__all__ = ["CheckColorSlope", "ColorSlopeMetric", "ColorSlope2NightMetric"]
import numpy as np
from rubin_scheduler.utils import int_binned_stat
from .base_metric import BaseMetric
[docs]
class CheckColorSlope(object):
"""Check if the data has a color and a slope
Parameters
----------
color_length : `float`
The maximum length of time different filters be observed
to still count as a color (hours). Default 1 hour.
slope_length : `float`
The length of time to demand observations in the
same filter be greater than (hours). Default 3 hours.
"""
def __init__(
self, color_length=1.0, slope_length=3.0, filter_col="filter", mjd_col="observationStartMJD"
):
self.color_length = color_length / 24.0
self.slope_length = slope_length / 24.0
self.filter_col = filter_col
self.mjd_col = mjd_col
def __call__(self, data_slice):
has_color = False
has_slope = False
if np.size(data_slice) < 3:
return 0
filters = data_slice[self.filter_col]
u_filters = np.unique(filters)
for filtername in u_filters:
in_filt = np.where(data_slice[self.filter_col] == filtername)[0]
time_gap = (
data_slice[self.mjd_col][in_filt].max() - data_slice[self.mjd_col][in_filt][np.newaxis].min()
)
if time_gap >= self.slope_length:
has_slope = True
break
for filtername1 in u_filters:
for filtername2 in u_filters:
if filtername1 != filtername2:
in_filt1 = np.where(filters == filtername1)[0]
in_filt2 = np.where(filters == filtername2)[0]
time_gaps = (
data_slice[self.mjd_col][in_filt1] - data_slice[self.mjd_col][in_filt2][np.newaxis].T
)
time_gaps = time_gaps[np.where(time_gaps > 0)]
if time_gaps.size > 0:
if np.min(time_gaps[np.where(time_gaps > 0)]) <= self.color_length:
has_color = True
break
if has_color & has_slope:
return 1
else:
return 0
[docs]
class ColorSlopeMetric(BaseMetric):
"""How many times do we get a color and slope in a night
A proxie metric for seeing how many times
there would be the possibility of identifying and
classifying a transient.
Parameters
----------
mag : `dict`
Dictionary with filternames as keys and minimum depth m5
magnitudes as values. If None, defaults to mag 20 in ugrizy.
color_length : `float`
The maximum length of time different filters be observed
to still count as a color (hours). Default 1 hour.
slope_length : `float`
The length of time to demand observations in the
same filter be greater than (hours). Default 3 hours."""
def __init__(
self,
mag=None,
night_col="night",
filter_col="filter",
m5_col="fiveSigmaDepth",
color_length=1.0,
slope_length=3.0,
time_col="observationStartMJD",
units="#",
metric_name="ColorSlope",
**kwargs,
):
cols = [filter_col, night_col, m5_col, time_col]
if mag is None:
mag = {"u": 20, "g": 20, "r": 20, "i": 20, "z": 20, "y": 20}
self.night_col = night_col
self.filter_col = filter_col
self.m5_col = m5_col
self.mag = mag
self.time_col = time_col
super().__init__(col=cols, units=units, metric_name=metric_name, **kwargs)
self.sequence_checker = CheckColorSlope(color_length=color_length, slope_length=slope_length)
[docs]
def run(self, data_slice, slice_point=None):
result = 0
deep_enough = np.zeros(data_slice.size, dtype=bool)
for filtername in np.unique(data_slice[self.filter_col]):
in_filt = np.where(data_slice[self.filter_col] == filtername)[0]
indx = np.where(data_slice[self.m5_col][in_filt] > self.mag[filtername])[0]
deep_enough[in_filt[indx]] = True
data = data_slice[deep_enough]
if data.size > 0:
_night, result = int_binned_stat(data[self.night_col], data, statistic=self.sequence_checker)
return np.sum(result)
[docs]
class ColorSlope2NightMetric(ColorSlopeMetric):
"""Like ColorSlopeMetric, but span over 2 nights
Parameters
----------
mag : `dict`
Dictionary with filternames as keys and minimum depth m5
magnitudes as values. If None, defaults to mag 20 in ugrizy.
color_length : `float`
The maximum length of time different filters be observed
to still count as a color (hours). Default 1 hour.
slope_length : `float`
The length of time to demand observations in the
same filter be greater than (hours). Default 15 hours."""
def __init__(
self,
mag=None,
night_col="night",
filter_col="filter",
m5_col="fiveSigmaDepth",
color_length=1.0,
slope_length=15.0,
time_col="observationStartMJD",
units="#",
metric_name="ColorSlope2Night",
**kwargs,
):
super().__init__(
mag=mag,
night_col=night_col,
filter_col=filter_col,
m5_col=m5_col,
color_length=color_length,
slope_length=slope_length,
time_col=time_col,
units=units,
metric_name=metric_name,
**kwargs,
)
[docs]
def run(self, data_slice, slice_point=None):
result = 0
deep_enough = np.zeros(data_slice.size, dtype=bool)
for filtername in np.unique(data_slice[self.filter_col]):
in_filt = np.where(data_slice[self.filter_col] == filtername)[0]
indx = np.where(data_slice[self.m5_col][in_filt] > self.mag[filtername])[0]
deep_enough[in_filt[indx]] = True
data = data_slice[deep_enough]
if data.size > 0:
# Send in nights as pairs, (0,1) (2,3), (4,5), etc
night_id = np.floor(data[self.night_col] / 2).astype(int)
_night, result1 = int_binned_stat(night_id, data, statistic=self.sequence_checker)
# Now to do pairs (1,2), (3,4)
night_id = np.ceil(data[self.night_col] / 2).astype(int)
_night, result2 = int_binned_stat(night_id, data, statistic=self.sequence_checker)
result = np.sum(result1) + np.sum(result2)
return result