snapshot...

modules/amv/intercompare.py

@@ -1184,12 +1184,13 @@ def analyze_best_fit_all(amvs, bfs, bin_size=200):
 def analyze_best_fit_single(amvs, bfs, bin_size=200):
     bfs = np.array(bfs)
 
-    keep = bfs[:, 4] == 0
-    keep_idxs = bfs[keep, 0]
-    keep_idxs = keep_idxs.astype(np.int32)
+    # keep = bfs[:, 4] == 0
+    # keep_idxs = bfs[keep, 0]
+    # keep_idxs = keep_idxs.astype(np.int32)
+    keep_idxs = bfs[:, 0]
 
     amv_p = amvs[keep_idxs, amv_pres_idx]
-    bf_p = bfs[keep, 3]
+    bf_p = bfs[:, 3]
     diff = amv_p - bf_p
     mad = np.average(np.abs(diff))
     bias = np.average(diff)
@@ -1206,7 +1207,7 @@ def analyze_best_fit_single(amvs, bfs, bin_size=200):
 
     amv_spd = amvs[keep_idxs, amv_spd_idx]
     amv_dir = amvs[keep_idxs, amv_dir_idx]
-    bf_spd, bf_dir = spd_dir_from_uv(bfs[keep, 1], bfs[keep, 2])
+    bf_spd, bf_dir = spd_dir_from_uv(bfs[:, 1], bfs[:, 2])
 
     diff = amv_spd * units('m/s') - bf_spd
     spd_mad = np.average(np.abs(diff))
@@ -1227,8 +1228,8 @@ def analyze_best_fit_single(amvs, bfs, bin_size=200):
     bin_dir = bin_data_by(dir_diff, amv_p, bin_ranges)
 
     amv_u, amv_v = uv_from_spd_dir(amvs[keep_idxs, amv_spd_idx], amvs[keep_idxs, amv_dir_idx])
-    u_diffs = amv_u - (bfs[keep, 1] * units('m/s'))
-    v_diffs = amv_v - (bfs[keep, 2] * units('m/s'))
+    u_diffs = amv_u - (bfs[:, 1] * units('m/s'))
+    v_diffs = amv_v - (bfs[:, 2] * units('m/s'))
 
     vd = np.sqrt(u_diffs**2 + v_diffs**2)
     vd_mean = np.mean(vd)