snapshot...

modules/deeplearning/cnn_cld_frac_mod_res.py

@@ -902,84 +902,6 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
         return out_sr, hr_grd_a, hr_grd_b, hr_grd_c
 
 
-def analyze(file='/Users/tomrink/cld_opd_out.npy'):
-    # Save this:
-    # nn.test_data_files = glob.glob('/Users/tomrink/data/clavrx_opd_valid_DAY/data_valid*.npy')
-    # idxs = np.arange(50)
-    # dat, lbl = nn.get_in_mem_data_batch(idxs, False)
-    # tmp = dat[:, 1:128, 1:128, 1]
-    # tmp = dat[:, 1:129, 1:129, 1]
-
-    tup = np.load(file, allow_pickle=True)
-    lbls = tup[0]
-    pred = tup[1]
-
-    lbls = lbls[:, :, :, 0]
-    pred = pred[:, :, :, 0]
-    print('Total num pixels: ', lbls.size)
-
-    pred = pred.flatten()
-    pred = np.where(pred < 0.0, 0.0, pred)
-    lbls = lbls.flatten()
-    diff = pred - lbls
-
-    mae = (np.sum(np.abs(diff))) / diff.size
-    print('MAE: ', mae)
-
-    bin_edges = []
-    bin_ranges = []
-
-    bin_ranges.append([0.0, 5.0])
-    bin_edges.append(0.0)
-
-    bin_ranges.append([5.0, 10.0])
-    bin_edges.append(5.0)
-
-    bin_ranges.append([10.0, 15.0])
-    bin_edges.append(10.0)
-
-    bin_ranges.append([15.0, 20.0])
-    bin_edges.append(15.0)
-
-    bin_ranges.append([20.0, 30.0])
-    bin_edges.append(20.0)
-
-    bin_ranges.append([30.0, 40.0])
-    bin_edges.append(30.0)
-
-    bin_ranges.append([40.0, 60.0])
-    bin_edges.append(40.0)
-
-    bin_ranges.append([60.0, 80.0])
-    bin_edges.append(60.0)
-
-    bin_ranges.append([80.0, 100.0])
-    bin_edges.append(80.0)
-
-    bin_ranges.append([100.0, 120.0])
-    bin_edges.append(100.0)
-
-    bin_ranges.append([120.0, 140.0])
-    bin_edges.append(120.0)
-
-    bin_ranges.append([140.0, 160.0])
-    bin_edges.append(140.0)
-
-    bin_edges.append(160.0)
-
-    diff_by_value_bins = util.util.bin_data_by(diff, lbls, bin_ranges)
-
-    values = []
-    for k in range(len(bin_ranges)):
-        diff_k = diff_by_value_bins[k]
-        mae_k = (np.sum(np.abs(diff_k)) / diff_k.size)
-        values.append(int(mae_k/bin_ranges[k][1] * 100.0))
-
-        print('MAE: ', diff_k.size, bin_ranges[k], mae_k)
-
-    return np.array(values), bin_edges
-
-
 def analyze2(nda_m, nda_i):
     n_imgs = nda_m.shape[0]
     nda_m = np.where(nda_m < 0.5, 0, 1)
@@ -1017,6 +939,39 @@ def analyze2(nda_m, nda_i):
     return cf_m, cf_i
 
 
+def helper(lbls, pred, file='/Users/tomrink/clavrx_surfrad_viirs_cld_prob_valid.npy'):
+    nda = np.load(file, allow_pickle=True)
+
+    bt = nda[:, 0, :, :]
+    refl = nda[:, 1, :, :]
+    cp = nda[:, 2, :, :]
+
+    bt = get_grid_cell_mean(bt)
+    bt = bt[:, 0:66, 0:66]
+
+    lo, hi, std, avg = get_min_max_std(refl)
+
+    cp = np.where(np.isnan(cp), 0, cp)
+    cp = get_grid_cell_mean(cp)
+    cp = np.where(np.isnan(cp), 0, cp)
+    cp = cp[:, 1:65, 1:65]
+    cp = cp.flatten()
+
+    lbls = lbls.flatten()
+    pred = pred.flatten()
+    print(lbls.shape, pred.shape, cp.shape)
+
+    cp_cm = np.zeros((5, 5))
+
+    for j in range(5):
+        for i in range(5):
+            keep = (lbls == j) & (pred == i)
+            cp_avg = np.sum(cp[keep])/ np.sum(keep)
+            cp_cm[j, i] = cp_avg
+
+    return cp_cm
+
+
 if __name__ == "__main__":
     nn = SRCNN()
     nn.run('matchup_filename')