generate circular grid points based on the maximum order

Author: narahahn

examples/modal_beamforming_open_circular_array.py

@@ -12,10 +12,9 @@
 pol_pwd = np.linspace(0, 2*np.pi, 91, endpoint=False)  # angles for plane wave decomposition
 k = np.linspace(0.1, 20, 100)  # wavenumber vector
 r = 1  # radius of array
-M = 181  # number of microphones
 
-# get uniform grid (microphone positions) of number M
-pol, weights = micarray.modal.angular.grid_equal_polar_angle(M)
+# get uniform grid (microphone positions) of order N
+pol, weights = micarray.modal.angular.grid_equal_polar_angle(N)
 # get circular harmonics matrix for sensors
 Psi_p = micarray.modal.angular.cht_matrix(N, pol, weights)
 # get circular harmonics matrix for a source ensemble of azimuthal plane wave

examples/modal_beamforming_rigid_circular_array.py

@@ -13,10 +13,9 @@
 pol_pwd = np.linspace(0, 2*np.pi, 180, endpoint=False)  # angles for plane wave decomposition
 k = np.linspace(0.1, 20, 100)  # wavenumber vector
 r = 1  # radius of array
-M = 61 # number of microphones
 
-# get uniform grid (microphone positions) of number M
-pol, weights = micarray.modal.angular.grid_equal_polar_angle(M)
+# get uniform grid (microphone positions) of order N
+pol, weights = micarray.modal.angular.grid_equal_polar_angle(N)
 
 # pressure on the surface of a rigid cylinder for an incident plane wave
 bn = micarray.modal.radial.circular_pw(Nsf, k, r, setup='rigid')

micarray/modal/angular.py

@@ -197,15 +197,13 @@ def grid_gauss(n):
     return azi, elev, weights
 
 
-def grid_equal_polar_angle(M, phi0=0):
+def grid_equal_polar_angle(n):
     """Equi-angular sampling points on a circle.
 
     Parameters
     ----------
-    M : int
-        Number of microphones.
-    phi0 : float
-        Angular shift
+    n : int
+        Maximum order
 
     Returns
     -------
@@ -214,6 +212,7 @@ def grid_equal_polar_angle(M, phi0=0):
     weights : array_like
         Weights.
     """
-    pol = np.linspace(0, 2*np.pi, num=M, endpoint=False) + phi0
-    weights = 1/M * np.ones(M)
+    num_mic = 2*n+1
+    pol = np.linspace(0, 2*np.pi, num=num_mic, endpoint=False)
+    weights = 1/num_mic * np.ones(num_mic)
     return pol, weights