more functions: added 2d mask and filtering of 1d diffs...

xray/__init__.py

@@ -0,0 +1,8 @@
+from . import storage
+from . import utils
+from . import mask
+try:
+  from . import azav
+except ImportError:
+  print("Can't import azav ...")
+from . import dataReduction

xray/azav.py

@@ -11,6 +11,7 @@ import glob
 import pathlib
 from . import storage
 from . import utils
+import fabio
 import pyFAI
 
 try:
@@ -18,15 +19,27 @@ try:
 except ImportError:
   log.warn("Can't import matplotlib !")
 
-def pyFAIread(fname):
+def _read(fname):
   """ read data from file using fabio """
-  import fabio
   f = fabio.open(fname)
   data = f.data
-  del f
+  del f; # close file
   return data
 
-def pyFAI_dict(ai):
+def read(fnames):
+  """ read data from file(s) using fabio """
+  if isinstance(fnames,str):
+    data = _read(fnames)
+  else:
+    # read one image to know img size
+    temp = _read(fnames[0])
+    shape = [len(fnames),]+list(temp.shape)
+    data  = np.empty(shape)
+    data[0] = temp
+    for i in range(1,len(fnames)): data[i] = _read(fnames[i])
+  return data
+    
+def ai_as_dict(ai):
   """ ai is a pyFAI azimuthal intagrator"""
   methods = dir(ai)
   methods = [m for m in methods if m.find("get_") == 0]
@@ -36,11 +49,11 @@ def pyFAI_dict(ai):
   ret["detector"] = ai.detector.get_name()
   return ret
 
-def pyFAI_info(ai):
+def ai_as_str(ai):
   """ ai is a pyFAI azimuthal intagrator"""
   return "#" + str(ai).replace("\n","\n#")
 
-def pyFAI1d(ai, imgs, mask = None, npt_radial = 600, method = 'csr',safe=True,dark=10., polCorr = 1):
+def do1d(ai, imgs, mask = None, npt_radial = 600, method = 'csr',safe=True,dark=10., polCorr = 1):
     """ ai is a pyFAI azimuthal intagrator 
               it can be defined with pyFAI.load(ponifile)
         mask: True are points to be masked out """
@@ -57,7 +70,7 @@ def pyFAI1d(ai, imgs, mask = None, npt_radial = 600, method = 'csr',safe=True,da
       out_s[_i] = sig
     return q,np.squeeze(out_i),np.squeeze(out_s)
 
-def pyFAI2d(ai, imgs, mask = None, npt_radial = 600, npt_azim=360,method = 'csr',safe=True,dark=10., polCorr = 1):
+def do2d(ai, imgs, mask = None, npt_radial = 600, npt_azim=360,method = 'csr',safe=True,dark=10., polCorr = 1):
     """ ai is a pyFAI azimuthal intagrator 
               it can be defined with pyFAI.load(ponifile)
         mask: True are points to be masked out """
@@ -72,32 +85,46 @@ def pyFAI2d(ai, imgs, mask = None, npt_radial = 600, npt_azim=360,method = 'csr'
       out[_i] = i2d
     return q,azTheta,np.squeeze(out)
 
-def _getAI(poni,folder):
+def getAI(poni,folder=None):
+  """ get AzimuthalIntegrator instance:
+      → if poni is a dictionary use it to define one
+      → if poni is a string look, it is used as filename to read.
+        in this case if folder is given it is used (together with all its 
+        subfolder) as search path (along with ./ and home folder)
+  """
   if isinstance(poni,pyFAI.azimuthalIntegrator.AzimuthalIntegrator):
     ai = poni
   elif isinstance(poni,dict):
     ai = pyFAI.azimuthalIntegrator.AzimuthalIntegrator(**poni)
+  elif isinstance(poni,str):
+    # look is file exists in cwd
+    if os.path.exists(poni):
+      ai = pyFAI.load(poni)
+    # if file does not exist look for one with that name around
     else:
-    if poni == 'auto':
+      # build search paths
+      folders = []
+      if folder is not None:
         temp = os.path.abspath(folder)
         path = pathlib.Path(temp)
         folders = [ str(path), ]
         for p in path.parents: folders.append(str(p))
       folders.append( "./" )
       folders.append( os.path.expanduser("~/") )
+      # look for file
       for path in folders:
-        poni = path + "/" + "pyfai.poni"
+        fname = path + "/" + poni
-        if os.path.exists(poni):
+        if os.path.exists(fname):
-          log.info("Found pyfai.poni in %s",path)
+          log.info("Found poni file %s",fname)
           break
         else:
-          log.debug("Could not find pyfai.poni in %s",path)
+          log.debug("Could not poni file %s",fname)
-    ai = pyFAI.load(poni)
+    ai = pyFAI.load(fname)
   return ai
 
 
 def doFolder(folder,files='*.edf*',nQ = 1500,force=False,mask=None,
-             saveChi=True,poni='auto',storageFile='auto',diagnostic=None):
+             saveChi=True,poni='pyfai.poni',storageFile='auto',diagnostic=None):
   """ calc 1D curves from files in folder, returning a dictionary of stuff
       nQ    : number of Q-points (equispaced)
       force : if True, redo from beginning even if previous data are found
@@ -107,16 +134,15 @@ def doFolder(folder,files='*.edf*',nQ = 1500,force=False,mask=None,
       saveChi: self-explanatory
       poni  : could be:
               → an AzimuthalIntegrator instance
-              → a filename
+              → a filename that will be look for in
-              → a dictionary (use to bootstrap an AzimuthalIntegrator using 
-                AzimuthalIntegrator(**poni)
-              → the string 'auto' that will look for the file 'pyfai.poni' in:
                  1 'folder' first
                  2 in ../folder
                  3 in ../../folder
                  ....
                  n-1 in pwd
                  n   in homefolder
+              → a dictionary (use to bootstrap an AzimuthalIntegrator using 
+                AzimuthalIntegrator(**poni)
  """
   if storageFile == 'auto': storageFile = folder + "/" + "pyfai_1d.h5"
 
@@ -126,7 +152,7 @@ def doFolder(folder,files='*.edf*',nQ = 1500,force=False,mask=None,
     saved = None
 
   # which poni file to use:
-  ai = _getAI(poni,folder)
+  ai = getAI(poni,folder)
 
   files = utils.getFiles(folder,files)
   if saved is not None:
@@ -137,18 +163,18 @@ def doFolder(folder,files='*.edf*',nQ = 1500,force=False,mask=None,
     if isinstance(mask,np.ndarray):
       mask = mask.astype(bool)
     elif mask is not None:
-      mask = pyFAIread(mask).astype(bool)
+      mask = read(mask).astype(bool)
       
     data   = np.empty( (len(files),nQ) )
     err    = np.empty( (len(files),nQ) )
     for ifname,fname in enumerate(files):
-      img = pyFAIread(fname)
+      img = read(fname)
-      q,i,e = pyFAI1d(ai,img,mask=mask,npt_radial=nQ)
+      q,i,e = do1d(ai,img,mask=mask,npt_radial=nQ)
       data[ifname] = i
       err[ifname]  = e
       if saveChi:
         chi_fname = utils.removeExt(fname) + ".chi"
-        utils.saveTxt(chi_fname,q,i,e,info=pyFAI_info(ai),overwrite=True)
+        utils.saveTxt(chi_fname,q,i,e,info=ai_as_str(ai),overwrite=True)
 
     files = [ utils.getBasename(f) for f in files ]
     files = np.asarray(files)
@@ -157,7 +183,7 @@ def doFolder(folder,files='*.edf*',nQ = 1500,force=False,mask=None,
       data  = np.concatenate( (saved["data"]  ,data  ) )
       err   = np.concatenate( (saved["err"]   ,err   ) )
     ret = dict(q=q,folder=folder,files=files,data=data,err=err,
-          pyfai=pyFAI_dict(ai),pyfai_info=pyFAI_info(ai),mask=mask)
+          pyfai=ai_as_dict(ai),pyfai_info=ai_as_str(ai),mask=mask)
 
     # add info from diagnostic if provided
     if diagnostic is not None:
@@ -191,7 +217,7 @@ def leastsq_circle(x,y):
   residu   = np.sum((Ri - R)**2)
   return xc, yc, R
 
-def pyFAI_find_center(img,psize=100e-6,dist=0.1,wavelength=0.8e-10,**kwargs):
+def find_center(img,psize=100e-6,dist=0.1,wavelength=0.8e-10,**kwargs):
   plt.ion()
   kw = dict( pixel1 = psize, pixel2 = psize, dist = dist,wavelength=wavelength )
   kw.update(kwargs)
@@ -199,8 +225,9 @@ def pyFAI_find_center(img,psize=100e-6,dist=0.1,wavelength=0.8e-10,**kwargs):
   fig_img,ax_img = plt.subplots(1,1)
   fig_pyfai,ax_pyfai = plt.subplots(1,1)
   fig_pyfai = plt.figure(2)
-  ax_img.imshow(img)
+  temp= ax_img.imshow(img)
   plt.sca(ax_img); # set figure to use for mouse interaction
+  temp.set_clim( *np.percentile(img,(2,95) ) )
   ans = ""
   print("Enter 'end' when done")
   while ans != "end":
@@ -213,8 +240,9 @@ def pyFAI_find_center(img,psize=100e-6,dist=0.1,wavelength=0.8e-10,**kwargs):
     print("Selected center:",xc,yc)
     ai.set_poni1(xc*psize)
     ai.set_poni2(yc*psize)
-    q,az,i = pyFAI2d(ai,img)
+    q,az,i = do2d(ai,img)
-    ax_pyfai.pcolormesh(q,az,i)
+    mesh = ax_pyfai.pcolormesh(q,az,i)
+    mesh.set_clim( *np.percentile(i,(2,95) ) )
     ax_pyfai.set_title(str( (xc,yc) ))
     plt.pause(0.01)
     plt.draw()

xray/dataReduction.py

@@ -13,7 +13,8 @@ def subtractReferences(i,idx_ref, useRatio = False):
   """ given data in i (first index is shot num) and the indeces of the 
       references (idx_ref, array of integers) it interpolates the closest
       reference data for each shot and subtracts it (or divides it, depending
-      on useRatio = [True|False];  """
+      on useRatio = [True|False]; 
+      Note: it works in place (i.e. it modifies i) """
   iref=np.empty_like(i)
   idx_ref = np.squeeze(idx_ref)
   idx_ref = np.atleast_1d(idx_ref)
@@ -47,7 +48,7 @@ def subtractReferences(i,idx_ref, useRatio = False):
   return i
 
 def averageScanPoints(scan,data,isRef=None,lpower=None,useRatio=False,\
-    funcForEveraging=np.nanmean):
+    funcForAveraging=np.nanmean):
   """ given scanpoints in 'scan' and corresponding data in 'data'
       average all data corresponding the exactly the same scanpoint.
       If the values in scan are coming from a readback, rounding might be
@@ -58,51 +59,55 @@ def averageScanPoints(scan,data,isRef=None,lpower=None,useRatio=False,\
          subtracted/divided by the interpolated reference
       if lpower is provided the data is divided by it (how it is done depends
          if one uses the ratio or not
-      funcForEveraging: is usually np.nanmean or np.nanmedian. it can be any 
+      funcForAveraging: is usually np.nanmean or np.nanmedian. it can be any 
          function that support axis=0 as keyword argument
 """
   data = data.astype(np.float)
   avData = np.nanmedian( data , axis = 0 )
+
   if isRef is None: isRef = np.zeros( data.shape[0], dtype=bool )
   assert data.shape[0] == isRef.shape[0]
 
   # subtract reference only is there is at least one
   if isRef.sum()>0:
-    data = subtractReferences(data,np.argwhere(isRef), useRatio=useRatio)
+    # create a copy (subtractReferences works in place)
+    diff = subtractReferences(data.copy(),np.argwhere(isRef), useRatio=useRatio)
   else:
-    data = data.copy(); # create local copy
+    diff = data
 
   # normalize signal for laser intensity if provided
   if lpower is not None:
     lpower = utils.reshapeToBroadcast(lpower,data)
     if useRatio is False:
-      data /= lpower
+      diff /= lpower
     else:
-      data = (data-1)/lpower+1
+      diff = (data-1)/lpower+1
 
   scan_pos = np.unique(scan)
-  shape_out = [len(scan_pos),] + list(data.shape[1:])
+  shape_out = [len(scan_pos),] + list(diff.shape[1:])
   ret       = np.empty(shape_out)
   err       = np.empty(shape_out)
-  dataInScanPoint = []
+  data_abs  = np.empty(shape_out)
+  diffsInScanPoint = []
   chi2_0 = []
   for i,t in enumerate(scan_pos):
     shot_idx = (scan == t)
 
     # select data for the scan point
-    data_for_scan = data[shot_idx]
+    diff_for_scan = diff[shot_idx]
-    dataInScanPoint.append( data_for_scan )
+    diffsInScanPoint.append( diff_for_scan )
 
     # calculate average
-    ret[i] = funcForEveraging(data_for_scan,axis=0)
+    ret[i] = funcForAveraging(diff_for_scan,axis=0)
+    data_abs[i] = funcForAveraging(data[shot_idx],axis=0)
 
     # calculate std
-    noise = np.nanstd(data[shot_idx], axis = 0)
+    noise = np.nanstd(diff[shot_idx], axis = 0)
 
     # calculate chi2 of different repetitions
-    chi2 = np.power( (data_for_scan - ret[i])/noise,2)
+    chi2 = np.power( (diff_for_scan - ret[i])/noise,2)
     # sum over all axis but first
-    for _ in range(data_for_scan.ndim-1):
+    for _ in range(diff_for_scan.ndim-1):
       chi2 = np.nansum( chi2, axis=-1 )
 
     # store chi2_0
@@ -111,8 +116,9 @@ def averageScanPoints(scan,data,isRef=None,lpower=None,useRatio=False,\
     # store error of mean
     err[i] = noise/np.sqrt(shot_idx.sum())
 
-  ret = dict(scan=scan_pos,data=ret,err=err,chi2_0=chi2_0,
+  ret = dict(scan=scan_pos,data=ret,dataUnmasked=ret.copy(),err=err,errUnmasked=err.copy(),
-             dataInScanPoint=dataInScanPoint,avData=avData)
+        chi2_0=chi2_0,diffsInScanPoint=diffsInScanPoint,dataAbsAvAll=avData,dataAbsAvScanPoint=data_abs,
+        dataAbs=data.copy())
   ret = storage.DataStorage(ret)
   return ret
 
@@ -148,51 +154,107 @@ def calcTimeResolvedSignal(scan,data,reference="min",monitor=None,q=None,
   if q is not None: ret["q"] = q
   return ret
 
-def errorFilter(data,threshold=4):
+def errorMask(data,threshold=5):
-  """ Very simple but effective filter for zinger like noise
+  """ Q-by-Q mask !
+      Very simple but effective mask for zinger like noise
       The noise is in general lower when using nanmean instead than
-      nanmedian but nanmean does not filter out 'spikes'.
+      nanmedian but nanmean does not mask out 'spikes'.
-      This filter mitigate this effect by using nanmedian for the q points
+      This mask mitigate this effect by using nanmedian for the q points
       that have an higher than usual error (proxy for spikes ...)
       tested with mi1245/dec2016/tiox/tiox1/run3
   """
   assert data.data.ndim == 2
-  for iscan in range(len(data.dataInScanPoint)):
+  idx_mask = []
-    median_err = np.nanmedian(data.err[iscan])
+  for iscan in range(len(data.diffsInScanPoint)):
-    idx = data.err[iscan] > threshold*median_err
+    temp = data.diffsInScanPoint[iscan]
-    data.data[iscan][idx] = \
+    # sqrt(len(temp)) = sqrt(numOfDiffs); it is needed to estimate error of single Diff
-      np.nanmedian( data.dataInScanPoint[iscan][:,idx],axis=0 )
+    idx = np.abs(temp-np.median(temp,axis=0)) > threshold*data.err[iscan]*np.sqrt(len(temp))
-    data.err[iscan][idx] = median_err
+    idx_mask.append( idx )
+  if "masks" not in data: data['masks'] = dict()
+  data['masks']['error'] = idx_mask
   return data
 
-def saveTxt(folder,data,delayToStr=True,info="",**kw):
+def chi2Mask(data,threshold=2):
-  """ data must be a data_storage instance """
+  """ 
+      The noise is in general lower when using nanmean instead than
+      nanmedian but nanmean does not mask out 'spikes'.
+      This mask mitigate this effect by using nanmedian for the q points
+      that have an higher than usual error (proxy for spikes ...)
+      tested with mi1245/dec2016/tiox/tiox1/run3
+  """
+  idx_mask = []
+  for iscan in range(len(data.diffsInScanPoint)):
+    idx = data.chi2_0[iscan] > threshold
+    # expand along other axis (q ...)
+    idx = utils.reshapeToBroadcast(idx,data.diffsInScanPoint[iscan])
+    idx_mask.append(idx)
+  if "masks" not in data: data['masks'] = dict()
+  data['masks']['chi2'] = idx_mask
+  return data
+
+def applyMasks(data,which='all',funcForAveraging=np.nanmean):
+  # don't do anything if no mask is defined
+  if 'masks' not in data: return data
+  if which == 'all': which = list(data['masks'].keys())
+  totmask = []
+  for iscan in range(len(data.diffsInScanPoint)):
+    mask = data['masks'][which[0]][iscan]
+    for w in which[1:]:
+      mask = np.logical_and(mask,data['masks'][w][iscan])
+    mask = np.squeeze(mask)
+    totmask.append(mask); # store for later
+    # check is a q-by-q mask
+    if mask.shape == data.diffsInScanPoint[iscan].shape:
+      temp = np.ma.MaskedArray(data=data.diffsInScanPoint[iscan],mask=mask)
+      data.data[iscan] = funcForAveraging( temp,axis=0 )
+    else:
+      data.data[iscan] = funcForAveraging( data.diffsInScanPoint[iscan][~mask],axis=0)
+  data['mask'] = totmask
+  return data
+
+  
+def saveTxt(folder,data,delayToStr=True,basename='auto',info="",**kw):
+  """ data must be a DataStorage instance """
+  # folder ends usually with sample/run so use the last two subfolders
+  if basename == 'auto':
+    basename = "_".join(folder.rstrip("/").split("/")[-2:]) + "_"
   q = data.q if "q" in data else np.arange(data.data.shape[-1])
   # save one file with all average diffs
-  fname = "%s/diff_av_matrix.txt" % (folder)
+  fname = "%s/%sdiff_av_matrix.txt" % (folder,basename)
   utils.saveTxt(fname,q,data.data,headerv=data.scan,**kw)
+  # save error bars in the matrix form
+  fname = "%s/%sdiff_av_matrix_err.txt" % (folder,basename)
+  utils.saveTxt(fname,q,data.err,headerv=data.scan,**kw)
+
   for iscan,scan in enumerate(data.scan):
     scan = utils.timeToStr(scan) if delayToStr else "%+10.5e" % scan
 
     # try retreiving info on chi2
     try:
       chi2_0  = data.chi2_0[iscan]
-      info_delay = [ "# rep_num : chi2_0 ", ]
+      info_delay = [ "# rep_num : chi2_0 , discarded by chi2masking ?", ]
       for irep,value in enumerate(chi2_0):
         info_delay.append( "# %d : %.3f" % (irep,value))
+        if 'chi2' in data.masks: info_delay[-1] += " %s"%str(data.masks['chi2'][iscan][irep])
       info_delay = "\n".join(info_delay)
       if info != '': info_delay = "%s\n%s" % (info,info_delay)
     except AttributeError:
       info_delay = info
 
     # save one file per timedelay with average diff (and err)
-    fname = "%s/diff_av_%s.txt" % (folder,scan)
+    fname = "%s/%sdiff_av_%s.txt" % (folder,basename,scan)
-    utils.saveTxt(fname,q,data.data[iscan],e=data.err[iscan],
+    if 'mask' in data:
-                  info=info_delay,**kw)
+      tosave = np.vstack( (data.data[iscan],data.err[iscan],
+               data.dataUnmasked[iscan],data.errUnmasked[iscan] ) ) 
+      columns = 'q diffmask errmask diffnomask errnomask'.split()
+    else: 
+      tosave = np.vstack( (data.data[iscan],data.err[iscan] ) )
+      columns = 'q diff err'.split()
+    utils.saveTxt(fname,q,tosave,info=info_delay,columns=columns)
 
     # save one file per timedelay with all diffs for given delay
-    fname = "%s/diffs_%s.txt" % (folder,scan)
+    fname = "%s/%sdiffs_%s.txt" % (folder,basename,scan)
-    utils.saveTxt(fname,q,data.dataInScanPoint[iscan],info=info_delay,**kw)
+    utils.saveTxt(fname,q,data.diffsInScanPoint[iscan],info=info_delay,**kw)
 
 
 def read_diff_av(folder,plot2D=False,save=None):

xray/example_main_tiox.py

@@ -8,28 +8,53 @@ id9 = xray.id9
 
 # use npz files (they can handle more stuff (list of arrays,unicode) than h5py)
 id9.default_extension = '.npz'
-#id9.default_extension_extension = '.h5'
+#id9.default_extension = '.h5'
 
-def azav(folder,nQ=1500,force=False,saveChi=True,
+def readCalc():
-  poni='auto',storageFile='auto',mask=470):
+  fold = "../tiox/calculated_patterns/"
+  names = "alpha beta lam".split()
+  fnames = "alpha500K beta290K lambda".split()
+  calc = dict()
+  for name,fname in zip(names,fnames):
+    q,i=np.loadtxt(fold + "%s.xye.q"%fname,unpack=True)
+    calc[name] = xray.storage.DataStorage( dict( q=q, i=i ) )
+  return xray.storage.DataStorage(calc)
+
+calc = readCalc()
+
+def azav(folder,nQ=1500,force=False,saveChi=True,mask=470):
   if isinstance(mask,int):
     files = xray.utils.getFiles(folder,"*.edf*")
-    img   = xray.azav.pyFAIread(files[0])
+    img   = xray.azav.read(files[0])
     temp = np.ones_like(img,dtype=bool)
     temp[:mask] = False
     mask = temp
-  return id9.doFolder_azav(folder,nQ=nQ,force=force,mask=mask,saveChi=saveChi,
+  return id9.doFolder_azav(folder,nQ=nQ,force=force,mask=mask,saveChi=saveChi)
-            poni=poni,storageFile=storageFile)
 
 def datared(folder,monitor=(1,5),showPlot=True,**kw):
   data,diffs = id9.doFolder_dataRed(folder,monitor=monitor,**kw)
-  if showPlot: xray.utils.plotdiffs(diffs.q,diffs.data,t=diffs.scan)
+  if showPlot:
+    xray.utils.plotdiffs(diffs.q,diffs.data,t=diffs.scan,
+         absSignal=diffs.dataAbsAvAll,absSignalScale=30)
+    plt.plot(calc.lam.q,calc.lam.i/1000+0.1,label='calc')
+    plt.title(folder + " norm %s" % str(monitor))
   return data,diffs
 
 def doall(folder,force=False):
   azav(folder,force=force)
   return datared(folder)
 
+def anaAmplitue(run=6):
+  fname = "../tiox/tiox1/run%d/diffs.npz" % run
+  data  = xray.storage.DataStorage(fname)
+  ranges = ( (1.75,1.85), (2.2,2.4), (3.25,3.4) )
+  nPlot = len(ranges)
+  fig,ax = plt.subplots(nPlot,1,sharex=True)
+  for r,a in zip(ranges,ax):
+    idx = (data.q>r[0]) & (data.q<r[1])
+    amplitude = np.abs(data.data[:,idx]).mean(axis=1)
+    a.plot(data.scan,amplitude,'-o')
+    a.set_title("Range %s"%(str(r)))
 
 def plotCalc(scale=1):
   fold = "../tiox/calculated_patterns/"

xray/id9.py

@@ -36,7 +36,7 @@ def readDelayFromDiagnostic(fname):
 
 
 def doFolder_azav(folder,nQ=1500,force=False,mask=None,saveChi=True,
-  poni='auto',storageFile='auto'):
+  poni='pyfai.poni',storageFile='auto'):
   """ very small wrapper around azav.doFolder, essentially just reading
       the diagnostics.log """
 
@@ -46,21 +46,28 @@ def doFolder_azav(folder,nQ=1500,force=False,mask=None,saveChi=True,
     saveChi=saveChi,poni=poni,storageFile=storageFile,diagnostic=diag)
 
 def doFolder_dataRed(folder,storageFile='auto',monitor=None,
-                     funcForEveraging=np.nanmean,errFilter=True):
+                     funcForAveraging=np.nanmean,errMask=5,chi2Mask=2,qlims=None):
 
   if storageFile == 'auto' : storageFile = folder +  "/" + "pyfai_1d" + default_extension
 
   # read azimuthal averaged curves
   data = storage.DataStorage(storageFile)
 
+  if qlims is not None:
+    idx = (data.q>qlims[0]) & (data.q<qlims[1])
+    data.data = data.data[:,idx]
+    data.q    = data.q[idx]
   
   # calculate differences
   diffs = dataReduction.calcTimeResolvedSignal(data.delays,data.data,q=data.q,\
-          reference="min",monitor=monitor,funcForEveraging=funcForEveraging)
+          reference="min",monitor=monitor,funcForAveraging=funcForAveraging)
 
-  # filter if asked so
+  # mask if asked so
-  if funcForEveraging == np.nanmean and errFilter: 
+  if errMask>0:
-    diffs = dataReduction.errorFilter(diffs)
+    diffs = dataReduction.errorMask(diffs,threshold=errMask)
+  if chi2Mask>0:
+    diffs = dataReduction.chi2Mask(diffs,threshold=chi2Mask)
+  diffs = dataReduction.applyMasks(diffs)
   
   # save txt and npz file
   dataReduction.saveTxt(folder,diffs,info=data.pyfai_info)

xray/mask.py

@@ -0,0 +1,138 @@
+from __future__ import print_function
+import sys
+if sys.version_info.major == 2: input=raw_input
+import logging as log
+log.basicConfig(level=log.INFO)
+import os
+import numpy as np
+import matplotlib.pyplot as plt
+
+class MyMask(object):
+  def __init__(self,img=None):
+    self.comp = []
+    self.img  = img
+    self.mask = None
+  
+  def addCircle(self,xcen,ycen,radius):
+    self.comp.append( ["add","circle", [xcen,ycen,radius] ] )
+  def subtractCircle(self,xcen,ycen,radius):
+    self.comp.append( ["subtract","circle", [xcen,ycen,radius] ] )
+  def addRectangle(self,x1,y1,x2,y2):
+    if x1>x2: x1,x2=x2,x1
+    if y1>y2: y1,y2=y2,y1
+    self.comp.append( ["add","rectangle", [x1,y1,x2,y2] ])
+  def subtractRectangle(self,x1,y1,x2,y2):
+    if x1>x2: x1,x2=x2,x1
+    if y1>y2: y1,y2=y2,y1
+    self.comp.append( ["subtract","rectangle", [x1,y1,x2,y2] ])
+
+  def getMask(self,shape=None):
+    if shape is None: shape = self.img.shape
+    m = []
+    X,Y = np.meshgrid ( range(shape[0]),range(shape[1]) )
+    for o in self.comp:
+      whattodo = o[0]
+      kind=o[1]
+      pars=o[2]
+      if kind == "circle":
+        (xc,yc,r) = pars
+        d = np.sqrt((X-xc)**2+(Y-yc)**2)
+        #plt.imshow(d<r)
+        #raw_input()
+        m.append( d<r )
+      if kind == "rectangle":
+        (x1,y1,x2,y2) = pars
+        temp = (X>x1) & (X<x2) & ( Y>y1) & (Y<y2)
+        m.append( temp )
+    mask = np.zeros(shape,dtype=np.bool)
+    for i in range(len(m)):
+      whattodo = self.comp[i][0]
+      if (whattodo == "add"):
+        mask[m[i]] = True
+      else:
+        mask[m[i]] = False
+    self.mask = mask
+    return mask
+
+  def getMatplotlibMask(self,shape=None):
+    mask = self.getMask(shape=shape)
+    # convert
+    mpl_mask = np.zeros( (mask.shape[0],mask.shape[1],4) )
+    mpl_mask[:,:,:3] = 0.5; # gray color
+    mpl_mask[:,:,3] = mask/2; # give some transparency
+    return mpl_mask
+
+  def save(self,fname,inverted=False):
+    import fabio
+    mask = self.mask
+    if (inverted): mask = ~mask
+    i=fabio.edfimage.edfimage(mask.astype(np.uint8)); # edf does not support bool
+    i.save(fname)
+
+def test():
+  mask = MyMask()
+  mask.addCircle(400,300,250)
+  mask.subtractCircle(400,300,150)
+  mask.addRectangle(350,250,1500,700)
+  mask.show()
+  return mask
+
+def snap(point,shape,snapRange=20):
+  snapped = list(point)
+  if snapped[0] < snapRange: snapped[0] = 0
+  if snapped[0] > shape[0]-snapRange: snapped[0] = shape[0]
+  if snapped[1] < snapRange: snapped[1] = 0
+  if snapped[1] > shape[1]-snapRange: snapped[1] = shape[1]
+  return snapped
+
+def getPoint(shape,snapRange):
+  c = plt.ginput()[0]
+  c = snap(c,shape,snapRange=snapRange)
+  return c
+
+def makeMaskGui(img,snapRange=60):
+  """ snapRange controls border snapping (in pixels, use <= 0 to disable """
+  mask = MyMask(img)
+  ans='ok'
+  while (ans != 'done'):
+    plt.imshow(img)
+    plt.clim(np.percentile(img,(2,98)))
+    plt.imshow(mask.getMatplotlibMask())
+    plt.pause(0.01)
+    ans = input("What's next c/r/done? ")
+    if ans == "c":
+      print("Adding circle, click on center")
+      c = getPoint(img.shape,snapRange)
+      print("Adding circle, click on another point to define radius")
+      p = getPoint(img.shape,snapRange)
+      r = np.sqrt( (p[0]-c[0])**2 + (p[1]-c[1])**2 )
+      mask.addCircle(c[0],c[1],r)
+    if ans == "r":
+      print("Adding rectangle, click on one corner")
+      c1 = getPoint(img.shape,snapRange)
+      print("Adding rectangle, click on opposite corner")
+      c2 = getPoint(img.shape,snapRange)
+      mask.addRectangle(c1[0],c1[1],c2[0],c2[1])
+    plt.imshow(mask.getMatplotlibMask())
+    plt.pause(0.01)
+  fname = input("Enter a valid filename (ext .edf or .npy) to save the mask")
+  try:
+    if fname != '':
+      ext = os.path.splitext(fname)[1]
+      if ext == '.edf':
+        mask.save(fname)
+      elif ext == '.npy':
+        np.save(fname,mask.getMask())
+  except Exception as e:
+    log.error("Error in saving mask")
+    log.error(e)
+  finally:
+    return mask
+    
+    
+
+
+if __name__ == "__main__":
+  test()
+  plt.show()
+  ans=input("Enter to finish") 

xray/utils.py

@@ -19,13 +19,14 @@ except ImportError:
 _time_regex =      re.compile( "(-?\d+\.?\d*(?:ps|ns|us|ms)?)")
 _timeInStr_regex = re.compile("_(-?\d+\.?\d*(?:ps|ns|us|ms)?)")
 
-def getFiles(folder,basename="*.edf*"):
+def getFiles(folder,basename="*.edf*",nFiles=None):
   files = glob.glob(folder + "/" + basename)
   files.sort()
+  if nFiles is not None: files = files[:nFiles]
   return files
 
-def getEdfFiles(folder):
+def getEdfFiles(folder,**kw):
-  return getFiles(folder,basename="*.edf*")
+  return getFiles(folder,basename="*.edf*",**kw)
 
 def getDelayFromString(string) :
   match = _timeInStr_regex_regex.search(string)
@@ -71,9 +72,9 @@ def removeExt(fname):
 def getBasename(fname):
   return removeExt(os.path.basename(fname));
 
-def plotdata(q,data,t=None,plot=True,showTrend=True,title=None,clim='auto'):
+def plotdata(q,data,x=None,plot=True,showTrend=True,title=None,clim='auto'):
   if not (plot or showTrend): return
-  if t is None: t = np.arange(data.shape[0])
+  if x is None: x = np.arange(data.shape[0])
   if clim == 'auto': clim = np.nanpercentile(data,(1.5,98.5))
   one_plot = showTrend or plot
   two_plot = showTrend and plot
@@ -84,7 +85,7 @@ def plotdata(q,data,t=None,plot=True,showTrend=True,title=None,clim='auto'):
   ax = np.atleast_1d(ax)
   if showTrend:
     plt.sca(ax[0])
-    plt.pcolormesh(t,q,data.T)
+    plt.pcolormesh(x,q,data.T)
     plt.xlabel("image number, 0 being older")
     plt.ylabel(r"q ($\AA^{-1}$)")
     plt.clim( *clim )
@@ -150,12 +151,11 @@ def plotdiffs(q,diffs,t,select=None,err=None,absSignal=None,absSignalScale=10,
   fig.canvas.mpl_connect('pick_event', onpick)
 
 
-def saveTxt(fname,q,i,e=None,headerv=None,info=None,overwrite=True):
+def saveTxt(fname,q,data,headerv=None,info=None,overwrite=True,columns=''):
   """ Write data to file 'fname' in text format.
       Inputs:
         q = x vector
-        i = 1D or 2D
+        data = one or 2D array (first axis is q)
-        e = 1D (discarded when i is 2D)
         info = dictionary (saved as '# key : value') or string
         headerv = vector to be used as header or string
   """
@@ -170,13 +170,16 @@ def saveTxt(fname,q,i,e=None,headerv=None,info=None,overwrite=True):
   else:
     header = ""
   if isinstance(headerv,str): header += "\n%s" % headerv
-  if i.ndim == 1:
+  if data.ndim == 1:
-    x = np.vstack( (q,i,e) ) if e is not None else np.vstack( (q,i) )
+    x = np.vstack( (q,data) )
-  if i.ndim == 2:
+  elif data.ndim == 2:
-    x = np.vstack( (q,i,) )
+    x = np.vstack( (q,data) )
     if headerv is not None:
-      headerv = np.concatenate(( (i.shape[1],),headerv))
+      headerv = np.concatenate(( (data.shape[1],),headerv))
       x = np.hstack( (headerv[:,np.newaxis],x) )
+  if columns != '':
+    s = "#" + " ".join( [str(c).center(12) for c in columns] )
+    header = header + "\n" + s if header != '' else s
   np.savetxt(fname,x.T,fmt="%+10.5e",header=header,comments='')
 
 def reshapeToBroadcast(what,ref):