Fix error handling in void_prob_func.py

Author: aphearin

halotools/mock_observables/pair_counters/pairs.py

@@ -5,10 +5,11 @@
 """
 
 from __future__ import absolute_import, division, print_function, unicode_literals
+
 import numpy as np
 
-__all__ = ['npairs', 'wnpairs', 'xy_z_npairs', 'xy_z_wnpairs', 's_mu_npairs']
-__author__ = ['Duncan Campbell']
+__all__ = ["npairs", "wnpairs", "xy_z_npairs", "xy_z_wnpairs", "s_mu_npairs"]
+__author__ = ["Duncan Campbell"]
 
 
 def npairs(sample1, sample2, rbins, period=None):
@@ -51,22 +52,24 @@ def npairs(sample1, sample2, rbins, period=None):
 
     # Process period entry and check for consistency.
     if period is None:
-        period = np.array([np.inf]*np.shape(sample1)[-1])
+        period = np.array([np.inf] * np.shape(sample1)[-1])
     else:
         period = np.asarray(period).astype("float64")
         if np.shape(period) == ():
-            period = np.array([period]*np.shape(sample1)[-1])
+            period = np.array([period] * np.shape(sample1)[-1])
         elif np.shape(period)[0] != np.shape(sample1)[-1]:
             raise ValueError("period should have len == dimension of points")
             return None
 
     N1 = len(sample1)
     N2 = len(sample2)
-    dd = np.zeros((N1*N2,))  # store radial pair separations
-    for i in range(0, N1):  # calculate distance between every point and every other point
+    dd = np.zeros((N1 * N2,))  # store radial pair separations
+    for i in range(
+        0, N1
+    ):  # calculate distance between every point and every other point
         x1 = sample1[i, :]
         x2 = sample2
-        dd[i*N2:i*N2+N2] = distance(x1, x2, period)
+        dd[i * N2 : i * N2 + N2] = distance(x1, x2, period)
 
     # sort results
     dd.sort()
@@ -127,23 +130,25 @@ def xy_z_npairs(sample1, sample2, rp_bins, pi_bins, period=None):
 
     # Process period entry and check for consistency.
     if period is None:
-        period = np.array([np.inf]*np.shape(sample1)[-1])
+        period = np.array([np.inf] * np.shape(sample1)[-1])
     else:
         period = np.asarray(period).astype("float64")
         if np.shape(period) == ():
-            period = np.array([period]*np.shape(sample1)[-1])
+            period = np.array([period] * np.shape(sample1)[-1])
         elif np.shape(period)[0] != np.shape(sample1)[-1]:
             raise ValueError("period should have len == dimension of points")
             return None
 
     N1 = len(sample1)
     N2 = len(sample2)
-    dd = np.zeros((N1*N2, 2))  # store pair separations
-    for i in range(0, N1):  # calculate distance between every point and every other point
+    dd = np.zeros((N1 * N2, 2))  # store pair separations
+    for i in range(
+        0, N1
+    ):  # calculate distance between every point and every other point
         x1 = sample1[i, :]
         x2 = sample2
-        dd[i*N2:i*N2+N2, 1] = parallel_distance(x1, x2, period)
-        dd[i*N2:i*N2+N2, 0] = perpendicular_distance(x1, x2, period)
+        dd[i * N2 : i * N2 + N2, 1] = parallel_distance(x1, x2, period)
+        dd[i * N2 : i * N2 + N2, 0] = perpendicular_distance(x1, x2, period)
 
     # count number less than r
     n = np.zeros((rp_bins.size, pi_bins.size), dtype=np.int64)
@@ -200,26 +205,26 @@ def wnpairs(sample1, sample2, r, period=None, weights1=None, weights2=None):
 
     # Process period entry and check for consistency.
     if period is None:
-        period = np.array([np.inf]*np.shape(sample1)[-1])
+        period = np.array([np.inf] * np.shape(sample1)[-1])
     else:
         period = np.asarray(period).astype("float64")
         if np.shape(period) == ():
-            period = np.array([period]*np.shape(sample1)[-1])
+            period = np.array([period] * np.shape(sample1)[-1])
         if np.shape(period)[0] != np.shape(sample1)[-1]:
             raise ValueError("period should have len == dimension of points")
             return None
 
     # Process weights1 entry and check for consistency.
     if weights1 is None:
-        weights1 = np.array([1.0]*np.shape(sample1)[0], dtype=np.float64)
+        weights1 = np.array([1.0] * np.shape(sample1)[0], dtype=np.float64)
     else:
         weights1 = np.asarray(weights1).astype("float64")
         if np.shape(weights1)[0] != np.shape(sample1)[0]:
             raise ValueError("weights1 should have same len as sample1")
             return None
     # Process weights2 entry and check for consistency.
     if weights2 is None:
-        weights2 = np.array([1.0]*np.shape(sample2)[0], dtype=np.float64)
+        weights2 = np.array([1.0] * np.shape(sample2)[0], dtype=np.float64)
     else:
         weights2 = np.asarray(weights2).astype("float64")
         if np.shape(weights2)[0] != np.shape(sample2)[0]:
@@ -229,7 +234,9 @@ def wnpairs(sample1, sample2, r, period=None, weights1=None, weights2=None):
     N1 = len(sample1)
     N2 = len(sample2)
     dd = np.zeros((N1, N2), dtype=np.float64)  # store radial pair separations
-    for i in range(0, N1):  # calculate distance between every point and every other point
+    for i in range(
+        0, N1
+    ):  # calculate distance between every point and every other point
         x1 = sample1[i, :]
         x2 = sample2
         dd[i, :] = distance(x1, x2, period)
@@ -238,12 +245,14 @@ def wnpairs(sample1, sample2, r, period=None, weights1=None, weights2=None):
     n = np.zeros((r.size,), dtype=np.float64)
     for i in range(r.size):
         for j in range(N1):
-            n[i] += np.sum(np.extract(dd[j, :] <= r[i], weights2))*weights1[j]
+            n[i] += np.sum(np.extract(dd[j, :] <= r[i], weights2)) * weights1[j]
 
     return n
 
 
-def xy_z_wnpairs(sample1, sample2, rp_bins, pi_bins, period=None, weights1=None, weights2=None):
+def xy_z_wnpairs(
+    sample1, sample2, rp_bins, pi_bins, period=None, weights1=None, weights2=None
+):
     r"""
     Calculate the number of weighted pairs with parellal separations less than or equal to
     pi_bins[i], and perpendicular separations less than or equal to rp_bins[i].
@@ -298,26 +307,26 @@ def xy_z_wnpairs(sample1, sample2, rp_bins, pi_bins, period=None, weights1=None,
 
     # Process period entry and check for consistency.
     if period is None:
-        period = np.array([np.inf]*np.shape(sample1)[-1])
+        period = np.array([np.inf] * np.shape(sample1)[-1])
     else:
         period = np.asarray(period).astype("float64")
         if np.shape(period) == ():
-            period = np.array([period]*np.shape(sample1)[-1])
+            period = np.array([period] * np.shape(sample1)[-1])
         elif np.shape(period)[0] != np.shape(sample1)[-1]:
             raise ValueError("period should have len == dimension of points")
             return None
 
     # Process weights1 entry and check for consistency.
     if weights1 is None:
-        weights1 = np.array([1.0]*np.shape(sample1)[0], dtype=np.float64)
+        weights1 = np.array([1.0] * np.shape(sample1)[0], dtype=np.float64)
     else:
         weights1 = np.asarray(weights1).astype("float64")
         if np.shape(weights1)[0] != np.shape(sample1)[0]:
             raise ValueError("weights1 should have same len as sample1")
             return None
     # Process weights2 entry and check for consistency.
     if weights2 is None:
-        weights2 = np.array([1.0]*np.shape(sample2)[0], dtype=np.float64)
+        weights2 = np.array([1.0] * np.shape(sample2)[0], dtype=np.float64)
     else:
         weights2 = np.asarray(weights2).astype("float64")
         if np.shape(weights2)[0] != np.shape(sample2)[0]:
@@ -326,20 +335,24 @@ def xy_z_wnpairs(sample1, sample2, rp_bins, pi_bins, period=None, weights1=None,
 
     N1 = len(sample1)
     N2 = len(sample2)
-    dd = np.zeros((N1*N2, 2))  # store pair separations
-    ww = np.zeros((N1*N2, 1))  # store pair separations
-    for i in range(0, N1):  # calculate distance between every point and every other point
+    dd = np.zeros((N1 * N2, 2))  # store pair separations
+    ww = np.zeros((N1 * N2, 1))  # store pair separations
+    for i in range(
+        0, N1
+    ):  # calculate distance between every point and every other point
         x1 = sample1[i, :]
         x2 = sample2
-        dd[i*N2:i*N2+N2, 1] = parallel_distance(x1, x2, period)
-        dd[i*N2:i*N2+N2, 0] = perpendicular_distance(x1, x2, period)
-        ww[i*N2:i*N2+N2] = weights1[i]*weights2
+        dd[i * N2 : i * N2 + N2, 1] = parallel_distance(x1, x2, period)
+        dd[i * N2 : i * N2 + N2, 0] = perpendicular_distance(x1, x2, period)
+        ww[i * N2 : i * N2 + N2] = weights1[i] * weights2
 
     # count number less than r
     n = np.zeros((rp_bins.size, pi_bins.size), dtype=np.float64)
     for i in range(rp_bins.size):
         for j in range(pi_bins.size):
-            n[i, j] += np.sum(np.extract((dd[:, 0] <= rp_bins[i]) & (dd[:, 1] <= pi_bins[j]), ww))
+            n[i, j] += np.sum(
+                np.extract((dd[:, 0] <= rp_bins[i]) & (dd[:, 1] <= pi_bins[j]), ww)
+            )
 
     return n
 
@@ -400,11 +413,11 @@ def s_mu_npairs(sample1, sample2, s_bins, mu_bins, period=None):
 
     # Process period entry and check for consistency.
     if period is None:
-        period = np.array([np.inf]*np.shape(sample1)[-1])
+        period = np.array([np.inf] * np.shape(sample1)[-1])
     else:
         period = np.asarray(period).astype("float64")
         if np.shape(period) == ():
-            period = np.array([period]*np.shape(sample1)[-1])
+            period = np.array([period] * np.shape(sample1)[-1])
         elif np.shape(period)[0] != np.shape(sample1)[-1]:
             raise ValueError("period should have len == dimension of points")
             return None
@@ -413,14 +426,14 @@ def s_mu_npairs(sample1, sample2, s_bins, mu_bins, period=None):
     # note that this array can be very large for large N1 and N2
     N1 = len(sample1)
     N2 = len(sample2)
-    dd = np.zeros((N1*N2, 2))
+    dd = np.zeros((N1 * N2, 2))
 
     # calculate distance between every point and every other point
     for i in range(0, N1):
         x1 = sample1[i, :]
         x2 = sample2
-        dd[i*N2:i*N2+N2, 0] = distance(x1, x2, period)
-        dd[i*N2:i*N2+N2, 1] = np.cos(theta_LOS(x1, x2, period))
+        dd[i * N2 : i * N2 + N2, 0] = distance(x1, x2, period)
+        dd[i * N2 : i * N2 + N2, 1] = np.cos(theta_LOS(x1, x2, period))
 
     # put mu bins in increasing theta_LOS order
     mu_bins = np.sort(mu_bins)[::-1]
@@ -458,7 +471,7 @@ def distance(x1, x2, period=None):
     x1 = np.atleast_2d(x1)
     x2 = np.atleast_2d(x2)
     if period is None:
-        period = np.array([np.inf]*np.shape(x1)[-1])
+        period = np.array([np.inf] * np.shape(x1)[-1])
 
     # check for consistency
     if np.shape(x1)[-1] != np.shape(x2)[-1]:
@@ -469,7 +482,7 @@ def distance(x1, x2, period=None):
         raise ValueError("period must have length equal to the dimension of x1 and x2.")
 
     m = np.minimum(np.fabs(x1 - x2), period - np.fabs(x1 - x2))
-    distance = np.sqrt(np.sum(m*m, axis=len(np.shape(m))-1))
+    distance = np.sqrt(np.sum(m * m, axis=len(np.shape(m)) - 1))
 
     return distance
 
@@ -500,7 +513,7 @@ def parallel_distance(x1, x2, period=None):
     x1 = np.atleast_2d(x1)
     x2 = np.atleast_2d(x2)
     if period is None:
-        period = np.array([np.inf]*np.shape(x1)[-1])
+        period = np.array([np.inf] * np.shape(x1)[-1])
 
     # check for consistency
     if np.shape(x1)[-1] != np.shape(x2)[-1]:
@@ -510,8 +523,10 @@ def parallel_distance(x1, x2, period=None):
     if np.shape(period)[0] != np.shape(x1)[-1]:
         raise ValueError("period must have length equal to the dimension of x1 and x2.")
 
-    m = np.minimum(np.fabs(x1[:, -1] - x2[:, -1]), period[-1] - np.fabs(x1[:, -1] - x2[:, -1]))
-    distance = np.sqrt(m*m)
+    m = np.minimum(
+        np.fabs(x1[:, -1] - x2[:, -1]), period[-1] - np.fabs(x1[:, -1] - x2[:, -1])
+    )
+    distance = np.sqrt(m * m)
 
     return distance
 
@@ -542,7 +557,7 @@ def perpendicular_distance(x1, x2, period=None):
     x1 = np.atleast_2d(x1)
     x2 = np.atleast_2d(x2)
     if period is None:
-        period = np.array([np.inf]*np.shape(x1)[-1])
+        period = np.array([np.inf] * np.shape(x1)[-1])
 
     # check for consistency
     if np.shape(x1)[-1] != np.shape(x2)[-1]:
@@ -552,8 +567,10 @@ def perpendicular_distance(x1, x2, period=None):
     if np.shape(period)[0] != np.shape(x1)[-1]:
         raise ValueError("period must have length equal to the dimension of x1 and x2.")
 
-    m = np.minimum(np.fabs(x1[:, :-1] - x2[:, :-1]), period[:-1] - np.fabs(x1[:, :-1] - x2[:, :-1]))
-    distance = np.sqrt(np.sum(m*m, axis=len(np.shape(m))-1))
+    m = np.minimum(
+        np.fabs(x1[:, :-1] - x2[:, :-1]), period[:-1] - np.fabs(x1[:, :-1] - x2[:, :-1])
+    )
+    distance = np.sqrt(np.sum(m * m, axis=len(np.shape(m)) - 1))
 
     return distance
 
@@ -589,7 +606,7 @@ def theta_LOS(x1, x2, period=None):
     x1 = np.atleast_2d(x1)
     x2 = np.atleast_2d(x2)
     if period is None:
-        period = np.array([np.inf]*np.shape(x1)[-1])
+        period = np.array([np.inf] * np.shape(x1)[-1])
 
     # check for consistency
     if np.shape(x1)[-1] != np.shape(x2)[-1]:
@@ -604,9 +621,9 @@ def theta_LOS(x1, x2, period=None):
 
     # deal with zero separation
     r = np.sqrt(r_perp**2 + r_parallel**2)
-    mask = (r>0.0)
+    mask = r > 0.0
 
-    theta = np.zeros(len(r)) # set to zero if r==0
-    theta[mask] = np.pi/2.0 - np.arctan2(r_parallel[mask],r_perp[mask])
+    theta = np.zeros(len(r))  # set to zero if r==0
+    theta[mask] = np.pi / 2.0 - np.arctan2(r_parallel[mask], r_perp[mask])
 
     return theta

halotools/mock_observables/pair_counters/test_pair_counters/test_marked_npairs_3d.py

@@ -1,17 +1,16 @@
-"""
-"""
+""" """
+
 from __future__ import absolute_import, division, print_function, unicode_literals
 
+from pathlib import Path
+
 import numpy as np
 import pytest
 from astropy.utils.misc import NumpyRNGContext
-from pathlib import Path
-
-from ..pairs import wnpairs as pure_python_weighted_pairs
-from ..marked_npairs_3d import marked_npairs_3d, _func_signature_int_from_wfunc
 
 from ....custom_exceptions import HalotoolsError
-
+from ..marked_npairs_3d import _func_signature_int_from_wfunc, marked_npairs_3d
+from ..pairs import wnpairs as pure_python_weighted_pairs
 
 error_msg = (
     "\nThe `test_marked_npairs_wfuncs_behavior` function performs \n"

halotools/mock_observables/void_statistics/tests/test_underdensity_prob_func.py

@@ -1,18 +1,18 @@
-""" Module providing unit-testing for the
+"""Module providing unit-testing for the
 `~halotools.mock_observables.underdensity_prob_func function.
 """
+
 from __future__ import absolute_import, division, print_function
 
 import numpy as np
 import pytest
 from astropy.utils.misc import NumpyRNGContext
 
+from ....custom_exceptions import HalotoolsError
+from ...tests.cf_helpers import generate_locus_of_3d_points
 from ..underdensity_prob_func import underdensity_prob_func
 from ..void_prob_func import void_prob_func
 
-from ...tests.cf_helpers import generate_locus_of_3d_points
-from ....custom_exceptions import HalotoolsError
-
 __all__ = ("test_upf1", "test_upf2", "test_upf3", "test_upf4")
 
 fixed_seed = 43