) self.button_save.setEnabled(False) vbox22.addWidget(self.button_save) hbox20.addLayout(vbox22) vbox2.addLayout(hbox20) vbox2.addSpacing(5) hbox21 = QtWidgets.QHBoxLayout() sizer22 = QtWidgets.QGroupBox('Image') vbox23 = QtWidgets.QVBoxLayout() self.rb_flux = QtWidgets.QRadioButton( 'Flux', self) self.rb_od = QtWidgets.QRadioButton('Optical Density',self) self.rb_flux.setChecked(True) self.rb_flux.toggled.connect(self.OnRb_fluxod) vbox23.addWidget(self.rb_flux) vbox23.addWidget(self.rb_od) vbox23.addStretch (1) self.rb_flux.setEnabled(False) self.rb_od.setEnabled(False) self.add_scale_cb = QtWidgets.QCheckBox('Scalebar', self) self.add_scale_cb.setChecked(True) self.add_scale_cb.stateChanged.connect(self.OnShowScale) vbox23.addWidget(self.add_scale_cb) hbox211 = QtWidgets.QHBoxLayout() hbox211.addSpacing(20) self.cb_white_scale_bar = QtWidgets.QCheckBox('White', self) self.cb_white_scale_bar.setChecked(False) self.cb_white_scale_bar.stateChanged.connect(self.OnWhiteScale) hbox211.addWidget(self.cb_white_scale_bar) vbox23.addLayout(hbox211) self.add_colbar_cb = QtWidgets.QCheckBox('Colorbar', self) self.add_colbar_cb.stateChanged.connect(self.OnShowColBar) vbox23.addWidget(self.add_colbar_cb) sizer22.setLayout(vbox23) hbox21.addWidget(sizer22) hbox21.addSpacing(5) vbox2.addLayout(hbox21) sizer23 = QtWidgets.QGroupBox('Display settings') hbox23 = QtWidgets.QHBoxLayout() fgs21 = QtWidgets.QGridLayout() self.tc_min = QtWidgets.QLabel(self) self.tc_min.setText('Minimum: \t{0:5d}%'.format(int(100*self.brightness_min))) self.tc_max = QtWidgets.QLabel(self) self.tc_max.setText('Maximum:{0:5d}%'.format(int(100*self.brightness_max))) self.slider_brightness_min = QtWidgets.QSlider(QtCore.Qt.Horizontal, self) self.slider_brightness_min.setRange(0,49) self.slider_brightness_min.setValue(self.dispbrightness_min) self.slider_brightness_min.setFocusPolicy(QtCore.Qt.StrongFocus) self.slider_brightness_min.valueChanged[int].connect(self.OnScrollBrightnessMin) self.slider_brightness_max = QtWidgets.QSlider(QtCore.Qt.Horizontal, self) self.slider_brightness_max.setRange(50,120) self.slider_brightness_max.setValue(self.dispbrightness_max) self.slider_brightness_max.setFocusPolicy(QtCore.Qt.StrongFocus) self.slider_brightness_max.valueChanged[int].connect(self.OnScrollBrightnessMax) self.tc_gamma = QtWidgets.QLabel(self) self.tc_gamma.setText('Gamma: \t{0:5.2f}'.format(self.gamma)) self.slider_gamma = QtWidgets.QSlider(QtCore.Qt.Horizontal, self) self.slider_gamma.setRange(1,20) self.slider_gamma.setValue(int(self.displaygamma)) self.slider_gamma.setFocusPolicy(QtCore.Qt.StrongFocus) self.slider_gamma.valueChanged[int].connect(self.OnScrollGamma) fgs21.addWidget(self.tc_min, 0, 0) fgs21.addWidget(self.slider_brightness_min, 0, 1) fgs21.addWidget(self.tc_max, 1, 0) fgs21.addWidget(self.slider_brightness_max, 1, 1) fgs21.addWidget(self.tc_gamma, 2, 0) fgs21.addWidget(self.slider_gamma, 2, 1) hbox23.addLayout(fgs21) vbox24 = QtWidgets.QVBoxLayout() self.button_despike = QtWidgets.QPushButton('Despike') self.button_despike.clicked.connect( self.OnDespike) self.button_despike.setEnabled(False) vbox24.addWidget(self.button_despike) self.button_resetdisplay = QtWidgets.QPushButton( 'Reset') self.button_resetdisplay.clicked.connect( self.OnResetDisplaySettings) self.button_resetdisplay.setEnabled(False) vbox24.addWidget(self.button_resetdisplay) self.button_displaycolor = QtWidgets.QPushButton('Color Table... ') self.button_displaycolor.clicked.connect( self.OnSetColorTable) self.button_displaycolor.setEnabled(False) vbox24.addWidget(self.button_displaycolor) hbox23.addSpacing(20) hbox23.addLayout(vbox24) sizer23.setLayout(hbox23) hbox21.addWidget(sizer23) sizer2.setLayout(vbox2) #panel 3 sizer3 = QtWidgets.QGroupBox('Region of Interest') vbox3 = QtWidgets.QVBoxLayout() vbox3.setSpacing(0) self.button_addROI = QtWidgets.QPushButton('Select ROI (Lasso)') self.button_addROI.clicked.connect( self.OnAddROI) self.button_addROI.setEnabled(False) vbox3.addWidget(self.button_addROI) self.button_resetROI = QtWidgets.QPushButton('Reset ROI') self.button_resetROI.clicked.connect( self.OnResetROI) self.button_resetROI.setEnabled(False) vbox3.addWidget(self.button_resetROI) self.button_setROII0 = QtWidgets.QPushButton('Set ROI As I0') self.button_setROII0.clicked.connect( self.OnSetROII0) self.button_setROII0.setHidden(True) self.button_setROII0.setEnabled(False) vbox3.addWidget(self.button_setROII0) self.button_saveROIspectr = QtWidgets.QPushButton( 'Save ROI Spectrum...') self.button_saveROIspectr.clicked.connect( self.OnSaveROISpectrum) self.button_saveROIspectr.setEnabled(False) vbox3.addWidget(self.button_saveROIspectr) self.button_ROIdosecalc = QtWidgets.QPushButton('ROI Dose Calculation...') self.button_ROIdosecalc.clicked.connect( self.OnROI_DoseCalc) self.button_ROIdosecalc.setEnabled(False) vbox3.addWidget(self.button_ROIdosecalc) self.button_spectralROI = QtWidgets.QPushButton('Spectral ROI...') self.button_spectralROI.clicked.connect( self.OnSpectralROI) self.button_spectralROI.setEnabled(False) vbox3.addWidget(self.button_spectralROI) sizer3.setLayout(vbox3) #panel 4 #vbox4 = QtWidgets.QVBoxLayout() gridsizer4 = QtWidgets.QGridLayout() self.tc_imageeng = QtWidgets.QLabel(self) self.tc_imageeng.setText("Image at energy: ") gridsizer4.addWidget(self.tc_imageeng, 0, 0, QtCore .Qt. AlignLeft) frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.absimgfig = Figure((PlotH, PlotH)) self.AbsImagePanel = FigureCanvas(self.absimgfig) self.AbsImagePanel.setParent(self) self.cid1 = self.AbsImagePanel.mpl_connect('button_press_event', self.OnPointAbsimage) fbox.addWidget(self.AbsImagePanel) frame.setLayout(fbox) gridsizer4.addWidget(frame, 1, 0, QtCore .Qt. AlignLeft) self.slider_eng = QtWidgets.QScrollBar(QtCore.Qt.Vertical) self.slider_eng.setFocusPolicy(QtCore.Qt.StrongFocus) self.slider_eng.valueChanged[int].connect(self.OnScrollEng) self.slider_eng.setRange(0, 100) gridsizer4.addWidget(self.slider_eng, 1, 1, QtCore .Qt. AlignLeft) self.slider_theta = QtWidgets.QScrollBar(QtCore.Qt.Horizontal) self.slider_theta.setFocusPolicy(QtCore.Qt.StrongFocus) self.slider_theta.valueChanged[int].connect(self.OnScrollTheta) self.slider_theta.setRange(0, 100) self.slider_theta.setVisible(False) self.tc_imagetheta = QtWidgets.QLabel(self) self.tc_imagetheta.setText("4D Data Angle: ") self.tc_imagetheta.setVisible(False) hbox41 = QtWidgets.QHBoxLayout() hbox41.addWidget(self.tc_imagetheta) hbox41.addWidget(self.slider_theta) gridsizer4.addLayout(hbox41, 2, 0) #panel 5 self.tc_spec = QtWidgets.QLabel(self) self.tc_spec.setText("Spectrum ") gridsizer4.addWidget(self.tc_spec, 0, 2, QtCore.Qt.AlignLeft) frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.specfig = Figure((PlotW, PlotH)) self.SpectrumPanel = FigureCanvas(self.specfig) self.SpectrumPanel.setParent(self) self.SpectrumPanel.mpl_connect('button_press_event', self.OnPointSpectrum) fbox.addWidget(self.SpectrumPanel) frame.setLayout(fbox) gridsizer4.addWidget(frame, 1, 2, QtCore.Qt.AlignLeft) vboxtop = QtWidgets.QVBoxLayout() hboxtop = QtWidgets.QHBoxLayout() hboxtop.addWidget(sizer1) hboxtop.addWidget(sizer1b) hboxtop.addWidget(sizer2) hboxtop.addWidget(sizer3) hboxbott = QtWidgets.QHBoxLayout() hboxbott2 = QtWidgets.QHBoxLayout() hboxbott2.addLayout(gridsizer4) hboxbott.addLayout(hboxbott2) vboxtop.addStretch (1) vboxtop.addLayout(hboxtop) vboxtop.addStretch (1) vboxtop.addLayout(hboxbott) vboxtop.addStretch (1) vboxtop.setContentsMargins(20,20,20,20) self.setLayout(vboxtop) #---------------------------------------------------------------------- def OnI0FFile(self, event): try: wildcard = "I0 CSV files (*.csv);; I0 files (*.xas);;SDF I0 files (*.hdr)" filepath, _filter = QtWidgets.QFileDialog.getOpenFileName(self, 'Open file', '', wildcard) filepath = str(filepath) if filepath == '': return filepath_i0 = os.path.dirname(str(filepath)) self.filename = os.path.basename(str(filepath)) basename, extension = os.path.splitext(self.filename) if extension == '.hdr': QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) x=self.stk.n_cols y=self.stk.n_rows z=self.iev self.ix = int(x/2) self.iy = int(y/2) self.stk.read_sdf_i0(filepath) self.com.i0_loaded = 1 self.showSpectrum(self.ix, self.iy) self.loadImage() QtWidgets.QApplication.restoreOverrideCursor() elif extension == '.xas': QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) x=self.stk.n_cols y=self.stk.n_rows z=self.iev self.ix = int(x/2) self.iy = int(y/2) self.stk.read_stk_i0(filepath, extension) self.com.i0_loaded = 1 self.showSpectrum(self.ix, self.iy) self.loadImage() QtWidgets.QApplication.restoreOverrideCursor() elif extension == '.csv': QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) x=self.stk.n_cols y=self.stk.n_rows z=self.iev self.ix = int(x/2) self.iy = int(y/2) self.stk.read_stk_i0(filepath, extension) self.com.i0_loaded = 1 self.showSpectrum(self.ix, self.iy) self.loadImage() QtWidgets.QApplication.restoreOverrideCursor() except: QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.com.i0_loaded = 0 QtWidgets.QApplication.restoreOverrideCursor() QtGui.QMessageBox.warning(self,'Error',"I0 file not loaded.") import sys; print(sys.exc_info()) self.window().refresh_widgets() #---------------------------------------------------------------------- def OnI0histogram(self, event): #self.window().Hide() histogram = ShowHistogram(self, self.stk) histogram.show() #---------------------------------------------------------------------- def I0histogramCalculated(self): plot = PlotFrame(self, self.stk.evi0hist,self.stk.i0datahist) plot.show() self.com.i0_loaded = 1 if self.com.stack_4d == 1: self.stk.od3d = self.stk.od4D[:,:,:,self.itheta] self.stk.od = self.stk.od3d.copy() self.stk.od = np.reshape(self.stk.od, (self.stk.n_cols*self.stk.n_rows, self.stk.n_ev), order='F') self.showSpectrum(self.ix, self.iy) self.loadImage() self.window().refresh_widgets() #---------------------------------------------------------------------- def OnShowI0(self, event): plot = PlotFrame(self, self.stk.evi0,self.stk.i0data) plot.show() #---------------------------------------------------------------------- def OnArtefacts(self, event): # self.window().Hide() artefacts = ShowArtefacts(self.window(), self.com, self.stk) artefacts.show() # ---------------------------------------------------------------------- def OnPreNormalizedData(self, event): self.stk.UsePreNormalizedData() self.com.i0_loaded = 1 self.showSpectrum(self.ix, self.iy) self.loadImage() self.window().refresh_widgets() #---------------------------------------------------------------------- def OnRefImgs(self, event): #Load .xrm reference images try: #if True: wildcard = "Reference images (*.xrm)" filepaths, _filter = QtWidgets.QFileDialog.getOpenFileNames(self, 'Select reference files', '', wildcard) #Check reference files if len(filepaths) != self.stk.n_ev: QtGui.QMessageBox.warning(self,'Error',"Wrong number of Reference image files.") return QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.stk.read_xrm_ReferenceImages(filepaths) x=self.stk.n_cols y=self.stk.n_rows self.ix = int(x/2) self.iy = int(y/2) self.showSpectrum(self.ix, self.iy) self.loadImage() self.com.i0_loaded = 1 QtWidgets.QApplication.restoreOverrideCursor() except: QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.com.i0_loaded = 0 QtWidgets.QApplication.restoreOverrideCursor() QtWidgets.QMessageBox.warning(self,'Error',"Reference image file not loaded.") import sys; print(sys.exc_info()) self.window().refresh_widgets() #---------------------------------------------------------------------- def OnResetI0(self, event): self.stk.reset_i0() self.com.i0_loaded = 0 self.showflux = True self.rb_flux.setChecked(True) self.showSpectrum(self.ix, self.iy) self.loadImage() self.window().refresh_widgets() #---------------------------------------------------------------------- def OnSaveStack(self, event): self.window().SaveProcessedStack() #---------------------------------------------------------------------- def OnSave(self, event): savewin = SaveWinP1(self.window()) savewin.show() # ---------------------------------------------------------------------- def OnMultiCrop(self, evt): multicropwin = MultiCrop(self.window(), self.com, self.stk) multicropwin.show() #---------------------------------------------------------------------- # def OnLimitEv(self, evt): # # limitevwin = LimitEv(self.window(), self.com, self.stk) # limitevwin.show() # # #---------------------------------------------------------------------- # def OnCliptoSubregion(self, evt): # clipwin = CliptoSubregion(self.window(), self.com, self.stk) # clipwin.show() #---------------------------------------------------------------------- def Save(self, filename, path, spec_png = True, spec_pdf = False, spec_svg = False, sp_csv = False, img_png = True, img_pdf = False, img_svg = False, img_tif = False, img_all = False, img_all_tif = False): self.SaveFileName = os.path.join(path,filename) try: ext = 'png' suffix = "." + ext matplotlib.rcParams['pdf.fonttype'] = 42 if spec_png: fileName_spec = self.SaveFileName+"_spectrum."+ext fig = self.specfig fig.savefig(fileName_spec) if img_png: fileName_img = self.SaveFileName+"_" +str(self.stk.ev[self.iev])+"eV."+ext fig = self.absimgfig fig.savefig(fileName_img, pad_inches = 0.0) #Save all images in the stack if img_all: QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas matplotlib.rcParams['pdf.fonttype'] = 42 for i in range (self.stk.n_ev): if self.showflux: #Show flux image image = self.stk.absdata[:,:,i] else: #Show OD image image = self.stk.od3d[:,:,i] fig = matplotlib.figure.Figure(figsize =(float(self.stk.n_rows)/10, float(self.stk.n_cols)/10)) fig.clf() canvas = FigureCanvas(fig) fig.add_axes((0.0,0.0,1.0,1.0)) axes = fig.gca() im = axes.imshow(np.rot90(image), cmap=matplotlib.cm.get_cmap(self.colortable)) axes.axis("off") fileName_img = self.SaveFileName+"_imnum_" +str(i+1)+"."+ext fig.savefig(fileName_img, dpi=ImgDpi, pad_inches = 0.0) QtWidgets.QApplication.restoreOverrideCursor() #Save all images in the stack if img_all_tif: QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) for i in range (self.stk.n_ev): if self.showflux: #Show flux image image = self.stk.absdata[:,:,i] else: #Show OD image image = self.stk.od3d[:,:,i] fileName_img = self.SaveFileName+"_imnum_" +str(i+1)+".tif" img1 = Image.fromarray(image) img1.save(fileName_img) QtWidgets.QApplication.restoreOverrideCursor() ext = 'pdf' suffix = "." + ext if spec_pdf: fileName_spec = self.SaveFileName+"_spectrum."+ext fig = self.specfig fig.savefig(fileName_spec) if img_pdf: fileName_img = self.SaveFileName+"_" +str(self.stk.ev[self.iev])+"eV."+ext fig = self.absimgfig fig.savefig(fileName_img, pad_inches = 0.0) if sp_csv: fileName_spec = self.SaveFileName+"_spectrum.csv" self.stk.write_csv(fileName_spec, self.stk.ev, self.spectrum) ext = 'svg' suffix = "." + ext if spec_svg: fileName_spec = self.SaveFileName+"_spectrum."+ext fig = self.specfig fig.savefig(fileName_spec) if img_svg: fileName_img = self.SaveFileName+"_" +str(self.stk.ev[self.iev])+"eV."+ext fig = self.absimgfig fig.savefig(fileName_img, pad_inches = 0.0) if img_tif: fileName_img = self.SaveFileName+"_" +str(self.stk.ev[self.iev])+"eV.tif" if self.showflux: image = self.stk.absdata[:,:,self.iev] else: image = self.stk.od3d[:,:,self.iev] img1 = Image.fromarray(image) img1.save(fileName_img) except IOError as e: if e.strerror: err = e.strerror else: err = e QtWidgets.QMessageBox.warning(self,'Error','Could not save file: %s' % err) #---------------------------------------------------------------------- def OnSaveOD(self, event): """ Browse for tiff """ #try: if True: wildcard = "TIFF files (.tif)" filepath, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save OD', '', wildcard) filepath = str(filepath) if filepath == '': return directory = os.path.dirname(str(filepath)) QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.stk.write_tif(filepath, self.stk.od3d) QtWidgets.QApplication.restoreOverrideCursor() #except: # QtWidgets.QApplication.restoreOverrideCursor() # QtWidgets.QMessageBox.warning(self, 'Error', 'Could not save OD stack file.') return # ---------------------------------------------------------------------- def OnAlignImgsDialog(self, event): # self.window().Hide() imgregwin = ImageRegistrationDialog(self.window(),self.com) imgregwin.show() #---------------------------------------------------------------------- def OnAlignImgs(self, event): #self.window().Hide() imgregwin = ImageRegistrationManual(self.window(), self.com, self.stk) imgregwin.show() # ---------------------------------------------------------------------- def OnAlignImgs2(self, event): imgreg2 = ImageRegistrationFFT(self.window(), self.com, self.stk) imgreg2.show() #---------------------------------------------------------------------- def OnDarkSignal(self, event): dswin = DarkSignal(self.window(), self.com, self.stk) dswin.show() #---------------------------------------------------------------------- def OnSlideshow(self, event): if (self.com.stack_loaded == 1) and (self.addroi == 0): if (self.movie_playing == 1): self.movie_playing = 0 return self.button_slideshow.setText("Stop stack movie") old_iev = self.iev self.movie_playing = 1 for i in range(self.stk.n_ev): QtWidgets.qApp.processEvents() if self.movie_playing == 0: break self.iev = i self.loadImage() self.slider_eng.setValue(self.iev) self.showSpectrum(self.ix, self.iy) self.iev = old_iev self.loadImage() self.slider_eng.setValue(self.iev) self.showSpectrum(self.ix, self.iy) self.button_slideshow.setText("Play stack movie") self.movie_playing = 0 #---------------------------------------------------------------------- def OnScrollEng(self, value): self.iev = value if self.com.stack_loaded == 1: self.loadImage() self.showSpectrum(self.ix, self.iy) #---------------------------------------------------------------------- def OnScrollTheta(self, value): self.itheta = value self.stk.absdata = self.stk.stack4D[:,:,:,self.itheta] if self.com.i0_loaded: self.stk.od3d = self.stk.od4D[:,:,:,self.itheta] self.stk.od = self.stk.od3d.copy() n_pixels = self.stk.n_cols*self.stk.n_rows self.stk.od = np.reshape(self.stk.od, (n_pixels, self.stk.n_ev), order='F') self.tc_imagetheta.setText("4D Data Angle: "+str(self.stk.theta[self.itheta])) if self.com.stack_loaded == 1: self.loadImage() self.showSpectrum(self.ix, self.iy) self.window().page0.itheta = self.itheta self.window().page0.slider_theta.setValue(self.itheta) self.window().page2.itheta = self.itheta self.window().page2.slider_theta.setValue(self.itheta) #---------------------------------------------------------------------- def OnPointSpectrum(self, evt): x = evt.xdata y = evt.ydata if (self.com.stack_loaded == 1) and (self.addroi == 0): if x < self.stk.ev[0]: sel_ev = 0 elif x > self.stk.ev[self.stk.n_ev-1]: sel_ev = self.stk.n_ev-1 else: indx = np.abs(self.stk.ev - x).argmin() sel_ev = indx self.iev = sel_ev self.showSpectrum(self.ix, self.iy) self.loadImage() self.slider_eng.setValue(self.iev) #---------------------------------------------------------------------- def OnPointAbsimage(self, evt): x = evt.xdata y = evt.ydata if (x == None) or (y == None): return if (self.com.stack_loaded == 1) and (self.addroi == 0): self.ix = int(np.floor(x)) self.iy = self.stk.n_rows-1-int(np.floor(y)) if self.ix<0 : self.ix=0 if self.ix>self.stk.n_cols-1 : self.ix=self.stk.n_cols-1 if self.iy<0 : self.iy=0 if self.iy>self.stk.n_rows-1 : self.iy=self.stk.n_rows-1 self.showSpectrum(self.ix, self.iy) self.loadImage() #---------------------------------------------------------------------- def OnRb_fluxod(self, enabled): state = enabled if state: self.showflux = True else: self.showflux = False self.ResetDisplaySettings() self.loadImage() #---------------------------------------------------------------------- def OnShowScale(self,state): if state == QtCore.Qt.Checked: self.show_scale_bar = 1 else: self.show_scale_bar = 0 if self.com.stack_loaded == 1: self.loadImage() #---------------------------------------------------------------------- def OnWhiteScale(self,state): if state == QtCore.Qt.Checked: self.white_scale_bar = 1 else: self.white_scale_bar = 0 self.com.white_scale_bar = self.white_scale_bar if self.com.stack_loaded == 1: self.loadImage() #self.window().page0.ShowImage() #---------------------------------------------------------------------- def OnShowColBar(self, state): if state == QtCore.Qt.Checked: self.show_colorbar = 1 else: self.show_colorbar = 0 if self.com.stack_loaded == 1: self.loadImage() #---------------------------------------------------------------------- def OnResetDisplaySettings(self, event): self.ResetDisplaySettings() self.loadImage() #---------------------------------------------------------------------- def OnScrollBrightnessMin(self, value): self.dispbrightness_min = value self.brightness_min = float(self.dispbrightness_min)/100.0 self.defaultdisplay = 0.0 self.tc_min.setText('Minimum: \t{0:5d}%'.format(int(100*self.brightness_min))) if self.com.stack_loaded == 1: self.loadImage() #---------------------------------------------------------------------- def OnScrollBrightnessMax(self, value): self.dispbrightness_max = value self.brightness_max = float(self.dispbrightness_max)/100.0 self.defaultdisplay = 0.0 self.tc_max.setText('Maximum:{0:5d}%'.format(int(100*self.brightness_max))) if self.com.stack_loaded == 1: self.loadImage() #---------------------------------------------------------------------- def OnScrollGamma(self, value): self.displaygamma = value self.gamma = float(self.displaygamma)/10.0 self.defaultdisplay = 0.0 self.tc_gamma.setText('Gamma: \t{0:5.2f}'.format(self.gamma)) if self.com.stack_loaded == 1: self.loadImage() #---------------------------------------------------------------------- def OnDespike(self, evt): image = self.stk.absdata[:,:,self.iev] image = self.stk.despike(image) self.stk.data_struct.exchange.data = self.stk.absdata if self.com.i0_loaded: self.stk.calculate_optical_density() self.loadImage() #---------------------------------------------------------------------- def OnSetColorTable(self, event): colorwin = ColorTableFrame(self.window()) colorwin.show() #---------------------------------------------------------------------- def loadImage(self): if self.defaultdisplay == 1.0: #use a pointer to the data not a copy if self.showflux: #Show flux image image = self.stk.absdata[:,:,self.iev]#.copy() else: #Show OD image image = self.stk.od3d[:,:,self.iev]#.copy() else: #Adjustment to the data display setting has been made so make a copy if self.showflux: image = self.stk.absdata[:,:,self.iev].copy() else: image = self.stk.od3d[:,:,self.iev].copy() fig = self.absimgfig fig.clf() if self.show_colorbar == 0: fig.add_axes(((0.0,0.0,1.0,1.0))) axes = fig.gca() else: axes = fig.gca() divider = make_axes_locatable(axes) axcb = divider.new_horizontal(size="3%", pad=0.03) fig.add_axes(axcb) axes.set_position([0.03,0.03,0.8,0.94]) self.axes = axes fig.patch.set_alpha(1.0) if (self.line != None) and (self.addroi == 1): axes.add_line(self.line) if self.defaultdisplay == 1.0: im = axes.imshow(np.rot90(image), cmap=matplotlib.cm.get_cmap(self.colortable)) else: imgmax = np.amax(image) imgmin = np.amin(image) if (self.gamma != 1.0) or (imgmin < 0.0): image = (image-imgmin)/(imgmax-imgmin) imgmax = 1.0 imgmin = 0.0 if (self.gamma != 1.0): image = np.power(image, self.gamma) vmin=(imgmin+imgmax*self.brightness_min) vmax=imgmax*self.brightness_max if vmin > vmax : vmax = vmin + 0.1 im = axes.imshow(np.rot90( image), cmap=matplotlib.cm.get_cmap(self.colortable), vmin=vmin,vmax=vmax) if (self.showROImask == 1) and (self.addroi == 1): im_red = axes.imshow(np.rot90( self.ROIpix_masked), cmap=matplotlib.cm.get_cmap("autumn")) axes.axis("off") if self.show_colorbar == 1: cbar = axes.figure.colorbar(im, orientation='vertical',cax=axcb) if self.show_scale_bar == 1: if self.white_scale_bar == 1: sbcolor = 'white' else: sbcolor = 'black' startx = int(self.stk.n_cols*0.05) starty = self.stk.n_rows-int(self.stk.n_rows*0.05)-self.stk.scale_bar_pixels_y um_string = ' $\mathrm{\mu m}$' microns = '$'+self.stk.scale_bar_string+' $'+um_string axes.text(self.stk.scale_bar_pixels_x+startx+1,starty+1, microns, horizontalalignment='left', verticalalignment='center', color = sbcolor, fontsize=14) #Matplotlib has flipped scales so I'm using rows instead of cols! p = matplotlib.patches.Rectangle((startx,starty), self.stk.scale_bar_pixels_x, self.stk.scale_bar_pixels_y, color = sbcolor, fill = True) axes.add_patch(p) self.AbsImagePanel.draw() self.tc_imageeng.setText("Image at energy: {0:5.2f} eV".format(float(self.stk.ev[self.iev]))) #---------------------------------------------------------------------- def showSpectrum(self, xpos, ypos): fig = self.specfig fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() axes.set_xlabel('Photon Energy [eV]') if self.com.i0_loaded == 1: self.spectrum = self.stk.od3d[int(xpos),int(ypos), :] axes.set_ylabel('Optical Density') else: self.spectrum = self.stk.absdata[int(xpos),int(ypos), :] axes.set_ylabel('Flux') if self.addroi == 0: self.tc_spec.setText('Spectrum at pixel [{0}, {1}] or position [{2:5.2f}, {3:5.2f}]'.format(str(xpos), str(ypos), float(self.stk.x_dist[int(xpos)]), float(self.stk.y_dist[int(ypos)]))) specplot = axes.plot(self.stk.ev,self.spectrum) else: self.tc_spec.setText("Average ROI Spectrum: ") self.CalcROISpectrum() specplot = axes.plot(self.stk.ev,self.ROIspectrum) axes.axvline(x=self.stk.ev[self.iev], color = 'g', alpha=0.5) self.SpectrumPanel.draw() #---------------------------------------------------------------------- def ResetDisplaySettings(self): self.defaultdisplay = 1.0 self.dispbrightness_min = 0 self.dispbrightness_max = 100 self.displaygamma = 10.0 self.brightness_min = 0.0 self.brightness_max = 1.0 self.gamma = 1.0 self.slider_brightness_max.setValue(self.dispbrightness_max) self.slider_brightness_min.setValue(self.dispbrightness_min) self.slider_gamma.setValue(int(self.displaygamma)) self.tc_min.setText('Minimum: \t{0:5d}%'.format(int(100*self.brightness_min))) self.tc_max.setText('Maximum:{0:5d}%'.format(int(100*self.brightness_max))) self.tc_gamma.setText('Gamma: \t{0:5.2f}'.format(self.gamma)) #---------------------------------------------------------------------- def OnAddROI(self, evt): self.addroi = 1 self.AbsImagePanel.mpl_disconnect(self.cid1) lineprops = dict(color='red', linestyle='-', linewidth = 1, alpha=1) self.lasso = LassoSelector(self.axes, onselect=self.OnSelectLasso, useblit=False, props=lineprops) fig = self.specfig fig.clf() self.button_resetROI.setEnabled(True) self.button_ROIdosecalc.setEnabled(False) self.window().refresh_widgets() return #---------------------------------------------------------------------- def CalcROISpectrum(self): self.ROIspectrum = np.zeros((self.stk.n_ev)) indices = np.where(self.ROIarray == 255) numroipix = self.ROIarray[indices].shape[0] if self.com.i0_loaded == 1: for ie in range(self.stk.n_ev): thiseng_od = self.stk.od3d[:,:,ie] self.ROIspectrum[ie] = np.sum(thiseng_od[indices])/numroipix else: for ie in range(self.stk.n_ev): thiseng_abs = self.stk.absdata[:,:,ie] self.ROIspectrum[ie] = np.sum(thiseng_abs[indices])/numroipix #---------------------------------------------------------------------- def OnSelectLasso(self,verts): path = matplotlib.path.Path(verts) #find pixels inside the polygon ROIpix = np.zeros((self.stk.n_cols,self.stk.n_rows)) for i in range(self.stk.n_cols): for j in range(self.stk.n_rows): Pinside = path.contains_point((i,j)) if Pinside == True: ROIpix[i, self.stk.n_rows-1-j] = 255 ROIpix = np.ma.array(ROIpix) self.ROIpix_masked = np.ma.masked_values(ROIpix, 0) self.ROIarray = ROIpix[:] self.showROImask = 1 self.line = None self.previous_point = [] self.start_point = [] self.end_point = [] self.roixdata = [] self.roiydata = [] self.button_saveROIspectr.setEnabled(True) self.button_setROII0.setEnabled(True) self.button_ROIdosecalc.setEnabled(True) self.window().refresh_widgets() self.loadImage() self.showSpectrum(0, 0) QtWidgets.QApplication.restoreOverrideCursor() #---------------------------------------------------------------------- def OnResetROI(self, evt): self.ResetROI() #---------------------------------------------------------------------- def ResetROI(self): self.addroi = 0 self.showROImask = 0 self.ROIpix = None #self.button_acceptROI.setEnabled(False) self.button_setROII0.setEnabled(False) self.button_resetROI.setEnabled(False) self.button_saveROIspectr.setEnabled(False) self.button_ROIdosecalc.setEnabled(False) self.cid1 = self.AbsImagePanel.mpl_connect('button_press_event', self.OnPointAbsimage) self.AbsImagePanel.draw() self.window().refresh_widgets() self.loadImage() self.showSpectrum(self.ix, self.iy) #---------------------------------------------------------------------- def CalcROI_I0Spectrum(self): self.ROIspectrum = np.zeros((self.stk.n_ev)) indices = np.where(self.ROIarray == 255) numroipix = self.ROIarray[indices].shape[0] for ie in range(self.stk.n_ev): thiseng_abs = self.stk.absdata[:,:,ie] self.ROIspectrum[ie] = np.sum(thiseng_abs[indices])/numroipix #---------------------------------------------------------------------- def OnSetROII0(self, evt): self.CalcROI_I0Spectrum() self.stk.set_i0(self.ROIspectrum, self.stk.ev) plot = PlotFrame(self, self.stk.evi0,self.stk.i0data) plot.show() x=self.stk.n_cols y=self.stk.n_rows self.ix = int(x/2) self.iy = int(y/2) self.com.i0_loaded = 1 self.addroi = 0 self.showROImask = 0 self.ROIpix = None self.showSpectrum(self.ix, self.iy) self.loadImage() self.button_acceptROI.setEnabled(False) self.button_setROII0.setEnabled(False) self.button_resetROI.setEnabled(False) self.button_saveROIspectr.setEnabled(False) self.button_ROIdosecalc.setEnabled(False) self.window().refresh_widgets() #---------------------------------------------------------------------- def OnROI_DoseCalc(self, event): self.CalcROISpectrum() dosewin = DoseCalculation(self, self.stk, self.ROIspectrum) dosewin.show() #---------------------------------------------------------------------- def OnSaveROISpectrum(self, event): wildcard = "CSV files (*.csv)" fileName, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save ROI Spectrum (.csv)', '', wildcard) fileName = str(fileName) if fileName == '': return path, ext = os.path.splitext(fileName) ext = ext[1:].lower() try: if (self.com.i0_loaded == 1): self.stk.write_csv(fileName, self.stk.ev, self.ROIspectrum, cname='from ROI') else: self.CalcROI_I0Spectrum() self.stk.write_csv(fileName, self.stk.ev, self.ROIspectrum, cname='from ROI') except IOError as e: if e.strerror: err = e.strerror else: err = e QtGui.QMessageBox.warning(self,'Error','Could not save file: %s' % err) #---------------------------------------------------------------------- def OnSpectralROI(self, evt): specroiwin = SpectralROI(self, self.com, self.stk) specroiwin.show() #---------------------------------------------------------------------- class SaveWinP1(QtWidgets.QDialog): def __init__(self, parent): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.resize(400, 300) self.setWindowTitle('Save') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) self.com = self.parent.common path, ext = os.path.splitext(self.com.filename) ext = ext[1:].lower() suffix = "." + ext path, fn = os.path.split(self.com.filename) filename = fn[:-len(suffix)] self.path = self.com.path self.filename = filename vboxtop = QtWidgets.QVBoxLayout() vboxtop.setContentsMargins(20,20,20,20) gridtop = QtWidgets.QGridLayout() gridtop.setVerticalSpacing(20) fontb = QtGui.QFont() fontb.setBold(True) st1 = QtWidgets.QLabel(self) st1.setText('Save') st1.setFont(fontb) st2 = QtWidgets.QLabel(self) st2.setText('.pdf') st2.setFont(fontb) st3 = QtWidgets.QLabel(self) st3.setText('.png') st3.setFont(fontb) st4 = QtWidgets.QLabel(self) st4.setText('.svg') st4.setFont(fontb) st9 = QtWidgets.QLabel(self) st9.setText('.tif (data)') st9.setFont(fontb) st5 = QtWidgets.QLabel(self) st5.setText('.csv') st5.setFont(fontb) st6 = QtWidgets.QLabel(self) st6.setText('_spectrum') self.cb11 = QtWidgets.QCheckBox('', self) self.cb11.setChecked(True) self.cb12 = QtWidgets.QCheckBox('', self) self.cb13 = QtWidgets.QCheckBox('', self) self.cb14 = QtWidgets.QCheckBox('', self) st7 = QtWidgets.QLabel(self) st7.setText('_image') self.cb21 = QtWidgets.QCheckBox('', self) self.cb21.setChecked(True) self.cb22 = QtWidgets.QCheckBox('', self) self.cb23 = QtWidgets.QCheckBox('', self) self.cb24 = QtWidgets.QCheckBox('', self) st8 = QtWidgets.QLabel(self) st8.setText('all images') self.cb32 = QtWidgets.QCheckBox('', self) self.cb34 = QtWidgets.QCheckBox('', self) gridtop.addWidget(st1, 0, 0) gridtop.addWidget(st2, 0, 1) gridtop.addWidget(st3, 0, 2) gridtop.addWidget(st4, 0, 3) gridtop.addWidget(st9, 0, 5) gridtop.addWidget(st5, 0, 4) gridtop.addWidget(st6, 1, 0) gridtop.addWidget(self.cb11, 1, 1) gridtop.addWidget(self.cb12, 1, 2) gridtop.addWidget(self.cb13, 1, 3) gridtop.addWidget(self.cb14, 1, 4) gridtop.addWidget(st7, 2, 0) gridtop.addWidget(self.cb21, 2, 1) gridtop.addWidget(self.cb22, 2, 2) gridtop.addWidget(self.cb23, 2, 3) gridtop.addWidget(self.cb24, 2, 5) gridtop.addWidget(st8, 3, 0) gridtop.addWidget(self.cb32, 3, 2) gridtop.addWidget(self.cb34, 3, 5) vboxtop.addStretch(1) vboxtop.addLayout(gridtop) vboxtop.addStretch(2) hbox0 = QtWidgets.QHBoxLayout() stf = QtWidgets.QLabel(self) stf.setText('Filename:\t') self.tc_savefn = QtWidgets.QLineEdit(self) self.tc_savefn.setText(self.filename) hbox0.addWidget(stf) hbox0.addWidget(self.tc_savefn) hbox1 = QtWidgets.QHBoxLayout() stp = QtWidgets.QLabel(self) stp.setText('Path: \t') self.tc_savepath = QtWidgets.QLineEdit(self) self.tc_savepath.setReadOnly(True) self.tc_savepath.setText(self.path) self.tc_savepath.setMinimumWidth(100) hbox1.addWidget(stp) hbox1.addWidget(self.tc_savepath) button_path = QtWidgets.QPushButton('Browse...') button_path.clicked.connect(self.OnBrowseDir) hbox1.addWidget(button_path) hbox2 = QtWidgets.QHBoxLayout() button_save = QtWidgets.QPushButton('Save') button_save.clicked.connect(self.OnSave) hbox2.addWidget(button_save) button_cancel = QtWidgets.QPushButton('Cancel') button_cancel.clicked.connect(self.close) hbox2.addWidget(button_cancel) vboxtop.addLayout(hbox0) vboxtop.addLayout(hbox1) vboxtop.addStretch(1) vboxtop.addLayout(hbox2) self.setLayout(vboxtop) #---------------------------------------------------------------------- def OnBrowseDir(self, evt): directory = QtWidgets.QFileDialog.getExistingDirectory(self, "Choose a directory", self.path, QtWidgets.QFileDialog.ShowDirsOnly|QtWidgets.QFileDialog.ReadOnly) if directory == '': return directory = str(directory) self.com.path = directory self.path = directory self.tc_savepath.setText(self.path) #---------------------------------------------------------------------- def OnSave(self, evt): self.filename = str(self.tc_savefn.text()) sp_pdf = self.cb11.isChecked() sp_png = self.cb12.isChecked() sp_svg = self.cb13.isChecked() sp_csv = self.cb14.isChecked() im_pdf = self.cb21.isChecked() im_png = self.cb22.isChecked() im_svg = self.cb23.isChecked() im_tif = self.cb24.isChecked() im_all = self.cb32.isChecked() im_all_tif = self.cb34.isChecked() self.close() self.parent.page1.Save(self.filename, self.path, spec_png = sp_png, spec_pdf = sp_pdf, spec_svg = sp_svg, sp_csv = sp_csv, img_png = im_png, img_pdf = im_pdf, img_svg = im_svg, img_tif = im_tif, img_all = im_all, img_all_tif = im_all_tif) #---------------------------------------------------------------------- class ShowHistogram(QtWidgets.QDialog, QtWidgets.QGraphicsScene): def __init__(self, parent, stack): QtWidgets.QWidget.__init__(self, parent) dir_path = os.path.dirname(os.path.realpath(__file__)) uic.loadUi(os.path.join(dir_path,'showhistogram.ui'), self) self.HistoWidget.setBackground("w") self.I0Widget.setBackground("w") self.button_ok.clicked.connect(self.OnAccept) self.button_cancel.clicked.connect(self.close) self.parent = parent self.setWindowTitle('Select I0') self.stack = stack self.stack.calc_histogram() self.vb = self.I0Widget.addViewBox() self.vb.setAspectLocked() self.AbsImage = pg.ImageItem(border="k",parent= self) self.lassopoints= [] self.vb.setMouseEnabled(x=False, y=False) self.vb.addItem(self.AbsImage, ignoreBounds=False) self.MaskImage = pg.ImageItem(border="k") self.vb.addItem(self.MaskImage) self.redpix = np.zeros([self.stack.n_cols, self.stack.n_rows, 4], dtype=np.uint8) px = int(self.stack.n_cols*self.stack.n_rows * 0.98) # 98% of total pixels fluxmax_limit = np.mean(np.partition(np.ravel(self.stack.averageflux), px)[:px]) # average brightness of the 2% of pixels with highest flux self.histmin = fluxmax_limit self.histmax = np.max(self.stack.averageflux)+1 self.draw_histogram() self.draw_image(self.histmin,self.histmax) self.I0box = pg.PolyLineROI([[1, 1], [0, 1], [0, 0], [1, 0]], pen=(5, 8), closed=True) self.vb.addItem(self.I0box, ignoreBounds=False) self.I0box.clearPoints() self.clickdetector = QtCore.QTimer() self.clickdetector.timeout.connect(self.OnRelease) self.radioLassoROI.toggled.connect(self.SetupROI) self.SetupROI() def SetupROI(self): self.I0instructions.setText("Select I0 by dragging the histogram lines or by drawing a ROI.") self.lassopoints = [] self.I0box.setZValue(-10) self.MaskImage.setZValue(-10) if self.radioLassoROI.isChecked(): self.proxy = pg.SignalProxy(self.vb.scene().sigMouseMoved, rateLimit=15, slot=self.OnMouseHover) # rate limit to avoid too many handles self.I0box.handlePen = QtGui.QPen(QtGui.QColor(255, 0, 128, 0)) ##Polygon is currently broken # elif self.radioPolyROI.isChecked(): # self.proxy = pg.SignalProxy(self.vb.scene().sigMouseMoved, rateLimit=30, slot=self.OnMouseHover) # self.I0box.handlePen = QtGui.QPen(QtGui.QColor(255, 0, 128, 255)) try: self.I0box.sigRegionChangeFinished.disconnect() self.I0box.clearPoints() self.proxy.block = False except: pass #---------------------------------------------------------------------- def draw_histogram(self): histogram_data = np.reshape(self.stack.histogram, (self.stack.n_cols*self.stack.n_rows), order='F') histogram_data = histogram_data[~np.isnan(histogram_data)] #remove non-finite values y, x = np.histogram(histogram_data, bins=100) self.region = pg.LinearRegionItem(brush=[255,0,0,45],bounds=[np.min(x),np.max(x)]) self.region.setZValue(10) plot = self.HistoWidget plot.addItem(self.region, ignoreBounds=False) plot.setMouseEnabled(x=False, y=False) plot.showGrid(y=True) plot.showAxis("top", show=True) plot.showAxis("right", show=True) by = plot.getAxis("right") bx = plot.getAxis("top") by.setStyle(showValues=False,tickLength=0) bx.setStyle(showValues=False,tickLength=0) ay = plot.getAxis("left") ax = plot.getAxis("bottom") ax.setLabel(text="Average Flux") ## Little hack to display vertical axis as log. plot.getViewBox().setLimits(yMin=0,yMax=np.max(y)) ay.setLabel(text="log10 (Number of pixels)") y[y < 1] = 1 y = np.log10(y) plot.setLogMode(x=False, y=False) ## Log mode is not working correctly at the moment. ## plot.plot(x, y, stepMode=True, fillLevel=0, brush=(0, 0, 255, 150)) def update(region): self.region.setZValue(10) minX, maxX = region self.draw_image(minX, maxX) self.SetupROI() self.MaskImage.setZValue(10) self.region.setRegion((self.histmin,self.histmax)) self.region.sigRegionChanged.connect(lambda: update(self.region.getRegion())) #---------------------------------------------------------------------- def OnRelease(self): if not self.vb.scene().clickEvents: self.proxy.block = True self.DrawROI() #self.I0instructions.setText("Drag the polygon or the handles. Add handles by clicking on a line segment.") if self.radioLassoROI.isChecked(): self.lassopoints= [] self.I0instructions.setText("") while len(self.I0box.getHandles()) > 0: self.I0box.removeHandle(self.I0box.getHandles()[0], updateSegments=True) self.I0box.sigRegionChangeFinished.connect(self.DrawROI) self.clickdetector.stop() def DrawROI(self): left = int(round(self.vb.itemBoundingRect(self.I0box).left(),0)) right = int(round(self.vb.itemBoundingRect(self.I0box).right(),0)) top = int(round(self.vb.itemBoundingRect(self.I0box).bottom(),0)) bottom = int(round(self.vb.itemBoundingRect(self.I0box).top(),0)) #fill the selection polygon with ones and map to image coords io = self.I0box.getArrayRegion(np.ones((self.stack.n_cols,self.stack.n_rows)),self.AbsImage) #fill left and right margins with zeros lzeros = np.zeros((max(0,left),np.shape(io)[1]), dtype=io.dtype) rzeros = np.zeros((max(0,self.stack.n_cols-right), np.shape(io)[1]), dtype=io.dtype) io = np.vstack((lzeros, io,rzeros)) # fill bottom and top margins with zeros bzeros = np.zeros((np.shape(io)[0],(max(0,bottom))), dtype=io.dtype) tzeros = np.zeros((np.shape(io)[0],(max(0,self.stack.n_rows-top))), dtype=io.dtype) io = np.hstack((bzeros, io,tzeros)) #dilate mask to include border pixels: if left <= 0: io = ndimage.binary_dilation(io,structure=([[0, 1, 0], [0, 1, 0], [0, 0, 0]])) if right >= self.stack.n_cols: io = ndimage.binary_dilation(io,structure=([[0, 0, 0], [0, 1, 0], [0, 1, 0]])) if bottom <= 0: io = ndimage.binary_dilation(io,structure=([[0, 0, 0], [1, 1, 0], [0, 0, 0]])) if top >= self.stack.n_rows: io = ndimage.binary_dilation(io,structure=([[0, 0, 0], [0, 1, 1], [0, 0, 0]])) #crop mask to common region: io = io[abs(min(left,0)):self.stack.n_cols+abs(min(left,0)),abs(min(bottom,0)):self.stack.n_rows+abs(min(bottom,0))] self.i0_indices = np.where(io == 1) self.redpix[:, :] = [0, 0, 0, 0] self.redpix[self.i0_indices] = (255,0,0,255) self.MaskImage.setImage(self.redpix, opacity=0.3) self.MaskImage.setZValue(10) def OnMouseHover(self,ev): pos = self.vb.mapSceneToView(ev[0]) roipos = pos-self.vb.mapFromViewToItem(self.I0box,pos) if self.vb.itemBoundingRect(self.AbsImage).contains(pos): if self.vb.scene().clickEvents: self.MaskImage.setZValue(-10) self.I0box.setZValue(10) self.clickdetector.start(10) if self.radioLassoROI.isChecked(): handle = self.I0box.addFreeHandle((np.round(pos.x()-roipos.x(),0),np.round(pos.y()-roipos.y(),0))) self.lassopoints.append(handle) if len(self.lassopoints) > 1: self.I0box.addSegment(self.lassopoints[0], self.lassopoints[1]) self.lassopoints.pop(0) ## Polygon ROI is currently broken in recent pyqtgraph version # elif self.radioPolyROI.isChecked(): # origin = self.vb.mapSceneToView(self.vb.scene().clickEvents[0].scenePos()) # if origin.x() < 0: # x0 = 0-roipos.x() # elif origin.x() > self.stack.n_cols: # x0 = self.stack.n_cols - roipos.x() # else: # x0 = np.round(origin.x()-roipos.x(),0) # if origin.y() < 0: # y0 = 0-roipos.y() # elif origin.y() > self.stack.n_rows: # y0 = self.stack.n_rows - roipos.y() # else: # y0 = np.round(origin.y()-roipos.y(),0) # self.I0box.setPoints([(np.round(pos.x()-roipos.x(),0),np.round(pos.y()-roipos.y(),0)), (x0,np.round(pos.y()-roipos.y(),0)),(x0,y0),(np.round(pos.x()-roipos.x(),0),y0)], closed=True) def draw_image(self,fluxmin, fluxmax): self.I0instructions.setText( "Select I0 by dragging the histogram lines or by drawing a ROI.") self.i0_indices = np.where((fluxmin<=self.stack.histogram)&(self.stack.histogram<=fluxmax)) self.redpix[:, :] = [0, 0, 0, 0] self.redpix[self.i0_indices] = (255,0,0,255) self.AbsImage.setImage(self.stack.histogram) self.MaskImage.setImage(self.redpix, opacity = 0.3) #---------------------------------------------------------------------- def OnAccept(self, evt): if self.MaskImage.zValue() > 0 and np.any(self.i0_indices[0]): self.stack.i0_from_histogram(self.i0_indices) self.parent.I0histogramCalculated() self.stack.i0_mask = self.redpix self.stack.i0_mask[:, :] = False self.stack.i0_mask[self.i0_indices] = True self.close() else: self.i0_indices = [] QtWidgets.QMessageBox.warning(self, 'Error', 'I0 region is empty!') #---------------------------------------------------------------------- class ShowArtefacts(QtWidgets.QDialog): def __init__(self, parent, common, stack): QtWidgets.QWidget.__init__(self, parent) uic.loadUi(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'showartefacts.ui'), self) self.parent = parent self.com = common self.stack = stack self.i0_h_warnflag = False self.i0_v_warnflag = False self.pglayout = pg.GraphicsLayout(border=None) self.canvas.setBackground("w") # canvas is a pg.GraphicsView widget self.canvas.setCentralWidget(self.pglayout) self.vb = self.pglayout.addViewBox() self.vb.setAspectLocked() self.i_item = pg.ImageItem(border="k",parent= self) self.button_ok.clicked.connect(self.OnAccept) self.button_cancel.clicked.connect(self.close) self.cb_h.stateChanged.connect(self.ShowImage) self.cb_v.stateChanged.connect(self.ShowImage) #ToDo: I initially wanted to use Qt.QueuedConnection to create a non-blocking Slider. self.weight_slider.valueChanged.connect(self.ShowCalcImage) self.slider_eng.sliderPressed.connect(self.ShowImage) self.slider_eng.sliderReleased.connect(self.ShowImage) self.slider_eng.valueChanged[int].connect(self.OnScrollEng) self.setWindowTitle('Artefacts & Leveling') self.slider_eng.setRange(0, self.stack.n_ev - 1) self.vb.setMouseEnabled(x=False, y=False) self.vb.addItem(self.i_item, ignoreBounds=False) self.stack.absdata_level = self.stack.absdata.copy() if self.com.i0_loaded: self.rb_median_i0.setCheckable(True) self.rb_median_i0.setChecked(True) self.label_4.setText('I0 mask contribution:') else: self.sliderWidget.hide() self.rb_median.toggled.connect(self.ShowImage) self.OnScrollEng(0) def ShowCalcImage(self): if any([self.cb_h.isChecked(),self.cb_v.isChecked()]): a = self.stack.absdata[:, :, self.slider_eng.value()].astype('float64') a, wf = self.LevelCalc(a) if self.rb_median_i0.isChecked(): self.label_3.setText(str('{:d}').format(int(wf))+' %') self.i_item.setImage(a) def CorrectionArray(self,array,axis,wf,mask = None,): # calculate median along given axis, ignoring nans if present array_i = np.nanmedian(array, axis=axis, keepdims=False) if mask is not None and wf != 0.0: array = np.where(mask, array, np.nan) # replace masked pixels by nans # calculate median along given axis, ignoring nans if present array = np.nanmedian(array, axis=axis, keepdims=False) bg_median = np.nanmedian(array, axis=0, keepdims=False) if mask is None or wf == 0.0: # returns the median filtered array neglecting the i0 mask return (np.nanmedian(array_i, axis=0, keepdims=False) - array_i) elif wf == 1: return ((bg_median - array)*(wf)) else: return ((bg_median - array)*(wf) + (bg_median - array_i)*(1-wf)) def LevelCalc(self,a,final=False): wf = 0.0 mask = None diff_v = 0 diff_h = 0 if self.cb_h.isChecked(): wf = (self.weight_slider.value()) / 100 if self.rb_median_i0.isChecked(): mask = self.stack.i0_mask[:, :, 0] if final: mask = mask[:, :, None] diff_h = self.CorrectionArray(a, 0, float(wf), mask)[None, :] #a += diff[None, :] if self.cb_v.isChecked(): wf = (self.weight_slider.value()) / 100 if self.rb_median_i0.isChecked(): mask = self.stack.i0_mask[:, :, 0] if final: mask = mask[:, :, None] diff_v = self.CorrectionArray(a, 1, float(wf), mask)[: ,None] a = a + diff_v + diff_h return(a,int(wf*100)) # ---------------------------------------------------------------------- def OnScrollEng(self, value): self.slider_eng.setValue(value) self.iev = value self.ShowImage() def ShowImage(self): if (self.slider_eng.isSliderDown()): self.i_item.setImage(self.stack.absdata[:, :, int(self.iev)]) elif any([self.cb_h.isChecked(), self.cb_v.isChecked()]): self.ShowCalcImage() else: self.i_item.setImage(self.stack.absdata[:, :, int(self.iev)]) self.label_3.setText(str('')) self.groupBox.setTitle(str('Stack Browser | Image at {0:5.2f} eV').format(float(self.stack.ev[self.iev]))) #---------------------------------------------------------------------- def OnAccept(self, evt): a, wf = self.LevelCalc(self.stack.absdata.astype('float64'),final=True) self.stack.absdata = a self.parent.page1.showSpectrum(self.parent.page1.ix, self.parent.page1.iy) self.parent.page1.loadImage() self.parent.page0.Clear() self.parent.page0.LoadEntries() if showmaptab: self.parent.page9.Clear() self.parent.page9.LoadEntries() self.close() #---------------------------------------------------------------------- class MultiCrop(QtWidgets.QDialog, QtWidgets.QGraphicsScene): evlistchanged = pyqtSignal([object]) thetalistchanged = pyqtSignal([object]) def __init__(self, parent, common, stack): QtWidgets.QWidget.__init__(self, parent) uic.loadUi(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'showmulticrop.ui'), self) self.parent = parent self.stack = stack self.com = common self.iev = 0 self.itheta = 0 #self.stack.absdata_shifted_cropped = self.stack.absdata_shifted.copy() self.poolthread = QtCore.QThread() self.aligned = False self.button_ok.setEnabled(True) self.setWindowTitle('Stack Cropping') self.pglayout = pg.GraphicsLayout(border=None) self.canvas.setBackground("w") # canvas is a pg.GraphicsView widget self.canvas.setCentralWidget(self.pglayout) self.vb = self.pglayout.addViewBox() self.vb.setAspectLocked() self.i_item = pg.ImageItem(border="k",parent= self) self.vb.setMouseEnabled(x=False, y=False) self.vb.addItem(self.i_item, ignoreBounds=False) self.button_ok.clicked.connect(self.OnAccept) self.button_cancel.clicked.connect(self.OnCancel) if self.com.stack_loaded == 1: self.label_theta_range.setVisible(False) self.slider_theta.setVisible(False) self.cb_remove_theta.setVisible(False) self.groupBox_theta.setVisible(False) if self.com.stack_4d == 1: self.label_theta_range.setVisible(True) self.slider_theta.setVisible(True) self.cb_remove_theta.setVisible(True) self.groupBox_theta.setVisible(True) self.slider_theta.setRange(0, self.stack.n_theta - 1) self.slider_theta.valueChanged[int].connect(self.OnScrollTheta) self.SetupListTheta() # self.maskedvals = [True] * int(self.stack.n_ev) # self.spinBoxError.setEnabled(False) self.slider_eng.sliderPressed.connect(self.ShowImage) self.slider_eng.sliderReleased.connect(self.ShowImage) self.slider_eng.valueChanged[int].connect(self.OnScrollEng) self.slider_eng.setRange(0, self.stack.n_ev - 1) self.pb_selectall.clicked.connect(self.OnSelectAll) self.pb_clearall.clicked.connect(self.OnClearAll) self.evlistchanged.connect(lambda row: self.qListChangeHandler(row, "energy")) self.thetalistchanged.connect(lambda row: self.qListChangeHandler(row, "theta")) #self.ev_widget.itemClicked.connect(lambda item: self.OnItemClicked(item)) self.ev_widget.mousePressEvent = self.mouseEventOnEVList self.ev_widget.mouseMoveEvent = self.mouseEventOnEVList self.theta_widget.mousePressEvent = self.mouseEventOnThetaList self.theta_widget.mouseMoveEvent = self.mouseEventOnThetaList #self.ev_widget.itemSelectionChanged.connect(lambda item: self.OnItemClicked(item)) self.SetupListEV() self.OnScrollEng(0) self.SetupROI() self.SetupPlot() def mouseEventOnEVList(self, e): if e.type() == QtCore.QEvent.MouseMove or e.type() == QtCore.QEvent.MouseButtonPress: qlist = self.ev_widget pos = qlist.mapFromGlobal(QtGui.QCursor.pos()) row = qlist.indexAt(pos).row() item = qlist.itemAt(pos) #print(row,self.latest_row) if row >= 0: if e.type() != QtCore.QEvent.MouseMove or row != self.latest_row: if e.buttons() == QtCore.Qt.LeftButton: qlist.setCurrentRow(row) self.evlistchanged.emit(item) self.latest_row = row return def mouseEventOnThetaList(self, e): if e.type() == QtCore.QEvent.MouseMove or e.type() == QtCore.QEvent.MouseButtonPress: qlist = self.theta_widget pos = qlist.mapFromGlobal(QtGui.QCursor.pos()) row = qlist.indexAt(pos).row() item = qlist.itemAt(pos) #print(row,self.latest_row) if row >= 0: if e.type() != QtCore.QEvent.MouseMove or row != self.latest_row: if e.buttons() == QtCore.Qt.LeftButton: qlist.setCurrentRow(row) self.thetalistchanged.emit(item) self.latest_row = row return def OnSelectionChanged(self): self.RedrawNewPlot() #self.UpdateIndices() self.region.blockSignals(True) if self.idx_selected: self.region.setRegion([self.stack.ev[min(self.idx_selected)], self.stack.ev[max(self.idx_selected)]]) self.region.blockSignals(False) self.spectrum_plotwidget.setXRange(*self.region.getRegion()) self.spectrum_plotwidget.setYRange(np.min(self.plotitem_new.yData),np.max(self.plotitem_new.yData)) return def UpdateIndices(self): self.idx_selected = sorted([self.ev_widget.row(i) for i in self.ev_selected]) if self.com.stack_4d: self.thetaidx_selected = sorted([self.theta_widget.row(i) for i in self.theta_selected]) def RedrawPlots(self): x,y = self.GenerateSpectrum(list(range(self.stack.n_ev))) self.plotitem.setData(x,y) self.OnSelectionChanged() def RedrawNewPlot(self): self.UpdateIndices() x,y = self.GenerateSpectrum(self.idx_selected) self.plotitem_new.setData(x,y) if self.idx_selected: self.region.show() def qListChangeHandler(self,row, dimension): if dimension == "theta": selection = self.theta_selected widget = self.theta_widget elif dimension == "energy": selection = self.ev_selected widget = self.ev_widget if row in selection: selection.remove(row) row.setBackground(QtGui.QColor(0, 0, 0, 0)) else: selection.append(row) row.setBackground(QtGui.QColor('#beaed4')) if dimension == "theta": self.OnScrollTheta(widget.row(row)) elif dimension == "energy": self.OnScrollEng(widget.row(row)) self.OnSelectionChanged() def SetupPlot(self): x,y = self.GenerateSpectrum(list(range(self.stack.n_ev))) self.spectrum_plotwidget.setBackground("w") self.region = pg.LinearRegionItem(brush=[255,0,0,45],bounds=[np.min(x),np.max(x)]) self.region.setZValue(10) plot = self.spectrum_plotwidget plot.setBackground("w") plot.addItem(self.region, ignoreBounds=False) plot.setMouseEnabled(x=False, y=False) plot.showGrid(y=True) plot.showAxis("top", show=True) plot.showAxis("right", show=True) by = plot.getAxis("right") bx = plot.getAxis("top") by.setStyle(showValues=False,tickLength=0) bx.setStyle(showValues=False,tickLength=0) ay = plot.getAxis("left") ax = plot.getAxis("bottom") ax.setLabel(text="Photon energy [eV]") if self.com.i0_loaded: ay.setLabel(text="Optical density") else: ay.setLabel(text="Photon flux [cps]") self.plotitem = plot.plot(x, y, pen=pg.mkPen(color=0.8, width=2)) self.plotitem_new = plot.plot(x, y, pen=pg.mkPen(color="b", width=2)) self.refmarker = pg.InfiniteLine(angle=90, movable=False, pen=pg.mkPen(color="b", width=2, style=QtCore.Qt.DashLine)) plot.addItem(self.refmarker, ignoreBounds=True) self.region.setRegion((min(x),max(x))) self.region.sigRegionChangeFinished.connect(lambda region: self.UpdateEVRegion(region)) # ---------------------------------------------------------------------- def getDataClosestToRegion(self,region,plotitem,snapregion=False): minidx, maxidx = region.getRegion() data = plotitem.getData()[0] index = lambda x: np.argmin(np.abs(data - x)) minidx = index(minidx) maxidx = index(maxidx) if minidx == maxidx: minidx = 0 maxidx = np.argmax(data) mindata = data[minidx] maxdata = data[maxidx] if snapregion: region.blockSignals(True) region.setRegion([mindata, maxdata]) # snap region to data points region.blockSignals(False) return minidx, maxidx, mindata, maxdata def UpdateEVRegion(self, region): min_idx, max_idx,*_ = self.getDataClosestToRegion(region,self.plotitem, True) y_vals = self.plotitem.yData[min_idx:max_idx + 1] x_vals = self.plotitem.xData[min_idx:max_idx + 1] self.spectrum_plotwidget.setRange(yRange=[np.min(y_vals), np.max(y_vals)], xRange=[np.min(x_vals), np.max(x_vals)], disableAutoRange=True, padding=0.05) if min_idx not in self.idx_selected: for idx in range(int(min_idx),np.min(self.idx_selected)): self.ev_selected.append(self.ev_widget.item(idx)) self.ev_widget.item(idx).setBackground(QtGui.QColor('#beaed4')) else: for idx in range(np.min(self.idx_selected),int(min_idx)): try: self.ev_selected.remove(self.ev_widget.item(idx)) except ValueError: pass self.ev_widget.item(idx).setBackground(QtGui.QColor(0, 0, 0, 0)) if max_idx not in self.idx_selected: for idx in range(np.max(self.idx_selected)+1,int(max_idx)+1): try: self.ev_selected.append(self.ev_widget.item(idx)) except ValueError: pass self.ev_widget.item(idx).setBackground(QtGui.QColor('#beaed4')) else: for idx in range(int(max_idx)+1,np.max(self.idx_selected)+1): try: self.ev_selected.remove(self.ev_widget.item(idx)) except ValueError: pass self.ev_widget.item(idx).setBackground(QtGui.QColor(0, 0, 0, 0)) self.RedrawNewPlot() def OnSelectAll(self): self.ev_widget.clear() self.SetupListEV() if self.com.stack_4d: self.theta_widget.clear() self.SetupListTheta() self.region.setRegion((min(self.stack.ev), max(self.stack.ev))) self.region.show() self.RedrawNewPlot() def OnClearAll(self): #self.ev_widget.clear() #self.SetupListEV() self.region.hide() for idx in self.idx_selected: self.ev_widget.item(idx).setBackground(QtGui.QColor(0, 0, 0, 0)) self.ev_selected = [] self.idx_selected = [] if self.com.stack_4d: for idx in self.thetaidx_selected: self.theta_widget.item(idx).setBackground(QtGui.QColor(0, 0, 0, 0)) self.theta_selected = [] self.thetaidx_selected = [] self.RedrawNewPlot() def SetupListTheta(self): self.theta_selected = [] self.thetaidx_selected = [] for i,e in enumerate(self.stack.theta): # Fill QList with energies #self.stk.shifts.append([1,0,(0.0,0.0)]) #checked [0,1]; pre, post, undefined state for map [-1,1,0],(xshift [float],yshift [float]) item = QtWidgets.QListWidgetItem(str(int(i)).zfill(3)+" at " + format(e, '.1f') + "°") self.theta_widget.addItem(item) self.theta_selected.append(item) self.thetaidx_selected.append(i) item.setBackground(QtGui.QColor('#beaed4')) item.setForeground(QtGui.QColor(0, 0, 0, 128)) def SetupListEV(self): self.ev_selected = [] self.idx_selected = [] for i,e in enumerate(self.stack.ev): # Fill QList with energies #self.stk.shifts.append([1,0,(0.0,0.0)]) #checked [0,1]; pre, post, undefined state for map [-1,1,0],(xshift [float],yshift [float]) item = QtWidgets.QListWidgetItem(str(int(i)).zfill(3)+" at " + format(e, '.2f') + " eV") self.ev_widget.addItem(item) self.ev_selected.append(item) self.idx_selected.append(i) item.setBackground(QtGui.QColor('#beaed4')) item.setForeground(QtGui.QColor(0, 0, 0, 128)) def OnScrollTheta(self, value): self.slider_theta.setValue(value) self.ResetAllItems(self.theta_widget) self.itheta = value #self.stack.absdata = self.stack.stack4D[:,:,:,self.itheta].copy() self.ShowImage() self.theta_widget.setCurrentRow(self.itheta) if self.com.stack_loaded == 1: self.theta_widget.item(value).setForeground(QtGui.QColor(0, 0, 0, 255)) self.RedrawPlots() def OnScrollEng(self, value): self.slider_eng.setValue(value) self.ResetAllItems(self.ev_widget) self.iev = value self.ShowImage() try: self.refmarker.setValue(self.stack.ev[self.iev]) except: pass self.ev_widget.setCurrentRow(self.iev) if self.com.stack_loaded == 1: self.ev_widget.item(value).setForeground(QtGui.QColor(0, 0, 0, 255)) def ShowImage(self): if self.com.stack_4d == 1: self.i_item.setImage(self.stack.stack4D[:, :, int(self.iev),int(self.itheta)]) self.groupBox.setTitle(str('Stack Browser | Image at {0:5.2f} eV and {1:5.1f}°').format(float(self.stack.ev[self.iev]),float(self.stack.theta[self.itheta]), )) else: if self.com.i0_loaded: self.i_item.setImage(self.stack.od3d[:, :, int(self.iev)]) else: self.i_item.setImage(self.stack.absdata[:, :, int(self.iev)]) self.groupBox.setTitle(str('Stack Browser | Image at {0:5.2f} eV').format(float(self.stack.ev[self.iev]))) ## Setup a ROI for an alignment rectangle. By default the whole image area is used. def SetupROI(self): #self.stack.absdata = self.stack.absdata.copy() self.box = pg.RectROI(self.i_item.boundingRect().topLeft(), self.i_item.boundingRect().bottomRight(), pen=(5, 8), handlePen=QtGui.QPen(QtGui.QColor(255, 0, 128, 255)), centered=False, sideScalers=False, removable=False, scaleSnap=True, translateSnap=True, maxBounds=self.i_item.boundingRect()) self.vb.addItem(self.box, ignoreBounds=False) self.box.sigRegionChangeFinished.connect(self.RedrawPlots) self.box.sigRegionChangeStarted.connect(self.OnBoxChanging) self.button_rstroi.clicked.connect(self.OnResetROI) ## The ROI is limited to the visible image area. OnMouseMoveOutside handles the behavior when def OnResetROI(self): self.box.setPos(0, 0, update=False, finish=False) self.box.setSize(self.i_item.boundingRect().bottomRight() - self.box.pos(), update=True, snap=True, finish=True) self.box.show() def OnBoxChanging(self): self.boxsize = self.box.size() self.proxy = pg.SignalProxy(self.vb.scene().sigMouseMoved, rateLimit=30, slot=self.OnMouseMoveOutside) def GetRegion(self): try: self.proxy.disconnect() except AttributeError: pass left = int(self.box.pos().x()) right = left + int(self.box.size().x()) bottom = int(self.box.pos().y()) top = bottom + int(self.box.size().y()) return (left,right,top,bottom) def GenerateSpectrum(self, evselection): left,right,top,bottom = self.GetRegion() if self.com.i0_loaded == 1: if self.com.stack_4d == 1: total = self.stack.od4d[left:right, bottom:top, :, int(self.itheta)].copy() else: total = self.stack.od3d[left:right, bottom:top, :].copy() else: if self.com.stack_4d == 1: t = [self.stack.theta[i] for i in self.thetaidx_selected] self.label_theta_range.setText( "Theta range: [ " + str(min(t, default=0)) + "° .. " + str( max(t, default=0)) + "° ], # values: " + str( len(t))) total = self.stack.stack4D[left:right, bottom:top, :, int(self.itheta)].copy() else: total = self.stack.absdata[left:right, bottom:top, :].copy() total = total.sum(axis=(0,1)) / (int(self.box.size().x()) * int(self.box.size().y())) x = [self.stack.ev[i] for i in evselection] y = [total[i] for i in evselection] self.label_spatial_range.setText("Stack size: [ "+str(int(self.box.size().x()))+" x "+str(int(self.box.size().y()))+" ] px²") self.label_ev_range.setText( "Energy range: [ " + str(min(x, default=0)) + " .. " + str(max(x, default=0)) + " ] eV, # values: "+ str(len(x))) return (x, y) def ResetAllItems(self,widget): for i in range(widget.count()): widget.item(i).setForeground(QtGui.QColor(0, 0, 0, 128)) def OnMouseMoveOutside(self, ev): mousepos = self.vb.mapSceneToView(ev[0]) if not self.vb.itemBoundingRect(self.i_item).contains(mousepos): maxrect = self.i_item.boundingRect().bottomRight() # if bounds exceeded out_x = max((mousepos-maxrect).x(),0) out_y = max((mousepos-maxrect).y(), 0) if self.box.size() != self.boxsize: # prevents taking action when the box is just dragged and not resized if out_x and out_y: self.box.setSize(self.i_item.boundingRect().bottomRight()-self.box.pos(), update=True, snap=True, finish=False) elif out_x: self.box.setSize([self.i_item.boundingRect().right()-self.box.pos().x(),mousepos.y()-self.box.pos().y()], update=True, snap=True, finish=False) elif out_y: self.box.setSize([mousepos.x()-self.box.pos().x(),self.i_item.boundingRect().bottom()-self.box.pos().y()], update=True, snap=True, finish=False) # ---------------------------------------------------------------------- def OnCancel(self, evt): self.close() # ---------------------------------------------------------------------- def OnAccept(self, evt): if self.cb_croptoroi.isChecked(): left, right, top, bottom = self.GetRegion() else: left, right, top, bottom = (None,None,None,None) if self.cb_remove_evs.isChecked(): selection = self.idx_selected if len(selection) == 0: QtWidgets.QMessageBox.warning(self, 'Error', 'Please select at least one energy value!') return self.stack.n_ev = np.array(len(selection)) self.stack.ev = self.stack.ev[selection] self.stack.data_dwell = self.stack.data_dwell[selection] else: selection = list(range(self.stack.n_ev)) self.stack.absdata = self.stack.absdata[left:right,bottom:top,selection] self.stack.n_cols = self.stack.absdata.shape[0] self.stack.n_rows = self.stack.absdata.shape[1] self.parent.page1.ix = int(self.stack.n_cols/2) self.parent.page1.iy = int(self.stack.n_rows/2) if self.com.stack_4d: if self.cb_remove_theta.isChecked(): thetas = self.thetaidx_selected if len(thetas) == 0: QtWidgets.QMessageBox.warning(self, 'Error', 'Please select at least one theta value!') return self.stack.n_theta = len(thetas) self.stack.theta = self.stack.theta[thetas] else: thetas = list(range(self.stack.n_theta)) self.stack.stack4D = self.stack.stack4D[left:right, bottom:top, selection, :] self.stack.stack4D = self.stack.stack4D[:,:, :, thetas] if self.com.i0_loaded: if self.com.stack_4d: self.stack.od4D = self.stack.od4D[left:right,bottom:top,selection, thetas] else: self.stack.od3d = self.stack.od3d[left:right,bottom:top,selection] self.stack.od = self.stack.od3d.copy() self.stack.od = np.reshape(self.stack.od, (self.stack.n_rows * self.stack.n_cols, self.stack.n_ev), order='F') self.stack.fill_h5_struct_from_stk() if self.com.i0_loaded == 1: self.stack.fill_h5_struct_normalization() # Fix the slider on Page 1! if self.com.stack_4d: self.parent.page1.slider_theta.setRange(0, self.stack.n_theta - 1) self.parent.page1.itheta = 0 self.parent.page1.slider_theta.blockSignals(True) self.parent.page1.slider_theta.setValue(int(self.parent.page1.itheta)) self.parent.page1.slider_theta.blockSignals(False) self.parent.page0.slider_theta.setRange(0, self.stack.n_theta - 1) self.parent.page0.itheta = 0 self.parent.page0.slider_theta.blockSignals(True) self.parent.page0.slider_theta.setValue(int(self.parent.page1.itheta)) self.parent.page0.slider_theta.blockSignals(False) self.parent.page1.slider_eng.setRange(0, self.stack.n_ev - 1) self.parent.page1.iev = 0 self.parent.page1.slider_eng.setValue(int(self.parent.page1.iev)) self.parent.page0.slider_eng.setRange(0, self.stack.n_ev - 1) self.parent.page0.iev = 0 self.parent.page0.slider_eng.setValue(int(self.parent.page1.iev)) self.parent.page1.showSpectrum(self.parent.page1.ix, self.parent.page1.iy) self.parent.page1.loadImage() self.parent.page0.Clear() self.parent.page0.LoadEntries() if showmaptab: self.parent.page9.Clear() self.parent.page9.LoadEntries() self.close() class ImageRegistrationDialog(QtWidgets.QDialog): def __init__(self, parent, common): QtWidgets.QWidget.__init__(self, parent) uic.loadUi(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'dialogalign.ui'), self) self.parent = parent self.com = common # Currently disabled for Tomo data # if self.com.stack_4d == 1: # self.bt_align2.setEnabled(True) # else: # self.bt_align2.setEnabled(True) self.bt_align.clicked.connect(self.parent.page1.OnAlignImgs) self.bt_align2.clicked.connect(self.parent.page1.OnAlignImgs2) self.bt_align.clicked.connect(self.done) self.bt_align2.clicked.connect(self.done) #---------------------------------------------------------------------- class ImageRegistrationManual(QtWidgets.QDialog): def __init__(self, parent, common, stack): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.resize(1050, 750) self.setWindowTitle('Stack Alignment') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) self.stack = stack self.com = common self.have_ref_image = 0 self.regist_calculated = 0 self.iev = 0 self.itheta = 0 self.ref_image_index = 0 self.ref_image_index_theta = 0 self.ref_image = 0 self.man_align = 0 self.man_xref = 0 self.man_yref = 0 self.maxshift = 0 self.auto = True self.xleft = 0 self.xright = self.stack.n_cols self.ybottom = 0 self.ytop = self.stack.n_rows if self.com.stack_4d == 0: self.aligned_stack = self.stack.absdata.copy() self.man_xs = np.zeros((self.stack.n_ev)) self.man_ys = np.zeros((self.stack.n_ev)) self.xshifts = np.zeros((self.stack.n_ev)) self.yshifts = np.zeros((self.stack.n_ev)) else: self.aligned_stack = self.stack.stack4D.copy() self.man_xs = np.zeros((self.stack.n_ev,self.stack.n_theta)) self.man_ys = np.zeros((self.stack.n_ev,self.stack.n_theta)) self.xshifts = np.zeros((self.stack.n_ev,self.stack.n_theta)) self.yshifts = np.zeros((self.stack.n_ev,self.stack.n_theta)) self.minxs = 0 self.maxxs = 0 self.minys = 0 self.maxys = 0 self.showccorr = 0 self.edgee = 0 self.subregion = 0 self.sr_x1 = 0 self.sr_x2 = 0 self.sr_y1 = 0 self.sr_y2 = 0 self.patch = None #panel 1 vbox1 = QtWidgets.QVBoxLayout() self.tc_imageeng = QtWidgets.QLabel(self) self.tc_imageeng.setText("Image at energy: ") gridsizertop = QtWidgets.QGridLayout() frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.absimgfig = Figure((4.0, 4.0)) self.AbsImagePanel = FigureCanvas(self.absimgfig) self.AbsImagePanel.setParent(self) self.AbsImagePanel.mpl_connect('button_press_event', self.OnPointCorrimage) self.AbsImagePanel.setCursor(Qt.CrossCursor) fbox.addWidget(self.AbsImagePanel) frame.setLayout(fbox) gridsizertop.addWidget(frame, 0, 0, QtCore .Qt. AlignLeft) self.slider_eng = QtWidgets.QScrollBar(QtCore.Qt.Vertical) self.slider_eng.setFocusPolicy(QtCore.Qt.StrongFocus) self.slider_eng.valueChanged[int].connect(self.OnScrollEng) self.slider_eng.setRange(0, self.stack.n_ev-1) self.slider_eng.setValue(self.iev) gridsizertop.addWidget(self.slider_eng, 0, 1, QtCore .Qt. AlignLeft) self.slider_theta = QtWidgets.QScrollBar(QtCore.Qt.Horizontal) self.slider_theta.setFocusPolicy(QtCore.Qt.StrongFocus) self.slider_theta.valueChanged[int].connect(self.OnScrollTheta) self.slider_theta.setRange(0, self.stack.n_theta-1) self.tc_imagetheta = QtWidgets.QLabel(self) self.tc_imagetheta.setText("4D Data Angle: ") if self.com.stack_4d == 0 : self.tc_imagetheta.setVisible(False) self.slider_theta.setVisible(False) hbox51 = QtWidgets.QHBoxLayout() hbox51.addWidget(self.tc_imagetheta) hbox51.addWidget(self.slider_theta) gridsizertop.addLayout(hbox51, 1, 0) vbox1.addWidget(self.tc_imageeng) vbox1.addLayout(gridsizertop) self.tc_shift1 = QtWidgets.QLabel(self) self.tc_shift2 = QtWidgets.QLabel(self) vbox1.addWidget(self.tc_shift1) vbox1.addWidget(self.tc_shift2) vbox1.addStretch(1) if self.com.stack_4d == 0: self.tc_shift1.setText('X shift: {0:5.2f} pixels'.format(self.xshifts[self.iev])) self.tc_shift2.setText('Y shift: {0:5.2f} pixels'.format(self.yshifts[self.iev])) else: self.tc_shift1.setText('X shift: {0:5.2f} pixels'.format(self.xshifts[self.iev,self.itheta])) self.tc_shift2.setText('Y shift: {0:5.2f} pixels'.format(self.yshifts[self.iev,self.itheta])) #panel 2 vbox2 = QtWidgets.QVBoxLayout() tc2 = QtWidgets.QLabel(self) tc2.setText('Cross-correlation') frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.cscorrfig = Figure((2.4, 2.4)) self.CscorrPanel = FigureCanvas(self.cscorrfig) self.CscorrPanel.setParent(self) fbox.addWidget(self.CscorrPanel) frame.setLayout(fbox) vbox2.addWidget(tc2) vbox2.addWidget(frame) #panel 3 vbox3 = QtWidgets.QVBoxLayout() tc3= QtWidgets.QLabel(self) tc3.setText('Image shifts') frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.shiftsfig = Figure((4.0, 2.4)) self.ShiftsPanel = FigureCanvas(self.shiftsfig) self.ShiftsPanel.setParent(self) self.ShiftsPanel.mpl_connect('button_press_event', self.OnPlotShifts) fbox.addWidget(self.ShiftsPanel) frame.setLayout(fbox) vbox3.addWidget(tc3) vbox3.addWidget(frame) #panel 9 vbox9 = QtWidgets.QVBoxLayout() vbox9.setSpacing(0) groupBox9 = QtWidgets.QGroupBox() self.rb_auto = QtWidgets.QRadioButton( 'Automatic Alignment') self.rb_man = QtWidgets.QRadioButton('Manual Alignment') self.rb_auto.setChecked(True) self.rb_auto.toggled.connect(self.Onrb_automanual) vbox9.addWidget(self.rb_auto) vbox9.addWidget(self.rb_man) groupBox9.setLayout(vbox9) #panel 8 sizer8 = QtWidgets.QGroupBox('This Image') vbox8 = QtWidgets.QVBoxLayout() vbox8.setSpacing(0) self.button_refimg = QtWidgets.QPushButton('Set as Reference Image') self.button_refimg.clicked.connect(self.SetRefImage) vbox8.addWidget(self.button_refimg) self.button_refimgsave = QtWidgets.QPushButton('Save Reference Image') self.button_refimgsave.clicked.connect(self.SaveRefImage) self.button_refimgsave.setEnabled(False) vbox8.addWidget(self.button_refimgsave) self.button_refimgsload = QtWidgets.QPushButton('Load Reference Image') self.button_refimgsload.clicked.connect(self.LoadRefImage) vbox8.addWidget(self.button_refimgsload) line = QtWidgets.QFrame() line.setFrameShape(QtWidgets.QFrame.HLine) line.setFrameShadow(QtWidgets.QFrame.Sunken) vbox8.addSpacing(5) vbox8.addWidget(line) vbox8.addSpacing(5) self.button_remove = QtWidgets.QPushButton('Remove energy from stack') self.button_remove.clicked.connect(self.OnRemoveImage) vbox8.addWidget(self.button_remove) sizer8.setLayout(vbox8) #panel 4 sizer4 = QtWidgets.QGroupBox('Automatic Alignment') vbox4 = QtWidgets.QVBoxLayout() self.button_register = QtWidgets.QPushButton('Calculate image shifts') self.button_register.clicked.connect(self.OnCalcRegistration) self.button_register.setEnabled(False) vbox4.addWidget(self.button_register) #vbox4.addStretch(1) self.button_subregion = QtWidgets.QPushButton('Select subregion on reference') self.button_subregion.clicked.connect(self.OnSelectSubregion) self.button_subregion.setEnabled(False) vbox4.addWidget(self.button_subregion) self.button_delsubregion = QtWidgets.QPushButton('Remove subregion selection') self.button_delsubregion.clicked.connect(self.OnDeleteSubregion) self.button_delsubregion.setEnabled(False) vbox4.addWidget(self.button_delsubregion) vbox4.addStretch(1) groupBox4 = QtWidgets.QGroupBox() hbox43 = QtWidgets.QHBoxLayout() self.cb_edgeenh = QtWidgets.QCheckBox('Edge Enhancement', self) self.cb_edgeenh.stateChanged.connect(self.OnEdgeE) hbox43.addWidget(self.cb_edgeenh) self.rb_sobel = QtWidgets.QRadioButton( 'Sobel') self.rb_prewitt = QtWidgets.QRadioButton('Prewitt') self.rb_prewitt.setChecked(True) self.rb_sobel.setEnabled(False) self.rb_prewitt.setEnabled(False) hbox43.addWidget(self.rb_prewitt) hbox43.addWidget(self.rb_sobel) groupBox4.setLayout(hbox43) vbox4.addWidget(groupBox4) hbox42 = QtWidgets.QHBoxLayout() text1 = QtWidgets.QLabel(self) text1.setText(' Max shift [pixels]: ') self.tc_maxshift = QtWidgets.QSpinBox() self.tc_maxshift.setMinimum(0) self.tc_maxshift.valueChanged[int].connect(self.OnSetMaxShift) hbox42.addWidget(text1) hbox42.addWidget(self.tc_maxshift) vbox4.addLayout(hbox42) vbox4.addStretch(1) self.showcscor_cb = QtWidgets.QCheckBox('Show Cross-correlation', self) self.showcscor_cb.stateChanged.connect(self.OnShowCCorr) vbox4.addWidget(self.showcscor_cb) vbox4.addStretch() sizer4.setLayout(vbox4) #panel 5 vbox5 = QtWidgets.QVBoxLayout() self.tc_refimg = QtWidgets.QLabel(self) self.tc_refimg.setText('Reference image') frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.refimgfig = Figure((4.0, 4.0)) self.RefImagePanel = FigureCanvas(self.refimgfig) self.RefImagePanel.setParent(self) self.RefImagePanel.setCursor(Qt.CrossCursor) fbox.addWidget(self.RefImagePanel) frame.setLayout(fbox) self.RefImagePanel.mpl_connect('button_press_event', self.OnPointRefimage) self.RefImagePanel.mpl_connect('button_release_event', self.OnSelection) vbox5.addWidget(self.tc_refimg) vbox5.addWidget(frame) vbox5.addStretch(1) #panel 6 sizer6 = QtWidgets.QGroupBox('Manual Alignment') vbox6 = QtWidgets.QVBoxLayout() vbox6.setSpacing(0) self.button_manalign = QtWidgets.QPushButton('Pick a point on reference image') self.button_manalign.clicked.connect(self.OnPickRefPoint) self.button_manalign.setEnabled(False) vbox6.addWidget(self.button_manalign) self.button_pick2ndpoint = QtWidgets.QPushButton('This image: click on same point') self.button_pick2ndpoint.clicked.connect(self.OnPickCorrPoint) self.button_pick2ndpoint.setEnabled(False) vbox6.addWidget(self.button_pick2ndpoint) self.button_applyman = QtWidgets.QPushButton('Apply manual shifts') self.button_applyman.clicked.connect(self.OnApplyManShifts) self.button_applyman.setEnabled(False) vbox6.addWidget(self.button_applyman) self.textctrl_ms1 = QtWidgets.QLabel(self) self.textctrl_ms2 = QtWidgets.QLabel(self) vbox6.addWidget(self.textctrl_ms1) vbox6.addWidget(self.textctrl_ms2) self.textctrl_ms1.setText('X manual shift: ') self.textctrl_ms2.setText('Y manual shift: ') sizer6.setLayout(vbox6) #panel 7 vbox7 = QtWidgets.QVBoxLayout() vbox7.setSpacing(0) self.button_saveimg = QtWidgets.QPushButton('Save image shifts plot') self.button_saveimg.clicked.connect(self.OnSaveShiftsPlot) self.button_saveimg.setEnabled(False) vbox7.addWidget(self.button_saveimg) self.button_saveshifts = QtWidgets.QPushButton('Save image shifts') self.button_saveshifts.clicked.connect(self.OnSaveShifts) self.button_saveshifts.setEnabled(False) vbox7.addWidget(self.button_saveshifts) self.button_loadshifts = QtWidgets.QPushButton('Load image shifts') self.button_loadshifts.clicked.connect(self.OnLoadShifts) vbox7.addWidget(self.button_loadshifts) self.button_crop = QtWidgets.QPushButton('Crop aligned images') self.button_crop.clicked.connect(self.OnCropShifts) self.button_crop.setEnabled(False) vbox7.addWidget(self.button_crop) self.button_accept = QtWidgets.QPushButton('Apply Alignment') self.button_accept.clicked.connect(self.OnAccept) self.button_accept.setEnabled(False) vbox7.addWidget(self.button_accept) self.button_close = QtWidgets.QPushButton('Dismiss') self.button_close.clicked.connect(self.close) vbox7.addWidget(self.button_close) hboxtop = QtWidgets.QHBoxLayout() vboxL = QtWidgets.QVBoxLayout() vboxR = QtWidgets.QVBoxLayout() vboxL.addWidget(sizer8) vboxL.addStretch(1) vboxL.addWidget(groupBox9) vboxL.addStretch(1) vboxL.addWidget(sizer4) vboxL.addStretch(1) vboxL.addWidget(sizer6) vboxL.addStretch(1) vboxL.addLayout(vbox7) hboxRT = QtWidgets.QHBoxLayout() hboxRB = QtWidgets.QHBoxLayout() hboxRT.addLayout(vbox1) hboxRT.addLayout(vbox5) hboxRB.addLayout(vbox3) hboxRB.addLayout(vbox2) hboxRB.addStretch(1) vboxR.addStretch(1) vboxR.addLayout(hboxRT) vboxR.addStretch(5) vboxR.addLayout(hboxRB) vboxR.addStretch(5) hboxtop.addLayout(vboxL) hboxtop.addStretch(1) hboxtop.addLayout(vboxR) hboxtop.setContentsMargins(20,20,20,20) self.setLayout(hboxtop) self.ShowImage() #---------------------------------------------------------------------- def ShowImage(self): if self.com.stack_4d == 0: image = self.aligned_stack[:,:,self.iev] else: image = self.aligned_stack[:,:,self.iev,self.itheta] fig = self.absimgfig fig.clf() fig.add_axes(((0.0,0.0,1.0,1.0))) axes = fig.gca() _im = axes.imshow(np.rot90(image), cmap=matplotlib.cm.get_cmap('gray')) axes.autoscale(False) # if self.man_align == 2: # lx=matplotlib.lines.Line2D([self.man_yref-self.man_ys[self.iev], # self.man_yref-self.man_ys[self.iev]], # [0,self.stack.n_cols],color='red') # ly=matplotlib.lines.Line2D([0,self.stack.n_rows], # [self.man_xref-self.man_xs[self.iev], # self.man_xref-self.man_xs[self.iev]] ,color='red') # axes.add_line(lx) # axes.add_line(ly) axes.axis("off") self.AbsImagePanel.draw() self.tc_imageeng.setText('Image at energy: {0:5.2f} eV'.format(float(self.stack.ev[self.iev]))) if self.com.stack_4d == 0: self.tc_shift1.setText('X shift: {0:5.2f} pixels'.format(self.xshifts[self.iev])) self.tc_shift2.setText('Y shift: {0:5.2f} pixels'.format(self.yshifts[self.iev])) else: self.tc_shift1.setText('X shift: {0:5.2f} pixels'.format(self.xshifts[self.iev,self.itheta])) self.tc_shift2.setText('Y shift: {0:5.2f} pixels'.format(self.yshifts[self.iev,self.itheta])) if (self.man_align == 2): if self.com.stack_4d == 0: self.textctrl_ms1.setText('X manual shift: {0:5.2f} pixels'.format(self.man_xs[self.iev])) self.textctrl_ms2.setText('Y manual shift: {0:5.2f} pixels'.format(self.man_ys[self.iev])) else: self.textctrl_ms1.setText('X manual shift: {0:5.2f} pixels'.format(self.man_xs[self.iev,self.itheta])) self.textctrl_ms2.setText('Y manual shift: {0:5.2f} pixels'.format(self.man_ys[self.iev,self.itheta])) #---------------------------------------------------------------------- def OnScrollEng(self, value): self.iev = value self.ShowImage() #---------------------------------------------------------------------- def OnScrollTheta(self, value): self.itheta = value self.tc_imagetheta.setText("4D Data Angle: "+str(self.stack.theta[self.itheta])) self.ShowImage() #---------------------------------------------------------------------- def SetRefImage(self): self.ref_image_index = self.iev self.ref_image_index_theta = self.itheta if self.com.stack_4d == 0: self.ref_image = self.aligned_stack[:,:,self.iev].copy() else: self.ref_image = self.aligned_stack[:,:,self.iev,self.itheta].copy() self.ShowRefImage() self.have_ref_image = 1 self.UpdateWidgets() #---------------------------------------------------------------------- def SaveRefImage(self): wildcard = "TIFF File (*.tif);;" fileName, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save Reference Image', '', wildcard) fileName = str(fileName) if fileName == '': return img1 = Image.fromarray(self.ref_image) img1.save(fileName) #---------------------------------------------------------------------- def LoadRefImage(self): wildcard = "TIFF File (*.tif);;" fileName, _filter = QtWidgets.QFileDialog.getOpenFileName(self, 'Save Reference Image', '', wildcard) fileName = str(fileName) if fileName == '': return from PIL import Image img = Image.open(fileName) self.ref_image = np.array((img)) self.ShowRefImage() self.have_ref_image = 1 self.UpdateWidgets() #---------------------------------------------------------------------- def ShowRefImage(self): fig = self.refimgfig fig.clf() fig.add_axes(((0.0,0.0,1.0,1.0))) axes = fig.gca() _im = axes.imshow(np.rot90(self.ref_image), cmap=matplotlib.cm.get_cmap('gray')) if (self.subregion == 1): from matplotlib.path import Path import matplotlib.patches as patches verts = [ (self.sr_x1, self.sr_y1), # left, bottom (self.sr_x1, self.sr_y2), # left, top (self.sr_x2, self.sr_y2), # right, top (self.sr_x2, self.sr_y1), # right, bottom (self.sr_x1, self.sr_y1), # ignored ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(verts, codes) self.patch = patches.PathPatch(path, facecolor='red') self.patch.set_alpha(0.3) axes.add_patch(self.patch) if self.man_align > 0: lx=matplotlib.lines.Line2D([self.man_xref,self.man_xref], [0,self.stack.n_rows],color='green') ly=matplotlib.lines.Line2D([0,self.stack.n_cols], [self.man_yref,self.man_yref] ,color='green') axes.add_line(lx) axes.add_line(ly) axes.axis("off") self.RefImagePanel.draw() #---------------------------------------------------------------------- def Onrb_automanual(self, enabled): state = enabled if state: self.auto = True self.man_align = 0 else: self.auto = False if self.com.stack_4d == 0: self.aligned_stack = self.stack.absdata.copy() self.man_xs = np.zeros((self.stack.n_ev)) self.man_ys = np.zeros((self.stack.n_ev)) self.xshifts = np.zeros((self.stack.n_ev)) self.yshifts = np.zeros((self.stack.n_ev)) else: self.aligned_stack = self.stack.stack4D.copy() self.man_xs = np.zeros((self.stack.n_ev,self.stack.n_theta)) self.man_ys = np.zeros((self.stack.n_ev,self.stack.n_theta)) self.xshifts = np.zeros((self.stack.n_ev,self.stack.n_theta)) self.yshifts = np.zeros((self.stack.n_ev,self.stack.n_theta)) if self.have_ref_image == 1: fig = self.shiftsfig fig.clf() self.ShiftsPanel.draw() fig = self.cscorrfig fig.clf() self.CscorrPanel.draw() self.ShowImage() self.ShowRefImage() self.UpdateWidgets() #---------------------------------------------------------------------- def ShowCrossCorrelation(self, ccorr, xshift, yshift): fig = self.cscorrfig fig.clf() fig.add_axes(((0.0,0.0,1.0,1.0))) axes = fig.gca() im = axes.imshow(np.rot90(ccorr), cmap=matplotlib.cm.get_cmap('gray')) nx = ccorr.shape[0] ny = ccorr.shape[1] xcenter = xshift + float(nx)/2.0 ycenter = yshift + float(ny)/2.0 xl = xcenter-10 if xl<0: xl=0 xr = xcenter+10 if xr>nx-1: xr=nx-1 yl = ycenter-10 if yl<0: yl=0 yr = ycenter+10 if yr>ny-1: yr=ny-1 lx=matplotlib.lines.Line2D([xl,xr], [ycenter,ycenter], color='green') ly=matplotlib.lines.Line2D([xcenter,xcenter], [yl,yr], color='green') axes.add_line(lx) axes.add_line(ly) axes.axis("off") self.CscorrPanel.draw() #---------------------------------------------------------------------- def OnShowCCorr(self, state): if state == QtCore.Qt.Checked: self.showccorr = 1 else: self.showccorr = 0 #---------------------------------------------------------------------- def OnEdgeE(self, state): if state == QtCore.Qt.Checked: self.edgee = 1 self.rb_sobel.setEnabled(True) self.rb_prewitt.setEnabled(True) else: self.edgee = 0 self.rb_sobel.setEnabled(False) self.rb_prewitt.setEnabled(False) #---------------------------------------------------------------------- def OnSetMaxShift(self, value): self.maxshift = value #---------------------------------------------------------------------- def OnRemoveImage(self, event): self.stack.absdata = np.delete(self.stack.absdata, self.iev, axis=2) self.aligned_stack = np.delete(self.aligned_stack, self.iev, axis=2) if self.com.stack_4d == 1: self.stack.stack4D = np.delete(self.stack.stack4D, self.iev, axis=2) self.stack.n_ev = self.stack.n_ev - 1 self.stack.ev = np.delete(self.stack.ev, self.iev) if self.com.stack_4d == 0: self.stack.data_struct.exchange.data = self.stack.absdata self.xshifts = np.delete(self.xshifts, self.iev) self.yshifts = np.delete(self.yshifts, self.iev) else: self.stack.data_struct.exchange.data = self.stack.stack4D self.xshifts = np.delete(self.xshifts, self.iev, axis=0) self.yshifts = np.delete(self.yshifts, self.iev, axis=0) self.stack.data_struct.exchange.energy = self.stack.ev if self.com.i0_loaded == 1: self.stack.calculate_optical_density() self.iev = self.iev-1 if self.iev < 0: self.iev = 0 self.slider_eng.setRange(0, self.stack.n_ev-1) self.parent.page1.slider_eng.setRange(0, self.stack.n_ev-1) self.parent.page1.iev = self.stack.n_ev/2 self.parent.page1.slider_eng.setValue(self.parent.page1.iev) self.parent.page1.showSpectrum(self.parent.page1.ix, self.parent.page1.iy) self.parent.page1.loadImage() self.ShowImage() #---------------------------------------------------------------------- def OnCalcRegistration(self, event): if self.com.stack_4d == 1: self.CalcRegistration4D() return QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) #Get Edge enhancement info edge = 0 if self.edgee > 0: if self.rb_sobel.isChecked(): edge = 1 else: edge = 2 #Subregion selection on a reference image if self.subregion == 0: self.sr_x1 = 0 self.sr_x2 = 0 self.sr_y1 = 0 self.sr_y2 = 0 referenceimage = self.ref_image else: referenceimage = self.ref_image[self.sr_x1:self.sr_x2, self.sr_y2:self.sr_y1] for i in range(self.stack.n_ev): if self.subregion == 0: img2 = self.aligned_stack[:,:,i] else: img2 = self.aligned_stack[self.sr_x1:self.sr_x2, self.sr_y2:self.sr_y1, i] if i==0: xshift, yshift, ccorr = self.stack.register_images(referenceimage, img2, have_ref_img_fft = False, edge_enhancement = edge) elif i==self.ref_image_index: xshift = 0 yshift = 0 else: xshift, yshift, ccorr = self.stack.register_images(referenceimage, img2, have_ref_img_fft = True, edge_enhancement = edge) #Limit the shifts to MAXSHIFT chosen by the user if (self.maxshift > 0): if (abs(xshift) > self.maxshift): xshift = np.sign(xshift)*self.maxshift if (abs(yshift) > self.maxshift): yshift = np.sign(yshift)*self.maxshift self.xshifts[i] = xshift self.yshifts[i] = yshift self.PlotShifts() if self.showccorr == 1: self.ShowCrossCorrelation(ccorr, xshift, yshift) QCoreApplication.processEvents() #Apply shifts for i in range(self.stack.n_ev): img = self.aligned_stack[:,:,i] if (abs(self.xshifts[i])>0.02) or (abs(self.yshifts[i])>0.02): shifted_img = self.stack.apply_image_registration(img, self.xshifts[i], self.yshifts[i]) self.aligned_stack[:,:,i] = shifted_img self.regist_calculated = 1 self.iev = 0 self.ShowImage() self.slider_eng.setValue(self.iev) self.UpdateWidgets() min_xshift = np.min(self.xshifts) max_xshift = np.max(self.xshifts) min_yshift = np.min(self.yshifts) max_yshift = np.max(self.yshifts) if min_xshift < self.minxs : self.minxs = min_xshift if max_xshift > self.maxxs : self.maxxs = max_xshift if min_yshift < self.minys : self.minys = min_yshift if max_yshift > self.maxys : self.maxys = max_yshift QtWidgets.QApplication.restoreOverrideCursor() #---------------------------------------------------------------------- def CalcRegistration4D(self): QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) #Get Edge enhancement info edge = 0 if self.edgee > 0: if self.rb_sobel.isChecked(): edge = 1 else: edge = 2 #Subregion selection on a reference image if self.subregion == 0: self.sr_x1 = 0 self.sr_x2 = 0 self.sr_y1 = 0 self.sr_y2 = 0 referenceimage = self.ref_image else: referenceimage = self.ref_image[self.sr_x1:self.sr_x2, self.sr_y2:self.sr_y1] temptheta = self.itheta for j in range(self.stack.n_theta): self.itheta = j for i in range(self.stack.n_ev): if self.subregion == 0: img2 = self.aligned_stack[:,:,i,j] else: img2 = self.aligned_stack[self.sr_x1:self.sr_x2, self.sr_y2:self.sr_y1, i, j] if i==0 and j==0: xshift, yshift, ccorr = self.stack.register_images(referenceimage, img2, have_ref_img_fft = False, edge_enhancement = edge) elif i==self.ref_image_index and j==self.ref_image_index_theta: xshift = 0 yshift = 0 else: xshift, yshift, ccorr = self.stack.register_images(referenceimage, img2, have_ref_img_fft = True, edge_enhancement = edge) #Limit the shifts to MAXSHIFT chosen by the user if (self.maxshift > 0): if (abs(xshift) > self.maxshift): xshift = np.sign(xshift)*self.maxshift if (abs(yshift) > self.maxshift): yshift = np.sign(yshift)*self.maxshift self.xshifts[i,j] = xshift self.yshifts[i,j] = yshift self.PlotShifts() if self.showccorr == 1: self.ShowCrossCorrelation(ccorr, xshift, yshift) QCoreApplication.processEvents() #Apply shifts for i in range(self.stack.n_ev): for j in range(self.stack.n_theta): img = self.aligned_stack[:,:,i,j] if (abs(self.xshifts[i,j])>0.02) or (abs(self.yshifts[i,j])>0.02): shifted_img = self.stack.apply_image_registration(img, self.xshifts[i,j], self.yshifts[i,j]) self.aligned_stack[:,:,i,j] = shifted_img self.itheta = temptheta self.regist_calculated = 1 self.iev = 0 self.ShowImage() self.slider_eng.setValue(self.iev) self.UpdateWidgets() min_xshift = np.min(self.xshifts) max_xshift = np.max(self.xshifts) min_yshift = np.min(self.yshifts) max_yshift = np.max(self.yshifts) if min_xshift < self.minxs : self.minxs = min_xshift if max_xshift > self.maxxs : self.maxxs = max_xshift if min_yshift < self.minys : self.minys = min_yshift if max_yshift > self.maxys : self.maxys = max_yshift QtWidgets.QApplication.restoreOverrideCursor() #---------------------------------------------------------------------- def OnCropShifts(self, event): QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.aligned_stack, self.xleft, self.xright, self.ybottom, self.ytop, = self.stack.crop_registed_images(self.aligned_stack, self.minxs, self.maxxs, self.minys, self.maxys) self.iev = 0 self.ShowImage() self.slider_eng.setValue(self.iev) QtWidgets.QApplication.restoreOverrideCursor() #---------------------------------------------------------------------- def OnPickRefPoint(self, event): self.man_align = 1 #---------------------------------------------------------------------- def OnPointRefimage(self, evt): x = evt.xdata y = evt.ydata if (x == None) or (y == None): return if self.subregion == 1: self.sr_x1 = x self.sr_y1 = y self.button_pressed = True self.mousemoveconn = self.RefImagePanel.mpl_connect('motion_notify_event', self.OnSelectionMotion) return if self.man_align == 0: pass if (self.man_align == 1): self.man_xref = int(np.floor(x)) self.man_yref = int(np.floor(y)) if self.man_xref<0 : self.man_xref=0 if self.man_xref>self.stack.n_cols : self.man_xref=self.stack.n_cols if self.man_yref<0 : self.man_yref=0 if self.man_yref>self.stack.n_rows : self.man_yref=self.stack.n_rows self.UpdateWidgets() self.ShowRefImage() #---------------------------------------------------------------------- def OnPickCorrPoint(self): self.man_align = 2 #---------------------------------------------------------------------- def OnPointCorrimage(self, evt): x = evt.xdata y = evt.ydata if (self.man_align == 2): xcorr = float(x) ycorr = float(y) if xcorr<0 : xcorr=0 if xcorr>self.stack.n_cols : xcorr=self.stack.n_cols if ycorr<0 : ycorr=0 if ycorr>self.stack.n_rows : ycorr=self.stack.n_rows if self.com.stack_4d == 0: self.man_xs[self.iev] = self.man_xref - xcorr self.man_ys[self.iev] = -1.0*(self.man_yref - ycorr) self.textctrl_ms1.setText('X manual shift: {0:5.2f} pixels\n'.format(self.man_xs[self.iev])) self.textctrl_ms2.setText('Y manual shift: {0:5.2f} pixels'.format(self.man_ys[self.iev])) else: self.man_xs[self.iev, self.itheta] = self.man_xref - xcorr self.man_ys[self.iev, self.itheta] = -1.0*(self.man_yref - ycorr) self.textctrl_ms1.setText('X manual shift: {0:5.2f} pixels\n'.format(self.man_xs[self.iev, self.itheta])) self.textctrl_ms2.setText('Y manual shift: {0:5.2f} pixels'.format(self.man_ys[self.iev, self.itheta])) self.iev = self.iev + 1 if self.iev > (self.stack.n_ev-1): self.iev = 0 self.slider_eng.setValue(self.iev) self.ShowImage() self.UpdateWidgets() #---------------------------------------------------------------------- def OnApplyManShifts(self): if self.com.stack_4d == 0: for i in range(self.stack.n_ev): img = self.aligned_stack[:,:,i] if (abs(self.man_xs[i])>0.02) or (abs(self.man_ys[i])>0.02): shifted_img = self.stack.apply_image_registration(img, self.man_xs[i], self.man_ys[i]) self.aligned_stack[:,:,i] = shifted_img self.xshifts[i] = self.xshifts[i] + self.man_xs[i] self.yshifts[i] = self.yshifts[i] + self.man_ys[i] self.man_xs[i] = 0 self.man_ys[i] = 0 else: for i in range(self.stack.n_ev): for j in range(self.stack.n_theta): img = self.aligned_stack[:,:,i,j] if (abs(self.man_xs[i,j])>0.02) or (abs(self.man_ys[i,j])>0.02): shifted_img = self.stack.apply_image_registration(img, self.man_xs[i,j], self.man_ys[i,j]) self.aligned_stack[:,:,i,j] = shifted_img self.xshifts[i,j] = self.xshifts[i,j] + self.man_xs[i,j] self.yshifts[i,j] = self.yshifts[i,j] + self.man_ys[i,j] self.man_xs[i,j] = 0 self.man_ys[i,j] = 0 self.regist_calculated = 1 self.man_align = 0 self.ShowRefImage() self.PlotShifts() self.textctrl_ms1.setText('X manual shift: \n') self.textctrl_ms2.setText('Y manual shift: ') self.UpdateWidgets() min_xshift = np.min(self.xshifts) max_xshift = np.max(self.xshifts) min_yshift = np.min(self.yshifts) max_yshift = np.max(self.yshifts) if min_xshift < self.minxs : self.minxs = min_xshift if max_xshift > self.maxxs : self.maxxs = max_xshift if min_yshift < self.minys : self.minys = min_yshift if max_yshift > self.maxys : self.maxys = max_yshift self.ShowImage() #---------------------------------------------------------------------- def OnAccept(self): if self.com.stack_4d == 0: self.stack.absdata = self.aligned_stack self.stack.data_struct.exchange.data = self.stack.absdata else: self.stack.stack4D = self.aligned_stack self.stack.absdata = self.stack.stack4D[:,:,:,self.itheta] self.stack.data_struct.exchange.data = self.stack.stack4D datadim = np.int32(self.stack.absdata.shape) self.stack.n_cols = datadim[0].copy() self.stack.n_rows = datadim[1].copy() self.stack.xshifts = self.xshifts self.stack.yshifts = self.yshifts if self.com.i0_loaded == 1: if self.com.stack_4d == 0: #Resize optical density for i in range(self.stack.n_ev): img = self.stack.od3d[:,:,i] shifted_img = self.stack.apply_image_registration(img, self.xshifts[i], self.yshifts[i]) self.stack.od3d[:,:,i] = shifted_img self.stack.od3d = self.stack.od3d[self.xleft:self.xright, self.ybottom:self.ytop, :] self.stack.od = self.stack.od3d.copy() self.stack.od = np.reshape(self.stack.od, (self.stack.n_cols*self.stack.n_rows, self.stack.n_ev), order='F') else: #Resize optical density for 4D stack for i in range(self.stack.n_ev): for j in range(self.stack.n_theta): img = self.stack.od4D[:,:,i,j] shifted_img = self.stack.apply_image_registration(img, self.xshifts[i,j], self.yshifts[i,j]) self.stack.od4D[:,:,i,j] = shifted_img self.stack.od4D = self.stack.od4D[self.xleft:self.xright, self.ybottom:self.ytop, :, :] self.stack.od3d = self.stack.od4D[:,:,:,self.itheta] self.stack.od = self.stack.od3d.copy() n_pixels = self.stack.n_cols*self.stack.n_rows self.stack.od = np.reshape(self.stack.od, (n_pixels, self.stack.n_ev), order='F') self.stack.data_struct.spectromicroscopy.optical_density = self.stack.od self.stack.data_struct.exchange.energy = self.stack.ev self.stack.data_struct.spectromicroscopy.xshifts = self.xshifts self.stack.data_struct.spectromicroscopy.yshifts = self.yshifts self.parent.page1.slider_eng.setRange(0,self.stack.n_ev-1) self.parent.page1.iev = int(self.stack.n_ev/2) self.parent.page1.slider_eng.setValue(self.parent.page1.iev) self.parent.page1.ix = int(self.stack.n_cols/2) self.parent.page1.iy = int(self.stack.n_rows/2) self.parent.page1.showSpectrum(self.parent.page1.ix, self.parent.page1.iy) self.parent.page1.loadImage() #self.parent.page9.loadImage() self.close() #---------------------------------------------------------------------- def PlotShifts(self, ): fig = self.shiftsfig fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) axes = fig.gca() #Matplotlib has inverted axes! axes.set_xlabel('Photon Energy [eV]') axes.set_ylabel('Shifts (x-red, y-green) [pixels]') if self.com.stack_4d == 0: plot = axes.plot(self.stack.ev,self.xshifts, color='green') plot = axes.plot(self.stack.ev,self.yshifts, color='red') else: plot = axes.plot(self.stack.ev,self.xshifts[:,self.itheta], color='green') plot = axes.plot(self.stack.ev,self.yshifts[:,self.itheta], color='red') self.ShiftsPanel.draw() #---------------------------------------------------------------------- def OnPlotShifts(self, evt): x = evt.xdata y = evt.ydata if self.xshifts.any(): if x < self.stack.ev[0]: sel_ev = 0 elif x > self.stack.ev[self.stack.n_ev-1]: sel_ev = self.stack.n_ev-1 else: indx = np.abs(self.stack.ev - x).argmin() sel_ev = indx self.iev = sel_ev self.ShowImage() self.slider_eng.setValue(self.iev) #---------------------------------------------------------------------- def OnSaveShiftsPlot(self, evt): wildcard = "Portable Network Graphics (*.png);;Adobe PDF Files (*.pdf);;" self.SaveFileName, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save Image Shifts Plot', '', wildcard) self.SaveFileName = str(self.SaveFileName) if self.SaveFileName == '': return path, ext = os.path.splitext(self.SaveFileName) ext = ext[1:].lower() try: matplotlib.rcParams['pdf.fonttype'] = 42 if ext == 'png': fig = self.shiftsfig fig.savefig(self.SaveFileName) if ext =='pdf': fig = self.shiftsfig fig.savefig(self.SaveFileName) except: pass #---------------------------------------------------------------------- def OnSelectSubregion(self, event): self.subregion = 1 self.sr_x1 = 0 self.sr_x2 = 0 self.sr_y1 = 0 self.sr_y2 = 0 self.button_delsubregion.setEnabled(True) #---------------------------------------------------------------------- def OnDeleteSubregion(self, event): self.subregion = 0 self.sr_x1 = 0 self.sr_x2 = 0 self.sr_y1 = 0 self.sr_y2 = 0 self.button_pressed = False self.patch = None self.RefImagePanel.mpl_disconnect(self.mousemoveconn) self.ShowRefImage() #---------------------------------------------------------------------- def OnSelection(self, evt): if (self.man_align > 0) and (self.subregion == 0): return x2, y2 = evt.xdata, evt.ydata if (x2 == None) or (y2 == None): return self.sr_x1 = int(self.sr_x1) self.sr_x2 = int(x2) self.sr_y2 = int(self.sr_y1) self.sr_y1 = int(y2) if self.sr_x1 > self.sr_x2: temp = self.sr_x1 self.sr_x1 = self.sr_x2 self.sr_x2 = temp if self.sr_y2 > self.sr_y1: temp = self.sr_y1 self.sr_y1 = self.sr_y2 self.sr_y2 = temp self.button_pressed = False self.RefImagePanel.mpl_disconnect(self.OnSelectionMotion) self.ShowRefImage() #---------------------------------------------------------------------- def OnSelectionMotion(self, event): x2, y2 = event.xdata, event.ydata if (x2 == None) or (y2 == None): return if self.button_pressed == False: return self.sr_x2 = int(x2) self.sr_y2 = int(y2) fig = self.refimgfig axes = fig.gca() from matplotlib.path import Path import matplotlib.patches as patches verts = [ (self.sr_x1, self.sr_y1), # left, bottom (self.sr_x1, self.sr_y2), # left, top (self.sr_x2, self.sr_y2), # right, top (self.sr_x2, self.sr_y1), # right, bottom (self.sr_x1, self.sr_y1), # ignored ] codes = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY, ] path = Path(verts, codes) if self.patch != None: self.patch.remove() self.patch = patches.PathPatch(path, facecolor='red') self.patch.set_alpha(0.3) axes.add_patch(self.patch) axes.axis("off") self.RefImagePanel.draw() #---------------------------------------------------------------------- def OnSaveShifts(self, evt): wildcard = "CSV files (*.csv)" filepath, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Please select an alignment file (.csv)', '', wildcard) filepath = str(filepath) if filepath == '': return f = open(filepath, 'w') if self.com.stack_4d == 0: print('********************* Alignment file ********************', file=f) print('*** for ', self.com.filename, file=f) print('*** ev, xshift, yshift', file=f) for ie in range(self.stack.n_ev): print('%.6f, %.6f, %.6f' %(self.stack.ev[ie], self.xshifts[ie], self.yshifts[ie]), file=f) else: print('********************* Alignment file ********************', file=f) print('*** for ', self.com.filename, file=f) print('*** ev, theta, xshift, yshift', file=f) for i in range(self.stack.n_ev): for j in range(self.stack.n_theta): print('%.6f, %.6f, %.6f, %.6f' %(self.stack.ev[i], self.stack.theta[j], self.xshifts[i,j], self.yshifts[i,j]), file=f) f.close() #---------------------------------------------------------------------- def OnLoadShifts(self, evt): wildcard = "I0 CSV files (*.csv)" filepath, _filter = QtWidgets.QFileDialog.getOpenFileName(self, 'Please select an alignment file (.csv)', '', wildcard) filepath = str(filepath) if filepath == '': return f = open(str(filepath),'r') elist = [] xshiftlist = [] yshiftlist = [] for line in f: if line.startswith('*'): continue else: e, xs, ys = [float (x) for x in line.split(',')] elist.append(e) xshiftlist.append(xs) yshiftlist.append(ys) f.close() self.xshifts = np.zeros((self.stack.n_ev)) self.yshifts = np.zeros((self.stack.n_ev)) for ie in range(self.stack.n_ev): engfl = '%.6f' % ( self.stack.ev[ie]) eng = float(engfl) if eng in elist: ind = elist.index(eng) self.xshifts[ie] = xshiftlist[ind] self.yshifts[ie] = yshiftlist[ind] #Apply shifts self.PlotShifts() for i in range(self.stack.n_ev): img = self.aligned_stack[:,:,i] if (abs(self.xshifts[i])>0.02) or (abs(self.yshifts[i])>0.02): shifted_img = self.stack.apply_image_registration(img, self.xshifts[i], self.yshifts[i]) self.aligned_stack[:,:,i] = shifted_img self.regist_calculated = 1 self.iev = 0 self.ShowImage() self.slider_eng.setValue(self.iev) self.regist_calculated = 1 self.man_align = 0 self.textctrl_ms1.setText('X manual shift: \n') self.textctrl_ms2.setText('Y manual shift: ') self.UpdateWidgets() min_xshift = np.min(self.xshifts) max_xshift = np.max(self.xshifts) min_yshift = np.min(self.yshifts) max_yshift = np.max(self.yshifts) if min_xshift < self.minxs : self.minxs = min_xshift if max_xshift > self.maxxs : self.maxxs = max_xshift if min_yshift < self.minys : self.minys = min_yshift if max_yshift > self.maxys : self.maxys = max_yshift #---------------------------------------------------------------------- def UpdateWidgets(self): if self.auto: self.button_manalign.setEnabled(False) if self.have_ref_image == 1: self.button_register.setEnabled(True) self.button_subregion.setEnabled(True) self.button_refimgsave.setEnabled(True) else: self.button_register.setEnabled(False) self.button_subregion.setEnabled(False) self.button_refimgsave.setEnabled(False) self.button_delsubregion.setEnabled(False) if self.regist_calculated == 1: self.button_crop.setEnabled(True) self.button_accept.setEnabled(True) self.button_saveshifts.setEnabled(True) self.button_saveimg.setEnabled(True) else: self.button_crop.setEnabled(False) self.button_accept.setEnabled(False) self.button_saveshifts.setEnabled(False) self.button_saveimg.setEnabled(False) else: self.button_register.setEnabled(False) self.button_delsubregion.setEnabled(False) self.button_subregion.setEnabled(False) if self.have_ref_image == 1: self.button_manalign.setEnabled(True) self.button_refimgsave.setEnabled(True) else: self.button_manalign.setEnabled(False) self.button_refimgsave.setEnabled(False) if self.regist_calculated == 1: self.button_crop.setEnabled(True) self.button_accept.setEnabled(True) self.button_saveshifts.setEnabled(True) else: self.button_crop.setEnabled(False) self.button_accept.setEnabled(False) self.button_saveshifts.setEnabled(False) if self.man_align == 0: self.button_pick2ndpoint.setEnabled(False) self.button_applyman.setEnabled(False) elif self.man_align == 1: self.button_pick2ndpoint.setEnabled(True) elif self.man_align == 2: self.button_applyman.setEnabled(True) class GeneralPurposeSignals(QtCore.QObject): finished = pyqtSignal() ithetaprogress = pyqtSignal(int) # ---------------------------------------------------------------------- class GeneralPurposeProcessor(QtCore.QRunnable): def __init__(self, parent, queue): super(GeneralPurposeProcessor, self).__init__() self.signals = GeneralPurposeSignals() self.funcdict = {"ShiftImg": self.ShiftImg, "AlignReferenced": self.AlignReferenced} self.parent = parent self.queue = queue self.current_itheta = 0 @pyqtSlot() def run(self): #print('worker', threading.get_ident()) while True: #print(self.parent.pool.pool.activeThreadCount()) #print(QtCore.QThreadPool.activeThreadCount()) try: workerfunc, *args = self.queue.get(False) self.funcdict[workerfunc](*args[0]) #print("busy with task {}".format(workerfunc)) except Empty: # if queue empty break def AlignReferenced(self, data, itheta): if self.current_itheta != itheta: self.current_itheta = itheta self.signals.ithetaprogress.emit(itheta) drift_x = [0,0] drift_y = [0,0] drift, error, _ = phase_cross_correlation(self.Gauss(self.parent.stack.absdata_cropped[:, :, data[0],itheta]), self.Gauss(self.parent.stack.absdata_cropped[:, :, data[1],itheta]),upsample_factor=20) ## 20 means 0.05 px precision self.parent.stack.shiftsdict[itheta]["errors"][data[0]] = round(error,4) if data[0] - data[1] > 0: drift_x[1] = round(drift[0],2) drift_y[1] = round(drift[1],2) self.parent.stack.shiftsdict[itheta]["xdots"][data[0]]= drift_x[1] self.parent.stack.shiftsdict[itheta]["ydots"][data[0]]= drift_y[1] else: drift_x[0] = round(drift[0],2) drift_y[0] = round(drift[1],2) self.parent.stack.shiftsdict[itheta]["xdots"][data[0]]= drift_x[0] self.parent.stack.shiftsdict[itheta]["ydots"][data[0]]= drift_y[0] def ShiftImg(self, row,x,y,itheta): shifted = ndimage.fourier_shift(np.fft.fft2(self.parent.stack.absdata4d[:, :, row,itheta]), [float(-x),float(-y)]) shifted = np.fft.ifft2(shifted) self.parent.stack.absdata4d_shifted[:, :, row, itheta] = shifted.real return def Gauss(self, im): gaussed = ndimage.gaussian_filter(im, self.parent.spinBoxGauss.value()) return gaussed class TaskDispatcher(QtCore.QObject): def __init__(self,parent): print("0 - dispatcher called, pool initiated") super(TaskDispatcher, self).__init__() try: self.worker.signals.ithetaprogress.disconnect() self.worker.signals.finished.disconnect() except: pass self.pool = QtCore.QThreadPool.globalInstance() try: cpus = len(os.sched_getaffinity(0)) # number of cpu threads. not supported on some platforms. except: cpus = os.cpu_count() self.pool.setMaxThreadCount(cpus) self.queue = SimpleQueue() self.parent = parent @pyqtSlot() def run(self): #print(self.queue.qsize()) #print('pool', threading.get_ident()) # if self.parent.com.stack_4d == 0: qsize = int(self.queue.qsize()) maxthreads = self.pool.maxThreadCount() preferred_thread_number = min(maxthreads, qsize) print("1 - starting threads",preferred_thread_number, "threads needed, number of tasks:",qsize) while int(self.queue.qsize()) and self.pool.activeThreadCount() < preferred_thread_number: #start as many threads as needed. #print("active threads"+str(self.pool.activeThreadCount())+" qsize "+str(int(self.queue.qsize()))) worker = GeneralPurposeProcessor(self.parent,self.queue) worker.signals.ithetaprogress.connect(self.parent.IThetaProgress) self.pool.start(worker) time.sleep(0.02) # artifical delay to start threads with a little time separation. Otherwise ShiftImgs freezes in Win10 and MacOS for small stacks! self.pool.waitForDone() print("2 - all threads dead") worker.signals.finished.connect(self.parent.ThreadPoolComplete) worker.signals.finished.emit() #add a task to the queue def enqueuetask(self, func, *args, **kargs): self.queue.put((func, args, kargs)) class ImageRegistrationFFT(QtWidgets.QDialog, QtWidgets.QGraphicsScene): def __init__(self, parent, common, stack): QtWidgets.QWidget.__init__(self, parent) uic.loadUi(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'showalign2.ui'), self) self.parent = parent self.stack = stack self.com = common self.iev = 0 self.itheta = 0 if self.com.stack_loaded == 1: if self.com.stack_4d: self.stack.absdata4d = self.stack.stack4D.copy() #self.button_alignbatch.setVisible(True) self.button_align.setText("Align Batch") self.thetathread = QtCore.QThread() else: self.stack.absdata4d = np.expand_dims(self.stack.absdata.copy(), axis=3) #self.button_alignbatch.setVisible(False) self.button_align.setText("Align") self.stack.absdata_cropped = self.stack.absdata4d.copy() # This image stack is used by the alignment/shift routine and cropped to the ROI rectangle self.stack.absdata4d_shifted = self.stack.absdata4d.copy() # This is the full-sized output of the alignment/shift routine self.stack.absdata4d_shifted_cropped = self.stack.absdata4d.copy() # This is the output cropped to the common region #self.stack.absdata_unaligned = self.stack.absdata_shifted_cropped self.poolthread = QtCore.QThread() self.aligned = False self.SetupUI() def SetupUI(self): self.button_ok.setEnabled(False) self.slider_theta.setVisible(False) self.setWindowTitle('Align Stack v2') self.pglayout = pg.GraphicsLayout(border=None) self.canvas.setBackground("w") # canvas is a pg.GraphicsView widget self.canvas.setCentralWidget(self.pglayout) self.vb = self.pglayout.addViewBox() self.vb.setAspectLocked() self.i_item = pg.ImageItem(border="k",parent= self) self.vb.setMouseEnabled(x=False, y=False) self.vb.addItem(self.i_item, ignoreBounds=False) self.DriftsWidget.setBackground("w") self.py = self.DriftsWidget.addPlot(row=0, col=0, rowspan=1, colspan=1) self.py.setMouseEnabled(x=False, y=True) #self.i_item = pg.ImageItem(border="k") #self.py.setAspectLocked(lock=True, ratio=1) self.py.showAxis("top", show=True) self.py.showAxis("bottom", show=True) self.py.showAxis("left", show=True) self.py.showAxis("right", show=True) ay1 = self.py.getAxis("left") by1 = self.py.getAxis("right") ax1 = self.py.getAxis("bottom") bx1 = self.py.getAxis("top") ay1.setLabel(text="y-drift", units="px") ay1.enableAutoSIPrefix(enable=True) ay1.setWidth(w=60) ax1.setLabel(text="Photon Energy",units="eV") ax1.enableAutoSIPrefix(enable=True) ay1.setStyle(tickLength=8) ax1.setStyle(tickLength=0) #ax1.setHeight(h=46.2) ax1.setStyle(tickLength=8) by1.setStyle(showValues=False, tickLength=0) bx1.setStyle(showValues=False, tickLength=0) self.px = self.DriftsWidget.addPlot(row=1, col=0, rowspan=1, colspan=1) self.px.setXLink(self.py) self.px.setMouseEnabled(x=False, y=True) #self.i_item = pg.ImageItem(border="k") #self.px.setAspectLocked(lock=True, ratio=1) self.px.showAxis("top", show=True) self.px.showAxis("bottom", show=True) self.px.showAxis("left", show=True) self.px.showAxis("right", show=True) ay2 = self.px.getAxis("left") by2 = self.px.getAxis("right") ax2 = self.px.getAxis("bottom") bx2 = self.px.getAxis("top") ay2.setLabel(text="x-drift",units="px") ay2.enableAutoSIPrefix(enable=True) ay2.setWidth(w=60) ax2.setLabel(text="Photon Energy",units="eV") ax2.enableAutoSIPrefix(enable=True) ay2.setStyle(tickLength=8) ax2.setStyle(tickLength=8) by2.setStyle(showValues=False,tickLength=0) bx2.setStyle(showValues=False,tickLength=0) self.button_ok.clicked.connect(self.OnAccept) self.button_cancel.clicked.connect(self.OnCancel) if self.com.stack_loaded == 1: if self.com.stack_4d: self.slider_theta.setVisible(True) self.slider_theta.setRange(0, self.stack.n_theta - 1) self.slider_theta.valueChanged[int].connect(self.OnScrollTheta) #self.maskedvals = [True] * int(self.stack.n_ev) self.spinBoxError.setEnabled(False) self.slider_eng.sliderPressed.connect(self.ShowImage) self.slider_eng.sliderReleased.connect(self.ShowImage) self.slider_eng.valueChanged[int].connect(self.OnScrollEng) self.slider_eng.setRange(0, self.stack.n_ev - 1) self.refmarkerx = pg.InfiniteLine(angle=90, movable=False, pen=pg.mkPen(color="b", width=2, style=QtCore.Qt.DashLine)) self.refmarkery = pg.InfiniteLine(angle=90, movable=False, pen=pg.mkPen(color="b", width=2, style=QtCore.Qt.DashLine)) self.px.addItem(self.refmarkerx, ignoreBounds=True) self.py.addItem(self.refmarkery, ignoreBounds=True) self.fit_x = pg.PlotCurveItem(pen=pg.mkPen(color="c", width=2)) self.fit_y = pg.PlotCurveItem(pen=pg.mkPen(color="c", width=2)) self.fit_x.setZValue(200) self.fit_y.setZValue(200) self.px.addItem(self.fit_x, ignoreBounds=True) self.py.addItem(self.fit_y, ignoreBounds=True) self.OnScrollEng(0) self.SetupROI() self.button_align.clicked.connect(self.ComposeAlignQueue) self.xregion = pg.LinearRegionItem(brush=[255, 0, 0, 45], bounds=[self.stack.ev[0], self.stack.ev[-1]]) self.yregion = pg.LinearRegionItem(brush=[255, 0, 0, 45], bounds=[self.stack.ev[0], self.stack.ev[-1]]) self.xregion.setRegion([self.stack.ev[0], self.stack.ev[-1]]) self.yregion.setRegion([self.stack.ev[0], self.stack.ev[-1]]) self.yregion.setZValue(100) self.xregion.setZValue(100) self.px.addItem(self.xregion, ignoreBounds=False) self.py.addItem(self.yregion, ignoreBounds=False) self.xregion.sigRegionChangeFinished.connect(lambda region: self.OnLinRegion(region)) self.yregion.sigRegionChangeFinished.connect(lambda region: self.OnLinRegion(region)) self.xscatter = pg.ScatterPlotItem(pxMode=False) self.yscatter = pg.ScatterPlotItem(pxMode=False) self.InitShiftsDict() self.px.addItem(self.xscatter) self.py.addItem(self.yscatter) self.MakeNewScatterPlots() #self.shifts = self.stack.shifts.copy() self.xscatter.sigClicked.connect(self.OnPointClicked) self.yscatter.sigClicked.connect(self.OnPointClicked) self.cb_autocrop.toggled.connect(self.OnAutoCrop) def InitShiftsDict(self): outer_keys = range(max(self.stack.n_theta,1)) inner_keys = ["xdots", "ydots", "xshifts", "yshifts", "errors", "errormaskedx","errormaskedy","manualmaskedx","manualmaskedy"] self.stack.shiftsdict = {intkey : {key: [False] * int(self.stack.n_ev) for key in inner_keys} for intkey in outer_keys} single_keys = ["filter", "method", "autoquality", "extrapolation", "threshold", "regionlimitx", "regionlimity"] for intkey in outer_keys: self.stack.shiftsdict[intkey].update({key: False for key in single_keys}) def CreateScatterDots(self,shifts,mask): scatterdots = [{'pos': tup[0:2], 'size': 10, #'pen': {'color': 'w', 'width': 2}, 'brush': QtGui.QColor('red')} if tup[2] else {'pos': tup[0:2], 'size': 10, #'pen': {'color': 'w', 'width': 2}, 'brush': QtGui.QColor('blue')} for tup in list(zip(self.stack.ev, shifts, mask))] return scatterdots def OnPointClicked(self, obj, points): # Manually add/remove points to/from fit if in selected region selectscatter = {self.xscatter: ["errormaskedx","manualmaskedx",self.xregion], self.yscatter: ["errormaskedy","manualmaskedy",self.yregion]} idx = points[0].index() mask1 = self.stack.shiftsdict[self.itheta][selectscatter[obj][1]] mask2 = self.stack.shiftsdict[self.itheta][selectscatter[obj][0]] min_idx, max_idx, *_ = self.getDataClosestToRegion(selectscatter[obj][2], obj) if min_idx <= idx <= max_idx : mask1[idx] = not np.logical_or(mask1[idx],mask2[idx]) mask2[idx] = mask1[idx] self.ColorizeScatterDots() self.OnScrollEng(points[0].index()) if self.aligned: self.ComposeShiftQueue() def OnAligned(self): self.aligned = True print("aligned") self.cb_autoerror.stateChanged.disconnect() self.cb_extrapolate.stateChanged.disconnect() self.spinBoxFiltersize.valueChanged.disconnect() self.spinBoxError.valueChanged.disconnect() self.comboBox_approx.currentIndexChanged.disconnect() self.MakeNewScatterPlots() self.OnLinRegion(self.xregion, update=False) self.OnLinRegion(self.yregion, update=False) self.ComposeShiftQueue(init=True) # ---------------------------------------------------------------------- def MaskedScatterDotsArray(self,region): selection = {self.xregion : ["errormaskedx","manualmaskedx"], self.yregion : ["errormaskedy","manualmaskedy"]} array = np.logical_or(self.stack.shiftsdict[self.itheta][selection[region][0]], self.stack.shiftsdict[self.itheta][selection[region][1]]) return array def MakeNewScatterPlots(self): print("makenewscatte") errorthreshold = self.stack.shiftsdict[self.itheta]["threshold"] errors = self.stack.shiftsdict[self.itheta]["errors"] self.MaskScatterDotsAboveErrorThreshold((errors,errorthreshold,self.itheta)) maskedx = self.MaskedScatterDotsArray(self.xregion) maskedy = self.MaskedScatterDotsArray(self.yregion) self.cb_autoerror.stateChanged.connect(self.OnAutoError) self.cb_extrapolate.stateChanged.connect(self.OnExtrapolate) self.spinBoxFiltersize.valueChanged.connect(self.OnFilter) self.spinBoxError.valueChanged.connect(lambda value: self.OnSpinBoxError(value)) self.comboBox_approx.currentIndexChanged.connect(lambda: self.ComposeShiftQueue(init=False)) self.spinBoxError.blockSignals(True) self.spinBoxError.setDecimals(4) self.spinBoxError.setMinimum(np.partition(errors, 1)[1]) # makes sure that at least two elements are selected self.spinBoxError.setStepType(QtWidgets.QAbstractSpinBox.AdaptiveDecimalStepType) self.OnAutoError() self.spinBoxError.blockSignals(False) #self.spinBoxError.setValue(self.errormean) #self.OnMaskScatterDotsAboveErrorThreshold(errorvals=(errors, round(np.mean(errors),4))) #self.xscatter.addPoints(spots=self.CreateScatterDots(xdots), pxMode=True) #self.yscatter.addPoints(spots=self.CreateScatterDots(ydots), pxMode=True) xdots = self.stack.shiftsdict[self.itheta]["xdots"] ydots = self.stack.shiftsdict[self.itheta]["ydots"] self.xscatter.setData(spots=self.CreateScatterDots(xdots,maskedx), pxMode=True) self.yscatter.setData(spots=self.CreateScatterDots(ydots,maskedy), pxMode=True) def OnAutoError(self): print("onautoerror",self.stack.shiftsdict[self.itheta]["threshold"]) self.stack.shiftsdict[self.itheta]["autoquality"] = self.cb_autoerror.isChecked() self.stack.shiftsdict[self.itheta]["threshold"] = round(np.mean(self.stack.shiftsdict[self.itheta]["errors"]), 4) if self.cb_autoerror.isChecked() and self.aligned: self.spinBoxError.setEnabled(True) self.spinBoxError.setValue(self.stack.shiftsdict[self.itheta]["threshold"]) else: self.spinBoxError.setEnabled(False) self.spinBoxError.setValue(1) def OnSpinBoxError(self,value): print("onspinboxerror") self.stack.shiftsdict[self.itheta]["threshold"] = value print(self.stack.shiftsdict[self.itheta]["threshold"]) self.MaskScatterDotsAboveErrorThreshold((self.stack.shiftsdict[self.itheta]["errors"], value, self.itheta)) self.ColorizeScatterDots() self.ComposeShiftQueue(init=False) def OnExtrapolate(self): self.ComposeShiftQueue(init=False) def OnFilter(self): self.ComposeShiftQueue(init=False) def ColorizeScatterDots(self): brushesx = [QtGui.QColor('red') if bool else QtGui.QColor('blue') for bool in self.MaskedScatterDotsArray(self.xregion)] brushesy = [QtGui.QColor('red') if bool else QtGui.QColor('blue') for bool in self.MaskedScatterDotsArray(self.yregion)] self.xscatter.setBrush(brushesx,update=True) self.yscatter.setBrush(brushesy,update=True) def MaskScatterDotsAboveErrorThreshold(self, errorvals=None): errors, errorthreshold, itheta = errorvals if self.cb_autoerror.isChecked(): self.stack.shiftsdict[itheta]["errormaskedx"] = (errors > np.float64(errorthreshold)) self.stack.shiftsdict[itheta]["errormaskedy"] = (errors > np.float64(errorthreshold)) else: self.stack.shiftsdict[itheta]["errormaskedx"] = [False] * len(errors) self.stack.shiftsdict[itheta]["errormaskedy"] = [False] * len(errors) # ---------------------------------------------------------------------- def initParams(self, itheta): self.stack.shiftsdict[itheta]["regionlimitx"] = self.getDataClosestToRegion(self.xregion,self.xscatter,False)[2:] self.stack.shiftsdict[itheta]["regionlimity"] = self.getDataClosestToRegion(self.yregion,self.yscatter,False)[2:] self.stack.shiftsdict[itheta]["filter"] = self.spinBoxFiltersize.value() self.stack.shiftsdict[itheta]["method"] = self.comboBox_approx.currentIndex() #print(self.stack.shiftsdict[itheta]["method"]) #self.stack.shiftsdict[itheta]["autoquality"] = self.cb_autoerror.isChecked() self.stack.shiftsdict[itheta]["extrapolation"] = self.cb_extrapolate.isChecked() self.stack.shiftsdict[itheta]["threshold"] = round(np.mean(self.stack.shiftsdict[itheta]["errors"]), 4) def restoreParams(self,itheta): self.xregion.blockSignals(True) self.yregion.blockSignals(True) self.cb_autoerror.blockSignals(True) self.cb_extrapolate.blockSignals(True) self.comboBox_approx.blockSignals(True) self.spinBoxFiltersize.blockSignals(True) self.xregion.setRegion(self.stack.shiftsdict[itheta]["regionlimitx"]) self.yregion.setRegion(self.stack.shiftsdict[itheta]["regionlimity"]) self.cb_autoerror.setChecked(self.stack.shiftsdict[itheta]["autoquality"]) self.spinBoxError.blockSignals(True) #self.spinBoxError.setValue(self.stack.shiftsdict[self.itheta]["threshold"]) if self.cb_autoerror.isChecked() and self.aligned: self.spinBoxError.setEnabled(True) self.spinBoxError.setValue(self.stack.shiftsdict[self.itheta]["threshold"]) else: self.spinBoxError.setEnabled(False) self.spinBoxError.setValue(1) self.spinBoxError.blockSignals(False) self.cb_extrapolate.setChecked(self.stack.shiftsdict[itheta]["extrapolation"]) self.comboBox_approx.setCurrentIndex(self.stack.shiftsdict[itheta]["method"]) if self.comboBox_approx.currentIndex() == 0: self.spinBoxFiltersize.setEnabled(True) elif self.comboBox_approx.currentIndex() == 1: # linear regression self.spinBoxFiltersize.setEnabled(False) self.spinBoxFiltersize.setValue(self.stack.shiftsdict[itheta]["filter"]) # if limits: # region.setRegion(list(limits)) # else: # region.setRegion([self.stack.ev[0],self.stack.ev[-1]]) #self.spinBoxError.blockSignals(False) self.xregion.blockSignals(False) self.yregion.blockSignals(False) self.cb_autoerror.blockSignals(False) self.cb_extrapolate.blockSignals(False) self.comboBox_approx.blockSignals(False) self.spinBoxFiltersize.blockSignals(False) def getDataClosestToRegion(self,region,plotitem,snapregion=False): selectregion = {self.xregion : "regionlimitx", self.yregion : "regionlimity"} limits = selectregion[region] minidx, maxidx = region.getRegion() data = plotitem.getData()[0] index = lambda x: np.argmin(np.abs(data - x)) minidx = index(minidx) maxidx = index(maxidx) if minidx == maxidx: minidx = 0 maxidx = np.argmax(data) mindata = data[minidx] maxdata = data[maxidx] if snapregion: self.stack.shiftsdict[self.itheta][limits] = (mindata,maxdata) # snap region to data points region.setRegion((mindata,maxdata)) return minidx, maxidx, mindata, maxdata def ApplyApproximationFunction(self,region): boolarray = self.MaskedScatterDotsArray(region) selectscatter = {self.xregion: [self.xscatter, "x"] , self.yregion: [self.yscatter, "y"]} selectfit= {self.xregion : self.fit_x, self.yregion : self.fit_y} scatter = selectscatter[region][0] fit = selectfit[region] #min_idx, max_idx, *_ = self.getDataClosestToRegion(region,scatter) selected = np.count_nonzero(boolarray == False) xdata, ydata = scatter.getData() #xdata = xdata[min_idx:max_idx] xdata = xdata[~boolarray] #ydata = ydata[min_idx:max_idx] ydata = ydata[~boolarray] #print(selected) if selected < 2: QtWidgets.QMessageBox.warning(self, 'Error', 'Select at least two images in {}-direction!'.format(selectscatter[region][1])) return [] if self.comboBox_approx.currentIndex() == 0: # moving average self.spinBoxFiltersize.setEnabled(True) approximated= ndimage.filters.uniform_filter1d(ydata,self.spinBoxFiltersize.value(),mode = "nearest") elif self.comboBox_approx.currentIndex() == 1: # linear regression self.spinBoxFiltersize.setEnabled(False) reg = linregress([xdata,ydata]) approximated = [reg.slope * i + reg.intercept for i in xdata] if self.cb_extrapolate.isChecked(): fillval= "extrapolate" else: fillval=(approximated[0],approximated[-1]) interpolate_func = interp1d(xdata, approximated,kind="linear",fill_value=fillval,bounds_error=False) fitdata = [self.stack.ev,np.around(interpolate_func(self.stack.ev),1)] # round fit to a tenth of a px fit.setData(x=fitdata[0], y=fitdata[1]) fit.show() return fitdata[1] def resetPoolThread(self): try: self.poolthread.started.disconnect() self.poolthread.quit() self.poolthread.wait() except: pass self.pool = TaskDispatcher(self) self.pool.moveToThread(self.poolthread) # GUI is not blocking during calculation due to this self.poolthread.started.connect(self.pool.run) def IThetaProgress(self,itheta): #print("ithetaprogress") # Each thread calls this function. The condition prevents multiple calls. if self.slider_theta.value() != itheta: #self.slider_theta.blockSignals(True) self.slider_theta.setValue(itheta) #self.slider_theta.blockSignals(False) #print(self.pool.pool.activeThreadCount()) def ThreadPoolComplete(self): #print(str(self.pool.pool.activeThreadCount())+" THREADS REMAINING FROM POOL.") #self.slider_theta.setValue(0) if not self.aligned and self.pool.pool.activeThreadCount() == 0: self.OnAligned() elif self.aligned and self.pool.pool.activeThreadCount() == 0: self.OnAutoCrop() def ComposeAlignQueue(self): self.button_align.setEnabled(False) ref_idx = self.iev # Reset reference img: self.resetPoolThread() itheta = 0 ntheta = max(self.stack.n_theta,1) # necessary work around for 3d stacks and if 4d stack is loaded with LoadStack() while itheta < ntheta: idx = copy.copy(self.stack.n_ev) while idx: # Generate pairs of indices starting at reference image index. running = 2 if (ref_idx + (self.stack.n_ev-idx)) < self.stack.n_ev-1: self.pool.enqueuetask("AlignReferenced", (ref_idx + (self.stack.n_ev-idx) + 1, ref_idx), itheta) #q.put((ref_idx + (self.stack.n_ev-idx) + 1, ref_idx)) else: running -= 1 if ref_idx - (self.stack.n_ev-idx) > 0: self.pool.enqueuetask("AlignReferenced", (ref_idx - (self.stack.n_ev-idx) - 1, ref_idx), itheta) #q.put(((ref_idx - (self.stack.n_ev-idx) - 1),ref_idx)) else: running -= 1 if running: idx -= 1 else: break itheta = itheta + 1 if not self.pool.queue.empty(): print("total alignqueue composed. starting poolthread") self.poolthread.start() def ComposeShiftQueue(self, init=False): print("composeshift initial?",str(init)) self.resetPoolThread() #array = np.logical_or(self.MaskedScatterDotsArray(self.xregion),self.MaskedScatterDotsArray(self.yregion)) if not init: ntheta = [self.itheta] else: ntheta = range(max(self.stack.n_theta,1)) # necessary work around for 3d stacks and if 4d stack is loaded with LoadStack() for itheta in ntheta: self.initParams(itheta) for itheta in ntheta: #print(itheta) self.stack.shiftsdict[self.itheta]["filter"] = self.spinBoxFiltersize.value() self.stack.shiftsdict[self.itheta]["method"] = self.comboBox_approx.currentIndex() self.slider_theta.setValue(itheta) #time.sleep(0.2) xshifts = self.ApplyApproximationFunction(self.xregion) yshifts = self.ApplyApproximationFunction(self.yregion) # if empty arrays, i.e., if less than 2 dots selected, do nothing if [] in (xshifts, yshifts): return for ev in range(self.stack.n_ev): # Only enqueue if new shift val is different to previous shift if (self.stack.shiftsdict[itheta]["xshifts"][ev],self.stack.shiftsdict[itheta]["yshifts"][ev]) != (xshifts[ev],yshifts[ev]): self.stack.shiftsdict[itheta]["xshifts"][ev] = xshifts[ev] self.stack.shiftsdict[itheta]["yshifts"][ev] = yshifts[ev] self.pool.enqueuetask("ShiftImg", ev, xshifts[ev], yshifts[ev],itheta) #print("ShiftImg", ev, xshifts[ev], yshifts[ev],itheta) if not self.pool.queue.empty(): print("total shiftqueue composed. starting poolthread with queue size",self.pool.queue.qsize()) self.poolthread.start() # ---------------------------------------------------------------------- def GetIndexPairs(self): idxtuplelst = [(i+1,i) for i in range(self.stack.n_ev-1)] return idxtuplelst # ---------------------------------------------------------------------- def OnAutoCrop(self): if self.aligned: self.cb_autocrop.blockSignals(True) self.CropStack4D() self.cb_autocrop.blockSignals(False) self.OnScrollEng(self.iev) self.button_align.setEnabled(True) self.button_ok.setEnabled(True) def CropStack4D(self): self.stack.absdata4d_shifted_cropped = self.stack.absdata4d_shifted.copy() if self.cb_autocrop.isChecked(): ntheta = range(max(self.stack.n_theta,1)) # necessary work around for 3d stacks and if 4d stack is loaded with LoadStack() #print(ntheta) globalminx = min([j for i in [self.stack.shiftsdict[theta]["xshifts"] for theta in ntheta] for j in i]) globalmaxx = max([j for i in [self.stack.shiftsdict[theta]["xshifts"] for theta in ntheta] for j in i]) globalminy = min([j for i in [self.stack.shiftsdict[theta]["yshifts"] for theta in ntheta] for j in i]) globalmaxy = max([j for i in [self.stack.shiftsdict[theta]["yshifts"] for theta in ntheta] for j in i]) print("cropstack4d: ",globalminx,globalmaxx,globalminy,globalmaxy) #if not self.com.stack_4d: self.box.hide() l = -int(np.floor(globalminx)) r = -int(np.ceil(globalmaxx)) cr = r if r < 0 else None if l < 0: l = 0 cr = cr - l b = -int(np.floor(globalminy)) t = -int(np.ceil(globalmaxy)) ct = t if t < 0 else None if b < 0: b = 0 ct = ct - b if 0 in self.stack.absdata4d_shifted_cropped[l:cr,b:ct,:,self.itheta].shape: QtWidgets.QMessageBox.warning(self, 'Error', 'The alignment failed. Cropping would result in a zero-dimensional image. Please check your settings. Auto-crop has been disabled. You can re-enable it manually.') self.cb_autocrop.setChecked(False) self.OnResetROI() else: self.stack.absdata4d_shifted_cropped = self.stack.absdata4d_shifted_cropped[l:cr,b:ct,:,:] else: self.box.show() def OnScrollEng(self, value): self.slider_eng.setValue(value) self.iev = value self.ShowImage() self.refmarkerx.setValue(self.stack.ev[self.iev]) self.refmarkery.setValue(self.stack.ev[self.iev]) def OnScrollTheta(self, value): #if value != self.itheta: #print("Theta slider" + str(value)) self.slider_theta.setValue(value) self.itheta = value #self.ClearShifts() self.ShowImage() if self.aligned: #self.MakeNewScatterPlots() errorthreshold = self.stack.shiftsdict[self.itheta]["threshold"] errors = self.stack.shiftsdict[self.itheta]["errors"] if not errorthreshold: errorthreshold = round(np.mean(errors), 4) self.restoreParams(self.itheta) self.MaskScatterDotsAboveErrorThreshold((errors, errorthreshold, self.itheta)) # if self.cb_autoerror.isChecked(): # self.spinBoxError.blockSignals(True) # self.spinBoxError.setValue(errorthreshold) # self.spinBoxError.blockSignals(False) xdots = self.stack.shiftsdict[self.itheta]["xdots"] ydots = self.stack.shiftsdict[self.itheta]["ydots"] maskedx = self.MaskedScatterDotsArray(self.xregion) maskedy = self.MaskedScatterDotsArray(self.yregion) self.xscatter.setData(spots=self.CreateScatterDots(xdots, maskedx), pxMode=True) self.yscatter.setData(spots=self.CreateScatterDots(ydots, maskedy), pxMode=True) self.fit_x.setData(x=self.stack.ev, y=self.stack.shiftsdict[self.itheta]["xshifts"]) self.fit_y.setData(x=self.stack.ev, y=self.stack.shiftsdict[self.itheta]["yshifts"]) #min_idx, max_idx, min_ev, max_ev = self.getDataClosestToRegion(self.xregion, self.xscatter, True) #min_idx, max_idx, min_ev, max_ev = self.getDataClosestToRegion(self.yregion, self.yscatter, True) #self.fit_x.show() self.OnLinRegion(self.xregion, update=False) self.OnLinRegion(self.yregion, update=False) def ShowImage(self): self.stack.absdata_shifted_cropped = self.stack.absdata4d_shifted_cropped[:, :, :, int(self.itheta)] self.i_item.setImage(self.stack.absdata_shifted_cropped[:, :, int(self.iev)]) if self.com.stack_4d == 1: self.groupBox.setTitle(str('Stack Browser | Image at {0:5.2f} eV and {1:5.1f}°').format(float(self.stack.ev[self.iev]),float(self.stack.theta[self.itheta]), )) else: self.groupBox.setTitle(str('Stack Browser | Image at {0:5.2f} eV').format(float(self.stack.ev[self.iev]))) ## Setup a ROI for an alignment rectangle. By default the whole image area is used. def SetupROI(self): self.box = pg.RectROI(self.i_item.boundingRect().topLeft(), self.i_item.boundingRect().bottomRight(), pen=(5, 8), handlePen=QtGui.QPen(QtGui.QColor(255, 0, 128, 255)), centered=False, sideScalers=False, removable=False, scaleSnap=True, translateSnap=True, maxBounds=self.i_item.boundingRect()) self.vb.addItem(self.box, ignoreBounds=False) self.box.sigRegionChangeFinished.connect(self.OnBoxChanged) self.box.sigRegionChangeStarted.connect(self.OnBoxChanging) self.button_rstroi.clicked.connect(self.OnResetROI) ## The ROI is limited to the visible image area. OnMouseMoveOutside handles the behavior when def ClearShifts(self): self.aligned = False self.cb_autoerror.stateChanged.disconnect() self.fit_x.hide() self.fit_y.hide() self.InitShiftsDict() self.button_align.setEnabled(True) self.MakeNewScatterPlots() def OnResetROI(self): self.ClearShifts() self.stack.absdata4d_shifted_cropped = self.stack.absdata4d.copy() self.OnScrollEng(self.iev) self.box.setPos(0, 0, update=False, finish=False) self.box.setSize(self.i_item.boundingRect().bottomRight() - self.box.pos(), update=True, snap=True, finish=True) self.box.show() def OnBoxChanging(self): self.boxsize = self.box.size() self.proxy = pg.SignalProxy(self.vb.scene().sigMouseMoved, rateLimit=30, slot=self.OnMouseMoveOutside) def OnBoxChanged(self): try: self.proxy.disconnect() except AttributeError: pass left = int(self.box.pos().x()) right = left + int(self.box.size().x()) bottom = int(self.box.pos().y()) top = bottom + int(self.box.size().y()) self.stack.absdata_cropped = self.stack.absdata4d[left:right, bottom:top, :,:].copy() def OnLinRegion(self, region, update=True): #print("OnLinRegion", str(update)) selectregion= {self.xregion : "manualmaskedx", self.yregion : "manualmaskedy"} selectscatter = {self.xregion : self.xscatter, self.yregion : self.yscatter} selectregionlimit= {self.xregion : "regionlimitx", self.yregion : "regionlimity"} selectplot = {self.xregion : self.px, self.yregion : self.py} #print("onlinregion "+ selectregion[region]) scatter = selectscatter[region] min_idx, max_idx, min_ev, max_ev = self.getDataClosestToRegion(region,scatter,update) selection = [*range(min_idx, max_idx+1)] for idx,val in enumerate(self.stack.shiftsdict[self.itheta]["manualmaskedx"]): if idx not in selection: self.stack.shiftsdict[self.itheta][selectregion[region]][idx] = True else: self.stack.shiftsdict[self.itheta][selectregion[region]][idx] = False y_vals= selectscatter[region].data["y"][min_idx:max_idx + 1] selectplot[region].setRange(yRange=[np.min(y_vals), np.max(y_vals)], disableAutoRange=True, padding=0.1) self.ColorizeScatterDots() #filter = [True if idx in selection else False for idx,bool in enumerate(self.stack.shiftsdict[self.itheta]["manualmaskedx"])] #print(selection,filter) #self.UpdateScatterPlots(region, id) if not self.aligned: for theta in range(self.stack.n_theta): self.stack.shiftsdict[theta][selectregionlimit[region]] = (min_ev, max_ev) if self.aligned and update: self.stack.shiftsdict[self.itheta][selectregionlimit[region]] = (min_ev, max_ev) #print("ComposeShiftQueue"+selectregion[region]) self.ComposeShiftQueue() def OnMouseMoveOutside(self, ev): mousepos = self.vb.mapSceneToView(ev[0]) if not self.vb.itemBoundingRect(self.i_item).contains(mousepos): maxrect = self.i_item.boundingRect().bottomRight() # if bounds exceeded out_x = max((mousepos-maxrect).x(),0) out_y = max((mousepos-maxrect).y(), 0) if self.box.size() != self.boxsize: # prevents taking action when the box is just dragged and not resized if out_x and out_y: self.box.setSize(self.i_item.boundingRect().bottomRight()-self.box.pos(), update=True, snap=True, finish=False) elif out_x: self.box.setSize([self.i_item.boundingRect().right()-self.box.pos().x(),mousepos.y()-self.box.pos().y()], update=True, snap=True, finish=False) elif out_y: self.box.setSize([mousepos.x()-self.box.pos().x(),self.i_item.boundingRect().bottom()-self.box.pos().y()], update=True, snap=True, finish=False) # ---------------------------------------------------------------------- def OnCancel(self, evt): self.stack.absdata_shifted_cropped = self.stack.absdata self.parent.page1.loadImage() if showmaptab: self.parent.page9.Clear() self.parent.page9.LoadEntries() self.close() # ---------------------------------------------------------------------- def OnAccept(self, evt): if self.com.stack_4d == 0: self.stack.absdata = self.stack.absdata4d_shifted_cropped[:,:,:,0] self.stack.data_struct.exchange.data = self.stack.absdata else: self.stack.stack4D = self.stack.absdata4d_shifted_cropped self.stack.absdata = self.stack.stack4D[:, :, :, self.itheta] self.stack.data_struct.exchange.data = self.stack.stack4D #QtWidgets.QMessageBox.warning(self, 'Error', '4D stack not yet supported.') datadim = np.int32(self.stack.absdata.shape) self.stack.n_cols = datadim[0].copy() self.stack.n_rows = datadim[1].copy() #ToDo: Handshake data #self.stack.xshifts = self.x_shiftstemp #self.stack.yshifts = self.y_shiftstemp #ToDo: How to handle aligned i0 data? # if self.com.i0_loaded == 1: # if self.com.stack_4d == 0: # #Resize optical density # for i in range(self.stack.n_ev): # # img = self.stack.od3d[:,:,i] # shifted_img = self.stack.apply_image_registration(img, self.xshifts[i], self.yshifts[i]) # self.stack.od3d[:,:,i] = shifted_img # # # self.stack.od3d = self.stack.od3d[self.xleft:self.xright, self.ybottom:self.ytop, :] # # self.stack.od = self.stack.od3d.copy() # self.stack.od = np.reshape(self.stack.od, (self.stack.n_cols*self.stack.n_rows, self.stack.n_ev), order='F') # # else: # #Resize optical density for 4D stack # # for i in range(self.stack.n_ev): # for j in range(self.stack.n_theta): # img = self.stack.od4D[:,:,i,j] # shifted_img = self.stack.apply_image_registration(img, self.xshifts[i,j], self.yshifts[i,j]) # self.stack.od4D[:,:,i,j] = shifted_img # # self.stack.od4D = self.stack.od4D[self.xleft:self.xright, self.ybottom:self.ytop, :, :] # # self.stack.od3d = self.stack.od4D[:,:,:,self.itheta] # self.stack.od = self.stack.od3d.copy() # n_pixels = self.stack.n_cols*self.stack.n_rows # self.stack.od = np.reshape(self.stack.od, (n_pixels, self.stack.n_ev), order='F') # # self.stack.data_struct.spectromicroscopy.optical_density = self.stack.od self.stack.data_struct.exchange.energy = self.stack.ev #ToDo: Handshake shift data #self.stack.data_struct.spectromicroscopy.xshifts = self.x_shiftstemp #self.stack.data_struct.spectromicroscopy.yshifts = self.y_shiftstemp self.parent.page1.slider_eng.setRange(0,self.stack.n_ev-1) self.parent.page1.iev = int(self.stack.n_ev/2) self.parent.page1.slider_eng.setValue(self.parent.page1.iev) self.parent.page1.ix = int(self.stack.n_cols/2) self.parent.page1.iy = int(self.stack.n_rows/2) self.parent.page1.showSpectrum(self.parent.page1.ix, self.parent.page1.iy) self.parent.page1.loadImage() if showmaptab: self.parent.page9.Clear() self.parent.page9.LoadEntries() self.close() #---------------------------------------------------------------------- class SpectralImageMap(QtWidgets.QDialog, QtWidgets.QGraphicsScene): #evlistchanged = pyqtSignal([object]) #thetalistchanged = pyqtSignal([object]) def __init__(self, parent, common, stack): QtWidgets.QWidget.__init__(self, parent) uic.loadUi(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'showspectralroi.ui'), self) self.parent = parent self.stack = stack self.com = common self.iev = 0 self.itheta = 0 # self.stack.absdata_shifted_cropped = self.stack.absdata_shifted.copy() # self.poolthread = QtCore.QThread() # self.aligned = False self.button_ok.setEnabled(True) self.setWindowTitle('Spectral Image Map') #self.pglayout = pg.GraphicsLayout(border=None) #self.canvas.setBackground("w") # canvas is a pg.GraphicsView widget #self.canvas.setCentralWidget(self.pglayout) #self.vb = self.pglayout.addViewBox() #self.vb.setAspectLocked() #self.i_item = pg.ImageItem(border="k", parent=self) #self.vb.setMouseEnabled(x=False, y=False) #self.vb.addItem(self.i_item, ignoreBounds=False) self.button_ok.clicked.connect(self.OnAccept) self.button_cancel.clicked.connect(self.OnCancel) if self.com.stack_loaded == 1: # self.label_theta_range.setVisible(False) # self.slider_theta.setVisible(False) # self.cb_remove_theta.setVisible(False) # self.groupBox_theta.setVisible(False) # if self.com.stack_4d == 1: # self.label_theta_range.setVisible(True) # self.slider_theta.setVisible(True) # self.cb_remove_theta.setVisible(True) # self.groupBox_theta.setVisible(True) # self.slider_theta.setRange(0, self.stack.n_theta - 1) # self.slider_theta.valueChanged[int].connect(self.OnScrollTheta) # self.SetupListTheta() # # self.maskedvals = [True] * int(self.stack.n_ev) # # self.spinBoxError.setEnabled(False) # self.slider_eng.sliderPressed.connect(self.ShowImage) # self.slider_eng.sliderReleased.connect(self.ShowImage) # self.slider_eng.valueChanged[int].connect(self.OnScrollEng) # self.slider_eng.setRange(0, self.stack.n_ev - 1) # self.pb_selectall.clicked.connect(self.OnSelectAll) # self.pb_clearall.clicked.connect(self.OnClearAll) # self.evlistchanged.connect(lambda row: self.qListChangeHandler(row, "energy")) # self.thetalistchanged.connect(lambda row: self.qListChangeHandler(row, "theta")) # # self.ev_widget.itemClicked.connect(lambda item: self.OnItemClicked(item)) # self.ev_widget.mousePressEvent = self.mouseEventOnEVList # self.ev_widget.mouseMoveEvent = self.mouseEventOnEVList # self.theta_widget.mousePressEvent = self.mouseEventOnThetaList # self.theta_widget.mouseMoveEvent = self.mouseEventOnThetaList # self.ev_widget.itemSelectionChanged.connect(lambda item: self.OnItemClicked(item)) #self.SetupListEV() #self.OnScrollEng(0) #self.SetupROI() self.SetupPlot() def OnSelectionChanged(self): #self.RedrawNewPlot() # self.UpdateIndices() self.region_i0.blockSignals(True) if self.idx_selected: self.region_i0.setRegion([self.stack.ev[min(self.idx_selected)], self.stack.ev[max(self.idx_selected)]]) self.region_i0.blockSignals(False) #self.spectrum_plotwidget.setXRange(*self.region_i0.getRegion()) #self.spectrum_plotwidget.setYRange(np.min(self.plotitem_new.yData), np.max(self.plotitem_new.yData)) return def GenerateSpectrum(self, evselection): #left,right,top,bottom = self.GetRegion() if self.com.i0_loaded == 1: if self.com.stack_4d == 1: total = self.stack.od4d[:, :, :, int(self.itheta)].copy() else: total = self.stack.od3d[:, :, :].copy() else: if self.com.stack_4d == 1: # t = [self.stack.theta[i] for i in self.thetaidx_selected] # self.label_theta_range.setText( # "Theta range: [ " + str(min(t, default=0)) + "° .. " + str( # max(t, default=0)) + "° ], # values: " + str( # len(t))) total = self.stack.stack4D[:, :, :, int(self.itheta)].copy() else: total = self.stack.absdata[:, :, :].copy() total = total.sum(axis=(0,1)) #/ (int(self.box.size().x()) * int(self.box.size().y())) x = self.stack.ev y = total #self.label_spatial_range.setText("Stack size: [ "+str(int(self.box.size().x()))+" x "+str(int(self.box.size().y()))+" ] px²") #self.label_ev_range.setText( # "Energy range: [ " + str(min(x, default=0)) + " .. " + str(max(x, default=0)) + " ] eV, # values: "+ str(len(x))) return (x, y) # def UpdateIndices(self): # self.idx_selected = sorted([self.ev_widget.row(i) for i in self.ev_selected]) # if self.com.stack_4d: # self.thetaidx_selected = sorted([self.theta_widget.row(i) for i in self.theta_selected]) # # def RedrawPlots(self): # x, y = self.GenerateSpectrum(list(range(self.stack.n_ev))) # self.plotitem.setData(x, y) # self.OnSelectionChanged() # # def RedrawNewPlot(self): # #self.UpdateIndices() # x, y = self.GenerateSpectrum(self.idx_selected) # self.plotitem_new.setData(x, y) # if self.idx_selected: # self.region_i0.show() # # def qListChangeHandler(self, row, dimension): # if dimension == "theta": # selection = self.theta_selected # widget = self.theta_widget # elif dimension == "energy": # selection = self.ev_selected # widget = self.ev_widget # # if row in selection: # selection.remove(row) # row.setBackground(QtGui.QColor(0, 0, 0, 0)) # else: # selection.append(row) # row.setBackground(QtGui.QColor('#beaed4')) # if dimension == "theta": # self.OnScrollTheta(widget.row(row)) # elif dimension == "energy": # self.OnScrollEng(widget.row(row)) # self.OnSelectionChanged() def SetupPlot(self): x, y = self.GenerateSpectrum(list(range(self.stack.n_ev))) self.spectrum_plotwidget.setBackground("w") self.region_i0 = pg.LinearRegionItem(brush=QtGui.QColor('#88beaed4'),hoverBrush=QtGui.QColor('#ccbeaed4'), bounds=[np.min(x), np.max(x)]) self.region_i0.setZValue(10) self.region_i = pg.LinearRegionItem(brush=QtGui.QColor('#887fc97f'),hoverBrush=QtGui.QColor('#cc7fc97f'), bounds=[np.min(x), np.max(x)]) self.region_i.setZValue(10) plot = self.spectrum_plotwidget plot.setBackground("w") plot.addItem(self.region_i0, ignoreBounds=False) plot.addItem(self.region_i, ignoreBounds=False) plot.setMouseEnabled(x=False, y=False) plot.showGrid(y=True) plot.showAxis("top", show=True) plot.showAxis("right", show=True) by = plot.getAxis("right") bx = plot.getAxis("top") by.setStyle(showValues=False, tickLength=0) bx.setStyle(showValues=False, tickLength=0) ay = plot.getAxis("left") ax = plot.getAxis("bottom") ax.setLabel(text="Photon energy [eV]") if self.com.i0_loaded: ay.setLabel(text="Optical density") else: ay.setLabel(text="Photon flux [cps]") self.plotitem = plot.plot(x, y, pen=pg.mkPen(color="b", width=2)) self.plotitem.setZValue(100) # self.plotitem_new = plot.plot(x, y, pen=pg.mkPen(color="b", width=2)) # self.refmarker = pg.InfiniteLine(angle=90, movable=False, # pen=pg.mkPen(color="b", width=2, style=QtCore.Qt.DashLine)) #plot.addItem(self.refmarker, ignoreBounds=True) self.region_i0.setRegion((min(x), min(x)+0.25*(max(x)-min(x)))) self.region_i.setRegion(((max(x)-0.25*(max(x)-min(x))), max(x))) #self.region_i0.sigRegionChange.connect(self.UpdateSelection) #self.region_i.sigRegionChange.connect(self.UpdateSelection) self.region_i0.sigRegionChangeFinished.connect(self.UpdateSelection) self.region_i.sigRegionChangeFinished.connect(self.UpdateSelection) # ---------------------------------------------------------------------- def getDataClosestToRegion(self, region, plotitem, snapregion=True): #otherregion = {self.region_i : self.region_i0, self.region_i0: self.region_i} minidx, maxidx = region.getRegion() data = plotitem.getData()[0] index = lambda x: np.argmin(np.abs(data - x)) minidx = index(minidx) maxidx = index(maxidx) if minidx == maxidx: minidx = 0 maxidx = np.argmax(data) mindata = data[minidx] maxdata = data[maxidx] if region == self.region_i: self.region_i0.setBounds((min(data), data[minidx])) elif region == self.region_i0: self.region_i.setBounds((data[maxidx], max(data))) if snapregion: region.blockSignals(True) region.setRegion([mindata, maxdata]) # snap region to data points region.blockSignals(False) #self.parent.OnSelectionChanged() return minidx, maxidx, mindata, maxdata def UpdateSelection(self): mini0, maxi0, mindi0, maxdi0 = self.getDataClosestToRegion(self.region_i0,self.plotitem) mini, maxi, mindi, maxdi = self.getDataClosestToRegion(self.region_i,self.plotitem) #self.region_i0.blockSignals(True) #self.region_i.blockSignals(True) #self.region_i0.setBounds((min(self.plotitem.getData()[0]),mindi)) #self.region_i.setBounds((maxdi0,max(self.plotitem.getData()[0]))) #self.region_i0.blockSignals(False) #self.region_i.blockSignals(False) qlist = self.parent.MapSelectWidget1 for row in range(qlist.count()): if row in range(mini0, maxi0): self.stack.shifts[row][1]= -1 qlist.item(row).setBackground(QtGui.QColor('#beaed4')) elif row in range(mini, maxi): self.stack.shifts[row][1]= 1 qlist.item(row).setBackground(QtGui.QColor('#7fc97f')) else: self.stack.shifts[row][1]= 0 qlist.item(row).setBackground(QtGui.QColor(0, 0, 0, 0)) self.parent.OnSelectionChanged() # ---------------------------------------------------------------------- def OnCancel(self, evt): self.close() # # ---------------------------------------------------------------------- def OnAccept(self, evt): pass # if self.cb_croptoroi.isChecked(): # left, right, top, bottom = self.GetRegion() # else: # left, right, top, bottom = (None, None, None, None) # # if self.cb_remove_evs.isChecked(): # selection = self.idx_selected # if len(selection) == 0: # QtWidgets.QMessageBox.warning(self, 'Error', 'Please select at least one energy value!') # return # self.stack.n_ev = np.array(len(selection)) # self.stack.ev = self.stack.ev[selection] # self.stack.data_dwell = self.stack.data_dwell[selection] # else: # selection = list(range(self.stack.n_ev)) # # self.stack.absdata = self.stack.absdata[left:right, bottom:top, selection] # self.stack.n_cols = self.stack.absdata.shape[0] # self.stack.n_rows = self.stack.absdata.shape[1] # self.parent.page1.ix = int(self.stack.n_cols / 2) # self.parent.page1.iy = int(self.stack.n_rows / 2) # # if self.com.stack_4d: # if self.cb_remove_theta.isChecked(): # thetas = self.thetaidx_selected # if len(thetas) == 0: # QtWidgets.QMessageBox.warning(self, 'Error', 'Please select at least one theta value!') # return # self.stack.n_theta = len(thetas) # self.stack.theta = self.stack.theta[thetas] # # else: # thetas = list(range(self.stack.n_theta)) # self.stack.stack4D = self.stack.stack4D[left:right, bottom:top, selection, :] # self.stack.stack4D = self.stack.stack4D[:, :, :, thetas] # if self.com.i0_loaded: # if self.com.stack_4d: # self.stack.od4D = self.stack.od4D[left:right, bottom:top, selection, thetas] # else: # self.stack.od3d = self.stack.od3d[left:right, bottom:top, selection] # self.stack.od = self.stack.od3d.copy() # self.stack.od = np.reshape(self.stack.od, (self.stack.n_rows * self.stack.n_cols, self.stack.n_ev), # order='F') # # self.stack.fill_h5_struct_from_stk() # if self.com.i0_loaded == 1: # self.stack.fill_h5_struct_normalization() # # # Fix the slider on Page 1! # if self.com.stack_4d: # self.parent.page1.slider_theta.setRange(0, self.stack.n_theta - 1) # self.parent.page1.itheta = 0 # self.parent.page1.slider_theta.blockSignals(True) # self.parent.page1.slider_theta.setValue(int(self.parent.page1.itheta)) # self.parent.page1.slider_theta.blockSignals(False) # # self.parent.page0.slider_theta.setRange(0, self.stack.n_theta - 1) # self.parent.page0.itheta = 0 # self.parent.page0.slider_theta.blockSignals(True) # self.parent.page0.slider_theta.setValue(int(self.parent.page1.itheta)) # self.parent.page0.slider_theta.blockSignals(False) # # self.parent.page1.slider_eng.setRange(0, self.stack.n_ev - 1) # self.parent.page1.iev = 0 # self.parent.page1.slider_eng.setValue(int(self.parent.page1.iev)) # # self.parent.page0.slider_eng.setRange(0, self.stack.n_ev - 1) # self.parent.page0.iev = 0 # self.parent.page0.slider_eng.setValue(int(self.parent.page1.iev)) # # self.parent.page1.showSpectrum(self.parent.page1.ix, self.parent.page1.iy) # self.parent.page1.loadImage() # self.parent.page0.Clear() # self.parent.page0.LoadEntries() # # if showmaptab: # self.parent.page9.Clear() # self.parent.page9.LoadEntries() # # self.close() class SpectralROI(QtWidgets.QDialog): def __init__(self, parent, common, stack): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.stack = stack self.com = common self.resize(630, 700) self.setWindowTitle('Spectral Regions of Interest') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) self.stack = stack self.com = common self.imin = 0 self.imax = 0 self.i0min = 0 self.i0max = 0 self.iselected = 0 self.i0selected = 0 self.odtotal = self.stack.od3d.sum(axis=0) self.odtotal = self.odtotal.sum(axis=0)/(self.stack.n_rows*self.stack.n_cols) self.image_i0 = np.zeros((self.stack.n_cols, self.stack.n_rows)) self.image_i = np.zeros((self.stack.n_cols, self.stack.n_rows)) self.odthickmap = np.zeros((self.stack.n_cols, self.stack.n_rows)) vbox = QtWidgets.QVBoxLayout() text = QtWidgets.QLabel(self) text.setText('First select I0 region below the edge, then select I region above the edge:') vbox.addWidget(text) frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.specfig = Figure((6.0, 4.2)) self.SpectrumPanel = FigureCanvas(self.specfig) self.SpectrumPanel.setParent(self) self.SpectrumPanel.mpl_connect('button_press_event', self.OnSelection1) self.SpectrumPanel.mpl_connect('button_release_event', self.OnSelection2) fbox.addWidget(self.SpectrumPanel) frame.setLayout(fbox) vbox.addWidget(frame) hbox2 = QtWidgets.QHBoxLayout() sizer2 =QtWidgets.QGroupBox('Selected Spectral Regions') sizer2.setMinimumWidth(350) vbox2 = QtWidgets.QVBoxLayout() text = QtWidgets.QLabel(self) text.setText('I Selection (red): ') self.textctrl1 = QtWidgets.QLabel(self) self.textctrl1.setText('[ ]' ) vbox2.addWidget(text) vbox2.addWidget(self.textctrl1) text = QtWidgets.QLabel(self) text.setText('I0 Selection (green): ') self.textctrl2 = QtWidgets.QLabel(self) self.textctrl2.setText('[ ]' ) vbox2.addWidget(text) vbox2.addWidget(self.textctrl2) sizer2.setLayout(vbox2) text = QtWidgets.QLabel(self) text.setText('Optical density map') vbox.addWidget(text) frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.odmfig = Figure((2.4,2.4)) self.ODMImagePanel = FigureCanvas(self.odmfig) self.ODMImagePanel.setParent(self) fbox.addWidget(self.ODMImagePanel, stretch=0) frame.setLayout(fbox) hbox2.addWidget(frame, stretch=0) hbox2.addStretch(1) hbox2.addWidget(sizer2) vbox.addLayout(hbox2) hbox = QtWidgets.QHBoxLayout() button_save = QtWidgets.QPushButton('Save') button_save.clicked.connect(self.OnSave) hbox.addWidget(button_save) button_cancel = QtWidgets.QPushButton('Dismiss') button_cancel.clicked.connect(self.close) hbox.addWidget(button_cancel) vbox.addLayout(hbox) self.setLayout(vbox) self.draw_spectrum() #---------------------------------------------------------------------- def draw_spectrum(self): fig = self.specfig fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) self.axes = fig.gca() specplot = self.axes.plot(self.stack.ev,self.odtotal) if self.i0selected == 1: self.axes.axvspan(self.stack.ev[self.i0min], self.stack.ev[self.i0max], facecolor='g', alpha=0.5) if self.iselected == 1: self.axes.axvspan(self.stack.ev[self.imin], self.stack.ev[self.imax], facecolor='r', alpha=0.5) self.axes.set_xlabel('Photon Energy [eV]') self.axes.set_ylabel('Optical Density') self.SpectrumPanel.draw() #---------------------------------------------------------------------- def draw_image(self): fig = self.odmfig fig.clf() fig.add_axes((0.02,0.02,0.96,0.96)) axes = fig.gca() divider = make_axes_locatable(axes) axcb = divider.new_horizontal(size="3%", pad=0.03) fig.add_axes(axcb) axes.set_position([0.03,0.03,0.8,0.94]) im = axes.imshow(np.rot90(self.odthickmap), cmap=matplotlib.cm.get_cmap("gray")) cbar = axes.figure.colorbar(im, orientation='vertical',cax=axcb) #Show Scale Bar startx = int(self.stack.n_rows*0.05) starty = self.stack.n_cols-int(self.stack.n_cols*0.05)-self.stack.scale_bar_pixels_y um_string = ' $\mathrm{\mu m}$' microns = '$'+self.stack.scale_bar_string+' $'+um_string axes.text(self.stack.scale_bar_pixels_x+startx+1,starty+1, microns, horizontalalignment='left', verticalalignment='center', color = 'white', fontsize=14) #Matplotlib has flipped scales so I'm using rows instead of cols! p = matplotlib.patches.Rectangle((startx,starty), self.stack.scale_bar_pixels_x, self.stack.scale_bar_pixels_y, color = 'white', fill = True) axes.add_patch(p) axes.axis("off") self.ODMImagePanel.draw() #---------------------------------------------------------------------- def OnSelection1(self, evt): x1 = evt.xdata self.button_pressed = True self.patch = None self.conn = self.SpectrumPanel.mpl_connect('motion_notify_event', self.OnSelectionMotion) if x1 == None: return self.x1 = x1 #---------------------------------------------------------------------- def OnSelection2(self, evt): x2 = evt.xdata self.button_pressed = False self.SpectrumPanel.mpl_disconnect(self.conn) if x2 == None: return x1 = self.x1 if (self.i0selected == 1) and (self.iselected ==1): self.i0selected = 0 self.iselected = 0 if self.i0selected == 0: self.i0min = np.abs(self.stack.ev - x1).argmin() self.i0max = np.abs(self.stack.ev - x2).argmin() self.image_i0 = np.sum(self.stack.absdata[:, :, self.i0min:self.i0max+1], axis=2)/(self.i0max+1-self.i0min) self.textctrl1.setText('Selection: [ '+str(self.stack.ev[self.i0min]) + ' eV, '+ str(self.stack.ev[self.i0max])+' eV ]' ) self.i0selected = 1 elif self.iselected == 0: self.imin = np.abs(self.stack.ev - x1).argmin() self.imax = np.abs(self.stack.ev - x2).argmin() self.image_i = np.sum(self.stack.absdata[:, :, self.imin:self.imax+1], axis=2)/(self.imax+1-self.imin) self.textctrl2.setText('Selection: [ '+str(self.stack.ev[self.imin]) + ' eV, '+ str(self.stack.ev[self.imax])+' eV ]' ) self.iselected = 1 if (self.i0selected == 1) and (self.iselected ==1): nonzeroind = self.image_i0.nonzero() self.odthickmap = np.zeros((self.stack.n_cols, self.stack.n_rows)) self.odthickmap[nonzeroind] = - np.log(self.image_i[nonzeroind]/self.image_i0[nonzeroind]) self.draw_image() self.draw_spectrum() #---------------------------------------------------------------------- def OnSelectionMotion(self, event): x2 = event.xdata if x2 == None: return x1 = self.x1 fig = self.specfig axes = fig.gca() if self.patch != None: self.patch.remove() self.patch = self.axes.axvspan(x1, x2, facecolor='w', alpha=0.5) self.SpectrumPanel.draw() #---------------------------------------------------------------------- def OnSave(self, evt): #Save images wildcard = "Portable Network Graphics (*.png);;Adobe PDF Files (*.pdf);;TIFF File (*.tif);;" fileName, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save OD Map', '', wildcard) fileName = str(fileName) if fileName == '': return path, ext = os.path.splitext(fileName) ext = ext[1:].lower() if ext != 'png' and ext != 'pdf' and ext != 'tif': error_message = ( 'Only the PNG and PDF image formats are supported.\n' 'A file extension of `png\' or `pdf\' must be used.') QtWidgets.QMessageBox.warning(self, 'Error', 'Error - Could not save file.') return if ext == 'tif': from PIL import Image img1 = Image.fromarray(self.odthickmap) img1.save(fileName) else: try: matplotlib.rcParams['pdf.fonttype'] = 42 fig = self.odmfig fig.savefig(fileName) except IOError as e: if e.strerror: err = e.strerror else: err = e QtGui.QMessageBox.warning(self, 'Error', 'Could not save file: %s' % err) #---------------------------------------------------------------------- class DoseCalculation(QtWidgets.QDialog): def __init__(self, parent, stack, ROIspectrum): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.resize(300, 170) self.setWindowTitle('Dose Calculation') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) self.stack = stack self.ROIspectrum = ROIspectrum vboxtop = QtWidgets.QVBoxLayout() gridtop = QtWidgets.QGridLayout() #fontb = wx.SystemSettings_GetFont(wx.SYS_DEFAULT_GUI_FONT) #fontb.SetWeight(wx.BOLD) st1 = QtWidgets.QLabel(self) st1.setText('Detector efficiency [%]:') #st1.SetFont(fontb) st2 = QtWidgets.QLabel(self) st2.setText('I region composition:') #st2.SetFont(fontb) # st3 = QtWidgets.QLabel(self,'Xray absorption length:') # st3.SetFont(fontb) st4 = QtWidgets.QLabel(self) st4.setText('Dose [Gray]:') #st4.SetFont(fontb) self.tc_1 = QtWidgets.QLineEdit(self) self.tc_1.setText('30') self.tc_2 = QtWidgets.QLineEdit(self) # self.tc_3 = wx.TextCtrl(panel1, -1, size=((200,-1)), style=wx.TE_RICH|wx.VSCROLL|wx.TE_READONLY, # value=' ') self.tc_4 = QtWidgets.QLabel(self) gridtop.addWidget(st1, 0,0) gridtop.addWidget( self.tc_1, 0,1) gridtop.addWidget(st2, 1,0) gridtop.addWidget( self.tc_2, 1,1) # gridtop.addWidget(st3, 0) # gridtop.addWidget( self.tc_3, 0) gridtop.addWidget(st4, 2,0) gridtop.addWidget( self.tc_4, 2,1) button_calcdose = QtWidgets.QPushButton('Calculate Dose') button_calcdose.clicked.connect(self.OnCalcDose) button_cancel = QtWidgets.QPushButton('Dismiss') button_cancel.clicked.connect(self.close) vboxtop.addLayout(gridtop) vboxtop.addWidget(button_calcdose) vboxtop.addWidget(button_cancel) self.setLayout(vboxtop) #---------------------------------------------------------------------- def CalcDose(self): try: detector_eff = 0.01*float(self.tc_1.text()) except: QtGui.QMessageBox.warning(self, 'Error', 'Please enter numeric number for detector efficiency.') print('Please enter numeric number for detector efficiency.') return i_composition = str(self.tc_2.text()) dose = 0. Chenke = henke.henke() #Check if composition array is recognizable try: z_array, atwt = Chenke.compound(i_composition,1.0) except: QtGui.QMessageBox.warning(self, 'Error', "Please enter new compound.") return try: dose = Chenke.dose_calc(self.stack, i_composition, self.ROIspectrum, self.stack.i0data, detector_eff) except: QtGui.QMessageBox.warning(self, 'Error', "Could not calculate dose. Please enter new compound.") return self.tc_4.setText(str(dose)) return #---------------------------------------------------------------------- def OnCalcDose(self, evt): QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.CalcDose() QtWidgets.QApplication.restoreOverrideCursor() #---------------------------------------------------------------------- class DarkSignal(QtWidgets.QDialog): def __init__(self, parent, common, stack): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.stack = stack self.com = common self.resize(300, 170) self.setWindowTitle('Dark Signal Correction') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) vboxtop = QtWidgets.QVBoxLayout() gridtop = QtWidgets.QGridLayout() #fontb = wx.SystemSettings_GetFont(wx.SYS_DEFAULT_GUI_FONT) #fontb.SetWeight(wx.BOLD) st1 = QtWidgets.QLabel(self) st1.setText('Dark Signal Value:') self.ntc_ds = QtWidgets.QLineEdit(self) self.ntc_ds.setFixedWidth(150) self.ntc_ds.setValidator(QtGui.QDoubleValidator(-99999, 99999, 2, self)) self.ntc_ds.setAlignment(QtCore.Qt.AlignRight) self.ntc_ds.setText(str(0.0)) gridtop.addWidget(st1, 0,0) gridtop.addWidget(self.ntc_ds, 0,1) button_dscalc = QtWidgets.QPushButton('Subtract Dark Signal') button_dscalc.clicked.connect(self.OnDSCalc) button_cancel = QtWidgets.QPushButton('Dismiss') button_cancel.clicked.connect(self.close) vboxtop.addLayout(gridtop) vboxtop.addWidget(button_dscalc) vboxtop.addWidget(button_cancel) self.setLayout(vboxtop) #---------------------------------------------------------------------- def OnDSCalc(self): QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) darksig = 0.0 try: value = self.ntc_ds.text() darksig = float(value) except: QtWidgets.QApplication.restoreOverrideCursor() QtGui.QMessageBox.warning(self, 'Error', 'Please enter numeric number for dark signal.') return self.stack.absdata = self.stack.absdata - darksig if self.com.i0_loaded == 1: self.stack.calculate_optical_density() self.stack.fill_h5_struct_from_stk() if self.com.i0_loaded == 1: self.stack.fill_h5_struct_normalization() self.parent.page1.showSpectrum(self.parent.page1.ix, self.parent.page1.iy) self.parent.page1.loadImage() QtWidgets.QApplication.restoreOverrideCursor() self.close() return #---------------------------------------------------------------------- class PlotFrame(QtWidgets.QDialog): def __init__(self, parent, datax, datay, title = "I0 data"): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.title = title self.datax = datax self.datay = datay self.resize(630, 500) self.setWindowTitle(title) pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) vbox = QtWidgets.QVBoxLayout() frame = QtWidgets.QFrame() frame.setFrameStyle(QtWidgets.QFrame.StyledPanel|QtWidgets.QFrame.Sunken) fbox = QtWidgets.QHBoxLayout() self.plotfig = Figure((6.0, 4.2)) self.PlotPanel = FigureCanvas(self.plotfig) self.PlotPanel.setParent(self) fbox.addWidget(self.PlotPanel) frame.setLayout(fbox) vbox.addWidget(frame) hbox = QtWidgets.QHBoxLayout() button_save = QtWidgets.QPushButton('Save Spectrum') button_save.clicked.connect(self.OnSave) hbox.addWidget(button_save) button_close = QtWidgets.QPushButton('Close') button_close.clicked.connect(self.close) hbox.addWidget(button_close) vbox.addLayout(hbox) self.setLayout(vbox) self.draw_plot(datax,datay) #---------------------------------------------------------------------- def draw_plot(self, datax, datay): fig = self.plotfig fig.clf() fig.add_axes((0.15,0.15,0.75,0.75)) self.axes = fig.gca() plot = self.axes.plot(datax,datay) self.axes.set_xlabel('Photon Energy [eV]') self.axes.set_ylabel('I0 Flux') self.PlotPanel.draw() #---------------------------------------------------------------------- def OnSave(self, event): try: wildcard = "CSV files (*.csv)" filepath, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save plot as .csv (.csv)', '', wildcard) filepath = str(filepath) if filepath == '': return QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.Save(filepath) QtWidgets.QApplication.restoreOverrideCursor() except: QtWidgets.QApplication.restoreOverrideCursor() QtGui.QMessageBox.warning(self, 'Error', "Could not save .csv file.") self.close() return #---------------------------------------------------------------------- def Save(self, filename): f = open(filename, 'w') print('********************* X-ray Absorption Data ********************', file=f) print('*', file=f) print('* Formula: ', file=f) print('* Common name: ', self.title, file=f) print('* Edge: ', file=f) print('* Acquisition mode: ', file=f) print('* Source and purity: ', file=f) print('* Comments: Stack list ROI ""', file=f) print('* Delta eV: ', file=f) print('* Min eV: ', file=f) print('* Max eV: ', file=f) print('* Y axis: ', file=f) print('* Contact person: ', file=f) print('* Write date: ', file=f) print('* Journal: ', file=f) print('* Authors: ', file=f) print('* Title: ', file=f) print('* Volume: ', file=f) print('* Issue number: ', file=f) print('* Year: ', file=f) print('* Pages: ', file=f) print('* Booktitle: ', file=f) print('* Editors: ', file=f) print('* Publisher: ', file=f) print('* Address: ', file=f) print('*--------------------------------------------------------------', file=f) dim = self.datax.shape n=dim[0] for ie in range(n): print('{0:06.2f}, {1:06f}'.format(self.datax[ie], self.datay[ie]), file=f) f.close() #---------------------------------------------------------------------- class ColorTableFrame(QtWidgets.QDialog): def __init__(self, parent): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.resize(200, 430) self.setWindowTitle('Pick Color Table') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) self.colors= ["gray","jet","autumn","bone", "cool","copper", "flag","hot","hsv","pink", "prism","spring","summer","winter", "spectral"] vboxtop = QtWidgets.QVBoxLayout() sizer1 = QtWidgets.QGroupBox('Color Tables') vbox = QtWidgets.QVBoxLayout() self.rb_grey = QtWidgets.QRadioButton(self.colors[0], self) self.rb_jet = QtWidgets.QRadioButton(self.colors[1], self) self.rb_autumn = QtWidgets.QRadioButton(self.colors[2], self) self.rb_bone = QtWidgets.QRadioButton(self.colors[3], self) self.rb_cool = QtWidgets.QRadioButton(self.colors[4], self) self.rb_copper = QtWidgets.QRadioButton(self.colors[5], self) self.rb_flag = QtWidgets.QRadioButton(self.colors[6], self) self.rb_hot = QtWidgets.QRadioButton(self.colors[7], self) self.rb_hsv = QtWidgets.QRadioButton(self.colors[8], self) self.rb_pink = QtWidgets.QRadioButton(self.colors[9], self) self.rb_prism = QtWidgets.QRadioButton(self.colors[10], self) self.rb_spring = QtWidgets.QRadioButton(self.colors[11], self) self.rb_summer = QtWidgets.QRadioButton(self.colors[12], self) self.rb_winter = QtWidgets.QRadioButton(self.colors[13], self) self.rb_spectral = QtWidgets.QRadioButton(self.colors[14], self) self.radios = [] self.radios.append(self.rb_grey) self.radios.append(self.rb_jet) self.radios.append(self.rb_autumn) self.radios.append(self.rb_bone) self.radios.append(self.rb_cool) self.radios.append(self.rb_copper) self.radios.append(self.rb_flag) self.radios.append(self.rb_hot) self.radios.append(self.rb_hsv) self.radios.append(self.rb_pink) self.radios.append(self.rb_prism) self.radios.append(self.rb_spring) self.radios.append(self.rb_summer) self.radios.append(self.rb_winter) self.radios.append(self.rb_spectral) self.ct_dict = dict([(self.colors[x], self.radios[x]) for x in range(len(self.colors))]) self.rb_grey.clicked.connect(self.OnColorTable) self.rb_jet.clicked.connect(self.OnColorTable) self.rb_autumn.clicked.connect(self.OnColorTable) self.rb_bone.clicked.connect(self.OnColorTable) self.rb_cool.clicked.connect(self.OnColorTable) self.rb_copper.clicked.connect(self.OnColorTable) self.rb_flag.clicked.connect(self.OnColorTable) self.rb_hot.clicked.connect(self.OnColorTable) self.rb_hsv.clicked.connect(self.OnColorTable) self.rb_pink.clicked.connect(self.OnColorTable) self.rb_prism.clicked.connect(self.OnColorTable) self.rb_spring.clicked.connect(self.OnColorTable) self.rb_summer.clicked.connect(self.OnColorTable) self.rb_winter.clicked.connect(self.OnColorTable) self.rb_spectral.clicked.connect(self.OnColorTable) vbox.addWidget(self.rb_grey) vbox.addWidget(self.rb_jet) vbox.addWidget(self.rb_autumn) vbox.addWidget(self.rb_bone) vbox.addWidget(self.rb_cool) vbox.addWidget(self.rb_copper) vbox.addWidget(self.rb_flag) vbox.addWidget(self.rb_hot) vbox.addWidget(self.rb_hsv) vbox.addWidget(self.rb_pink) vbox.addWidget(self.rb_prism) vbox.addWidget(self.rb_spring) vbox.addWidget(self.rb_summer) vbox.addWidget(self.rb_winter) vbox.addWidget(self.rb_spectral) sizer1.setLayout(vbox) vboxtop.addWidget(sizer1) button_close = QtWidgets.QPushButton('Close') button_close.clicked.connect(self.close) vboxtop.addWidget(button_close) self.setLayout(vboxtop) self.ct_dict[self.parent.page1.colortable].setChecked(True) #---------------------------------------------------------------------- def OnColorTable(self, event): for radioButton in self.findChildren(QtWidgets.QRadioButton): if radioButton.isChecked(): radioButtonText = radioButton.text() break self.parent.page1.colortable = str(radioButtonText) self.parent.page1.loadImage() """ ------------------------------------------------------------------------------------------------""" class PageLoadData(QtWidgets.QWidget): def __init__(self, common, data_struct, stack): super(PageLoadData, self).__init__() uic.loadUi(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'pageloaddata.ui'), self) self.show() self.cmaps = [('Perceptually Uniform Sequential', [ 'viridis', 'plasma', 'inferno', 'magma']), ('Sequential', [ 'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds', 'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu', 'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']), ('Sequential (2)', [ 'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink', 'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia', 'hot', 'afmhot', 'gist_heat', 'copper']), ('Diverging', [ 'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu', 'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']), ('Qualitative', [ 'Pastel1', 'Pastel2', 'Paired', 'Accent', 'Dark2', 'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b', 'tab20c']), ('Miscellaneous', [ 'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv', 'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])] self.initUI(common, data_struct, stack) #---------------------------------------------------------------------- def initUI(self, common, data_struct, stack): self.scale = 0.000001 self.data_struct = data_struct self.stk = stack self.com = common self.filename = " " self.slider_eng.valueChanged[int].connect(self.OnScrollEng) self.slider_theta.valueChanged[int].connect(self.OnScrollTheta) self.iev = 0 self.itheta = 0 self.button_multiload.clicked.connect( self.OnLoadMulti) self.button_multiload.setToolTip('Supported Formats .hdf .hdf5 .ncb .nxs .hdr .stk .tif .tiff .txrm') self.button_4d.setToolTip('Supported Formats .hdf5 .ncb') self.button_4d.clicked.connect( self.OnLoad4D) #ToDo: Repair Stack Builder #ToDo: Repair Scaling/scalebar for HDF5 files. self.button_sm.setToolTip('Supported Formats .sm, .xrm') self.button_sm.clicked.connect( self.OnBuildStack) self.MetricCheckBox.toggled.connect(lambda: self.OnMetricScale(self.MetricCheckBox.isChecked(), self.ZeroOriginCheckBox.isChecked(),self.SquarePxCheckBox.isChecked())) self.ZeroOriginCheckBox.toggled.connect(lambda: self.OnMetricScale(self.MetricCheckBox.isChecked(), self.ZeroOriginCheckBox.isChecked(),self.SquarePxCheckBox.isChecked())) self.SquarePxCheckBox.toggled.connect(lambda: self.OnMetricScale(self.MetricCheckBox.isChecked(), self.ZeroOriginCheckBox.isChecked(),self.SquarePxCheckBox.isChecked())) self.SquarePxCheckBox.setVisible(False) self.CMCatBox.addItems([self.cmaps[0][0],self.cmaps[1][0],self.cmaps[2][0],self.cmaps[3][0],self.cmaps[4][0],self.cmaps[5][0]]) self.CMMapBox.addItems(self.cmaps[2][1]) self.CMCatBox.setCurrentIndex(2) self.CMMapBox.setCurrentIndex(3) self.CMCatBox.currentIndexChanged.connect(self.OnCatChanged) self.CMMapBox.currentIndexChanged.connect(lambda: self.OnColormap(map=self.CMMapBox.currentText(),colors=self.StepSpin.value())) self.StepSpin.valueChanged.connect(lambda: self.OnColormap(map=self.CMMapBox.currentText(),colors=self.StepSpin.value())) self.pb_rotate.clicked.connect(self.OnRotate) self.pb_mirror.clicked.connect(self.OnMirror) self.pb_copy.clicked.connect(self.OnCopy) self.pglayout = pg.GraphicsLayout(border=None) self.canvas.setBackground("w") # canvas is a pg.GraphicsView widget self.canvas.setCentralWidget(self.pglayout) self.p1 = self.pglayout.addPlot(row=0, col=0, rowspan=1, colspan=1) self.p1.setMouseEnabled(x=False, y=False) self.i_item = pg.ImageItem(border="k") self.p1.setAspectLocked(lock=True, ratio=1) self.p1.showAxis("top", show=True) self.p1.showAxis("bottom", show=True) self.p1.showAxis("left", show=True) self.p1.showAxis("right", show=True) self.ay1 = self.p1.getAxis("left") by1 = self.p1.getAxis("right") self.ax1 = self.p1.getAxis("bottom") bx1 = self.p1.getAxis("top") self.ay1.setLabel(text="y",units="px") self.ay1.enableAutoSIPrefix(enable=True) self.ax1.setLabel(text="x",units="px") self.ax1.enableAutoSIPrefix(enable=True) self.ay1.setStyle(tickLength=8) self.ax1.setStyle(tickLength=8) by1.setStyle(showValues=False,tickLength=0) bx1.setStyle(showValues=False,tickLength=0) self.p1.setTitle("No data loaded") self.cmimg = pg.ImageItem(border=None) self.cm = self.pglayout.addPlot(row=0, col=1, rowspan=1, colspan=1) self.cm.addItem(self.cmimg) self.cm.setMouseEnabled(x=False, y=False) self.cm.getViewBox().autoRange(padding=0) self.cm.showAxis("top", show=True) self.cm.showAxis("bottom", show=True) self.cm.showAxis("left", show=True) self.cm.showAxis("right", show=True) self.cm.setTitle("") ay3 = self.cm.getAxis("left") ay3.setZValue(1000) by3 = self.cm.getAxis("right") by3.setZValue(1000) bx3 = self.cm.getAxis("top") bx3.setZValue(1000) ax3 = self.cm.getAxis("bottom") ax3.setHeight(h=46.2) #workaround for overly long colorbar in linux by3.setWidth(w=60) ax3.setZValue(1000) ax3.setTicks([]) ax3.setLabel(text="", units="") by3 = self.cm.getAxis("right") by3.setLabel(text="counts", units="") by3.setStyle(tickLength=8) ay3.setStyle(showValues=False,tickLength=0) bx3.setStyle(showValues=False,tickLength=0) self.pglayout.layout.setColumnMinimumWidth(1, 80) self.pglayout.layout.setColumnMaximumWidth(1, 80) self.tc_file.setText('File name') self.tc_path.setText('D:/') self.slider_theta.setVisible(False) def Clear(self): self.p1.clear() def LoadEntries(self): # Called when fresh data are loaded. self.p1.addItem(self.i_item) self.OnScrollEng(0) # Plot first image & set Scrollbar self.OnMetricScale(self.MetricCheckBox.isChecked(), True, False) def keyPressEvent(self, e): if e.key() == 67 and (e.modifiers() & QtCore.Qt.ControlModifier): self.OnCopy() def OnMetricScale(self, setmetric= True, zeroorigin= True, square= False): if self.com.stack_loaded == 1: if setmetric==True: self.SquarePxCheckBox.setVisible(False) self.ZeroOriginCheckBox.setVisible(True) self.p1.setAspectLocked(lock=True, ratio=1) #self.p2.setAspectLocked(lock=True, ratio=1) if not zeroorigin: x_start = self.stk.x_start*self.scale y_start = self.stk.y_start*self.scale else: x_start = 0 y_start = 0 self.ay1.setLabel(text="y", units="m") self.ax1.setLabel(text="x", units="m") #self.ay2.setLabel(text="y", units="m") #self.ax2.setLabel(text="x", units="m") self.i_item.setRect(QtCore.QRectF(x_start, y_start, self.scale*self.stk.n_cols*self.stk.x_pxsize, self.scale*self.stk.n_rows*self.stk.y_pxsize)) #if hasattr(self, "OD"): # self.m_item.setRect(QtCore.QRectF(x_start, y_start, self.scale*np.shape(self.OD)[0]*self.stk.x_pxsize, self.scale*np.shape(self.OD)[1]*self.stk.y_pxsize)) # self.setCrosshair() else: self.ZeroOriginCheckBox.setVisible(False) self.SquarePxCheckBox.setVisible(True) if square == True or self.stk.x_pxsize == 0 or self.stk.y_pxsize == 0: aspect = 1 else: aspect = self.stk.x_pxsize/self.stk.y_pxsize #print(aspect) self.p1.setAspectLocked(lock=True, ratio=aspect) self.ay1.setLabel(text="y", units="px") self.ax1.setLabel(text="x", units="px") self.i_item.setRect(QtCore.QRectF(0, 0, self.stk.n_cols, self.stk.n_rows)) # ---------------------------------------------------------------------- def OnCatChanged(self): self.CMMapBox.blockSignals(True) self.CMMapBox.clear() self.CMMapBox.blockSignals(False) self.CMMapBox.addItems(self.cmaps[self.CMCatBox.currentIndex()][1]) def calcBinSize(self,i,N): return int(round(256*(i+1)/N) - round(256*i/N)) def OnCopy(self): self.exp = pg.exporters.ImageExporter(self.pglayout) self.exp.export(copy=True) return def OnMirror(self): if self.com.stack_loaded == 1: if self.com.stack_4d == 1: self.stk.stack4D = np.flip(self.stk.stack4D, axis=0) self.stk.absdata = self.stk.stack4D[:, :, :, self.itheta].copy() else: self.stk.absdata = np.flip(self.stk.absdata, axis=0) if self.com.i0_loaded: if self.com.stack_4d: self.stk.od4D = np.flip(self.stk.od4D, axis=0) else: self.stk.od3d = np.flip(self.stk.od3d, axis=0) self.stk.od = self.stk.od3d.copy() self.stk.od = np.reshape(self.stk.od, (self.stk.n_rows * self.stk.n_cols, self.stk.n_ev), order='F') self.stk.fill_h5_struct_from_stk() if self.com.i0_loaded == 1: self.stk.fill_h5_struct_normalization() self.OnScrollEng(self.iev) # Update/Refresh widgets: if showmaptab: self.window().page9.Clear() self.window().page9.LoadEntries() self.window().page1.loadImage() return def OnRotate(self): if self.com.stack_loaded == 1: if self.com.stack_4d == 1: self.stk.stack4D = np.rot90(self.stk.stack4D, 3) self.stk.absdata = self.stk.stack4D[:, :, :, self.itheta].copy() else: self.stk.absdata = np.rot90(self.stk.absdata, 3) # Swap x/y constants: self.stk.n_cols, self.stk.n_rows = self.stk.n_rows, self.stk.n_cols self.stk.x_pxsize, self.stk.y_pxsize = self.stk.y_pxsize, self.stk.x_pxsize self.stk.x_start, self.stk.y_start = self.stk.y_start, self.stk.x_start self.stk.x_dist, self.stk.y_dist = self.stk.y_dist, self.stk.x_dist if self.com.i0_loaded: if self.com.stack_4d: self.stk.od4D = np.rot90(self.stk.od4D, 3) else: self.stk.od3d = np.rot90(self.stk.od3d, 3) self.stk.od = self.stk.od3d.copy() self.stk.od = np.reshape(self.stk.od, (self.stk.n_rows * self.stk.n_cols, self.stk.n_ev), order='F') self.stk.fill_h5_struct_from_stk() if self.com.i0_loaded == 1: self.stk.fill_h5_struct_normalization() # Update/Refresh widgets: self.OnScrollEng(self.iev) self.OnMetricScale(self.MetricCheckBox.isChecked(), self.ZeroOriginCheckBox.isChecked(),self.SquarePxCheckBox.isChecked()) if showmaptab: self.window().page9.Clear() self.window().page9.LoadEntries() self.window().page1.ix = int(self.stk.n_cols / 2) self.window().page1.iy = int(self.stk.n_rows / 2) self.window().page1.showSpectrum(self.window().page1.ix, self.window().page1.iy) self.window().page1.loadImage() return def OnColormap(self,map="gray", colors=256): if self.com.stack_loaded == 1: colormap = cm.get_cmap(map, colors) colormap = colormap(np.arange(colors)) cm_lst = [[colormap[idx][0], colormap[idx][1], colormap[idx][2], colormap[idx][3]] for idx in range(np.shape(colormap)[0])] #convert to r,g,b,a list cm_lst = [item for sub in [[cm_lst[i]]*self.calcBinSize(i,colors) for i in range(colors)] for item in sub] #fills 256 bins as equal as possible with n colors cm_array = np.array([np.asarray(cm_lst)]) #vertical colorbar cm_lst.extend((cm_lst[-1],cm_lst[-1],cm_lst[-1])) lut = np.asarray(cm_lst) lut = (lut * 255).view(np.ndarray) self.cmimg.setImage(cm_array) self.i_item.setLookupTable(lut) self.setODbar(self.ODmin, self.ODmax) def setODbar(self,min=None,max=None): self.cm.setRange(xRange=[0,1], yRange=[min,max], update=False, disableAutoRange=True,padding=0) self.cmimg.setRect(QtCore.QRectF(0,min,1,max-min)) #---------------------------------------------------------------------- def OnLoadMulti(self, event): self.window().LoadStack() #---------------------------------------------------------------------- def OnLoad4D(self, event): self.window().LoadStack4D() #---------------------------------------------------------------------- def OnBuildStack(self, event): self.window().BuildStack() #---------------------------------------------------------------------- def OnScrollEng(self, value): self.slider_eng.setValue(value) #self.MapSelectWidget1.setCurrentRow(value) self.iev = value if self.com.stack_loaded == 1: image = self.stk.absdata[:, :, int(self.iev)].copy() if self.com.stack_4d == 1: self.p1.setTitle("
Image at {0:5.2f} eV and {1:5.1f}°
".format(float(self.stk.ev[self.iev]), float(self.stk.theta[self.itheta]))) else: self.p1.setTitle("
Image at energy {0:5.2f} eV
".format(float(self.stk.ev[self.iev]))) self.ODmin = np.min(image) self.ODmax = np.max(image) self.i_item.setImage(image) if np.isfinite(self.ODmin) or np.isfinite(self.ODmax): #incomplete stacks can contain NAN values that mess up the function on the next line self.OnColormap(map=self.CMMapBox.currentText(),colors=self.StepSpin.value()) #---------------------------------------------------------------------- def OnScrollTheta(self, value): self.slider_theta.setValue(value) self.itheta = value self.stk.absdata = self.stk.stack4D[:,:,:,self.itheta].copy() image = self.stk.absdata[:, :, int(self.slider_eng.value())].copy() #self.tc_imagetheta.setText("4D Data Angle: "+str(self.stk.theta[self.itheta])) if self.com.stack_loaded == 1: self.p1.setTitle("
Image at {0:5.2f} eV and {1:5.1f}°
".format(float(self.stk.ev[self.iev]), float(self.stk.theta[self.itheta]))) self.ODmin = np.min(image) self.ODmax = np.max(image) self.i_item.setImage(image) self.OnColormap(map=self.CMMapBox.currentText(),colors=self.StepSpin.value()) #self.window().page1.itheta = self.itheta #self.window().page1.slider_theta.setValue(self.itheta) #self.window().page2.itheta = self.itheta #self.window().page2.slider_theta.setValue(self.itheta) #---------------------------------------------------------------------- def ShowInfo(self, filename, filepath): self.tc_file.setText(filename) self.tc_path.setText(filepath) """ ------------------------------------------------------------------------------------------------""" class PageMap(QtWidgets.QWidget): qlistchanged = pyqtSignal([tuple]) def __init__(self, common, data_struct, stack): super(PageMap, self).__init__() dir_path = os.path.dirname(os.path.realpath(__file__)) uic.loadUi(os.path.join(dir_path,'pagemap.ui'), self) self.show() self.cmaps = [('Perceptually Uniform Sequential', [ 'viridis', 'plasma', 'inferno', 'magma']), ('Sequential', [ 'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds', 'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu', 'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn']), ('Sequential (2)', [ 'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink', 'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia', 'hot', 'afmhot', 'gist_heat', 'copper']), ('Diverging', [ 'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu', 'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic']), ('Qualitative', [ 'Pastel1', 'Pastel2', 'Paired', 'Accent', 'Dark2', 'Set1', 'Set2', 'Set3', 'tab10', 'tab20', 'tab20b', 'tab20c']), ('Miscellaneous', [ 'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern', 'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg', 'hsv', 'gist_rainbow', 'rainbow', 'jet', 'nipy_spectral', 'gist_ncar'])] self.initUI(common, data_struct, stack) #---------------------------------------------------------------------- def initUI(self, common, data_struct, stack): self.xoffset = 0 self.yoffset = 0 self.scale = 0.000001 self.pbRST.clicked.connect(lambda: self.setShifts(0, 0)) self.pbL.clicked.connect(lambda: self.setShifts(-0.2,0)) self.pbR.clicked.connect(lambda: self.setShifts(0.2,0)) self.pbU.clicked.connect(lambda: self.setShifts(0,0.2)) self.pbD.clicked.connect(lambda: self.setShifts(0,-0.2)) self.pbRD.clicked.connect(lambda: self.setShifts(0.2,-0.2)) self.pbRU.clicked.connect(lambda: self.setShifts(0.2,0.2)) self.pbLD.clicked.connect(lambda: self.setShifts(-0.2,-0.2)) self.pbLU.clicked.connect(lambda: self.setShifts(-0.2,0.2)) self.pbLL.clicked.connect(lambda: self.setShifts(-1,0)) self.pbRR.clicked.connect(lambda: self.setShifts(1,0)) self.pbUU.clicked.connect(lambda: self.setShifts(0,1)) self.pbDD.clicked.connect(lambda: self.setShifts(0,-1)) self.pbLLDD.clicked.connect(lambda: self.setShifts(-1,-1)) self.pbRRUU.clicked.connect(lambda: self.setShifts(1,1)) self.pbLLUU.clicked.connect(lambda: self.setShifts(-1,1)) self.pbRRDD.clicked.connect(lambda: self.setShifts(1,-1)) self.pbExpData.clicked.connect(self.OnSaveData) self.pbExpImg.clicked.connect(self.OnSaveImage) self.pbCopy.clicked.connect(self.OnCopy) self.MetricCheckBox.toggled.connect(lambda: self.OnMetricScale(self.MetricCheckBox.isChecked(), self.ZeroOriginCheckBox.isChecked(),self.SquarePxCheckBox.isChecked())) self.ZeroOriginCheckBox.toggled.connect(lambda: self.OnMetricScale(self.MetricCheckBox.isChecked(), self.ZeroOriginCheckBox.isChecked(),self.SquarePxCheckBox.isChecked())) self.SquarePxCheckBox.toggled.connect(lambda: self.OnMetricScale(self.MetricCheckBox.isChecked(), self.ZeroOriginCheckBox.isChecked(),self.SquarePxCheckBox.isChecked())) self.SquarePxCheckBox.setVisible(False) self.CropCheckBox.toggled.connect(lambda: self.OnCropCB(self.CropCheckBox.isChecked())) self.cropflag = True #self.ShiftLabel.setText("x = %0.1f \ny = %0.1f" % (0, 0)) self.ODHighSpinBox.valueChanged.connect(lambda: self.setODlimits(self.ODLowSpinBox.value(),self.ODHighSpinBox.value())) self.ODLowSpinBox.valueChanged.connect(lambda: self.setODlimits(self.ODLowSpinBox.value(),self.ODHighSpinBox.value())) self.pbRSTOD.clicked.connect(lambda: self.ShowMap(self.prelst, self.postlst)) self.pbClrShifts.clicked.connect(self.OnClrShifts) self.pbClrSel.clicked.connect(self.OnClrSelection) self.CMCatBox.addItems([self.cmaps[0][0],self.cmaps[1][0],self.cmaps[2][0],self.cmaps[3][0],self.cmaps[4][0],self.cmaps[5][0]]) self.CMMapBox.addItems(self.cmaps[2][1]) self.CMCatBox.setCurrentIndex(2) self.CMMapBox.setCurrentIndex(14) self.CMCatBox.currentIndexChanged.connect(self.OnCatChanged) self.CMMapBox.currentIndexChanged.connect(lambda: self.OnColormap(map=self.CMMapBox.currentText(),colors=self.StepSpin.value())) self.StepSpin.valueChanged.connect(lambda: self.OnColormap(map=self.CMMapBox.currentText(),colors=self.StepSpin.value())) self.filterSpinBox.valueChanged.connect(lambda: self.ShowMap(self.prelst, self.postlst)) self.filterSpinBox.setEnabled(False) self.MapSelectWidget1.mousePressEvent = self.mouseEventOnQList self.MapSelectWidget1.mouseMoveEvent = self.mouseEventOnQList self.data_struct = data_struct self.stk = stack self.com = common self.iev = 0 self.latest_row = -1 self.pglayout = pg.GraphicsLayout(border=None) self.canvas.setBackground("w") # canvas is a pg.GraphicsView widget self.canvas.setCentralWidget(self.pglayout) #self.pglayout.addItem(pg.AxisItem('left',vPolicy =QtWidgets.QSizePolicy.Maximum), row=1, col=0, rowspan=1, colspan=3) self.p1 = self.pglayout.addPlot(row=0, col=0, rowspan=1, colspan=1) self.p1.setMouseEnabled(x=False, y=False) self.i_item = pg.ImageItem(border="k") self.p1.setAspectLocked(lock=True, ratio=1) self.p1.showAxis("top", show=True) self.p1.showAxis("bottom", show=True) self.p1.showAxis("left", show=True) self.p1.showAxis("right", show=True) self.ay1 = self.p1.getAxis("left") by1 = self.p1.getAxis("right") self.ax1 = self.p1.getAxis("bottom") bx1 = self.p1.getAxis("top") self.ay1.setLabel(text="y",units="px") self.ay1.enableAutoSIPrefix(enable=True) self.ax1.setLabel(text="x",units="px") self.ax1.enableAutoSIPrefix(enable=True) self.ay1.setStyle(tickLength=8) self.ax1.setStyle(tickLength=8) by1.setStyle(showValues=False,tickLength=0) bx1.setStyle(showValues=False,tickLength=0) self.p2 = self.pglayout.addPlot(row=0, col=1, rowspan=1, colspan=1) self.p2.setMouseEnabled(x=False, y=False) self.m_item = pg.ImageItem(border="k") self.m_item.setZValue(1000) self.p2.setAspectLocked(lock=True, ratio=1) self.p2.showAxis("top", show=True) self.p2.showAxis("bottom", show=True) self.p2.showAxis("left", show=True) self.p2.showAxis("right", show=True) self.ay2 = self.p2.getAxis("left") self.ay2.setZValue(1000) by2 = self.p2.getAxis("right") by2.setZValue(1000) self.ax2 = self.p2.getAxis("bottom") self.ax2.setZValue(1000) bx2 = self.p2.getAxis("top") bx2.setZValue(1000) self.ay2.setLabel(text="y",units="px") self.si_prefix = self.ay2.enableAutoSIPrefix(enable=True) self.ax2.setLabel(text="x",units="px") self.ax2.enableAutoSIPrefix(enable=True) self.ay2.setStyle(tickLength=8) self.ax2.setStyle(tickLength=8) by2.setStyle(showValues=False,tickLength=0) bx2.setStyle(showValues=False,tickLength=0) self.p1.setTitle("") self.p1.titleLabel.setText("
No images loaded
", size='10pt') self.p2.setTitle("") self.p2.titleLabel.setText("
No map available
", size='10pt') #self.p1.titleLabel.item.setTextWidth(self.p1.width() * 0.7) #self.p2.titleLabel.item.setTextWidth(self.p2.width() * 0.7) self.cmimg = pg.ImageItem(border=None) self.cm = self.pglayout.addPlot(row=0, col=2, rowspan=1, colspan=1) self.cm.addItem(self.cmimg) self.cm.setMouseEnabled(x=False, y=False) self.cm.getViewBox().autoRange(padding=0) self.cm.showAxis("top", show=True) self.cm.showAxis("bottom", show=True) self.cm.showAxis("left", show=True) self.cm.showAxis("right", show=True) self.cm.setTitle("") self.cm.titleLabel.setText("", size='10pt') ay3 = self.cm.getAxis("left") ay3.setZValue(1000) by3 = self.cm.getAxis("right") by3.setZValue(1000) bx3 = self.cm.getAxis("top") bx3.setZValue(1000) ax3 = self.cm.getAxis("bottom") ax3.setZValue(1000) ax3.setTicks([]) ax3.setLabel(text="", units="") ax3.setStyle(tickLength=8) by3 = self.cm.getAxis("right") by3.setLabel(text="OD", units="") by3.setStyle(tickLength=8) #by3.textWidth() ay3.setStyle(showValues=False,tickLength=0) bx3.setStyle(showValues=False,tickLength=0) ax3.setHeight(h=46.2) # workaround for overly long colorbar by3.setWidth(w=60) self.pglayout.layout.setColumnMinimumWidth(2, 80) self.pglayout.layout.setColumnMaximumWidth(2, 80) #self.pglayout.layout.setColumnMinimumWidth(1, int((self.p1.width() + self.p2.width()) / 2)) #self.pglayout.layout.setColumnMinimumWidth(0, int((self.p1.width() + self.p2.width()) / 2)) self.p1.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Preferred) self.p2.setSizePolicy(QtWidgets.QSizePolicy.Expanding, QtWidgets.QSizePolicy.Preferred) self.cm.setSizePolicy(QtWidgets.QSizePolicy.Maximum, QtWidgets.QSizePolicy.Preferred) def mouseEventOnQList(self, e): if e.type() == QtCore.QEvent.MouseMove or e.type() == QtCore.QEvent.MouseButtonPress: qlist = self.MapSelectWidget1 pos = qlist.mapFromGlobal(QtGui.QCursor.pos()) row = qlist.indexAt(pos).row() #print(row,self.latest_row) if self.stk.shifts: params = [self.stk.shifts[row][0],self.stk.shifts[row][1],self.stk.shifts[row][2]] if row >= 0: if e.type() != QtCore.QEvent.MouseMove or row != self.latest_row: if e.buttons() == QtCore.Qt.RightButton: qlist.setCurrentRow(row) params[1] = 1 elif e.buttons() == QtCore.Qt.LeftButton: qlist.setCurrentRow(row) params[1] = -1 self.qlistchanged.emit((row,params)) self.latest_row = row return def qListChangeHandler(self,paramtup): qlist = self.MapSelectWidget1 row, params = paramtup if self.stk.shifts[row][1] == params[1]: #print("deselect it!") qlist.item(row).setBackground(QtGui.QColor(0, 0, 0, 0)) self.stk.shifts[row][1] = 0 elif self.stk.shifts[row][1] != params[1] and params[1] == 1: # select right #print("select right!") qlist.item(row).setBackground(QtGui.QColor('#7fc97f')) self.stk.shifts[row][1] = 1 elif self.stk.shifts[row][1] != params[1] and params[1] == -1: # select left #print("select left!") qlist.item(row).setBackground(QtGui.QColor('#beaed4')) self.stk.shifts[row][1] = -1 self.OnSelectionChanged() #print(row, params) def OnSelectionChanged(self): self.prelst = [index for index, value in enumerate([x[1] for x in self.stk.shifts]) if value == -1] self.postlst = [index for index, value in enumerate([x[1] for x in self.stk.shifts]) if value == 1] #print(self.prelst,self.postlst) self.OnScrollEng(self.MapSelectWidget1.currentRow()) if len(self.prelst) == 0 or len(self.postlst) == 0: if len(self.prelst + self.postlst) == 0: self.pbClrSel.setEnabled(False) else: self.pbClrSel.setEnabled(True) self.p2.clear() self.cm.clear() self.p2.titleLabel.setText("
Select at least one pre- and post-edge image!
",size='10pt') self.ODHighSpinBox.setEnabled(False) self.ODLowSpinBox.setEnabled(False) self.pbRSTOD.setEnabled(False) self.filterSpinBox.setEnabled(False) self.pbExpData.setEnabled(False) self.pbExpImg.setEnabled(False) else: self.pbClrSel.setEnabled(True) #self.InfWarning = False self.ShowMap(self.prelst,self.postlst) # self.OnScrollEng(self.MapSelectWidget1.currentRow()) self.OnMetricScale(self.MetricCheckBox.isChecked(), self.ZeroOriginCheckBox.isChecked(), self.SquarePxCheckBox.isChecked()) return # ---------------------------------------------------------------------- def OnSaveData(self,event): #Save Data wildcard = "Float32 TIFF File (*.tif);;TextImage (*.txt);;" fileName, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save OD Map', '', wildcard) fileName = str(fileName) if fileName == '': return path, ext = os.path.splitext(fileName) ext = ext[1:].lower() if ext != 'tif' and ext != 'txt': error_message = ( 'Only the TIF and TXT data formats are supported.\n' 'A file extension of `tif\' or `txt\' must be used.') QtWidgets.QMessageBox.warning(self, 'Error', 'Error - Could not save file.') return if ext == 'tif': from PIL import Image img1 = Image.fromarray(np.rot90(self.OD)) img1.save(fileName) if ext == 'txt': np.savetxt(fileName, np.rot90(self.OD), delimiter='\t', newline='\n',fmt='%.5f') def OnCopy(self): self.exp = pg.exporters.ImageExporter(self.pglayout) self.exp.export(copy=True) return def OnSaveImage(self, event): # Save Image wildcard = "TIFF (*.tif);;PNG (*.png);;JPG (*.jpg);;SVG (*.svg);;" fileName, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save OD Map', '', wildcard) fileName = str(fileName) if fileName == '': return path, ext = os.path.splitext(fileName) ext = ext[1:].lower() padding = 10 # clearance between OD color bar and p2 in px units width = self.p2.size().width() + self.cm.size().width() height = self.p2.size().height() if ext == 'svg': p2exp = pg.exporters.SVGExporter(self.p2) cmexp = pg.exporters.SVGExporter(self.cm) else: p2exp = pg.exporters.ImageExporter(self.p2) cmexp = pg.exporters.ImageExporter(self.cm) p2exp = p2exp.export(toBytes=True) cmexp = cmexp.export(toBytes=True) if ext == 'tif' or ext == 'png' or ext == 'jpg': # The subsequent code was tested with pyqtgraph 0.11.0dev0 ; pyqtgraph < 0.11.0 shows a TypeError: 'float'... # To combine self.p2 and self.cm (OD bar) in a single image, the two QImage objects are redrawn using a QPainter object. # If self.cm is not needed, the following two lines are sufficient: # p2exp = pg.exporters.ImageExporter(self.p2) # p2exp.export(FileName) qimg = QtGui.QImage(width + padding, height, QtGui.QImage.Format_ARGB32) if ext == 'jpg': qimg.fill(QtGui.QColor(255, 255, 255, 255)); # BG white if export as jpg else: qimg.fill(QtGui.QColor(0, 0, 0, 0)); painter = QtGui.QPainter() painter.begin(qimg) painter.drawImage(0, 0, p2exp) painter.drawImage(p2exp.size().width() + padding, 0, cmexp) painter.end() #pixmap = QPixmap.fromImage(tile) #QCoreApplication.instance().clipboard().setImage(qimg) qimg.save(fileName,quality=100) elif ext == 'svg': fig1 = self.SVGClipPathRemover(etree.fromstring(p2exp)) fig2 = self.SVGClipPathRemover(etree.fromstring(cmexp)) mergedfig = self.SVGMerger(fig1,fig2,self.p2.size().width(), self.cm.size().width(),height,padding) out = etree.tostring(mergedfig, xml_declaration=True, standalone=True, pretty_print=True) with open(fileName, 'wb') as svg: svg.write(out) def SVGMerger(self, svg1, svg2, width1,width2, height, padding=0): #merges two lxml.etree elements horizontally with padding ns = {'': svg1.tag.split('}')[0].strip('{')} svg = etree.Element(svg1.tag, nsmap={None: 'http://www.w3.org/2000/svg', 'xlink': 'http://www.w3.org/1999/xlink'}) svg.set("version", "1.2") svg.set('width', str(width1 + width2 + padding)+"px") #target width svg.set('height', str(height)+"px") #target height svg.set("viewBox", "0 0 %s %s" % (str(width1 + width2 +padding)+"px", str(height)+"px")) g1 = etree.SubElement(svg, 'g') g1.append(svg1.find('./g', ns)) g = etree.SubElement(g1, 'g') g.set('transform','translate('+str(width1 + padding)+', 0) scale(1)') g.append(svg2.find('./g', ns)) return svg def SVGClipPathRemover(self, elem): #takes etree elements and removes clip-path tags and keys inside g tags if present. ns = {'': elem.tag.split('}')[0].strip('{')} try: for clippath in elem.findall(".//clipPath", ns): clippath.getparent().remove(clippath) except: pass try: for g in elem.findall(".//g", ns): if g.keys()[0] == "clip-path": del g.attrib['clip-path'] except: pass return elem def OnCatChanged(self): self.CMMapBox.blockSignals(True) self.CMMapBox.clear() self.CMMapBox.blockSignals(False) self.CMMapBox.addItems(self.cmaps[self.CMCatBox.currentIndex()][1]) def setODlimits(self,low, high): self.ODHighSpinBox.setMaximum(self.ODmax) self.ODHighSpinBox.setMinimum(low) self.ODLowSpinBox.setMaximum(high) self.ODLowSpinBox.setMinimum(self.ODmin) OD = np.clip(self.OD,low,high) self.OnColormap(map=self.CMMapBox.currentText(), colors=self.StepSpin.value()) self.setODbar(low, high) self.m_item.setImage(OD) def setCrosshair(self): if hasattr(self, 'vLine'): self.p2.removeItem(self.vLine) self.p2.removeItem(self.vLine) self.canvas.removeItem(self.ODlabel) self.vLine = pg.InfiniteLine(angle=90, movable=False, pen="0000") self.hLine = pg.InfiniteLine(angle=0, movable=False,pen="0000") self.ODlabel = pg.LabelItem(justify="left") self.canvas.addItem(self.ODlabel) self.p2.addItem(self.vLine, ignoreBounds=True) self.p2.addItem(self.hLine, ignoreBounds=True) self.proxy = pg.SignalProxy(self.p2.scene().sigMouseMoved, rateLimit=30, slot=self.OnMouseHover) def OnMouseHover(self,evt): pos = evt[0] #print(pos) if hasattr(self, 'OD'): if self.p2.getViewBox().itemBoundingRect(self.m_item).contains(self.p2.getViewBox().mapSceneToView(pos)): self.vLine.setPen("r") self.hLine.setPen("r") self.vLine.setZValue(1000) self.hLine.setZValue(1000) mousePoint = self.p2.getViewBox().mapSceneToView(pos) if self.MetricCheckBox.isChecked(): x_min = (self.p2.getViewBox().itemBoundingRect(self.m_item).x() / (self.scale * self.stk.x_pxsize)) # minimum x value in px y_min = (self.p2.getViewBox().itemBoundingRect(self.m_item).y() / (self.scale * self.stk.y_pxsize)) # minimum y value in px x_off = x_min - int(x_min) y_off = y_min - int(y_min) x_pos = mousePoint.x() / (self.scale * self.stk.x_pxsize) +1 #x mousepos in number of rows y_pos = mousePoint.y() / (self.scale * self.stk.y_pxsize) +1 #y mousepos in number of rows if mousePoint.x() < 0: xi = int(x_pos - x_off-1) else: xi = int(x_pos - x_off) if mousePoint.y() < 0: yi = int(y_pos - y_off -1) else: yi = int(y_pos - y_off) x = (xi - 0.5 + x_off) * self.scale * self.stk.x_pxsize y = (yi - 0.5 + y_off) * self.scale * self.stk.y_pxsize pt = QtCore.QPointF(x,y) self.ODlabel.setText(" x = %0.3f µm, y = %0.3f µm, OD = %0.2f" % (x/self.scale - 0.5 * self.stk.x_pxsize, y/self.scale - 0.5 * self.stk.y_pxsize, self.OD[xi-1-int(x_min),yi-1-int(y_min)])) else: x = int(mousePoint.x()) + 0.5 y = int(mousePoint.y()) + 0.5 pt = QtCore.QPointF(x,y) self.ODlabel.setText(" x = %0.0f, y = %0.0f, OD = %0.2f" % (x, y,self.OD[int(x),int(y)])) #print(x,y) self.vLine.setPos(x) self.hLine.setPos(y) else: self.ODlabel.setText("") self.vLine.setZValue(-1000) self.hLine.setZValue(-1000) self.vLine.setPen("0000") self.hLine.setPen("0000") def setODbar(self,min=None,max=None): self.cm.setRange(xRange=[0,1], yRange=[min,max], update=False, disableAutoRange=True,padding=0) self.cmimg.setRect(QtCore.QRectF(0,min,1,max-min)) def keyPressEvent(self, e): modifiers = QtWidgets.QApplication.keyboardModifiers() if modifiers == QtCore.Qt.ShiftModifier: noshift = False elif modifiers == QtCore.Qt.KeypadModifier: noshift = True elif modifiers == (QtCore.Qt.KeypadModifier | QtCore.Qt.ShiftModifier): noshift = False else: noshift = True if e.key() == 67 and (e.modifiers() & QtCore.Qt.ControlModifier): self.OnCopy() if e.key() == Qt.Key_Up or (e.key() == QtCore.Qt.Key_8): if noshift: self.pbUU.click() else: self.pbU.click() elif e.key() == Qt.Key_Down or (e.key() == QtCore.Qt.Key_2): if noshift: self.pbDD.click() else: self.pbD.click() elif e.key() == Qt.Key_Left or (e.key() == QtCore.Qt.Key_4): if noshift: self.pbLL.click() else: self.pbL.click() elif e.key() == Qt.Key_Right or (e.key() == QtCore.Qt.Key_6): if noshift: self.pbRR.click() else: self.pbR.click() elif e.key() == QtCore.Qt.Key_Home: if noshift: self.pbLLUU.click() else: self.pbLU.click() elif e.key() == QtCore.Qt.Key_PageUp: if noshift: self.pbRRUU.click() else: self.pbRU.click() elif e.key() == QtCore.Qt.Key_End: if noshift: self.pbLLDD.click() else: self.pbLD.click() elif e.key() == QtCore.Qt.Key_PageDown: if noshift: self.pbRRDD.click() else: self.pbRD.click() elif e.key() == QtCore.Qt.Key_Clear: self.pbRST.click() def Clear(self): try: self.slider_eng.valueChanged.disconnect() self.qlistchanged.disconnect() except: pass self.prelst = [] self.postlst =[] self.stk.shifts = [] self.stk.absdata_shifted= [] self.p1.clear() self.p2.clear() self.cm.clear() self.MapSelectWidget1.clear() def LoadEntries(self): # Called when fresh data are loaded. self.ODHighSpinBox.setEnabled(False) self.ODLowSpinBox.setEnabled(False) self.pbRSTOD.setEnabled(False) self.filterSpinBox.setEnabled(False) self.pbExpData.setEnabled(False) self.pbExpImg.setEnabled(False) self.pbClrSel.setEnabled(False) self.stk.absdata_shifted = self.stk.absdata.copy() self.p1.addItem(self.i_item) for i,e in enumerate(self.stk.ev): # Fill QList with energies self.stk.shifts.append([1,0,(0.0,0.0)]) #checked [0,1]; pre, post, undefined state for map [-1,1,0],(xshift [float],yshift [float]) item = QtWidgets.QListWidgetItem(str(int(i)).zfill(3)+" at " + format(e, '.2f') + " eV "+"+0.0"+" +0.0") self.MapSelectWidget1.addItem(item) self.slider_eng.valueChanged[int].connect(self.OnScrollEng) self.qlistchanged.connect(self.qListChangeHandler) self.OnScrollEng(0) # Plot first image & set Scrollbar self.OnMetricScale(self.MetricCheckBox.isChecked(), True, False) self.pbSelfromSpec.setEnabled(True) self.pbSelfromSpec.clicked.connect(self.OnSelfromSpec) def UpdateEntry(self,row): self.MapSelectWidget1.item(row).setText(str(int(row)).zfill(3)+" at " + format(self.stk.ev[row], '.2f') + " eV "+format(self.stk.shifts[row][2][0], '+.1f')+" "+format(self.stk.shifts[row][2][1], '+.1f')) #self.MapSelectWidget1.addItem(self.MapSelectWidget1.item(row)) self.i_item.setImage(self.Shift(row)) def ResetAllItems(self,widget): for i in range(widget.count()): widget.item(i).setForeground(QtGui.QColor(0, 0, 0, 128)) def OnScrollEng(self, value): self.slider_eng.blockSignals(True) self.slider_eng.setValue(value) self.slider_eng.blockSignals(False) self.MapSelectWidget1.setCurrentRow(value) self.ResetAllItems(self.MapSelectWidget1) self.iev = value self.p1.titleLabel.item.setTextWidth(self.p1.width() * 0.7) self.p2.titleLabel.item.setTextWidth(self.p2.width() * 0.7) #self.canvas.resizeEvent(None) if self.com.stack_loaded == 1: self.p1.titleLabel.setText("
Image at energy {0:5.2f} eV
".format(float(self.stk.ev[self.iev])),size='10pt') self.i_item.setImage(self.Shift(int(self.iev))) self.MapSelectWidget1.item(value).setForeground(QtGui.QColor(0, 0, 0, 255)) def OnCropCB(self, value=True): if self.com.stack_loaded == 1: if value == True: self.cropflag = True else: self.cropflag = False self.ShowMap(self.prelst, self.postlst) def OnMetricScale(self, setmetric= True, zeroorigin= True, square= False): if self.com.stack_loaded == 1: if setmetric==True: self.SquarePxCheckBox.setVisible(False) self.ZeroOriginCheckBox.setVisible(True) self.p1.setAspectLocked(lock=True, ratio=1) self.p2.setAspectLocked(lock=True, ratio=1) if not zeroorigin: x_start = self.stk.x_start*self.scale y_start = self.stk.y_start*self.scale else: x_start = 0 y_start = 0 self.ay1.setLabel(text="y", units="m") self.ax1.setLabel(text="x", units="m") self.ay2.setLabel(text="y", units="m") self.ax2.setLabel(text="x", units="m") self.i_item.setRect(QtCore.QRectF(x_start, y_start, self.scale*self.stk.n_cols*self.stk.x_pxsize, self.scale*self.stk.n_rows*self.stk.y_pxsize)) if hasattr(self, "OD"): self.m_item.setRect(QtCore.QRectF(x_start, y_start, self.scale*np.shape(self.OD)[0]*self.stk.x_pxsize, self.scale*np.shape(self.OD)[1]*self.stk.y_pxsize)) self.setCrosshair() else: self.ZeroOriginCheckBox.setVisible(False) self.SquarePxCheckBox.setVisible(True) if square == True: aspect = 1 else: aspect = self.stk.x_pxsize/self.stk.y_pxsize #print(aspect) self.p1.setAspectLocked(lock=True, ratio=aspect) self.ay1.setLabel(text="y", units="px") self.ax1.setLabel(text="x", units="px") self.i_item.setRect(QtCore.QRectF(0, 0, self.stk.n_cols, self.stk.n_rows)) if hasattr(self, "OD"): self.ay2.setLabel(text="y", units="px") self.ax2.setLabel(text="x", units="px") self.p2.setAspectLocked(lock=True, ratio=aspect) self.m_item.setRect(QtCore.QRectF(0, 0, np.shape(self.OD)[0], np.shape(self.OD)[1])) def OnClrSelection(self): for row in self.prelst + self.postlst: self.MapSelectWidget1.item(row).setBackground(QtGui.QColor(0, 0, 0, 0)) self.stk.shifts[row][1] = 0 self.OnSelectionChanged() def OnSelfromSpec(self,evt): spectralimgmap = SpectralImageMap(self, self.com, self.stk) spectralimgmap.show() def OnClrShifts(self): for row in [index for index, value in enumerate([x[2] for x in self.stk.shifts]) if value != (0.0,0.0)]: self.stk.shifts[row].pop(2) # remove tuple self.stk.shifts[row].insert(2, (0.0, 0.0)) self.stk.absdata_shifted[:, :, row] = self.stk.absdata[:, :, row] self.MapSelectWidget1.item(row).setText( str(int(row)).zfill(3) + " at " + format(self.stk.ev[row], '.2f') + " eV " + format( self.stk.shifts[row][2][0], '+.1f') + " " + format(self.stk.shifts[row][2][1], '+.1f')) if hasattr(self, 'prelst') and hasattr(self, 'postlst'): if len(self.prelst) != 0 and len(self.postlst) != 0: self.ShowMap(self.prelst,self.postlst) if self.stk.shifts: self.UpdateEntry(self.MapSelectWidget1.currentRow()) def setShifts(self,shift_x, shift_y): #print("setshifts called") # if hasattr(self, "OD"): if self.stk.shifts: row = self.MapSelectWidget1.currentRow() xoffset, yoffset = self.stk.shifts[row][2] # current offset stored as tuple in table stk.shifts #print(self.stk.shifts[self.MapSelectWidget1.currentRow()]) #yoffset = self.stk.shifts[self.MapSelectWidget1.currentRow()][2][1] if shift_x == 0 and shift_y == 0: # if reset button pressed if xoffset != 0 or yoffset != 0: self.stk.shifts[row].pop(2) # remove tuple self.stk.shifts[row].insert(2,(0.0,0.0)) else: print("Reset has no effect") return else: self.stk.shifts[row].pop(2) xoffset = round(xoffset + shift_x,1) yoffset = round(yoffset + shift_y,1) self.stk.shifts[row].insert(2,(xoffset, yoffset)) #current_img = self.stk.absdata[:, :, self.MapSelectWidget1.currentRow()] #print(type(self.stk.absdata), self.stk.shifts[self.MapSelectWidget1.currentRow()]) #self.Shift(row) self.UpdateEntry(row) if hasattr(self, 'prelst') and hasattr(self, 'postlst'): if len(self.prelst) != 0 and len(self.postlst) != 0: self.ShowMap(self.prelst,self.postlst) def Shift(self,row): #current_img = self.stk.absdata_shifted[:, :, row] original_img = self.stk.absdata[:, :, row] xoffset, yoffset = self.stk.shifts[row][2] # x and y offsets of current image if xoffset == 0 and yoffset == 0: self.stk.absdata_shifted[:, :, row] = original_img # replace with original if no shift is applied #self.ShiftLabel.setText("x = %0.1f \ny = %0.1f" % (self.xoffset, self.yoffset)) #return original_img else: shifted = ndimage.fourier_shift(np.fft.fft2(original_img), [float(xoffset), float(yoffset)]) shifted = np.fft.ifft2(shifted) shifted_real = shifted.real self.stk.absdata_shifted[:, :, row] = shifted_real return self.stk.absdata_shifted[:,:, row] #self.ShiftLabel.setText("x = %0.1f \ny = %0.1f" % (xoffset, yoffset)) def CalcODMap(self,im_idx1,im_idx2): if len(im_idx1) == 1 and len(im_idx2) == 1: im1 = self.stk.absdata_shifted[:, :, int(im_idx1[0])] im2 = self.stk.absdata_shifted[:, :, int(im_idx2[0])] else: im1 = np.mean([self.stk.absdata_shifted[:, :, i] for i in im_idx1], axis=0) im2 = np.mean([self.stk.absdata_shifted[:, :, i] for i in im_idx2], axis=0) OD = np.log(im1/im2) if self.cropflag: shiftlst = [self.stk.shifts[i][2] for i in im_idx1 + im_idx2] #print(shiftlst) # print(max(shiftlst, key=itemgetter(0))[0]) # possible alternative to lambda function #Calculate crops l = int(np.floor(min(shiftlst, key=lambda item: item[0])[0])) cl = l if l < 0 else None r = int(np.ceil(max(shiftlst, key=lambda item: item[0])[0])) t = int(np.ceil(max(shiftlst, key=lambda item: item[1])[1])) b = int(np.floor(min(shiftlst, key=lambda item: item[1])[1])) cb = b if b < 0 else None # Crop map OD = OD[r:cl,t:cb] #print(r,cl,t,cb) inf_idx = np.where(np.isinf(OD)) nan_idx = np.where(np.isnan(OD)) if np.any(inf_idx) or np.any(nan_idx): #if not self.InfWarning: # self.InfWarning = True # QtGui.QMessageBox.warning(self, 'Warning!', "The OD map contained infinite or nan values. Please note that they have been zeroed.") OD[inf_idx] = 0 #infinite values get replaced by zero. This is an ugly work around, but with nans the image is not displayed correctly. OD[nan_idx] = 0 return OD def calcBinSize(self,i,N): return int(round(256*(i+1)/N) - round(256*i/N)) def OnColormap(self,map="afmhot", colors=256): if hasattr(self, "OD"): colormap = cm.get_cmap(map, colors) colormap = colormap(np.arange(colors)) cm_lst = [[colormap[idx][0], colormap[idx][1], colormap[idx][2], colormap[idx][3]] for idx in range(np.shape(colormap)[0])] #convert to r,g,b,a list cm_lst = [item for sub in [[cm_lst[i]]*self.calcBinSize(i,colors) for i in range(colors)] for item in sub] #fills 256 bins as equal as possible with n colors cm_array = np.array([np.asarray(cm_lst)]) #vertical colorbar cm_lst.extend((cm_lst[-1],cm_lst[-1],cm_lst[-1])) lut = np.asarray(cm_lst) lut = (lut * 255).view(np.ndarray) #lut for OD map self.cmimg.setImage(cm_array) self.m_item.setLookupTable(lut) if hasattr(self, "OD"): self.setODbar(self.ODmin, self.ODmax) def ShowMap(self, preidx, postidx): self.p2.clear() self.p2.addItem(self.m_item) self.cm.clear() self.cm.addItem(self.cmimg) self.setCrosshair() try: ## Optional sorting switched off for convenience. Allows maps to range to negative OD #selection = preidx + postidx # selection.sort() if len(preidx + postidx) == 2: self.p2.titleLabel.setText("
Binary map from energies " + str(round(self.stk.ev[preidx[0]], 2)) + " and " + str( round(self.stk.ev[postidx[0]], 2)) + " eV
", size='10pt') elif len(preidx + postidx) <= 6: self.p2.titleLabel.setText("
Map from energies " + str([round(self.stk.ev[e], 2) for e in preidx]).strip('[]') + " and " + str([round(self.stk.ev[e], 2) for e in postidx]).strip('[]') + " eV
", size='10pt') else: self.p2.titleLabel.setText("
Map from "+ str(len(preidx + postidx)) +" energies: " + str(round(self.stk.ev[preidx[0]], 2)) + ' ... ' + str(round(self.stk.ev[preidx[-1]], 2)) + " and " + str(round(self.stk.ev[postidx[0]], 2)) + ' ... ' + str(round(self.stk.ev[postidx[-1]], 2)) + " eV
", size='10pt') self.OD = self.CalcODMap(preidx, postidx) if self.filterSpinBox.value() > 1: self.OD = ndimage.filters.uniform_filter(self.OD, size=self.filterSpinBox.value(), mode='nearest') self.ODmin = np.min(self.OD) self.ODmax = np.max(self.OD) self.ODHighSpinBox.setEnabled(True) self.ODLowSpinBox.setEnabled(True) self.ODHighSpinBox.blockSignals(True) self.ODLowSpinBox.blockSignals(True) self.ODHighSpinBox.setMaximum(self.ODmax) self.ODHighSpinBox.setMinimum(self.ODmin) self.ODLowSpinBox.setMaximum(self.ODmax) self.ODLowSpinBox.setMinimum(self.ODmin) self.ODLowSpinBox.setValue(self.ODmin) self.ODHighSpinBox.setValue(self.ODmax) self.setODlimits(self.ODmin, self.ODmax) self.ODHighSpinBox.blockSignals(False) self.ODLowSpinBox.blockSignals(False) self.pbRSTOD.setEnabled(True) self.filterSpinBox.setEnabled(True) self.pbExpData.setEnabled(True) self.pbExpImg.setEnabled(True) #self.pglayout.layout.setColumnMaximumWidth(1, self.p1.width()) except IndexError: self.p2.clear() self.cm.clear() #self.p2.setTitle("Please select a second image!") #print("Select a second image!") #---------------------------------------------------------------------- class StackListFrame(QtWidgets.QDialog): def __init__(self, parent, filepath, com, stack, data_struct): QtWidgets.QWidget.__init__(self, parent) self.parent = parent self.data_struct = data_struct self.stk = stack self.common = com self.resize(600, 500) self.setWindowTitle('Stack File List') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) self.filepath = str(filepath) self.have1st = 0 self.havelast = 0 self.file1st = ' ' self.filelast = ' ' self.filetype = '' vbox = QtWidgets.QVBoxLayout() self.textt = QtWidgets.QLabel(self) self.textt.setText('Select first stack file') vbox.addStretch(1) vbox.addWidget(self.textt) self.filelist = QtGui.QTableWidget() self.filelist.setMinimumHeight(450) self.filelist.setColumnCount(4) self.filelist.setHorizontalHeaderLabels(('File list', 'X', 'Y', 'eV')) self.filelist.setShowGrid(False) self.filelist.verticalHeader().setVisible(False) self.filelist.setColumnWidth(0,400) self.filelist.setColumnWidth(1,50) self.filelist.setColumnWidth(2,50) self.filelist.setColumnWidth(3,50) self.filelist.setRowCount(0) self.filelist.cellClicked.connect(self.OnFileList) vbox.addWidget(self.filelist) vbox.addStretch(1) self.tc_first = QtWidgets.QLabel(self) self.tc_first.setText('First stack file: ') self.tc_last = QtWidgets.QLabel(self) self.tc_last.setText('Last stack file: ') vbox.addWidget(self.tc_first) vbox.addWidget(self.tc_last) vbox.addStretch(1) hbox = QtWidgets.QHBoxLayout() self.button_accept = QtWidgets.QPushButton('Accept') #self.button_accept.setEnabled(False) self.button_accept.clicked.connect( self.OnAccept) hbox.addWidget(self.button_accept) button_cancel = QtWidgets.QPushButton('Cancel') button_cancel.clicked.connect( self.close) hbox.addWidget(button_cancel) vbox.addLayout(hbox) vbox.addStretch(1) self.setLayout(vbox) self.ShowFileList() #---------------------------------------------------------------------- def OnFileList(self, row, column): if (self.have1st == 1) and (self.havelast==1): self.have1st = 0 self.havelast = 0 #self.button_accept.setEnabled(False) self.tc_first.setText('First stack file: ') self.tc_last.setText('Last stack file: ') item = self.filelist.item(row, 0) fn = item.text() if self.have1st == 0: self.tc_first.setText('First stack file: ' + fn) self.textt.setText('Select last stack file') self.file1st = fn self.have1st = 1 elif self.havelast == 0: self.tc_last.setText('Last stack file: ' + fn) self.textt.setText('Select first stack file') self.filelast = fn #self.button_accept.setEnabled(True) self.havelast = 1 #---------------------------------------------------------------------- def ShowFileList(self): filepath = str(self.filepath) self.sm_files = [x for x in os.listdir(filepath) if x.endswith('.sm')] if self.sm_files: self.filetype = 'sm' try: from netCDF4 import Dataset from file_plugins import file_sm_netcdf except: QtGui.QMessageBox.warning(self, 'Error', "Could not import netCDF4 library.") return count = 0 for i in range(len(self.sm_files)): filename = self.sm_files[i] thisfile = os.path.join(filepath, filename) filever, ncols, nrows, iev = file_sm_netcdf.read_sm_header(thisfile) if filever > 0: self.filelist.insertRow(count) self.filelist.setRowHeight(count,20) self.filelist.setItem(count, 0, QtGui.QTableWidgetItem(filename)) self.filelist.setItem(count, 1, QtGui.QTableWidgetItem(str(ncols))) self.filelist.setItem(count, 2, QtGui.QTableWidgetItem(str(nrows))) self.filelist.setItem(count, 3, QtGui.QTableWidgetItem('{0:5.2f}'.format(iev))) count += 1 else: continue self.xrm_files = [x for x in os.listdir(filepath) if x.endswith('.xrm')] if self.xrm_files: self.filetype = 'xrm' count = 0 for i in range(len(self.xrm_files)): filename = self.xrm_files[i] thisfile = os.path.join(filepath, filename) ncols, nrows, iev = file_xrm.read_xrm_fileinfo(thisfile) if ncols > 0: self.filelist.insertRow(count) self.filelist.setRowHeight(count,20) self.filelist.setItem(count, 0, QtGui.QTableWidgetItem(filename)) self.filelist.setItem(count, 1, QtGui.QTableWidgetItem(str(ncols))) self.filelist.setItem(count, 2, QtGui.QTableWidgetItem(str(nrows))) self.filelist.setItem(count, 3, QtGui.QTableWidgetItem('{0:5.2f}'.format(iev))) count += 1 self.sm_files = self.xrm_files self.bim_files = [x for x in os.listdir(filepath) if x.endswith('.bim')] if self.bim_files: self.filetype = 'bim' count = 0 for i in range(len(self.bim_files)): #print sm_files filename = self.bim_files[i] thisfile = os.path.join(filepath, filename) ncols, nrows, iev = file_bim.read_bim_info(thisfile) if ncols >0 : self.filelist.insertRow(count) self.filelist.setRowHeight(count,20) self.filelist.setItem(count, 0, QtGui.QTableWidgetItem(filename)) self.filelist.setItem(count, 1, QtGui.QTableWidgetItem(str(ncols))) self.filelist.setItem(count, 2, QtGui.QTableWidgetItem(str(nrows))) self.filelist.setItem(count, 3, QtGui.QTableWidgetItem('{0:5.2f}'.format(iev))) count += 1 self.sm_files = self.bim_files return #---------------------------------------------------------------------- def OnAccept(self, evt): QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.parent.new_stack_refresh() self.stk.new_data() ind1st = self.sm_files.index(self.file1st) indlast = self.sm_files.index(self.filelast) filelist = self.sm_files[ind1st:indlast+1] if self.filetype == 'sm': from file_plugins import file_sm_netcdf file_sm_netcdf.read_sm_list(self, filelist, self.filepath, self.data_struct) elif self.filetype == 'xrm': file_xrm.read_xrm_list(self, filelist, self.filepath, self.data_struct) elif self.filetype == 'bim': file_bim.read_bim_list(self, filelist, self.filepath, self.data_struct) else: print('Wrong file type') return #fill the gui structure data self.stk.absdata = self.data_struct.exchange.data datadim = np.int32(self.stk.absdata.shape) self.stk.n_cols = datadim[0].copy() self.stk.n_rows = datadim[1].copy() self.stk.ev = self.data_struct.exchange.energy self.stk.n_ev = np.int32(self.stk.ev.shape[0]).copy() npixels = self.stk.n_cols*self.stk.n_rows*self.stk.n_ev self.stk.x_dist = self.data_struct.exchange.x self.stk.y_dist = self.data_struct.exchange.y self.stk.data_dwell = self.data_struct.spectromicroscopy.data_dwell self.stk.fill_h5_struct_from_stk() self.stk.scale_bar() self.parent.page1.iev = int(self.stk.n_ev/3) #Is this correct? self.parent.ix = int(self.stk.n_cols/2) self.parent.iy = int(self.stk.n_rows/2) self.common.stack_loaded = 1 self.parent.refresh_widgets() self.parent.page1.ResetDisplaySettings() self.parent.page1.filename = filelist[0] self.parent.page1.textctrl.setText(filelist[0]) self.parent.page0.slider_eng.setRange(0,self.stk.n_ev-1) self.parent.page0.iev = int(self.stk.n_ev/2) self.parent.page0.slider_eng.setValue(self.parent.page1.iev) self.parent.page1.slider_eng.setRange(0,self.stk.n_ev-1) self.parent.page1.iev = self.stk.n_ev/2 self.parent.page1.slider_eng.setValue(self.parent.page1.iev) self.parent.page1.showSpectrum(self.parent.page1.ix, self.parent.page1.iy) self.parent.page1.loadImage() self.parent.page0.ShowInfo(filelist[0], self.filepath) QtWidgets.QApplication.restoreOverrideCursor() self.close() #---------------------------------------------------------------------- class InputRegionDialog(QtWidgets.QDialog): def __init__(self, parent, nregions, title='Multi Region Stack'): QtWidgets.QWidget.__init__(self, parent) self.parent = parent mainLayout = QtWidgets.QVBoxLayout() layout = QtWidgets.QHBoxLayout() label = QtWidgets.QLabel() label.setText('Select Region to load:') layout.addWidget(label) combo = QtWidgets.QComboBox(self) combo.addItem("All") for i in range(nregions): combo.addItem(str(i+1)) combo.setCurrentIndex(1) self.parent.loadregion = 1 combo.activated[int].connect(self.OnSelectRegion) layout.addWidget(combo) mainLayout.addLayout(layout) layout = QtWidgets.QHBoxLayout() button = QtWidgets.QPushButton("Submit") #string or icon self.connect(button, QtCore.SIGNAL("clicked()"), self.close) layout.addWidget(button) mainLayout.addLayout(layout) self.setLayout(mainLayout) self.resize(250, 60) self.setWindowTitle(title) #---------------------------------------------------------------------- def OnSelectRegion(self, value): item = value selregion = item self.parent.loadregion = selregion #---------------------------------------------------------------------- class AboutFrame(QtWidgets.QDialog): def __init__(self, parent = None, title='About'): QtWidgets.QWidget.__init__(self, parent) self.resize(360, 660) self.setWindowTitle('About Mantis') pal = QtGui.QPalette() self.setAutoFillBackground(True) pal.setColor(QtGui.QPalette.Window,QtGui.QColor('white')) self.setPalette(pal) vbox = QtWidgets.QVBoxLayout() self.image = QtGui.QImage(resource_path(os.path.join('images','Mantis_logo_about.png'))) self.imageLabel = QtWidgets.QLabel() self.imageLabel.setBackgroundRole(QtGui.QPalette.Base) self.imageLabel.setPixmap(QtGui.QPixmap.fromImage(self.image)) vbox.addWidget(self.imageLabel) text1 = QtWidgets.QLabel(self) text1.setText("www.2ndlookconsulting.com") text1.setStyleSheet('color: rgb(53,159,217);font-size: 14pt; font-family: SansSerif;') #text1.setFont(QtGui.QFont('SansSerif', 14)) #font2 = wx.Font(12, wx.SWISS, wx.NORMAL, wx.NORMAL) text2 = QtWidgets.QLabel(self) text2.setText('Mantis '+version) text2.setStyleSheet('font-size: 12pt') #text2.SetFont(font2) #font3 = wx.Font(8, wx.SWISS, wx.NORMAL, wx.NORMAL) text3 = QtWidgets.QLabel(self) text3.setText( ''' Developed by Mirna Lerotic, based on earlier programs by Mirna Lerotic and Chris Jacobsen. Initial development supported by Argonne National Laboratory LDRD 2010-193-R1 9113. ''') #text3.SetFont(font3) #font4 = wx.Font(8, wx.SWISS, wx.NORMAL, wx.NORMAL) text4 = QtWidgets.QLabel(self) text4.setText( ''' 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/.''') #text4.SetFont(font4) vbox.addStretch(1) hbox = QtWidgets.QHBoxLayout() hbox.addStretch(1) vbox2 = QtWidgets.QVBoxLayout() vbox2.addWidget(text1) vbox2.addStretch(1) vbox2.addWidget(text2) vbox2.addWidget(text3) vbox2.addWidget(text4) hbox.addLayout(vbox2) hbox.addStretch(1) vbox.addLayout(hbox) vbox.addStretch(1) button_close = QtWidgets.QPushButton('Close') button_close.clicked.connect( self.close) vbox.addWidget(button_close) vbox.addStretch(1) self.setLayout(vbox) """ ------------------------------------------------------------------------------------------------""" class MainFrame(QtWidgets.QMainWindow): def __init__(self): super(MainFrame, self).__init__() self.initUI() #---------------------------------------------------------------------- def initUI(self): self.data_struct = data_struct.h5() self.stk = data_stack.data(self.data_struct) self.anlz = analyze.analyze(self.stk) self.nnma = nnma.nnma(self.stk) self.common = common() self.resize(Winsizex, Winsizey) self.setWindowTitle('Mantis v.{0}'.format(version)) self.initToolbar() ico = QtGui.QIcon(resource_path(os.path.join('images','logo-2l-32.ico'))) self.setWindowIcon(ico) tabs = QtWidgets.QTabWidget() # create the page windows as tabs self.page0 = PageLoadData(self.common, self.data_struct, self.stk) self.page1 = PageStack(self.common, self.data_struct, self.stk) self.page2 = PagePCA(self.common, self.data_struct, self.stk, self.anlz) self.page3 = PageCluster(self.common, self.data_struct, self.stk, self.anlz) self.page4 = PageSpectral(self.common, self.data_struct, self.stk, self.anlz) self.page5 = PagePeakID(self.common, self.data_struct, self.stk, self.anlz) self.page6 = PageXrayPeakFitting(self.common, self.data_struct, self.stk, self.anlz) self.page7 = PageNNMA(self.common, self.data_struct, self.stk, self.anlz, self.nnma) tabs.addTab(self.page0,"Load Data") tabs.addTab(self.page1, "Preprocess Data") tabs.addTab(self.page2,"PCA") tabs.addTab(self.page3,"Cluster Analysis") tabs.addTab(self.page4,"Spectral Maps") tabs.addTab(self.page7, "NNMA Analysis") tabs.addTab(self.page5,"Peak ID") tabs.addTab(self.page6, "XrayPeakFitting") if showtomotab: self.page8 = PageTomo(self.common, self.data_struct, self.stk, self.anlz) tabs.addTab(self.page8, "Tomography") if showmaptab: self.page9 = PageMap(self.common, self.data_struct, self.stk) tabs.addTab(self.page9, "Image Maps") if sys.platform == 'win32': tabs.setMinimumHeight(750) else: tabs.setMinimumHeight(400) tabs.tabBar().setTabTextColor(0, QtGui.QColor('green')) tabs.tabBar().setTabTextColor(1, QtGui.QColor('green')) tabs.tabBar().setTabTextColor(2, QtGui.QColor('darkRed')) tabs.tabBar().setTabTextColor(3, QtGui.QColor('darkRed')) tabs.tabBar().setTabTextColor(4, QtGui.QColor('darkRed')) tabs.tabBar().setTabTextColor(5, QtGui.QColor('darkRed')) tabs.tabBar().setTabTextColor(6, QtGui.QColor('purple')) tabs.tabBar().setTabTextColor(7, QtGui.QColor('purple')) if showtomotab: tabs.tabBar().setTabTextColor(8, QtGui.QColor('darkblue')) if showmaptab: tabs.tabBar().setTabTextColor(9, QtGui.QColor('darkblue')) # Only add "expert" pages if option "--key" is given in command line try: options, extraParams = getopt.getopt(sys.argv[1:], '', ['wx', 'batch', 'nnma', 'ica', 'keyeng']) except: print('Error - wrong command line option used. Available options are --wx, --batch and --nnma') return # for opt, arg in options: # if opt in '--nnma': # if verbose: print "Running with NNMA." # self.page7 = PageNNMA(self.common, self.data_struct, self.stk, self.anlz, self.nnma) # tabs.addTab(self.page7, "NNMA Analysis") layout = QtWidgets.QVBoxLayout() layout.addWidget(tabs) #self.setCentralWidget(tabs) self.scrollArea = QtWidgets.QScrollArea() #self.scrollArea.setBackgroundRole(QtGui.QPalette.Dark) self.scrollArea.setWidget(tabs) #self.scrollArea.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOn) #self.scrollArea.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff) self.scrollArea.setWidgetResizable(True) self.setCentralWidget(self.scrollArea) screen = QtWidgets.QDesktopWidget().screenGeometry() if screen.height() < Winsizey - 50: self.showMaximized() self.show() if sys.platform == "darwin": self.raise_() #---------------------------------------------------------------------- def initToolbar(self): self.actionOpen = QtWidgets.QAction(self) self.actionOpen.setObjectName('actionOpen') self.actionOpen.setIcon(QtGui.QIcon(resource_path(os.path.join('images','document-open.png')))) self.toolbar = self.addToolBar('actionOpen') self.toolbar.addAction(self.actionOpen) self.actionOpen.triggered.connect(self.LoadStack) self.actionOpenSL = QtWidgets.QAction(self) self.actionOpenSL.setObjectName('actionOpenSL') self.actionOpenSL.setIcon(QtGui.QIcon(resource_path(os.path.join('images','open-sl.png')))) self.toolbar.addAction(self.actionOpenSL) self.actionOpenSL.triggered.connect(self.BuildStack) self.actionSave = QtWidgets.QAction(self) self.actionSave.setObjectName('actionSave') self.actionSave.setIcon(QtGui.QIcon(resource_path(os.path.join('images','media-floppy.png')))) self.toolbar.addAction(self.actionSave) self.actionSave.triggered.connect(self.onSaveResultsToH5) self.actionSave.setEnabled(False) self.actionInfo = QtWidgets.QAction(self) self.actionInfo.setObjectName('actionInfo') self.actionInfo.setIcon(QtGui.QIcon(resource_path(os.path.join('images','help-browser.png')))) self.toolbar.addAction(self.actionInfo) self.actionInfo.triggered.connect(self.onAbout) #---------------------------------------------------------------------- def LoadStack(self): """ Browse for a stack file: """ filepath, plugin = File_GUI.SelectFile('read','stack') print(filepath, plugin) JSONconvert = False if filepath is not None: if plugin is None: plugin = file_plugins.identify(filepath) FileStruct = file_plugins.GetFileStructure(filepath, plugin=plugin) FileInternalSelection = [(0,0)] if FileStruct is not None: dlg = File_GUI.DataChoiceDialog(filepath=filepath, filestruct=FileStruct, plugin=plugin) if not dlg.exec_(): return # do nothing if GUI is cancelled FileInternalSelection = dlg.selection if dlg.filepath != filepath: filepath = dlg.filepath plugin = file_plugins.identify(dlg.filepath) if plugin is None: QtWidgets.QMessageBox.warning(self, 'Error!', "Unknown file type") QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) if self.common.stack_loaded == 1: self.new_stack_refresh() self.stk.new_data() self.anlz.delete_data() try: JSONconvert = dlg.jsoncheck.isChecked() except UnboundLocalError: print("DataChoiceDialog skipped") file_plugins.load(filepath, stack_object=self.stk, plugin=plugin, selection=FileInternalSelection,json=JSONconvert) directory = os.path.dirname(str(filepath)) self.page1.filename = os.path.basename(str(filepath)) #Update widgets x=self.stk.n_cols y=self.stk.n_rows self.page1.imgrgb = np.zeros(x*y*3,dtype = "uint8") self.page1.maxval = np.amax(self.stk.absdata) self.ix = int(x/2) self.iy = int(y/2) self.page1.ix = self.ix self.page1.iy = self.iy self.iev = int(self.stk.n_ev/2) self.page0.slider_eng.setRange(0,self.stk.n_ev-1) self.page0.iev = self.iev self.page0.slider_eng.setValue(self.iev) self.page1.slider_eng.setRange(0,self.stk.n_ev-1) self.page1.iev = self.iev self.page1.slider_eng.setValue(self.iev) if showmaptab: self.page9.Clear() self.page9.slider_eng.setRange(0,self.stk.n_ev-1) self.stk.scale_bar() self.common.stack_loaded = 1 self.common.path = directory if self.stk.data_struct.spectromicroscopy.normalization.white_spectrum is not None: self.common.i0_loaded = 1 self.stk.calculate_optical_density() self.stk.fill_h5_struct_normalization() self.page1.ResetDisplaySettings() self.page1.loadImage() self.page1.button_multicrop.setText('Crop stack 3D...') #print (x,y), (self.ix,self.iy), self.stk.absdata.shape self.page1.showSpectrum(self.ix, self.iy) self.page1.textctrl.setText(self.page1.filename) self.page0.Clear() self.page0.LoadEntries() self.page0.ShowInfo(self.page1.filename, directory) self.page5.updatewidgets() QtWidgets.QApplication.restoreOverrideCursor() if showmaptab: self.page9.Clear() self.page9.LoadEntries() self.refresh_widgets() #---------------------------------------------------------------------- def BuildStack(self): """ Browse for .sm files """ #try: if True: directory = QtWidgets.QFileDialog.getExistingDirectory(self, "Choose a directory", '', QtWidgets.QFileDialog.ShowDirsOnly|QtWidgets.QFileDialog.ReadOnly ) if directory == '': return self.common.path = directory stackframe = StackListFrame(self, directory, self.common, self.stk, self.data_struct) stackframe.show() # except: # print 'Error could not build stack list.' # self.common.stack_loaded = 0 # self.common.i0_loaded = 0 # self.new_stack_refresh() # self.refresh_widgets() # # QtGui.QMessageBox.warning(self,'Error',"Error could not build stack list") # import sys; print sys.exc_info() #---------------------------------------------------------------------- def LoadStack4D(self): try: #if True: wildcard = "Supported 4D formats (*.hdf5 *.ncb);;HDF5 files (*.hdf5);;NCB files (*.ncb);;" filepath, _filter = QtWidgets.QFileDialog.getOpenFileNames(self, 'Open files', '', wildcard) if filepath == '': return filenames = [] for name in filepath: filenames.append(str(name)) directory = os.path.dirname(str(filenames[0])) self.page1.filename = os.path.basename(filenames[0]) QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) basename, extension = os.path.splitext(self.page1.filename) extension = extension.strip() self.common.path = directory self.common.filename = self.page1.filename if extension == '.hdf5': if self.common.stack_loaded == 1: self.new_stack_refresh() self.stk.new_data() self.anlz.delete_data() self.stk.read_h54D(filenames[0]) print('Finished reading 4D stack', filenames[0]) elif extension == '.ncb': if self.common.stack_loaded == 1: self.new_stack_refresh() self.stk.new_data() #self.stk.data_struct.delete_data() self.anlz.delete_data() self.stk.read_ncb4D(filenames) #Get energy list wildcard = "Text file (*.txt);;" engfilepath, _filter = QtWidgets.QFileDialog.getOpenFileName(self, 'Open file', directory, wildcard) self.stk.read_ncb4Denergy(str(engfilepath)) #Update widgets if not hasattr(self.stk, 'theta') or type(self.stk.theta) == int: raise TypeError("Not a 4D stack") self.common.stack_4d = 1 x=self.stk.n_cols y=self.stk.n_rows self.page1.imgrgb = np.zeros(x*y*3,dtype = "uint8") self.page1.maxval = np.amax(self.stk.absdata) self.ix = int(x/2) self.iy = int(y/2) self.page1.ix = self.ix self.page1.iy = self.iy self.iev = int(self.stk.n_ev/2) self.page0.slider_eng.setRange(0,self.stk.n_ev-1) self.page0.iev = self.iev self.page0.slider_eng.setValue(self.iev) self.page1.slider_eng.setRange(0,self.stk.n_ev-1) self.page1.iev = self.iev self.page1.slider_eng.setValue(self.iev) self.page0.slider_theta.setVisible(True) #self.page0.tc_imagetheta.setVisible(True) self.itheta = 0 self.page0.slider_theta.setRange(0,self.stk.n_theta-1) self.page0.itheta = self.itheta self.page0.slider_theta.setValue(self.itheta) #self.page0.tc_imagetheta.setText("4D Data Angle: "+str(self.stk.theta[self.itheta])) self.page1.slider_theta.setVisible(True) self.page1.tc_imagetheta.setVisible(True) self.page1.slider_theta.setRange(0,self.stk.n_theta-1) self.page1.itheta = self.itheta self.page1.slider_theta.setValue(self.itheta) self.page1.button_multicrop.setText('Crop stack 4D...') self.page1.tc_imagetheta.setText("4D Data Angle: "+str(self.stk.theta[self.itheta])) self.page2.button_calcpca4D.setVisible(True) self.page4.button_calc4d.setVisible(True) self.common.stack_loaded = 1 if self.stk.data_struct.spectromicroscopy.normalization.white_spectrum is not None: self.common.i0_loaded = 1 self.page1.ResetDisplaySettings() self.page1.loadImage() self.page1.showSpectrum(self.ix, self.iy) self.page1.textctrl.setText(self.page1.filename) self.page0.Clear() self.page0.LoadEntries() self.page0.ShowInfo(self.page1.filename, directory) self.page5.updatewidgets() QtWidgets.QApplication.restoreOverrideCursor() if showmaptab: self.page9.Clear() self.page9.LoadEntries() except: self.common.stack_loaded = 0 self.common.i0_loaded = 0 self.new_stack_refresh() self.page1.button_multicrop.setText('Crop stack 3D/4D...') QtWidgets.QApplication.restoreOverrideCursor() QtGui.QMessageBox.warning(self, 'Error', 'Image stack not loaded.') import sys print(sys.exc_info()) self.refresh_widgets() #---------------------------------------------------------------------- def OnSaveProcessedStack(self, event): self.SaveProcessedStack() #---------------------------------------------------------------------- def SaveProcessedStack(self): """ Export processed stack to file """ filepath, plugin = File_GUI.SelectFile('write','stack') if filepath is not None and plugin is not None: QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) try: file_plugins.save(filepath, self.stk, 'stack', plugin=plugin) QtWidgets.QApplication.restoreOverrideCursor() except: QtWidgets.QApplication.restoreOverrideCursor() QtWidgets.QMessageBox.warning(self, 'Error', 'Could not save processed stack file.') return #---------------------------------------------------------------------- def onSaveResultsToH5(self, event): self.SaveResultsToH5() #---------------------------------------------------------------------- def SaveResultsToH5(self): """ Browse for .hdf5 file """ try: #print self.data_struct.exchange.data.shape #print self.data_struct.exchange.data_axes wildcard = "HDF5 files (*.hdf5)" filepath, _filter = QtWidgets.QFileDialog.getSaveFileName(self, 'Save as .hdf5', '', wildcard) filepath = str(filepath) if filepath == '': return directory = os.path.dirname(str(filepath)) self.page1.filename = os.path.basename(str(filepath)) QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(Qt.WaitCursor)) self.common.path = directory self.common.filename = self.page1.filename file_dataexch_hdf5.write_results_h5(filepath, self.data_struct, self.anlz) QtWidgets.QApplication.restoreOverrideCursor() except: QtWidgets.QApplication.restoreOverrideCursor() QtGui.QMessageBox.warning(self, 'Error', 'Could not save HDF5 file.') self.refresh_widgets() return #---------------------------------------------------------------------- def onAbout(self): self.popup = AboutFrame(self) self.popup.show() #---------------------------------------------------------------------- def refresh_widgets(self): if self.common.stack_loaded == 0: self.page1.button_i0ffile.setEnabled(False) self.page1.button_i0histogram.setEnabled(False) self.page1.button_artefacts.setEnabled(False) self.page1.button_prenorm.setEnabled(False) self.page1.button_refimgs.setEnabled(False) self.page1.button_multicrop.setEnabled(False) #self.page1.button_limitev.setEnabled(False) #self.page1.button_subregion.setEnabled(False) self.page1.button_darksig.setEnabled(False) self.page1.button_save.setEnabled(False) self.page1.button_savestack.setEnabled(False) self.page1.button_align.setEnabled(False) self.page1.button_slideshow.setEnabled(False) self.page1.button_addROI.setEnabled(False) self.page1.button_spectralROI.setEnabled(False) self.page1.button_resetdisplay.setEnabled(False) self.page1.button_despike.setEnabled(False) self.page1.button_displaycolor.setEnabled(False) self.actionSave.setEnabled(False) else: self.page1.button_i0ffile.setEnabled(True) self.page1.button_i0histogram.setEnabled(True) self.page1.button_artefacts.setEnabled(True) self.page1.button_prenorm.setEnabled(True) self.page1.button_multicrop.setEnabled(True) #self.page1.button_limitev.setEnabled(True) if self.common.stack_4d == 0: self.page1.button_refimgs.setEnabled(True) #self.page1.button_subregion.setEnabled(True) self.page1.button_darksig.setEnabled(True) else: self.page1.button_refimgs.setEnabled(False) #self.page1.button_subregion.setEnabled(False) self.page1.button_darksig.setEnabled(False) self.page1.button_save.setEnabled(True) self.page1.button_savestack.setEnabled(True) self.page1.button_align.setEnabled(True) self.page1.button_slideshow.setEnabled(True) self.page1.button_addROI.setEnabled(True) self.page1.button_spectralROI.setEnabled(True) self.page1.button_resetdisplay.setEnabled(True) self.page1.button_despike.setEnabled(True) self.page1.button_displaycolor.setEnabled(True) self.actionSave.setEnabled(True) if self.common.i0_loaded == 0: self.page1.button_showi0.setEnabled(False) self.page1.rb_flux.setEnabled(False) self.page1.rb_od.setEnabled(False) self.page1.button_reseti0.setEnabled(False) self.page1.button_saveod.setEnabled(False) self.page2.button_calcpca.setEnabled(False) self.page2.button_calcpca4D.setEnabled(False) self.page4.button_loadtspec.setEnabled(False) self.page4.button_addflat.setEnabled(False) self.page5.button_save.setEnabled(False) if self.page7: self.page7.button_calcnnma.setEnabled(False) self.page7.button_mufile.setEnabled(False) self.page7.button_murand.setEnabled(False) if showtomotab: self.page8.button_engdata.setEnabled(False) else: self.page1.button_showi0.setEnabled(True) self.page1.rb_flux.setEnabled(True) self.page1.rb_od.setEnabled(True) self.page1.button_reseti0.setEnabled(True) self.page1.button_saveod.setEnabled(True) self.page2.button_calcpca.setEnabled(True) self.page2.button_calcpca4D.setEnabled(True) self.page4.button_loadtspec.setEnabled(True) self.page4.button_addflat.setEnabled(True) self.page5.button_save.setEnabled(True) if self.page7: self.page7.button_calcnnma.setEnabled(True) self.page7.button_mufile.setEnabled(True) self.page7.button_murand.setEnabled(True) if showtomotab: self.page8.button_engdata.setEnabled(True) if self.common.pca_calculated == 0: self.page2.button_savepca.setEnabled(False) self.page2.slidershow.setEnabled(False) self.page2.button_movepcup.setEnabled(False) self.page3.button_calcca.setEnabled(False) self.page4.rb_fit.setEnabled(False) else: self.page2.button_savepca.setEnabled(True) self.page2.slidershow.setEnabled(True) self.page2.button_movepcup.setEnabled(True) self.page3.button_calcca.setEnabled(True) self.page4.rb_fit.setEnabled(True) if self.common.cluster_calculated == 0: self.page3.button_scatterplots.setEnabled(False) self.page3.button_savecluster.setEnabled(False) self.page3.slidershow.setEnabled(False) self.page4.button_addclspec.setEnabled(False) self.page5.button_addclspec.setEnabled(False) if self.page7: self.page7.button_mucluster.setEnabled(False) else: self.page3.button_scatterplots.setEnabled(True) self.page3.button_savecluster.setEnabled(True) self.page3.slidershow.setEnabled(True) self.page4.button_addclspec.setEnabled(True) self.page5.button_addclspec.setEnabled(True) if self.page7: self.page7.button_mucluster.setEnabled(True) if self.common.spec_anl_calculated == 0: self.page4.button_removespec.setEnabled(False) self.page4.button_movespdown.setEnabled(False) self.page4.button_movespup.setEnabled(False) self.page4.button_save.setEnabled(False) self.page4.button_showrgb.setEnabled(False) self.page4.button_histogram.setEnabled(False) self.page4.button_calc4d.setEnabled(False) else: self.page4.button_removespec.setEnabled(True) self.page4.button_movespdown.setEnabled(True) self.page4.button_movespup.setEnabled(True) self.page4.button_save.setEnabled(True) self.page4.button_showrgb.setEnabled(True) self.page4.button_histogram.setEnabled(True) self.page4.button_calc4d.setEnabled(True) if showtomotab: if self.common.spec_anl4D_calculated == 0: self.page8.button_spcomp.setEnabled(False) else: self.page8.button_spcomp.setEnabled(True) if self.page6 != None: if self.common.cluster_calculated == 0: self.page6.button_addclspec.setEnabled(False) else: self.page6.button_addclspec.setEnabled(True) if self.common.xpf_loaded == 1: self.page6.slider_spec.setEnabled(True) else: self.page6.slider_spec.setEnabled(False) self.page1.ResetDisplaySettings() #---------------------------------------------------------------------- def new_stack_refresh(self): self.common.i0_loaded = 0 self.common.pca_calculated = 0 self.common.cluster_calculated = 0 self.common.spec_anl_calculated = 0 self.common.xpf_loaded = 0 self.common.stack_4d = 0 self.common.pca4D_calculated = 0 self.common.spec_anl4D_calculated = 0 self.refresh_widgets() #page 0 self.page0.slider_theta.setVisible(False) #self.page0.tc_imagetheta.setVisible(False) #page 1 self.page1.rb_flux.setChecked(True) self.page1.rb_od.setChecked(False) self.page1.showflux = True fig = self.page1.specfig fig.clf() self.page1.SpectrumPanel.draw() self.page1.tc_spec.setText("Spectrum at point: ") fig = self.page1.absimgfig fig.clf() self.page1.AbsImagePanel.draw() self.page1.tc_imageeng.setText("Image at energy: ") self.page1.textctrl.setText(' ') self.page1.ResetROI() self.page1.ResetDisplaySettings() #page 0 self.page1.slider_theta.setVisible(False) self.page1.tc_imagetheta.setVisible(False) #page 2 fig = self.page2.pcaevalsfig fig.clf() self.page2.PCAEvalsPan.draw() fig = self.page2.pcaimgfig fig.clf() self.page2.PCAImagePan.draw() fig = self.page2.pcaspecfig fig.clf() self.page2.PCASpecPan.draw() self.page2.vartc.setText('0%') self.page2.npcaspin.setValue(1) self.page2.tc_PCAcomp.setText("PCA component ") self.page2.text_pcaspec.setText("PCA spectrum ") self.page2.selpca = 1 self.page2.numsigpca = 2 self.page2.slidershow.setValue(self.page2.selpca) self.page2.slider_theta.setVisible(False) self.page2.tc_imagetheta.setVisible(False) self.page2.button_calcpca4D.setVisible(False) #page 3 fig = self.page3.clusterimgfig fig.clf() self.page3.ClusterImagePan.draw() fig = self.page3.clusterindvimgfig fig.clf() self.page3.ClusterIndvImagePan.draw() fig = self.page3.clusterspecfig fig.clf() self.page3.ClusterSpecPan.draw() fig = self.page3.clusterdistmapfig fig.clf() self.page3.ClusterDistMapPan.draw() self.page3.selcluster = 1 self.page3.slidershow.setValue(self.page3.selcluster) self.page3.numclusters = 5 self.page3.nclusterspin.setValue(self.page3.numclusters) self.page3.tc_cluster.setText("Cluster ") self.page3.tc_clustersp.setText("Cluster spectrum") self.page3.wo_1st_pca = 0 self.page3.remove1stpcacb.setChecked(False) #page 4 self.page4.ClearWidgets() #page 5 fig = self.page5.kespecfig fig.clf() self.page5.KESpecPan.draw() fig = self.page5.absimgfig fig.clf() self.page5.AbsImagePanel.draw() #page 6 if self.page6 != None: fig = self.page6.Specfig fig.clf() self.page6.SpectrumPanel.draw() self.page6.slider_spec.setEnabled(False) #page8 if showtomotab: self.page8.NewStackClear() """ ------------------------------------------------------------------------------------------------""" def main(): app = QtWidgets.QApplication(sys.argv) #print('main', threading.get_ident()) with open(qsspath, "r") as stylesheet: app.setStyleSheet(stylesheet.read()) frame = MainFrame() sys.exit(app.exec_()) if __name__ == '__main__': main() ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/nnma.py0000644000175000017500000002035314332463747017064 0ustar00wattswattsimport numpy as np import scipy as sp from scipy.integrate import trapz import sys, time from . import analyze class nnma(): def __init__(self, stkdata): self.stack = stkdata # Set default values for various NNMA parameters self.kNNMA = 5 # use no. of significant PCA components, otherwise default = 5 self.maxIters = 10 # default no. of iterations self.deltaErrorThreshold = 1e-3 # default threshold for change in reconstruction error self.initMatrices = 'Random' # default initialization for matrices self.lambdaSparse = 0. # sparseness regularization param self.lambdaClusterSim = 0. # cluster spectra similarity regularization param self.lambdaSmooth = 0. # smoothness regularization param # Fill initial matrices depending on user-specified initialization method def setParameters(self, kNNMA = 5, maxIters = 10, deltaErrorThreshold = 1e-3, initMatrices = 'Random', lambdaSparse = 0., lambdaClusterSim = 0., lambdaSmooth = 0.): # Set the values for various NNMA parameters self.kNNMA = kNNMA self.maxIters = maxIters self.deltaErrorThreshold = deltaErrorThreshold self.initMatrices = initMatrices self.lambdaSparse = lambdaSparse self.lambdaClusterSim = lambdaClusterSim self.lambdaSmooth = lambdaSmooth def setClusterSpectra(self, clusterspectra): self.clusterspectra = clusterspectra.T.copy() def setStandardsSpectra(self, standspectra): self.standspectra = standspectra #--------------------------------------------------------------------------------------- # Define some helper functions for NNMA #--------------------------------------------------------------------------------------- # Fill initial matrices depending on user-specified initialization method def fillInitMatrices(self): print ('Init method:', self.initMatrices) if self.initMatrices == 'Random': muInit = np.random.rand(self.nEnergies, self.kNNMA) tInit = np.random.rand(self.kNNMA, self.nPixels) elif self.initMatrices == "Cluster": muInit = self.clusterspectra tInit = np.random.rand(self.kNNMA, self.nPixels) # use SVD on mu to find t instead? elif self.initMatrices == "Standards": muInit = self.standspectra tInit = np.random.rand(self.kNNMA, self.nPixels) # use SVD on mu to find t instead? return muInit, tInit # Update t def tUpdate(self, mu, t): tUpdateFactor = np.dot(mu.T, self.OD) / ( np.dot(mu.T, np.dot(mu, t)) + self.lambdaSparse + 1e-9 ) tUpdated = t * tUpdateFactor return tUpdated # Update mu def muUpdate(self, mu, tUpdated): dCostSmooth_dMu = self.calcDCostSmooth_dMu(mu) muDiff = mu - self.muCluster muUpdateFactor = np.dot(self.OD, tUpdated.T) / ( np.dot(mu, np.dot(tUpdated, tUpdated.T)) + self.lambdaSmooth*dCostSmooth_dMu + self.lambdaClusterSim*2*muDiff + 1e-9 ) muUpdated = mu * muUpdateFactor # Normalize each column of mu return muUpdated # Calculate sparseness contribution (JSparse) to cost function def calcCostSparse(self, t): costSparse = np.sum(np.sum(np.abs(t))) return costSparse # Calculate cluster spectra similarity contribution (JClusterSim) to cost function def calcCostClusterSim(self, mu): muDiff = mu - self.muCluster costClusterSim = (np.linalg.norm(muDiff))**2 return costClusterSim # Calculate smoothness contribution (JSmooth) to cost function def calcCostSmooth(self, mu): return 0. # Calculate dJSmooth/dMu needed in mu update algorithm def calcDCostSmooth_dMu(self, mu): return 0. # Calculate integral of each column in mu for normalization def calcMuColNorm(self, mu, muRefNorm): for k in range(self.kNNMA): muNorm = trapz(mu[:, k], x=self.energies) mu[:, k] = (mu[:, k] / muNorm) * muRefNorm[k] return mu # Calculate current total cost function, fill cost function array def calcCostFn(self, mu, t, count): D = np.dot(mu, t) costDataMatch = 0.5 * (np.linalg.norm(self.OD - D))**2 costSparse = self.calcCostSparse(t) costClusterSim = self.calcCostClusterSim(mu) costSmooth = self.calcCostSmooth(mu) costTotal = ( costDataMatch + self.lambdaSparse*costSparse + self.lambdaClusterSim*costClusterSim + self.lambdaSmooth*costSmooth ) if count > 0: deltaError = self.costFnArray[count, 1] - self.costFnArray[count-1, 1] elif count == 0: deltaError = -1e-13 self.costFnArray[count, :] = np.array([costTotal, deltaError, costSparse, costClusterSim, costSmooth]) return costTotal, deltaError #--------------------------------------------------------------------------------------- # Calculate NNMA #--------------------------------------------------------------------------------------- def calcNNMA(self, initmatrices = 'Random'): print ('calculating nnma') self.initMatrices = initmatrices self.OD = self.stack.od.copy() self.energies = self.stack.ev self.nEnergies = self.stack.n_ev self.nCols = self.stack.n_cols self.nRows = self.stack.n_rows self.nPixels = self.nCols * self.nRows # Transpose optical density matrix since NNMA needs dim NxP self.OD = self.OD.T # Zero out negative values in OD matrix negInd = np.where(self.OD < 0.) if negInd: self.OD[negInd] = 0. self.muRecon = np.zeros((self.nEnergies, self.kNNMA)) self.tRecon = np.zeros((self.kNNMA, self.nPixels)) self.DRecon = np.zeros((self.nEnergies, self.nPixels)) self.costFnArray = np.zeros((self.maxIters+1, 5)) self.costFnArray[0, 0] = 1e99 self.costTotal = 0. # stores current value of total cost function self.deltaError = 0 # stores current value of cost function change # If doing cluster spectra similarity regularization, import cluster spectra if initmatrices == 'Cluster': self.muCluster = self.clusterspectra else: self.muCluster = 0. self.timeTaken = 0. # stores time taken to complete NNMA analysis # Initialize matrices muInit, tInit = self.fillInitMatrices() self.muinit = muInit.copy() muCurrent = muInit tCurrent = tInit count = 0 costCurrent, deltaErrorCurrent = self.calcCostFn(muCurrent, tCurrent, count) # Start NNMA startTime = time.time() while ((count < self.maxIters) and (self.deltaError < self.deltaErrorThreshold)): # Store values from previous iterations before updating self.tRecon = tCurrent self.muRecon = muCurrent self.costTotal = costCurrent self.deltaError = deltaErrorCurrent # Now do NNMA update tUpdated = self.tUpdate(muCurrent, tCurrent) muUpdated = self.muUpdate(muCurrent, tUpdated) # Zero out any negative values in t and mu negIndT = np.where(tUpdated < 0.) if negIndT: tUpdated[negIndT] = 0. negIndMu = np.where(muUpdated < 0.) if negIndMu: muUpdated[negIndMu] = 0. tCurrent = tUpdated muCurrent = muUpdated # Calculate cost function costCurrent, deltaErrorCurrent = self.calcCostFn(muCurrent, tCurrent, count) count = count + 1 print ('Iteration number {0}/{1}'.format(count,self.maxIters)) endTime = time.time() self.timeTaken = endTime - startTime if count < self.maxIters: self.maxIters = count self.tRecon = np.reshape(self.tRecon, (self.kNNMA, self.nCols, self.nRows), order='F') return 1 ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1667917799.0 mantis-xray-3.1.15/mantis_xray/pageloaddata.ui0000755000175000017500000006666614332463747020552 0ustar00wattswatts Form 0 0 900 750 0 0 Form 12 6 0 0 Ubuntu 8 12 QLayout::SetDefaultConstraint 1 1 1 250 0 250 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See the # GNU General Public License for more details . from __future__ import division from __future__ import absolute_import import os import numpy as np import scipy as sp from time import time import multiprocessing from functools import partial try: from skimage.transform import iradon, radon except: print ('SIRT reconstruction requires skimage library.') import warnings warnings.simplefilter('ignore', DeprecationWarning) from .TomoCS.forward_backward_tv import fista_tv,gfb_tv, gfb_tv_weng from .TomoCS.projections import build_projection_operator from .TomoCS.util import generate_synthetic_data from .TomoCS import sirt as st from . import Mrc #---------------------------------------------------------------------- def write_mrc(stack, mrcfn): #stackf32 = stack.astype(np.float32) Mrc.save(stack, mrcfn,ifExists='overwrite',calcMMM=False) #---------------------------------------------------------------------- def load_mrc( mrcfn): stack = Mrc.load(mrcfn) return stack #---------------------------------------------------------------------- def calc_sirt(R, theta, Rmin, Rmax, nonnegconst, maxiter, nrows): R = (R-Rmin)/(Rmax-Rmin) S1 = np.sum(R) At = iradon(R, theta=theta, output_size=nrows) S2 = np.sum(At) At = (At/S2)*S1 xk = At for k in range(maxiter): t = iradon(radon(xk,theta),theta) #normalize St = np.sum(t) t = (t/St)*S1 #update using (At g - At A x_k) #new xk = xk + difference between reconstruction At_starting - t_previuous_step xk = xk + At - t if nonnegconst == 1: #delete values <0 aka not real! xk = xk.clip(min=0) return xk #---------------------------------------------------------------------- def calc_cs(R, theta, Rmin, Rmax, l, beta, initx0, nonnegconst, maxiter): R = (R-Rmin)/(Rmax-Rmin) proj = R.ravel()[:, np.newaxis] proj_operator = build_projection_operator(l, n_dir=len(theta), angles=theta) res, engs = gfb_tv(proj, beta, maxiter, H=proj_operator, x0=initx0, nonnegconst=nonnegconst) return res[-1] #---------------------------------------------------------------------- class Ctomo: def __init__(self, stkdata): self.stack = stkdata self.tomorec = [] #---------------------------------------------------------------------- # Calculate tomo reconstruction # Algorithm = 0 : CS reconstruction for 1 dataset # Algorithm = 1 : SIRT reconstruction for 1 dataset def calc_tomo(self, tomodata, theta, maxiter, beta, samplethickness, algorithm = 0, x0=[], comp = 0, beta2 = 0, nonnegconst = 1, nprocessors=1): tomodata = tomodata #Algorithm if algorithm == 0: if nprocessors <= 1: self.calc_tomo_cs(tomodata.astype(np.float32), theta, maxiter, beta, samplethickness, nonnegconst = nonnegconst) else: self.calc_tomo_cs_multi(tomodata.astype(np.float32), theta, maxiter, beta, samplethickness, nonnegconst = nonnegconst, nprocessors=nprocessors) elif algorithm == 1: self.calc_tomo_sirt(tomodata.astype(np.float32), theta, maxiter, beta, samplethickness, nonnegconst = nonnegconst, nprocessors=nprocessors) else: self.calc_tomo_cs_tveng(tomodata.astype(np.float32), theta, maxiter, beta, samplethickness, x0, comp, beta2, nonnegconst = nonnegconst) #---------------------------------------------------------------------- # Calculate tomo - CS reconstruction for 1 dataset with multiprocessing def calc_tomo_cs_multi(self, tomodata, theta, maxiter, beta, samplethickness, nonnegconst=1, nprocessors=6): print ('Compressed sensing TV regression') #print ('Angles ', theta) print ('TV beta ', beta) print ('MAX iterations ', maxiter) print ("Sample thickness ", samplethickness) #Check if we have negative angles, if yes convert to 0-360deg for i in range(len(theta)): if theta[i] < 0: theta[i] = theta[i] + 360 #print ('Angles in 0-360 range: ', theta) nang = len(theta) #print ('Number of angles ', nang) dims = tomodata.shape print ('Dimensions ', dims, tomodata.dtype) stack = np.swapaxes(tomodata, 0, 1) theta = np.deg2rad(theta) dims = stack.shape ncols = dims[0] nrows = dims[1] l = dims[1] Rmax = np.amax(stack) Rmin = np.amin(stack) recondata = [] projections = [] initx0 = np.zeros((nrows,nrows), dtype=np.float32) t1 = time() R = stack[int(ncols/2),:, :].T R = (R-Rmin)/(Rmax-Rmin) proj = R.ravel()[:, np.newaxis] l = dims[1] proj_operator = build_projection_operator(l, n_dir=len(theta), angles=theta) res, engs = gfb_tv(proj, beta, maxiter, H=proj_operator, x0=initx0, nonnegconst = nonnegconst) initx0 = res[-1] for j in range(ncols): projections.append(stack[j,:, :].T) Rmax = np.amax(stack) Rmin = np.amin(stack) partial_calc_cs = partial(calc_cs, theta=theta, Rmin=Rmin, Rmax=Rmax, l=l, beta=beta, initx0=initx0, nonnegconst=nonnegconst, maxiter=maxiter) pool = multiprocessing.Pool(processes=nprocessors) res = pool.map(partial_calc_cs, projections) pool.close() pool.join() recondata = res t2 = time() print ("reconstruction done in %f s" %(t2 - t1)) #Save the 3D tomo reconstruction to a HDF5 file recondata=np.array(recondata) dims = recondata.shape print ('final dims', dims) #Crop the data is sample thickness is defined if (samplethickness > 0) and (samplethickness 0) and (samplethickness 0) and (samplethickness 0) and (samplethickness 0) and (samplethickness=3 Description-Content-Type: text/markdown License-File: LICENSE Requires-Dist: PyQt5 Requires-Dist: numpy Requires-Dist: scipy Requires-Dist: matplotlib Requires-Dist: h5py Requires-Dist: Pillow Requires-Dist: lxml Requires-Dist: pyqtgraph<=0.12.4 Requires-Dist: scikit-image Provides-Extra: netcdf Requires-Dist: netcdf4-python; extra == "netcdf" # Spectromicroscopy # [Spectromicroscopy](http://spectromicroscopy.com) combines spectral data with microscopy, where typical datasets consist of a stack of microscopic images taken across an energy range. Due to the data complexity, manual analysis can be time consuming and inefficient, whereas multivariate analysis tools not only reduce the time needed but also can uncover hidden trends in the data. # Mantis # [MANTiS](http://spectromicroscopy.com) is Multivariate ANalysis Tool for Spectromicroscopy developed in Python by [2nd Look Consulting](http://2ndlookconsulting.com). It uses principal component analysis and cluster analysis to classify pixels according to spectral similarity. ## Download ## Mantis package and binaries can be downloaded from [spectromicroscopy.com](http://spectromicroscopy.com). Alternatively, you can install [Python](https://www.python.org/downloads/) and then run the command: `python3 -m pip install mantis-xray` ## Update ## You can upgrade to the latest package release with the command: `pip3 install mantis-xray -U`. It is recommended that you also upgrade the dependencies with: `pip3 install mantis-xray -U --upgrade-strategy "eager"` ## Run ## Installation via pip provides the `mantis-xray` command (alternatively `python3 -m mantis_xray`) to start the Mantis GUI. ## User Guide ## Mantis User Guide can be found on the project Wiki pages [Home](https://github.com/mlerotic/spectromicroscopy/wiki). ## References ## Please use the following reference when quoting Mantis Lerotic M, Mak R, Wirick S, Meirer F, Jacobsen C. MANTiS: a program for the analysis of X-ray spectromicroscopy data. J. Synchrotron Rad. 2014 Sep; 21(5); 1206–1212 [http://dx.doi.org/10.1107/S1600577514013964] ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1701535927.0 mantis-xray-3.1.15/mantis_xray.egg-info/SOURCES.txt0000664000175000017500000000360014532660267021133 0ustar00wattswattsLICENSE MANIFEST.in README.md setup.py mantis_xray/Mantis_batch_settings.txt mantis_xray/Mrc.py mantis_xray/__init__.py mantis_xray/__main__.py mantis_xray/analyze.py mantis_xray/data_stack.py mantis_xray/data_struct.py mantis_xray/dialogalign.ui mantis_xray/helpers.py mantis_xray/henke.py mantis_xray/henke.xdr mantis_xray/logos.py mantis_xray/mantis.py mantis_xray/mantis_qt.py mantis_xray/nnma.py mantis_xray/pageloaddata.ui mantis_xray/pagemap.ui mantis_xray/showalign2.ui mantis_xray/showartefacts.ui mantis_xray/showhistogram.ui mantis_xray/showmulticrop.ui mantis_xray/showspectralroi.ui mantis_xray/stylesheet_global.qss mantis_xray/tomo_reconstruction.py mantis_xray.egg-info/PKG-INFO mantis_xray.egg-info/SOURCES.txt mantis_xray.egg-info/dependency_links.txt mantis_xray.egg-info/entry_points.txt mantis_xray.egg-info/requires.txt mantis_xray.egg-info/top_level.txt mantis_xray/TomoCS/__init__.py mantis_xray/TomoCS/_rank_order.py mantis_xray/TomoCS/forward_backward_tv.py mantis_xray/TomoCS/projections.py mantis_xray/TomoCS/sirt.py mantis_xray/TomoCS/tv_denoising.py mantis_xray/TomoCS/util.py mantis_xray/file_plugins/__init__.py mantis_xray/file_plugins/file_bim.py mantis_xray/file_plugins/file_csv.py mantis_xray/file_plugins/file_dataexch_hdf5.py mantis_xray/file_plugins/file_json.py mantis_xray/file_plugins/file_ncb.py mantis_xray/file_plugins/file_nexus_hdf5.py mantis_xray/file_plugins/file_sdf.py mantis_xray/file_plugins/file_sm_netcdf.py mantis_xray/file_plugins/file_stk.py mantis_xray/file_plugins/file_tif.py mantis_xray/file_plugins/file_xrm.py mantis_xray/images/Mantis_logo.icns mantis_xray/images/Mantis_logo.ico mantis_xray/images/Mantis_logo_about.png mantis_xray/images/document-open.png mantis_xray/images/help-browser.png mantis_xray/images/logo-2l-32.ico mantis_xray/images/media-floppy.png mantis_xray/images/mirror.png mantis_xray/images/open-sl.png mantis_xray/images/rotate.png././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1701535927.0 mantis-xray-3.1.15/mantis_xray.egg-info/dependency_links.txt0000664000175000017500000000000114532660267023316 0ustar00wattswatts ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1701535927.0 mantis-xray-3.1.15/mantis_xray.egg-info/entry_points.txt0000664000175000017500000000006714532660267022551 0ustar00wattswatts[gui_scripts] mantis-xray = mantis_xray.mantis_qt:main ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1701535927.0 mantis-xray-3.1.15/mantis_xray.egg-info/requires.txt0000664000175000017500000000014614532660267021651 0ustar00wattswattsPyQt5 numpy scipy matplotlib h5py Pillow lxml pyqtgraph<=0.12.4 scikit-image [netCDF] netcdf4-python ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1701535927.0 mantis-xray-3.1.15/mantis_xray.egg-info/top_level.txt0000664000175000017500000000001414532660267021775 0ustar00wattswattsmantis_xray ././@PaxHeader0000000000000000000000000000003400000000000010212 xustar0028 mtime=1701535927.1322315 mantis-xray-3.1.15/setup.cfg0000664000175000017500000000004614532660267015041 0ustar00wattswatts[egg_info] tag_build = tag_date = 0 ././@PaxHeader0000000000000000000000000000002600000000000010213 xustar0022 mtime=1678690458.0 mantis-xray-3.1.15/setup.py0000664000175000017500000000237114403544232014723 0ustar00wattswattsimport setuptools import mantis_xray with open("README.md", "r", encoding="utf-8") as fh: long_description = fh.read() setuptools.setup( name="mantis-xray", version=mantis_xray.__version__, author="Mirna Lerotic", author_email="mirna@2ndlookconsulting.com", description="MANTiS is a Multivariate ANalysis Tool for x-ray Spectromicroscopy", long_description=long_description, long_description_content_type="text/markdown", url="https://spectromicroscopy.com/", project_urls={ "Code": "https://github.com/mlerotic/spectromicroscopy", "Documentation": "https://docs.spectromicroscopy.com", }, install_requires=['PyQt5','numpy', 'scipy', 'matplotlib', 'h5py', 'Pillow', 'lxml', 'pyqtgraph<=0.12.4', "scikit-image"], extras_require={ "netCDF": "netcdf4-python"}, entry_points={ "gui_scripts": "mantis-xray = mantis_xray.mantis_qt:main"}, classifiers=[ "Programming Language :: Python :: 3", "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", "Operating System :: OS Independent", "Topic :: Scientific/Engineering", ], packages=setuptools.find_packages(), include_package_data=True, python_requires=">=3", )