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Commit 05706f68 authored by tomrink's avatar tomrink
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...@@ -37,8 +37,6 @@ NOISE_TRAINING = False ...@@ -37,8 +37,6 @@ NOISE_TRAINING = False
NOISE_STDDEV = 0.01 NOISE_STDDEV = 0.01
DO_AUGMENT = True DO_AUGMENT = True
DO_SMOOTH = False
SIGMA = 1.0
DO_ZERO_OUT = False DO_ZERO_OUT = False
DO_ESPCN = False # Note: If True, cannot do mixed resolution input fields (Adjust accordingly below) DO_ESPCN = False # Note: If True, cannot do mixed resolution input fields (Adjust accordingly below)
...@@ -138,7 +136,7 @@ def upsample(tmp): ...@@ -138,7 +136,7 @@ def upsample(tmp):
return tmp return tmp
def upsample_nearest(grd): def upsample_mean(grd):
bsize, ylen, xlen = grd.shape bsize, ylen, xlen = grd.shape
grd = get_grid_cell_mean(grd) grd = get_grid_cell_mean(grd)
up = np.zeros(bsize, ylen, xlen) up = np.zeros(bsize, ylen, xlen)
...@@ -345,13 +343,13 @@ class SRCNN: ...@@ -345,13 +343,13 @@ class SRCNN:
tmp = input_data[:, label_idx, :, :] tmp = input_data[:, label_idx, :, :]
tmp = tmp.copy() tmp = tmp.copy()
tmp = np.where(np.isnan(tmp), 0, tmp) tmp = np.where(np.isnan(tmp), 0, tmp)
if DO_SMOOTH:
tmp = smooth_2d(tmp, sigma=SIGMA)
# tmp = median_filter_2d(tmp)
if DO_ESPCN: if DO_ESPCN:
tmp = tmp[:, slc_y_2, slc_x_2] tmp = tmp[:, slc_y_2, slc_x_2]
else: # Half res upsampled to full res: else: # Half res upsampled to full res:
tmp = upsample(tmp) # tmp = upsample(tmp)
tmp = upsample_mean(tmp)
tmp = tmp[:, slc_y, slc_x]
if label_param != 'cloud_probability': if label_param != 'cloud_probability':
tmp = normalize(tmp, label_param, mean_std_dct) tmp = normalize(tmp, label_param, mean_std_dct)
if DO_ADD_NOISE: if DO_ADD_NOISE:
......
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