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Commit 454c87bd authored by tomrink's avatar tomrink
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import glob
import tensorflow as tf
import util.util
from util.setup import logdir, modeldir, cachepath, now, ancillary_path
from util.util import EarlyStop, normalize, denormalize, resample, resample_2d_linear, resample_one,\
resample_2d_linear_one, get_grid_values_all, add_noise, smooth_2d, smooth_2d_single
......@@ -766,6 +768,82 @@ def run_evaluate_static(in_file, out_file, ckpt_dir):
return out_sr, hr_grd_a, 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]
diff = pred - lbls
mae = (np.sum(np.abs(diff))) / diff.size
print('MAE: ', mae)
bin_ranges = util.util.get_bin_ranges(0.0, 160.0, 20.0)
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.flatten(), lbls.flatten(), 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
if __name__ == "__main__":
nn = SRCNN()
nn.run('matchup_filename')
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