/usr/share/pyshared/PyMca/HDF5Stack1D.py is in pymca 4.5.0-4.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 | #/*##########################################################################
# Copyright (C) 2004-2012 European Synchrotron Radiation Facility
#
# This file is part of the PyMCA X-ray Fluorescence Toolkit developed at
# the ESRF by the Beamline Instrumentation Software Support (BLISS) group.
#
# This toolkit 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 2 of the License, or (at your option)
# any later version.
#
# PyMCA 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
# PyMCA; if not, write to the Free Software Foundation, Inc., 59 Temple Place,
# Suite 330, Boston, MA 02111-1307, USA.
#
# PyMCA follows the dual licensing model of Trolltech's Qt and Riverbank's PyQt
# and cannot be used as a free plugin for a non-free program.
#
# Please contact the ESRF industrial unit (industry@esrf.fr) if this license
# is a problem for you.
#############################################################################*/
import posixpath
import h5py
try:
from PyMca import DataObject
except:
import DataObject
try:
from PyMca import NexusDataSource
except:
import NexusDataSource
import posixpath
import numpy
DEBUG = 0
SOURCE_TYPE = "HDF5Stack1D"
class HDF5Stack1D(DataObject.DataObject):
def __init__(self, filelist, selection,
scanlist=None,
dtype=None):
DataObject.DataObject.__init__(self)
#the data type of the generated stack
self.__dtype0 = dtype
self.__dtype = dtype
if filelist is not None:
if selection is not None:
self.loadFileList(filelist, selection, scanlist)
def loadFileList(self, filelist, selection, scanlist=None):
"""
loadFileList(self, filelist, y, scanlist=None, monitor=None, x=None)
filelist is the list of file names belonging to the stack
selection is a dictionary with the keys x, y, m.
x is the path to the x data (the channels) in the spectrum,
without the first level "directory". It is unused (for now).
y is the path to the 1D data (the counts) in the spectrum,
without the first level "directory"
m is the path to the normalizing data (I0 or whatever)
without the first level "directory".
scanlist is the list of first level "directories" containing the 1D data
Example: The actual path has the form:
/whatever1/whatever2/counts
That means scanlist = ["/whatever1"]
and selection['y'] = "/whatever2/counts"
"""
# all the files in the same source
hdfStack = NexusDataSource.NexusDataSource(filelist)
#if there is more than one file, it is assumed all the files have
#the same structure.
tmpHdf = hdfStack._sourceObjectList[0]
entryNames = []
for key in tmpHdf["/"].keys():
if isinstance(tmpHdf["/"+key], h5py.Group):
entryNames.append(key)
#built the selection in terms of HDF terms
#for the time being, the x selection will be ignored but not the
#monitor and only one y is taken
ySelection = selection['y']
if type(ySelection) == type([]):
ySelection = ySelection[0]
mSelection = selection['m']
if type(mSelection) == type([]):
if len(mSelection):
mSelection = mSelection[0]
else:
mSelection = None
else:
mSelection = None
#deal with the pathological case where the scanlist corresponds to a selected top level dataset
if len(entryNames) == 0:
if scanlist is not None:
if len(scanlist) == 1:
if scanlist[0] == ySelection:
scanlist = None
if scanlist in [None, []]:
#if the scanlist is None, it is assumed we are interested on all
#the scans containing the selection, not that all the scans
#contain the selection.
scanlist = []
if 0:
JUST_KEYS = False
#expect same entry names in the files
for entry in entryNames:
path = "/"+entry + ySelection
dirname = posixpath.dirname(path)
base = posixpath.basename(path)
try:
if base in tmpHdf[dirname].keys():
scanlist.append(entry)
except:
pass
else:
JUST_KEYS = True
#expect same structure in the files
if len(entryNames):
i = 0
for entry in entryNames:
i += 1
path = "/"+entry + ySelection
dirname = posixpath.dirname(path)
base = posixpath.basename(path)
try:
if base in tmpHdf[dirname].keys():
scanlist.append("1.%d" % i)
except:
pass
if not len(scanlist):
path = "/" + ySelection
dirname = posixpath.dirname(path)
base = posixpath.basename(path)
try:
if base in tmpHdf[dirname].keys():
JUST_KEYS = False
scanlist.append("")
except:
#it will crash later on
pass
else:
JUST_KEYS = False
scanlist.append("")
else:
try:
number, order = [int(x) for x in scanlist[0].split(".")]
JUST_KEYS = True
except:
JUST_KEYS = False
if not JUST_KEYS:
for scan in scanlist:
if scan.startswith("/"):
t = scan[1:]
else:
t = scan
if t not in entryNames:
raise ValueError("Entry %s not in file" % scan)
nFiles = len(filelist)
nScans = len(scanlist)
if JUST_KEYS:
if not nScans:
raise IOError("No entry contains the required data")
#Now is to decide the number of mca ...
#I assume all the scans contain the same number of mca
if JUST_KEYS:
path = "/" + entryNames[int(scanlist[0].split(".")[-1])-1] + ySelection
if mSelection is not None:
mpath = "/" + entryNames[int(scanlist[0].split(".")[-1])-1] + mSelection
else:
path = scanlist[0] + ySelection
if mSelection is not None:
mpath = scanlist[0] + mSelection
yDataset = tmpHdf[path]
if self.__dtype is None:
self.__dtype = yDataset.dtype
#figure out the shape of the stack
shape = yDataset.shape
mcaIndex = selection.get('index', len(shape)-1)
if mcaIndex == -1:
mcaIndex = len(shape) - 1
dim0, dim1, mcaDim = self.getDimensions(nFiles, nScans, shape,
index=mcaIndex)
try:
if self.__dtype in [numpy.float32, numpy.int32]:
bytefactor = 4
elif self.__dtype in [numpy.int16, numpy.uint16]:
bytefactor = 2
elif self.__dtype in [numpy.int8, numpy.uint8]:
bytefactor = 1
else:
bytefactor = 8
neededMegaBytes = nFiles * dim0 * dim1 * mcaDim * bytefactor/(1024*1024.)
if (neededMegaBytes > 2000) and (nFiles == 1) and (len(shape) == 3):
if self.__dtype0 is None:
if (bytefactor == 8) and (neededMegaBytes <4000):
#try reading as float32
self.__dtype = numpy.float32
else:
raise MemoryError("Force dynamic loading")
else:
raise MemoryError("Force dynamic loading")
self.data = numpy.zeros((dim0, dim1, mcaDim), self.__dtype)
DONE = False
except (MemoryError, ValueError):
#some versions report ValueError instead of MemoryError
if (nFiles == 1) and (len(shape) == 3):
print("Attempting dynamic loading")
self.data = yDataset
if mSelection is not None:
mDataset = tmpHdf[mpath]
self.monitor = mDataset
if h5py.version.version < '2.0':
#prevent automatic closing keeping a reference
#to the open file
self._fileReference = hdfStack
DONE = True
else:
#what to do if the number of dimensions is only 2?
raise
if not DONE:
self.info["McaIndex"] = 2
n = 0
i_idx = dim0 * dim1
if dim0 == 1:
self.onBegin(dim1)
else:
self.onBegin(dim0)
self.incrProgressBar=0
for hdf in hdfStack._sourceObjectList:
entryNames = list(hdf["/"].keys())
for scan in scanlist:
if JUST_KEYS:
entryName = entryNames[int(scan.split(".")[-1])-1]
path = entryName + ySelection
if mSelection is not None:
mpath = entryName + mSelection
mDataset = hdf[mpath].value
else:
path = scan + ySelection
if mSelection is not None:
mpath = scan + mSelection
mDataset = hdf[mpath].value
try:
yDataset = hdf[path]
tmpShape = yDataset.shape
totalBytes = numpy.ones((1,), yDataset.dtype).itemsize
for nItems in tmpShape:
totalBytes *= nItems
if (totalBytes/(1024.*1024.)) > 500:
#read from disk
IN_MEMORY = False
else:
#read the data into memory
yDataset = hdf[path].value
IN_MEMORY = True
except (MemoryError, ValueError):
yDataset = hdf[path]
IN_MEMORY = False
nMcaInYDataset = 1
for dim in yDataset.shape:
nMcaInYDataset *= dim
nMcaInYDataset = int(nMcaInYDataset/mcaDim)
if mcaIndex != 0:
if IN_MEMORY:
yDataset.shape = -1, mcaDim
if mSelection is not None:
case = -1
nMonitorData = 1
for v in mDataset.shape:
nMonitorData *= v
if nMonitorData == nMcaInYDataset:
mDataset.shape = nMcaInYDataset
case = 0
elif nMonitorData == (nMcaInYDataset * mcaDim):
case = 1
mDataset.shape = nMcaInYDataset, mcaDim
if case == -1:
raise ValueError(\
"I do not know how to handle this monitor data")
if (len(yDataset.shape) == 3) and\
(dim1 == yDataset.shape[1]):
mca = 0
deltaI = int(yDataset.shape[1]/dim1)
for ii in range(yDataset.shape[0]):
i = int(n/dim1)
yData = yDataset[ii:(ii+1)]
yData.shape = -1, mcaDim
if mSelection is not None:
if case == 0:
mData = numpy.outer(mDataset[mca:(mca+dim1)],
numpy.ones((mcaDim)))
self.data[i, :, :] = yData/mData
elif case == 1:
mData = mDataset[mca:(mca+dim1), :]
mData.shape = -1, mcaDim
self.data[i, :, :] = yData/mData
else:
self.data[i:(i+deltaI), :] = yData
n += yDataset.shape[1]
mca += dim1
else:
for mca in range(nMcaInYDataset):
i = int(n/dim1)
j = n % dim1
if len(yDataset.shape) == 3:
ii = int(mca/yDataset.shape[1])
jj = mca % yDataset.shape[1]
yData = yDataset[ii, jj]
elif len(yDataset.shape) == 2:
yData = yDataset[mca,:]
elif len(yDataset.shape) == 1:
yData = yDataset
if mSelection is not None:
if case == 0:
self.data[i, j, :] = yData/mDataset[mca]
elif case == 1:
self.data[i, j, :] = yData/mDataset[mca, :]
else:
self.data[i, j, :] = yData
n += 1
else:
if mSelection is not None:
case = -1
nMonitorData = 1
for v in mDataset.shape:
nMonitorData *= v
if nMonitorData == yDataset.shape[0]:
case = 3
mDataset.shape = yDataset.shape[0]
elif nMonitorData == nMcaInYDataset:
mDataset.shape = nMcaInYDataset
case = 0
#elif nMonitorData == (yDataset.shape[1] * yDataset.shape[2]):
# case = 1
# mDataset.shape = yDataset.shape[1], yDataset.shape[2]
if case == -1:
raise ValueError(\
"I do not know how to handle this monitor data")
if IN_MEMORY:
yDataset.shape = mcaDim, -1
if len(yDataset.shape) != 3:
for mca in range(nMcaInYDataset):
i = int(n/dim1)
j = n % dim1
if len(yDataset.shape) == 3:
ii = int(mca/yDataset.shape[2])
jj = mca % yDataset.shape[2]
yData = yDataset[:, ii, jj]
elif len(yDataset.shape) == 2:
yData = yDataset[:, mca]
elif len(yDataset.shape) == 1:
yData = yDataset[:]
if mSelection is not None:
if case == 0:
self.data[i, j, :] = yData/mDataset[mca]
elif case == 1:
self.data[i, j, :] = yData/mDataset[:, mca]
elif case == 3:
self.data[i, j, :] = yData/mDataset
else:
self.data[i, j, :] = yData
n += 1
else:
#stack of images to be read as MCA
for nImage in range(yDataset.shape[0]):
tmp = yDataset[nImage:(nImage+1)]
if len(tmp.shape) == 3:
i = int(n/dim1)
j = n % dim1
if 0:
#this loop is extremely SLOW!!!(and useless)
for ii in range(tmp.shape[1]):
for jj in range(tmp.shape[2]):
self.data[i+ii, j+jj, nImage] = tmp[0, ii, jj]
else:
self.data[i:i+tmp.shape[1],
j:j+tmp.shape[2], nImage] = tmp[0]
if mSelection is not None:
for mca in range(yDataset.shape[0]):
i = int(n/dim1)
j = n % dim1
yData = self.data[i, j, :]
if case == 0:
self.data[i, j, :] = yData/mDataset[mca]
elif case == 1:
self.data[i, j, :] = yData/mDataset[:, mca]
n += 1
else:
n += tmp.shape[1] * tmp.shape[2]
if dim0 == 1:
self.onProgress(j)
if dim0 != 1:
self.onProgress(i)
self.onEnd()
else:
self.info["McaIndex"] = mcaIndex
self.info["SourceType"] = SOURCE_TYPE
self.info["SourceName"] = filelist
self.info["Size"] = 1
self.info["NumberOfFiles"] = 1
if mcaIndex == 0:
self.info["FileIndex"] = 1
else:
self.info["FileIndex"] = 0
self.info['McaCalib'] = [ 0.0, 1.0, 0.0]
self.info['Channel0'] = 0
shape = self.data.shape
for i in range(len(shape)):
key = 'Dim_%d' % (i+1,)
self.info[key] = shape[i]
def getDimensions(self, nFiles, nScans, shape, index=None):
#some body may want to overwrite this
"""
Returns the shape of the final stack as (Dim0, Dim1, Nchannels)
"""
if index is None:
index = -1
if index == -1:
index = len(shape) - 1
if DEBUG:
print("INDEX = %d" % index)
#figure out the shape of the stack
if len(shape) == 0:
#a scalar?
raise ValueError("Selection corresponds to a scalar")
elif len(shape) == 1:
#nchannels
nMca = 1
elif len(shape) == 2:
if index == 0:
#npoints x nchannels
nMca = shape[1]
else:
#npoints x nchannels
nMca = shape[0]
elif len(shape) == 3:
if index in [2, -1]:
#dim1 x dim2 x nchannels
nMca = shape[0] * shape[1]
elif index == 0:
nMca = shape[1] * shape[2]
else:
raise IndexError("Only first and last dimensions handled")
else:
nMca = 1
for i in range(len(shape)):
if i == index:
continue
nMca *= shape[i]
mcaDim = shape[index]
if DEBUG:
print("nMca = %d" % nMca)
print("mcaDim = ", mcaDim)
# HDF allows to work directly from the files without loading
# them into memory.
if (nScans == 1) and (nFiles > 1):
if nMca == 1:
#specfile like case
dim0 = nFiles
dim1 = nMca * nScans # nScans is 1
else:
#ESRF EDF like case
dim0 = nFiles
dim1 = nMca * nScans # nScans is 1
elif (nScans == 1) and (nFiles == 1):
if nMca == 1:
#specfile like single mca
dim0 = nFiles # it is 1
dim1 = nMca * nScans # nScans is 1
elif len(shape) == 2:
dim0 = nFiles # it is 1
dim1 = nMca * nScans # nScans is 1
elif len(shape) == 3:
if index == 0:
dim0 = shape[1]
dim1 = shape[2]
else:
dim0 = shape[0]
dim1 = shape[1]
else:
#specfile like multiple mca
dim0 = nFiles # it is 1
dim1 = nMca * nScans # nScans is 1
elif (nScans > 1) and (nFiles == 1):
if nMca == 1:
#specfile like case
dim0 = nFiles
dim1 = nMca * nScans
elif nMca > 1:
if len(shape) == 1:
#specfile like case
dim0 = nFiles
dim1 = nMca * nScans
elif len(shape) == 2:
dim0 = nScans
dim1 = nMca #shape[0]
elif len(shape) == 3:
if (shape[0] == 1) or (shape[1] == 1):
dim0 = nScans
dim1 = nMca
else:
#The user will have to decide the shape
dim0 = 1
dim1 = nScans * nMca
else:
#The user will have to decide the shape
dim0 = 1
dim1 = nScans * nMca
elif (nScans > 1) and (nFiles > 1):
dim0 = nFiles
dim1 = nMca * nScans
else:
#I should not reach this point
raise ValueError("Unhandled case")
return dim0, dim1, shape[index]
def onBegin(self, n):
pass
def onProgress(self, n):
pass
def onEnd(self):
pass
if __name__ == "__main__":
import sys
|