Coverage for python/lsst/pipe/tasks/functors.py : 35%

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1import yaml
2import re
3from itertools import product
5import pandas as pd
6import numpy as np
7import astropy.units as u
9from lsst.daf.persistence import doImport
10from lsst.daf.butler import DeferredDatasetHandle
11from .parquetTable import ParquetTable, MultilevelParquetTable
14def init_fromDict(initDict, basePath='lsst.pipe.tasks.functors',
15 typeKey='functor', name=None):
16 """Initialize an object defined in a dictionary
18 The object needs to be importable as
19 f'{basePath}.{initDict[typeKey]}'
20 The positional and keyword arguments (if any) are contained in
21 "args" and "kwargs" entries in the dictionary, respectively.
22 This is used in `functors.CompositeFunctor.from_yaml` to initialize
23 a composite functor from a specification in a YAML file.
25 Parameters
26 ----------
27 initDict : dictionary
28 Dictionary describing object's initialization. Must contain
29 an entry keyed by ``typeKey`` that is the name of the object,
30 relative to ``basePath``.
31 basePath : str
32 Path relative to module in which ``initDict[typeKey]`` is defined.
33 typeKey : str
34 Key of ``initDict`` that is the name of the object
35 (relative to `basePath`).
36 """
37 initDict = initDict.copy()
38 # TO DO: DM-21956 We should be able to define functors outside this module
39 pythonType = doImport(f'{basePath}.{initDict.pop(typeKey)}')
40 args = []
41 if 'args' in initDict:
42 args = initDict.pop('args')
43 if isinstance(args, str):
44 args = [args]
45 try:
46 element = pythonType(*args, **initDict)
47 except Exception as e:
48 message = f'Error in constructing functor "{name}" of type {pythonType.__name__} with args: {args}'
49 raise type(e)(message, e.args)
50 return element
53class Functor(object):
54 """Define and execute a calculation on a ParquetTable
56 The `__call__` method accepts either a `ParquetTable` object or a
57 `DeferredDatasetHandle`, and returns the
58 result of the calculation as a single column. Each functor defines what
59 columns are needed for the calculation, and only these columns are read
60 from the `ParquetTable`.
62 The action of `__call__` consists of two steps: first, loading the
63 necessary columns from disk into memory as a `pandas.DataFrame` object;
64 and second, performing the computation on this dataframe and returning the
65 result.
68 To define a new `Functor`, a subclass must define a `_func` method,
69 that takes a `pandas.DataFrame` and returns result in a `pandas.Series`.
70 In addition, it must define the following attributes
72 * `_columns`: The columns necessary to perform the calculation
73 * `name`: A name appropriate for a figure axis label
74 * `shortname`: A name appropriate for use as a dictionary key
76 On initialization, a `Functor` should declare what filter (`filt` kwarg)
77 and dataset (e.g. `'ref'`, `'meas'`, `'forced_src'`) it is intended to be
78 applied to. This enables the `_get_data` method to extract the proper
79 columns from the parquet file. If not specified, the dataset will fall back
80 on the `_defaultDataset`attribute. If filter is not specified and `dataset`
81 is anything other than `'ref'`, then an error will be raised when trying to
82 perform the calculation.
84 As currently implemented, `Functor` is only set up to expect a
85 dataset of the format of the `deepCoadd_obj` dataset; that is, a
86 dataframe with a multi-level column index,
87 with the levels of the column index being `filter`,
88 `dataset`, and `column`. This is defined in the `_columnLevels` attribute,
89 as well as being implicit in the role of the `filt` and `dataset` attributes
90 defined at initialization. In addition, the `_get_data` method that reads
91 the dataframe from the `ParquetTable` will return a dataframe with column
92 index levels defined by the `_dfLevels` attribute; by default, this is
93 `column`.
95 The `_columnLevels` and `_dfLevels` attributes should generally not need to
96 be changed, unless `_func` needs columns from multiple filters or datasets
97 to do the calculation.
98 An example of this is the `lsst.pipe.tasks.functors.Color` functor, for
99 which `_dfLevels = ('filter', 'column')`, and `_func` expects the dataframe
100 it gets to have those levels in the column index.
102 Parameters
103 ----------
104 filt : str
105 Filter upon which to do the calculation
107 dataset : str
108 Dataset upon which to do the calculation
109 (e.g., 'ref', 'meas', 'forced_src').
111 """
113 _defaultDataset = 'ref'
114 _columnLevels = ('filter', 'dataset', 'column')
115 _dfLevels = ('column',)
116 _defaultNoDup = False
118 def __init__(self, filt=None, dataset=None, noDup=None):
119 self.filt = filt
120 self.dataset = dataset if dataset is not None else self._defaultDataset
121 self._noDup = noDup
123 @property
124 def noDup(self):
125 if self._noDup is not None:
126 return self._noDup
127 else:
128 return self._defaultNoDup
130 @property
131 def columns(self):
132 """Columns required to perform calculation
133 """
134 if not hasattr(self, '_columns'):
135 raise NotImplementedError('Must define columns property or _columns attribute')
136 return self._columns
138 def _get_data_columnLevels(self, data, columnIndex=None):
139 """Gets the names of the column index levels
141 This should only be called in the context of a multilevel table.
142 The logic here is to enable this to work both with the gen2 `MultilevelParquetTable`
143 and with the gen3 `DeferredDatasetHandle`.
145 Parameters
146 ----------
147 data : `MultilevelParquetTable` or `DeferredDatasetHandle`
149 columnnIndex (optional): pandas `Index` object
150 if not passed, then it is read from the `DeferredDatasetHandle`
151 """
152 if isinstance(data, DeferredDatasetHandle):
153 if columnIndex is None:
154 columnIndex = data.get(component="columns")
155 if columnIndex is not None:
156 return columnIndex.names
157 if isinstance(data, MultilevelParquetTable):
158 return data.columnLevels
159 else:
160 raise TypeError(f"Unknown type for data: {type(data)}!")
162 def _get_data_columnLevelNames(self, data, columnIndex=None):
163 """Gets the content of each of the column levels for a multilevel table
165 Similar to `_get_data_columnLevels`, this enables backward compatibility with gen2.
167 Mirrors original gen2 implementation within `pipe.tasks.parquetTable.MultilevelParquetTable`
168 """
169 if isinstance(data, DeferredDatasetHandle):
170 if columnIndex is None:
171 columnIndex = data.get(component="columns")
172 if columnIndex is not None:
173 columnLevels = columnIndex.names
174 columnLevelNames = {
175 level: list(np.unique(np.array([c for c in columnIndex])[:, i]))
176 for i, level in enumerate(columnLevels)
177 }
178 return columnLevelNames
179 if isinstance(data, MultilevelParquetTable):
180 return data.columnLevelNames
181 else:
182 raise TypeError(f"Unknown type for data: {type(data)}!")
184 def _colsFromDict(self, colDict, columnIndex=None):
185 """Converts dictionary column specficiation to a list of columns
187 This mirrors the original gen2 implementation within `pipe.tasks.parquetTable.MultilevelParquetTable`
188 """
189 new_colDict = {}
190 columnLevels = self._get_data_columnLevels(None, columnIndex=columnIndex)
192 for i, l in enumerate(columnLevels):
193 if l in colDict:
194 if isinstance(colDict[l], str):
195 new_colDict[l] = [colDict[l]]
196 else:
197 new_colDict[l] = colDict[l]
198 else:
199 new_colDict[l] = columnIndex.levels[i]
201 levelCols = [new_colDict[l] for l in columnLevels]
202 cols = product(*levelCols)
203 return list(cols)
205 def multilevelColumns(self, data, columnIndex=None, returnTuple=False):
206 """Returns columns needed by functor from multilevel dataset
208 To access tables with multilevel column structure, the `MultilevelParquetTable`
209 or `DeferredDatasetHandle` need to be passed either a list of tuples or a
210 dictionary.
212 Parameters
213 ----------
214 data : `MultilevelParquetTable` or `DeferredDatasetHandle`
216 columnIndex (optional): pandas `Index` object
217 either passed or read in from `DeferredDatasetHandle`.
219 `returnTuple` : bool
220 If true, then return a list of tuples rather than the column dictionary
221 specification. This is set to `True` by `CompositeFunctor` in order to be able to
222 combine columns from the various component functors.
224 """
225 if isinstance(data, DeferredDatasetHandle) and columnIndex is None:
226 columnIndex = data.get(component="columns")
228 # Confirm that the dataset has the column levels the functor is expecting it to have.
229 columnLevels = self._get_data_columnLevels(data, columnIndex)
231 if not set(columnLevels) == set(self._columnLevels):
232 raise ValueError(
233 "ParquetTable does not have the expected column levels. "
234 f"Got {columnLevels}; expected {self._columnLevels}."
235 )
237 columnDict = {'column': self.columns,
238 'dataset': self.dataset}
239 if self.filt is None:
240 columnLevelNames = self._get_data_columnLevelNames(data, columnIndex)
241 if "filter" in columnLevels:
242 if self.dataset == "ref":
243 columnDict["filter"] = columnLevelNames["filter"][0]
244 else:
245 raise ValueError(f"'filt' not set for functor {self.name}"
246 f"(dataset {self.dataset}) "
247 "and ParquetTable "
248 "contains multiple filters in column index. "
249 "Set 'filt' or set 'dataset' to 'ref'.")
250 else:
251 columnDict['filter'] = self.filt
253 if isinstance(data, MultilevelParquetTable):
254 return data._colsFromDict(columnDict)
255 elif isinstance(data, DeferredDatasetHandle):
256 if returnTuple:
257 return self._colsFromDict(columnDict, columnIndex=columnIndex)
258 else:
259 return columnDict
261 def _func(self, df, dropna=True):
262 raise NotImplementedError('Must define calculation on dataframe')
264 def _get_columnIndex(self, data):
265 """Return columnIndex
266 """
268 if isinstance(data, DeferredDatasetHandle):
269 return data.get(component="columns")
270 else:
271 return None
273 def _get_data(self, data):
274 """Retrieve dataframe necessary for calculation.
276 The data argument can be a DataFrame, a ParquetTable instance, or a gen3 DeferredDatasetHandle
278 Returns dataframe upon which `self._func` can act.
280 N.B. while passing a raw pandas `DataFrame` *should* work here, it has not been tested.
281 """
282 if isinstance(data, pd.DataFrame):
283 return data
285 # First thing to do: check to see if the data source has a multilevel column index or not.
286 columnIndex = self._get_columnIndex(data)
287 is_multiLevel = isinstance(data, MultilevelParquetTable) or isinstance(columnIndex, pd.MultiIndex)
289 # Simple single-level parquet table, gen2
290 if isinstance(data, ParquetTable) and not is_multiLevel:
291 columns = self.columns
292 df = data.toDataFrame(columns=columns)
293 return df
295 # Get proper multi-level columns specification for this functor
296 if is_multiLevel:
297 columns = self.multilevelColumns(data, columnIndex=columnIndex)
299 if isinstance(data, MultilevelParquetTable):
300 # Load in-memory dataframe with appropriate columns the gen2 way
301 df = data.toDataFrame(columns=columns, droplevels=False)
302 elif isinstance(data, DeferredDatasetHandle):
303 # Load in-memory dataframe with appropriate columns the gen3 way
304 df = data.get(parameters={"columns": columns})
306 # Drop unnecessary column levels
307 df = self._setLevels(df)
308 return df
310 def _setLevels(self, df):
311 levelsToDrop = [n for n in df.columns.names if n not in self._dfLevels]
312 df.columns = df.columns.droplevel(levelsToDrop)
313 return df
315 def _dropna(self, vals):
316 return vals.dropna()
318 def __call__(self, data, dropna=False):
319 try:
320 df = self._get_data(data)
321 vals = self._func(df)
322 except Exception:
323 vals = self.fail(df)
324 if dropna:
325 vals = self._dropna(vals)
327 return vals
329 def difference(self, data1, data2, **kwargs):
330 """Computes difference between functor called on two different ParquetTable objects
331 """
332 return self(data1, **kwargs) - self(data2, **kwargs)
334 def fail(self, df):
335 return pd.Series(np.full(len(df), np.nan), index=df.index)
337 @property
338 def name(self):
339 """Full name of functor (suitable for figure labels)
340 """
341 return NotImplementedError
343 @property
344 def shortname(self):
345 """Short name of functor (suitable for column name/dict key)
346 """
347 return self.name
350class CompositeFunctor(Functor):
351 """Perform multiple calculations at once on a catalog
353 The role of a `CompositeFunctor` is to group together computations from
354 multiple functors. Instead of returning `pandas.Series` a
355 `CompositeFunctor` returns a `pandas.Dataframe`, with the column names
356 being the keys of `funcDict`.
358 The `columns` attribute of a `CompositeFunctor` is the union of all columns
359 in all the component functors.
361 A `CompositeFunctor` does not use a `_func` method itself; rather,
362 when a `CompositeFunctor` is called, all its columns are loaded
363 at once, and the resulting dataframe is passed to the `_func` method of each component
364 functor. This has the advantage of only doing I/O (reading from parquet file) once,
365 and works because each individual `_func` method of each component functor does not
366 care if there are *extra* columns in the dataframe being passed; only that it must contain
367 *at least* the `columns` it expects.
369 An important and useful class method is `from_yaml`, which takes as argument the path to a YAML
370 file specifying a collection of functors.
372 Parameters
373 ----------
374 funcs : `dict` or `list`
375 Dictionary or list of functors. If a list, then it will be converted
376 into a dictonary according to the `.shortname` attribute of each functor.
378 """
379 dataset = None
381 def __init__(self, funcs, **kwargs):
383 if type(funcs) == dict:
384 self.funcDict = funcs
385 else:
386 self.funcDict = {f.shortname: f for f in funcs}
388 self._filt = None
390 super().__init__(**kwargs)
392 @property
393 def filt(self):
394 return self._filt
396 @filt.setter
397 def filt(self, filt):
398 if filt is not None:
399 for _, f in self.funcDict.items():
400 f.filt = filt
401 self._filt = filt
403 def update(self, new):
404 if isinstance(new, dict):
405 self.funcDict.update(new)
406 elif isinstance(new, CompositeFunctor):
407 self.funcDict.update(new.funcDict)
408 else:
409 raise TypeError('Can only update with dictionary or CompositeFunctor.')
411 # Make sure new functors have the same 'filt' set
412 if self.filt is not None:
413 self.filt = self.filt
415 @property
416 def columns(self):
417 return list(set([x for y in [f.columns for f in self.funcDict.values()] for x in y]))
419 def multilevelColumns(self, data, **kwargs):
420 # Get the union of columns for all component functors. Note the need to have `returnTuple=True` here.
421 return list(
422 set(
423 [
424 x
425 for y in [
426 f.multilevelColumns(data, returnTuple=True, **kwargs) for f in self.funcDict.values()
427 ]
428 for x in y
429 ]
430 )
431 )
433 def __call__(self, data, **kwargs):
434 columnIndex = self._get_columnIndex(data)
436 # First, determine whether data has a multilevel index (either gen2 or gen3)
437 is_multiLevel = isinstance(data, MultilevelParquetTable) or isinstance(columnIndex, pd.MultiIndex)
439 # Simple single-level column index, gen2
440 if isinstance(data, ParquetTable) and not is_multiLevel:
441 columns = self.columns
442 df = data.toDataFrame(columns=columns)
443 valDict = {k: f._func(df) for k, f in self.funcDict.items()}
445 # Multilevel index, gen2 or gen3
446 if is_multiLevel:
447 columns = self.multilevelColumns(data, columnIndex=columnIndex)
449 if isinstance(data, MultilevelParquetTable):
450 # Read data into memory the gen2 way
451 df = data.toDataFrame(columns=columns, droplevels=False)
452 elif isinstance(data, DeferredDatasetHandle):
453 # Read data into memory the gen3 way
454 df = data.get(parameters={"columns": columns})
456 valDict = {}
457 for k, f in self.funcDict.items():
458 try:
459 subdf = f._setLevels(
460 df[f.multilevelColumns(data, returnTuple=True, columnIndex=columnIndex)]
461 )
462 valDict[k] = f._func(subdf)
463 except Exception:
464 valDict[k] = f.fail(subdf)
466 # non-multilevel, gen3 (TODO: this should work, but this case is not tested in test_functors.py)
467 elif isinstance(data, DeferredDatasetHandle):
468 columns = self.columns
469 df = data.get(parameters={"columns": columns})
470 valDict = {k: f._func(df) for k, f in self.funcDict.items()}
472 try:
473 valDf = pd.concat(valDict, axis=1)
474 except TypeError:
475 print([(k, type(v)) for k, v in valDict.items()])
476 raise
478 if kwargs.get('dropna', False):
479 valDf = valDf.dropna(how='any')
481 return valDf
483 @classmethod
484 def renameCol(cls, col, renameRules):
485 if renameRules is None:
486 return col
487 for old, new in renameRules:
488 if col.startswith(old):
489 col = col.replace(old, new)
490 return col
492 @classmethod
493 def from_file(cls, filename, **kwargs):
494 with open(filename) as f:
495 translationDefinition = yaml.safe_load(f)
497 return cls.from_yaml(translationDefinition, **kwargs)
499 @classmethod
500 def from_yaml(cls, translationDefinition, **kwargs):
501 funcs = {}
502 for func, val in translationDefinition['funcs'].items():
503 funcs[func] = init_fromDict(val, name=func)
505 if 'flag_rename_rules' in translationDefinition:
506 renameRules = translationDefinition['flag_rename_rules']
507 else:
508 renameRules = None
510 if 'refFlags' in translationDefinition:
511 for flag in translationDefinition['refFlags']:
512 funcs[cls.renameCol(flag, renameRules)] = Column(flag, dataset='ref')
514 if 'flags' in translationDefinition:
515 for flag in translationDefinition['flags']:
516 funcs[cls.renameCol(flag, renameRules)] = Column(flag, dataset='meas')
518 return cls(funcs, **kwargs)
521def mag_aware_eval(df, expr):
522 """Evaluate an expression on a DataFrame, knowing what the 'mag' function means
524 Builds on `pandas.DataFrame.eval`, which parses and executes math on dataframes.
526 Parameters
527 ----------
528 df : pandas.DataFrame
529 Dataframe on which to evaluate expression.
531 expr : str
532 Expression.
533 """
534 try:
535 expr_new = re.sub(r'mag\((\w+)\)', r'-2.5*log(\g<1>)/log(10)', expr)
536 val = df.eval(expr_new, truediv=True)
537 except Exception: # Should check what actually gets raised
538 expr_new = re.sub(r'mag\((\w+)\)', r'-2.5*log(\g<1>_instFlux)/log(10)', expr)
539 val = df.eval(expr_new, truediv=True)
540 return val
543class CustomFunctor(Functor):
544 """Arbitrary computation on a catalog
546 Column names (and thus the columns to be loaded from catalog) are found
547 by finding all words and trying to ignore all "math-y" words.
549 Parameters
550 ----------
551 expr : str
552 Expression to evaluate, to be parsed and executed by `mag_aware_eval`.
553 """
554 _ignore_words = ('mag', 'sin', 'cos', 'exp', 'log', 'sqrt')
556 def __init__(self, expr, **kwargs):
557 self.expr = expr
558 super().__init__(**kwargs)
560 @property
561 def name(self):
562 return self.expr
564 @property
565 def columns(self):
566 flux_cols = re.findall(r'mag\(\s*(\w+)\s*\)', self.expr)
568 cols = [c for c in re.findall(r'[a-zA-Z_]+', self.expr) if c not in self._ignore_words]
569 not_a_col = []
570 for c in flux_cols:
571 if not re.search('_instFlux$', c):
572 cols.append(f'{c}_instFlux')
573 not_a_col.append(c)
574 else:
575 cols.append(c)
577 return list(set([c for c in cols if c not in not_a_col]))
579 def _func(self, df):
580 return mag_aware_eval(df, self.expr)
583class Column(Functor):
584 """Get column with specified name
585 """
587 def __init__(self, col, **kwargs):
588 self.col = col
589 super().__init__(**kwargs)
591 @property
592 def name(self):
593 return self.col
595 @property
596 def columns(self):
597 return [self.col]
599 def _func(self, df):
600 return df[self.col]
603class Index(Functor):
604 """Return the value of the index for each object
605 """
607 columns = ['coord_ra'] # just a dummy; something has to be here
608 _defaultDataset = 'ref'
609 _defaultNoDup = True
611 def _func(self, df):
612 return pd.Series(df.index, index=df.index)
615class IDColumn(Column):
616 col = 'id'
617 _allow_difference = False
618 _defaultNoDup = True
620 def _func(self, df):
621 return pd.Series(df.index, index=df.index)
624class FootprintNPix(Column):
625 col = 'base_Footprint_nPix'
628class CoordColumn(Column):
629 """Base class for coordinate column, in degrees
630 """
631 _radians = True
633 def __init__(self, col, **kwargs):
634 super().__init__(col, **kwargs)
636 def _func(self, df):
637 # Must not modify original column in case that column is used by another functor
638 output = df[self.col] * 180 / np.pi if self._radians else df[self.col]
639 return output
642class RAColumn(CoordColumn):
643 """Right Ascension, in degrees
644 """
645 name = 'RA'
646 _defaultNoDup = True
648 def __init__(self, **kwargs):
649 super().__init__('coord_ra', **kwargs)
651 def __call__(self, catalog, **kwargs):
652 return super().__call__(catalog, **kwargs)
655class DecColumn(CoordColumn):
656 """Declination, in degrees
657 """
658 name = 'Dec'
659 _defaultNoDup = True
661 def __init__(self, **kwargs):
662 super().__init__('coord_dec', **kwargs)
664 def __call__(self, catalog, **kwargs):
665 return super().__call__(catalog, **kwargs)
668def fluxName(col):
669 if not col.endswith('_instFlux'):
670 col += '_instFlux'
671 return col
674def fluxErrName(col):
675 if not col.endswith('_instFluxErr'):
676 col += '_instFluxErr'
677 return col
680class Mag(Functor):
681 """Compute calibrated magnitude
683 Takes a `calib` argument, which returns the flux at mag=0
684 as `calib.getFluxMag0()`. If not provided, then the default
685 `fluxMag0` is 63095734448.0194, which is default for HSC.
686 This default should be removed in DM-21955
688 This calculation hides warnings about invalid values and dividing by zero.
690 As for all functors, a `dataset` and `filt` kwarg should be provided upon
691 initialization. Unlike the default `Functor`, however, the default dataset
692 for a `Mag` is `'meas'`, rather than `'ref'`.
694 Parameters
695 ----------
696 col : `str`
697 Name of flux column from which to compute magnitude. Can be parseable
698 by `lsst.pipe.tasks.functors.fluxName` function---that is, you can pass
699 `'modelfit_CModel'` instead of `'modelfit_CModel_instFlux'`) and it will
700 understand.
701 calib : `lsst.afw.image.calib.Calib` (optional)
702 Object that knows zero point.
703 """
704 _defaultDataset = 'meas'
706 def __init__(self, col, calib=None, **kwargs):
707 self.col = fluxName(col)
708 self.calib = calib
709 if calib is not None:
710 self.fluxMag0 = calib.getFluxMag0()[0]
711 else:
712 # TO DO: DM-21955 Replace hard coded photometic calibration values
713 self.fluxMag0 = 63095734448.0194
715 super().__init__(**kwargs)
717 @property
718 def columns(self):
719 return [self.col]
721 def _func(self, df):
722 with np.warnings.catch_warnings():
723 np.warnings.filterwarnings('ignore', r'invalid value encountered')
724 np.warnings.filterwarnings('ignore', r'divide by zero')
725 return -2.5*np.log10(df[self.col] / self.fluxMag0)
727 @property
728 def name(self):
729 return f'mag_{self.col}'
732class MagErr(Mag):
733 """Compute calibrated magnitude uncertainty
735 Takes the same `calib` object as `lsst.pipe.tasks.functors.Mag`.
737 Parameters
738 col : `str`
739 Name of flux column
740 calib : `lsst.afw.image.calib.Calib` (optional)
741 Object that knows zero point.
742 """
744 def __init__(self, *args, **kwargs):
745 super().__init__(*args, **kwargs)
746 if self.calib is not None:
747 self.fluxMag0Err = self.calib.getFluxMag0()[1]
748 else:
749 self.fluxMag0Err = 0.
751 @property
752 def columns(self):
753 return [self.col, self.col + 'Err']
755 def _func(self, df):
756 with np.warnings.catch_warnings():
757 np.warnings.filterwarnings('ignore', r'invalid value encountered')
758 np.warnings.filterwarnings('ignore', r'divide by zero')
759 fluxCol, fluxErrCol = self.columns
760 x = df[fluxErrCol] / df[fluxCol]
761 y = self.fluxMag0Err / self.fluxMag0
762 magErr = (2.5 / np.log(10.)) * np.sqrt(x*x + y*y)
763 return magErr
765 @property
766 def name(self):
767 return super().name + '_err'
770class NanoMaggie(Mag):
771 """
772 """
774 def _func(self, df):
775 return (df[self.col] / self.fluxMag0) * 1e9
778class MagDiff(Functor):
779 _defaultDataset = 'meas'
781 """Functor to calculate magnitude difference"""
783 def __init__(self, col1, col2, **kwargs):
784 self.col1 = fluxName(col1)
785 self.col2 = fluxName(col2)
786 super().__init__(**kwargs)
788 @property
789 def columns(self):
790 return [self.col1, self.col2]
792 def _func(self, df):
793 with np.warnings.catch_warnings():
794 np.warnings.filterwarnings('ignore', r'invalid value encountered')
795 np.warnings.filterwarnings('ignore', r'divide by zero')
796 return -2.5*np.log10(df[self.col1]/df[self.col2])
798 @property
799 def name(self):
800 return f'(mag_{self.col1} - mag_{self.col2})'
802 @property
803 def shortname(self):
804 return f'magDiff_{self.col1}_{self.col2}'
807class Color(Functor):
808 """Compute the color between two filters
810 Computes color by initializing two different `Mag`
811 functors based on the `col` and filters provided, and
812 then returning the difference.
814 This is enabled by the `_func` expecting a dataframe with a
815 multilevel column index, with both `'filter'` and `'column'`,
816 instead of just `'column'`, which is the `Functor` default.
817 This is controlled by the `_dfLevels` attribute.
819 Also of note, the default dataset for `Color` is `forced_src'`,
820 whereas for `Mag` it is `'meas'`.
822 Parameters
823 ----------
824 col : str
825 Name of flux column from which to compute; same as would be passed to
826 `lsst.pipe.tasks.functors.Mag`.
828 filt2, filt1 : str
829 Filters from which to compute magnitude difference.
830 Color computed is `Mag(filt2) - Mag(filt1)`.
831 """
832 _defaultDataset = 'forced_src'
833 _dfLevels = ('filter', 'column')
834 _defaultNoDup = True
836 def __init__(self, col, filt2, filt1, **kwargs):
837 self.col = fluxName(col)
838 if filt2 == filt1:
839 raise RuntimeError("Cannot compute Color for %s: %s - %s " % (col, filt2, filt1))
840 self.filt2 = filt2
841 self.filt1 = filt1
843 self.mag2 = Mag(col, filt=filt2, **kwargs)
844 self.mag1 = Mag(col, filt=filt1, **kwargs)
846 super().__init__(**kwargs)
848 @property
849 def filt(self):
850 return None
852 @filt.setter
853 def filt(self, filt):
854 pass
856 def _func(self, df):
857 mag2 = self.mag2._func(df[self.filt2])
858 mag1 = self.mag1._func(df[self.filt1])
859 return mag2 - mag1
861 @property
862 def columns(self):
863 return [self.mag1.col, self.mag2.col]
865 def multilevelColumns(self, parq, **kwargs):
866 return [(self.dataset, self.filt1, self.col), (self.dataset, self.filt2, self.col)]
868 @property
869 def name(self):
870 return f'{self.filt2} - {self.filt1} ({self.col})'
872 @property
873 def shortname(self):
874 return f"{self.col}_{self.filt2.replace('-', '')}m{self.filt1.replace('-', '')}"
877class Labeller(Functor):
878 """Main function of this subclass is to override the dropna=True
879 """
880 _null_label = 'null'
881 _allow_difference = False
882 name = 'label'
883 _force_str = False
885 def __call__(self, parq, dropna=False, **kwargs):
886 return super().__call__(parq, dropna=False, **kwargs)
889class StarGalaxyLabeller(Labeller):
890 _columns = ["base_ClassificationExtendedness_value"]
891 _column = "base_ClassificationExtendedness_value"
893 def _func(self, df):
894 x = df[self._columns][self._column]
895 mask = x.isnull()
896 test = (x < 0.5).astype(int)
897 test = test.mask(mask, 2)
899 # TODO: DM-21954 Look into veracity of inline comment below
900 # are these backwards?
901 categories = ['galaxy', 'star', self._null_label]
902 label = pd.Series(pd.Categorical.from_codes(test, categories=categories),
903 index=x.index, name='label')
904 if self._force_str:
905 label = label.astype(str)
906 return label
909class NumStarLabeller(Labeller):
910 _columns = ['numStarFlags']
911 labels = {"star": 0, "maybe": 1, "notStar": 2}
913 def _func(self, df):
914 x = df[self._columns][self._columns[0]]
916 # Number of filters
917 n = len(x.unique()) - 1
919 labels = ['noStar', 'maybe', 'star']
920 label = pd.Series(pd.cut(x, [-1, 0, n-1, n], labels=labels),
921 index=x.index, name='label')
923 if self._force_str:
924 label = label.astype(str)
926 return label
929class DeconvolvedMoments(Functor):
930 name = 'Deconvolved Moments'
931 shortname = 'deconvolvedMoments'
932 _columns = ("ext_shapeHSM_HsmSourceMoments_xx",
933 "ext_shapeHSM_HsmSourceMoments_yy",
934 "base_SdssShape_xx", "base_SdssShape_yy",
935 "ext_shapeHSM_HsmPsfMoments_xx",
936 "ext_shapeHSM_HsmPsfMoments_yy")
938 def _func(self, df):
939 """Calculate deconvolved moments"""
940 if "ext_shapeHSM_HsmSourceMoments_xx" in df.columns: # _xx added by tdm
941 hsm = df["ext_shapeHSM_HsmSourceMoments_xx"] + df["ext_shapeHSM_HsmSourceMoments_yy"]
942 else:
943 hsm = np.ones(len(df))*np.nan
944 sdss = df["base_SdssShape_xx"] + df["base_SdssShape_yy"]
945 if "ext_shapeHSM_HsmPsfMoments_xx" in df.columns:
946 psf = df["ext_shapeHSM_HsmPsfMoments_xx"] + df["ext_shapeHSM_HsmPsfMoments_yy"]
947 else:
948 # LSST does not have shape.sdss.psf. Could instead add base_PsfShape to catalog using
949 # exposure.getPsf().computeShape(s.getCentroid()).getIxx()
950 # raise TaskError("No psf shape parameter found in catalog")
951 raise RuntimeError('No psf shape parameter found in catalog')
953 return hsm.where(np.isfinite(hsm), sdss) - psf
956class SdssTraceSize(Functor):
957 """Functor to calculate SDSS trace radius size for sources"""
958 name = "SDSS Trace Size"
959 shortname = 'sdssTrace'
960 _columns = ("base_SdssShape_xx", "base_SdssShape_yy")
962 def _func(self, df):
963 srcSize = np.sqrt(0.5*(df["base_SdssShape_xx"] + df["base_SdssShape_yy"]))
964 return srcSize
967class PsfSdssTraceSizeDiff(Functor):
968 """Functor to calculate SDSS trace radius size difference (%) between object and psf model"""
969 name = "PSF - SDSS Trace Size"
970 shortname = 'psf_sdssTrace'
971 _columns = ("base_SdssShape_xx", "base_SdssShape_yy",
972 "base_SdssShape_psf_xx", "base_SdssShape_psf_yy")
974 def _func(self, df):
975 srcSize = np.sqrt(0.5*(df["base_SdssShape_xx"] + df["base_SdssShape_yy"]))
976 psfSize = np.sqrt(0.5*(df["base_SdssShape_psf_xx"] + df["base_SdssShape_psf_yy"]))
977 sizeDiff = 100*(srcSize - psfSize)/(0.5*(srcSize + psfSize))
978 return sizeDiff
981class HsmTraceSize(Functor):
982 """Functor to calculate HSM trace radius size for sources"""
983 name = 'HSM Trace Size'
984 shortname = 'hsmTrace'
985 _columns = ("ext_shapeHSM_HsmSourceMoments_xx",
986 "ext_shapeHSM_HsmSourceMoments_yy")
988 def _func(self, df):
989 srcSize = np.sqrt(0.5*(df["ext_shapeHSM_HsmSourceMoments_xx"]
990 + df["ext_shapeHSM_HsmSourceMoments_yy"]))
991 return srcSize
994class PsfHsmTraceSizeDiff(Functor):
995 """Functor to calculate HSM trace radius size difference (%) between object and psf model"""
996 name = 'PSF - HSM Trace Size'
997 shortname = 'psf_HsmTrace'
998 _columns = ("ext_shapeHSM_HsmSourceMoments_xx",
999 "ext_shapeHSM_HsmSourceMoments_yy",
1000 "ext_shapeHSM_HsmPsfMoments_xx",
1001 "ext_shapeHSM_HsmPsfMoments_yy")
1003 def _func(self, df):
1004 srcSize = np.sqrt(0.5*(df["ext_shapeHSM_HsmSourceMoments_xx"]
1005 + df["ext_shapeHSM_HsmSourceMoments_yy"]))
1006 psfSize = np.sqrt(0.5*(df["ext_shapeHSM_HsmPsfMoments_xx"]
1007 + df["ext_shapeHSM_HsmPsfMoments_yy"]))
1008 sizeDiff = 100*(srcSize - psfSize)/(0.5*(srcSize + psfSize))
1009 return sizeDiff
1012class HsmFwhm(Functor):
1013 name = 'HSM Psf FWHM'
1014 _columns = ('ext_shapeHSM_HsmPsfMoments_xx', 'ext_shapeHSM_HsmPsfMoments_yy')
1015 # TODO: DM-21403 pixel scale should be computed from the CD matrix or transform matrix
1016 pixelScale = 0.168
1017 SIGMA2FWHM = 2*np.sqrt(2*np.log(2))
1019 def _func(self, df):
1020 return self.pixelScale*self.SIGMA2FWHM*np.sqrt(
1021 0.5*(df['ext_shapeHSM_HsmPsfMoments_xx'] + df['ext_shapeHSM_HsmPsfMoments_yy']))
1024class E1(Functor):
1025 name = "Distortion Ellipticity (e1)"
1026 shortname = "Distortion"
1028 def __init__(self, colXX, colXY, colYY, **kwargs):
1029 self.colXX = colXX
1030 self.colXY = colXY
1031 self.colYY = colYY
1032 self._columns = [self.colXX, self.colXY, self.colYY]
1033 super().__init__(**kwargs)
1035 @property
1036 def columns(self):
1037 return [self.colXX, self.colXY, self.colYY]
1039 def _func(self, df):
1040 return df[self.colXX] - df[self.colYY] / (df[self.colXX] + df[self.colYY])
1043class E2(Functor):
1044 name = "Ellipticity e2"
1046 def __init__(self, colXX, colXY, colYY, **kwargs):
1047 self.colXX = colXX
1048 self.colXY = colXY
1049 self.colYY = colYY
1050 super().__init__(**kwargs)
1052 @property
1053 def columns(self):
1054 return [self.colXX, self.colXY, self.colYY]
1056 def _func(self, df):
1057 return 2*df[self.colXY] / (df[self.colXX] + df[self.colYY])
1060class RadiusFromQuadrupole(Functor):
1062 def __init__(self, colXX, colXY, colYY, **kwargs):
1063 self.colXX = colXX
1064 self.colXY = colXY
1065 self.colYY = colYY
1066 super().__init__(**kwargs)
1068 @property
1069 def columns(self):
1070 return [self.colXX, self.colXY, self.colYY]
1072 def _func(self, df):
1073 return (df[self.colXX]*df[self.colYY] - df[self.colXY]**2)**0.25
1076class LocalWcs(Functor):
1077 """Computations using the stored localWcs.
1078 """
1079 name = "LocalWcsOperations"
1081 def __init__(self,
1082 colCD_1_1,
1083 colCD_1_2,
1084 colCD_2_1,
1085 colCD_2_2,
1086 **kwargs):
1087 self.colCD_1_1 = colCD_1_1
1088 self.colCD_1_2 = colCD_1_2
1089 self.colCD_2_1 = colCD_2_1
1090 self.colCD_2_2 = colCD_2_2
1091 super().__init__(**kwargs)
1093 def computeDeltaRaDec(self, x, y, cd11, cd12, cd21, cd22):
1094 """Compute the distance on the sphere from x2, y1 to x1, y1.
1096 Parameters
1097 ----------
1098 x : `pandas.Series`
1099 X pixel coordinate.
1100 y : `pandas.Series`
1101 Y pixel coordinate.
1102 cd11 : `pandas.Series`
1103 [1, 1] element of the local Wcs affine transform.
1104 cd11 : `pandas.Series`
1105 [1, 1] element of the local Wcs affine transform.
1106 cd12 : `pandas.Series`
1107 [1, 2] element of the local Wcs affine transform.
1108 cd21 : `pandas.Series`
1109 [2, 1] element of the local Wcs affine transform.
1110 cd22 : `pandas.Series`
1111 [2, 2] element of the local Wcs affine transform.
1113 Returns
1114 -------
1115 raDecTuple : tuple
1116 RA and dec conversion of x and y given the local Wcs. Returned
1117 units are in radians.
1119 """
1120 return (x * cd11 + y * cd12, x * cd21 + y * cd22)
1122 def computeSkySeperation(self, ra1, dec1, ra2, dec2):
1123 """Compute the local pixel scale conversion.
1125 Parameters
1126 ----------
1127 ra1 : `pandas.Series`
1128 Ra of the first coordinate in radians.
1129 dec1 : `pandas.Series`
1130 Dec of the first coordinate in radians.
1131 ra2 : `pandas.Series`
1132 Ra of the second coordinate in radians.
1133 dec2 : `pandas.Series`
1134 Dec of the second coordinate in radians.
1136 Returns
1137 -------
1138 dist : `pandas.Series`
1139 Distance on the sphere in radians.
1140 """
1141 deltaDec = dec2 - dec1
1142 deltaRa = ra2 - ra1
1143 return 2 * np.arcsin(
1144 np.sqrt(
1145 np.sin(deltaDec / 2) ** 2
1146 + np.cos(dec2) * np.cos(dec1) * np.sin(deltaRa / 2) ** 2))
1148 def getSkySeperationFromPixel(self, x1, y1, x2, y2, cd11, cd12, cd21, cd22):
1149 """Compute the distance on the sphere from x2, y1 to x1, y1.
1151 Parameters
1152 ----------
1153 x1 : `pandas.Series`
1154 X pixel coordinate.
1155 y1 : `pandas.Series`
1156 Y pixel coordinate.
1157 x2 : `pandas.Series`
1158 X pixel coordinate.
1159 y2 : `pandas.Series`
1160 Y pixel coordinate.
1161 cd11 : `pandas.Series`
1162 [1, 1] element of the local Wcs affine transform.
1163 cd11 : `pandas.Series`
1164 [1, 1] element of the local Wcs affine transform.
1165 cd12 : `pandas.Series`
1166 [1, 2] element of the local Wcs affine transform.
1167 cd21 : `pandas.Series`
1168 [2, 1] element of the local Wcs affine transform.
1169 cd22 : `pandas.Series`
1170 [2, 2] element of the local Wcs affine transform.
1172 Returns
1173 -------
1174 Distance : `pandas.Series`
1175 Arcseconds per pixel at the location of the local WC
1176 """
1177 ra1, dec1 = self.computeDeltaRaDec(x1, y1, cd11, cd12, cd21, cd22)
1178 ra2, dec2 = self.computeDeltaRaDec(x2, y2, cd11, cd12, cd21, cd22)
1179 # Great circle distance for small separations.
1180 return self.computeSkySeperation(ra1, dec1, ra2, dec2)
1183class ComputePixelScale(LocalWcs):
1184 """Compute the local pixel scale from the stored CDMatrix.
1185 """
1186 name = "PixelScale"
1188 @property
1189 def columns(self):
1190 return [self.colCD_1_1,
1191 self.colCD_1_2,
1192 self.colCD_2_1,
1193 self.colCD_2_2]
1195 def pixelScaleArcseconds(self, cd11, cd12, cd21, cd22):
1196 """Compute the local pixel to scale conversion in arcseconds.
1198 Parameters
1199 ----------
1200 cd11 : `pandas.Series`
1201 [1, 1] element of the local Wcs affine transform in radians.
1202 cd11 : `pandas.Series`
1203 [1, 1] element of the local Wcs affine transform in radians.
1204 cd12 : `pandas.Series`
1205 [1, 2] element of the local Wcs affine transform in radians.
1206 cd21 : `pandas.Series`
1207 [2, 1] element of the local Wcs affine transform in radians.
1208 cd22 : `pandas.Series`
1209 [2, 2] element of the local Wcs affine transform in radians.
1211 Returns
1212 -------
1213 pixScale : `pandas.Series`
1214 Arcseconds per pixel at the location of the local WC
1215 """
1216 return 3600 * np.degrees(np.sqrt(np.fabs(cd11 * cd22 - cd12 * cd21)))
1218 def _func(self, df):
1219 return self.pixelScaleArcseconds(df[self.colCD_1_1],
1220 df[self.colCD_1_2],
1221 df[self.colCD_2_1],
1222 df[self.colCD_2_2])
1225class ConvertPixelToArcseconds(ComputePixelScale):
1226 """Convert a value in units pixels to units arcseconds.
1227 """
1229 def __init__(self,
1230 col,
1231 colCD_1_1,
1232 colCD_1_2,
1233 colCD_2_1,
1234 colCD_2_2,
1235 **kwargs):
1236 self.col = col
1237 super().__init__(colCD_1_1,
1238 colCD_1_2,
1239 colCD_2_1,
1240 colCD_2_2,
1241 **kwargs)
1243 @property
1244 def name(self):
1245 return f"{self.col}_asArcseconds"
1247 @property
1248 def columns(self):
1249 return [self.col,
1250 self.colCD_1_1,
1251 self.colCD_1_2,
1252 self.colCD_2_1,
1253 self.colCD_2_2]
1255 def _func(self, df):
1256 return df[self.col] * self.pixelScaleArcseconds(df[self.colCD_1_1],
1257 df[self.colCD_1_2],
1258 df[self.colCD_2_1],
1259 df[self.colCD_2_2])
1262class ReferenceBand(Functor):
1263 name = 'Reference Band'
1264 shortname = 'refBand'
1266 @property
1267 def columns(self):
1268 return ["merge_measurement_i",
1269 "merge_measurement_r",
1270 "merge_measurement_z",
1271 "merge_measurement_y",
1272 "merge_measurement_g"]
1274 def _func(self, df):
1275 def getFilterAliasName(row):
1276 # get column name with the max value (True > False)
1277 colName = row.idxmax()
1278 return colName.replace('merge_measurement_', '')
1280 return df[self.columns].apply(getFilterAliasName, axis=1)
1283class Photometry(Functor):
1284 # AB to NanoJansky (3631 Jansky)
1285 AB_FLUX_SCALE = (0 * u.ABmag).to_value(u.nJy)
1286 LOG_AB_FLUX_SCALE = 12.56
1287 FIVE_OVER_2LOG10 = 1.085736204758129569
1288 # TO DO: DM-21955 Replace hard coded photometic calibration values
1289 COADD_ZP = 27
1291 def __init__(self, colFlux, colFluxErr=None, calib=None, **kwargs):
1292 self.vhypot = np.vectorize(self.hypot)
1293 self.col = colFlux
1294 self.colFluxErr = colFluxErr
1296 self.calib = calib
1297 if calib is not None:
1298 self.fluxMag0, self.fluxMag0Err = calib.getFluxMag0()
1299 else:
1300 self.fluxMag0 = 1./np.power(10, -0.4*self.COADD_ZP)
1301 self.fluxMag0Err = 0.
1303 super().__init__(**kwargs)
1305 @property
1306 def columns(self):
1307 return [self.col]
1309 @property
1310 def name(self):
1311 return f'mag_{self.col}'
1313 @classmethod
1314 def hypot(cls, a, b):
1315 if np.abs(a) < np.abs(b):
1316 a, b = b, a
1317 if a == 0.:
1318 return 0.
1319 q = b/a
1320 return np.abs(a) * np.sqrt(1. + q*q)
1322 def dn2flux(self, dn, fluxMag0):
1323 return self.AB_FLUX_SCALE * dn / fluxMag0
1325 def dn2mag(self, dn, fluxMag0):
1326 with np.warnings.catch_warnings():
1327 np.warnings.filterwarnings('ignore', r'invalid value encountered')
1328 np.warnings.filterwarnings('ignore', r'divide by zero')
1329 return -2.5 * np.log10(dn/fluxMag0)
1331 def dn2fluxErr(self, dn, dnErr, fluxMag0, fluxMag0Err):
1332 retVal = self.vhypot(dn * fluxMag0Err, dnErr * fluxMag0)
1333 retVal *= self.AB_FLUX_SCALE / fluxMag0 / fluxMag0
1334 return retVal
1336 def dn2MagErr(self, dn, dnErr, fluxMag0, fluxMag0Err):
1337 retVal = self.dn2fluxErr(dn, dnErr, fluxMag0, fluxMag0Err) / self.dn2flux(dn, fluxMag0)
1338 return self.FIVE_OVER_2LOG10 * retVal
1341class NanoJansky(Photometry):
1342 def _func(self, df):
1343 return self.dn2flux(df[self.col], self.fluxMag0)
1346class NanoJanskyErr(Photometry):
1347 @property
1348 def columns(self):
1349 return [self.col, self.colFluxErr]
1351 def _func(self, df):
1352 retArr = self.dn2fluxErr(df[self.col], df[self.colFluxErr], self.fluxMag0, self.fluxMag0Err)
1353 return pd.Series(retArr, index=df.index)
1356class Magnitude(Photometry):
1357 def _func(self, df):
1358 return self.dn2mag(df[self.col], self.fluxMag0)
1361class MagnitudeErr(Photometry):
1362 @property
1363 def columns(self):
1364 return [self.col, self.colFluxErr]
1366 def _func(self, df):
1367 retArr = self.dn2MagErr(df[self.col], df[self.colFluxErr], self.fluxMag0, self.fluxMag0Err)
1368 return pd.Series(retArr, index=df.index)
1371class LocalPhotometry(Functor):
1372 """Base class for calibrating the specified instrument flux column using
1373 the local photometric calibration.
1375 Parameters
1376 ----------
1377 instFluxCol : `str`
1378 Name of the instrument flux column.
1379 instFluxErrCol : `str`
1380 Name of the assocated error columns for ``instFluxCol``.
1381 photoCalibCol : `str`
1382 Name of local calibration column.
1383 photoCalibErrCol : `str`
1384 Error associated with ``photoCalibCol``
1386 See also
1387 --------
1388 LocalPhotometry
1389 LocalNanojansky
1390 LocalNanojanskyErr
1391 LocalMagnitude
1392 LocalMagnitudeErr
1393 """
1394 logNJanskyToAB = (1 * u.nJy).to_value(u.ABmag)
1396 def __init__(self,
1397 instFluxCol,
1398 instFluxErrCol,
1399 photoCalibCol,
1400 photoCalibErrCol,
1401 **kwargs):
1402 self.instFluxCol = instFluxCol
1403 self.instFluxErrCol = instFluxErrCol
1404 self.photoCalibCol = photoCalibCol
1405 self.photoCalibErrCol = photoCalibErrCol
1406 super().__init__(**kwargs)
1408 def instFluxToNanojansky(self, instFlux, localCalib):
1409 """Convert instrument flux to nanojanskys.
1411 Parameters
1412 ----------
1413 instFlux : `numpy.ndarray` or `pandas.Series`
1414 Array of instrument flux measurements
1415 localCalib : `numpy.ndarray` or `pandas.Series`
1416 Array of local photometric calibration estimates.
1418 Returns
1419 -------
1420 calibFlux : `numpy.ndarray` or `pandas.Series`
1421 Array of calibrated flux measurements.
1422 """
1423 return instFlux * localCalib
1425 def instFluxErrToNanojanskyErr(self, instFlux, instFluxErr, localCalib, localCalibErr):
1426 """Convert instrument flux to nanojanskys.
1428 Parameters
1429 ----------
1430 instFlux : `numpy.ndarray` or `pandas.Series`
1431 Array of instrument flux measurements
1432 instFluxErr : `numpy.ndarray` or `pandas.Series`
1433 Errors on associated ``instFlux`` values
1434 localCalib : `numpy.ndarray` or `pandas.Series`
1435 Array of local photometric calibration estimates.
1436 localCalibErr : `numpy.ndarray` or `pandas.Series`
1437 Errors on associated ``localCalib`` values
1439 Returns
1440 -------
1441 calibFluxErr : `numpy.ndarray` or `pandas.Series`
1442 Errors on calibrated flux measurements.
1443 """
1444 return np.hypot(instFluxErr * localCalib, instFlux * localCalibErr)
1446 def instFluxToMagnitude(self, instFlux, localCalib):
1447 """Convert instrument flux to nanojanskys.
1449 Parameters
1450 ----------
1451 instFlux : `numpy.ndarray` or `pandas.Series`
1452 Array of instrument flux measurements
1453 localCalib : `numpy.ndarray` or `pandas.Series`
1454 Array of local photometric calibration estimates.
1456 Returns
1457 -------
1458 calibMag : `numpy.ndarray` or `pandas.Series`
1459 Array of calibrated AB magnitudes.
1460 """
1461 return -2.5 * np.log10(self.instFluxToNanojansky(instFlux, localCalib)) + self.logNJanskyToAB
1463 def instFluxErrToMagnitudeErr(self, instFlux, instFluxErr, localCalib, localCalibErr):
1464 """Convert instrument flux err to nanojanskys.
1466 Parameters
1467 ----------
1468 instFlux : `numpy.ndarray` or `pandas.Series`
1469 Array of instrument flux measurements
1470 instFluxErr : `numpy.ndarray` or `pandas.Series`
1471 Errors on associated ``instFlux`` values
1472 localCalib : `numpy.ndarray` or `pandas.Series`
1473 Array of local photometric calibration estimates.
1474 localCalibErr : `numpy.ndarray` or `pandas.Series`
1475 Errors on associated ``localCalib`` values
1477 Returns
1478 -------
1479 calibMagErr: `numpy.ndarray` or `pandas.Series`
1480 Error on calibrated AB magnitudes.
1481 """
1482 err = self.instFluxErrToNanojanskyErr(instFlux, instFluxErr, localCalib, localCalibErr)
1483 return 2.5 / np.log(10) * err / self.instFluxToNanojansky(instFlux, instFluxErr)
1486class LocalNanojansky(LocalPhotometry):
1487 """Compute calibrated fluxes using the local calibration value.
1489 See also
1490 --------
1491 LocalNanojansky
1492 LocalNanojanskyErr
1493 LocalMagnitude
1494 LocalMagnitudeErr
1495 """
1497 @property
1498 def columns(self):
1499 return [self.instFluxCol, self.photoCalibCol]
1501 @property
1502 def name(self):
1503 return f'flux_{self.instFluxCol}'
1505 def _func(self, df):
1506 return self.instFluxToNanojansky(df[self.instFluxCol], df[self.photoCalibCol])
1509class LocalNanojanskyErr(LocalPhotometry):
1510 """Compute calibrated flux errors using the local calibration value.
1512 See also
1513 --------
1514 LocalNanojansky
1515 LocalNanojanskyErr
1516 LocalMagnitude
1517 LocalMagnitudeErr
1518 """
1520 @property
1521 def columns(self):
1522 return [self.instFluxCol, self.instFluxErrCol,
1523 self.photoCalibCol, self.photoCalibErrCol]
1525 @property
1526 def name(self):
1527 return f'fluxErr_{self.instFluxCol}'
1529 def _func(self, df):
1530 return self.instFluxErrToNanojanskyErr(df[self.instFluxCol], df[self.instFluxErrCol],
1531 df[self.photoCalibCol], df[self.photoCalibErrCol])
1534class LocalMagnitude(LocalPhotometry):
1535 """Compute calibrated AB magnitudes using the local calibration value.
1537 See also
1538 --------
1539 LocalNanojansky
1540 LocalNanojanskyErr
1541 LocalMagnitude
1542 LocalMagnitudeErr
1543 """
1545 @property
1546 def columns(self):
1547 return [self.instFluxCol, self.photoCalibCol]
1549 @property
1550 def name(self):
1551 return f'mag_{self.instFluxCol}'
1553 def _func(self, df):
1554 return self.instFluxToMagnitude(df[self.instFluxCol],
1555 df[self.photoCalibCol])
1558class LocalMagnitudeErr(LocalPhotometry):
1559 """Compute calibrated AB magnitude errors using the local calibration value.
1561 See also
1562 --------
1563 LocalNanojansky
1564 LocalNanojanskyErr
1565 LocalMagnitude
1566 LocalMagnitudeErr
1567 """
1569 @property
1570 def columns(self):
1571 return [self.instFluxCol, self.instFluxErrCol,
1572 self.photoCalibCol, self.photoCalibErrCol]
1574 @property
1575 def name(self):
1576 return f'magErr_{self.instFluxCol}'
1578 def _func(self, df):
1579 return self.instFluxErrToMagnitudeErr(df[self.instFluxCol],
1580 df[self.instFluxErrCol],
1581 df[self.photoCalibCol],
1582 df[self.photoCalibErrCol])