1 | # Licensed under a 3-clause BSD style license - see LICENSE.rst |
||
0 ignored issues
–
show
coding-style
introduced
by
Loading history...
|
|||
2 | from __future__ import absolute_import, division, print_function, unicode_literals |
||
3 | import abc |
||
4 | import copy |
||
5 | import inspect |
||
6 | import json |
||
7 | import numpy as np |
||
8 | from collections import OrderedDict |
||
9 | from astropy import units as u |
||
10 | from astropy.utils.misc import InheritDocstrings |
||
11 | from astropy.io import fits |
||
12 | from .geom import pix_tuple_to_idx, MapCoord |
||
13 | from .utils import unpack_seq |
||
14 | from ..extern import six |
||
15 | from ..utils.scripts import make_path |
||
16 | |||
17 | __all__ = ["Map"] |
||
18 | |||
19 | |||
20 | class MapMeta(InheritDocstrings, abc.ABCMeta): |
||
21 | pass |
||
22 | |||
23 | |||
24 | @six.add_metaclass(MapMeta) |
||
25 | class Map(object): |
||
26 | """Abstract map class. |
||
27 | |||
28 | This can represent WCS- or HEALPIX-based maps |
||
29 | with 2 spatial dimensions and N non-spatial dimensions. |
||
30 | |||
31 | Parameters |
||
32 | ---------- |
||
33 | geom : `~gammapy.maps.MapGeom` |
||
34 | Geometry |
||
35 | data : `~numpy.ndarray` |
||
36 | Data array |
||
37 | meta : `~collections.OrderedDict` |
||
38 | Dictionary to store meta data. |
||
39 | unit : str or `~astropy.units.Unit` |
||
40 | Data unit |
||
41 | """ |
||
42 | |||
43 | def __init__(self, geom, data, meta=None, unit=""): |
||
44 | self.geom = geom |
||
45 | self.data = data |
||
46 | self.unit = unit |
||
47 | |||
48 | if meta is None: |
||
49 | self.meta = {} |
||
50 | else: |
||
51 | self.meta = meta |
||
52 | |||
53 | def _init_copy(self, **kwargs): |
||
54 | """Init map instance by copying missing init arguments from self. |
||
55 | """ |
||
56 | argnames = inspect.getargspec(self.__init__).args |
||
57 | argnames.remove("self") |
||
58 | argnames.remove("dtype") |
||
59 | |||
60 | for arg in argnames: |
||
61 | value = getattr(self, "_" + arg) |
||
62 | kwargs.setdefault(arg, copy.deepcopy(value)) |
||
63 | |||
64 | return self.from_geom(**kwargs) |
||
65 | |||
66 | @property |
||
67 | def geom(self): |
||
68 | """Map geometry (`~gammapy.maps.MapGeom`)""" |
||
69 | return self._geom |
||
70 | |||
71 | @geom.setter |
||
72 | def geom(self, val): |
||
73 | self._geom = val |
||
74 | |||
75 | @property |
||
76 | def data(self): |
||
77 | """Data array (`~numpy.ndarray`)""" |
||
78 | return self._data |
||
79 | |||
80 | @data.setter |
||
81 | def data(self, val): |
||
82 | if val.shape != self.geom.data_shape: |
||
83 | raise ValueError( |
||
84 | "Shape {!r} does not match map data shape {!r}" |
||
85 | "".format(val.shape, self.geom.data_shape) |
||
86 | ) |
||
87 | |||
88 | if isinstance(val, u.Quantity): |
||
89 | raise TypeError("Map data must be a Numpy array. Set unit separately") |
||
90 | |||
91 | self._data = val |
||
92 | |||
93 | @property |
||
94 | def unit(self): |
||
95 | """Map unit (`~astropy.units.Unit`)""" |
||
96 | return self._unit |
||
97 | |||
98 | @unit.setter |
||
99 | def unit(self, val): |
||
100 | self._unit = u.Unit(val) |
||
101 | |||
102 | @property |
||
103 | def meta(self): |
||
104 | """Map meta (`~collections.OrderedDict`)""" |
||
105 | return self._meta |
||
106 | |||
107 | @meta.setter |
||
108 | def meta(self, val): |
||
109 | self._meta = OrderedDict(val) |
||
110 | |||
111 | @property |
||
112 | def quantity(self): |
||
113 | """Map data times unit (`~astropy.units.Quantity`)""" |
||
114 | return u.Quantity(self.data, self.unit, copy=False) |
||
115 | |||
116 | @quantity.setter |
||
117 | def quantity(self, val): |
||
118 | val = u.Quantity(val, copy=False) |
||
119 | self.data = val.value |
||
120 | self.unit = val.unit |
||
121 | |||
122 | @staticmethod |
||
123 | def create(**kwargs): |
||
124 | """Create an empty map object. |
||
125 | |||
126 | This method accepts generic options listed below, as well as options |
||
127 | for `HpxMap` and `WcsMap` objects. For WCS-specific options, see |
||
128 | `WcsMap.create` and for HPX-specific options, see `HpxMap.create`. |
||
129 | |||
130 | Parameters |
||
131 | ---------- |
||
132 | coordsys : str |
||
133 | Coordinate system, either Galactic ('GAL') or Equatorial |
||
134 | ('CEL'). |
||
135 | map_type : {'wcs', 'wcs-sparse', 'hpx', 'hpx-sparse'} |
||
136 | Map type. Selects the class that will be used to |
||
137 | instantiate the map. |
||
138 | binsz : float or `~numpy.ndarray` |
||
139 | Pixel size in degrees. |
||
140 | skydir : `~astropy.coordinates.SkyCoord` |
||
141 | Coordinate of map center. |
||
142 | axes : list |
||
143 | List of `~MapAxis` objects for each non-spatial dimension. |
||
144 | If None then the map will be a 2D image. |
||
145 | dtype : str |
||
146 | Data type, default is 'float32' |
||
147 | unit : str or `~astropy.units.Unit` |
||
148 | Data unit. |
||
149 | meta : `~collections.OrderedDict` |
||
150 | Dictionary to store meta data. |
||
151 | |||
152 | Returns |
||
153 | ------- |
||
154 | map : `Map` |
||
155 | Empty map object. |
||
156 | """ |
||
157 | from .hpxmap import HpxMap |
||
158 | from .wcsmap import WcsMap |
||
159 | |||
160 | map_type = kwargs.setdefault("map_type", "wcs") |
||
161 | if "wcs" in map_type.lower(): |
||
162 | return WcsMap.create(**kwargs) |
||
163 | elif "hpx" in map_type.lower(): |
||
164 | return HpxMap.create(**kwargs) |
||
165 | else: |
||
166 | raise ValueError("Unrecognized map type: {!r}".format(map_type)) |
||
167 | |||
168 | @staticmethod |
||
169 | def read(filename, hdu=None, hdu_bands=None, map_type="auto"): |
||
170 | """Read a map from a FITS file. |
||
171 | |||
172 | Parameters |
||
173 | ---------- |
||
174 | filename : str or `~pathlib.Path` |
||
175 | Name of the FITS file. |
||
176 | hdu : str |
||
177 | Name or index of the HDU with the map data. |
||
178 | hdu_bands : str |
||
179 | Name or index of the HDU with the BANDS table. If not |
||
180 | defined this will be inferred from the FITS header of the |
||
181 | map HDU. |
||
182 | map_type : {'wcs', 'wcs-sparse', 'hpx', 'hpx-sparse', 'auto'} |
||
183 | Map type. Selects the class that will be used to |
||
184 | instantiate the map. The map type should be consistent |
||
185 | with the format of the input file. If map_type is 'auto' |
||
186 | then an appropriate map type will be inferred from the |
||
187 | input file. |
||
188 | |||
189 | Returns |
||
190 | ------- |
||
191 | map_out : `Map` |
||
192 | Map object |
||
193 | """ |
||
194 | filename = str(make_path(filename)) |
||
195 | with fits.open(filename, memmap=False) as hdulist: |
||
196 | return Map.from_hdulist(hdulist, hdu, hdu_bands, map_type) |
||
197 | |||
198 | @staticmethod |
||
199 | def from_geom(geom, meta=None, data=None, map_type="auto", unit=""): |
||
200 | """Generate an empty map from a `MapGeom` instance. |
||
201 | |||
202 | Parameters |
||
203 | ---------- |
||
204 | geom : `MapGeom` |
||
205 | Map geometry. |
||
206 | data : `numpy.ndarray` |
||
207 | data array |
||
208 | meta : `~collections.OrderedDict` |
||
209 | Dictionary to store meta data. |
||
210 | map_type : {'wcs', 'wcs-sparse', 'hpx', 'hpx-sparse', 'auto'} |
||
211 | Map type. Selects the class that will be used to |
||
212 | instantiate the map. The map type should be consistent |
||
213 | with the geometry. If map_type is 'auto' then an |
||
214 | appropriate map type will be inferred from type of ``geom``. |
||
215 | unit : str or `~astropy.units.Unit` |
||
216 | Data unit. |
||
217 | |||
218 | Returns |
||
219 | ------- |
||
220 | map_out : `Map` |
||
221 | Map object |
||
222 | |||
223 | """ |
||
224 | if map_type == "auto": |
||
225 | |||
226 | from .hpx import HpxGeom |
||
227 | from .wcs import WcsGeom |
||
228 | |||
229 | if isinstance(geom, HpxGeom): |
||
230 | map_type = "hpx" |
||
231 | elif isinstance(geom, WcsGeom): |
||
232 | map_type = "wcs" |
||
233 | else: |
||
234 | raise ValueError("Unrecognized geom type.") |
||
235 | |||
236 | cls_out = Map._get_map_cls(map_type) |
||
237 | return cls_out(geom, data=data, meta=meta, unit=unit) |
||
238 | |||
239 | @staticmethod |
||
240 | def from_hdulist(hdulist, hdu=None, hdu_bands=None, map_type="auto"): |
||
241 | """Create from `astropy.io.fits.HDUList`.""" |
||
242 | if map_type == "auto": |
||
243 | map_type = Map._get_map_type(hdulist, hdu) |
||
244 | cls_out = Map._get_map_cls(map_type) |
||
245 | return cls_out.from_hdulist(hdulist, hdu=hdu, hdu_bands=hdu_bands) |
||
246 | |||
247 | @staticmethod |
||
248 | def _get_meta_from_header(header): |
||
249 | """Load meta data from a FITS header.""" |
||
250 | if "META" in header: |
||
251 | meta = json.loads(header["META"], object_pairs_hook=OrderedDict) |
||
252 | else: |
||
253 | meta = OrderedDict() |
||
254 | return meta |
||
255 | |||
256 | @staticmethod |
||
257 | def _get_map_type(hdu_list, hdu_name): |
||
258 | """Infer map type from a FITS HDU. |
||
259 | |||
260 | Only read header, never data, to have good performance. |
||
261 | """ |
||
262 | if hdu_name is None: |
||
263 | # Find the header of the first non-empty HDU |
||
264 | header = hdu_list[0].header |
||
265 | if header["NAXIS"] == 0: |
||
266 | header = hdu_list[1].header |
||
267 | else: |
||
268 | header = hdu_list[hdu_name].header |
||
269 | |||
270 | if ("PIXTYPE" in header) and (header["PIXTYPE"] == "HEALPIX"): |
||
271 | return "hpx" |
||
272 | else: |
||
273 | return "wcs" |
||
274 | |||
275 | @staticmethod |
||
276 | def _get_map_cls(map_type): |
||
277 | """Get map class for given `map_type` string. |
||
278 | |||
279 | This should probably be a registry dict so that users |
||
280 | can add supported map types to the `gammapy.maps` I/O |
||
281 | (see e.g. the Astropy table format I/O registry), |
||
282 | but that's non-trivial to implement without avoiding circular imports. |
||
283 | """ |
||
284 | if map_type == "wcs": |
||
285 | from .wcsnd import WcsNDMap |
||
286 | |||
287 | return WcsNDMap |
||
288 | elif map_type == "wcs-sparse": |
||
289 | raise NotImplementedError() |
||
290 | elif map_type == "hpx": |
||
291 | from .hpxnd import HpxNDMap |
||
292 | |||
293 | return HpxNDMap |
||
294 | elif map_type == "hpx-sparse": |
||
295 | from .hpxsparse import HpxSparseMap |
||
296 | |||
297 | return HpxSparseMap |
||
298 | else: |
||
299 | raise ValueError("Unrecognized map type: {!r}".format(map_type)) |
||
300 | |||
301 | def write(self, filename, overwrite=False, **kwargs): |
||
302 | """Write to a FITS file. |
||
303 | |||
304 | Parameters |
||
305 | ---------- |
||
306 | filename : str |
||
307 | Output file name. |
||
308 | overwrite : bool |
||
309 | Overwrite existing file? |
||
310 | hdu : str |
||
311 | Set the name of the image extension. By default this will |
||
312 | be set to SKYMAP (for BINTABLE HDU) or PRIMARY (for IMAGE |
||
313 | HDU). |
||
314 | hdu_bands : str |
||
315 | Set the name of the bands table extension. By default this will |
||
316 | be set to BANDS. |
||
317 | conv : str |
||
318 | FITS format convention. By default files will be written |
||
319 | to the gamma-astro-data-formats (GADF) format. This |
||
320 | option can be used to write files that are compliant with |
||
321 | format conventions required by specific software (e.g. the |
||
322 | Fermi Science Tools). Supported conventions are 'gadf', |
||
323 | 'fgst-ccube', 'fgst-ltcube', 'fgst-bexpcube', |
||
324 | 'fgst-template', 'fgst-srcmap', 'fgst-srcmap-sparse', |
||
325 | 'galprop', and 'galprop2'. |
||
326 | sparse : bool |
||
327 | Sparsify the map by dropping pixels with zero amplitude. |
||
328 | This option is only compatible with the 'gadf' format. |
||
329 | """ |
||
330 | hdulist = self.to_hdulist(**kwargs) |
||
331 | hdulist.writeto(filename, overwrite=overwrite) |
||
332 | |||
333 | def iter_by_image(self): |
||
334 | """Iterate over image planes of the map returning a tuple with the image |
||
335 | array and image plane index. |
||
336 | |||
337 | The image plane index is in data order, so that the data array can be |
||
338 | indexed directly. See :ref:`mapiter` for further information. |
||
339 | |||
340 | Returns |
||
341 | ------- |
||
342 | val : `~numpy.ndarray` |
||
343 | Array of image plane values. |
||
344 | idx : tuple |
||
345 | Index of image plane. |
||
346 | """ |
||
347 | for idx in np.ndindex(self.geom.shape): |
||
348 | yield self.data[idx[::-1]], idx[::-1] |
||
349 | |||
350 | def iter_by_pix(self, buffersize=1): |
||
351 | """Iterate over elements of the map returning a tuple with values and |
||
352 | pixel coordinates. |
||
353 | |||
354 | Parameters |
||
355 | ---------- |
||
356 | buffersize : int |
||
357 | Set the size of the buffer. The map will be returned in |
||
358 | chunks of the given size. |
||
359 | |||
360 | Returns |
||
361 | ------- |
||
362 | val : `~numpy.ndarray` |
||
363 | Map values. |
||
364 | pix : tuple |
||
365 | Tuple of pixel coordinates. |
||
366 | """ |
||
367 | pix = list(self.geom.get_idx(flat=True)) |
||
368 | vals = self.data[np.isfinite(self.data)] |
||
369 | x = [vals] + pix |
||
370 | return unpack_seq( |
||
371 | np.nditer(x, flags=["external_loop", "buffered"], buffersize=buffersize) |
||
372 | ) |
||
373 | |||
374 | def iter_by_coord(self, buffersize=1): |
||
375 | """Iterate over elements of the map returning a tuple with values and |
||
376 | map coordinates. |
||
377 | |||
378 | Parameters |
||
379 | ---------- |
||
380 | buffersize : int |
||
381 | Set the size of the buffer. The map will be returned in |
||
382 | chunks of the given size. |
||
383 | |||
384 | Returns |
||
385 | ------- |
||
386 | val : `~numpy.ndarray` |
||
387 | Map values. |
||
388 | coords : tuple |
||
389 | Tuple of map coordinates. |
||
390 | """ |
||
391 | coords = list(self.geom.get_coord(flat=True)) |
||
392 | vals = self.data[np.isfinite(self.data)] |
||
393 | x = [vals] + coords |
||
394 | return unpack_seq( |
||
395 | np.nditer(x, flags=["external_loop", "buffered"], buffersize=buffersize) |
||
396 | ) |
||
397 | |||
398 | @abc.abstractmethod |
||
399 | def sum_over_axes(self): |
||
400 | """Reduce to a 2D image by summing over non-spatial dimensions.""" |
||
401 | pass |
||
402 | |||
403 | def coadd(self, map_in): |
||
404 | """Add the contents of ``map_in`` to this map. This method can be |
||
405 | used to combine maps containing integral quantities (e.g. counts) |
||
406 | or differential quantities if the maps have the same binning. |
||
407 | |||
408 | Parameters |
||
409 | ---------- |
||
410 | map_in : `Map` |
||
411 | Input map. |
||
412 | """ |
||
413 | if not self.unit.is_equivalent(map_in.unit): |
||
414 | raise ValueError("Incompatible units") |
||
415 | |||
416 | # TODO: Check whether geometries are aligned and if so sum the |
||
417 | # data vectors directly |
||
418 | idx = map_in.geom.get_idx() |
||
419 | coords = map_in.geom.get_coord() |
||
420 | vals = u.Quantity(map_in.get_by_idx(idx), map_in.unit) |
||
421 | self.fill_by_coord(coords, vals) |
||
422 | |||
423 | def reproject(self, geom, order=1, mode="interp"): |
||
424 | """Reproject this map to a different geometry. |
||
425 | |||
426 | Only spatial axes are reprojected, if you would like to reproject |
||
427 | non-spatial axes consider using `Map.interp_by_coord()` instead. |
||
428 | |||
429 | Parameters |
||
430 | ---------- |
||
431 | geom : `MapGeom` |
||
432 | Geometry of projection. |
||
433 | mode : {'interp', 'exact'} |
||
434 | Method for reprojection. 'interp' method interpolates at pixel |
||
435 | centers. 'exact' method integrates over intersection of pixels. |
||
436 | order : int or str |
||
437 | Order of interpolating polynomial (0 = nearest-neighbor, 1 = |
||
438 | linear, 2 = quadratic, 3 = cubic). |
||
439 | |||
440 | Returns |
||
441 | ------- |
||
442 | map : `Map` |
||
443 | Reprojected map. |
||
444 | """ |
||
445 | if geom.is_image: |
||
446 | axes = [ax.copy() for ax in self.geom.axes] |
||
447 | geom = geom.copy(axes=axes) |
||
448 | else: |
||
449 | axes_eq = geom.ndim == self.geom.ndim |
||
450 | axes_eq &= np.all( |
||
451 | [ax0 == ax1 for ax0, ax1 in zip(geom.axes, self.geom.axes)] |
||
452 | ) |
||
453 | |||
454 | if not axes_eq: |
||
455 | raise ValueError( |
||
456 | "Map and target geometry non-spatial axes must match." |
||
457 | "Use interp_by_coord to interpolate in non-spatial axes." |
||
458 | ) |
||
459 | |||
460 | if geom.is_hpx: |
||
461 | return self._reproject_to_hpx(geom, mode=mode, order=order) |
||
462 | else: |
||
463 | return self._reproject_to_wcs(geom, mode=mode, order=order) |
||
464 | |||
465 | @abc.abstractmethod |
||
466 | def pad(self, pad_width, mode="constant", cval=0, order=1): |
||
467 | """Pad the spatial dimension of the map by extending the edge of the |
||
468 | map by the given number of pixels. |
||
469 | |||
470 | Parameters |
||
471 | ---------- |
||
472 | pad_width : {sequence, array_like, int} |
||
473 | Number of pixels padded to the edges of each axis. |
||
474 | mode : {'edge', 'constant', 'interp'} |
||
475 | Padding mode. 'edge' pads with the closest edge value. |
||
476 | 'constant' pads with a constant value. 'interp' pads with |
||
477 | an extrapolated value. |
||
478 | cval : float |
||
479 | Padding value when mode='consant'. |
||
480 | order : int |
||
481 | Order of interpolation when mode='constant' (0 = |
||
482 | nearest-neighbor, 1 = linear, 2 = quadratic, 3 = cubic). |
||
483 | |||
484 | Returns |
||
485 | ------- |
||
486 | map : `Map` |
||
487 | Padded map. |
||
488 | |||
489 | """ |
||
490 | pass |
||
491 | |||
492 | @abc.abstractmethod |
||
493 | def crop(self, crop_width): |
||
494 | """Crop the spatial dimension of the map by removing a number of |
||
495 | pixels from the edge of the map. |
||
496 | |||
497 | Parameters |
||
498 | ---------- |
||
499 | crop_width : {sequence, array_like, int} |
||
500 | Number of pixels cropped from the edges of each axis. |
||
501 | Defined analogously to ``pad_with`` from `numpy.pad`. |
||
502 | |||
503 | Returns |
||
504 | ------- |
||
505 | map : `Map` |
||
506 | Cropped map. |
||
507 | """ |
||
508 | pass |
||
509 | |||
510 | @abc.abstractmethod |
||
511 | def downsample(self, factor, preserve_counts=True): |
||
512 | """Downsample the spatial dimension by a given factor. |
||
513 | |||
514 | Parameters |
||
515 | ---------- |
||
516 | factor : int |
||
517 | Downsampling factor. |
||
518 | preserve_counts : bool |
||
519 | Preserve the integral over each bin. This should be true |
||
520 | if the map is an integral quantity (e.g. counts) and false if |
||
521 | the map is a differential quantity (e.g. intensity). |
||
522 | |||
523 | Returns |
||
524 | ------- |
||
525 | map : `Map` |
||
526 | Downsampled map. |
||
527 | """ |
||
528 | pass |
||
529 | |||
530 | @abc.abstractmethod |
||
531 | def upsample(self, factor, order=0, preserve_counts=True): |
||
532 | """Upsample the spatial dimension by a given factor. |
||
533 | |||
534 | Parameters |
||
535 | ---------- |
||
536 | factor : int |
||
537 | Upsampling factor. |
||
538 | order : int |
||
539 | Order of the interpolation used for upsampling. |
||
540 | preserve_counts : bool |
||
541 | Preserve the integral over each bin. This should be true |
||
542 | if the map is an integral quantity (e.g. counts) and false if |
||
543 | the map is a differential quantity (e.g. intensity). |
||
544 | |||
545 | Returns |
||
546 | ------- |
||
547 | map : `Map` |
||
548 | Upsampled map. |
||
549 | |||
550 | """ |
||
551 | pass |
||
552 | |||
553 | def slice_by_idx(self, slices): |
||
554 | """Slice sub map from map object. |
||
555 | |||
556 | For usage examples, see :ref:`mapslicing`. |
||
557 | |||
558 | Parameters |
||
559 | ---------- |
||
560 | slices : dict |
||
561 | Dict of axes names and integers or `slice` object pairs. Contains one |
||
562 | element for each non-spatial dimension. For integer indexing the |
||
563 | corresponding axes is dropped from the map. Axes not specified in the |
||
564 | dict are kept unchanged. |
||
565 | |||
566 | Returns |
||
567 | ------- |
||
568 | map_out : `Map` |
||
569 | Sliced map object. |
||
570 | """ |
||
571 | geom = self.geom.slice_by_idx(slices) |
||
572 | slices = tuple([slices.get(ax.name, slice(None)) for ax in self.geom.axes]) |
||
573 | data = self.data[slices[::-1]] |
||
574 | return self.__class__(geom=geom, data=data, unit=self.unit, meta=self.meta) |
||
575 | |||
576 | def get_image_by_coord(self, coords): |
||
577 | """Return spatial map at the given axis coordinates. |
||
578 | |||
579 | Parameters |
||
580 | ---------- |
||
581 | coords : tuple or dict |
||
582 | Tuple should be ordered as (x_0, ..., x_n) where x_i are coordinates |
||
583 | for non-spatial dimensions of the map. Dict should specify the axis |
||
584 | names of the non-spatial axes such as {'axes0': x_0, ..., 'axesn': x_n}. |
||
585 | |||
586 | Examples |
||
587 | -------- |
||
588 | |||
589 | :: |
||
590 | |||
591 | import numpy as np |
||
592 | from gammapy.maps import Map, MapAxis |
||
593 | from astropy.coordinates import SkyCoord |
||
594 | from astropy import units as u |
||
595 | |||
596 | # Define map axes |
||
597 | energy_axis = MapAxis.from_edges( |
||
598 | np.logspace(-1., 1., 4), unit='TeV', name='energy', |
||
599 | ) |
||
600 | |||
601 | time_axis = MapAxis.from_edges( |
||
602 | np.linspace(0., 10, 20), unit='h', name='time', |
||
603 | ) |
||
604 | |||
605 | # Define map center |
||
606 | skydir = SkyCoord(0, 0, frame='galactic', unit='deg') |
||
607 | |||
608 | # Create map |
||
609 | m_wcs = Map.create( |
||
610 | map_type='wcs', |
||
611 | binsz=0.02, |
||
612 | skydir=skydir, |
||
613 | width=10.0, |
||
614 | axes=[energy_axis, time_axis], |
||
615 | ) |
||
616 | |||
617 | # Get image by coord tuple |
||
618 | image = m_wcs.get_image_by_coord(('500 GeV', '1 h')) |
||
619 | |||
620 | # Get image by coord dict with strings |
||
621 | image = m_wcs.get_image_by_coord({'energy': '500 GeV', 'time': '1 h'}) |
||
622 | |||
623 | # Get image by coord dict with quantities |
||
624 | image = m_wcs.get_image_by_coord({'energy': 0.5 * u.TeV, 'time': 1 * u.h}) |
||
625 | |||
626 | See Also |
||
627 | -------- |
||
628 | get_image_by_idx, get_image_by_pix |
||
629 | |||
630 | Returns |
||
631 | ------- |
||
632 | map_out : `Map` |
||
633 | Map with spatial dimensions only. |
||
634 | """ |
||
635 | if isinstance(coords, tuple): |
||
636 | axes_names = [_.name for _ in self.geom.axes] |
||
637 | coords = OrderedDict(zip(axes_names, coords)) |
||
638 | |||
639 | idx = [] |
||
640 | for ax in self.geom.axes: |
||
641 | value = coords[ax.name] |
||
642 | idx.append(ax.coord_to_idx(value)) |
||
643 | |||
644 | return self.get_image_by_idx(idx) |
||
645 | |||
646 | def get_image_by_pix(self, pix): |
||
647 | """Return spatial map at the given axis pixel coordinates |
||
648 | |||
649 | Parameters |
||
650 | ---------- |
||
651 | pix : tuple |
||
652 | Tuple of scalar pixel coordinates for each non-spatial dimension of |
||
653 | the map. Tuple should be ordered as (I_0, ..., I_n). Pixel coordinates |
||
654 | can be either float or integer type. |
||
655 | |||
656 | See Also |
||
657 | -------- |
||
658 | get_image_by_coord, get_image_by_idx |
||
659 | |||
660 | Returns |
||
661 | ------- |
||
662 | map_out : `Map` |
||
663 | Map with spatial dimensions only. |
||
664 | """ |
||
665 | idx = self.geom.pix_to_idx(pix) |
||
666 | return self.get_image_by_idx(idx) |
||
667 | |||
668 | def get_image_by_idx(self, idx): |
||
669 | """Return spatial map at the given axis pixel indices. |
||
670 | |||
671 | Parameters |
||
672 | ---------- |
||
673 | idx : tuple |
||
674 | Tuple of scalar indices for each non spatial dimension of the map. |
||
675 | Tuple should be ordered as (I_0, ..., I_n). |
||
676 | |||
677 | See Also |
||
678 | -------- |
||
679 | get_image_by_coord, get_image_by_pix |
||
680 | |||
681 | Returns |
||
682 | ------- |
||
683 | map_out : `Map` |
||
684 | Map with spatial dimensions only. |
||
685 | """ |
||
686 | if len(idx) != len(self.geom.axes): |
||
687 | raise ValueError("Tuple length must equal number of non-spatial dimensions") |
||
688 | |||
689 | # Only support scalar indices per axis |
||
690 | idx = tuple([int(_) for _ in idx]) |
||
691 | |||
692 | geom = self.geom.to_image() |
||
693 | data = self.data[idx[::-1]] |
||
694 | return self.__class__(geom=geom, data=data, unit=self.unit, meta=self.meta) |
||
695 | |||
696 | def get_by_coord(self, coords): |
||
697 | """Return map values at the given map coordinates. |
||
698 | |||
699 | Parameters |
||
700 | ---------- |
||
701 | coords : tuple or `~gammapy.maps.MapCoord` |
||
702 | Coordinate arrays for each dimension of the map. Tuple |
||
703 | should be ordered as (lon, lat, x_0, ..., x_n) where x_i |
||
704 | are coordinates for non-spatial dimensions of the map. |
||
705 | |||
706 | Returns |
||
707 | ------- |
||
708 | vals : `~numpy.ndarray` |
||
709 | Values of pixels in the map. np.nan used to flag coords |
||
710 | outside of map. |
||
711 | """ |
||
712 | coords = MapCoord.create(coords, coordsys=self.geom.coordsys) |
||
713 | msk = self.geom.contains(coords) |
||
714 | vals = np.empty(coords.shape, dtype=self.data.dtype) |
||
715 | coords = coords.apply_mask(msk) |
||
716 | idx = self.geom.coord_to_idx(coords) |
||
717 | vals[msk] = self.get_by_idx(idx) |
||
718 | vals[~msk] = np.nan |
||
719 | return vals |
||
720 | |||
721 | def get_by_pix(self, pix): |
||
722 | """Return map values at the given pixel coordinates. |
||
723 | |||
724 | Parameters |
||
725 | ---------- |
||
726 | pix : tuple |
||
727 | Tuple of pixel index arrays for each dimension of the map. |
||
728 | Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n) |
||
729 | for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps. |
||
730 | Pixel indices can be either float or integer type. |
||
731 | |||
732 | Returns |
||
733 | ---------- |
||
734 | vals : `~numpy.ndarray` |
||
735 | Array of pixel values. np.nan used to flag coordinates |
||
736 | outside of map |
||
737 | """ |
||
738 | # FIXME: Support local indexing here? |
||
739 | # FIXME: Support slicing? |
||
740 | pix = [np.array(p, copy=False, ndmin=1) for p in pix] |
||
741 | pix = np.broadcast_arrays(*pix) |
||
742 | msk = self.geom.contains_pix(pix) |
||
743 | vals = np.empty(pix[0].shape, dtype=self.data.dtype) |
||
744 | pix = tuple([p[msk] for p in pix]) |
||
745 | idx = self.geom.pix_to_idx(pix) |
||
746 | vals[msk] = self.get_by_idx(idx) |
||
747 | vals[~msk] = np.nan |
||
748 | return vals |
||
749 | |||
750 | @abc.abstractmethod |
||
751 | def get_by_idx(self, idx): |
||
752 | """Return map values at the given pixel indices. |
||
753 | |||
754 | Parameters |
||
755 | ---------- |
||
756 | idx : tuple |
||
757 | Tuple of pixel index arrays for each dimension of the map. |
||
758 | Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n) |
||
759 | for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps. |
||
760 | |||
761 | Returns |
||
762 | ---------- |
||
763 | vals : `~numpy.ndarray` |
||
764 | Array of pixel values. |
||
765 | np.nan used to flag coordinate outside of map |
||
766 | """ |
||
767 | pass |
||
768 | |||
769 | @abc.abstractmethod |
||
770 | def interp_by_coord(self, coords, interp=None, fill_value=None): |
||
771 | """Interpolate map values at the given map coordinates. |
||
772 | |||
773 | Parameters |
||
774 | ---------- |
||
775 | coords : tuple or `~gammapy.maps.MapCoord` |
||
776 | Coordinate arrays for each dimension of the map. Tuple |
||
777 | should be ordered as (lon, lat, x_0, ..., x_n) where x_i |
||
778 | are coordinates for non-spatial dimensions of the map. |
||
779 | |||
780 | interp : {None, 'nearest', 'linear', 'cubic', 0, 1, 2, 3} |
||
781 | Method to interpolate data values. By default no |
||
782 | interpolation is performed and the return value will be |
||
783 | the amplitude of the pixel encompassing the given |
||
784 | coordinate. Integer values can be used in lieu of strings |
||
785 | to choose the interpolation method of the given order |
||
786 | (0='nearest', 1='linear', 2='quadratic', 3='cubic'). Note |
||
787 | that only 'nearest' and 'linear' methods are supported for |
||
788 | all map types. |
||
789 | fill_value : None or float value |
||
790 | The value to use for points outside of the interpolation domain. |
||
791 | If None, values outside the domain are extrapolated. |
||
792 | |||
793 | Returns |
||
794 | ------- |
||
795 | vals : `~numpy.ndarray` |
||
796 | Interpolated pixel values. |
||
797 | """ |
||
798 | pass |
||
799 | |||
800 | @abc.abstractmethod |
||
801 | def interp_by_pix(self, pix, interp=None, fill_value=None): |
||
802 | """Interpolate map values at the given pixel coordinates. |
||
803 | |||
804 | Parameters |
||
805 | ---------- |
||
806 | pix : tuple |
||
807 | Tuple of pixel coordinate arrays for each dimension of the |
||
808 | map. Tuple should be ordered as (p_lon, p_lat, p_0, ..., |
||
809 | p_n) where p_i are pixel coordinates for non-spatial |
||
810 | dimensions of the map. |
||
811 | |||
812 | interp : {None, 'nearest', 'linear', 'cubic', 0, 1, 2, 3} |
||
813 | Method to interpolate data values. By default no |
||
814 | interpolation is performed and the return value will be |
||
815 | the amplitude of the pixel encompassing the given |
||
816 | coordinate. Integer values can be used in lieu of strings |
||
817 | to choose the interpolation method of the given order |
||
818 | (0='nearest', 1='linear', 2='quadratic', 3='cubic'). Note |
||
819 | that only 'nearest' and 'linear' methods are supported for |
||
820 | all map types. |
||
821 | fill_value : None or float value |
||
822 | The value to use for points outside of the interpolation domain. |
||
823 | If None, values outside the domain are extrapolated. |
||
824 | |||
825 | Returns |
||
826 | ------- |
||
827 | vals : `~numpy.ndarray` |
||
828 | Interpolated pixel values. |
||
829 | """ |
||
830 | pass |
||
831 | |||
832 | def fill_by_coord(self, coords, weights=None): |
||
833 | """Fill pixels at ``coords`` with given ``weights``. |
||
834 | |||
835 | Parameters |
||
836 | ---------- |
||
837 | coords : tuple or `~gammapy.maps.MapCoord` |
||
838 | Coordinate arrays for each dimension of the map. Tuple |
||
839 | should be ordered as (lon, lat, x_0, ..., x_n) where x_i |
||
840 | are coordinates for non-spatial dimensions of the map. |
||
841 | weights : `~numpy.ndarray` |
||
842 | Weights vector. Default is weight of one. |
||
843 | """ |
||
844 | idx = self.geom.coord_to_idx(coords) |
||
845 | self.fill_by_idx(idx, weights) |
||
846 | |||
847 | def fill_by_pix(self, pix, weights=None): |
||
848 | """Fill pixels at ``pix`` with given ``weights``. |
||
849 | |||
850 | Parameters |
||
851 | ---------- |
||
852 | pix : tuple |
||
853 | Tuple of pixel index arrays for each dimension of the map. |
||
854 | Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n) |
||
855 | for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps. |
||
856 | Pixel indices can be either float or integer type. Float |
||
857 | indices will be rounded to the nearest integer. |
||
858 | weights : `~numpy.ndarray` |
||
859 | Weights vector. Default is weight of one. |
||
860 | """ |
||
861 | idx = pix_tuple_to_idx(pix) |
||
862 | return self.fill_by_idx(idx, weights=weights) |
||
863 | |||
864 | @abc.abstractmethod |
||
865 | def fill_by_idx(self, idx, weights=None): |
||
866 | """Fill pixels at ``idx`` with given ``weights``. |
||
867 | |||
868 | Parameters |
||
869 | ---------- |
||
870 | idx : tuple |
||
871 | Tuple of pixel index arrays for each dimension of the map. |
||
872 | Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n) |
||
873 | for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps. |
||
874 | weights : `~numpy.ndarray` |
||
875 | Weights vector. Default is weight of one. |
||
876 | """ |
||
877 | pass |
||
878 | |||
879 | def set_by_coord(self, coords, vals): |
||
880 | """Set pixels at ``coords`` with given ``vals``. |
||
881 | |||
882 | Parameters |
||
883 | ---------- |
||
884 | coords : tuple or `~gammapy.maps.MapCoord` |
||
885 | Coordinate arrays for each dimension of the map. Tuple |
||
886 | should be ordered as (lon, lat, x_0, ..., x_n) where x_i |
||
887 | are coordinates for non-spatial dimensions of the map. |
||
888 | vals : `~numpy.ndarray` |
||
889 | Values vector. |
||
890 | """ |
||
891 | idx = self.geom.coord_to_pix(coords) |
||
892 | self.set_by_pix(idx, vals) |
||
893 | |||
894 | def set_by_pix(self, pix, vals): |
||
895 | """Set pixels at ``pix`` with given ``vals``. |
||
896 | |||
897 | Parameters |
||
898 | ---------- |
||
899 | pix : tuple |
||
900 | Tuple of pixel index arrays for each dimension of the map. |
||
901 | Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n) |
||
902 | for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps. |
||
903 | Pixel indices can be either float or integer type. Float |
||
904 | indices will be rounded to the nearest integer. |
||
905 | vals : `~numpy.ndarray` |
||
906 | Values vector. |
||
907 | """ |
||
908 | idx = pix_tuple_to_idx(pix) |
||
909 | return self.set_by_idx(idx, vals) |
||
910 | |||
911 | @abc.abstractmethod |
||
912 | def set_by_idx(self, idx, vals): |
||
913 | """Set pixels at ``idx`` with given ``vals``. |
||
914 | |||
915 | Parameters |
||
916 | ---------- |
||
917 | idx : tuple |
||
918 | Tuple of pixel index arrays for each dimension of the map. |
||
919 | Tuple should be ordered as (I_lon, I_lat, I_0, ..., I_n) |
||
920 | for WCS maps and (I_hpx, I_0, ..., I_n) for HEALPix maps. |
||
921 | vals : `~numpy.ndarray` |
||
922 | Values vector. |
||
923 | """ |
||
924 | pass |
||
925 | |||
926 | def plot_interactive(self, rc_params=None, **kwargs): |
||
927 | """ |
||
928 | Plot map with interactive widgets to explore the non spatial axes. |
||
929 | |||
930 | Parameters |
||
931 | ---------- |
||
932 | rc_params : dict |
||
933 | Passed to ``matplotlib.rc_context(rc=rc_params)`` to style the plot. |
||
934 | **kwargs : dict |
||
935 | Keyword arguments passed to `WcsNDMap.plot`. |
||
936 | |||
937 | Examples |
||
938 | -------- |
||
939 | You can try this out e.g. using a Fermi-LAT diffuse model cube with an energy axis:: |
||
940 | |||
941 | from gammapy.maps import Map |
||
942 | |||
943 | m = Map.read("$GAMMAPY_EXTRA/datasets/vela_region/gll_iem_v05_rev1_cutout.fits") |
||
944 | m.plot_interactive(cmap='gnuplot2') |
||
945 | |||
946 | If you would like to adjust the figure size you can use the ``rc_params`` argument:: |
||
947 | |||
948 | rc_params = {'figure.figsize': (12, 6), 'font.size': 12} |
||
949 | m.plot_interactive(rc_params=rc_params) |
||
950 | """ |
||
951 | import matplotlib as mpl |
||
952 | import matplotlib.pyplot as plt |
||
953 | from ipywidgets.widgets.interaction import interact, fixed |
||
954 | from ipywidgets import SelectionSlider, RadioButtons |
||
955 | |||
956 | if self.geom.is_image: |
||
957 | raise TypeError("Use .plot() for 2D Maps") |
||
958 | |||
959 | kwargs.setdefault("interpolation", "nearest") |
||
960 | kwargs.setdefault("origin", "lower") |
||
961 | kwargs.setdefault("cmap", "afmhot") |
||
962 | |||
963 | rc_params = rc_params or {} |
||
964 | stretch = kwargs.pop("stretch", "sqrt") |
||
965 | |||
966 | interact_kwargs = {} |
||
967 | |||
968 | for axis in self.geom.axes: |
||
969 | if axis.node_type == "edges": |
||
970 | options = [ |
||
971 | "{:.2e} - {:.2e} {}".format(val_min, val_max, axis.unit) |
||
972 | for val_min, val_max in zip(axis.edges[:-1], axis.edges[1:]) |
||
973 | ] |
||
974 | else: |
||
975 | options = ["{:.2e} {}".format(val, axis.unit) for val in axis.center] |
||
976 | |||
977 | interact_kwargs[axis.name] = SelectionSlider( |
||
978 | options=options, |
||
979 | description="Select {}:".format(axis.name), |
||
980 | continuous_update=False, |
||
981 | style={"description_width": "initial"}, |
||
982 | layout={"width": "50%"}, |
||
983 | ) |
||
984 | interact_kwargs[axis.name + "_options"] = fixed(options) |
||
985 | |||
986 | interact_kwargs["stretch"] = RadioButtons( |
||
987 | options=["linear", "sqrt", "log"], |
||
988 | value=stretch, |
||
989 | description="Select stretch:", |
||
990 | style={"description_width": "initial"}, |
||
991 | ) |
||
992 | |||
993 | @interact(**interact_kwargs) |
||
994 | def _plot_interactive(**ikwargs): |
||
995 | idx = [ |
||
996 | ikwargs[ax.name + "_options"].index(ikwargs[ax.name]) |
||
997 | for ax in self.geom.axes |
||
998 | ] |
||
999 | img = self.get_image_by_idx(idx) |
||
1000 | stretch = ikwargs["stretch"] |
||
1001 | with mpl.rc_context(rc=rc_params): |
||
1002 | fig, ax, cbar = img.plot(stretch=stretch, **kwargs) |
||
1003 | plt.show() |
||
1004 | |||
1005 | def copy(self, **kwargs): |
||
1006 | """Copy map instance and overwrite given attributes, except for geometry. |
||
1007 | |||
1008 | Parameters |
||
1009 | ---------- |
||
1010 | **kwargs : dict |
||
1011 | Keyword arguments to overwrite in the map constructor. |
||
1012 | |||
1013 | Returns |
||
1014 | -------- |
||
1015 | copy : `Map` |
||
1016 | Copied Map. |
||
1017 | """ |
||
1018 | if "geom" in kwargs: |
||
1019 | raise ValueError("Can't copy and change geometry of the map.") |
||
1020 | return self._init_copy(**kwargs) |
||
1021 | |||
1022 | def __repr__(self): |
||
1023 | str_ = self.__class__.__name__ |
||
1024 | str_ += "\n\n" |
||
1025 | geom = self.geom.__class__.__name__ |
||
1026 | str_ += "\tgeom : {} \n ".format(geom) |
||
1027 | axes = ["skycoord"] if self.geom.is_hpx else ["lon", "lat"] |
||
1028 | axes = axes + [_.name for _ in self.geom.axes] |
||
1029 | str_ += "\taxes : {}\n".format(", ".join(axes)) |
||
1030 | str_ += "\tshape : {}\n".format(self.geom.data_shape[::-1]) |
||
1031 | str_ += "\tndim : {}\n".format(self.geom.ndim) |
||
1032 | str_ += "\tunit : {!r} \n".format(str(self.unit)) |
||
1033 | str_ += "\tdtype : {} \n".format(self.data.dtype) |
||
1034 | return str_ |
||
1035 | |||
1036 | def _arithmetics(self, operator, other, copy): |
||
1037 | """ Perform arithmetics on maps after checking geometry consistency""" |
||
1038 | if isinstance(other, Map): |
||
1039 | # TODO: check consistency |
||
1040 | q = other.quantity |
||
1041 | else: |
||
1042 | q = u.Quantity(other, copy=False) |
||
1043 | |||
1044 | out = self.copy() if copy else self |
||
1045 | out.quantity = operator(out.quantity, q) |
||
1046 | return out |
||
1047 | |||
1048 | def __add__(self, other): |
||
1049 | return self._arithmetics(np.add, other, copy=True) |
||
1050 | |||
1051 | def __iadd__(self, other): |
||
1052 | return self._arithmetics(np.add, other, copy=False) |
||
1053 | |||
1054 | def __sub__(self, other): |
||
1055 | return self._arithmetics(np.subtract, other, copy=True) |
||
1056 | |||
1057 | def __isub__(self, other): |
||
1058 | return self._arithmetics(np.subtract, other, copy=False) |
||
1059 | |||
1060 | def __mul__(self, other): |
||
1061 | return self._arithmetics(np.multiply, other, copy=True) |
||
1062 | |||
1063 | def __imul__(self, other): |
||
1064 | return self._arithmetics(np.multiply, other, copy=False) |
||
1065 | |||
1066 | def __truediv__(self, other): |
||
1067 | return self._arithmetics(np.true_divide, other, copy=True) |
||
1068 | |||
1069 | def __itruediv__(self, other): |
||
1070 | return self._arithmetics(np.true_divide, other, copy=False) |
||
1071 |