pycraf.antenna: fix imt2020_composite_pattern tests

bwinkel · Aug 15, 2017 · e1b58fa · e1b58fa
1 parent 678f193
commit e1b58fa
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Showing 3 changed files with 49 additions and 12 deletions.
diff --git a/CHANGES.rst b/CHANGES.rst
@@ -10,6 +10,10 @@ pycraf.pathprof
   ellipsoid. Only rectangular limits (absolute coordinates) are supported.
   This can be used to determine the area of SRTM pixels (in km^2).
 
+pycraf.antenna
+^^^^^^^^^^^^^^^
+- Add correlation-level parameter to `imt2020_composite_pattern` function.
+
 
 0.25.3 (2017-08-09)
 ====================

diff --git a/pycraf/antenna/imt.py b/pycraf/antenna/imt.py
@@ -233,8 +233,8 @@ def imt2020_composite_pattern(
         [dimless]
     N_H, N_V : int
         Horizontal/Vertical number of single antenna elements
-    rho : float, optional
-        Correlation level (see 3GPP TR 37.840, 5.4.4.1.4, default: 1)
+    rho : `~astropy.units.Quantity`, optional
+        Correlation level (see 3GPP TR 37.840, 5.4.4.1.4, default: 1) [dimless]
 
     Returns
     -------

diff --git a/pycraf/antenna/tests/test_antenna.py b/pycraf/antenna/tests/test_antenna.py
@@ -153,8 +153,8 @@ def test_imt2020_composite_pattern():
     # not working:
     # check_astro_quantities(imt.imt2020_composite_pattern, args_list)
 
-    azims = np.arange(-180, 180, 0.5)[::200] * apu.deg
-    elevs = np.arange(-90, 90, 0.5)[::100] * apu.deg
+    azims = np.linspace(-170, 170, 7) * apu.deg
+    elevs = np.linspace(-65, 65, 4) * apu.deg
 
     # BS (outdoor) according to IMT.PARAMETER table 10 (multipage!)
     G_Emax = 5 * cnv.dB
@@ -179,10 +179,13 @@ def test_imt2020_composite_pattern():
     assert_quantity_allclose(
         gains_array,
         [
-            [-3.3119858e+02, -2.3773064e+01, -1.5343370e+01, -2.4861577e+01],
-            [-3.4144141e+02, -5.9032086e+01, -3.0494818e+01, -5.8627716e+01],
-            [-3.4980321e+02, -2.6020485e+01, 7.2968710e-02, -1.4303173e+01],
-            [-3.2730426e+02, -4.1613638e+01, -3.3511436e+01, -4.1442556e+01]
+            [-26.84474397, -26.78742409, -26.78742337, -26.84474599],
+            [-38.93066406, -37.95333672, -37.95333672, -38.93066406],
+            [-29.54158595, -27.4920591, -27.4920591, -29.54159263],
+            [-7.59590143, 8.86832778, 8.86832778, -7.59590143],
+            [-29.54159263, -27.4920591, -27.4920591, -29.54158595],
+            [-38.93066406, -37.95333672, -37.95333672, -38.93066406],
+            [-26.84474599, -26.78742337, -26.78742409, -26.84474397]
             ] * cnv.dB,
         atol=1.e-2 * cnv.dB, rtol=1.e-4,
         )
@@ -202,10 +205,40 @@ def test_imt2020_composite_pattern():
     assert_quantity_allclose(
         gains_array,
         [
-            [-71.65367508, -48.39750099, -51.72422600, -53.09899139],
-            [-71.65367508, -34.74055308, -42.48623672, -23.25435181],
-            [-65.79568691, -37.13879018, -14.86999894, -23.49184427],
-            [-71.65367508, -38.27782536, -41.34740448, -44.02411652]
+            [-38.41604328, -23.76623774, -37.31241322, -40.32924271],
+            [-26.08025503, -22.76059723, -36.30677032, -27.99344826],
+            [-19.27646115, -7.04822194, -20.59439408, -21.18965438],
+            [-34.2128334, -9.48487504, -23.03104146, -36.12606049],
+            [-33.92118076, -26.211614, -39.75806176, -35.83437732],
+            [-41.83896255, -27.74344563, -41.28961372, -43.75215149],
+            [-43.43812561, -26.77295995, -40.31912613, -45.35131454]
+            ] * cnv.dB
+        )
+
+    # test rho
+    rho = 50 * apu.percent
+
+    gains_array = imt.imt2020_composite_pattern(
+        azims[:, np.newaxis], elevs[np.newaxis],
+        azim_i, elev_j,
+        G_Emax,
+        A_m, SLA_nu,
+        azim_3db, elev_3db,
+        d_H, d_V,
+        N_H, N_V,
+        rho,
+        )
+
+    assert_quantity_allclose(
+        gains_array,
+        [
+            [-27.81689191, -24.33945388, -27.76250386, -27.88482141],
+            [-25.50662625, -23.73752856, -27.70019412, -26.24376309],
+            [-17.4177759, -6.17815893, -8.33298694, -17.95386317],
+            [-10.00205684, 0.8616368, 0.66564671, -10.00499249],
+            [-19.05914643, -8.42762696, -8.46233689, -19.08447626],
+            [-27.92129159, -26.15860176, -27.9094286, -27.95279717],
+            [-27.94851589, -25.79662728, -27.88453221, -27.97042871]
             ] * cnv.dB
         )