Revert "run black on pycbc/events/ranking in order to try and get cod…

…eclimate to be quiet"

This reverts commit 4f082ea.

pycbc/events/ranking.py

@@ -1,24 +1,26 @@
 """ This module contains functions for calculating single-ifo ranking
 statistic values
 """
-
 import numpy
 
 
-def effsnr(snr, reduced_x2, fac=250.0, **kwargs):  # pylint:disable=unused-argument
-    """Calculate the effective SNR statistic. See (S5y1 paper) for definition."""
+def effsnr(snr, reduced_x2, fac=250.,
+           **kwargs):  # pylint:disable=unused-argument
+    """Calculate the effective SNR statistic. See (S5y1 paper) for definition.
+    """
     snr = numpy.array(snr, ndmin=1, dtype=numpy.float64)
     rchisq = numpy.array(reduced_x2, ndmin=1, dtype=numpy.float64)
-    esnr = snr / (1 + snr**2 / fac) ** 0.25 / rchisq**0.25
+    esnr = snr / (1 + snr ** 2 / fac) ** 0.25 / rchisq ** 0.25
 
     # If snr input is float, return a float. Otherwise return numpy array.
-    if hasattr(snr, "__len__"):
+    if hasattr(snr, '__len__'):
         return esnr
     else:
         return esnr[0]
 
 
-def newsnr(snr, reduced_x2, q=6.0, n=2.0, **kwargs):  # pylint:disable=unused-argument
+def newsnr(snr, reduced_x2, q=6., n=2.,
+           **kwargs):  # pylint:disable=unused-argument
     """Calculate the re-weighted SNR statistic ('newSNR') from given SNR and
     reduced chi-squared values. See http://arxiv.org/abs/1208.3491 for
     definition. Previous implementation in glue/ligolw/lsctables.py
@@ -27,63 +29,69 @@ def newsnr(snr, reduced_x2, q=6.0, n=2.0, **kwargs):  # pylint:disable=unused-ar
     reduced_x2 = numpy.array(reduced_x2, ndmin=1, dtype=numpy.float64)
 
     # newsnr is only different from snr if reduced chisq > 1
-    ind = numpy.where(reduced_x2 > 1.0)[0]
-    nsnr[ind] *= (0.5 * (1.0 + reduced_x2[ind] ** (q / n))) ** (-1.0 / q)
+    ind = numpy.where(reduced_x2 > 1.)[0]
+    nsnr[ind] *= (0.5 * (1. + reduced_x2[ind] ** (q/n))) ** (-1./q)
 
     # If snr input is float, return a float. Otherwise return numpy array.
-    if hasattr(snr, "__len__"):
+    if hasattr(snr, '__len__'):
         return nsnr
     else:
         return nsnr[0]
 
 
-def newsnr_sgveto(snr, brchisq, sgchisq, **kwargs):  # pylint:disable=unused-argument
-    """Combined SNR derived from NewSNR and Sine-Gaussian Chisq"""
-    nsnr = numpy.array(newsnr(snr, brchisq, **kwargs), ndmin=1)
+def newsnr_sgveto(snr, brchisq, sgchisq,
+                  **kwargs):  # pylint:disable=unused-argument
+    """ Combined SNR derived from NewSNR and Sine-Gaussian Chisq"""
+    nsnr = numpy.array(
+        newsnr(
+            snr,
+            brchisq,
+            **kwargs),
+        ndmin=1)
     sgchisq = numpy.array(sgchisq, ndmin=1)
     t = numpy.array(sgchisq > 4, ndmin=1)
     if len(t):
         nsnr[t] = nsnr[t] / (sgchisq[t] / 4.0) ** 0.5
 
     # If snr input is float, return a float. Otherwise return numpy array.
-    if hasattr(snr, "__len__"):
+    if hasattr(snr, '__len__'):
         return nsnr
     else:
         return nsnr[0]
 
 
-def newsnr_sgveto_psdvar(
-    snr, brchisq, sgchisq, psd_var_val, min_expected_psdvar=0.65, **kwargs
-):  # pylint:disable=unused-argument
-    """Combined SNR derived from SNR, reduced Allen chisq, sine-Gaussian chisq and
+def newsnr_sgveto_psdvar(snr, brchisq, sgchisq, psd_var_val,
+                         min_expected_psdvar=0.65,
+                         **kwargs):  # pylint:disable=unused-argument
+    """ Combined SNR derived from SNR, reduced Allen chisq, sine-Gaussian chisq and
     PSD variation statistic"""
     # If PSD var is lower than the 'minimum usually expected value' stop this
     # being used in the statistic. This low value might arise because a
     # significant fraction of the "short" PSD period was gated (for instance).
     psd_var_val = numpy.array(psd_var_val, copy=True)
-    psd_var_val[psd_var_val < min_expected_psdvar] = 1.0
-    scaled_snr = snr * (psd_var_val**-0.5)
-    scaled_brchisq = brchisq * (psd_var_val**-1.0)
-    nsnr = newsnr_sgveto(scaled_snr, scaled_brchisq, sgchisq, **kwargs)
+    psd_var_val[psd_var_val < min_expected_psdvar] = 1.
+    scaled_snr = snr * (psd_var_val ** -0.5)
+    scaled_brchisq = brchisq * (psd_var_val ** -1.)
+    nsnr = newsnr_sgveto(
+        scaled_snr,
+        scaled_brchisq,
+        sgchisq,
+        **kwargs
+    )
 
     # If snr input is float, return a float. Otherwise return numpy array.
-    if hasattr(snr, "__len__"):
+    if hasattr(snr, '__len__'):
         return nsnr
     else:
         return nsnr[0]
 
 
-def newsnr_sgveto_psdvar_threshold(
-    snr,
-    brchisq,
-    sgchisq,
-    psd_var_val,
-    min_expected_psdvar=0.65,
-    brchisq_threshold=10.0,
-    psd_var_val_threshold=10.0,
-    **kwargs
-):  # pylint:disable=unused-argument
-    """newsnr_sgveto_psdvar with thresholds applied.
+def newsnr_sgveto_psdvar_threshold(snr, brchisq, sgchisq, psd_var_val,
+                                   min_expected_psdvar=0.65,
+                                   brchisq_threshold=10.0,
+                                   psd_var_val_threshold=10.0,
+                                   **kwargs):  # pylint:disable=unused-argument
+    """ newsnr_sgveto_psdvar with thresholds applied.
 
     This is the newsnr_sgveto_psdvar statistic with additional options
     to threshold on chi-squared or PSD variation.
@@ -97,47 +105,59 @@ def newsnr_sgveto_psdvar_threshold(
         **kwargs
     )
     nsnr = numpy.array(nsnr, ndmin=1)
-    nsnr[brchisq > brchisq_threshold] = 1.0
-    nsnr[psd_var_val > psd_var_val_threshold] = 1.0
+    nsnr[brchisq > brchisq_threshold] = 1.
+    nsnr[psd_var_val > psd_var_val_threshold] = 1.
 
     # If snr input is float, return a float. Otherwise return numpy array.
-    if hasattr(snr, "__len__"):
+    if hasattr(snr, '__len__'):
         return nsnr
     else:
         return nsnr[0]
 
 
-def newsnr_sgveto_psdvar_scaled(
-    snr, brchisq, sgchisq, psd_var_val, scaling=0.33, min_expected_psdvar=0.65, **kwargs
-):  # pylint:disable=unused-argument
-    """Combined SNR derived from NewSNR, Sine-Gaussian Chisq and scaled PSD
-    variation statistic."""
-    nsnr = numpy.array(newsnr_sgveto(snr, brchisq, sgchisq, **kwargs), ndmin=1)
+def newsnr_sgveto_psdvar_scaled(snr, brchisq, sgchisq, psd_var_val,
+                                scaling=0.33, min_expected_psdvar=0.65,
+                                **kwargs):  # pylint:disable=unused-argument
+    """ Combined SNR derived from NewSNR, Sine-Gaussian Chisq and scaled PSD
+    variation statistic. """
+    nsnr = numpy.array(
+        newsnr_sgveto(
+            snr,
+            brchisq,
+            sgchisq,
+            **kwargs),
+        ndmin=1)
     psd_var_val = numpy.array(psd_var_val, ndmin=1, copy=True)
-    psd_var_val[psd_var_val < min_expected_psdvar] = 1.0
+    psd_var_val[psd_var_val < min_expected_psdvar] = 1.
 
     # Default scale is 0.33 as tuned from analysis of data from O2 chunks
-    nsnr = nsnr / psd_var_val**scaling
+    nsnr = nsnr / psd_var_val ** scaling
 
     # If snr input is float, return a float. Otherwise return numpy array.
-    if hasattr(snr, "__len__"):
+    if hasattr(snr, '__len__'):
         return nsnr
     else:
         return nsnr[0]
 
 
-def newsnr_sgveto_psdvar_scaled_threshold(
-    snr, bchisq, sgchisq, psd_var_val, threshold=2.0, **kwargs
-):  # pylint:disable=unused-argument
-    """Combined SNR derived from NewSNR and Sine-Gaussian Chisq, and
+def newsnr_sgveto_psdvar_scaled_threshold(snr, bchisq, sgchisq, psd_var_val,
+                                          threshold=2.0,
+                                          **kwargs):  # pylint:disable=unused-argument
+    """ Combined SNR derived from NewSNR and Sine-Gaussian Chisq, and
     scaled psd variation.
     """
-    nsnr = newsnr_sgveto_psdvar_scaled(snr, bchisq, sgchisq, psd_var_val, **kwargs)
+    nsnr = newsnr_sgveto_psdvar_scaled(
+        snr,
+        bchisq,
+        sgchisq,
+        psd_var_val,
+        **kwargs
+    )
     nsnr = numpy.array(nsnr, ndmin=1)
-    nsnr[bchisq > threshold] = 1.0
+    nsnr[bchisq > threshold] = 1.
 
     # If snr input is float, return a float. Otherwise return numpy array.
-    if hasattr(snr, "__len__"):
+    if hasattr(snr, '__len__'):
         return nsnr
     else:
         return nsnr[0]
@@ -158,7 +178,7 @@ def get_snr(trigs, **kwargs):  # pylint:disable=unused-argument
     numpy.ndarray
         Array of snr values
     """
-    return numpy.array(trigs["snr"][:], ndmin=1, dtype=numpy.float32)
+    return numpy.array(trigs['snr'][:], ndmin=1, dtype=numpy.float32)
 
 
 def get_newsnr(trigs, **kwargs):  # pylint:disable=unused-argument
@@ -176,8 +196,12 @@ def get_newsnr(trigs, **kwargs):  # pylint:disable=unused-argument
     numpy.ndarray
         Array of newsnr values
     """
-    dof = 2.0 * trigs["chisq_dof"][:] - 2.0
-    nsnr = newsnr(trigs["snr"][:], trigs["chisq"][:] / dof, **kwargs)
+    dof = 2. * trigs['chisq_dof'][:] - 2.
+    nsnr = newsnr(
+        trigs['snr'][:],
+        trigs['chisq'][:] / dof,
+        **kwargs
+    )
     return numpy.array(nsnr, ndmin=1, dtype=numpy.float32)
 
 
@@ -196,9 +220,12 @@ def get_newsnr_sgveto(trigs, **kwargs):  # pylint:disable=unused-argument
     numpy.ndarray
         Array of newsnr values
     """
-    dof = 2.0 * trigs["chisq_dof"][:] - 2.0
+    dof = 2. * trigs['chisq_dof'][:] - 2.
     nsnr_sg = newsnr_sgveto(
-        trigs["snr"][:], trigs["chisq"][:] / dof, trigs["sg_chisq"][:], **kwargs
+        trigs['snr'][:],
+        trigs['chisq'][:] / dof,
+        trigs['sg_chisq'][:],
+        **kwargs
     )
     return numpy.array(nsnr_sg, ndmin=1, dtype=numpy.float32)
 
@@ -219,20 +246,18 @@ def get_newsnr_sgveto_psdvar(trigs, **kwargs):  # pylint:disable=unused-argument
      numpy.ndarray
         Array of newsnr values
     """
-    dof = 2.0 * trigs["chisq_dof"][:] - 2.0
+    dof = 2. * trigs['chisq_dof'][:] - 2.
     nsnr_sg_psd = newsnr_sgveto_psdvar(
-        trigs["snr"][:],
-        trigs["chisq"][:] / dof,
-        trigs["sg_chisq"][:],
-        trigs["psd_var_val"][:],
+        trigs['snr'][:],
+        trigs['chisq'][:] / dof,
+        trigs['sg_chisq'][:],
+        trigs['psd_var_val'][:],
         **kwargs
     )
     return numpy.array(nsnr_sg_psd, ndmin=1, dtype=numpy.float32)
 
 
-def get_newsnr_sgveto_psdvar_threshold(
-    trigs, **kwargs
-):  # pylint:disable=unused-argument
+def get_newsnr_sgveto_psdvar_threshold(trigs, **kwargs):  # pylint:disable=unused-argument
     """
     Calculate newsnr re-weighted by the sine-gaussian veto and scaled
     psd variation statistic
@@ -248,12 +273,11 @@ def get_newsnr_sgveto_psdvar_threshold(
      numpy.ndarray
         Array of newsnr values
     """
-    dof = 2.0 * trigs["chisq_dof"][:] - 2.0
+    dof = 2. * trigs['chisq_dof'][:] - 2.
     nsnr_sg_psdt = newsnr_sgveto_psdvar_threshold(
-        trigs["snr"][:],
-        trigs["chisq"][:] / dof,
-        trigs["sg_chisq"][:],
-        trigs["psd_var_val"][:],
+        trigs['snr'][:], trigs['chisq'][:] / dof,
+        trigs['sg_chisq'][:],
+        trigs['psd_var_val'][:],
         **kwargs
     )
     return numpy.array(nsnr_sg_psdt, ndmin=1, dtype=numpy.float32)
@@ -275,20 +299,18 @@ def get_newsnr_sgveto_psdvar_scaled(trigs, **kwargs):  # pylint:disable=unused-a
      numpy.ndarray
         Array of newsnr values
     """
-    dof = 2.0 * trigs["chisq_dof"][:] - 2.0
+    dof = 2. * trigs['chisq_dof'][:] - 2.
     nsnr_sg_psdscale = newsnr_sgveto_psdvar_scaled(
-        trigs["snr"][:],
-        trigs["chisq"][:] / dof,
-        trigs["sg_chisq"][:],
-        trigs["psd_var_val"][:],
+        trigs['snr'][:],
+        trigs['chisq'][:] / dof,
+        trigs['sg_chisq'][:],
+        trigs['psd_var_val'][:],
         **kwargs
     )
     return numpy.array(nsnr_sg_psdscale, ndmin=1, dtype=numpy.float32)
 
 
-def get_newsnr_sgveto_psdvar_scaled_threshold(
-    trigs, **kwargs
-):  # pylint:disable=unused-argument
+def get_newsnr_sgveto_psdvar_scaled_threshold(trigs, **kwargs):  # pylint:disable=unused-argument
     """
     Calculate newsnr re-weighted by the sine-gaussian veto and scaled
     psd variation statistic. A further threshold is applied to the
@@ -305,40 +327,43 @@ def get_newsnr_sgveto_psdvar_scaled_threshold(
      numpy.ndarray
         Array of newsnr values
     """
-    dof = 2.0 * trigs["chisq_dof"][:] - 2.0
+    dof = 2. * trigs['chisq_dof'][:] - 2.
     nsnr_sg_psdt = newsnr_sgveto_psdvar_scaled_threshold(
-        trigs["snr"][:],
-        trigs["chisq"][:] / dof,
-        trigs["sg_chisq"][:],
-        trigs["psd_var_val"][:],
+        trigs['snr'][:],
+        trigs['chisq'][:] / dof,
+        trigs['sg_chisq'][:],
+        trigs['psd_var_val'][:],
         **kwargs
     )
     return numpy.array(nsnr_sg_psdt, ndmin=1, dtype=numpy.float32)
 
 
 sngls_ranking_function_dict = {
-    "snr": get_snr,
-    "newsnr": get_newsnr,
-    "new_snr": get_newsnr,
-    "newsnr_sgveto": get_newsnr_sgveto,
-    "newsnr_sgveto_psdvar": get_newsnr_sgveto_psdvar,
-    "newsnr_sgveto_psdvar_threshold": get_newsnr_sgveto_psdvar_threshold,
-    "newsnr_sgveto_psdvar_scaled": get_newsnr_sgveto_psdvar_scaled,
-    "newsnr_sgveto_psdvar_scaled_threshold": get_newsnr_sgveto_psdvar_scaled_threshold,
+    'snr': get_snr,
+    'newsnr': get_newsnr,
+    'new_snr': get_newsnr,
+    'newsnr_sgveto': get_newsnr_sgveto,
+    'newsnr_sgveto_psdvar': get_newsnr_sgveto_psdvar,
+    'newsnr_sgveto_psdvar_threshold': get_newsnr_sgveto_psdvar_threshold,
+    'newsnr_sgveto_psdvar_scaled': get_newsnr_sgveto_psdvar_scaled,
+    'newsnr_sgveto_psdvar_scaled_threshold':
+    get_newsnr_sgveto_psdvar_scaled_threshold,
 }
 
 # Lists of datasets required in the trigs object for each function
 reqd_datasets = {}
-reqd_datasets["snr"] = ["snr"]
-reqd_datasets["newsnr"] = reqd_datasets["snr"] + ["chisq", "chisq_dof"]
-reqd_datasets["new_snr"] = reqd_datasets["newsnr"]
-reqd_datasets["newsnr_sgveto"] = reqd_datasets["newsnr"] + ["sg_chisq"]
-reqd_datasets["newsnr_sgveto_psdvar"] = reqd_datasets["newsnr_sgveto"] + ["psd_var_val"]
-reqd_datasets["newsnr_sgveto_psdvar_threshold"] = reqd_datasets["newsnr_sgveto_psdvar"]
-reqd_datasets["newsnr_sgveto_psdvar_scaled"] = reqd_datasets["newsnr_sgveto_psdvar"]
-reqd_datasets["newsnr_sgveto_psdvar_scaled_threshold"] = reqd_datasets[
-    "newsnr_sgveto_psdvar"
-]
+reqd_datasets['snr'] = ['snr']
+reqd_datasets['newsnr'] = reqd_datasets['snr'] + ['chisq', 'chisq_dof']
+reqd_datasets['new_snr'] = reqd_datasets['newsnr']
+reqd_datasets['newsnr_sgveto'] = reqd_datasets['newsnr'] + ['sg_chisq']
+reqd_datasets['newsnr_sgveto_psdvar'] = \
+    reqd_datasets['newsnr_sgveto'] + ['psd_var_val']
+reqd_datasets['newsnr_sgveto_psdvar_threshold'] = \
+    reqd_datasets['newsnr_sgveto_psdvar']
+reqd_datasets['newsnr_sgveto_psdvar_scaled'] = \
+    reqd_datasets['newsnr_sgveto_psdvar']
+reqd_datasets['newsnr_sgveto_psdvar_scaled_threshold'] = \
+    reqd_datasets['newsnr_sgveto_psdvar']
 
 
 def get_sngls_ranking_from_trigs(trigs, statname, **kwargs):
@@ -359,7 +384,7 @@ def get_sngls_ranking_from_trigs(trigs, statname, **kwargs):
     try:
         sngl_func = sngls_ranking_function_dict[statname]
     except KeyError as exc:
-        err_msg = "Single-detector ranking {} not recognized".format(statname)
+        err_msg = 'Single-detector ranking {} not recognized'.format(statname)
         raise ValueError(err_msg) from exc
 
     # NOTE: In the sngl_funcs all the kwargs are explicitly stated, so any