Moves spherical histogram into doa sub-package. Fixes test_rejection …

…pre-test.

pyroomacoustics/__init__.py

@@ -120,7 +120,7 @@
 
 import warnings
 
-from . import adaptive, bss, datasets, denoise, doa, experimental
+from . import adaptive, bss, datasets, denoise, doa, experimental, random
 from . import libroom as libroom
 from . import phase, transform
 from .acoustics import *

pyroomacoustics/directivities/analytic.py

@@ -416,7 +416,7 @@ def _pattern(self, x):
         return cardioid_func(
             x.T,
             direction=self._loc,
-            coef=self._coeff,
+            p=self._coeff,
             gain=1.0,
             normalize=False,
             magnitude=True,

pyroomacoustics/doa/__init__.py

@@ -118,6 +118,7 @@
 from .tops import *
 from .utils import *
 from .waves import *
+from .histogram import SphericalHistogram
 
 # Create this dictionary as a shortcut to different algorithms
 algorithms = {

pyroomacoustics/doa/grid.py

@@ -209,7 +209,7 @@ def __init__(
             # If no list was provided, samples points on the sphere
             # as uniformly as possible
 
-            self.x, self.y, self.z = fibonacci_spherical_sampling(n_points)
+            self.x[:], self.y[:], self.z[:] = fibonacci_spherical_sampling(n_points)
 
             # Create convenient arrays
             # to access both in cartesian and spherical coordinates
@@ -389,10 +389,7 @@ def plot(
     def plot_old(self, plot_points=False, mark_peaks=0):
         """Plot the points on the sphere with their values"""
 
-        from scipy import rand
-
         try:
-            import matplotlib.colors as colors
             import matplotlib.pyplot as plt
 
             # from mpl_toolkits.mplot3d import Axes3D

pyroomacoustics/doa/histogram.py

@@ -38,11 +38,15 @@ def __init__(self, n_bins, dim=3, enable_peak_finding=False):
 
         if self.n_dim == 3:
             self._grid = GridSphere(
-                n_points=self.n_bins, enable_peak_finding=enable_peak_finding
+                n_points=self.n_bins, precompute_neighbors=enable_peak_finding
             )
         else:
             raise NotImplementedError("Only 3D histogram has been implemented")
 
+        import pdb
+
+        pdb.set_trace()
+
         # we need to know the area of each bin
         self._voronoi = SphericalVoronoi(self._grid.cartesian.T)
         self._areas = self._voronoi.calculate_areas()
@@ -51,7 +55,7 @@ def __init__(self, n_bins, dim=3, enable_peak_finding=False):
         self._kd_tree = cKDTree(self._grid.cartesian.T)
 
         # the counter variables for every bin
-        self._bins = np.zeros(self.n_bins, dtype=np.int)
+        self._bins = np.zeros(self.n_bins, dtype=int)
 
         # the total number of points in the histogram
         self._total_count = 0

pyroomacoustics/random/tests/test_rejection.py

@@ -20,14 +20,13 @@
 
     for loc, scale in zip([[1, 1, 1]], [10, 1, 0.1]):
         print(loc, scale)
-        sampler = CardioidFamilySampler(
-            loc=loc, coeff=pra.DirectivityPattern.FIGURE_EIGHT.value
-        )
+        # Figure-of-eight
+        sampler = CardioidFamilySampler(loc=loc, p=0)
 
         points = sampler(size=10000).T  # shape (n_dim, n_points)
 
         # Create a spherical histogram
-        hist = pra.SphericalHistogram(n_bins=500)
+        hist = pra.doa.SphericalHistogram(n_bins=500)
         hist.push(points)
         hist.plot()