updates to align more with DM style guide.

rubin_sim/maf/batches/radar_limited.py

@@ -511,7 +511,7 @@ def radar_limited(
     subgroupCount = 1
 
     displayDict["subgroup"] = f"{subgroupCount}: Static Science"
-    ## Static Science
+    # Static Science
     # Calculate the static science metrics - effective survey area,
     # mean/median coadded depth, stdev of
     # coadded depth and the 3x2ptFoM emulator.
@@ -565,7 +565,7 @@ def radar_limited(
         displayDict["order"] += 1
         bundleList.append(bundle)
 
-    ## LSS Science
+    # LSS Science
     # The only metric we have from LSS is the NGals metric -
     # which is similar to the GalaxyCountsExtended
     # metric, but evaluated only on the depth/dust cuts footprint.
@@ -574,7 +574,7 @@ def radar_limited(
     displayDict["order"] = 0
     plotDict = {"n_ticks": 5}
 
-    ## WL metrics
+    # WL metrics
     # Calculates the number of visits per pointing, after removing
     # parts of the footprint due to dust/depth
     # Count visits in gri bands.

rubin_sim/maf/batches/science_radar_batch.py

@@ -550,7 +550,7 @@ def science_radar_batch(
     subgroupCount = 1
 
     displayDict["subgroup"] = f"{subgroupCount}: Static Science"
-    ## Static Science
+    # Static Science
     # Calculate the static science metrics - effective survey area,
     # mean/median coadded depth, stdev of coadded depth and
     # the 3x2ptFoM emulator.
@@ -602,7 +602,7 @@ def science_radar_batch(
         displayDict["order"] += 1
         bundleList.append(bundle)
 
-    ## LSS Science
+    # LSS Science
     # The only metric we have from LSS is the NGals metric -
     # which is similar to the GalaxyCountsExtended
     # metric, but evaluated only on the depth/dust cuts footprint.
@@ -645,7 +645,7 @@ def science_radar_batch(
     )
     bundleList.append(bundle)
 
-    ## WL metrics
+    # WL metrics
     # Calculates the number of visits per pointing,
     # after removing parts of the footprint due to dust/depth.
     # Count visits in gri bands.
@@ -1815,7 +1815,7 @@ def science_radar_batch(
         )
     )
 
-    ## Local Volume Dwarf Satellites
+    # Local Volume Dwarf Satellites
     displayDict["group"] = "Local Volume"
     displayDict["subgroup"] = "LV dwarf satellites"
     displayDict["order"] = 0

rubin_sim/maf/ddf_dir.py

@@ -1,8 +1,3 @@
-__all__ = ("ddf_dir",)
-
-import matplotlib
-
-matplotlib.use("Agg")
 import argparse
 import glob
 import os
@@ -12,6 +7,12 @@
 import rubin_sim.maf.db as db
 import rubin_sim.maf.metric_bundles as mb
 
+__all__ = ("ddf_dir",)
+
+import matplotlib
+
+matplotlib.use("Agg")
+
 
 def ddf_dir():
     """

rubin_sim/maf/glance_dir.py

@@ -1,8 +1,3 @@
-__all__ = ("glance_dir",)
-
-import matplotlib
-
-matplotlib.use("Agg")
 import argparse
 import glob
 import os
@@ -12,6 +7,12 @@
 import rubin_sim.maf.db as db
 import rubin_sim.maf.metric_bundles as mb
 
+__all__ = ("glance_dir",)
+
+import matplotlib
+
+matplotlib.use("Agg")
+
 
 def glance_dir():
     """

rubin_sim/maf/maf_contrib/tdes_pop_metric.py

@@ -49,7 +49,7 @@ def interp(self, t, filtername, lc_indx=0):
         filtername : str
             The filter. one of ugrizy
         lc_index : int (0)
-            Which file to use.
+            Which file to use.
         """
 
         result = np.interp(

rubin_sim/maf/maf_contrib/transient_ascii_sed_metric.py

@@ -325,7 +325,7 @@ def evaluate_all_detection_criteria(self, data_slice):
 
         # Find the unique number of light curves that passed the required
         # number of conditions
-        self.num_detected += len(np.where(self.transient_detected == True)[0])
+        self.num_detected += len(np.where(self.transient_detected is True)[0])
 
     def evaluate_pre_time_detection_criteria(self, t_id):
         """

rubin_sim/maf/maf_night_report.py

@@ -1,3 +1,10 @@
+from . import db as db
+from . import metricBundles as metricBundles
+from . import metrics as metrics
+from . import plots as plots
+from . import slicers as slicers
+from .batches import col_map_dict
+
 __all__ = ("make_bundle_list", "maf_night_report")
 
 import argparse
@@ -6,13 +13,6 @@
 
 # Set matplotlib backend (to create plots where DISPLAY is not set).
 matplotlib.use("Agg")
-from . import db as db
-from . import metricBundles as metricBundles
-from . import metrics as metrics
-from . import plots as plots
-from . import slicers as slicers
-from . import utils as utils
-from .batches import col_map_dict
 
 
 def make_bundle_list(

rubin_sim/maf/metadata_dir.py

@@ -1,3 +1,8 @@
+from . import batches as batches
+from .db import ResultsDb
+from .metric_bundles import MetricBundleGroup
+from .slicers import HealpixSlicer, make_wfd_subset_slicer
+
 __all__ = ("metadata_dir",)
 
 import argparse
@@ -9,11 +14,6 @@
 
 matplotlib.use("Agg")
 
-from . import batches as batches
-from .db import ResultsDb
-from .metric_bundles import MetricBundleGroup
-from .slicers import HealpixSlicer, make_wfd_subset_slicer
-
 
 def metadata_dir():
     """

rubin_sim/maf/metrics/galplane_time_sampling_metrics.py

@@ -183,7 +183,7 @@ def __init__(
             self.tau_var = tau_var
         else:
             self.tau_var = TAU_OBS * 5
-        ### NOTE: I would recommend dropping tau_var 10 and 25 from this
+        # NOTE: I would recommend dropping tau_var 10 and 25 from this
         # analysis unless the metric is changed
         # these intervals are so short they will *always* be dropped
         # during the season gap

rubin_sim/maf/metrics/sky_sat_metric.py

@@ -14,5 +14,5 @@ def __init__(self, metric_name="SkySaturation", units="#", **kwargs):
     def run(self, data_slice, slice_point):
         # Saturation stacker returns NaN if the sky saturates
         finite = np.isfinite(data_slice["saturation_mag"])
-        result = np.size(np.where(finite == False)[0])
+        result = np.size(np.where(not finite)[0])
         return result

rubin_sim/maf/metrics/sn_n_sn_metric.py

@@ -558,7 +558,7 @@ def duration_z(self, grp, min_duration=60.0):
         `pd.DataFrame` with season_length, z, T0_min and T0_max cols
 
         """
-        ## IS THIS CALLED FROM ANYWHERE?
+        # IS THIS CALLED FROM ANYWHERE?
         daymin = grp["MJD_min"].values
         daymax = grp["MJD_max"].values
         dur_z = pd.DataFrame(self.zrange, columns=["z"])

rubin_sim/maf/metrics/technical_metrics.py

@@ -25,7 +25,7 @@ def __init__(self, col="filter", order_by="observationStartMJD", **kwargs):
     def run(self, data_slice, slice_point=None):
         idxs = np.argsort(data_slice[self.order_by])
         diff = data_slice[self.col][idxs][1:] != data_slice[self.col][idxs][:-1]
-        return np.size(np.where(diff == True)[0])
+        return np.size(np.where(diff is True)[0])
 
 
 class MinTimeBetweenStatesMetric(BaseMetric):
@@ -60,7 +60,7 @@ def run(self, data_slice, slice_point=None):
         # Sort on time, to be sure we've got changes in the right order.
         idxs = np.argsort(data_slice[self.time_col])
         changes = data_slice[self.change_col][idxs][1:] != data_slice[self.change_col][idxs][:-1]
-        condition = np.where(changes == True)[0]
+        condition = np.where(changes is True)[0]
         changetimes = data_slice[self.time_col][idxs][1:][condition]
         prevchangetime = np.concatenate(
             (
@@ -115,7 +115,7 @@ def run(self, data_slice, slice_point=None):
         # Sort on time, to be sure we've got changes in the right order.
         idxs = np.argsort(data_slice[self.time_col])
         changes = data_slice[self.change_col][idxs][1:] != data_slice[self.change_col][idxs][:-1]
-        condition = np.where(changes == True)[0]
+        condition = np.where(changes is True)[0]
         changetimes = data_slice[self.time_col][idxs][1:][condition]
         prevchangetime = np.concatenate(
             (
@@ -172,7 +172,7 @@ def run(self, data_slice, slice_point=None):
         # Sort on time, to be sure we've got changes in the right order.
         idxs = np.argsort(data_slice[self.time_col])
         changes = data_slice[self.change_col][idxs][:-1] != data_slice[self.change_col][idxs][1:]
-        condition = np.where(changes == True)[0]
+        condition = np.where(changes is True)[0]
         changetimes = data_slice[self.time_col][idxs][1:][condition]
         # If there are 0 filter changes ...
         if changetimes.size == 0:

rubin_sim/maf/run_moving_join.py

@@ -1,3 +1,5 @@
+from . import batches as batches
+
 __all__ = ("run_moving_join",)
 
 import argparse
@@ -9,8 +11,6 @@
 
 matplotlib.use("Agg")
 
-from . import batches as batches
-
 
 def run_moving_join():
     """Join split metric outputs into a single metric output file."""

rubin_sim/maf/run_selfcal_metric.py

@@ -1,3 +1,10 @@
+from .db import ResultsDb
+from .maf_contrib import PhotometricSelfCalUniformityMetric
+from .metric_bundles import MetricBundle, MetricBundleGroup
+from .metrics import IdentityMetric
+from .plots import HealpixHistogram, HealpixSkyMap, PlotHandler
+from .slicers import HealpixSlicer, UniSlicer
+
 __all__ = ("run_selfcal_metric",)
 
 import argparse
@@ -10,13 +17,6 @@
 
 matplotlib.use("Agg")
 
-from .db import ResultsDb
-from .maf_contrib import PhotometricSelfCalUniformityMetric
-from .metric_bundles import MetricBundle, MetricBundleGroup
-from .metrics import IdentityMetric
-from .plots import HealpixHistogram, HealpixSkyMap, PlotHandler
-from .slicers import HealpixSlicer, UniSlicer
-
 
 def run_selfcal_metric():
     """

rubin_sim/maf/scimaf_dir.py

@@ -1,3 +1,7 @@
+from . import batches as batches
+from . import db as db
+from . import metricBundles as mmB
+
 __all__ = ("scimaf_dir",)
 
 import argparse
@@ -12,10 +16,6 @@
 
 matplotlib.use("Agg")
 
-from . import batches as batches
-from . import db as db
-from . import metricBundles as mmB
-
 
 def scimaf_dir():
     """Run the science batch on all .db files in a directory."""

rubin_sim/maf/web/maf_run_results.py

@@ -542,7 +542,7 @@ def order_plots(self, sky_plots):
             #  that do NOT match original _*_ pattern.
             pattern = "_[ugrizy]_"
             nonmatches = np.array([bool(re.search(pattern, x)) for x in sky_plots["plot_file"]])
-            nonmatch_sky_plots = sky_plots[nonmatches == False]
+            nonmatch_sky_plots = sky_plots[nonmatches is False]
             if len(nonmatch_sky_plots) > 0:
                 for sky_plot in nonmatch_sky_plots:
                     ordered_sky_plots.append(self.plot_dict(np.array([sky_plot])))

rubin_sim/satellite_constellations/sat_utils.py

@@ -298,7 +298,7 @@ def update_mjd(self, mjd):
         self.azimuth_rad = np.array(self.azimuth_rad)
         self.illum = np.array(self.illum)
         # Keep track of the ones that are up and illuminated
-        self.visible = np.where((self.altitudes_rad >= self.alt_limit_rad) & (self.illum == True))[0]
+        self.visible = np.where((self.altitudes_rad >= self.alt_limit_rad) & (self.illum is True))[0]
 
     def paths_array(self, mjds):
         """Calculate and return the RA/Dec/Alt and illumination status
@@ -409,7 +409,7 @@ def check_pointings(
         # np.where confuses me when used on a 2d array.
         above_illum_indx = np.where(
             ((sat_alt_1 > self.alt_limit_rad) | (sat_alt_2 > self.alt_limit_rad))
-            & ((sat_illum_1 == True) | (sat_illum_2 == True))
+            & ((sat_illum_1 is True) | (sat_illum_2 is True))
         )
 
         # point_to_line_distance can take arrays,

rubin_sim/skybrightness/data/ESO_Spectra/eso_tools.py

@@ -9,7 +9,7 @@
 from rubin_sim.data import get_data_dir
 from rubin_sim.phot_utils import Bandpass, Sed
 
-## Tools for calling and reading things from the ESO sky model.
+# Tools for calling and reading things from the ESO sky model.
 # Installing 'calcskymodel' is a major headache,
 # let's hope we don't have to do this again anytime soon
 

tests/maf/test_healpixslicer.py

@@ -1,15 +1,15 @@
-import matplotlib
-
-matplotlib.use("Agg")
 import unittest
 
 import healpy as hp
+import matplotlib
 import numpy as np
 import numpy.lib.recfunctions as rfn
 import numpy.ma as ma
 
 from rubin_sim.maf.slicers import HealpixSlicer
 
+matplotlib.use("Agg")
+
 
 def make_data_values(
     size=100,