Coverage for python/lsst/pipe/tasks/functors.py: 36%
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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 "IDColumn", "FootprintNPix", "CoordColumn", "RAColumn", "DecColumn",
24 "HtmIndex20", "Mag", "MagErr", "NanoMaggie", "MagDiff", "Color",
25 "Labeller", "StarGalaxyLabeller", "NumStarLabeller", "DeconvolvedMoments",
26 "SdssTraceSize", "PsfSdssTraceSizeDiff", "HsmTraceSize", "PsfHsmTraceSizeDiff",
27 "HsmFwhm", "E1", "E2", "RadiusFromQuadrupole", "LocalWcs", "ComputePixelScale",
28 "ConvertPixelToArcseconds", "ConvertPixelSqToArcsecondsSq", "ReferenceBand",
29 "Photometry", "NanoJansky", "NanoJanskyErr", "Magnitude", "MagnitudeErr",
30 "LocalPhotometry", "LocalNanojansky", "LocalNanojanskyErr",
31 "LocalMagnitude", "LocalMagnitudeErr", "LocalDipoleMeanFlux",
32 "LocalDipoleMeanFluxErr", "LocalDipoleDiffFlux", "LocalDipoleDiffFluxErr",
33 "Ratio", "Ebv"]
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 IDColumn(Column):
660 col = 'id'
661 _allow_difference = False
662 _defaultNoDup = True
664 def _func(self, df):
665 return pd.Series(df.index, index=df.index)
668class FootprintNPix(Column):
669 col = 'base_Footprint_nPix'
672class CoordColumn(Column):
673 """Base class for coordinate column, in degrees
674 """
675 _radians = True
677 def __init__(self, col, **kwargs):
678 super().__init__(col, **kwargs)
680 def _func(self, df):
681 # Must not modify original column in case that column is used by another functor
682 output = df[self.col] * 180 / np.pi if self._radians else df[self.col]
683 return output
686class RAColumn(CoordColumn):
687 """Right Ascension, in degrees
688 """
689 name = 'RA'
690 _defaultNoDup = True
692 def __init__(self, **kwargs):
693 super().__init__('coord_ra', **kwargs)
695 def __call__(self, catalog, **kwargs):
696 return super().__call__(catalog, **kwargs)
699class DecColumn(CoordColumn):
700 """Declination, in degrees
701 """
702 name = 'Dec'
703 _defaultNoDup = True
705 def __init__(self, **kwargs):
706 super().__init__('coord_dec', **kwargs)
708 def __call__(self, catalog, **kwargs):
709 return super().__call__(catalog, **kwargs)
712class HtmIndex20(Functor):
713 """Compute the level 20 HtmIndex for the catalog.
715 Notes
716 -----
717 This functor was implemented to satisfy requirements of old APDB interface
718 which required ``pixelId`` column in DiaObject with HTM20 index. APDB
719 interface had migrated to not need that information, but we keep this
720 class in case it may be useful for something else.
721 """
722 name = "Htm20"
723 htmLevel = 20
724 _radians = True
726 def __init__(self, ra, decl, **kwargs):
727 self.pixelator = sphgeom.HtmPixelization(self.htmLevel)
728 self.ra = ra
729 self.decl = decl
730 self._columns = [self.ra, self.decl]
731 super().__init__(**kwargs)
733 def _func(self, df):
735 def computePixel(row):
736 if self._radians:
737 sphPoint = geom.SpherePoint(row[self.ra],
738 row[self.decl],
739 geom.radians)
740 else:
741 sphPoint = geom.SpherePoint(row[self.ra],
742 row[self.decl],
743 geom.degrees)
744 return self.pixelator.index(sphPoint.getVector())
746 return df.apply(computePixel, axis=1, result_type='reduce').astype('int64')
749def fluxName(col):
750 if not col.endswith('_instFlux'):
751 col += '_instFlux'
752 return col
755def fluxErrName(col):
756 if not col.endswith('_instFluxErr'):
757 col += '_instFluxErr'
758 return col
761class Mag(Functor):
762 """Compute calibrated magnitude
764 Takes a `calib` argument, which returns the flux at mag=0
765 as `calib.getFluxMag0()`. If not provided, then the default
766 `fluxMag0` is 63095734448.0194, which is default for HSC.
767 This default should be removed in DM-21955
769 This calculation hides warnings about invalid values and dividing by zero.
771 As for all functors, a `dataset` and `filt` kwarg should be provided upon
772 initialization. Unlike the default `Functor`, however, the default dataset
773 for a `Mag` is `'meas'`, rather than `'ref'`.
775 Parameters
776 ----------
777 col : `str`
778 Name of flux column from which to compute magnitude. Can be parseable
779 by `lsst.pipe.tasks.functors.fluxName` function---that is, you can pass
780 `'modelfit_CModel'` instead of `'modelfit_CModel_instFlux'`) and it will
781 understand.
782 calib : `lsst.afw.image.calib.Calib` (optional)
783 Object that knows zero point.
784 """
785 _defaultDataset = 'meas'
787 def __init__(self, col, calib=None, **kwargs):
788 self.col = fluxName(col)
789 self.calib = calib
790 if calib is not None:
791 self.fluxMag0 = calib.getFluxMag0()[0]
792 else:
793 # TO DO: DM-21955 Replace hard coded photometic calibration values
794 self.fluxMag0 = 63095734448.0194
796 super().__init__(**kwargs)
798 @property
799 def columns(self):
800 return [self.col]
802 def _func(self, df):
803 with np.warnings.catch_warnings():
804 np.warnings.filterwarnings('ignore', r'invalid value encountered')
805 np.warnings.filterwarnings('ignore', r'divide by zero')
806 return -2.5*np.log10(df[self.col] / self.fluxMag0)
808 @property
809 def name(self):
810 return f'mag_{self.col}'
813class MagErr(Mag):
814 """Compute calibrated magnitude uncertainty
816 Takes the same `calib` object as `lsst.pipe.tasks.functors.Mag`.
818 Parameters
819 col : `str`
820 Name of flux column
821 calib : `lsst.afw.image.calib.Calib` (optional)
822 Object that knows zero point.
823 """
825 def __init__(self, *args, **kwargs):
826 super().__init__(*args, **kwargs)
827 if self.calib is not None:
828 self.fluxMag0Err = self.calib.getFluxMag0()[1]
829 else:
830 self.fluxMag0Err = 0.
832 @property
833 def columns(self):
834 return [self.col, self.col + 'Err']
836 def _func(self, df):
837 with np.warnings.catch_warnings():
838 np.warnings.filterwarnings('ignore', r'invalid value encountered')
839 np.warnings.filterwarnings('ignore', r'divide by zero')
840 fluxCol, fluxErrCol = self.columns
841 x = df[fluxErrCol] / df[fluxCol]
842 y = self.fluxMag0Err / self.fluxMag0
843 magErr = (2.5 / np.log(10.)) * np.sqrt(x*x + y*y)
844 return magErr
846 @property
847 def name(self):
848 return super().name + '_err'
851class NanoMaggie(Mag):
852 """
853 """
855 def _func(self, df):
856 return (df[self.col] / self.fluxMag0) * 1e9
859class MagDiff(Functor):
860 _defaultDataset = 'meas'
862 """Functor to calculate magnitude difference"""
864 def __init__(self, col1, col2, **kwargs):
865 self.col1 = fluxName(col1)
866 self.col2 = fluxName(col2)
867 super().__init__(**kwargs)
869 @property
870 def columns(self):
871 return [self.col1, self.col2]
873 def _func(self, df):
874 with np.warnings.catch_warnings():
875 np.warnings.filterwarnings('ignore', r'invalid value encountered')
876 np.warnings.filterwarnings('ignore', r'divide by zero')
877 return -2.5*np.log10(df[self.col1]/df[self.col2])
879 @property
880 def name(self):
881 return f'(mag_{self.col1} - mag_{self.col2})'
883 @property
884 def shortname(self):
885 return f'magDiff_{self.col1}_{self.col2}'
888class Color(Functor):
889 """Compute the color between two filters
891 Computes color by initializing two different `Mag`
892 functors based on the `col` and filters provided, and
893 then returning the difference.
895 This is enabled by the `_func` expecting a dataframe with a
896 multilevel column index, with both `'band'` and `'column'`,
897 instead of just `'column'`, which is the `Functor` default.
898 This is controlled by the `_dfLevels` attribute.
900 Also of note, the default dataset for `Color` is `forced_src'`,
901 whereas for `Mag` it is `'meas'`.
903 Parameters
904 ----------
905 col : str
906 Name of flux column from which to compute; same as would be passed to
907 `lsst.pipe.tasks.functors.Mag`.
909 filt2, filt1 : str
910 Filters from which to compute magnitude difference.
911 Color computed is `Mag(filt2) - Mag(filt1)`.
912 """
913 _defaultDataset = 'forced_src'
914 _dfLevels = ('band', 'column')
915 _defaultNoDup = True
917 def __init__(self, col, filt2, filt1, **kwargs):
918 self.col = fluxName(col)
919 if filt2 == filt1:
920 raise RuntimeError("Cannot compute Color for %s: %s - %s " % (col, filt2, filt1))
921 self.filt2 = filt2
922 self.filt1 = filt1
924 self.mag2 = Mag(col, filt=filt2, **kwargs)
925 self.mag1 = Mag(col, filt=filt1, **kwargs)
927 super().__init__(**kwargs)
929 @property
930 def filt(self):
931 return None
933 @filt.setter
934 def filt(self, filt):
935 pass
937 def _func(self, df):
938 mag2 = self.mag2._func(df[self.filt2])
939 mag1 = self.mag1._func(df[self.filt1])
940 return mag2 - mag1
942 @property
943 def columns(self):
944 return [self.mag1.col, self.mag2.col]
946 def multilevelColumns(self, parq, **kwargs):
947 return [(self.dataset, self.filt1, self.col), (self.dataset, self.filt2, self.col)]
949 @property
950 def name(self):
951 return f'{self.filt2} - {self.filt1} ({self.col})'
953 @property
954 def shortname(self):
955 return f"{self.col}_{self.filt2.replace('-', '')}m{self.filt1.replace('-', '')}"
958class Labeller(Functor):
959 """Main function of this subclass is to override the dropna=True
960 """
961 _null_label = 'null'
962 _allow_difference = False
963 name = 'label'
964 _force_str = False
966 def __call__(self, parq, dropna=False, **kwargs):
967 return super().__call__(parq, dropna=False, **kwargs)
970class StarGalaxyLabeller(Labeller):
971 _columns = ["base_ClassificationExtendedness_value"]
972 _column = "base_ClassificationExtendedness_value"
974 def _func(self, df):
975 x = df[self._columns][self._column]
976 mask = x.isnull()
977 test = (x < 0.5).astype(int)
978 test = test.mask(mask, 2)
980 # TODO: DM-21954 Look into veracity of inline comment below
981 # are these backwards?
982 categories = ['galaxy', 'star', self._null_label]
983 label = pd.Series(pd.Categorical.from_codes(test, categories=categories),
984 index=x.index, name='label')
985 if self._force_str:
986 label = label.astype(str)
987 return label
990class NumStarLabeller(Labeller):
991 _columns = ['numStarFlags']
992 labels = {"star": 0, "maybe": 1, "notStar": 2}
994 def _func(self, df):
995 x = df[self._columns][self._columns[0]]
997 # Number of filters
998 n = len(x.unique()) - 1
1000 labels = ['noStar', 'maybe', 'star']
1001 label = pd.Series(pd.cut(x, [-1, 0, n-1, n], labels=labels),
1002 index=x.index, name='label')
1004 if self._force_str:
1005 label = label.astype(str)
1007 return label
1010class DeconvolvedMoments(Functor):
1011 name = 'Deconvolved Moments'
1012 shortname = 'deconvolvedMoments'
1013 _columns = ("ext_shapeHSM_HsmSourceMoments_xx",
1014 "ext_shapeHSM_HsmSourceMoments_yy",
1015 "base_SdssShape_xx", "base_SdssShape_yy",
1016 "ext_shapeHSM_HsmPsfMoments_xx",
1017 "ext_shapeHSM_HsmPsfMoments_yy")
1019 def _func(self, df):
1020 """Calculate deconvolved moments"""
1021 if "ext_shapeHSM_HsmSourceMoments_xx" in df.columns: # _xx added by tdm
1022 hsm = df["ext_shapeHSM_HsmSourceMoments_xx"] + df["ext_shapeHSM_HsmSourceMoments_yy"]
1023 else:
1024 hsm = np.ones(len(df))*np.nan
1025 sdss = df["base_SdssShape_xx"] + df["base_SdssShape_yy"]
1026 if "ext_shapeHSM_HsmPsfMoments_xx" in df.columns:
1027 psf = df["ext_shapeHSM_HsmPsfMoments_xx"] + df["ext_shapeHSM_HsmPsfMoments_yy"]
1028 else:
1029 # LSST does not have shape.sdss.psf. Could instead add base_PsfShape to catalog using
1030 # exposure.getPsf().computeShape(s.getCentroid()).getIxx()
1031 # raise TaskError("No psf shape parameter found in catalog")
1032 raise RuntimeError('No psf shape parameter found in catalog')
1034 return hsm.where(np.isfinite(hsm), sdss) - psf
1037class SdssTraceSize(Functor):
1038 """Functor to calculate SDSS trace radius size for sources"""
1039 name = "SDSS Trace Size"
1040 shortname = 'sdssTrace'
1041 _columns = ("base_SdssShape_xx", "base_SdssShape_yy")
1043 def _func(self, df):
1044 srcSize = np.sqrt(0.5*(df["base_SdssShape_xx"] + df["base_SdssShape_yy"]))
1045 return srcSize
1048class PsfSdssTraceSizeDiff(Functor):
1049 """Functor to calculate SDSS trace radius size difference (%) between object and psf model"""
1050 name = "PSF - SDSS Trace Size"
1051 shortname = 'psf_sdssTrace'
1052 _columns = ("base_SdssShape_xx", "base_SdssShape_yy",
1053 "base_SdssShape_psf_xx", "base_SdssShape_psf_yy")
1055 def _func(self, df):
1056 srcSize = np.sqrt(0.5*(df["base_SdssShape_xx"] + df["base_SdssShape_yy"]))
1057 psfSize = np.sqrt(0.5*(df["base_SdssShape_psf_xx"] + df["base_SdssShape_psf_yy"]))
1058 sizeDiff = 100*(srcSize - psfSize)/(0.5*(srcSize + psfSize))
1059 return sizeDiff
1062class HsmTraceSize(Functor):
1063 """Functor to calculate HSM trace radius size for sources"""
1064 name = 'HSM Trace Size'
1065 shortname = 'hsmTrace'
1066 _columns = ("ext_shapeHSM_HsmSourceMoments_xx",
1067 "ext_shapeHSM_HsmSourceMoments_yy")
1069 def _func(self, df):
1070 srcSize = np.sqrt(0.5*(df["ext_shapeHSM_HsmSourceMoments_xx"]
1071 + df["ext_shapeHSM_HsmSourceMoments_yy"]))
1072 return srcSize
1075class PsfHsmTraceSizeDiff(Functor):
1076 """Functor to calculate HSM trace radius size difference (%) between object and psf model"""
1077 name = 'PSF - HSM Trace Size'
1078 shortname = 'psf_HsmTrace'
1079 _columns = ("ext_shapeHSM_HsmSourceMoments_xx",
1080 "ext_shapeHSM_HsmSourceMoments_yy",
1081 "ext_shapeHSM_HsmPsfMoments_xx",
1082 "ext_shapeHSM_HsmPsfMoments_yy")
1084 def _func(self, df):
1085 srcSize = np.sqrt(0.5*(df["ext_shapeHSM_HsmSourceMoments_xx"]
1086 + df["ext_shapeHSM_HsmSourceMoments_yy"]))
1087 psfSize = np.sqrt(0.5*(df["ext_shapeHSM_HsmPsfMoments_xx"]
1088 + df["ext_shapeHSM_HsmPsfMoments_yy"]))
1089 sizeDiff = 100*(srcSize - psfSize)/(0.5*(srcSize + psfSize))
1090 return sizeDiff
1093class HsmFwhm(Functor):
1094 name = 'HSM Psf FWHM'
1095 _columns = ('ext_shapeHSM_HsmPsfMoments_xx', 'ext_shapeHSM_HsmPsfMoments_yy')
1096 # TODO: DM-21403 pixel scale should be computed from the CD matrix or transform matrix
1097 pixelScale = 0.168
1098 SIGMA2FWHM = 2*np.sqrt(2*np.log(2))
1100 def _func(self, df):
1101 return self.pixelScale*self.SIGMA2FWHM*np.sqrt(
1102 0.5*(df['ext_shapeHSM_HsmPsfMoments_xx'] + df['ext_shapeHSM_HsmPsfMoments_yy']))
1105class E1(Functor):
1106 name = "Distortion Ellipticity (e1)"
1107 shortname = "Distortion"
1109 def __init__(self, colXX, colXY, colYY, **kwargs):
1110 self.colXX = colXX
1111 self.colXY = colXY
1112 self.colYY = colYY
1113 self._columns = [self.colXX, self.colXY, self.colYY]
1114 super().__init__(**kwargs)
1116 @property
1117 def columns(self):
1118 return [self.colXX, self.colXY, self.colYY]
1120 def _func(self, df):
1121 return df[self.colXX] - df[self.colYY] / (df[self.colXX] + df[self.colYY])
1124class E2(Functor):
1125 name = "Ellipticity e2"
1127 def __init__(self, colXX, colXY, colYY, **kwargs):
1128 self.colXX = colXX
1129 self.colXY = colXY
1130 self.colYY = colYY
1131 super().__init__(**kwargs)
1133 @property
1134 def columns(self):
1135 return [self.colXX, self.colXY, self.colYY]
1137 def _func(self, df):
1138 return 2*df[self.colXY] / (df[self.colXX] + df[self.colYY])
1141class RadiusFromQuadrupole(Functor):
1143 def __init__(self, colXX, colXY, colYY, **kwargs):
1144 self.colXX = colXX
1145 self.colXY = colXY
1146 self.colYY = colYY
1147 super().__init__(**kwargs)
1149 @property
1150 def columns(self):
1151 return [self.colXX, self.colXY, self.colYY]
1153 def _func(self, df):
1154 return (df[self.colXX]*df[self.colYY] - df[self.colXY]**2)**0.25
1157class LocalWcs(Functor):
1158 """Computations using the stored localWcs.
1159 """
1160 name = "LocalWcsOperations"
1162 def __init__(self,
1163 colCD_1_1,
1164 colCD_1_2,
1165 colCD_2_1,
1166 colCD_2_2,
1167 **kwargs):
1168 self.colCD_1_1 = colCD_1_1
1169 self.colCD_1_2 = colCD_1_2
1170 self.colCD_2_1 = colCD_2_1
1171 self.colCD_2_2 = colCD_2_2
1172 super().__init__(**kwargs)
1174 def computeDeltaRaDec(self, x, y, cd11, cd12, cd21, cd22):
1175 """Compute the distance on the sphere from x2, y1 to x1, y1.
1177 Parameters
1178 ----------
1179 x : `pandas.Series`
1180 X pixel coordinate.
1181 y : `pandas.Series`
1182 Y pixel coordinate.
1183 cd11 : `pandas.Series`
1184 [1, 1] element of the local Wcs affine transform.
1185 cd11 : `pandas.Series`
1186 [1, 1] element of the local Wcs affine transform.
1187 cd12 : `pandas.Series`
1188 [1, 2] element of the local Wcs affine transform.
1189 cd21 : `pandas.Series`
1190 [2, 1] element of the local Wcs affine transform.
1191 cd22 : `pandas.Series`
1192 [2, 2] element of the local Wcs affine transform.
1194 Returns
1195 -------
1196 raDecTuple : tuple
1197 RA and dec conversion of x and y given the local Wcs. Returned
1198 units are in radians.
1200 """
1201 return (x * cd11 + y * cd12, x * cd21 + y * cd22)
1203 def computeSkySeparation(self, ra1, dec1, ra2, dec2):
1204 """Compute the local pixel scale conversion.
1206 Parameters
1207 ----------
1208 ra1 : `pandas.Series`
1209 Ra of the first coordinate in radians.
1210 dec1 : `pandas.Series`
1211 Dec of the first coordinate in radians.
1212 ra2 : `pandas.Series`
1213 Ra of the second coordinate in radians.
1214 dec2 : `pandas.Series`
1215 Dec of the second coordinate in radians.
1217 Returns
1218 -------
1219 dist : `pandas.Series`
1220 Distance on the sphere in radians.
1221 """
1222 deltaDec = dec2 - dec1
1223 deltaRa = ra2 - ra1
1224 return 2 * np.arcsin(
1225 np.sqrt(
1226 np.sin(deltaDec / 2) ** 2
1227 + np.cos(dec2) * np.cos(dec1) * np.sin(deltaRa / 2) ** 2))
1229 def getSkySeparationFromPixel(self, x1, y1, x2, y2, cd11, cd12, cd21, cd22):
1230 """Compute the distance on the sphere from x2, y1 to x1, y1.
1232 Parameters
1233 ----------
1234 x1 : `pandas.Series`
1235 X pixel coordinate.
1236 y1 : `pandas.Series`
1237 Y pixel coordinate.
1238 x2 : `pandas.Series`
1239 X pixel coordinate.
1240 y2 : `pandas.Series`
1241 Y pixel coordinate.
1242 cd11 : `pandas.Series`
1243 [1, 1] element of the local Wcs affine transform.
1244 cd11 : `pandas.Series`
1245 [1, 1] element of the local Wcs affine transform.
1246 cd12 : `pandas.Series`
1247 [1, 2] element of the local Wcs affine transform.
1248 cd21 : `pandas.Series`
1249 [2, 1] element of the local Wcs affine transform.
1250 cd22 : `pandas.Series`
1251 [2, 2] element of the local Wcs affine transform.
1253 Returns
1254 -------
1255 Distance : `pandas.Series`
1256 Arcseconds per pixel at the location of the local WC
1257 """
1258 ra1, dec1 = self.computeDeltaRaDec(x1, y1, cd11, cd12, cd21, cd22)
1259 ra2, dec2 = self.computeDeltaRaDec(x2, y2, cd11, cd12, cd21, cd22)
1260 # Great circle distance for small separations.
1261 return self.computeSkySeparation(ra1, dec1, ra2, dec2)
1264class ComputePixelScale(LocalWcs):
1265 """Compute the local pixel scale from the stored CDMatrix.
1266 """
1267 name = "PixelScale"
1269 @property
1270 def columns(self):
1271 return [self.colCD_1_1,
1272 self.colCD_1_2,
1273 self.colCD_2_1,
1274 self.colCD_2_2]
1276 def pixelScaleArcseconds(self, cd11, cd12, cd21, cd22):
1277 """Compute the local pixel to scale conversion in arcseconds.
1279 Parameters
1280 ----------
1281 cd11 : `pandas.Series`
1282 [1, 1] element of the local Wcs affine transform in radians.
1283 cd11 : `pandas.Series`
1284 [1, 1] element of the local Wcs affine transform in radians.
1285 cd12 : `pandas.Series`
1286 [1, 2] element of the local Wcs affine transform in radians.
1287 cd21 : `pandas.Series`
1288 [2, 1] element of the local Wcs affine transform in radians.
1289 cd22 : `pandas.Series`
1290 [2, 2] element of the local Wcs affine transform in radians.
1292 Returns
1293 -------
1294 pixScale : `pandas.Series`
1295 Arcseconds per pixel at the location of the local WC
1296 """
1297 return 3600 * np.degrees(np.sqrt(np.fabs(cd11 * cd22 - cd12 * cd21)))
1299 def _func(self, df):
1300 return self.pixelScaleArcseconds(df[self.colCD_1_1],
1301 df[self.colCD_1_2],
1302 df[self.colCD_2_1],
1303 df[self.colCD_2_2])
1306class ConvertPixelToArcseconds(ComputePixelScale):
1307 """Convert a value in units pixels squared to units arcseconds squared.
1308 """
1310 def __init__(self,
1311 col,
1312 colCD_1_1,
1313 colCD_1_2,
1314 colCD_2_1,
1315 colCD_2_2,
1316 **kwargs):
1317 self.col = col
1318 super().__init__(colCD_1_1,
1319 colCD_1_2,
1320 colCD_2_1,
1321 colCD_2_2,
1322 **kwargs)
1324 @property
1325 def name(self):
1326 return f"{self.col}_asArcseconds"
1328 @property
1329 def columns(self):
1330 return [self.col,
1331 self.colCD_1_1,
1332 self.colCD_1_2,
1333 self.colCD_2_1,
1334 self.colCD_2_2]
1336 def _func(self, df):
1337 return df[self.col] * self.pixelScaleArcseconds(df[self.colCD_1_1],
1338 df[self.colCD_1_2],
1339 df[self.colCD_2_1],
1340 df[self.colCD_2_2])
1343class ConvertPixelSqToArcsecondsSq(ComputePixelScale):
1344 """Convert a value in units pixels to units arcseconds.
1345 """
1347 def __init__(self,
1348 col,
1349 colCD_1_1,
1350 colCD_1_2,
1351 colCD_2_1,
1352 colCD_2_2,
1353 **kwargs):
1354 self.col = col
1355 super().__init__(colCD_1_1,
1356 colCD_1_2,
1357 colCD_2_1,
1358 colCD_2_2,
1359 **kwargs)
1361 @property
1362 def name(self):
1363 return f"{self.col}_asArcsecondsSq"
1365 @property
1366 def columns(self):
1367 return [self.col,
1368 self.colCD_1_1,
1369 self.colCD_1_2,
1370 self.colCD_2_1,
1371 self.colCD_2_2]
1373 def _func(self, df):
1374 pixScale = self.pixelScaleArcseconds(df[self.colCD_1_1],
1375 df[self.colCD_1_2],
1376 df[self.colCD_2_1],
1377 df[self.colCD_2_2])
1378 return df[self.col] * pixScale * pixScale
1381class ReferenceBand(Functor):
1382 name = 'Reference Band'
1383 shortname = 'refBand'
1385 @property
1386 def columns(self):
1387 return ["merge_measurement_i",
1388 "merge_measurement_r",
1389 "merge_measurement_z",
1390 "merge_measurement_y",
1391 "merge_measurement_g",
1392 "merge_measurement_u"]
1394 def _func(self, df: pd.DataFrame) -> pd.Series:
1395 def getFilterAliasName(row):
1396 # get column name with the max value (True > False)
1397 colName = row.idxmax()
1398 return colName.replace('merge_measurement_', '')
1400 # Skip columns that are unavailable, because this functor requests the
1401 # superset of bands that could be included in the object table
1402 columns = [col for col in self.columns if col in df.columns]
1403 # Makes a Series of dtype object if df is empty
1404 return df[columns].apply(getFilterAliasName, axis=1,
1405 result_type='reduce').astype('object')
1408class Photometry(Functor):
1409 # AB to NanoJansky (3631 Jansky)
1410 AB_FLUX_SCALE = (0 * u.ABmag).to_value(u.nJy)
1411 LOG_AB_FLUX_SCALE = 12.56
1412 FIVE_OVER_2LOG10 = 1.085736204758129569
1413 # TO DO: DM-21955 Replace hard coded photometic calibration values
1414 COADD_ZP = 27
1416 def __init__(self, colFlux, colFluxErr=None, calib=None, **kwargs):
1417 self.vhypot = np.vectorize(self.hypot)
1418 self.col = colFlux
1419 self.colFluxErr = colFluxErr
1421 self.calib = calib
1422 if calib is not None:
1423 self.fluxMag0, self.fluxMag0Err = calib.getFluxMag0()
1424 else:
1425 self.fluxMag0 = 1./np.power(10, -0.4*self.COADD_ZP)
1426 self.fluxMag0Err = 0.
1428 super().__init__(**kwargs)
1430 @property
1431 def columns(self):
1432 return [self.col]
1434 @property
1435 def name(self):
1436 return f'mag_{self.col}'
1438 @classmethod
1439 def hypot(cls, a, b):
1440 if np.abs(a) < np.abs(b):
1441 a, b = b, a
1442 if a == 0.:
1443 return 0.
1444 q = b/a
1445 return np.abs(a) * np.sqrt(1. + q*q)
1447 def dn2flux(self, dn, fluxMag0):
1448 return self.AB_FLUX_SCALE * dn / fluxMag0
1450 def dn2mag(self, dn, fluxMag0):
1451 with np.warnings.catch_warnings():
1452 np.warnings.filterwarnings('ignore', r'invalid value encountered')
1453 np.warnings.filterwarnings('ignore', r'divide by zero')
1454 return -2.5 * np.log10(dn/fluxMag0)
1456 def dn2fluxErr(self, dn, dnErr, fluxMag0, fluxMag0Err):
1457 retVal = self.vhypot(dn * fluxMag0Err, dnErr * fluxMag0)
1458 retVal *= self.AB_FLUX_SCALE / fluxMag0 / fluxMag0
1459 return retVal
1461 def dn2MagErr(self, dn, dnErr, fluxMag0, fluxMag0Err):
1462 retVal = self.dn2fluxErr(dn, dnErr, fluxMag0, fluxMag0Err) / self.dn2flux(dn, fluxMag0)
1463 return self.FIVE_OVER_2LOG10 * retVal
1466class NanoJansky(Photometry):
1467 def _func(self, df):
1468 return self.dn2flux(df[self.col], self.fluxMag0)
1471class NanoJanskyErr(Photometry):
1472 @property
1473 def columns(self):
1474 return [self.col, self.colFluxErr]
1476 def _func(self, df):
1477 retArr = self.dn2fluxErr(df[self.col], df[self.colFluxErr], self.fluxMag0, self.fluxMag0Err)
1478 return pd.Series(retArr, index=df.index)
1481class Magnitude(Photometry):
1482 def _func(self, df):
1483 return self.dn2mag(df[self.col], self.fluxMag0)
1486class MagnitudeErr(Photometry):
1487 @property
1488 def columns(self):
1489 return [self.col, self.colFluxErr]
1491 def _func(self, df):
1492 retArr = self.dn2MagErr(df[self.col], df[self.colFluxErr], self.fluxMag0, self.fluxMag0Err)
1493 return pd.Series(retArr, index=df.index)
1496class LocalPhotometry(Functor):
1497 """Base class for calibrating the specified instrument flux column using
1498 the local photometric calibration.
1500 Parameters
1501 ----------
1502 instFluxCol : `str`
1503 Name of the instrument flux column.
1504 instFluxErrCol : `str`
1505 Name of the assocated error columns for ``instFluxCol``.
1506 photoCalibCol : `str`
1507 Name of local calibration column.
1508 photoCalibErrCol : `str`
1509 Error associated with ``photoCalibCol``
1511 See also
1512 --------
1513 LocalPhotometry
1514 LocalNanojansky
1515 LocalNanojanskyErr
1516 LocalMagnitude
1517 LocalMagnitudeErr
1518 """
1519 logNJanskyToAB = (1 * u.nJy).to_value(u.ABmag)
1521 def __init__(self,
1522 instFluxCol,
1523 instFluxErrCol,
1524 photoCalibCol,
1525 photoCalibErrCol,
1526 **kwargs):
1527 self.instFluxCol = instFluxCol
1528 self.instFluxErrCol = instFluxErrCol
1529 self.photoCalibCol = photoCalibCol
1530 self.photoCalibErrCol = photoCalibErrCol
1531 super().__init__(**kwargs)
1533 def instFluxToNanojansky(self, instFlux, localCalib):
1534 """Convert instrument flux to nanojanskys.
1536 Parameters
1537 ----------
1538 instFlux : `numpy.ndarray` or `pandas.Series`
1539 Array of instrument flux measurements
1540 localCalib : `numpy.ndarray` or `pandas.Series`
1541 Array of local photometric calibration estimates.
1543 Returns
1544 -------
1545 calibFlux : `numpy.ndarray` or `pandas.Series`
1546 Array of calibrated flux measurements.
1547 """
1548 return instFlux * localCalib
1550 def instFluxErrToNanojanskyErr(self, instFlux, instFluxErr, localCalib, localCalibErr):
1551 """Convert instrument flux to nanojanskys.
1553 Parameters
1554 ----------
1555 instFlux : `numpy.ndarray` or `pandas.Series`
1556 Array of instrument flux measurements
1557 instFluxErr : `numpy.ndarray` or `pandas.Series`
1558 Errors on associated ``instFlux`` values
1559 localCalib : `numpy.ndarray` or `pandas.Series`
1560 Array of local photometric calibration estimates.
1561 localCalibErr : `numpy.ndarray` or `pandas.Series`
1562 Errors on associated ``localCalib`` values
1564 Returns
1565 -------
1566 calibFluxErr : `numpy.ndarray` or `pandas.Series`
1567 Errors on calibrated flux measurements.
1568 """
1569 return np.hypot(instFluxErr * localCalib, instFlux * localCalibErr)
1571 def instFluxToMagnitude(self, instFlux, localCalib):
1572 """Convert instrument flux to nanojanskys.
1574 Parameters
1575 ----------
1576 instFlux : `numpy.ndarray` or `pandas.Series`
1577 Array of instrument flux measurements
1578 localCalib : `numpy.ndarray` or `pandas.Series`
1579 Array of local photometric calibration estimates.
1581 Returns
1582 -------
1583 calibMag : `numpy.ndarray` or `pandas.Series`
1584 Array of calibrated AB magnitudes.
1585 """
1586 return -2.5 * np.log10(self.instFluxToNanojansky(instFlux, localCalib)) + self.logNJanskyToAB
1588 def instFluxErrToMagnitudeErr(self, instFlux, instFluxErr, localCalib, localCalibErr):
1589 """Convert instrument flux err to nanojanskys.
1591 Parameters
1592 ----------
1593 instFlux : `numpy.ndarray` or `pandas.Series`
1594 Array of instrument flux measurements
1595 instFluxErr : `numpy.ndarray` or `pandas.Series`
1596 Errors on associated ``instFlux`` values
1597 localCalib : `numpy.ndarray` or `pandas.Series`
1598 Array of local photometric calibration estimates.
1599 localCalibErr : `numpy.ndarray` or `pandas.Series`
1600 Errors on associated ``localCalib`` values
1602 Returns
1603 -------
1604 calibMagErr: `numpy.ndarray` or `pandas.Series`
1605 Error on calibrated AB magnitudes.
1606 """
1607 err = self.instFluxErrToNanojanskyErr(instFlux, instFluxErr, localCalib, localCalibErr)
1608 return 2.5 / np.log(10) * err / self.instFluxToNanojansky(instFlux, instFluxErr)
1611class LocalNanojansky(LocalPhotometry):
1612 """Compute calibrated fluxes using the local calibration value.
1614 See also
1615 --------
1616 LocalNanojansky
1617 LocalNanojanskyErr
1618 LocalMagnitude
1619 LocalMagnitudeErr
1620 """
1622 @property
1623 def columns(self):
1624 return [self.instFluxCol, self.photoCalibCol]
1626 @property
1627 def name(self):
1628 return f'flux_{self.instFluxCol}'
1630 def _func(self, df):
1631 return self.instFluxToNanojansky(df[self.instFluxCol], df[self.photoCalibCol])
1634class LocalNanojanskyErr(LocalPhotometry):
1635 """Compute calibrated flux errors using the local calibration value.
1637 See also
1638 --------
1639 LocalNanojansky
1640 LocalNanojanskyErr
1641 LocalMagnitude
1642 LocalMagnitudeErr
1643 """
1645 @property
1646 def columns(self):
1647 return [self.instFluxCol, self.instFluxErrCol,
1648 self.photoCalibCol, self.photoCalibErrCol]
1650 @property
1651 def name(self):
1652 return f'fluxErr_{self.instFluxCol}'
1654 def _func(self, df):
1655 return self.instFluxErrToNanojanskyErr(df[self.instFluxCol], df[self.instFluxErrCol],
1656 df[self.photoCalibCol], df[self.photoCalibErrCol])
1659class LocalMagnitude(LocalPhotometry):
1660 """Compute calibrated AB magnitudes using the local calibration value.
1662 See also
1663 --------
1664 LocalNanojansky
1665 LocalNanojanskyErr
1666 LocalMagnitude
1667 LocalMagnitudeErr
1668 """
1670 @property
1671 def columns(self):
1672 return [self.instFluxCol, self.photoCalibCol]
1674 @property
1675 def name(self):
1676 return f'mag_{self.instFluxCol}'
1678 def _func(self, df):
1679 return self.instFluxToMagnitude(df[self.instFluxCol],
1680 df[self.photoCalibCol])
1683class LocalMagnitudeErr(LocalPhotometry):
1684 """Compute calibrated AB magnitude errors using the local calibration value.
1686 See also
1687 --------
1688 LocalNanojansky
1689 LocalNanojanskyErr
1690 LocalMagnitude
1691 LocalMagnitudeErr
1692 """
1694 @property
1695 def columns(self):
1696 return [self.instFluxCol, self.instFluxErrCol,
1697 self.photoCalibCol, self.photoCalibErrCol]
1699 @property
1700 def name(self):
1701 return f'magErr_{self.instFluxCol}'
1703 def _func(self, df):
1704 return self.instFluxErrToMagnitudeErr(df[self.instFluxCol],
1705 df[self.instFluxErrCol],
1706 df[self.photoCalibCol],
1707 df[self.photoCalibErrCol])
1710class LocalDipoleMeanFlux(LocalPhotometry):
1711 """Compute absolute mean of dipole fluxes.
1713 See also
1714 --------
1715 LocalNanojansky
1716 LocalNanojanskyErr
1717 LocalMagnitude
1718 LocalMagnitudeErr
1719 LocalDipoleMeanFlux
1720 LocalDipoleMeanFluxErr
1721 LocalDipoleDiffFlux
1722 LocalDipoleDiffFluxErr
1723 """
1724 def __init__(self,
1725 instFluxPosCol,
1726 instFluxNegCol,
1727 instFluxPosErrCol,
1728 instFluxNegErrCol,
1729 photoCalibCol,
1730 photoCalibErrCol,
1731 **kwargs):
1732 self.instFluxNegCol = instFluxNegCol
1733 self.instFluxPosCol = instFluxPosCol
1734 self.instFluxNegErrCol = instFluxNegErrCol
1735 self.instFluxPosErrCol = instFluxPosErrCol
1736 self.photoCalibCol = photoCalibCol
1737 self.photoCalibErrCol = photoCalibErrCol
1738 super().__init__(instFluxNegCol,
1739 instFluxNegErrCol,
1740 photoCalibCol,
1741 photoCalibErrCol,
1742 **kwargs)
1744 @property
1745 def columns(self):
1746 return [self.instFluxPosCol,
1747 self.instFluxNegCol,
1748 self.photoCalibCol]
1750 @property
1751 def name(self):
1752 return f'dipMeanFlux_{self.instFluxPosCol}_{self.instFluxNegCol}'
1754 def _func(self, df):
1755 return 0.5*(np.fabs(self.instFluxToNanojansky(df[self.instFluxNegCol], df[self.photoCalibCol]))
1756 + np.fabs(self.instFluxToNanojansky(df[self.instFluxPosCol], df[self.photoCalibCol])))
1759class LocalDipoleMeanFluxErr(LocalDipoleMeanFlux):
1760 """Compute the error on the absolute mean of dipole fluxes.
1762 See also
1763 --------
1764 LocalNanojansky
1765 LocalNanojanskyErr
1766 LocalMagnitude
1767 LocalMagnitudeErr
1768 LocalDipoleMeanFlux
1769 LocalDipoleMeanFluxErr
1770 LocalDipoleDiffFlux
1771 LocalDipoleDiffFluxErr
1772 """
1774 @property
1775 def columns(self):
1776 return [self.instFluxPosCol,
1777 self.instFluxNegCol,
1778 self.instFluxPosErrCol,
1779 self.instFluxNegErrCol,
1780 self.photoCalibCol,
1781 self.photoCalibErrCol]
1783 @property
1784 def name(self):
1785 return f'dipMeanFluxErr_{self.instFluxPosCol}_{self.instFluxNegCol}'
1787 def _func(self, df):
1788 return 0.5*np.sqrt(
1789 (np.fabs(df[self.instFluxNegCol]) + np.fabs(df[self.instFluxPosCol])
1790 * df[self.photoCalibErrCol])**2
1791 + (df[self.instFluxNegErrCol]**2 + df[self.instFluxPosErrCol]**2)
1792 * df[self.photoCalibCol]**2)
1795class LocalDipoleDiffFlux(LocalDipoleMeanFlux):
1796 """Compute the absolute difference of dipole fluxes.
1798 Value is (abs(pos) - abs(neg))
1800 See also
1801 --------
1802 LocalNanojansky
1803 LocalNanojanskyErr
1804 LocalMagnitude
1805 LocalMagnitudeErr
1806 LocalDipoleMeanFlux
1807 LocalDipoleMeanFluxErr
1808 LocalDipoleDiffFlux
1809 LocalDipoleDiffFluxErr
1810 """
1812 @property
1813 def columns(self):
1814 return [self.instFluxPosCol,
1815 self.instFluxNegCol,
1816 self.photoCalibCol]
1818 @property
1819 def name(self):
1820 return f'dipDiffFlux_{self.instFluxPosCol}_{self.instFluxNegCol}'
1822 def _func(self, df):
1823 return (np.fabs(self.instFluxToNanojansky(df[self.instFluxPosCol], df[self.photoCalibCol]))
1824 - np.fabs(self.instFluxToNanojansky(df[self.instFluxNegCol], df[self.photoCalibCol])))
1827class LocalDipoleDiffFluxErr(LocalDipoleMeanFlux):
1828 """Compute the error on the absolute difference of dipole fluxes.
1830 See also
1831 --------
1832 LocalNanojansky
1833 LocalNanojanskyErr
1834 LocalMagnitude
1835 LocalMagnitudeErr
1836 LocalDipoleMeanFlux
1837 LocalDipoleMeanFluxErr
1838 LocalDipoleDiffFlux
1839 LocalDipoleDiffFluxErr
1840 """
1842 @property
1843 def columns(self):
1844 return [self.instFluxPosCol,
1845 self.instFluxNegCol,
1846 self.instFluxPosErrCol,
1847 self.instFluxNegErrCol,
1848 self.photoCalibCol,
1849 self.photoCalibErrCol]
1851 @property
1852 def name(self):
1853 return f'dipDiffFluxErr_{self.instFluxPosCol}_{self.instFluxNegCol}'
1855 def _func(self, df):
1856 return np.sqrt(
1857 ((np.fabs(df[self.instFluxPosCol]) - np.fabs(df[self.instFluxNegCol]))
1858 * df[self.photoCalibErrCol])**2
1859 + (df[self.instFluxPosErrCol]**2 + df[self.instFluxNegErrCol]**2)
1860 * df[self.photoCalibCol]**2)
1863class Ratio(Functor):
1864 """Base class for returning the ratio of 2 columns.
1866 Can be used to compute a Signal to Noise ratio for any input flux.
1868 Parameters
1869 ----------
1870 numerator : `str`
1871 Name of the column to use at the numerator in the ratio
1872 denominator : `str`
1873 Name of the column to use as the denominator in the ratio.
1874 """
1875 def __init__(self,
1876 numerator,
1877 denominator,
1878 **kwargs):
1879 self.numerator = numerator
1880 self.denominator = denominator
1881 super().__init__(**kwargs)
1883 @property
1884 def columns(self):
1885 return [self.numerator, self.denominator]
1887 @property
1888 def name(self):
1889 return f'ratio_{self.numerator}_{self.denominator}'
1891 def _func(self, df):
1892 with np.warnings.catch_warnings():
1893 np.warnings.filterwarnings('ignore', r'invalid value encountered')
1894 np.warnings.filterwarnings('ignore', r'divide by zero')
1895 return df[self.numerator] / df[self.denominator]
1898class Ebv(Functor):
1899 """Compute E(B-V) from dustmaps.sfd
1900 """
1901 _defaultDataset = 'ref'
1902 name = "E(B-V)"
1903 shortname = "ebv"
1905 def __init__(self, **kwargs):
1906 # import is only needed for Ebv
1907 from dustmaps.sfd import SFDQuery
1908 self._columns = ['coord_ra', 'coord_dec']
1909 self.sfd = SFDQuery()
1910 super().__init__(**kwargs)
1912 def _func(self, df):
1913 coords = SkyCoord(df['coord_ra'].values * u.rad, df['coord_dec'].values * u.rad)
1914 ebv = self.sfd(coords)
1915 # Double precision unnecessary scientifically
1916 # but currently needed for ingest to qserv
1917 return pd.Series(ebv, index=df.index).astype('float64')