Coverage for python / lsst / analysis / tools / actions / keyedData / stellarLocusFit.py: 10%
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5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
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14# This program is distributed in the hope that it will be useful,
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22from __future__ import annotations
24__all__ = ("StellarLocusFitAction",)
26from typing import cast
28import numpy as np
29import scipy.odr as scipyODR
31from lsst.pex.config import DictField
32from lsst.pipe.base import Struct
34from ...interfaces import KeyedData, KeyedDataAction, KeyedDataSchema, Scalar, Vector
35from ...math import sigmaMad
38def _stellarLocusFit(xs, ys, mags, paramDict):
39 """Make a fit to the stellar locus.
41 Parameters
42 ----------
43 xs : `numpy.ndarray` [`float`]
44 The color on the xaxis.
45 ys : `numpy.ndarray` [`float`]
46 The color on the yaxis.
47 mags : `numpy.ndarray` [`float`]
48 The magnitude of the reference band flux (in mag).
49 paramDict : `dict` [`str`, `float`]
50 A dictionary of parameters for line fitting:
52 ``"xMin"``
53 The minimum x edge of the box to use for initial fitting (`float`).
54 ``"xMax"``
55 The maximum x edge of the box to use for initial fitting (`float`).
56 ``"yMin"``
57 The minimum y edge of the box to use for initial fitting (`float`).
58 ``"yMax"``
59 The maximum y edge of the box to use for initial fitting (`float`).
60 ``"mHW"``
61 The hardwired gradient for the fit (`float`).
62 ``"bHw"``
63 The hardwired intercept of the fit (`float`).
64 ``"nSigmaToClip1"``
65 The number of sigma perpendicular to the fit to clip in the initial
66 fitting loop (`float`). This should probably be stricter than the
67 final iteration (i.e. nSigmaToClip1 < nSigmaToClip2).
68 ``"nSigmaToClip2"``
69 The number of sigma perpendicular to the fit to clip in the final
70 fitting loop (`float`).
71 ``"minObjectForFit"``
72 Minimum number of objects surviving cuts to attempt fit. If not
73 met, return NANs for values in ``fitParams`` (`int`).
75 Returns
76 -------
77 fitParams : `dict`
78 A dictionary of the calculated fit parameters:
80 ``"bPerpMin"``
81 The intercept of the perpendicular line that goes through xMin
82 (`float`).
83 ``"bPerpMax"``
84 The intercept of the perpendicular line that goes through xMax
85 (`float`).
86 ``"mODR"``
87 The gradient from the final round of fitting (`float`).
88 ``"bODR"``
89 The intercept from the final round of fitting (`float`).
90 ``"mPerp"``
91 The gradient of the line perpendicular to the line from the final
92 fit (`float`).
93 ``"fitPoints"``
94 A boolean list indicating which points were used in the final fit
95 (`list` [`bool`]).
97 Notes
98 -----
99 The code does two rounds of fitting, the first is initiated using the
100 fixed values given in ``paramDict`` and is done using an Orthogonal
101 Distance Regression (ODR) fit to the points defined by the box with limits
102 defined by the keys: xMin, xMax, yMin, and yMax. Once this fitting has been
103 done a perpendicular bisector is calculated at either end of the line and
104 only points that fall within these lines are used to recalculate the fit.
105 We also perform clipping of points perpendicular to the fit line that have
106 distances that deviate more than nSigmaToClip1/2 (for an initial and final
107 iteration) from the fit.
108 """
109 fitParams = {}
110 # Initial subselection of points to use for the fit
111 # Check for nans/infs
112 goodPoints = np.isfinite(xs) & np.isfinite(ys) & np.isfinite(mags)
114 fitPoints = (
115 goodPoints
116 & (xs > paramDict["xMin"])
117 & (xs < paramDict["xMax"])
118 & (ys > paramDict["yMin"])
119 & (ys < paramDict["yMax"])
120 )
121 if sum(fitPoints) < paramDict["minObjectForFit"]:
122 fitParams = _setFitParamsNans(fitParams, fitPoints, paramDict)
123 return fitParams
125 linear = scipyODR.polynomial(1)
127 fitData = scipyODR.Data(xs[fitPoints], ys[fitPoints])
128 odr = scipyODR.ODR(fitData, linear, beta0=[paramDict["bFixed"], paramDict["mFixed"]])
129 params = odr.run()
130 mODR0 = float(params.beta[1])
131 bODR0 = float(params.beta[0])
132 mPerp0 = -1.0 / mODR0
134 # Loop twice over the fit and include sigma clipping of points
135 # perpendicular to the fit line (stricter on first iteration).
136 for nSigmaToClip in [paramDict["nSigmaToClip1"], paramDict["nSigmaToClip2"]]:
137 # Having found the initial fit calculate perpendicular ends.
138 # When the gradient is really steep we need to use the
139 # y limits of the fit line rather than the x ones.
140 if np.abs(mODR0) > 1:
141 yPerpMin = paramDict["yMin"]
142 xPerpMin = (yPerpMin - bODR0) / mODR0
143 yPerpMax = paramDict["yMax"]
144 xPerpMax = (yPerpMax - bODR0) / mODR0
145 else:
146 yPerpMin = mODR0 * paramDict["xMin"] + bODR0
147 xPerpMin = paramDict["xMin"]
148 yPerpMax = mODR0 * paramDict["xMax"] + bODR0
149 xPerpMax = paramDict["xMax"]
151 bPerpMin = yPerpMin - mPerp0 * xPerpMin
152 bPerpMax = yPerpMax - mPerp0 * xPerpMax
154 # Use these perpendicular lines to choose the data and refit.
155 fitPoints = (ys > mPerp0 * xs + bPerpMin) & (ys < mPerp0 * xs + bPerpMax)
156 if sum(fitPoints) < paramDict["minObjectForFit"]:
157 fitParams = _setFitParamsNans(fitParams, fitPoints, paramDict)
158 return fitParams
160 # Compute two points along the line (making sure not to extrapolate
161 # way off the plot limits, especially for the near vertical fits).
162 if np.abs(mODR0) > 1:
163 p1 = np.array([1.0, mODR0 + bODR0])
164 p2 = np.array([(1.0 - bODR0) / mODR0, 1.0])
165 else:
166 p1 = np.array([0, bODR0])
167 p2 = np.array([-bODR0 / mODR0, 0])
168 if np.abs(sum(p1 - p2)) < 1e-12: # p1 and p2 must be different.
169 if np.abs(mODR0) > 1:
170 p2 = np.array([(1.5 - bODR0) / mODR0, 1.5])
171 else:
172 p2 = np.array([(1.0 - bODR0) / mODR0, 1.0])
174 # Sigma clip points based on perpendicular distance (in mmag) to
175 # current fit.
176 fitDists = np.array(perpDistance(p1, p2, zip(xs[fitPoints], ys[fitPoints]))) * 1000
177 clippedStats = calcQuartileClippedStats(fitDists, nSigmaToClip=nSigmaToClip)
178 allDists = np.array(perpDistance(p1, p2, zip(xs, ys))) * 1000
179 keep = np.abs(allDists) <= clippedStats.clipValue
180 fitPoints &= keep
181 if sum(fitPoints) < paramDict["minObjectForFit"]:
182 fitParams = _setFitParamsNans(fitParams, fitPoints, paramDict)
183 return fitParams
184 fitData = scipyODR.Data(xs[fitPoints], ys[fitPoints])
185 odr = scipyODR.ODR(fitData, linear, beta0=[bODR0, mODR0])
186 params = odr.run()
187 mODR0 = params.beta[1]
188 bODR0 = params.beta[0]
190 fitParams["bPerpMin"] = bPerpMin
191 fitParams["bPerpMax"] = bPerpMax
193 fitParams["mODR"] = float(params.beta[1])
194 fitParams["bODR"] = float(params.beta[0])
196 fitParams["mPerp"] = -1.0 / fitParams["mODR"]
197 fitParams["goodPoints"] = goodPoints
198 fitParams["fitPoints"] = fitPoints
199 fitParams["paramDict"] = paramDict
201 return fitParams
204def _setFitParamsNans(fitParams, fitPoints, paramDict):
205 fitParams["bPerpMin"] = np.nan
206 fitParams["bPerpMax"] = np.nan
207 fitParams["mODR"] = np.nan
208 fitParams["bODR"] = np.nan
209 fitParams["mPerp"] = np.nan
210 fitParams["goodPoints"] = np.nan
211 fitParams["fitPoints"] = fitPoints
212 fitParams["paramDict"] = paramDict
213 return fitParams
216def perpDistance(p1, p2, points):
217 """Calculate the perpendicular distance to a line from a point.
219 Parameters
220 ----------
221 p1 : `numpy.ndarray` [`float`]
222 A point on the line.
223 p2 : `numpy.ndarray` [`float`]
224 Another point on the line.
225 points : `zip` [(`float`, `float`)]
226 The points to calculate the distance to.
228 Returns
229 -------
230 dists : `numpy.ndarray` [`float`]
231 The distances from the line to the points. Uses the cross
232 product to work this out.
233 """
234 if sum(p2 - p1) == 0:
235 raise ValueError(f"Must supply two different points for p1, p2. Got {p1}, {p2}")
236 points = list(points)
237 if len(points) == 0:
238 raise ValueError("Must provied a non-empty zip() list of points.")
240 # Recommendation from numpy docs for 2d cross product.
241 def cross2d(x, y):
242 return x[..., 0] * y[..., 1] - x[..., 1] * y[..., 0]
244 dists = cross2d(p2 - p1, points - p1) / np.linalg.norm(p2 - p1)
246 return dists
249def calcQuartileClippedStats(dataArray, nSigmaToClip=3.0):
250 """Calculate the quartile-based clipped statistics of a data array.
252 The difference between quartiles[2] and quartiles[0] is the interquartile
253 distance. 0.74*interquartileDistance is an estimate of standard deviation
254 so, in the case that ``dataArray`` has an approximately Gaussian
255 distribution, this is equivalent to nSigma clipping.
257 Parameters
258 ----------
259 dataArray : `list` or `numpy.ndarray` [`float`]
260 List or array containing the values for which the quartile-based
261 clipped statistics are to be calculated.
262 nSigmaToClip : `float`, optional
263 Number of \"sigma\" outside of which to clip data when computing the
264 statistics.
266 Returns
267 -------
268 result : `lsst.pipe.base.Struct`
269 The quartile-based clipped statistics with ``nSigmaToClip`` clipping.
270 Atributes are:
272 ``median``
273 The median of the full ``dataArray`` (`float`).
274 ``mean``
275 The quartile-based clipped mean (`float`).
276 ``stdDev``
277 The quartile-based clipped standard deviation (`float`).
278 ``rms``
279 The quartile-based clipped root-mean-squared (`float`).
280 ``clipValue``
281 The value outside of which to clip the data before computing the
282 statistics (`float`).
283 ``goodArray``
284 A boolean array indicating which data points in ``dataArray`` were
285 used in the calculation of the statistics, where `False` indicates
286 a clipped datapoint (`numpy.ndarray` of `bool`).
287 """
288 quartiles = np.percentile(dataArray, [25, 50, 75])
289 assert len(quartiles) == 3
290 median = quartiles[1]
291 interQuartileDistance = quartiles[2] - quartiles[0]
292 clipValue = nSigmaToClip * 0.74 * interQuartileDistance
293 good = np.abs(dataArray - median) <= clipValue
294 quartileClippedMean = dataArray[good].mean()
295 quartileClippedStdDev = dataArray[good].std()
296 quartileClippedRms = np.sqrt(np.mean(dataArray[good] ** 2))
298 return Struct(
299 median=median,
300 mean=quartileClippedMean,
301 stdDev=quartileClippedStdDev,
302 rms=quartileClippedRms,
303 clipValue=clipValue,
304 goodArray=good,
305 )
308class StellarLocusFitAction(KeyedDataAction):
309 r"""Determine Stellar Locus fit parameters from given input `Vector`\ s."""
311 stellarLocusFitDict = DictField[str, float](
312 doc="The parameters to use for the stellar locus fit. For xMin/Max, yMin/Max, "
313 "and m/bFixed, the default parameters are examples and are not generally useful "
314 "for any of the fits, so should be updated in the PlotAction definition in the "
315 "atools directory. The dict needs to contain xMin/xMax/yMin/yMax which are the "
316 "limits of the initial point selection box for fitting the stellar locus, mFixed "
317 "and bFixed are meant to represent the intercept and gradient of a canonical fit "
318 "for a given dataset (and should be derived from data). They are used here as an "
319 "initial guess for the fitting. nSigmaToClip1/2 set the number of sigma to clip "
320 "perpendicular the fit in the first and second fit iterations after the initial "
321 "guess and point selection fit. minObjectForFit sets a minimum number of points "
322 "deemed suitable for inclusion in the fit in order to bother attempting the fit.",
323 default={
324 "xMin": 0.1,
325 "xMax": 0.2,
326 "yMin": 0.1,
327 "yMax": 0.2,
328 "mHW": 0.5,
329 "bHW": 0.0,
330 "nSigmaToClip1": 3.5,
331 "nSigmaToClip2": 5.0,
332 "minObjectForFit": 7,
333 },
334 )
336 def getInputSchema(self) -> KeyedDataSchema:
337 return (("x", Vector), ("y", Vector))
339 def getOutputSchema(self) -> KeyedDataSchema:
340 value = (
341 (f"{self.identity or ''}_sigmaMAD", Scalar),
342 (f"{self.identity or ''}_median", Scalar),
343 )
344 return value
346 def __call__(self, data: KeyedData, **kwargs) -> KeyedData:
347 xs = cast(Vector, data["x"])
348 ys = cast(Vector, data["y"])
349 mags = cast(Vector, data["mag"])
351 fitParams = _stellarLocusFit(xs, ys, mags, self.stellarLocusFitDict)
352 # Bail out if there were not enough points to fit.
353 for value in fitParams.values():
354 if isinstance(value, float):
355 if np.isnan(value):
356 fitParams[f"{self.identity or ''}_sigmaMAD"] = np.nan
357 fitParams[f"{self.identity or ''}_median"] = np.nan
358 return fitParams
359 fitPoints = fitParams["fitPoints"]
361 if np.fabs(self.stellarLocusFitDict["mFixed"]) > 1:
362 ysFitLineFixed = np.array([self.stellarLocusFitDict["yMin"], self.stellarLocusFitDict["yMax"]])
363 xsFitLineFixed = (ysFitLineFixed - self.stellarLocusFitDict["bFixed"]) / self.stellarLocusFitDict[
364 "mFixed"
365 ]
366 ysFitLine = np.array([self.stellarLocusFitDict["yMin"], self.stellarLocusFitDict["yMax"]])
367 xsFitLine = (ysFitLine - fitParams["bODR"]) / fitParams["mODR"]
369 else:
370 xsFitLineFixed = np.array([self.stellarLocusFitDict["xMin"], self.stellarLocusFitDict["xMax"]])
371 ysFitLineFixed = (
372 self.stellarLocusFitDict["mFixed"] * xsFitLineFixed + self.stellarLocusFitDict["bFixed"]
373 )
374 xsFitLine = [self.stellarLocusFitDict["xMin"], self.stellarLocusFitDict["xMax"]]
375 ysFitLine = np.array(
376 [
377 fitParams["mODR"] * xsFitLine[0] + fitParams["bODR"],
378 fitParams["mODR"] * xsFitLine[1] + fitParams["bODR"],
379 ]
380 )
382 # Calculate the distances to that line.
383 # Need two points to characterize the lines we want to get the
384 # distances to.
385 p1 = np.array([xsFitLine[0], ysFitLine[0]])
386 p2 = np.array([xsFitLine[1], ysFitLine[1]])
388 # Convert this to mmag.
389 dists = np.array(perpDistance(p1, p2, zip(xs[fitPoints], ys[fitPoints]))) * 1000
391 # Now we have the information for the perpendicular line we
392 # can use it to calculate the points at the ends of the
393 # perpendicular lines that intersect at the box edges.
394 if np.fabs(self.stellarLocusFitDict["mFixed"]) > 1:
395 xMid = (self.stellarLocusFitDict["yMin"] - fitParams["bODR"]) / fitParams["mODR"]
396 xs = np.array([xMid - 0.5, xMid, xMid + 0.5])
397 ys = fitParams["mPerp"] * xs + fitParams["bPerpMin"]
398 else:
399 xs = np.array(
400 [
401 self.stellarLocusFitDict["xMin"] - 0.2,
402 self.stellarLocusFitDict["xMin"],
403 self.stellarLocusFitDict["xMin"] + 0.2,
404 ]
405 )
406 ys = xs * fitParams["mPerp"] + fitParams["bPerpMin"]
408 if np.fabs(self.stellarLocusFitDict["mFixed"]) > 1:
409 xMid = (self.stellarLocusFitDict["yMax"] - fitParams["bODR"]) / fitParams["mODR"]
410 xs = np.array([xMid - 0.5, xMid, xMid + 0.5])
411 ys = fitParams["mPerp"] * xs + fitParams["bPerpMax"]
412 else:
413 xs = np.array(
414 [
415 self.stellarLocusFitDict["xMax"] - 0.2,
416 self.stellarLocusFitDict["xMax"],
417 self.stellarLocusFitDict["xMax"] + 0.2,
418 ]
419 )
420 ys = xs * fitParams["mPerp"] + fitParams["bPerpMax"]
422 fit_sigma, fit_med = (sigmaMad(dists), np.median(dists)) if len(dists) else (np.nan, np.nan)
423 fitParams[f"{self.identity or ''}_sigmaMAD"] = fit_sigma
424 fitParams[f"{self.identity or ''}_median"] = fit_med
426 return fitParams