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Commit c117f065 authored by Chris Smith's avatar Chris Smith
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Removed duplicate ptychography

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pycroscopy/io/translators/__init__.py
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import beutils
import utils
import translator
import ptychographytranslator
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pycroscopy/io/translators/ptychographytranslator.py deleted 100644 → 0
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− 229
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"""
Created on Feb 9, 2016
@author: Chris Smith
"""
import numpy as np
import os
from scipy.misc import imread
from ..iohdf5 import ioHDF5
from ..microdata import MicroDataGroup, MicroDataset
from ..hdfutils import getH5DsetRefs
from .translator import Translator
from .utils import generateDummyMainParms, makePositionMat, getPositionSlicing
class PtychographyTranslator(Translator):
"""
Translate Pytchography data from a set of images to an HDF5 file
"""
def translate(self, h5_path, image_path):
"""
Basic method that adds Ptychography data to existing hdf5 thisfile
You must have already done the basic translation with BEodfTranslator
Parameters
----------------
h5_path : Absolute thisfile path for the data thisfile.
Must be in hdf5 format
image_path : Absolute path to folder holding the image files
Returns
----------
None
"""
# Get the list of all files with the .tif extension and the number of files in the list
file_list, num_files = self.__getfilelist(image_path,'.tif')
# Open the hdf5 thisfile and delete any contents
try:
hdf = ioHDF5(h5_path)
hdf.clear()
except:
raise
# Set up the basic parameters associated with this set of images
(usize,vsize),data_type = self.__getimagesize(os.path.join(image_path,file_list[0]))
num_pixels = usize*vsize
scan_size = np.int(np.sqrt(num_files))
num_files = scan_size**2
mean_ronch = np.zeros(num_pixels,dtype=np.float32)
h5_main, h5_mean_spec, h5_ronch = self.__setupH5(num_files, hdf, usize,
vsize, np.float32,
num_pixels,
scan_size)
for ifile, thisfile in enumerate(file_list[:num_files]):
selected = (ifile+1) % round(num_files/16) == 0
if selected:
print('Processing file...{}% - reading: {}'.format(round(100*ifile/num_files),thisfile))
image = imread(os.path.join(image_path,thisfile))
image = image.reshape(num_pixels)
h5_main[ifile,:] = image
h5_mean_spec[ifile] = np.mean(image)
mean_ronch += image
hdf.flush()
print np.max(mean_ronch), np.min(mean_ronch)
h5_ronch[:] = mean_ronch/num_files
hdf.flush()
hdf.close()
def __getfilelist(self, path, ftype='all'):
"""
Returns a list of all files in the directory given by path
Parameters
---------------
path : string / unicode
absolute path to directory containing files
ftype : (optional) string / unicode
file type to return in file_list, Default is 'all'
Returns
----------
file_list : list of strings
names of all files in directory located at path
numfiles : unsigned integer
number of files in file_list
"""
# Get all files in directory
file_list = os.listdir(path)
# If no file type specified, return full list
if ftype=='all':
numfiles = len(file_list)
return (file_list, numfiles)
# Remove files of type other than the request ftype from the list
new_file_list = []
for this_file in file_list:
split = os.path.splitext(this_file)
if len(split) <2:
continue
ext = split[1]
if ext == ftype:
new_file_list.append(this_file)
numfiles = len(new_file_list)
return (new_file_list,numfiles)
def __getimagesize(self,image):
"""
Returns the x and y size of the image in pixels
Parameters
------------
image : string / unicode
absolute path to the image file
Returns
-----------
(size, tmp.dtype) : Tuple
size : unsigned integer
x and y dimenstions of image
dtype : data type
Datatype of the image
"""
tmp = imread(image)
size = tmp.shape
return (size, tmp.dtype)
def __buildSpectroscopic(self, usize, vsize, num_pixels):
spec_mat = makePositionMat([usize, vsize])
spec_slices = {'U': (slice(0,1,None),slice(0,num_pixels,None)),
'V': (slice(1,2,None),slice(0,num_pixels,None))}
ds_spec_ind = MicroDataset('Spectroscopic_Indices', np.transpose(spec_mat), dtype=np.uint32)
ds_spec_ind.attrs['labels'] = spec_slices
ds_spec_vals = MicroDataset('Spectroscopic_Values', np.transpose(spec_mat), dtype=np.float32)
ds_spec_vals.attrs['labels'] = spec_slices
ds_spec_vals.attrs['units'] = ['', '']
return ds_spec_ind, ds_spec_vals
def __buildPosition(self, num_files, scan_size):
pos_mat = makePositionMat([scan_size, scan_size])
pos_slices = getPositionSlicing(['X', 'Y'], num_files)
ds_pos_ind = MicroDataset('Position_Indices', pos_mat, dtype=np.uint32)
ds_pos_ind.attrs['labels'] = pos_slices
ds_pos_val = MicroDataset('Position_Values', pos_mat)
ds_pos_val.attrs['labels'] = pos_slices
ds_pos_val.attrs['units'] = ['pixel', 'pixel']
return ds_pos_ind, ds_pos_val
def __setupH5(self, num_files, hdf, usize, vsize, data_type, num_pixels, scan_size):
root_parms = generateDummyMainParms()
main_parms = {'num_images': num_files,
'image_size_u': usize,
'image_size_v': vsize,
'num_pixels': num_pixels,
'translator': 'Ptychography',
'scan_size_x': scan_size,
'scan_size_y': scan_size}
# Create the hdf5 data Group
root_grp = MicroDataGroup('/')
root_grp.attrs = root_parms
root_grp.attrs['data_type'] = 'PtychographyData'
meas_grp = MicroDataGroup('Measurement_000')
meas_grp.attrs = main_parms
chan_grp = MicroDataGroup('Channel_000')
# Get the Position and Spectroscopic Datasets
ds_spec_ind, ds_spec_vals = self.__buildSpectroscopic(usize, vsize, num_pixels)
ds_pos_ind, ds_pos_val = self.__buildPosition(num_files, scan_size)
# Allocate space for Main_Data and Pixel averaged Data
ds_main_data = MicroDataset('Raw_Data', data=[],
maxshape=(num_files, num_pixels),
chunking=(1, num_pixels),
dtype=data_type,
compression='gzip')
ds_mean_ronch_data = MicroDataset('Mean_Ronchigram',
data=np.zeros(num_pixels,
dtype=np.float32),
dtype=np.float32)
ds_mean_spec_data = MicroDataset('Spectroscopic_Mean',
data=np.zeros(num_files,
dtype=np.float32),
dtype=np.float32)
# Add datasets as children of Measurement_000 data group
chan_grp.addChildren([ds_main_data, ds_spec_ind, ds_spec_vals, ds_pos_ind, ds_pos_val, ds_mean_ronch_data, ds_mean_spec_data])
meas_grp.addChildren([chan_grp])
root_grp.addChildren([meas_grp])
print('Writing following tree to thisfile:')
root_grp.showTree()
h5_refs = hdf.writeData(root_grp)
h5_main = getH5DsetRefs(['Raw_Data'], h5_refs)[0]
h5_ronch = getH5DsetRefs(['Mean_Ronchigram'], h5_refs)[0]
h5_mean_spec = getH5DsetRefs(['Spectroscopic_Mean'], h5_refs)[0]
aux_ds_names = ['Spectroscopic_Indices',
'Spectroscopic_Values',
'Position_Indices',
'Position_Values']
hdf.linkRefs(h5_main, getH5DsetRefs(aux_ds_names, h5_refs))
hdf.flush()
return h5_main, h5_mean_spec, h5_ronch
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