Coverage for python/lsst/pipe/tasks/functors.py: 34%
724 statements
« prev ^ index » next coverage.py v6.5.0, created at 2023-06-07 12:50 +0000
« prev ^ index » next coverage.py v6.5.0, created at 2023-06-07 12:50 +0000
1# This file is part of pipe_tasks.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22__all__ = ["Functor", "CompositeFunctor", "CustomFunctor", "Column", "Index",
23 "CoordColumn", "RAColumn", "DecColumn", "HtmIndex20", "Mag",
24 "MagErr", "MagDiff", "Color", "DeconvolvedMoments", "SdssTraceSize",
25 "PsfSdssTraceSizeDiff", "HsmTraceSize", "PsfHsmTraceSizeDiff",
26 "HsmFwhm", "E1", "E2", "RadiusFromQuadrupole", "LocalWcs",
27 "ComputePixelScale", "ConvertPixelToArcseconds",
28 "ConvertPixelSqToArcsecondsSq", "ReferenceBand", "Photometry",
29 "NanoJansky", "NanoJanskyErr", "LocalPhotometry", "LocalNanojansky",
30 "LocalNanojanskyErr", "LocalDipoleMeanFlux",
31 "LocalDipoleMeanFluxErr", "LocalDipoleDiffFlux",
32 "LocalDipoleDiffFluxErr", "Ebv",
33 ]
35import yaml
36import re
37from itertools import product
38import logging
39import os.path
41import pandas as pd
42import numpy as np
43import astropy.units as u
44from astropy.coordinates import SkyCoord
46from lsst.utils import doImport
47from lsst.utils.introspection import get_full_type_name
48from lsst.daf.butler import DeferredDatasetHandle
49from lsst.pipe.base import InMemoryDatasetHandle
50import lsst.geom as geom
51import lsst.sphgeom as sphgeom
54def init_fromDict(initDict, basePath='lsst.pipe.tasks.functors',
55 typeKey='functor', name=None):
56 """Initialize an object defined in a dictionary
58 The object needs to be importable as
59 f'{basePath}.{initDict[typeKey]}'
60 The positional and keyword arguments (if any) are contained in
61 "args" and "kwargs" entries in the dictionary, respectively.
62 This is used in `functors.CompositeFunctor.from_yaml` to initialize
63 a composite functor from a specification in a YAML file.
65 Parameters
66 ----------
67 initDict : dictionary
68 Dictionary describing object's initialization. Must contain
69 an entry keyed by ``typeKey`` that is the name of the object,
70 relative to ``basePath``.
71 basePath : str
72 Path relative to module in which ``initDict[typeKey]`` is defined.
73 typeKey : str
74 Key of ``initDict`` that is the name of the object
75 (relative to `basePath`).
76 """
77 initDict = initDict.copy()
78 # TO DO: DM-21956 We should be able to define functors outside this module
79 pythonType = doImport(f'{basePath}.{initDict.pop(typeKey)}')
80 args = []
81 if 'args' in initDict:
82 args = initDict.pop('args')
83 if isinstance(args, str):
84 args = [args]
85 try:
86 element = pythonType(*args, **initDict)
87 except Exception as e:
88 message = f'Error in constructing functor "{name}" of type {pythonType.__name__} with args: {args}'
89 raise type(e)(message, e.args)
90 return element
93class Functor(object):
94 """Define and execute a calculation on a DataFrame or Handle holding a DataFrame.
96 The `__call__` method accepts either a `DataFrame` object or a
97 `DeferredDatasetHandle` or `InMemoryDatasetHandle`, and returns the
98 result of the calculation as a single column. Each functor defines what
99 columns are needed for the calculation, and only these columns are read
100 from the dataset handle.
102 The action of `__call__` consists of two steps: first, loading the
103 necessary columns from disk into memory as a `pandas.DataFrame` object;
104 and second, performing the computation on this dataframe and returning the
105 result.
108 To define a new `Functor`, a subclass must define a `_func` method,
109 that takes a `pandas.DataFrame` and returns result in a `pandas.Series`.
110 In addition, it must define the following attributes
112 * `_columns`: The columns necessary to perform the calculation
113 * `name`: A name appropriate for a figure axis label
114 * `shortname`: A name appropriate for use as a dictionary key
116 On initialization, a `Functor` should declare what band (`filt` kwarg)
117 and dataset (e.g. `'ref'`, `'meas'`, `'forced_src'`) it is intended to be
118 applied to. This enables the `_get_data` method to extract the proper
119 columns from the underlying data. If not specified, the dataset will fall back
120 on the `_defaultDataset`attribute. If band is not specified and `dataset`
121 is anything other than `'ref'`, then an error will be raised when trying to
122 perform the calculation.
124 Originally, `Functor` was set up to expect
125 datasets formatted like the `deepCoadd_obj` dataset; that is, a
126 dataframe with a multi-level column index, with the levels of the
127 column index being `band`, `dataset`, and `column`.
128 It has since been generalized to apply to dataframes without mutli-level
129 indices and multi-level indices with just `dataset` and `column` levels.
130 In addition, the `_get_data` method that reads
131 the columns from the underlying data will return a dataframe with column
132 index levels defined by the `_dfLevels` attribute; by default, this is
133 `column`.
135 The `_dfLevels` attributes should generally not need to
136 be changed, unless `_func` needs columns from multiple filters or datasets
137 to do the calculation.
138 An example of this is the `lsst.pipe.tasks.functors.Color` functor, for
139 which `_dfLevels = ('band', 'column')`, and `_func` expects the dataframe
140 it gets to have those levels in the column index.
142 Parameters
143 ----------
144 filt : str
145 Filter upon which to do the calculation
147 dataset : str
148 Dataset upon which to do the calculation
149 (e.g., 'ref', 'meas', 'forced_src').
150 """
152 _defaultDataset = 'ref'
153 _dfLevels = ('column',)
154 _defaultNoDup = False
156 def __init__(self, filt=None, dataset=None, noDup=None):
157 self.filt = filt
158 self.dataset = dataset if dataset is not None else self._defaultDataset
159 self._noDup = noDup
160 self.log = logging.getLogger(type(self).__name__)
162 @property
163 def noDup(self):
164 if self._noDup is not None:
165 return self._noDup
166 else:
167 return self._defaultNoDup
169 @property
170 def columns(self):
171 """Columns required to perform calculation
172 """
173 if not hasattr(self, '_columns'):
174 raise NotImplementedError('Must define columns property or _columns attribute')
175 return self._columns
177 def _get_data_columnLevels(self, data, columnIndex=None):
178 """Gets the names of the column index levels
180 This should only be called in the context of a multilevel table.
182 Parameters
183 ----------
184 data : various
185 The data to be read, can be a `DeferredDatasetHandle` or
186 `InMemoryDatasetHandle`.
187 columnnIndex (optional): pandas `Index` object
188 If not passed, then it is read from the `DeferredDatasetHandle`
189 for `InMemoryDatasetHandle`.
190 """
191 if columnIndex is None:
192 columnIndex = data.get(component="columns")
193 return columnIndex.names
195 def _get_data_columnLevelNames(self, data, columnIndex=None):
196 """Gets the content of each of the column levels for a multilevel table.
197 """
198 if columnIndex is None:
199 columnIndex = data.get(component="columns")
201 columnLevels = columnIndex.names
202 columnLevelNames = {
203 level: list(np.unique(np.array([c for c in columnIndex])[:, i]))
204 for i, level in enumerate(columnLevels)
205 }
206 return columnLevelNames
208 def _colsFromDict(self, colDict, columnIndex=None):
209 """Converts dictionary column specficiation to a list of columns
210 """
211 new_colDict = {}
212 columnLevels = self._get_data_columnLevels(None, columnIndex=columnIndex)
214 for i, lev in enumerate(columnLevels):
215 if lev in colDict:
216 if isinstance(colDict[lev], str):
217 new_colDict[lev] = [colDict[lev]]
218 else:
219 new_colDict[lev] = colDict[lev]
220 else:
221 new_colDict[lev] = columnIndex.levels[i]
223 levelCols = [new_colDict[lev] for lev in columnLevels]
224 cols = list(product(*levelCols))
225 colsAvailable = [col for col in cols if col in columnIndex]
226 return colsAvailable
228 def multilevelColumns(self, data, columnIndex=None, returnTuple=False):
229 """Returns columns needed by functor from multilevel dataset
231 To access tables with multilevel column structure, the `DeferredDatasetHandle`
232 or `InMemoryDatasetHandle` need to be passed either a list of tuples or a
233 dictionary.
235 Parameters
236 ----------
237 data : various
238 The data as either `DeferredDatasetHandle`, or `InMemoryDatasetHandle`.
239 columnIndex (optional): pandas `Index` object
240 either passed or read in from `DeferredDatasetHandle`.
241 `returnTuple` : `bool`
242 If true, then return a list of tuples rather than the column dictionary
243 specification. This is set to `True` by `CompositeFunctor` in order to be able to
244 combine columns from the various component functors.
246 """
247 if not isinstance(data, (DeferredDatasetHandle, InMemoryDatasetHandle)):
248 raise RuntimeError(f"Unexpected data type. Got {get_full_type_name(data)}.")
250 if columnIndex is None:
251 columnIndex = data.get(component="columns")
253 # Confirm that the dataset has the column levels the functor is expecting it to have.
254 columnLevels = self._get_data_columnLevels(data, columnIndex)
256 columnDict = {'column': self.columns,
257 'dataset': self.dataset}
258 if self.filt is None:
259 columnLevelNames = self._get_data_columnLevelNames(data, columnIndex)
260 if "band" in columnLevels:
261 if self.dataset == "ref":
262 columnDict["band"] = columnLevelNames["band"][0]
263 else:
264 raise ValueError(f"'filt' not set for functor {self.name}"
265 f"(dataset {self.dataset}) "
266 "and DataFrame "
267 "contains multiple filters in column index. "
268 "Set 'filt' or set 'dataset' to 'ref'.")
269 else:
270 columnDict['band'] = self.filt
272 if returnTuple:
273 return self._colsFromDict(columnDict, columnIndex=columnIndex)
274 else:
275 return columnDict
277 def _func(self, df, dropna=True):
278 raise NotImplementedError('Must define calculation on dataframe')
280 def _get_columnIndex(self, data):
281 """Return columnIndex
282 """
284 if isinstance(data, (DeferredDatasetHandle, InMemoryDatasetHandle)):
285 return data.get(component="columns")
286 else:
287 return None
289 def _get_data(self, data):
290 """Retrieve dataframe necessary for calculation.
292 The data argument can be a `DataFrame`, a `DeferredDatasetHandle`, or an
293 `InMemoryDatasetHandle`.
295 Returns dataframe upon which `self._func` can act.
296 """
297 # We wrap a dataframe in a handle here to take advantage of the dataframe
298 # delegate dataframe column wrangling abilities.
299 if isinstance(data, pd.DataFrame):
300 _data = InMemoryDatasetHandle(data, storageClass="DataFrame")
301 elif isinstance(data, (DeferredDatasetHandle, InMemoryDatasetHandle)):
302 _data = data
303 else:
304 raise RuntimeError(f"Unexpected type provided for data. Got {get_full_type_name(data)}.")
306 # First thing to do: check to see if the data source has a multilevel column index or not.
307 columnIndex = self._get_columnIndex(_data)
308 is_multiLevel = isinstance(columnIndex, pd.MultiIndex)
310 # Get proper columns specification for this functor
311 if is_multiLevel:
312 columns = self.multilevelColumns(_data, columnIndex=columnIndex)
313 else:
314 columns = self.columns
316 # Load in-memory dataframe with appropriate columns the gen3 way
317 df = _data.get(parameters={"columns": columns})
319 # Drop unnecessary column levels
320 if is_multiLevel:
321 df = self._setLevels(df)
323 return df
325 def _setLevels(self, df):
326 levelsToDrop = [n for n in df.columns.names if n not in self._dfLevels]
327 df.columns = df.columns.droplevel(levelsToDrop)
328 return df
330 def _dropna(self, vals):
331 return vals.dropna()
333 def __call__(self, data, dropna=False):
334 df = self._get_data(data)
335 try:
336 vals = self._func(df)
337 except Exception as e:
338 self.log.error("Exception in %s call: %s: %s", self.name, type(e).__name__, e)
339 vals = self.fail(df)
340 if dropna:
341 vals = self._dropna(vals)
343 return vals
345 def difference(self, data1, data2, **kwargs):
346 """Computes difference between functor called on two different DataFrame/Handle objects
347 """
348 return self(data1, **kwargs) - self(data2, **kwargs)
350 def fail(self, df):
351 return pd.Series(np.full(len(df), np.nan), index=df.index)
353 @property
354 def name(self):
355 """Full name of functor (suitable for figure labels)
356 """
357 return NotImplementedError
359 @property
360 def shortname(self):
361 """Short name of functor (suitable for column name/dict key)
362 """
363 return self.name
366class CompositeFunctor(Functor):
367 """Perform multiple calculations at once on a catalog.
369 The role of a `CompositeFunctor` is to group together computations from
370 multiple functors. Instead of returning `pandas.Series` a
371 `CompositeFunctor` returns a `pandas.Dataframe`, with the column names
372 being the keys of `funcDict`.
374 The `columns` attribute of a `CompositeFunctor` is the union of all columns
375 in all the component functors.
377 A `CompositeFunctor` does not use a `_func` method itself; rather,
378 when a `CompositeFunctor` is called, all its columns are loaded
379 at once, and the resulting dataframe is passed to the `_func` method of each component
380 functor. This has the advantage of only doing I/O (reading from parquet file) once,
381 and works because each individual `_func` method of each component functor does not
382 care if there are *extra* columns in the dataframe being passed; only that it must contain
383 *at least* the `columns` it expects.
385 An important and useful class method is `from_yaml`, which takes as argument the path to a YAML
386 file specifying a collection of functors.
388 Parameters
389 ----------
390 funcs : `dict` or `list`
391 Dictionary or list of functors. If a list, then it will be converted
392 into a dictonary according to the `.shortname` attribute of each functor.
394 """
395 dataset = None
396 name = "CompositeFunctor"
398 def __init__(self, funcs, **kwargs):
400 if type(funcs) == dict:
401 self.funcDict = funcs
402 else:
403 self.funcDict = {f.shortname: f for f in funcs}
405 self._filt = None
407 super().__init__(**kwargs)
409 @property
410 def filt(self):
411 return self._filt
413 @filt.setter
414 def filt(self, filt):
415 if filt is not None:
416 for _, f in self.funcDict.items():
417 f.filt = filt
418 self._filt = filt
420 def update(self, new):
421 if isinstance(new, dict):
422 self.funcDict.update(new)
423 elif isinstance(new, CompositeFunctor):
424 self.funcDict.update(new.funcDict)
425 else:
426 raise TypeError('Can only update with dictionary or CompositeFunctor.')
428 # Make sure new functors have the same 'filt' set
429 if self.filt is not None:
430 self.filt = self.filt
432 @property
433 def columns(self):
434 return list(set([x for y in [f.columns for f in self.funcDict.values()] for x in y]))
436 def multilevelColumns(self, data, **kwargs):
437 # Get the union of columns for all component functors. Note the need to have `returnTuple=True` here.
438 return list(
439 set(
440 [
441 x
442 for y in [
443 f.multilevelColumns(data, returnTuple=True, **kwargs) for f in self.funcDict.values()
444 ]
445 for x in y
446 ]
447 )
448 )
450 def __call__(self, data, **kwargs):
451 """Apply the functor to the data table
453 Parameters
454 ----------
455 data : various
456 The data represented as `lsst.daf.butler.DeferredDatasetHandle`,
457 `lsst.pipe.base.InMemoryDatasetHandle`,
458 or `pandas.DataFrame`.
459 The table or a pointer to a table on disk from which columns can
460 be accessed
461 """
462 if isinstance(data, pd.DataFrame):
463 _data = InMemoryDatasetHandle(data, storageClass="DataFrame")
464 elif isinstance(data, (DeferredDatasetHandle, InMemoryDatasetHandle)):
465 _data = data
466 else:
467 raise RuntimeError(f"Unexpected type provided for data. Got {get_full_type_name(data)}.")
469 columnIndex = self._get_columnIndex(_data)
471 if isinstance(columnIndex, pd.MultiIndex):
472 columns = self.multilevelColumns(_data, columnIndex=columnIndex)
473 df = _data.get(parameters={"columns": columns})
475 valDict = {}
476 for k, f in self.funcDict.items():
477 try:
478 subdf = f._setLevels(
479 df[f.multilevelColumns(_data, returnTuple=True, columnIndex=columnIndex)]
480 )
481 valDict[k] = f._func(subdf)
482 except Exception as e:
483 self.log.exception(
484 "Exception in %s (funcs: %s) call: %s",
485 self.name,
486 str(list(self.funcDict.keys())),
487 type(e).__name__,
488 )
489 try:
490 valDict[k] = f.fail(subdf)
491 except NameError:
492 raise e
494 else:
495 df = _data.get(parameters={"columns": self.columns})
497 valDict = {k: f._func(df) for k, f in self.funcDict.items()}
499 # Check that output columns are actually columns
500 for name, colVal in valDict.items():
501 if len(colVal.shape) != 1:
502 raise RuntimeError("Transformed column '%s' is not the shape of a column. "
503 "It is shaped %s and type %s." % (name, colVal.shape, type(colVal)))
505 try:
506 valDf = pd.concat(valDict, axis=1)
507 except TypeError:
508 print([(k, type(v)) for k, v in valDict.items()])
509 raise
511 if kwargs.get('dropna', False):
512 valDf = valDf.dropna(how='any')
514 return valDf
516 @classmethod
517 def renameCol(cls, col, renameRules):
518 if renameRules is None:
519 return col
520 for old, new in renameRules:
521 if col.startswith(old):
522 col = col.replace(old, new)
523 return col
525 @classmethod
526 def from_file(cls, filename, **kwargs):
527 # Allow environment variables in the filename.
528 filename = os.path.expandvars(filename)
529 with open(filename) as f:
530 translationDefinition = yaml.safe_load(f)
532 return cls.from_yaml(translationDefinition, **kwargs)
534 @classmethod
535 def from_yaml(cls, translationDefinition, **kwargs):
536 funcs = {}
537 for func, val in translationDefinition['funcs'].items():
538 funcs[func] = init_fromDict(val, name=func)
540 if 'flag_rename_rules' in translationDefinition:
541 renameRules = translationDefinition['flag_rename_rules']
542 else:
543 renameRules = None
545 if 'calexpFlags' in translationDefinition:
546 for flag in translationDefinition['calexpFlags']:
547 funcs[cls.renameCol(flag, renameRules)] = Column(flag, dataset='calexp')
549 if 'refFlags' in translationDefinition:
550 for flag in translationDefinition['refFlags']:
551 funcs[cls.renameCol(flag, renameRules)] = Column(flag, dataset='ref')
553 if 'forcedFlags' in translationDefinition:
554 for flag in translationDefinition['forcedFlags']:
555 funcs[cls.renameCol(flag, renameRules)] = Column(flag, dataset='forced_src')
557 if 'flags' in translationDefinition:
558 for flag in translationDefinition['flags']:
559 funcs[cls.renameCol(flag, renameRules)] = Column(flag, dataset='meas')
561 return cls(funcs, **kwargs)
564def mag_aware_eval(df, expr, log):
565 """Evaluate an expression on a DataFrame, knowing what the 'mag' function means
567 Builds on `pandas.DataFrame.eval`, which parses and executes math on dataframes.
569 Parameters
570 ----------
571 df : pandas.DataFrame
572 Dataframe on which to evaluate expression.
574 expr : str
575 Expression.
576 """
577 try:
578 expr_new = re.sub(r'mag\((\w+)\)', r'-2.5*log(\g<1>)/log(10)', expr)
579 val = df.eval(expr_new)
580 except Exception as e: # Should check what actually gets raised
581 log.error("Exception in mag_aware_eval: %s: %s", type(e).__name__, e)
582 expr_new = re.sub(r'mag\((\w+)\)', r'-2.5*log(\g<1>_instFlux)/log(10)', expr)
583 val = df.eval(expr_new)
584 return val
587class CustomFunctor(Functor):
588 """Arbitrary computation on a catalog
590 Column names (and thus the columns to be loaded from catalog) are found
591 by finding all words and trying to ignore all "math-y" words.
593 Parameters
594 ----------
595 expr : str
596 Expression to evaluate, to be parsed and executed by `mag_aware_eval`.
597 """
598 _ignore_words = ('mag', 'sin', 'cos', 'exp', 'log', 'sqrt')
600 def __init__(self, expr, **kwargs):
601 self.expr = expr
602 super().__init__(**kwargs)
604 @property
605 def name(self):
606 return self.expr
608 @property
609 def columns(self):
610 flux_cols = re.findall(r'mag\(\s*(\w+)\s*\)', self.expr)
612 cols = [c for c in re.findall(r'[a-zA-Z_]+', self.expr) if c not in self._ignore_words]
613 not_a_col = []
614 for c in flux_cols:
615 if not re.search('_instFlux$', c):
616 cols.append(f'{c}_instFlux')
617 not_a_col.append(c)
618 else:
619 cols.append(c)
621 return list(set([c for c in cols if c not in not_a_col]))
623 def _func(self, df):
624 return mag_aware_eval(df, self.expr, self.log)
627class Column(Functor):
628 """Get column with specified name
629 """
631 def __init__(self, col, **kwargs):
632 self.col = col
633 super().__init__(**kwargs)
635 @property
636 def name(self):
637 return self.col
639 @property
640 def columns(self):
641 return [self.col]
643 def _func(self, df):
644 return df[self.col]
647class Index(Functor):
648 """Return the value of the index for each object
649 """
651 columns = ['coord_ra'] # just a dummy; something has to be here
652 _defaultDataset = 'ref'
653 _defaultNoDup = True
655 def _func(self, df):
656 return pd.Series(df.index, index=df.index)
659class CoordColumn(Column):
660 """Base class for coordinate column, in degrees
661 """
662 _radians = True
664 def __init__(self, col, **kwargs):
665 super().__init__(col, **kwargs)
667 def _func(self, df):
668 # Must not modify original column in case that column is used by another functor
669 output = df[self.col] * 180 / np.pi if self._radians else df[self.col]
670 return output
673class RAColumn(CoordColumn):
674 """Right Ascension, in degrees
675 """
676 name = 'RA'
677 _defaultNoDup = True
679 def __init__(self, **kwargs):
680 super().__init__('coord_ra', **kwargs)
682 def __call__(self, catalog, **kwargs):
683 return super().__call__(catalog, **kwargs)
686class DecColumn(CoordColumn):
687 """Declination, in degrees
688 """
689 name = 'Dec'
690 _defaultNoDup = True
692 def __init__(self, **kwargs):
693 super().__init__('coord_dec', **kwargs)
695 def __call__(self, catalog, **kwargs):
696 return super().__call__(catalog, **kwargs)
699class HtmIndex20(Functor):
700 """Compute the level 20 HtmIndex for the catalog.
702 Notes
703 -----
704 This functor was implemented to satisfy requirements of old APDB interface
705 which required ``pixelId`` column in DiaObject with HTM20 index. APDB
706 interface had migrated to not need that information, but we keep this
707 class in case it may be useful for something else.
708 """
709 name = "Htm20"
710 htmLevel = 20
711 _radians = True
713 def __init__(self, ra, dec, **kwargs):
714 self.pixelator = sphgeom.HtmPixelization(self.htmLevel)
715 self.ra = ra
716 self.dec = dec
717 self._columns = [self.ra, self.dec]
718 super().__init__(**kwargs)
720 def _func(self, df):
722 def computePixel(row):
723 if self._radians:
724 sphPoint = geom.SpherePoint(row[self.ra],
725 row[self.dec],
726 geom.radians)
727 else:
728 sphPoint = geom.SpherePoint(row[self.ra],
729 row[self.dec],
730 geom.degrees)
731 return self.pixelator.index(sphPoint.getVector())
733 return df.apply(computePixel, axis=1, result_type='reduce').astype('int64')
736def fluxName(col):
737 if not col.endswith('_instFlux'):
738 col += '_instFlux'
739 return col
742def fluxErrName(col):
743 if not col.endswith('_instFluxErr'):
744 col += '_instFluxErr'
745 return col
748class Mag(Functor):
749 """Compute calibrated magnitude
751 Takes a `calib` argument, which returns the flux at mag=0
752 as `calib.getFluxMag0()`. If not provided, then the default
753 `fluxMag0` is 63095734448.0194, which is default for HSC.
754 This default should be removed in DM-21955
756 This calculation hides warnings about invalid values and dividing by zero.
758 As for all functors, a `dataset` and `filt` kwarg should be provided upon
759 initialization. Unlike the default `Functor`, however, the default dataset
760 for a `Mag` is `'meas'`, rather than `'ref'`.
762 Parameters
763 ----------
764 col : `str`
765 Name of flux column from which to compute magnitude. Can be parseable
766 by `lsst.pipe.tasks.functors.fluxName` function---that is, you can pass
767 `'modelfit_CModel'` instead of `'modelfit_CModel_instFlux'`) and it will
768 understand.
769 calib : `lsst.afw.image.calib.Calib` (optional)
770 Object that knows zero point.
771 """
772 _defaultDataset = 'meas'
774 def __init__(self, col, calib=None, **kwargs):
775 self.col = fluxName(col)
776 self.calib = calib
777 if calib is not None:
778 self.fluxMag0 = calib.getFluxMag0()[0]
779 else:
780 # TO DO: DM-21955 Replace hard coded photometic calibration values
781 self.fluxMag0 = 63095734448.0194
783 super().__init__(**kwargs)
785 @property
786 def columns(self):
787 return [self.col]
789 def _func(self, df):
790 with np.warnings.catch_warnings():
791 np.warnings.filterwarnings('ignore', r'invalid value encountered')
792 np.warnings.filterwarnings('ignore', r'divide by zero')
793 return -2.5*np.log10(df[self.col] / self.fluxMag0)
795 @property
796 def name(self):
797 return f'mag_{self.col}'
800class MagErr(Mag):
801 """Compute calibrated magnitude uncertainty
803 Takes the same `calib` object as `lsst.pipe.tasks.functors.Mag`.
805 Parameters
806 col : `str`
807 Name of flux column
808 calib : `lsst.afw.image.calib.Calib` (optional)
809 Object that knows zero point.
810 """
812 def __init__(self, *args, **kwargs):
813 super().__init__(*args, **kwargs)
814 if self.calib is not None:
815 self.fluxMag0Err = self.calib.getFluxMag0()[1]
816 else:
817 self.fluxMag0Err = 0.
819 @property
820 def columns(self):
821 return [self.col, self.col + 'Err']
823 def _func(self, df):
824 with np.warnings.catch_warnings():
825 np.warnings.filterwarnings('ignore', r'invalid value encountered')
826 np.warnings.filterwarnings('ignore', r'divide by zero')
827 fluxCol, fluxErrCol = self.columns
828 x = df[fluxErrCol] / df[fluxCol]
829 y = self.fluxMag0Err / self.fluxMag0
830 magErr = (2.5 / np.log(10.)) * np.sqrt(x*x + y*y)
831 return magErr
833 @property
834 def name(self):
835 return super().name + '_err'
838class MagDiff(Functor):
839 _defaultDataset = 'meas'
841 """Functor to calculate magnitude difference"""
843 def __init__(self, col1, col2, **kwargs):
844 self.col1 = fluxName(col1)
845 self.col2 = fluxName(col2)
846 super().__init__(**kwargs)
848 @property
849 def columns(self):
850 return [self.col1, self.col2]
852 def _func(self, df):
853 with np.warnings.catch_warnings():
854 np.warnings.filterwarnings('ignore', r'invalid value encountered')
855 np.warnings.filterwarnings('ignore', r'divide by zero')
856 return -2.5*np.log10(df[self.col1]/df[self.col2])
858 @property
859 def name(self):
860 return f'(mag_{self.col1} - mag_{self.col2})'
862 @property
863 def shortname(self):
864 return f'magDiff_{self.col1}_{self.col2}'
867class Color(Functor):
868 """Compute the color between two filters
870 Computes color by initializing two different `Mag`
871 functors based on the `col` and filters provided, and
872 then returning the difference.
874 This is enabled by the `_func` expecting a dataframe with a
875 multilevel column index, with both `'band'` and `'column'`,
876 instead of just `'column'`, which is the `Functor` default.
877 This is controlled by the `_dfLevels` attribute.
879 Also of note, the default dataset for `Color` is `forced_src'`,
880 whereas for `Mag` it is `'meas'`.
882 Parameters
883 ----------
884 col : str
885 Name of flux column from which to compute; same as would be passed to
886 `lsst.pipe.tasks.functors.Mag`.
888 filt2, filt1 : str
889 Filters from which to compute magnitude difference.
890 Color computed is `Mag(filt2) - Mag(filt1)`.
891 """
892 _defaultDataset = 'forced_src'
893 _dfLevels = ('band', 'column')
894 _defaultNoDup = True
896 def __init__(self, col, filt2, filt1, **kwargs):
897 self.col = fluxName(col)
898 if filt2 == filt1:
899 raise RuntimeError("Cannot compute Color for %s: %s - %s " % (col, filt2, filt1))
900 self.filt2 = filt2
901 self.filt1 = filt1
903 self.mag2 = Mag(col, filt=filt2, **kwargs)
904 self.mag1 = Mag(col, filt=filt1, **kwargs)
906 super().__init__(**kwargs)
908 @property
909 def filt(self):
910 return None
912 @filt.setter
913 def filt(self, filt):
914 pass
916 def _func(self, df):
917 mag2 = self.mag2._func(df[self.filt2])
918 mag1 = self.mag1._func(df[self.filt1])
919 return mag2 - mag1
921 @property
922 def columns(self):
923 return [self.mag1.col, self.mag2.col]
925 def multilevelColumns(self, parq, **kwargs):
926 return [(self.dataset, self.filt1, self.col), (self.dataset, self.filt2, self.col)]
928 @property
929 def name(self):
930 return f'{self.filt2} - {self.filt1} ({self.col})'
932 @property
933 def shortname(self):
934 return f"{self.col}_{self.filt2.replace('-', '')}m{self.filt1.replace('-', '')}"
937class DeconvolvedMoments(Functor):
938 name = 'Deconvolved Moments'
939 shortname = 'deconvolvedMoments'
940 _columns = ("ext_shapeHSM_HsmSourceMoments_xx",
941 "ext_shapeHSM_HsmSourceMoments_yy",
942 "base_SdssShape_xx", "base_SdssShape_yy",
943 "ext_shapeHSM_HsmPsfMoments_xx",
944 "ext_shapeHSM_HsmPsfMoments_yy")
946 def _func(self, df):
947 """Calculate deconvolved moments"""
948 if "ext_shapeHSM_HsmSourceMoments_xx" in df.columns: # _xx added by tdm
949 hsm = df["ext_shapeHSM_HsmSourceMoments_xx"] + df["ext_shapeHSM_HsmSourceMoments_yy"]
950 else:
951 hsm = np.ones(len(df))*np.nan
952 sdss = df["base_SdssShape_xx"] + df["base_SdssShape_yy"]
953 if "ext_shapeHSM_HsmPsfMoments_xx" in df.columns:
954 psf = df["ext_shapeHSM_HsmPsfMoments_xx"] + df["ext_shapeHSM_HsmPsfMoments_yy"]
955 else:
956 # LSST does not have shape.sdss.psf. Could instead add base_PsfShape to catalog using
957 # exposure.getPsf().computeShape(s.getCentroid()).getIxx()
958 # raise TaskError("No psf shape parameter found in catalog")
959 raise RuntimeError('No psf shape parameter found in catalog')
961 return hsm.where(np.isfinite(hsm), sdss) - psf
964class SdssTraceSize(Functor):
965 """Functor to calculate SDSS trace radius size for sources"""
966 name = "SDSS Trace Size"
967 shortname = 'sdssTrace'
968 _columns = ("base_SdssShape_xx", "base_SdssShape_yy")
970 def _func(self, df):
971 srcSize = np.sqrt(0.5*(df["base_SdssShape_xx"] + df["base_SdssShape_yy"]))
972 return srcSize
975class PsfSdssTraceSizeDiff(Functor):
976 """Functor to calculate SDSS trace radius size difference (%) between object and psf model"""
977 name = "PSF - SDSS Trace Size"
978 shortname = 'psf_sdssTrace'
979 _columns = ("base_SdssShape_xx", "base_SdssShape_yy",
980 "base_SdssShape_psf_xx", "base_SdssShape_psf_yy")
982 def _func(self, df):
983 srcSize = np.sqrt(0.5*(df["base_SdssShape_xx"] + df["base_SdssShape_yy"]))
984 psfSize = np.sqrt(0.5*(df["base_SdssShape_psf_xx"] + df["base_SdssShape_psf_yy"]))
985 sizeDiff = 100*(srcSize - psfSize)/(0.5*(srcSize + psfSize))
986 return sizeDiff
989class HsmTraceSize(Functor):
990 """Functor to calculate HSM trace radius size for sources"""
991 name = 'HSM Trace Size'
992 shortname = 'hsmTrace'
993 _columns = ("ext_shapeHSM_HsmSourceMoments_xx",
994 "ext_shapeHSM_HsmSourceMoments_yy")
996 def _func(self, df):
997 srcSize = np.sqrt(0.5*(df["ext_shapeHSM_HsmSourceMoments_xx"]
998 + df["ext_shapeHSM_HsmSourceMoments_yy"]))
999 return srcSize
1002class PsfHsmTraceSizeDiff(Functor):
1003 """Functor to calculate HSM trace radius size difference (%) between object and psf model"""
1004 name = 'PSF - HSM Trace Size'
1005 shortname = 'psf_HsmTrace'
1006 _columns = ("ext_shapeHSM_HsmSourceMoments_xx",
1007 "ext_shapeHSM_HsmSourceMoments_yy",
1008 "ext_shapeHSM_HsmPsfMoments_xx",
1009 "ext_shapeHSM_HsmPsfMoments_yy")
1011 def _func(self, df):
1012 srcSize = np.sqrt(0.5*(df["ext_shapeHSM_HsmSourceMoments_xx"]
1013 + df["ext_shapeHSM_HsmSourceMoments_yy"]))
1014 psfSize = np.sqrt(0.5*(df["ext_shapeHSM_HsmPsfMoments_xx"]
1015 + df["ext_shapeHSM_HsmPsfMoments_yy"]))
1016 sizeDiff = 100*(srcSize - psfSize)/(0.5*(srcSize + psfSize))
1017 return sizeDiff
1020class HsmFwhm(Functor):
1021 name = 'HSM Psf FWHM'
1022 _columns = ('ext_shapeHSM_HsmPsfMoments_xx', 'ext_shapeHSM_HsmPsfMoments_yy')
1023 # TODO: DM-21403 pixel scale should be computed from the CD matrix or transform matrix
1024 pixelScale = 0.168
1025 SIGMA2FWHM = 2*np.sqrt(2*np.log(2))
1027 def _func(self, df):
1028 return self.pixelScale*self.SIGMA2FWHM*np.sqrt(
1029 0.5*(df['ext_shapeHSM_HsmPsfMoments_xx'] + df['ext_shapeHSM_HsmPsfMoments_yy']))
1032class E1(Functor):
1033 name = "Distortion Ellipticity (e1)"
1034 shortname = "Distortion"
1036 def __init__(self, colXX, colXY, colYY, **kwargs):
1037 self.colXX = colXX
1038 self.colXY = colXY
1039 self.colYY = colYY
1040 self._columns = [self.colXX, self.colXY, self.colYY]
1041 super().__init__(**kwargs)
1043 @property
1044 def columns(self):
1045 return [self.colXX, self.colXY, self.colYY]
1047 def _func(self, df):
1048 return df[self.colXX] - df[self.colYY] / (df[self.colXX] + df[self.colYY])
1051class E2(Functor):
1052 name = "Ellipticity e2"
1054 def __init__(self, colXX, colXY, colYY, **kwargs):
1055 self.colXX = colXX
1056 self.colXY = colXY
1057 self.colYY = colYY
1058 super().__init__(**kwargs)
1060 @property
1061 def columns(self):
1062 return [self.colXX, self.colXY, self.colYY]
1064 def _func(self, df):
1065 return 2*df[self.colXY] / (df[self.colXX] + df[self.colYY])
1068class RadiusFromQuadrupole(Functor):
1070 def __init__(self, colXX, colXY, colYY, **kwargs):
1071 self.colXX = colXX
1072 self.colXY = colXY
1073 self.colYY = colYY
1074 super().__init__(**kwargs)
1076 @property
1077 def columns(self):
1078 return [self.colXX, self.colXY, self.colYY]
1080 def _func(self, df):
1081 return (df[self.colXX]*df[self.colYY] - df[self.colXY]**2)**0.25
1084class LocalWcs(Functor):
1085 """Computations using the stored localWcs.
1086 """
1087 name = "LocalWcsOperations"
1089 def __init__(self,
1090 colCD_1_1,
1091 colCD_1_2,
1092 colCD_2_1,
1093 colCD_2_2,
1094 **kwargs):
1095 self.colCD_1_1 = colCD_1_1
1096 self.colCD_1_2 = colCD_1_2
1097 self.colCD_2_1 = colCD_2_1
1098 self.colCD_2_2 = colCD_2_2
1099 super().__init__(**kwargs)
1101 def computeDeltaRaDec(self, x, y, cd11, cd12, cd21, cd22):
1102 """Compute the distance on the sphere from x2, y1 to x1, y1.
1104 Parameters
1105 ----------
1106 x : `pandas.Series`
1107 X pixel coordinate.
1108 y : `pandas.Series`
1109 Y pixel coordinate.
1110 cd11 : `pandas.Series`
1111 [1, 1] element of the local Wcs affine transform.
1112 cd11 : `pandas.Series`
1113 [1, 1] element of the local Wcs affine transform.
1114 cd12 : `pandas.Series`
1115 [1, 2] element of the local Wcs affine transform.
1116 cd21 : `pandas.Series`
1117 [2, 1] element of the local Wcs affine transform.
1118 cd22 : `pandas.Series`
1119 [2, 2] element of the local Wcs affine transform.
1121 Returns
1122 -------
1123 raDecTuple : tuple
1124 RA and dec conversion of x and y given the local Wcs. Returned
1125 units are in radians.
1127 """
1128 return (x * cd11 + y * cd12, x * cd21 + y * cd22)
1130 def computeSkySeparation(self, ra1, dec1, ra2, dec2):
1131 """Compute the local pixel scale conversion.
1133 Parameters
1134 ----------
1135 ra1 : `pandas.Series`
1136 Ra of the first coordinate in radians.
1137 dec1 : `pandas.Series`
1138 Dec of the first coordinate in radians.
1139 ra2 : `pandas.Series`
1140 Ra of the second coordinate in radians.
1141 dec2 : `pandas.Series`
1142 Dec of the second coordinate in radians.
1144 Returns
1145 -------
1146 dist : `pandas.Series`
1147 Distance on the sphere in radians.
1148 """
1149 deltaDec = dec2 - dec1
1150 deltaRa = ra2 - ra1
1151 return 2 * np.arcsin(
1152 np.sqrt(
1153 np.sin(deltaDec / 2) ** 2
1154 + np.cos(dec2) * np.cos(dec1) * np.sin(deltaRa / 2) ** 2))
1156 def getSkySeparationFromPixel(self, x1, y1, x2, y2, cd11, cd12, cd21, cd22):
1157 """Compute the distance on the sphere from x2, y1 to x1, y1.
1159 Parameters
1160 ----------
1161 x1 : `pandas.Series`
1162 X pixel coordinate.
1163 y1 : `pandas.Series`
1164 Y pixel coordinate.
1165 x2 : `pandas.Series`
1166 X pixel coordinate.
1167 y2 : `pandas.Series`
1168 Y pixel coordinate.
1169 cd11 : `pandas.Series`
1170 [1, 1] element of the local Wcs affine transform.
1171 cd11 : `pandas.Series`
1172 [1, 1] element of the local Wcs affine transform.
1173 cd12 : `pandas.Series`
1174 [1, 2] element of the local Wcs affine transform.
1175 cd21 : `pandas.Series`
1176 [2, 1] element of the local Wcs affine transform.
1177 cd22 : `pandas.Series`
1178 [2, 2] element of the local Wcs affine transform.
1180 Returns
1181 -------
1182 Distance : `pandas.Series`
1183 Arcseconds per pixel at the location of the local WC
1184 """
1185 ra1, dec1 = self.computeDeltaRaDec(x1, y1, cd11, cd12, cd21, cd22)
1186 ra2, dec2 = self.computeDeltaRaDec(x2, y2, cd11, cd12, cd21, cd22)
1187 # Great circle distance for small separations.
1188 return self.computeSkySeparation(ra1, dec1, ra2, dec2)
1191class ComputePixelScale(LocalWcs):
1192 """Compute the local pixel scale from the stored CDMatrix.
1193 """
1194 name = "PixelScale"
1196 @property
1197 def columns(self):
1198 return [self.colCD_1_1,
1199 self.colCD_1_2,
1200 self.colCD_2_1,
1201 self.colCD_2_2]
1203 def pixelScaleArcseconds(self, cd11, cd12, cd21, cd22):
1204 """Compute the local pixel to scale conversion in arcseconds.
1206 Parameters
1207 ----------
1208 cd11 : `pandas.Series`
1209 [1, 1] element of the local Wcs affine transform in radians.
1210 cd11 : `pandas.Series`
1211 [1, 1] element of the local Wcs affine transform in radians.
1212 cd12 : `pandas.Series`
1213 [1, 2] element of the local Wcs affine transform in radians.
1214 cd21 : `pandas.Series`
1215 [2, 1] element of the local Wcs affine transform in radians.
1216 cd22 : `pandas.Series`
1217 [2, 2] element of the local Wcs affine transform in radians.
1219 Returns
1220 -------
1221 pixScale : `pandas.Series`
1222 Arcseconds per pixel at the location of the local WC
1223 """
1224 return 3600 * np.degrees(np.sqrt(np.fabs(cd11 * cd22 - cd12 * cd21)))
1226 def _func(self, df):
1227 return self.pixelScaleArcseconds(df[self.colCD_1_1],
1228 df[self.colCD_1_2],
1229 df[self.colCD_2_1],
1230 df[self.colCD_2_2])
1233class ConvertPixelToArcseconds(ComputePixelScale):
1234 """Convert a value in units pixels to units arcseconds.
1235 """
1237 def __init__(self,
1238 col,
1239 colCD_1_1,
1240 colCD_1_2,
1241 colCD_2_1,
1242 colCD_2_2,
1243 **kwargs):
1244 self.col = col
1245 super().__init__(colCD_1_1,
1246 colCD_1_2,
1247 colCD_2_1,
1248 colCD_2_2,
1249 **kwargs)
1251 @property
1252 def name(self):
1253 return f"{self.col}_asArcseconds"
1255 @property
1256 def columns(self):
1257 return [self.col,
1258 self.colCD_1_1,
1259 self.colCD_1_2,
1260 self.colCD_2_1,
1261 self.colCD_2_2]
1263 def _func(self, df):
1264 return df[self.col] * self.pixelScaleArcseconds(df[self.colCD_1_1],
1265 df[self.colCD_1_2],
1266 df[self.colCD_2_1],
1267 df[self.colCD_2_2])
1270class ConvertPixelSqToArcsecondsSq(ComputePixelScale):
1271 """Convert a value in units pixels squared to units arcseconds squared.
1272 """
1274 def __init__(self,
1275 col,
1276 colCD_1_1,
1277 colCD_1_2,
1278 colCD_2_1,
1279 colCD_2_2,
1280 **kwargs):
1281 self.col = col
1282 super().__init__(colCD_1_1,
1283 colCD_1_2,
1284 colCD_2_1,
1285 colCD_2_2,
1286 **kwargs)
1288 @property
1289 def name(self):
1290 return f"{self.col}_asArcsecondsSq"
1292 @property
1293 def columns(self):
1294 return [self.col,
1295 self.colCD_1_1,
1296 self.colCD_1_2,
1297 self.colCD_2_1,
1298 self.colCD_2_2]
1300 def _func(self, df):
1301 pixScale = self.pixelScaleArcseconds(df[self.colCD_1_1],
1302 df[self.colCD_1_2],
1303 df[self.colCD_2_1],
1304 df[self.colCD_2_2])
1305 return df[self.col] * pixScale * pixScale
1308class ReferenceBand(Functor):
1309 name = 'Reference Band'
1310 shortname = 'refBand'
1312 @property
1313 def columns(self):
1314 return ["merge_measurement_i",
1315 "merge_measurement_r",
1316 "merge_measurement_z",
1317 "merge_measurement_y",
1318 "merge_measurement_g",
1319 "merge_measurement_u"]
1321 def _func(self, df: pd.DataFrame) -> pd.Series:
1322 def getFilterAliasName(row):
1323 # get column name with the max value (True > False)
1324 colName = row.idxmax()
1325 return colName.replace('merge_measurement_', '')
1327 # Skip columns that are unavailable, because this functor requests the
1328 # superset of bands that could be included in the object table
1329 columns = [col for col in self.columns if col in df.columns]
1330 # Makes a Series of dtype object if df is empty
1331 return df[columns].apply(getFilterAliasName, axis=1,
1332 result_type='reduce').astype('object')
1335class Photometry(Functor):
1336 # AB to NanoJansky (3631 Jansky)
1337 AB_FLUX_SCALE = (0 * u.ABmag).to_value(u.nJy)
1338 LOG_AB_FLUX_SCALE = 12.56
1339 FIVE_OVER_2LOG10 = 1.085736204758129569
1340 # TO DO: DM-21955 Replace hard coded photometic calibration values
1341 COADD_ZP = 27
1343 def __init__(self, colFlux, colFluxErr=None, calib=None, **kwargs):
1344 self.vhypot = np.vectorize(self.hypot)
1345 self.col = colFlux
1346 self.colFluxErr = colFluxErr
1348 self.calib = calib
1349 if calib is not None:
1350 self.fluxMag0, self.fluxMag0Err = calib.getFluxMag0()
1351 else:
1352 self.fluxMag0 = 1./np.power(10, -0.4*self.COADD_ZP)
1353 self.fluxMag0Err = 0.
1355 super().__init__(**kwargs)
1357 @property
1358 def columns(self):
1359 return [self.col]
1361 @property
1362 def name(self):
1363 return f'mag_{self.col}'
1365 @classmethod
1366 def hypot(cls, a, b):
1367 if np.abs(a) < np.abs(b):
1368 a, b = b, a
1369 if a == 0.:
1370 return 0.
1371 q = b/a
1372 return np.abs(a) * np.sqrt(1. + q*q)
1374 def dn2flux(self, dn, fluxMag0):
1375 return self.AB_FLUX_SCALE * dn / fluxMag0
1377 def dn2mag(self, dn, fluxMag0):
1378 with np.warnings.catch_warnings():
1379 np.warnings.filterwarnings('ignore', r'invalid value encountered')
1380 np.warnings.filterwarnings('ignore', r'divide by zero')
1381 return -2.5 * np.log10(dn/fluxMag0)
1383 def dn2fluxErr(self, dn, dnErr, fluxMag0, fluxMag0Err):
1384 retVal = self.vhypot(dn * fluxMag0Err, dnErr * fluxMag0)
1385 retVal *= self.AB_FLUX_SCALE / fluxMag0 / fluxMag0
1386 return retVal
1388 def dn2MagErr(self, dn, dnErr, fluxMag0, fluxMag0Err):
1389 retVal = self.dn2fluxErr(dn, dnErr, fluxMag0, fluxMag0Err) / self.dn2flux(dn, fluxMag0)
1390 return self.FIVE_OVER_2LOG10 * retVal
1393class NanoJansky(Photometry):
1394 def _func(self, df):
1395 return self.dn2flux(df[self.col], self.fluxMag0)
1398class NanoJanskyErr(Photometry):
1399 @property
1400 def columns(self):
1401 return [self.col, self.colFluxErr]
1403 def _func(self, df):
1404 retArr = self.dn2fluxErr(df[self.col], df[self.colFluxErr], self.fluxMag0, self.fluxMag0Err)
1405 return pd.Series(retArr, index=df.index)
1408class LocalPhotometry(Functor):
1409 """Base class for calibrating the specified instrument flux column using
1410 the local photometric calibration.
1412 Parameters
1413 ----------
1414 instFluxCol : `str`
1415 Name of the instrument flux column.
1416 instFluxErrCol : `str`
1417 Name of the assocated error columns for ``instFluxCol``.
1418 photoCalibCol : `str`
1419 Name of local calibration column.
1420 photoCalibErrCol : `str`
1421 Error associated with ``photoCalibCol``
1423 See also
1424 --------
1425 LocalNanojansky
1426 LocalNanojanskyErr
1427 """
1428 logNJanskyToAB = (1 * u.nJy).to_value(u.ABmag)
1430 def __init__(self,
1431 instFluxCol,
1432 instFluxErrCol,
1433 photoCalibCol,
1434 photoCalibErrCol,
1435 **kwargs):
1436 self.instFluxCol = instFluxCol
1437 self.instFluxErrCol = instFluxErrCol
1438 self.photoCalibCol = photoCalibCol
1439 self.photoCalibErrCol = photoCalibErrCol
1440 super().__init__(**kwargs)
1442 def instFluxToNanojansky(self, instFlux, localCalib):
1443 """Convert instrument flux to nanojanskys.
1445 Parameters
1446 ----------
1447 instFlux : `numpy.ndarray` or `pandas.Series`
1448 Array of instrument flux measurements
1449 localCalib : `numpy.ndarray` or `pandas.Series`
1450 Array of local photometric calibration estimates.
1452 Returns
1453 -------
1454 calibFlux : `numpy.ndarray` or `pandas.Series`
1455 Array of calibrated flux measurements.
1456 """
1457 return instFlux * localCalib
1459 def instFluxErrToNanojanskyErr(self, instFlux, instFluxErr, localCalib, localCalibErr):
1460 """Convert instrument flux to nanojanskys.
1462 Parameters
1463 ----------
1464 instFlux : `numpy.ndarray` or `pandas.Series`
1465 Array of instrument flux measurements
1466 instFluxErr : `numpy.ndarray` or `pandas.Series`
1467 Errors on associated ``instFlux`` values
1468 localCalib : `numpy.ndarray` or `pandas.Series`
1469 Array of local photometric calibration estimates.
1470 localCalibErr : `numpy.ndarray` or `pandas.Series`
1471 Errors on associated ``localCalib`` values
1473 Returns
1474 -------
1475 calibFluxErr : `numpy.ndarray` or `pandas.Series`
1476 Errors on calibrated flux measurements.
1477 """
1478 return np.hypot(instFluxErr * localCalib, instFlux * localCalibErr)
1480 def instFluxToMagnitude(self, instFlux, localCalib):
1481 """Convert instrument flux to nanojanskys.
1483 Parameters
1484 ----------
1485 instFlux : `numpy.ndarray` or `pandas.Series`
1486 Array of instrument flux measurements
1487 localCalib : `numpy.ndarray` or `pandas.Series`
1488 Array of local photometric calibration estimates.
1490 Returns
1491 -------
1492 calibMag : `numpy.ndarray` or `pandas.Series`
1493 Array of calibrated AB magnitudes.
1494 """
1495 return -2.5 * np.log10(self.instFluxToNanojansky(instFlux, localCalib)) + self.logNJanskyToAB
1497 def instFluxErrToMagnitudeErr(self, instFlux, instFluxErr, localCalib, localCalibErr):
1498 """Convert instrument flux err to nanojanskys.
1500 Parameters
1501 ----------
1502 instFlux : `numpy.ndarray` or `pandas.Series`
1503 Array of instrument flux measurements
1504 instFluxErr : `numpy.ndarray` or `pandas.Series`
1505 Errors on associated ``instFlux`` values
1506 localCalib : `numpy.ndarray` or `pandas.Series`
1507 Array of local photometric calibration estimates.
1508 localCalibErr : `numpy.ndarray` or `pandas.Series`
1509 Errors on associated ``localCalib`` values
1511 Returns
1512 -------
1513 calibMagErr: `numpy.ndarray` or `pandas.Series`
1514 Error on calibrated AB magnitudes.
1515 """
1516 err = self.instFluxErrToNanojanskyErr(instFlux, instFluxErr, localCalib, localCalibErr)
1517 return 2.5 / np.log(10) * err / self.instFluxToNanojansky(instFlux, instFluxErr)
1520class LocalNanojansky(LocalPhotometry):
1521 """Compute calibrated fluxes using the local calibration value."""
1523 @property
1524 def columns(self):
1525 return [self.instFluxCol, self.photoCalibCol]
1527 @property
1528 def name(self):
1529 return f'flux_{self.instFluxCol}'
1531 def _func(self, df):
1532 return self.instFluxToNanojansky(df[self.instFluxCol], df[self.photoCalibCol])
1535class LocalNanojanskyErr(LocalPhotometry):
1536 """Compute calibrated flux errors using the local calibration value."""
1538 @property
1539 def columns(self):
1540 return [self.instFluxCol, self.instFluxErrCol,
1541 self.photoCalibCol, self.photoCalibErrCol]
1543 @property
1544 def name(self):
1545 return f'fluxErr_{self.instFluxCol}'
1547 def _func(self, df):
1548 return self.instFluxErrToNanojanskyErr(df[self.instFluxCol], df[self.instFluxErrCol],
1549 df[self.photoCalibCol], df[self.photoCalibErrCol])
1552class LocalDipoleMeanFlux(LocalPhotometry):
1553 """Compute absolute mean of dipole fluxes.
1555 See also
1556 --------
1557 LocalNanojansky
1558 LocalNanojanskyErr
1559 LocalDipoleMeanFluxErr
1560 LocalDipoleDiffFlux
1561 LocalDipoleDiffFluxErr
1562 """
1563 def __init__(self,
1564 instFluxPosCol,
1565 instFluxNegCol,
1566 instFluxPosErrCol,
1567 instFluxNegErrCol,
1568 photoCalibCol,
1569 photoCalibErrCol,
1570 **kwargs):
1571 self.instFluxNegCol = instFluxNegCol
1572 self.instFluxPosCol = instFluxPosCol
1573 self.instFluxNegErrCol = instFluxNegErrCol
1574 self.instFluxPosErrCol = instFluxPosErrCol
1575 self.photoCalibCol = photoCalibCol
1576 self.photoCalibErrCol = photoCalibErrCol
1577 super().__init__(instFluxNegCol,
1578 instFluxNegErrCol,
1579 photoCalibCol,
1580 photoCalibErrCol,
1581 **kwargs)
1583 @property
1584 def columns(self):
1585 return [self.instFluxPosCol,
1586 self.instFluxNegCol,
1587 self.photoCalibCol]
1589 @property
1590 def name(self):
1591 return f'dipMeanFlux_{self.instFluxPosCol}_{self.instFluxNegCol}'
1593 def _func(self, df):
1594 return 0.5*(np.fabs(self.instFluxToNanojansky(df[self.instFluxNegCol], df[self.photoCalibCol]))
1595 + np.fabs(self.instFluxToNanojansky(df[self.instFluxPosCol], df[self.photoCalibCol])))
1598class LocalDipoleMeanFluxErr(LocalDipoleMeanFlux):
1599 """Compute the error on the absolute mean of dipole fluxes.
1601 See also
1602 --------
1603 LocalNanojansky
1604 LocalNanojanskyErr
1605 LocalDipoleMeanFlux
1606 LocalDipoleDiffFlux
1607 LocalDipoleDiffFluxErr
1608 """
1610 @property
1611 def columns(self):
1612 return [self.instFluxPosCol,
1613 self.instFluxNegCol,
1614 self.instFluxPosErrCol,
1615 self.instFluxNegErrCol,
1616 self.photoCalibCol,
1617 self.photoCalibErrCol]
1619 @property
1620 def name(self):
1621 return f'dipMeanFluxErr_{self.instFluxPosCol}_{self.instFluxNegCol}'
1623 def _func(self, df):
1624 return 0.5*np.sqrt(
1625 (np.fabs(df[self.instFluxNegCol]) + np.fabs(df[self.instFluxPosCol])
1626 * df[self.photoCalibErrCol])**2
1627 + (df[self.instFluxNegErrCol]**2 + df[self.instFluxPosErrCol]**2)
1628 * df[self.photoCalibCol]**2)
1631class LocalDipoleDiffFlux(LocalDipoleMeanFlux):
1632 """Compute the absolute difference of dipole fluxes.
1634 Value is (abs(pos) - abs(neg))
1636 See also
1637 --------
1638 LocalNanojansky
1639 LocalNanojanskyErr
1640 LocalDipoleMeanFlux
1641 LocalDipoleMeanFluxErr
1642 LocalDipoleDiffFluxErr
1643 """
1645 @property
1646 def columns(self):
1647 return [self.instFluxPosCol,
1648 self.instFluxNegCol,
1649 self.photoCalibCol]
1651 @property
1652 def name(self):
1653 return f'dipDiffFlux_{self.instFluxPosCol}_{self.instFluxNegCol}'
1655 def _func(self, df):
1656 return (np.fabs(self.instFluxToNanojansky(df[self.instFluxPosCol], df[self.photoCalibCol]))
1657 - np.fabs(self.instFluxToNanojansky(df[self.instFluxNegCol], df[self.photoCalibCol])))
1660class LocalDipoleDiffFluxErr(LocalDipoleMeanFlux):
1661 """Compute the error on the absolute difference of dipole fluxes.
1663 See also
1664 --------
1665 LocalNanojansky
1666 LocalNanojanskyErr
1667 LocalDipoleMeanFlux
1668 LocalDipoleMeanFluxErr
1669 LocalDipoleDiffFlux
1670 """
1672 @property
1673 def columns(self):
1674 return [self.instFluxPosCol,
1675 self.instFluxNegCol,
1676 self.instFluxPosErrCol,
1677 self.instFluxNegErrCol,
1678 self.photoCalibCol,
1679 self.photoCalibErrCol]
1681 @property
1682 def name(self):
1683 return f'dipDiffFluxErr_{self.instFluxPosCol}_{self.instFluxNegCol}'
1685 def _func(self, df):
1686 return np.sqrt(
1687 ((np.fabs(df[self.instFluxPosCol]) - np.fabs(df[self.instFluxNegCol]))
1688 * df[self.photoCalibErrCol])**2
1689 + (df[self.instFluxPosErrCol]**2 + df[self.instFluxNegErrCol]**2)
1690 * df[self.photoCalibCol]**2)
1693class Ebv(Functor):
1694 """Compute E(B-V) from dustmaps.sfd
1695 """
1696 _defaultDataset = 'ref'
1697 name = "E(B-V)"
1698 shortname = "ebv"
1700 def __init__(self, **kwargs):
1701 # import is only needed for Ebv
1702 from dustmaps.sfd import SFDQuery
1703 self._columns = ['coord_ra', 'coord_dec']
1704 self.sfd = SFDQuery()
1705 super().__init__(**kwargs)
1707 def _func(self, df):
1708 coords = SkyCoord(df['coord_ra'].values * u.rad, df['coord_dec'].values * u.rad)
1709 ebv = self.sfd(coords)
1710 # Double precision unnecessary scientifically
1711 # but currently needed for ingest to qserv
1712 return pd.Series(ebv, index=df.index).astype('float64')