/usr/lib/python2.7/dist-packages/pyfits/hdu/groups.py is in python-pyfits 1:3.2-1build2.
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 | import sys
import numpy as np
from pyfits.column import Column, ColDefs, FITS2NUMPY
from pyfits.fitsrec import FITS_rec, FITS_record
from pyfits.hdu.image import _ImageBaseHDU, PrimaryHDU
from pyfits.hdu.table import _TableLikeHDU
from pyfits.util import (lazyproperty, _is_int, _is_pseudo_unsigned,
_unsigned_zero)
class Group(FITS_record):
"""
One group of the random group data.
"""
def __init__(self, input, row=0, start=None, end=None, step=None,
base=None):
super(Group, self).__init__(input, row, start, end, step, base)
@property
def parnames(self):
return self.array.parnames
@property
def data(self):
# The last column in the coldefs is the data portion of the group
return self.field(self.array._coldefs.names[-1])
@lazyproperty
def _unique(self):
return _par_indices(self.parnames)
def par(self, parname):
"""
Get the group parameter value.
"""
if _is_int(parname):
result = self.array[self.row][parname]
else:
indx = self._unique[parname.upper()]
if len(indx) == 1:
result = self.array[self.row][indx[0]]
# if more than one group parameter have the same name
else:
result = self.array[self.row][indx[0]].astype('f8')
for i in indx[1:]:
result += self.array[self.row][i]
return result
def setpar(self, parname, value):
"""
Set the group parameter value.
"""
# TODO: It would be nice if, instead of requiring a multi-part value to
# be an array, there were an *option* to automatically split the value
# into multiple columns if it doesn't already fit in the array data
# type.
if _is_int(parname):
self.array[self.row][parname] = value
else:
indx = self._unique[parname.upper()]
if len(indx) == 1:
self.array[self.row][indx[0]] = value
# if more than one group parameter have the same name, the
# value must be a list (or tuple) containing arrays
else:
if isinstance(value, (list, tuple)) and \
len(indx) == len(value):
for i in range(len(indx)):
self.array[self.row][indx[i]] = value[i]
else:
raise ValueError('Parameter value must be a sequence '
'with %d arrays/numbers.' % len(indx))
class GroupData(FITS_rec):
"""
Random groups data object.
Allows structured access to FITS Group data in a manner analogous
to tables.
"""
_record_type = Group
def __new__(cls, input=None, bitpix=None, pardata=None, parnames=[],
bscale=None, bzero=None, parbscales=None, parbzeros=None):
"""
Parameters
----------
input : array or FITS_rec instance
input data, either the group data itself (a
`numpy.ndarray`) or a record array (`FITS_rec`) which will
contain both group parameter info and the data. The rest
of the arguments are used only for the first case.
bitpix : int
data type as expressed in FITS ``BITPIX`` value (8, 16, 32,
64, -32, or -64)
pardata : sequence of arrays
parameter data, as a list of (numeric) arrays.
parnames : sequence of str
list of parameter names.
bscale : int
``BSCALE`` of the data
bzero : int
``BZERO`` of the data
parbscales : sequence of int
list of bscales for the parameters
parbzeros : sequence of int
list of bzeros for the parameters
"""
if not isinstance(input, FITS_rec):
if pardata is None:
npars = 0
else:
npars = len(pardata)
if parbscales is None:
parbscales = [None] * npars
if parbzeros is None:
parbzeros = [None] * npars
if parnames is None:
parnames = ['PAR%d' % (idx + 1) for idx in range(npars)]
if len(parnames) != npars:
raise ValueError('The number of paramater data arrays does '
'not match the number of paramaters.')
unique_parnames = _unique_parnames(parnames + ['DATA'])
if bitpix is None:
bitpix = _ImageBaseHDU.ImgCode[input.dtype.name]
fits_fmt = GroupsHDU._width2format[bitpix] # -32 -> 'E'
format = FITS2NUMPY[fits_fmt] # 'E' -> 'f4'
data_fmt = '%s%s' % (str(input.shape[1:]), format)
formats = ','.join(([format] * npars) + [data_fmt])
gcount = input.shape[0]
cols = [Column(name=unique_parnames[idx], format=fits_fmt,
bscale=parbscales[idx], bzero=parbzeros[idx])
for idx in range(npars)]
cols.append(Column(name=unique_parnames[-1], format=fits_fmt,
bscale=bscale, bzero=bzero))
coldefs = ColDefs(cols)
self = FITS_rec.__new__(cls,
np.rec.array(None,
formats=formats,
names=coldefs.names,
shape=gcount))
self._coldefs = coldefs
self.parnames = parnames
for idx in range(npars):
scale, zero = self._get_scale_factors(idx)[3:5]
if scale or zero:
self._convert[idx] = pardata[idx]
else:
np.rec.recarray.field(self, idx)[:] = pardata[idx]
scale, zero = self._get_scale_factors(npars)[3:5]
if scale or zero:
self._convert[npars] = input
else:
np.rec.recarray.field(self, npars)[:] = input
else:
self = FITS_rec.__new__(cls, input)
self.parnames = None
return self
def __array_finalize__(self, obj):
super(GroupData, self).__array_finalize__(obj)
if isinstance(obj, GroupData):
self.parnames = obj.parnames
elif isinstance(obj, FITS_rec):
self.parnames = obj._coldefs.names
def __getitem__(self, key):
out = super(GroupData, self).__getitem__(key)
if isinstance(out, GroupData):
out.parnames = self.parnames
return out
@property
def data(self):
# The last column in the coldefs is the data portion of the group
return self.field(self._coldefs.names[-1])
@lazyproperty
def _unique(self):
return _par_indices(self.parnames)
def par(self, parname):
"""
Get the group parameter values.
"""
if _is_int(parname):
result = self.field(parname)
else:
indx = self._unique[parname.upper()]
if len(indx) == 1:
result = self.field(indx[0])
# if more than one group parameter have the same name
else:
result = self.field(indx[0]).astype('f8')
for i in indx[1:]:
result += self.field(i)
return result
class GroupsHDU(PrimaryHDU, _TableLikeHDU):
"""
FITS Random Groups HDU class.
"""
_width2format = {8: 'B', 16: 'I', 32: 'J', 64: 'K', -32: 'E', -64: 'D'}
_data_type = GroupData
def __init__(self, data=None, header=None):
"""
TODO: Write me
"""
super(GroupsHDU, self).__init__(data=data, header=header)
# The name of the table record array field that will contain the group
# data for each group; 'data' by default, but may be precdeded by any
# number of underscores if 'data' is already a parameter name
self._data_field = 'DATA'
# Update the axes; GROUPS HDUs should always have at least one axis
if len(self._axes) <= 0:
self._axes = [0]
self._header['NAXIS'] = 1
self._header.set('NAXIS1', 0, after='NAXIS')
@classmethod
def match_header(cls, header):
keyword = header.cards[0].keyword
return (keyword == 'SIMPLE' and 'GROUPS' in header and
header['GROUPS'] == True)
@lazyproperty
def data(self):
"""
The data of a random group FITS file will be like a binary table's
data.
"""
data = self._get_tbdata()
data._coldefs = self.columns
data.formats = self.columns.formats
data.parnames = self.parnames
del self.columns
return data
@lazyproperty
def parnames(self):
"""The names of the group parameters as described by the header."""
pcount = self._header['PCOUNT']
# The FITS standard doesn't really say what to do if a parname is
# missing, so for now just assume that won't happen
return [self._header['PTYPE' + str(idx + 1)] for idx in range(pcount)]
@lazyproperty
def columns(self):
if self._has_data and hasattr(self.data, '_coldefs'):
return self.data._coldefs
format = self._width2format[self._header['BITPIX']]
pcount = self._header['PCOUNT']
parnames = []
bscales = []
bzeros = []
for idx in range(pcount):
bscales.append(self._header.get('PSCAL' + str(idx + 1), 1))
bzeros.append(self._header.get('PZERO' + str(idx + 1), 0))
parnames.append(self._header['PTYPE' + str(idx + 1)])
# Now create columns from collected parameters, but first add the DATA
# column too, to contain the group data.
formats = [format] * len(parnames)
parnames.append('DATA')
bscales.append(self._header.get('BSCALE', 1))
bzeros.append(self._header.get('BZEROS', 0))
data_shape = self.shape[:-1]
formats.append(str(int(np.array(data_shape).sum())) + format)
parnames = _unique_parnames(parnames)
self._data_field = parnames[-1]
cols = [Column(name=name, format=fmt, bscale=bscale, bzero=bzero)
for name, fmt, bscale, bzero in
zip(parnames, formats, bscales, bzeros)]
coldefs = ColDefs(cols)
# TODO: Something has to be done about this spaghetti code of arbitrary
# attributes getting tacked on to the coldefs here.
coldefs._shape = self._header['GCOUNT']
coldefs._dat_format = FITS2NUMPY[format]
return coldefs
@lazyproperty
def _theap(self):
# Only really a lazyproperty for symmetry with _TableBaseHDU
return 0
@property
def size(self):
"""
Returns the size (in bytes) of the HDU's data part.
"""
size = 0
naxis = self._header.get('NAXIS', 0)
# for random group image, NAXIS1 should be 0, so we skip NAXIS1.
if naxis > 1:
size = 1
for idx in range(1, naxis):
size = size * self._header['NAXIS' + str(idx + 1)]
bitpix = self._header['BITPIX']
gcount = self._header.get('GCOUNT', 1)
pcount = self._header.get('PCOUNT', 0)
size = abs(bitpix) * gcount * (pcount + size) // 8
return size
def update_header(self):
old_naxis = self._header.get('NAXIS', 0)
if self._data_loaded:
if isinstance(self.data, GroupData):
self._axes = list(self.data.data.shape)[1:]
self._axes.reverse()
self._axes = [0] + self._axes
field0 = self.data.dtype.names[0]
field0_code = self.data.dtype.fields[field0][0].name
elif self.data is None:
self._axes = [0]
field0_code = 'uint8' # For lack of a better default
else:
raise ValueError('incorrect array type')
self._header['BITPIX'] = _ImageBaseHDU.ImgCode[field0_code]
self._header['NAXIS'] = len(self._axes)
# add NAXISi if it does not exist
for idx, axis in enumerate(self._axes):
if (idx == 0):
after = 'NAXIS'
else:
after = 'NAXIS' + str(idx)
self._header.set('NAXIS' + str(idx + 1), axis, after=after)
# delete extra NAXISi's
for idx in range(len(self._axes) + 1, old_naxis + 1):
try:
del self._header['NAXIS' + str(idx)]
except KeyError:
pass
if self._has_data and isinstance(self.data, GroupData):
self._header.set('GROUPS', True,
after='NAXIS' + str(len(self._axes)))
self._header.set('PCOUNT', len(self.data.parnames), after='GROUPS')
self._header.set('GCOUNT', len(self.data), after='PCOUNT')
npars = len(self.data.parnames)
scale, zero = self.data._get_scale_factors(npars)[3:5]
if scale:
self._header.set('BSCALE', self.data._coldefs.bscales[npars])
if zero:
self._header.set('BZERO', self.data._coldefs.bzeros[npars])
for idx in range(npars):
self._header.set('PTYPE' + str(idx + 1),
self.data.parnames[idx])
scale, zero = self.data._get_scale_factors(idx)[3:5]
if scale:
self._header.set('PSCAL' + str(idx + 1),
self.data._coldefs.bscales[idx])
if zero:
self._header.set('PZERO' + str(idx + 1),
self.data._coldefs.bzeros[idx])
# Update the position of the EXTEND keyword if it already exists
if 'EXTEND' in self._header:
if len(self._axes):
after = 'NAXIS' + str(len(self._axes))
else:
after = 'NAXIS'
self._header.set('EXTEND', after=after)
def _get_tbdata(self):
# get the right shape for the data part of the random group,
# since binary table does not support ND yet
self.columns._recformats[-1] = (repr(self.shape[:-1]) +
self.columns._dat_format)
return super(GroupsHDU, self)._get_tbdata()
def _writedata_internal(self, fileobj):
"""
Basically copy/pasted from `_ImageBaseHDU._writedata_internal()`, but
we have to get the data's byte order a different way...
TODO: Might be nice to store some indication of the data's byte order
as an attribute or function so that we don't have to do this.
"""
size = 0
if self.data is not None:
self.data._scale_back()
# Based on the system type, determine the byteorders that
# would need to be swapped to get to big-endian output
if sys.byteorder == 'little':
swap_types = ('<', '=')
else:
swap_types = ('<',)
# deal with unsigned integer 16, 32 and 64 data
if _is_pseudo_unsigned(self.data.dtype):
# Convert the unsigned array to signed
output = np.array(
self.data - _unsigned_zero(self.data.dtype),
dtype='>i%d' % self.data.dtype.itemsize)
should_swap = False
else:
output = self.data
fname = self.data.dtype.names[0]
byteorder = self.data.dtype.fields[fname][0].str[0]
should_swap = (byteorder in swap_types)
if not fileobj.simulateonly:
if should_swap:
output.byteswap(True)
try:
fileobj.writearray(output)
finally:
output.byteswap(True)
else:
fileobj.writearray(output)
size += output.size * output.itemsize
return size
def _verify(self, option='warn'):
errs = super(GroupsHDU, self)._verify(option=option)
# Verify locations and values of mandatory keywords.
self.req_cards('NAXIS', 2,
lambda v: (_is_int(v) and v >= 1 and v <= 999), 1,
option, errs)
self.req_cards('NAXIS1', 3, lambda v: (_is_int(v) and v == 0), 0,
option, errs)
after = self._header['NAXIS'] + 3
pos = lambda x: x >= after
self.req_cards('GCOUNT', pos, _is_int, 1, option, errs)
self.req_cards('PCOUNT', pos, _is_int, 0, option, errs)
self.req_cards('GROUPS', pos, lambda v: (v == True), True, option,
errs)
return errs
def _calculate_datasum(self, blocking):
"""
Calculate the value for the ``DATASUM`` card in the HDU.
"""
if self._has_data:
# We have the data to be used.
# Check the byte order of the data. If it is little endian we
# must swap it before calculating the datasum.
byteorder = \
self.data.dtype.fields[self.data.dtype.names[0]][0].str[0]
if byteorder != '>':
byteswapped = True
d = self.data.byteswap(True)
d.dtype = d.dtype.newbyteorder('>')
else:
byteswapped = False
d = self.data
cs = self._compute_checksum(np.fromstring(d, dtype='ubyte'),
blocking=blocking)
# If the data was byteswapped in this method then return it to
# its original little-endian order.
if byteswapped:
d.byteswap(True)
d.dtype = d.dtype.newbyteorder('<')
return cs
else:
# This is the case where the data has not been read from the file
# yet. We can handle that in a generic manner so we do it in the
# base class. The other possibility is that there is no data at
# all. This can also be handled in a gereric manner.
return super(GroupsHDU, self)._calculate_datasum(blocking=blocking)
def _summary(self):
summary = super(GroupsHDU, self)._summary()
name, classname, length, shape, format, gcount = summary
# Drop the first axis from the shape
if shape:
shape = shape[1:]
if shape and all(shape):
# Update the format
format = self.columns[0].dtype.name
# Update the GCOUNT report
gcount = '%d Groups %d Parameters' % (self._gcount, self._pcount)
return (name, classname, length, shape, format, gcount)
def _par_indices(names):
"""
Given a list of objects, returns a mapping of objects in that list to the
index or indices at which that object was found in the list.
"""
unique = {}
for idx, name in enumerate(names):
# Case insensitive
name = name.upper()
if name in unique:
unique[name].append(idx)
else:
unique[name] = [idx]
return unique
def _unique_parnames(names):
"""
Given a list of parnames, including possible duplicates, returns a new list
of parnames with duplicates prepended by one or more underscores to make
them unique. This is also case insensitive.
"""
upper_names = set()
unique_names = []
for name in names:
name_upper = name.upper()
while name_upper in upper_names:
name = '_' + name
name_upper = '_' + name_upper
unique_names.append(name)
upper_names.add(name_upper)
return unique_names
|