# -*- coding: utf-8 -*-
########### SVN repository information ###################
# $Date: 2023-05-11 18:08:12 -0500 (Thu, 11 May 2023) $
# $Author: toby $
# $Revision: 5577 $
# $URL: https://subversion.xray.aps.anl.gov/pyGSAS/trunk/imports/G2img_HDF5.py $
# $Id: G2img_HDF5.py 5577 2023-05-11 23:08:12Z toby $
########### SVN repository information ###################
'''
'''
from __future__ import division, print_function
try:
import h5py
except ImportError:
h5py = None
import GSASIIobj as G2obj
import GSASIIpath
GSASIIpath.SetVersionNumber("$Revision: 5577 $")
[docs]
class HDF5_Reader(G2obj.ImportImage):
'''Routine to read a HD5 image, typically from APS Sector 6.
B. Frosik/SDM.
'''
dsetlist = []
buffer = {}
init = False
def __init__(self):
if h5py is None:
self.UseReader = False
print('HDF5 Reader skipped because h5py library is not installed')
import os,sys
os.path.split(sys.executable)[0]
conda = os.path.join(os.path.split(sys.executable)[0],'conda')
if os.path.exists(conda):
print('To fix this use command:\n\t'+conda+' install h5py hdf5')
super(self.__class__,self).__init__( # fancy way to self-reference
extensionlist=('.hdf5','.hd5','.h5','.hdf'),strictExtension=True,
formatName = 'HDF5 image',longFormatName = 'HDF5 image file')
[docs]
def ContentsValidator(self, filename):
'''Test if valid by seeing if the HDF5 library recognizes the file.
'''
try:
fp = h5py.File(filename, 'r')
fp.close()
return True
except IOError:
return False
[docs]
def Reader(self, filename, ParentFrame=None, **kwarg):
'''Scan file structure using :meth:`visit` and map out locations of image(s)
then read one image using :meth:`readDataset`. Save map of file structure in
buffer arg, if used.
'''
imagenum = kwarg.get('blocknum')
if imagenum is None: imagenum = 1
self.buffer = kwarg.get('buffer',{})
try:
fp = h5py.File(filename, 'r')
if not self.buffer.get('init'):
self.buffer['init'] = True
self.Comments = self.visit(fp)
if imagenum > len(self.buffer['imagemap']):
self.errors = 'No valid images found in file'
return False
self.Data,self.Npix,self.Image = self.readDataset(fp,imagenum)
if self.Npix == 0:
self.errors = 'No valid images found in file'
return False
self.LoadImage(ParentFrame,filename,imagenum)
self.repeatcount = imagenum
self.repeat = imagenum < len(self.buffer['imagemap'])
if GSASIIpath.GetConfigValue('debug'): print('Read image #'+str(imagenum)+' from file '+filename)
return True
except IOError:
print ('cannot open file '+ filename)
return False
finally:
fp.close()
[docs]
def visit(self, fp):
'''Recursively visit each node in an HDF5 file. For nodes
ending in 'data' look at dimensions of contents. If the shape is
length 2 or 4 assume an image and index in self.buffer['imagemap']
'''
datakeywords = ['data','images']
head = []
def func(name, dset):
if not hasattr(dset,'shape'): return # not array, can't be image
if isinstance(dset, h5py.Dataset):
if len(dset.shape) < 2:
head.append('%s: %s'%(dset.name,str(dset[()][0])))
for datakeyword in datakeywords:
if dset.name.endswith(datakeyword):
dims = dset.shape
if len(dims) == 4:
self.buffer['imagemap'] += [(dset.name,i) for i in range(dims[1])]
elif len(dims) == 3:
self.buffer['imagemap'] += [(dset.name,i) for i in range(dims[0])]
elif len(dims) == 2:
self.buffer['imagemap'] += [(dset.name,None)]
else:
print('Skipping entry '+str(dset.name)+'. Shape is '+str(dims))
break
self.buffer['imagemap'] = []
fp.visititems(func)
return head
[docs]
def readDataset(self,fp,imagenum=1):
'''Read a specified image number from a file
'''
name,num = self.buffer['imagemap'][imagenum-1] # look up in map
dset = fp[name]
if num is None:
image = dset[()]
elif len(dset.shape) == 4:
image = dset[0,num,...]
elif len(dset.shape) == 3:
image = dset[num,...]
else:
msg = 'Unexpected image dimensions '+name
print(msg)
raise Exception(msg)
sizexy = list(image.shape)
Npix = sizexy[0]*sizexy[1]
data = {'pixelSize':[74.8,74.8],'wavelength':0.15,'distance':1000.,
'center':[sizexy[0]*0.1,sizexy[1]*0.1],'size':sizexy,'det2theta':0.0}
for item in self.Comments:
name,val = item.split(':',1)
if 'wavelength' in name and 'spread' not in name:
try:
data['wavelength'] = float(val)
except ValueError:
pass
elif 'distance' in name:
data['distance'] = float(val)
elif 'x_pixel_size' in name:
data['pixelSize'][0] = float(val)*1000.
elif 'y_pixel_size' in name:
data['pixelSize'][1] = float(val)*1000.
elif 'beam_center_x' in name:
data['center'][0] = float(val)
elif 'beam_center_y' in name:
data['center'][1] = float(val)
return data,Npix,image.T