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Commit 30d6ebd0 authored by tomrink's avatar tomrink
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......@@ -134,6 +134,13 @@ def upsample(tmp):
return tmp
def upsample_one(tmp):
tmp = tmp[slc_y_2, slc_x_2]
tmp = resample_2d_linear_one(x_2, y_2, tmp, t, s)
tmp = tmp[y_k, x_k]
return tmp
class SRCNN:
def __init__(self):
......@@ -676,11 +683,6 @@ class SRCNN:
pred = self.model([data], training=False)
self.test_probs = pred
pred = pred.numpy()
if label_param != 'cloud_probability':
pred = denormalize(pred, label_param, mean_std_dct)
return pred
def run(self, directory, ckpt_dir=None, num_data_samples=50000):
train_data_files = glob.glob(directory+'data_train_*.npy')
......@@ -718,54 +720,40 @@ def run_restore_static(directory, ckpt_dir, out_file=None):
def run_evaluate_static(in_file, out_file, ckpt_dir):
N_X = N_Y = 10
slc_x = slice(2, N_X*128 + 4)
slc_y = slice(2, N_Y*128 + 4)
slc_x_2 = slice(1, N_X*128 + 6, 2)
slc_y_2 = slice(1, N_Y*128 + 6, 2)
x_2 = np.arange(int((N_X*128)/2) + 3)
y_2 = np.arange(int((N_Y*128)/2) + 3)
t = np.arange(0, int((N_X*128)/2) + 3, 0.5)
s = np.arange(0, int((N_Y*128)/2) + 3, 0.5)
x_k = slice(1, N_X*128 + 3)
y_k = slice(1, N_Y*128 + 3)
x_128 = slice(3, N_X*128 + 3)
y_128 = slice(3, N_Y*128 + 3)
sub_y, sub_x = (N_Y * 128) + 10, (N_X * 128) + 10
y_0, x_0, = 3232 - int(sub_y/2), 3200 - int(sub_x/2)
y_0, x_0, = 3232 - int(sub_y/2), 1100 - int(sub_x/2)
h5f = h5py.File(in_file, 'r')
grd_a = get_grid_values_all(h5f, 'temp_11_0um_nom')
grd_a = get_grid_values_all(h5f, 'super/temp_11_0um')
grd_a = grd_a[y_0:y_0+sub_y, x_0:x_0+sub_x]
grd_a = np.where(np.isnan(grd_a), 0, grd_a)
hr_grd_a = grd_a.copy()
grd_a = upsample(grd_a)
grd_a = normalize(grd_a, 'temp_11_0um_nom', mean_std_dct)
grd_a = upsample_one(grd_a)
grd_a = normalize(grd_a, 'super/temp_11_0um', mean_std_dct)
hr_grd_a = hr_grd_a[y_128, x_128]
# ------------------------------------------------------
grd_b = get_grid_values_all(h5f, 'refl_0_65um_nom')
grd_b = get_grid_values_all(h5f, 'super/refl_0_65um')
grd_b = grd_b[y_0:y_0+sub_y, x_0:x_0+sub_x]
grd_b = np.where(np.isnan(grd_b), 0, grd_b)
hr_grd_b = grd_b.copy()
hr_grd_b = hr_grd_b[y_128, x_128]
# Full res:
grd_b = grd_b[:, slc_y, slc_x]
grd_b = normalize(grd_b, 'refl_0_65um_nom', mean_std_dct)
grd_b = grd_b[slc_y, slc_x]
grd_b = normalize(grd_b, 'super/refl_0_65um', mean_std_dct)
grd_c = get_grid_values_all(h5f, label_param)
grd_c = get_grid_values_all(h5f, 'super/'+label_param)
grd_c = grd_c[y_0:y_0+sub_y, x_0:x_0+sub_x]
hr_grd_c = grd_c.copy()
hr_grd_c = np.where(np.isnan(hr_grd_c), 0, grd_c)
hr_grd_c = hr_grd_c[y_128, x_128]
grd_c = np.where(np.isnan(grd_c), 0, grd_c)
grd_c = upsample(grd_c)
grd_c = upsample_one(grd_c)
if label_param != 'cloud_probability':
grd_c = normalize(grd_c, label_param, mean_std_dct)
grd_c = normalize(grd_c, 'super/'+label_param, mean_std_dct)
data = np.stack([grd_a, grd_b, grd_c], axis=2)
data = np.expand_dims(data, axis=0)
......@@ -773,7 +761,12 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
h5f.close()
nn = SRCNN()
out_sr = nn.run_evaluate(data, ckpt_dir)
nn.run_evaluate(data, ckpt_dir)
out_sr = nn.test_probs
out_sr = out_sr.numpy()
if label_param != 'cloud_probability':
out_sr = denormalize(out_sr, label_param, mean_std_dct)
if out_file is not None:
np.save(out_file, (out_sr[0, :, :, 0], hr_grd_a, hr_grd_b, hr_grd_c))
else:
......
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