From bd773b48c15c73da214d3fcfb39184a8db59db56 Mon Sep 17 00:00:00 2001
From: tomrink <rink@ssec.wisc.edu>
Date: Fri, 1 Apr 2022 12:50:18 -0500
Subject: [PATCH] minor...
---
modules/icing/pirep_goes.py | 72 +++++++++++++++++++++++++++----------
1 file changed, 54 insertions(+), 18 deletions(-)
diff --git a/modules/icing/pirep_goes.py b/modules/icing/pirep_goes.py
index de8e00a3..0ece9432 100644
--- a/modules/icing/pirep_goes.py
+++ b/modules/icing/pirep_goes.py
@@ -1717,26 +1717,62 @@ def split_data(times):
[get_timestamp('2022-01-01_00:00'), get_timestamp('2022-01-07_23:59')],
[get_timestamp('2022-03-01_00:00'), get_timestamp('2022-03-07_23:59')]]
- test_time_idxs = []
+ keep_out = 10800 # 3 hrs
+
+ vld_time_idxs = []
+ for t_rng in time_ranges:
+ t_rng[0] -= keep_out
+ t_rng[1] += keep_out
+ tidxs = np.searchsorted(times, t_rng)
+ vld_time_idxs.append(np.arange(tidxs[0], tidxs[1], 1))
+ vld_time_idxs = np.concatenate(vld_time_idxs, axis=None)
+ # train_time_idxs = time_idxs[np.in1d(time_idxs, vld_time_idxs, invert=True)]
+
+ # Save this just in case.
+ # # Keep out
+ # out_idxs = []
+ # for k, t_rng in enumerate(time_ranges):
+ # t_a = time_ranges[k][0]
+ # t_b = time_ranges[k][1]
+ # tidxs = np.searchsorted(times, [t_a - 10800, t_a])
+ # out_idxs.append(np.arange(tidxs[0], tidxs[1], 1))
+ # tidxs = np.searchsorted(times, [t_b, t_b + 10800])
+ # out_idxs.append(np.arange(tidxs[0], tidxs[1], 1))
+ # out_idxs = np.concatenate(out_idxs, axis=None)
+ # train_time_idxs = train_time_idxs[np.in1d(train_time_idxs, out_idxs, invert=True)]
+
+ time_ranges = [[get_timestamp('2018-02-01_00:00'), get_timestamp('2018-02-05_23:59')],
+ [get_timestamp('2018-04-01_00:00'), get_timestamp('2018-04-05_23:59')],
+ [get_timestamp('2018-06-01_00:00'), get_timestamp('2018-06-05_23:59')],
+ [get_timestamp('2018-08-01_00:00'), get_timestamp('2018-08-05_23:59')],
+ [get_timestamp('2018-10-01_00:00'), get_timestamp('2018-10-05_23:59')],
+ [get_timestamp('2018-12-01_00:00'), get_timestamp('2018-12-05_23:59')],
+ [get_timestamp('2019-02-01_00:00'), get_timestamp('2019-02-05_23:59')],
+ [get_timestamp('2019-04-01_00:00'), get_timestamp('2019-04-05_23:59')],
+ [get_timestamp('2019-06-01_00:00'), get_timestamp('2019-06-05_23:59')],
+ [get_timestamp('2019-08-01_00:00'), get_timestamp('2019-08-05_23:59')],
+ [get_timestamp('2019-10-01_00:00'), get_timestamp('2019-10-05_23:59')],
+ [get_timestamp('2019-12-01_00:00'), get_timestamp('2019-12-05_23:59')],
+
+ [get_timestamp('2021-10-05_00:00'), get_timestamp('2021-10-10_23:59')],
+ [get_timestamp('2021-12-01_00:00'), get_timestamp('2021-12-05_23:59')],
+ [get_timestamp('2022-02-01_00:00'), get_timestamp('2022-02-05_23:59')],
+ [get_timestamp('2022-03-25_00:00'), get_timestamp('2022-03-30_23:59')]]
+
+ tst_time_idxs = []
for t_rng in time_ranges:
+ t_rng[0] -= keep_out
+ t_rng[1] += keep_out
tidxs = np.searchsorted(times, t_rng)
- test_time_idxs.append(np.arange(tidxs[0], tidxs[1], 1))
- test_time_idxs = np.concatenate(test_time_idxs, axis=None)
- train_time_idxs = time_idxs[np.in1d(time_idxs, test_time_idxs, invert=True)]
-
- # Keep out
- out_idxs = []
- for k, t_rng in enumerate(time_ranges):
- t_a = time_ranges[k][0]
- t_b = time_ranges[k][1]
- tidxs = np.searchsorted(times, [t_a - 10800, t_a])
- out_idxs.append(np.arange(tidxs[0], tidxs[1], 1))
- tidxs = np.searchsorted(times, [t_b, t_b + 10800])
- out_idxs.append(np.arange(tidxs[0], tidxs[1], 1))
- out_idxs = np.concatenate(out_idxs, axis=None)
- train_time_idxs = train_time_idxs[np.in1d(train_time_idxs, out_idxs, invert=True)]
-
- return train_time_idxs, test_time_idxs
+ tst_time_idxs.append(np.arange(tidxs[0], tidxs[1], 1))
+ tst_time_idxs = np.concatenate(tst_time_idxs, axis=None)
+
+ vld_tst_time_idxs = np.concatenate([vld_time_idxs, tst_time_idxs])
+ vld_tst_time_idxs = np.sort(vld_tst_time_idxs)
+
+ train_time_idxs = time_idxs[np.in1d(time_idxs, vld_tst_time_idxs, invert=True)]
+
+ return train_time_idxs, vld_time_idxs, tst_time_idxs
def normalize(data, param, mean_std_dict, add_noise=False, noise_scale=1.0, seed=None):
--
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