Source code for jscatter.sasimagelib

# -*- coding: utf-8 -*-
# written by Ralf Biehl at the Forschungszentrum Jülich ,
# Jülich Center for Neutron Science 1 and Institute of Complex Systems 1
#    Jscatter is a program to read, analyse and plot data
#    Copyright (C) 2015  Ralf Biehl
#
#    This program 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
#    (at your option) any later version.
#
#    This program 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.
#
#    You should have received a copy of the GNU General Public License
#    along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
"""
Read 2D image files (TIFF) from SAXS cameras and extract the corresponding data.

The sasImage is a 2D array that allows direct subtraction and multiplication (e.g. transmission)
respecting given masks in operations. E.g. ::

 sample=js.sas.sasImage('sample.tiff')
 solvent=js.sas.sasImage('solvent.tiff')
 corrected = sample/sampletransmission - solvent/solventtransmission

Calibration of detector distance, radial average, size reduction and more.
.pickBeamcenter allows sensitive detection of the beamcenter.

An example is shown in :py:class:`~.sasimagelib.sasImage` .


------

"""

import os
import glob
import copy
import time
import numpy as np
import numpy.ma as ma
import scipy
import scipy.linalg as la
from scipy import ndimage
from scipy.interpolate import griddata
import PIL
import PIL.ImageOps
import PIL.ExifTags
import PIL.ImageSequence
from xml.etree import ElementTree
import matplotlib
import matplotlib.cm as cm
from matplotlib.patches import Circle
from matplotlib import pyplot

from . import formel
from .dataarray import dataArray as dA
from .dataarray import dataList as dL
from . import mpl

try:
  basestring
except NameError:
  basestring = str

# normalized gaussian function
_gauss=lambda x,A,mean,sigma,bgr:A*np.exp(-0.5*(x-mean)**2/sigma**2)/sigma/np.sqrt(2*np.pi) + bgr

[docs]def shortprint(values,threshold=6,edgeitems=2): """ Creates a short handy representation string for array values. Parameters ---------- values : object Values to print. threshold: int default 6 Number of elements to switch to reduced form. edgeitems : int default 2 Items at the edge. """ opt = np.get_printoptions() np.set_printoptions(threshold=threshold,edgeitems=edgeitems) valuestr=np.array_str(values) np.set_printoptions(**opt) return valuestr
def _w2f(word): """ Converts strings if possible to float. """ try: return float(word) except ValueError: return word
[docs]def parseXML(text): root = ElementTree.fromstring(text) r=etree_to_dict(root) return r
[docs]def etree_to_dict(root): #d = {root.tag : map(etree_to_dict, root.getchildren())} d={ child.attrib['name']:child.text for child in root.iter() if child.text is not None} return d
[docs]def phase(phases): """Transform to [-pi,pi] range.""" return ( phases + np.pi) % (2 * np.pi ) - np.pi
# calc peak positions of AgBe #q=np.r_[0.5:10:0.0001] #iq=js.sas.AgBeReference(q,data.wavelength[0]/10,n=np.r_[1:15]) #iq.iX[scipy.signal.argrelmax(iq.iY,order=3)[0]] #: AgBe peak positions AgBepeaks=[ 1.0753, 2.1521, 3.2286, 4.3049, 5.3813, 6.4576, 7.5339, 8.6102, 9.6865, 10.7628] #: Create AgBe peak positions profile def _agbpeak(q, center=0, fwhm=1, lg=1, asym=0, amplitude=1, bgr=0): peak=formel.voigt(x=q, center=center, fwhm=fwhm,lg=lg,asym=asym, amplitude=amplitude) peak.Y+=bgr return peak # While reading the image file, data are extracted from XML string or text in the EXIF data of the image. # The following describe what to extract in an line/entry and how to replace: # 1 name to look for # 2 the new attribute name (to have later unique names from different detectors) # 3 a dictionary of char to replace in the line before looking for the keyword/content # 4 factor to convert to specific units # 5 return value 'list' or 'string', default list with possible conversion to float # Not extracted information is in .artist or .imageDescription exchangekeywords = [] exchangekeywords.append(['Wavelength', 'wavelength', None,1,None]) exchangekeywords.append(['Flux', 'flux', None,1,None]) exchangekeywords.append(['det_exposure_time','exposure_time', None,1,None]) exchangekeywords.append(['det_pixel_size','pixel_size', None,1,None]) exchangekeywords.append(['beamcenter_actual','beamcenter',None,1,None]) exchangekeywords.append(['detector_dist','detector_distance',None,0.001,None]) # conversion to m exchangekeywords.append(['Meas.Description','description',None,1,'string']) # return a string exchangekeywords.append(['wavelength', 'wavelength', None,1,None]) exchangekeywords.append(['Exposure_time' , 'exposure_time', None,1,None]) exchangekeywords.append(['Pixel_size', 'pixel_size', {'m':'','x ':''},1,None]) exchangekeywords.append(['Detector_distance','detector_distance',None,1,None])
[docs]class SubArray(np.ndarray): # Defines a generic np.ndarray subclass, that stores some metadata # in attributes # It seems to be the default way for subclassing maskedArrays # to have the array_finalize from this subclass. def __new__(cls,arr): x = np.asanyarray(arr).view(cls) x.comment=[] return x def __array_finalize__(self, obj): if callable(getattr(super(SubArray, self),'__array_finalize__', None)): super(SubArray, self).__array_finalize__(obj) if hasattr(obj,'attr'): for attribut in obj.attr: self.__dict__[attribut]=getattr(obj,attribut) try: # copy tags from reading self._tags=getattr(obj,'_tags') except:pass return @property def array(self): return self.view(np.ndarray)
[docs] def setattr(self,objekt,prepend='',keyadd='_'): """ Set (copy) attributes from objekt. Parameters ---------- objekt : objekt with attr or dictionary Can be a dictionary of names:value pairs like {'name':[1,2,3,7,9]} If object has property attr the returned attribut names are copied. prepend : string, default '' Prepend this string to all attribute names. keyadd : char, default='_' If reserved attributes (T, mean, ..) are found the name is 'T'+keyadd """ if hasattr(objekt,'attr'): for attribut in objekt.attr: try: setattr(self,prepend+attribut,getattr(objekt,attribut)) except AttributeError: self.comment.append('mapped '+attribut+' to '+attribut+keyadd) setattr(self,prepend+attribut+keyadd,getattr(objekt,attribut)) elif type(objekt)==type({}): for key in objekt: try: setattr(self,prepend+key,objekt[key]) except AttributeError: self.comment.append('mapped '+key+' to '+key+keyadd) setattr(self,prepend+key+keyadd,objekt[key])
@property def attr(self): """ Show specific attribute names as sorted list of attribute names. """ if hasattr(self,'__dict__'): return sorted([key for key in self.__dict__ if key[0]!='_']) else: return []
[docs] def showattr(self,maxlength=None,exclude=['comment']): """ Show specific attributes with values as overview. Parameters ---------- maxlength : int Truncate string representation after maxlength char. exclude : list of str List of attribute names to exclude from result. """ for attr in self.attr: if attr not in exclude: values=getattr(self,attr) try: valstr=shortprint(values.split('\n')) print( '{:>24} = {:}'.format(attr, valstr[0])) for vstr in valstr[1:]: print( '{:>25} {:}'.format('', vstr)) except: print( '%24s = %s' %(attr,str(values)[:maxlength]))
def __repr__(self): # hide that we have a ndarray subclass, just not to confuse people return self.view(np.ndarray).__repr__()
subarray = SubArray
[docs]class Picker: def __init__(self, circle, image,destination,symmetry=6): self.circle = circle self.fig=circle.figure self.ax=circle.figure.axes[0] self.image=image self.iX=image.iX self.iY=image.iY self.symmetry=symmetry self.destination=destination self.cidpress = circle.figure.canvas.mpl_connect('button_press_event', self.on_button_press) self.cidscroll = circle.figure.canvas.mpl_connect('scroll_event', self.on_scroll) self.keypress = circle.figure.canvas.mpl_connect('key_press_event', self.on_keypress) self.destination.text(circle.radius,0.95, 'beamcenter \n[{0:.1f}, {1:.1f}]'.format(self.circle.center[1],self.circle.center[0]), fontsize=8) self.update()
[docs] def on_button_press(self, event): if event.inaxes is None: return #print('click', event.xdata,event.ydata) self.circle.center=event.xdata,event.ydata self.update()
[docs] def on_scroll(self,event): if event.inaxes is None: return if event.button == 'down': self.circle.set_radius(self.circle.radius-1) elif event.button == 'up': self.circle.set_radius(self.circle.radius+1) self.update()
[docs] def on_keypress(self,event): pressedkey=event.key if pressedkey=='up': self.circle.center = self.circle.center[0],self.circle.center[1]-1 elif pressedkey=='down': self.circle.center = self.circle.center[0],self.circle.center[1]+1 elif pressedkey=='left': self.circle.center = self.circle.center[0]-1 ,self.circle.center[1] elif pressedkey=='right': self.circle.center = self.circle.center[0]+1 ,self.circle.center[1] elif pressedkey=='ctrl+up': self.circle.center = self.circle.center[0],self.circle.center[1]-0.1 elif pressedkey=='ctrl+down': self.circle.center = self.circle.center[0],self.circle.center[1]+0.1 elif pressedkey=='ctrl+left': self.circle.center = self.circle.center[0]-0.1 ,self.circle.center[1] elif pressedkey=='ctrl+right': self.circle.center = self.circle.center[0]+0.1 ,self.circle.center[1] self.update()
[docs] def update(self): dphi=2*np.pi/self.symmetry self.image._polarCoordinates([self.circle.center[1],self.circle.center[0]]) azimuth=self.image._azimuth radial=self.image._radial awidth=dphi/2 image=ndimage.filters.gaussian_filter(self.image.data,0.8) for i,angle in enumerate(np.r_[-np.pi:np.pi:dphi]): mask=((azimuth>(angle-awidth)) & (azimuth<(angle+awidth)) & (radial>self.circle.radius*0.7) & (radial<self.circle.radius*1.3)) try: rad=dA(np.stack([radial[mask],image[mask]])) rad.isort() # sorts along X by default # return lower number of points from prune result = rad[:,rad.Y>0].prune(number=50,type='sum',kind='mean') result.Y/=result.Y.max() if len(self.destination.lines)>i: # update data self.destination.lines[i].set_xdata(result.X) self.destination.lines[i].set_ydata(result.Y) else: # line not yet plotted self.destination.plot(result.X, result.Y) except:pass self.destination.texts[0].set_text('beamcenter \n[{0:.1f}, {1:.1f}]'.format(self.circle.center[1],self.circle.center[0])) self.fig.canvas.draw()
[docs]class sasImage(SubArray,np.ma.MaskedArray): def __new__(cls, file,detector_distance=None,beamcenter=None,flip=PIL.Image.FLIP_TOP_BOTTOM): """ Creates sasImage as maskedArray from a detector image for evaluation. Reads a .tif file including the information in the EXIF tag. - All methods of maskedArrays including masking of invalid areas work. - Masked areas are automatically masked for all math operations. - Arithmetic operations for sasImages work as for numpy arrays e.g. to subtract background image or multiplying with transmission. - coordinates are [height,width] Parameters ---------- file : string Filename to open. detector_distance : float, sasImage Detector distance from calibration measurement or calibrated image. Overwrites value in the file EXIF tag. beamcenter : None, list 2xfloat, sasImage Beamcenter is [height, width] position of primary beam. If sasImage is given the corresponding beamcenter is copied. Overwrites value given in the file EXIF tag. flip : 0,1,2,3,4,5,6, default 1=FLIP_TOP_BOTTOM TIFF images may have a orientation (stored in EXIF tag 'orientation') that we may want to flip the image to get the native orientation as seen from the sample. Most Xray cameras have 1 (E.g. our Pilatus detectors (0,0) at top left). You may use PIL.Image.FLIP_TOP_BOTTOM (which is 1). ( 0 FLIP_LEFT_RIGHT, 1 FLIP_TOP_BOTTOM, 2 ROTATE_90, 3 ROTATE_180, 4 ROTATE_270, 5 TRANSPOSE, 6 TRANSVERSE) Returns ------- image : sasImage with attributes - .beamcenter : beam center - .iX : Height pixel positions - .iY : Width pixel positions - .filename - .artist : Additional attributes from EXIF Tag Artist - .imageDescription : Additional attributes from EXIF Tag ImageDescription Notes ----- - Unmasked data can be accessed as .data - The mask is .mask and initial set to all negative values. - Masking of a pixel is done as image[i,j]=np.ma.masked. Use mask methods as implemented. - TIFF tags with index above 700 are ignored. - Tested for reading tiff image files from Pilatus detectors as given from our metal jet SAXS machines Ganesha and Galaxi at JCNS, Jülich. - Additional SAXSpace TIFF files are supported which show frames per pixel on the Y axis. This allows to examine the time evolution of the measurement on these line collimation cameras (Kratky camera). Instead of the old PIL the newer fork Pillow is needed for the multi page TIFFs. Additional the pixel_size is set to 0.024 (µm) as for the JCNS CCD camera. - Beamcenter & orientation: The x,y orientation is not well defined and dependent on the implementation on the specific camera setup. The default used here corresponds to our in house Ganesha which needs to revert the EXIF beamcenter. We use the lower left image corner as zero with X as lower axis. Please check if your beamcenter corresponds to this. If not just change it. Examples -------- :: import jscatter as js # # Look at calibration measurement calibration = js.sas.sasImage(js.examples.datapath+'/calibration.tiff') # Check beamcenter # For correct beamcenter it should show straight lines (change beamcenter to see change) calibration.showPolar(beamcenter=[254,122],scaleR=3) # or use pickBeamcenter which seems to be more accurate calibration.pickBeamcenter() # Recalibrate with previous found beamcenter (calibration sets it already) calibration.recalibrateDetDistance(showfits=True) iqcal=calibration.radialAverage() # This might be used to calibrate detector distance for following measurements as # empty.setDetectorDistance(calibration) # empty = js.sas.sasImage(js.examples.datapath+'/emptycell.tiff') # Mask beamstop (not the same as calibration, unluckily) empty.mask4Polygon([185,92],[190,92],[233,0],[228,0]) empty.maskCircle(empty.beamcenter, 9) empty.show() buffer = js.sas.sasImage(js.examples.datapath+'/buffer.tiff') buffer.maskFromImage(empty) buffer.show() bsa = js.sas.sasImage(js.examples.datapath+'/BSA11mg.tiff') bsa.maskFromImage(empty) bsa.show() # by default a log scaled image # # subtract buffer (transmission factor is just a guess here, sorry) new=bsa-buffer*0.2 new.show() # iqempty=empty.radialAverage() iqbuffer=buffer.radialAverage() iqbsa=bsa.radialAverage() # p=js.grace(1,1) p.plot(iqempty,le='empty cell') p.plot(iqbuffer,le='buffer') p.plot(iqbsa,le='bsa 11 mg/ml') p.title('raw data, no transmission correction') p.yaxis(min=1,max=1e3,scale='l',label='I(q) / a.u.') p.xaxis(scale='l',label='q / nm\S-1') p.legend() References ---------- .. [1] Everything SAXS: small-angle scattering pattern collection and correction Brian Richard Pauw J. Phys.: Condens. Matter 25, 383201 (2013) DOI https://doi.org/10.1088/0953-8984/25/38/383201 """ # open file if isinstance(file,str): # read tiff image image=PIL.Image.open(file) else: # try if this was an opened image image=file # get EXIF tags tags= image.tag_v2 if hasattr(image,'tag_v2') else image.tag try: # try im we have multiple frames as for SAXSpace # seek(1) returns error for single frame image.seek(1) image.seek(0) if hasattr(PIL,'__version__'): # squeeze for single columns im=np.asarray([np.asarray(image) for _ii in PIL.ImageSequence.Iterator(image)]).squeeze() else: raise ImportWarning('Current version of PIL does not support multi frame images. Install Pillow>=5.2.0 ') except EOFError: # tif to array conversion for single frame #im = np.asarray(image.transpose(PIL.Image.FLIP_TOP_BOTTOM)) im = np.asarray(image) # set it to writable im.flags.writeable=True # create the maskedArray from the base class as view # create default mask from negative values # Pilatus detectors have negative values outside sensitive detector area. sub_im = SubArray(im) data = np.ma.MaskedArray.__new__(cls, data=sub_im, mask=sub_im<0) # default values data.imageDescription=[] data.artist=[] data.set_fill_value(0) # the EXIF tags contain all meta information. # Take them as dictionary and add to artist, imageDescription or respective name from PIL.ExifTags.TAGS. try: data._getEXIF(tags) except: pass # set attributes from exif and extract some of these data data.filename=file data.description='---' # keywords to replace data._extractAttributes_(exchangekeywords) if beamcenter is not None: self.setBeamcenter(beamcenter) if detector_distance is not None: data.setDetectorDistance(detector_distance) data._issasImage=True return data def _extractAttributes_(self,attriblist): # extract attributes from EXIF entries # first words in comments firstwords=[line.split()[0] for line in self.imageDescription+self.artist if len(line.strip()) >0] for attribs in attriblist: if attribs[0] in firstwords: self.getfromcomment(attribs[0],replace=attribs[2],newname=attribs[1]) if attribs[4]=='string': setattr(self, attribs[1],' '.join([str(v) for v in getattr(self, attribs[1])])) else: setattr(self,attribs[1],[v*attribs[3] if isinstance(v,(float,int)) else v for v in getattr(self,attribs[1])]) def _getEXIF(self,tags): # Take them as dictionary and add to artist, imageDescription or respective name from PIL.ExifTags.TAGS. self._tags=tags # extract EXIF data and save them in artist and imageDescription for k,v in dict(self._tags).items(): if k > 700: continue elif k==270: # TAGS[270] = 'ImageDescription' # from Galaxy or Ganesha self.setattr({'imageDescription':[ vv[1:].strip() if vv[0]=='#' else vv.strip() for vv in v.splitlines()]}) elif k==315: # TAGS[315] = 'Artist' # in XML tag from Ganesha. Throws error if not a XML tag as for Galaxy try: self.entriesXML=parseXML(self._tags[315]) self.setattr({'artist':[str(k)+' '+str(v) for k,v in self.entriesXML.items()]}) except ElementTree.ParseError: if isinstance(self._tags[315],basestring): # catch if it is a single string as for SAXSPACE self.setattr({'artist':[self._tags[315]]}) else: self.setattr({'artist':[]}) else: if k in PIL.ExifTags.TAGS: self.setattr({PIL.ExifTags.TAGS[k]: v if isinstance(v, (list, set)) else [v]}) try: if self.artist[0] == 'Anton Paar GmbH': # catches SAXSPACE TIFF files # iv are specific for SAXSPACE for k,iv in dict({'wavelength':65024,'detector_distance':65060}).items(): v=self._tags[iv] self.setattr({k: v if isinstance(v, (list, set)) else [v]}) self.pixelSize=0.024 # 24 µm except:pass return def _setEXIF(self): # set Exif entries according to attributes if these were changed # see PIL.TiffTags.TYPES for types # we add anything new to TAGS[270] for k,v in dict(self._tags).items(): if k > 700: continue elif k==270: # TAGS[270] = 'ImageDescription' content=['processed by Jscatter'] content+= self.imageDescription for ekw in exchangekeywords: try: content.append(ekw[0]+' '+' '.join([str(a) for a in getattr(self,ekw[1])])) except:pass self._tags[k]='\n'.join(content) elif k==315: # TAGS[315] = 'Artist' self._tags[k]='\n'.join(self.artist) else: if k in PIL.ExifTags.TAGS: content=getattr(self,PIL.ExifTags.TAGS[k])[0] type=self._tags.tagtype[k] if type==2: self._tags[k] = ' '.join(content) elif type in [3,4,8,9]: self._tags[k] = content else: self._tags[k] = content return @property def iY(self): """ Y pixel coordinates """ return np.repeat(np.r_[0:self.shape[1]][None,:],self.shape[0],axis=0) @property def iX(self): """ X pixel coordinates """ return np.repeat(np.r_[0:self.shape[0]][:,None], self.shape[1], axis=1) @property def array(self): """ Strip of all attributes and return a simple array without mask. """ return self.data.array def __repr__(self): beamcenter=self.beamcenter if hasattr(self,'beamcenter') else None detector_distance = self.detector_distance if hasattr(self, 'detector_distance') else None desc="sasImage-> \n{0} \nbeamcenter={1} \ndetector distance={2} \nshape={3} " return desc.format(self,beamcenter,detector_distance ,self.shape)
[docs] def getfromcomment(self, name, replace=None, newname=None): """ Extract name from .artist or .imageDescription with attribute name in front. If multiple names start with parname first one is used. Used line is deleted from .artist or .imageDescription. Parameters ---------- name : string Name of the parameter in first place. replace : dict Dictionary with pairs to replace in all lines. newname : string New attribute name """ if newname is None: newname=name #first look in imageDescription for i,line in enumerate(self.imageDescription): if isinstance(replace, dict): for k,v in replace.items(): line=line.replace(k,str(v)) words=line.split() if len(words)>0 and words[0]==name: setattr(self,newname,[_w2f(word) for word in words[1:]]) del self.imageDescription[i] return # then in artist for i, line in enumerate(self.artist): if isinstance(replace, dict): for k, v in replace.items(): line = line.replace(k, str(v)) words = line.split() if len(words) > 0 and words[0] == name: setattr(self, newname, [_w2f(word) for word in words[1:]]) del self.artist[i] return
[docs] def setDetectorDistance(self,detector_distance,offset=0): """ Set detector distance from calibration . Parameters ---------- detector_distance : float, sasImage New value for detector distance. If sasImage the detector_distance is copied. offset : float Offset for sample compared to calibration sample. """ if isinstance(detector_distance,(float,int)): self.detector_distance = [detector_distance+offset] elif isinstance(detector_distance,(list,set)): self.detector_distance = [v+offset if isinstance(v,(float,int)) else v for v in detector_distance] elif isinstance(detector_distance,sasImage): self.detector_distance = [v+offset if isinstance(v,(float,int)) else v for v in detector_distance.detector_distance]
[docs] def setBeamcenter(self,beamcenter): """ Set beamcenter . Parameters ---------- beamcenter : float, sasImage New value for beamcenter as [height, width]. If sasImage the beamcenter is copied. """ if hasattr(beamcenter,'beamcenter'): self.beamcenter=list(beamcenter.beamcenter) else: # copy from object self.beamcenter = list(beamcenter)
[docs] def pickBeamcenter(self,levels=8,symmetry=6): """ Open image to pick the beamcenter from a calibration sample as AgBe. Radial averaged sectors allow to find the optimal beamcenter with best overlap of peaks. Closing the image accepts the actual selected beamcenter. Parameters ---------- levels : int Number of levels in contour image. symmetry : int Number of sectors around beamcenter for radial averages. Returns ------- After closing the selected beamcenter is saved in the sasImage. Notes ----- A figure with the AgBe picture (right) and a radial average over sectors (left, symmetry defines number of sectors) is shown. - A circle is shown around the mouse point after clicking. By default the radius corresponds to an AgBe reflex. The radius can be change by mouse wheel. - The beamcenter can be changed by the mouse pointer (click). - The beamcenter can be moved by arrow keys (+-1) or ctrl+arrow (+-0.1) - In the sectors a radial average (after some smoothing) is calculated and shown in the left axes. - The beamcenter is OK if the peaks show maximum overlap. """ colorMap='jet' origin='lower' fontsize=10 extend=None wl=self.wavelength[0]/10. # conversion to nm dd=self.detector_distance[0] # pixel r from q pfq=lambda q:dd*np.tan(2*np.arcsin(np.asarray(q)*wl/4./np.pi)) pixelpeaks = pfq(AgBepeaks)/self.pixel_size[0] # guess good AgBe peak pixelradius=pixelpeaks[np.abs(pixelpeaks-np.min(self.shape)/5).argmin()] fig = pyplot.figure() ax1 = fig.add_axes([0.4,0.05,0.6,0.85]) ax0 = fig.add_axes([0.1, 0.1, 0.3, 0.8]) cmap = pyplot.get_cmap(colorMap) lmap = pyplot.get_cmap(None) fig.suptitle('Move beamcenter: Pick with mouse; Close to accept \narrows(+-1 pixel) or ctrl+arrow (+-0.1 pixel) ',fontsize=10) ax1.yaxis.tick_right() ax1.yaxis.set_label_position("right") logself=np.ma.log(self) im = ax1.imshow(logself, cmap=cmap, extent=extend, origin=origin) im.cset = ax1.contour(logself,levels=levels,linewidths=1,cmap=lmap,extent=extend,origin=origin) im.labels=ax1.clabel(im.cset,inline=True,fmt='%1.1f',fontsize=fontsize) fig.colorbar(im,ax=ax1,orientation='horizontal',shrink=0.7,fraction=0.03,pad=0.1) # note that colorbar is a method of the figure, not the axes ax1.invert_yaxis() ax1.set_xlabel('Y dimension') ax1.set_ylabel('X dimension') # create circle and add it to figure if hasattr(self,'beamcenter'): center=[self.beamcenter[1],self.beamcenter[0],] print('Old position of beamcenter: [{0:.2f},{1:.2f}]'.format(center[1],center[0])) else: center=(self.shape[1]/2, self.shape[0]/2) print('No beamcenter defined') circle=Circle(center,pixelradius, color='k', fill=False,linewidth=2,linestyle=(0, (6, 3))) #np.min(self.shape)/3 ax1.add_artist(circle) # create picker and turn matplotlib to blocking mode to wait until window is closed pick = Picker(circle=circle,image=self,destination=ax0,symmetry=symmetry) mpl.pyplot.show(block=True) # now set beamcenter self.setBeamcenter([pick.circle.center[1],pick.circle.center[0]]) print('Set beamcenter to [{0:.2f},{1:.2f}]'.format(self.beamcenter[0],self.beamcenter[1]))
[docs] def maskFromImage(self,image): """ Use/copy mask from image. Parameters ---------- image : sasImage sasImage to use mask for resetting mask. image needs to have same dimension. """ if image.shape==self.shape: self.mask=image.mask
[docs] def maskRegion(self,xmin,xmax,ymin,ymax): """ Mask rectangular region. Parameters ---------- xmin,xmax,ymin,ymax : int Corners of the region to mask """ self[xmin:xmax,ymin:ymax]=ma.masked
[docs] def maskRegions(self,regions): """ Mask several regions. Parameters ---------- regions : list List of regions as in maskRegion. """ for region in regions: self.maskRegion(*region)
[docs] def maskbelowLine(self,p1,p2): """ Mask points at one side of line. The masked side is left looking from p1 to p2. Parameters ---------- p1, p2 : list of 2x float Points in pixel coordinates defining line. """ points=np.stack([self.iX,self.iY]) pp1=np.array(p1) pp2 = np.array(p2) d = np.cross((pp1-pp2)[:,None,None], pp2[:,None,None]-points,axis=0) self[d>0]=ma.masked
[docs] def maskTriangle(self,p1,p2,p3,invert=False): """ Mask inside triangle. Parameters ---------- p1,p2,p3 : list of 2x float Edge points of triangle. invert : bool Invert region. Mask outside circle. """ points=np.stack([self.iX,self.iY], axis=2) pp1 = np.array(p1) pp2 = np.array(p2) pp3 = np.array(p3) # cross to get sides of lines d1 = np.sign(np.cross((pp1 - pp2)[ None, None,:], points - pp2[ None, None,:], axis=2).reshape(points.shape[0],-1)) d2 = np.sign(np.cross((pp2 - pp3)[ None, None,:], points - pp3[ None, None,:], axis=2).reshape(points.shape[0],-1)) d3 = np.sign(np.cross((pp3 - pp1)[ None, None,:], points - pp1[ None, None,:], axis=2).reshape(points.shape[0],-1)) # equal side if sign equal sign of 3rd point mask=((d1 == d1[p3[0], p3[1]]) & (d2 == d2[p1[0], p1[1]]) & (d3 == d3[p2[0], p2[1]])) if invert: self[~mask]=ma.masked else: self[mask] = ma.masked
[docs] def mask4Polygon(self,p1,p2,p3,p4, invert=False): """ Mask inside polygon of 4 points. Points need to be given in right hand order. Parameters ---------- p1,p2,p3,p4 : list of 2x float Edge points. invert : bool Invert region. Mask outside circle. """ points=np.stack([self.iX,self.iY], axis=2) pp1 = np.array(p1,dtype=np.int32) pp2 = np.array(p2,dtype=np.int32) pp3 = np.array(p3,dtype=np.int32) pp4 = np.array(p4,dtype=np.int32) # cross to get sides of lines d1 = np.sign(np.cross((pp1 - pp2)[ None, None,:], points - pp2[ None, None,:], axis=2).reshape(points.shape[0],-1)) d2 = np.sign(np.cross((pp2 - pp3)[ None, None,:], points - pp3[ None, None,:], axis=2).reshape(points.shape[0],-1)) d3 = np.sign(np.cross((pp3 - pp4)[ None, None,:], points - pp4[ None, None,:], axis=2).reshape(points.shape[0],-1)) d4 = np.sign(np.cross((pp4 - pp1)[None, None, :], points - pp1[None, None, :], axis=2).reshape(points.shape[0], -1)) # equal side if sign equal sign of 3rd point mask=((d1 == d1[pp3[0], pp3[1]]) & (d2 == d2[pp4[0], pp4[1]]) & (d3 == d3[pp1[0], pp1[1]]) & (d4 == d3[pp2[0], pp2[1]]) ) if invert: self[~mask]=ma.masked else: self[mask] = ma.masked
[docs] def maskCircle(self,center,radius,invert=False): """ Mask points inside circle. Parameters ---------- center : list of 2x float Center point. radius : float Radius in pixel units invert : bool Invert region. Mask outside circle. """ points=np.stack([self.iX,self.iY]) distance=la.norm(points-np.array(center)[:, None, None],axis=0) mask=distance<radius if invert: self[~mask]=ma.masked else: self[mask] = ma.masked
[docs] def maskSectors(self,angles,width,radialmax=None,beamcenter=None,invert=False): """ Mask sector around beamcenter. Zero angle is Parameters ---------- angles : list of float Center angles of sectors in grad. width : float or list of float Width of the sectors in grad. If single value all sectors are equal. radialmax : float Maximum radius in pixels. beamcenter : 2x float Center if different from stored beamcenter. invert : bool Invert mask or not. Examples -------- :: import jscatter as js cal = js.sas.sasImage(js.examples.datapath+'/calibration.tiff') cal.maskSectors([0,90,180],20,radialmax=100,invert=True) cal.show() """ if beamcenter is None: beamcenter=self.beamcenter self._polarCoordinates(beamcenter) angles=np.asarray(angles) if isinstance(width,(float,int)): width=np.ones_like(angles)*width mask=self.mask.copy() mask[:]=False for a,w in zip(np.deg2rad(angles),np.deg2rad(np.abs(width))): limits=np.r_[a-w/2,a+w/2] % (2*np.pi)-np.pi if radialmax is None: if limits[0]<limits[1]: mask= np.logical_or(mask, (self._azimuth>limits[0]) & (self._azimuth<limits[1]) ) else: mask= np.logical_or(mask, ~((self._azimuth<limits[0]) & (self._azimuth>limits[1]))) else: if limits[0]<limits[1]: mask = np.logical_or(mask, (self._azimuth>limits[0]) & (self._azimuth<limits[1]) & (self._radial<radialmax)) else: mask = np.logical_or(mask, ~((self._azimuth<limits[0]) & (self._azimuth>limits[1])) & (self._radial<radialmax)) if invert: self[~mask]=ma.masked else: self[mask] = ma.masked
[docs] def findCenterOfIntensity(self,beamcenter=None,size=100): """ Find beam center as center of intensity around beamcenter. Only values above the mean value are used to calc center of intensity. Use an image with a clear symmetric and strong scattering sample as AgBe. Use *.showPolar([600,699],scaleR=5)* to see if peak is symmetric. Parameters ---------- beamcenter : list 2x int First estimate of beamcenter as [height, width] position. If not given preliminary beamcenter is estimated as center of intensity of full image. size : int Defines size of rectangular region of interest (ROI) around the beamcenter to look at. Returns ------- Adds (replaces) .beamcenter as attribute. Notes ----- If ROI is to large the result may be biased due to asymmetry of the intensity distribution inside of ROI. """ if isinstance(size,float): size=np.rint(size).astype(np.int) med=(self.max()+self.min()).array/2. if beamcenter is None: # as first guess beamcenter = ndimage.measurements.center_of_mass( ma.masked_less(self, med , copy=True).filled(0).array ) if size is not None: # take smaller portion to reduce bias from image size bc=np.rint(beamcenter).astype(np.int) data=self[bc[0]-size:bc[0]+size,bc[1]-size:bc[1]+size] # mask values smaller than mean and take centerofmass med=(data.max()+data.min()).array/2. center = ndimage.measurements.center_of_mass( ma.masked_less(data, med, copy=True).filled(0).array ) beamcenter=[center[0]+bc[0]-size,center[1]+bc[1]-size] self.setBeamcenter(beamcenter)
def _findCenterAgBe(self,beamcenter=None,size=40): """ Currently not working!! Find beamcenter as center of Debye-Scherrer rings of AgBe powder. Parameters ---------- beamcenter : 2x int Estimate of center. If not given findCenterOfIntensity is used to estimate center. size : int Rectangular region around the beamcenter to look at. Returns ------- Adds .beamcenter as attribute. """ if beamcenter is None: if not hasattr(self,'beamcenter'): self.findCenterOfIntensity(size=size) print('Found new beamcenter at ',self.beamcenter) else: print('Use beamcenter at ', self.beamcenter) beamcenter=self.beamcenter # get original mask orgmask=self.mask X=self.iX-beamcenter[0] Y=self.iY-beamcenter[1] mean=self.mean() # calc approximate radial wavevectors in real coordinates xxyy=((X*self.pixel_size[0])**2+(Y*self.pixel_size[1])**2)**0.5 phi=np.arctan2(X,Y) # scattering angle angle=np.arctan(xxyy/self.detector_distance[0]) wl=self.wavelength[0]/10. # conversion to nm q=4*np.pi/wl*np.sin(angle/2) dq=0.3 # around peak positions nn=20 dphi=np.pi/nn bc=[] # AgBepeaks contains a list of AgBe peak positions to test for print(beamcenter) for agp in AgBepeaks: qmask=(q>agp-dq) & (q<agp+dq) centers = [] print('-----------------------------------') for i,a1 in enumerate(np.r_[0:nn]*dphi): # symmetric side is -pi a2=a1-np.pi # make masks pi1 = qmask & ((phi > a1 ) & (phi < (a1 + dphi))) & ~orgmask pi2 = qmask & ((phi > a2 ) & (phi < (a2 + dphi))) & ~orgmask # only proceed if sizes of both sides are equal (no mask involved) # print('#1 ',a1,pi1.sum(),a2,pi2.sum() ) if pi1.sum() == pi2.sum() and pi1.sum()>10: # only above mean pi1max = pi1 & (self>mean) # X and Y mean Xpi1mean = np.mean(X[pi1max].astype(np.float64))# * self[pi1max]) / self[pi1max].sum() Ypi1mean = np.mean(Y[pi1max].astype(np.float64))# * self[pi1max]) / self[pi1max].sum() # same for other side pi2max = pi2 & (self > mean) Xpi2mean = np.mean(X[pi2max].astype(np.float64))# * self[pi2max]) / self[pi2max].sum() Ypi2mean = np.mean(Y[pi2max].astype(np.float64))# * self[pi2max]) / self[pi2max].sum() centers.append([(Xpi1mean+Xpi2mean)/2,(Ypi1mean+Ypi2mean)/2,abs(Xpi1mean-Xpi2mean),abs(Ypi1mean-Ypi2mean)]) print('pi1 ',["%0.2f" % i for i in [pi1.sum(),Xpi1mean,Ypi1mean,Xpi2mean,Ypi2mean]]) if len(centers)>nn*0.7: centers=np.array(centers).T # use only better 45 degree choose=(centers[2]/(centers[2] ** 2 + centers[3] ** 2) ** 0.5) >0.5**0.5 bc = [np.mean(centers[0,~choose]),np.mean(centers[1,choose])] print(bc) # self.beamcenter= centers print(centers) # restore original mask self.mask=ma.nomask self[orgmask]=ma.masked
[docs] def radialAverage(self, beamcenter=None, number=300,kind='log'): """ Radial average of image and conversion to wavevector q. Remember to set .detector_distance to calibrated value. Parameters ---------- beamcenter : list 2x float Sets beam center or radial center in data and uses this. If not given the attribut beamcenter in the data is used. number : int, default 500 Number of intervals on new X scale. kind : 'lin', default 'log' Determines how points are distributed. Returns ------- dataArray Notes ----- - Correction of pixel size for flat detector projected to Ewald sphere included. - The value in a q binning is the average count rate :math:`c(q)=(\sum c_i)/N` with counts in pixel *i* :math:`c_i` and number of pixels :math:`N` - The error (standard deviation) is calculated in a q binning as :math:`e=(\sum c_i)^{1/2}/N` - The error is valid for single photon counting detectors showing Poisson statistics as the today typical Pilatus detectors from DECTRIS. Examples -------- Mask and do radial average over sectors. :: import jscatter as js cal = js.sas.sasImage(js.examples.datapath+'/calibration.tiff') p=js.grace() calc=cal.copy() calc.maskSectors([0,180],20,radialmax=100,invert=True) calc.show() icalc=calc.radialAverage() p.plot(icalc,le='horizontal') calc=cal.copy() calc.maskSectors([90+0,90+180],20,radialmax=100,invert=True) calc.show() icalc=calc.radialAverage() p.plot(icalc,le='vertical') p.yaxis(scale='l') p.legend() p.title('The AgBe is not isotropically ordered') """ if beamcenter is not None: self.setBeamcenter(beamcenter) X=(self.iX-self.beamcenter[0])*self.pixel_size[0] Y=(self.iY-self.beamcenter[1])*self.pixel_size[1] # calc radial wavevectors r=np.linalg.norm([X,Y],axis=0) angle=np.arctan(r/self.detector_distance[0]) wl=self.wavelength[0]/10. # conversion to nm self.q=4*np.pi/wl*np.sin(angle/2) # correction for flat detector with pixel area lpl0=1./np.cos(angle) data=self.data*lpl0**3 mask=self.mask radial=dA(np.stack([self.q[~mask],data[~mask]])) radial.isort() # sorts along X by default # return lower number of points from prune result = radial[:,radial.Y>0].prune(number=number,type='sum',kind=kind) # average without error err=result[1]**0.5 result[1] /= result[2] # mean result[2] = err / result[2] # mean error result.setColumnIndex(iey=2) result.filename=self.filename result.detector_distance=self.detector_distance result.description=self.description return result
[docs] def getPixelQ(self): """ Get scattering vector along pixel dimension around beamcenter. Returns ------- qx,qy with image x and y dimension """ wl=self.wavelength[0]/10. # conversion to nm dd=self.detector_distance[0] # pixel distances X=(np.r_[0:self.shape[0]]-self.beamcenter[0])*self.pixel_size[0] Y=(np.r_[0:self.shape[1]]-self.beamcenter[1])*self.pixel_size[1] # q from pixel qfp=lambda r: 4*np.pi/wl*np.sin(0.5 * np.arctan( r / dd )) return qfp(X),qfp(Y)
[docs] def lineAverage(self, beamcenter=None, number=None, minmax='auto', show=False): """ Line average of image and conversion to wavevector q for line collimation cameras. Remember to set .detector_distance to calibrated value. Parameters ---------- beamcenter : float Sets beam center in data and uses this. If not given the beam center is determined from semitransparent beam. number : int, default None Number of intervals on new X scale. None means all pixels. minmax : [int,int], 'auto' Interval for determination of beamcenter. show : bool Show the fit of the primary beam. Returns ------- dataArray - .filename - .detector_distance - .description - .beamcenter Notes ----- - Detector distance in attributes is used. - The primary beam is automatically detected. - Correction for flat detector projected to Ewald sphere included. """ if beamcenter is None: # take average imageav=dA(np.c_[np.r_[0:self.shape[0]],self.mean(axis=1)].T) # find minima from argmax if not given explicitly if minmax[0]=='a': # auto # for normal empty cell or buffer measurement the primary beam is the maximum imax=imin=imageav.Y.argmax() while imageav.Y[imax+1]<imageav.Y[imax]: imax+=1 while imageav.Y[imin-1]<imageav.Y[imin]: imin-=1 xmax=imageav.X[imax] xmin=imageav.X[imin] else: xmin=minmax[0] xmax=minmax[1] # prune to smaller interval primarybeam=imageav.prune(lower=xmin,upper=xmax) # subtract min value , which is basically dark current primarybeam.Y-=primarybeam.Y.min() norm= scipy.integrate.simps(primarybeam.Y, primarybeam.X) primarybeam.Y/=norm # fit mean position and width primarybeam.fit(_gauss,{'mean':imageav.Y.argmax(),'sigma':0.015,'bgr':0,'A':1},{},{'x':'X'}) beamcenter=primarybeam.mean self.primarybeam_hwhm=primarybeam.sigma*np.sqrt(np.log(2.0)) self.primarybeam_peakmax=primarybeam.modelValues(x=primarybeam.mean).Y[0]*norm if show: primarybeam.showlastErrPlot() self.beamcenter=beamcenter r=(self.iX[0]-self.beamcenter)*self.pixelSize # µm pixel size # calc radial wavevectors angle=np.arctan(r/self.detector_distance[0]) wl=self.wavelength[0] self.q=4*np.pi/wl*np.sin(angle/2) # correction for flat detector with pixel area lpl0=1./np.cos(angle) data = self.mean(axis=0) * lpl0 # because of line collimation only power 1 error = self.std(axis=0) * lpl0 # because of line collimation only power 1 result=dA(np.stack([self.q,data,error])) if number is not None: # return lower number of points from prune result = result.prune(number=number,kind='mean+') # makes averages with errors result.filename=self.filename result.detector_distance=self.detector_distance result.description=self.description return result
[docs] def recalibrateDetDistance(self, beamcenter=None, number=500,showfits=False): """ Recalibration of detectorDistance by AgBe reference for point collimation. Use only for AgBe reference measurements to determine the correction factor. For non AgBe measurements set during reading or .detector_distance to the new value. May not work if the detector distance is totally wrong. Parameters ---------- beamcenter : list 2x float Sets beam center or radial center in data and uses this. If not given the attribut beamcenter in the data is used. number : int, default 1000 number of intervals on new X scale. showfits : bool Show the AgBe peak fits. Notes ----- - .distanceCorrection will contain factor for correction. Repeating this results in a .distanceCorrection close to 1. """ # do radial average iq=self.radialAverage(beamcenter=beamcenter, number=number) # later distance corrections self.distanceCorrection=[] dq=0.3 # around peak positions for agp in AgBepeaks: # AgBepeaks contains a list of AgBe peak positions to test for # we fit each with a voigt function and take later the average if iq.X.max()>agp+dq and iq.X.min()<agp-dq: # cut between lower and upper and fit Voigt function for peak iqq=iq.prune(lower=agp-dq,upper=agp+dq) #iqq.setColumnIndex(iey=None) if iqq.shape[1]<5: continue iqq.setLimit(amplitude=[0],bgr=[0],fwhm=[0.001,agp]) ret=iqq.fit(_agbpeak, {'center':agp, 'amplitude': iqq.Y.max() / 4., 'fwhm':0.1, 'asym':1, 'bgr': iqq.Y.min() / 2.}, {}, {'q':'X'}, output=False) if ret== -1: continue if showfits : iqq.showlastErrPlot() self.distanceCorrection+=[iqq.center/agp] corfactor=np.mean(self.distanceCorrection) corstd=np.std(self.distanceCorrection)/corfactor # set new detectorDistance self.detector_distance[0]*=corfactor print('\nCorrection factor %.4f to new distance %.4f (rel error : %.4f )' %(corfactor, self.detector_distance[0], corstd) )
[docs] def show(self,scale=None,levels=8,axis='pixel',block=False): """ Show image. Parameters ---------- scale : 'norm', default 'log', If log the image is first log scaled levels : int, None Number of contour levels. axis : 'pixel', None Force pixel as coordinates, otherwise wavevectors if possible. block : bool Open in blocking or non-blocking mode Returns ------- image handle """ if scale is None or scale[0]=='l': fig=mpl.contourImage(x=np.ma.log(self),levels=levels,axis=axis,block=block,invert_yaxis=True) else: fig=mpl.contourImage(x=self, levels=levels,axis=axis,block=block,invert_yaxis=True) fig.axes[0].set_xlabel('Y dimension ') fig.axes[0].set_ylabel('X dimension') mpl.pyplot.show(block=block) return fig
[docs] def gaussianFilter(self,sigma=2): """ Gaussian filter in place. Uses ndimage.filters.gaussian_filter with default parameters except sigma. Parameters ---------- sigma : float Gaussian kernel sigma. """ self[self.mask]=ndimage.filters.gaussian_filter(self.data,sigma)[self.mask]
[docs] def reduceSize(self,pixelsize=2,center=None,border=None): """ Reduce size of image using uniform average in box. XResolution,YResolution,beamcenter are scaled correspondingly. Parameters ---------- pixelsize : int Pixel size of the box to average. Also factor for reduction. center : [int,int] Center of crop region. border : int Size of crop region. - If center is given a box with 2*size around center is used. - If center is None the border is cut by size. Returns ------- sasImage """ i1=i3=0 i2=i4=100000 if border is not None: # set box around center or from border if center is not None : center = np.asarray(center, int) i1 = center[0] - border i2 = center[0] + border i3 = center[1] - border i4 = center[1] + border else: i1 = border i2 = self.shape[0]- border i3 = border i4 = self.shape[1]- border data=self[max(i1,0):min(i2,self.shape[0]),max(i3,0):min(i4,self.shape[1])].copy() data[data.mask]=ndimage.filters.uniform_filter(data.data,size=pixelsize)[data.mask] smalldata=data[::pixelsize,::pixelsize] try: # increase pixel size smalldata.pixel_size = [pz * pixelsize for pz in smalldata.pixel_size] except:pass try: bc=self.beamcenter[:] bc[0]=( bc[0] - max(i1,0) ) / pixelsize bc[1]=( bc[1] - max(i3,0) ) / pixelsize smalldata.setBeamcenter(bc) except:pass # set pixel coordinates smalldata.ImageWidth=[smalldata.shape[1]] smalldata.ImageLength = [smalldata.shape[0]] smalldata.XResolution[0]=data.XResolution[0]*float(pixelsize) smalldata.YResolution[0]=data.YResolution[0]*float(pixelsize) smalldata._setEXIF() return smalldata
[docs] def showPolar(self,beamcenter=None,scaleR=1,offset=0): """ Show image transformed to polar coordinates around beamcenter. Azimuth corresponds: center line to beamcenter upwards, upper quarter beamcenter to right upper/lower edge = beamcenter downwards, lower quarter beamcenter to left Parameters ---------- beamcenter : [int,int] Beamcenter scaleR : float Scaling factor for radial component to zoom the beamcenter. offset : float Offset to remove beamcenter from polar image. Returns ------- Handle to figure """ if beamcenter is not None: self.beamcenter=beamcenter # transform from polar coordinates to cartesian with bc shift and scaling of radial component to magnify def transform(rp,bc,shape,scale,offset): phi=rp[0]/shape[0]*2*np.pi-np.pi r=max(0,rp[1]+abs(offset))/scale return r * np.cos(phi) + bc[0] , r * np.sin(phi) + bc[1] newimage=np.zeros_like(self.data) ndimage.geometric_transform(self,mapping=transform,output=newimage, extra_keywords={'bc':self.beamcenter,'shape':self.shape,'scale':scaleR,'offset':offset}) f= mpl.contourImage(newimage) f.axes[0].set_ylabel('azimuth ') f.axes[0].set_xlabel('radial ') mpl.pyplot.show(block=False) return f
[docs] def save(self,file,format=None,**params): """ Saves image under the given filename. If no format is specified, the format to use is determined from the filename extension. See PIL save for formats and options. Parameters ---------- file : string Filename to save to. format : Optional format override. If omitted, the format to use is determined from the filename extension. If a file object was used instead of a filename, this parameter should always be used. params : - Additional parameters. Returns ------- """ #create image image=PIL.Image.fromarray(self.data) # write image params.update({'tiffinfo':self._tags}) image.save(fp=file,format=format,**params)
def _polarCoordinates(self,beamcenter): X=(self.iX-beamcenter[0]) Y=(self.iY-beamcenter[1]) self._azimuth = np.arctan2(X,Y) self._radial = np.linalg.norm([X,Y],axis=0)
[docs] def asdataArray(self,masked=0): """ Return representation of sasImage as dataArray representing wavevectors (qx,qy) against intensity. Parameters ---------- masked : float, None, string, default=0 How to deal with masked values. - float : Set masked pixels to this value - None : Remove from dataArray. To recover the image the masked pixels need to be interpolated on a regular grid. - ‘linear’, ‘cubic’, ‘nearest’ : interpolate masked points by scipy.interpolate.griddata using specified order of interpolation. Returns ------- dataArray with [qx,qy,I(qx,qy) ] - .qx, .qz : original qx values to recover the image Examples -------- :: # this demo will show the interpolation in the masked regions # of an artificial intensity distribution import jscatter as js import numpy as np calibration = js.sas.sasImage(js.examples.datapath+'/calibration.tiff') # manipulate data (not the mask) calibration.data[:150,30:60]=100 calibration.data[:150,60:90]=300 calibration.data[:150,90:]=600 # mask a circle calibration.maskCircle([100,100], 60) cal=calibration.asdataArray('linear') cal.Y[cal.Y<=0.1]=1.1 js.mpl.surface(cal.X, cal.Z, cal.Y) cal2=calibration.asdataArray(None) # this is reduced in size due to the mask """ qxz=self.getPixelQ() # array of qx and qy qx=np.repeat(qxz[0][:,None],self.shape[1],axis=1) qz=np.repeat(qxz[1][None,:], self.shape[0], axis=0) # return flat array without masked data mask=~self.mask.flatten() if isinstance(masked,(float,int)): out=dA(np.stack([qx.flatten(),qz.flatten(),self.data.flatten()]),XYeYeX=[0, 2, None, None, 1, None]) out[2,~mask]=masked elif isinstance(masked,basestring) and self.mask.sum()>0: if masked not in ['linear', 'cubic', 'nearest']: masked='nearest' qxf = qx.flatten() qzf = qz.flatten() dat = self.data.flatten() f = griddata( np.stack([qxf[mask],qzf[mask]],axis=1), dat[mask],(qxf[~mask],qzf[~mask]), method=masked) dat[~mask]=f out = dA(np.stack([qxf,qzf , dat ]), XYeYeX=[0, 2, None, None, 1, None]) else: out=dA(np.stack([qx.flatten()[mask],qz.flatten()[mask],self.flatten()[mask]]),XYeYeX=[0, 2, None, None, 1, None]) out.qx=qxz[0] out.qz=qxz[1] return out
[docs]def readImages(filenames): """ Read a list of images returning sasImage`s. Parameters ---------- filenames : string Glob pattern to read Returns ------- list of sasImage`s Notes ----- To get a list of image descriptions:: images=js.sas.readImages(path+'/latest*.tiff') [i.description for i in images] """ try: filelist=glob.glob(filenames) except AttributeError: raise AttributeError('No filename pattern in ', filenames) else: data=[] for ff in filelist: data.append(sasImage(ff)) return data
[docs]def createImageDescriptions(images): """ Create text file with image descriptions as list of content. Parameters ---------- images : list of sasImages or glob pattern List of images Returns ------- """ if not isinstance(images,(list,set)): images=readImages(filenames) commonprefix = os.path.commonprefix([i.filename for i in images]) description=[i.filename[len(os.path.dirname(commonprefix))+1:]+' '+i.description for i in images] description.sort() commonname=os.path.split(commonprefix)[-1] if commonname=='': commonname='--' with open('ContentOf_'+commonname+'.txt', 'w') as f: f.writelines("%s\n" % l for l in ['Content of dir '+os.path.dirname(commonprefix),' ']) f.writelines("%s\n" % l for l in description)
[docs]def createLogPNG(filenames,center=None,size=None,colormap='jet',equalize=False,contrast=None): """ Create .png files from grayscale images with log scale conversion to values between [1,255]. This generates images viewable in simple image viewers as overview. The new files are stored in the same folder as the original files. Parameters ---------- filenames : string Filename with glob pattern as 'file*.tif' center : [int,int] Center of crop region. size : int Size of crop region. - If center is given a box with 2*size around center is used. - If center is None the border is cut by size. colormap : string, None Colormap from matplotlib or None for grayscale. For standard colormap names look in mpl.showColors(). equalize : bool Equalize the images. contrast : None, float Autocontrast for the image. The value (0.1=10%) determines how much percent are cut from the intensity histogram before linear spread of intensities. """ if colormap is not None: cmap=mpl.pyplot.get_cmap(colormap) else: cmap=None i1=i3=0 i2=i4=100000 if size is not None: # set box around center or from border if center is not None : i1 = center[1] - size i2 = center[1] + size i3 = center[0] - size i4 = center[0] + size else: i1 = size i2 = - size i3 = size i4 = - size try: filelist=glob.glob(filenames) except AttributeError: raise AttributeError('No filename pattern in ', filenames) else: for ff in filelist: image=PIL.Image.open(ff) # crop image arary image2=np.array(image)[max(i1,0):min(i2,image.height),max(i3,0):min(i4,image.width)] # log scale mapped to 0-255 image2[image2<1]=1 image2 = np.log(image2) image2 = image2 / np.max(image2) * 255 if cmap is None: newimage=PIL.Image.fromarray(image2.astype(np.uint8)).convert('L') if contrast is not None: newimage=PIL.ImageOps.autocontrast(newimage,contrast) if equalize: newimage=PIL.ImageOps.equalize(newimage) newimage.save(ff+'.png') else: # cmap needs uint to work properly image2=cmap(image2.astype(np.uint8),bytes=True) newimage=PIL.Image.fromarray(image2[:,:,:-1],mode='RGB') if contrast is not None: newimage=PIL.ImageOps.autocontrast(newimage,contrast) if equalize: newimage=PIL.ImageOps.equalize(newimage) newimage.save(ff + '.png') return