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ruff and doc strings maf
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@rhiannonlynne
rhiannonlynne committed Jan 14, 2024
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146 changes: 96 additions & 50 deletions rubin_sim/maf/metrics/simple_metrics.py
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Expand Up @@ -38,14 +38,18 @@

from .base_metric import BaseMetric

# A collection of commonly used simple metrics, operating on a single column and returning a float.
# A collection of commonly used simple metrics,
# operating on a single column and returning a float.

twopi = 2.0 * np.pi


class PassMetric(BaseMetric):
"""
Just pass the entire array through
"""Pass the entire dataslice array back to the MetricBundle.
This is most likely useful while prototyping metrics and wanting to
just 'get the data at a point in the sky', while using a HealpixSlicer
or a UserPointSlicer.
"""

def __init__(self, cols=None, **kwargs):
Expand All @@ -58,64 +62,86 @@ def run(self, data_slice, slice_point=None):


class Coaddm5Metric(BaseMetric):
"""Calculate the coadded m5 value at this gridpoint.
"""Calculate the coadded m5 value.
Parameters
----------
m5Col : `str`, optional
m5_col : `str`, optional
Name of the m5 column. Default fiveSigmaDepth.
metric_name : `str`, optional
Name to associate with the metric output.
Name to associate with the metric output. Default "CoaddM5".
filter_name : `str`, optional
Optionally specify a filter to sub-select visits.
Default None, does no sub-selection or checking.
filter_col : `str`, optional
Name of the filter column.
"""

def __init__(self, m5_col="fiveSigmaDepth", metric_name="CoaddM5", **kwargs):
def __init__(self, m5_col="fiveSigmaDepth", metric_name="CoaddM5",
filter_name=None, filter_col="Filter", **kwargs):
self.filter_name = filter_name
self.filter_col = filter_col
self.m5_col = m5_col
super(Coaddm5Metric, self).__init__(col=m5_col, metric_name=metric_name, **kwargs)

@staticmethod
def coadd(single_visit_m5s):
return 1.25 * np.log10(np.sum(10.0 ** (0.8 * single_visit_m5s)))

def run(self, data_slice, slice_point=None):
# Running this metric directly from the slicer, this should never come up.
# However, other metrics call this one and maybe had visits in other filters ..
if len(data_slice) == 0:
return self.badval
return 1.25 * np.log10(np.sum(10.0 ** (0.8 * data_slice[self.colname])))
if self.filter_name is not None:
matched = np.where(data_slice[self.filter_col] == self.filter_name)
coadd = self.coadd(data_slice[matched][self.m5_col])
else:
coadd = self.coadd(data_slice[self.m5_col])
return coadd


class MaxMetric(BaseMetric):
"""Calculate the maximum of a simData column slice."""
"""Calculate the maximum of a simData column slice.
"""

def run(self, data_slice, slice_point=None):
return np.max(data_slice[self.colname])


class AbsMaxMetric(BaseMetric):
"""Calculate the max of the absolute value of a simData column slice."""
"""Calculate the max of the absolute value of a simData column slice.
"""

def run(self, data_slice, slice_point=None):
return np.max(np.abs(data_slice[self.colname]))


class MeanMetric(BaseMetric):
"""Calculate the mean of a simData column slice."""
"""Calculate the mean of a simData column slice.
"""

def run(self, data_slice, slice_point=None):
return np.mean(data_slice[self.colname])


class AbsMeanMetric(BaseMetric):
"""Calculate the mean of the absolute value of a simData column slice."""
"""Calculate the mean of the absolute value of a simData column slice.
"""

def run(self, data_slice, slice_point=None):
return np.mean(np.abs(data_slice[self.colname]))


class MedianMetric(BaseMetric):
"""Calculate the median of a simData column slice."""
"""Calculate the median of a simData column slice.
"""

def run(self, data_slice, slice_point=None):
return np.median(data_slice[self.colname])


class AbsMedianMetric(BaseMetric):
"""Calculate the median of the absolute value of a simData column slice."""
"""Calculate the median of the absolute value of a simData column slice.
"""

def run(self, data_slice, slice_point=None):
return np.median(np.abs(data_slice[self.colname]))
Expand Down Expand Up @@ -164,7 +190,8 @@ def run(self, data_slice, slice_point=None):


class UniqueRatioMetric(BaseMetric):
"""Return the number of unique values divided by the total number of values."""
"""Return the number of unique values divided by the
total number of values."""

def run(self, data_slice, slice_point=None):
ntot = float(np.size(data_slice[self.colname]))
Expand All @@ -184,7 +211,8 @@ def run(self, data_slice, slice_point=None):


class CountExplimMetric(BaseMetric):
"""Count the number of x second visits. Useful for rejecting very short exposures
"""Count the number of x second visits.
Useful for rejecting very short exposures
and counting 60s exposures as 2 visits."""

def __init__(self, col=None, min_exp=20.0, expected_exp=30.0, exp_col="visitExposureTime", **kwargs):
Expand All @@ -202,7 +230,7 @@ def run(self, data_slice, slice_point=None):


class CountRatioMetric(BaseMetric):
"""Count the length of a simData column slice, then divide by 'norm_val'."""
"""Count the length of a column slice, then divide by `norm_val`."""

def __init__(self, col=None, norm_val=1.0, metric_name=None, units="", **kwargs):
self.norm_val = float(norm_val)
Expand All @@ -215,7 +243,7 @@ def run(self, data_slice, slice_point=None):


class CountSubsetMetric(BaseMetric):
"""Count the length of a simData column slice which matches 'subset'."""
"""Count the length of a column slice which matches `subset`."""

def __init__(self, col=None, subset=None, units="#", **kwargs):
super(CountSubsetMetric, self).__init__(col=col, units=units, **kwargs)
Expand All @@ -229,7 +257,8 @@ def run(self, data_slice, slice_point=None):


class CountBeyondThreshold(BaseMetric):
"""Count the number of entries in a data column above or below the threshold."""
"""Count the number of entries in a data column above or below
the `threshold`."""

def __init__(self, col=None, lower_threshold=None, upper_threshold=None, **kwargs):
super().__init__(col=col, **kwargs)
Expand All @@ -256,7 +285,7 @@ def run(self, data_slice, slice_point=None):

class RobustRmsMetric(BaseMetric):
"""Use the inter-quartile range of the data to estimate the RMS.
Robust since this calculation does not include outliers in the distribution.
Robust, as this calculation does not include outliers in the distribution.
"""

def run(self, data_slice, slice_point=None):
Expand All @@ -266,7 +295,8 @@ def run(self, data_slice, slice_point=None):


class MaxPercentMetric(BaseMetric):
"""Return the percent of the data which has the maximum value."""
"""Return the percent of data which matches the maximum value of the data.
"""

def run(self, data_slice, slice_point=None):
n_max = np.size(np.where(data_slice[self.colname] == np.max(data_slice[self.colname]))[0])
Expand All @@ -275,7 +305,9 @@ def run(self, data_slice, slice_point=None):


class AbsMaxPercentMetric(BaseMetric):
"""Return the percent of the data which has the absolute value of the max value of the data."""
"""Return the percent of data which matches the absolute value of the
max value of the data.
"""

def run(self, data_slice, slice_point=None):
max_val = np.abs(np.max(data_slice[self.colname]))
Expand All @@ -285,7 +317,8 @@ def run(self, data_slice, slice_point=None):


class BinaryMetric(BaseMetric):
"""Return 1 if there is data."""
"""Return 1 if there is data, `badval` otherwise.
"""

def run(self, data_slice, slice_point=None):
if data_slice.size > 0:
Expand All @@ -295,7 +328,8 @@ def run(self, data_slice, slice_point=None):


class FracAboveMetric(BaseMetric):
"""Find the fraction of data values above a given value."""
"""Find the fraction of data values above a given `cutoff`.
"""

def __init__(self, col=None, cutoff=0.5, scale=1, metric_name=None, **kwargs):
# Col could just get passed in bundle with kwargs, but by explicitly pulling it out
Expand All @@ -314,7 +348,8 @@ def run(self, data_slice, slice_point=None):


class FracBelowMetric(BaseMetric):
"""Find the fraction of data values below a given value."""
"""Find the fraction of data values below a given `cutoff`.
"""

def __init__(self, col=None, cutoff=0.5, scale=1, metric_name=None, **kwargs):
if metric_name is None:
Expand All @@ -331,7 +366,8 @@ def run(self, data_slice, slice_point=None):


class PercentileMetric(BaseMetric):
"""Find the value of a column at a given percentile."""
"""Find the value of a column at a given `percentile`.
"""

def __init__(self, col=None, percentile=90, metric_name=None, **kwargs):
if metric_name is None:
Expand All @@ -345,8 +381,8 @@ def run(self, data_slice, slice_point=None):


class NoutliersNsigmaMetric(BaseMetric):
"""Calculate the # of visits less than n_sigma below the mean (n_sigma<0) or
more than n_sigma above the mean of 'col'.
"""Calculate the # of visits less than n_sigma below the mean (n_sigma<0)
or more than n_sigma above the mean.
"""

def __init__(self, col=None, n_sigma=3.0, metric_name=None, **kwargs):
Expand Down Expand Up @@ -374,9 +410,10 @@ def _rotate_angles(angles):
"""Private utility for the '*Angle' Metrics below.
This takes a series of angles between 0-2pi and rotates them so that the
first angle is at 0, ensuring the biggest 'gap' is at the end of the series.
This simplifies calculations like the 'mean' and 'rms' or 'fullrange', removing
the discontinuity at 0/2pi.
first angle is at 0, ensuring the biggest 'gap' is at the end of the
series.
This simplifies calculations like the 'mean' and 'rms' or 'fullrange',
removing the discontinuity at 0/2pi.
"""
angleidx = np.argsort(angles)
diffangles = np.diff(angles[angleidx])
Expand All @@ -395,14 +432,15 @@ def _rotate_angles(angles):


class MeanAngleMetric(BaseMetric):
"""Calculate the mean of an angular (degree) simData column slice.
"""Calculate the mean of an angular (degree) column slice.
'MeanAngle' differs from 'Mean' in that it accounts for wraparound at 2pi.
"""

def run(self, data_slice, slice_point=None):
"""Calculate mean angle via unit vectors.
If unit vector 'strength' is less than 0.1, then just set mean to 180 degrees
If unit vector 'strength' is less than 0.1,
then just set mean to 180 degrees
(as this indicates nearly uniformly distributed angles).
"""
x = np.cos(np.radians(data_slice[self.colname]))
Expand All @@ -418,7 +456,7 @@ def run(self, data_slice, slice_point=None):


class RmsAngleMetric(BaseMetric):
"""Calculate the standard deviation of an angular (degrees) simData column slice.
"""Calculate the standard deviation of an angular (degrees) column slice.
'RmsAngle' differs from 'Rms' in that it accounts for wraparound at 2pi.
"""
Expand All @@ -429,9 +467,10 @@ def run(self, data_slice, slice_point=None):


class FullRangeAngleMetric(BaseMetric):
"""Calculate the full range of an angular (degrees) simData column slice.
"""Calculate the full range of an angular (degrees) column slice.
'FullRangeAngle' differs from 'FullRange' in that it accounts for wraparound at 2pi.
'FullRangeAngle' differs from 'FullRange' in that it accounts for
wraparound at 2pi.
"""

def run(self, data_slice, slice_point=None):
Expand All @@ -440,24 +479,30 @@ def run(self, data_slice, slice_point=None):


class AngularSpreadMetric(BaseMetric):
"""Compute the angular spread statistic which measures uniformity of a distribution angles
accounting for 2pi periodicity.
The strategy is to first map angles into unit vectors on the unit circle, and then compute the
2D centroid of those vectors. A uniform distribution of angles will lead to a distribution of
unit vectors with mean that approaches the origin. In contrast, a delta function distribution
of angles leads to a delta function distribution of unit vectors with a mean that lands on the
"""Compute the angular spread statistic which measures
uniformity of a distribution angles accounting for 2pi periodicity.
The strategy is to first map angles into unit vectors on the unit circle,
and then compute the 2D centroid of those vectors.
A uniform distribution of angles will lead to a distribution of
unit vectors with mean that approaches the origin.
In contrast, a delta function distribution of angles leads to a
delta function distribution of unit vectors with a mean that lands on the
unit circle.
The angular spread statistic is then defined as 1 - R, where R is the radial offset of the mean
of the unit vectors derived from the input angles. R approaches 1 for a uniform distribution
The angular spread statistic is then defined as 1 - R,
where R is the radial offset of the mean
of the unit vectors derived from the input angles.
R approaches 1 for a uniform distribution
of angles and 0 for a delta function distribution of angles.
The optional parameter `period` may be used to specificy periodicity other than 2 pi.
The optional parameter `period` may be used to specificy periodicity
other than 2 pi.
"""

def __init__(self, col=None, period=2.0 * np.pi, **kwargs):
# https://en.wikipedia.org/wiki/Directional_statistics#Measures_of_location_and_spread
# https://en.wikipedia.org/wiki/Directional_statistics
# #Measures_of_location_and_spread
# [email protected]
self.period = period
super(AngularSpreadMetric, self).__init__(col=col, **kwargs)
Expand All @@ -474,7 +519,8 @@ def run(self, data_slice, slice_point=None):


class RealMeanMetric(BaseMetric):
"""Calculate the mean of a simData column slice with no nans or infs."""
"""Calculate the mean of a column with no nans or infs.
"""

def run(self, data_slice, slice_point=None):
return np.mean(data_slice[self.colname][np.isfinite(data_slice[self.colname])])
6 changes: 4 additions & 2 deletions rubin_sim/maf/metrics/summary_metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -157,8 +157,10 @@ def run(self, data_slice, slice_point=None):


class IdentityMetric(BaseMetric):
"""
Return the metric value itself .. this is primarily useful as a summary statistic for UniSlicer metrics.
"""Return the metric value.
This is primarily useful as a summary statistic for UniSlicer metrics,
to propagate the ~MetricBundle.metric_value into the results database.
"""

def run(self, data_slice, slice_point=None):
Expand Down
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