From ade6b7cf49441b2c1c665bbf05a091a38e8e242b Mon Sep 17 00:00:00 2001
From: Paolo Veglio <paolo.veglio@ssec.wisc.edu>
Date: Fri, 3 Feb 2023 20:42:53 +0000
Subject: [PATCH] added more tests
---
mvcm/spectral_tests.py | 106 ++++++++++++++++-------------------
tests/test_spectral_tests.py | 38 +++++++++++++
2 files changed, 85 insertions(+), 59 deletions(-)
diff --git a/mvcm/spectral_tests.py b/mvcm/spectral_tests.py
index 1599327..e237b4f 100644
--- a/mvcm/spectral_tests.py
+++ b/mvcm/spectral_tests.py
@@ -143,81 +143,74 @@ class CloudTests(object):
# return cmin, np.abs(1-test_bit)*qa_bit
return cmin, kwargs['test_bit']
- @run_if_test_exists_for_scene
+ @run_if_test_exists_for_scene('8.6-11um_Test')
def bt_diff_86_11um(self,
band: str,
cmin: np.ndarray,
- test_name: str = '8.6-11um_Test') -> np.ndarray:
+ **kwargs) -> np.ndarray:
- confidence = np.ones(self.data[band].shape)
- qa_bit = np.zeros(self.data[band].shape)
- test_bit = np.zeros(self.data[band].shape)
- threshold = self.thresholds[self.scene_name][test_name]
+ threshold = kwargs['thresholds']
if (threshold['perform'] is True and self.pixels_in_scene is True):
- qa_bit[self.scene_idx] = 1
+ kwargs['qa_bit'][self.scene_idx] = 1
print(f'Testing "{self.scene_name}"\n')
rad = self.data[band].values[self.scene_idx]
idx = np.nonzero((self.data[band].values < threshold['thr'][1]) &
(self.data[self.scene_name] == 1))
- test_bit[idx] = 1
- confidence[self.scene_idx] = conf.conf_test_new(rad, threshold['thr'])
+ kwargs['test_bit'][idx] = 1
+ kwargs['confidence'][self.scene_idx] = conf.conf_test_new(rad, threshold['thr'])
- cmin = np.fmin(cmin, confidence)
+ cmin = np.fmin(cmin, kwargs['confidence'])
- return cmin, test_bit # np.abs(1-test_bit)*qa_bit
+ return cmin, kwargs['test_bit'] # np.abs(1-test_bit)*qa_bit
- @run_if_test_exists_for_scene
+ @run_if_test_exists_for_scene('11-12um_Cirrus_Test')
def test_11_12um_diff(self,
band: str,
cmin: np.ndarray,
- test_name: str = '11-12um_Cirrus_Test') -> np.ndarray:
+ **kwargs) -> np.ndarray:
- confidence = np.ones(self.data.M15.shape)
- qa_bit = np.zeros(self.data[band].shape)
- test_bit = np.zeros(self.data[band].shape)
- threshold = self.thresholds[self.scene_name][test_name]
+ threshold = kwargs['thresholds']
if (threshold['perform'] is True and self.pixels_in_scene is True):
- qa_bit[self.scene_idx] = 1
+ kwargs['qa_bit'][self.scene_idx] = 1
thr = preproc.thresholds_11_12um(self.data, threshold, self.scene_name, self.scene_idx)
print(f'Testing "{self.scene_name}"\n')
rad = self.data[band].values[self.scene_idx]
idx = np.nonzero((rad <= thr[1, :]) &
(self.data[self.scene_name].values[self.scene_idx] == 1))
- tmp_bit = test_bit[self.scene_idx]
+ tmp_bit = kwargs['test_bit'][self.scene_idx]
tmp_bit[idx] = 1
- test_bit[self.scene_idx] = tmp_bit
- confidence[self.scene_idx] = conf.conf_test_new(rad, thr)
+ kwargs['test_bit'][self.scene_idx] = tmp_bit
+ kwargs['confidence'][self.scene_idx] = conf.conf_test_new(rad, thr)
- cmin = np.fmin(cmin, confidence)
- test_bit = test_bit.reshape(self.data[band].shape)
+ cmin = np.fmin(cmin, kwargs['confidence'])
+ kwargs['test_bit'] = kwargs['test_bit'].reshape(self.data[band].shape)
# return cmin, np.abs(1-test_bit)*qa_bit
- return cmin, test_bit
+ return cmin, kwargs['test_bit']
- @run_if_test_exists_for_scene
+ # THIS NEEDS TO BE TESTED
+ @run_if_test_exists_for_scene('11-4um_BT_Difference_Test')
def bt_difference_11_4um_test(self,
band: str,
cmin: np.ndarray,
- test_name: str = '11-4um_BT_Difference_Test') -> np.ndarray:
+ **kwargs) -> np.ndarray:
- confidence = np.ones(self.data.M15.shape)
- qa_bit = np.zeros(self.data[band].shape)
- test_bit = np.zeros(self.data[band].shape)
- threshold = self.thresholds[self.scene_name][test_name]
+ threshold = kwargs['thresholds']
if (threshold['perform'] is True and self.pixels_in_scene is True):
- qa_bit[self.scene_idx] = 1
+ kwargs['qa_bit'][self.scene_idx] = 1
print(f'Testing "{self.scene_name}"\n')
thr = preproc.bt_diff_11_4um_thresholds(self.data, threshold, self.scene_idx)
rad = self.data[band].values[self.scene_idx]
- test_bit[self.scene_idx] = 1 # THIS NEEDS TO BE CALCULATED PROPERLY
- confidence[self.scene_idx] = conf.conf_test_dble(rad, thr)
+ kwargs['test_bit'][self.scene_idx] = 1 # THIS NEEDS TO BE CALCULATED PROPERLY
+ kwargs['confidence'][self.scene_idx] = conf.conf_test_dble(rad, thr)
- cmin = np.fmin(cmin, confidence)
+ cmin = np.fmin(cmin, kwargs['confidence'])
- return cmin, test_bit
+ return cmin, kwargs['test_bit']
+ # THIS NEEDS TO BE TESTED
@run_if_test_exists_for_scene
def bt_difference_11_4um_test_land(self,
band1: str,
@@ -311,21 +304,18 @@ class CloudTests(object):
def water_vapor_cloud_test():
pass
- @run_if_test_exists_for_scene
+ @run_if_test_exists_for_scene('11um_Variability_Test')
def variability_11um_test(self,
band: str,
cmin: np.ndarray,
- test_name: str = '11um_Variability_Test') -> np.ndarray:
+ **kwargs) -> np.ndarray:
- confidence = np.ones(self.data.M15.shape)
- qa_bit = np.zeros(self.data[band].shape)
- test_bit = np.zeros(self.data[band].shape)
- threshold = self.thresholds[self.scene_name][test_name]
+ threshold = kwargs['thresholds']
var = np.zeros([self.data.M15.shape[0], self.data.M15.shape[1], 9])
radshape = self.data.M15.shape
if (threshold['perform'] is True and self.pixels_in_scene is True):
- qa_bit[self.scene_idx] = 1
+ kwargs['qa_bit'][self.scene_idx] = 1
print(f'Testing "{self.scene_name}"\n')
rad = self.data[band].values
@@ -335,35 +325,33 @@ class CloudTests(object):
var = var.sum(axis=2)
idx = np.where(var == 9)
- test_bit[idx] = 1
+ kwargs['test_bit'][idx] = 1
thr = np.array(threshold['thr'])
- confidence[idx] = conf.conf_test_new(rad[idx], thr)
+ kwargs['confidence'][idx] = conf.conf_test_new(rad[idx], thr)
- cmin = np.fmin(cmin, confidence)
+ cmin = np.fmin(cmin, kwargs['confidence'])
- return cmin, test_bit
+ return cmin, kwargs['test_bit']
- @run_if_test_exists_for_scene
+ # NEED TO CHECK WHAT'S GOING ON WITH THIS TEST
+ @run_if_test_exists_for_scene('11-4um_Oceanic_Stratus_Test')
def oceanic_stratus_11_4um_test(self,
band: str,
cmin: np.ndarray,
- test_name: str = '11-4um_Oceanic_Stratus_Test') -> np.ndarray:
+ **kwargs) -> np.ndarray:
- confidence = np.ones(self.data.M15.shape)
- qa_bit = np.zeros(self.data[band].shape)
- test_bit = np.zeros(self.data[band].shape)
- threshold = self.thresholds[self.scene_name][test_name]
+ threshold = kwargs['thresholds']
if (threshold['perform'] is True and self.pixels_in_scene is True):
- qa_bit[self.scene_idx] = 1
+ kwargs['qa_bit'][self.scene_idx] = 1
print(f'Testing "{self.scene_name}"\n')
rad = self.data[band].values[self.scene_idx]
thr = threshold['thr']
if self.scene_name in ['Land_Day_Desert', 'Land_Day_Desert_Coast', 'Polar_Day_Desert',
'Polar_Day_Desert_Coast']:
- confidence[self.scene_idx] = conf.conf_test_dble(rad, thr)
+ kwargs['confidence'][self.scene_idx] = conf.conf_test_dble(rad, thr)
- # these scenes have not been implemented yet
+ # these scenes have not been implemented yet... and it might not be necessary
# elif self.scene_name in ['Land_Night', 'Polar_Night_Land', 'Polar_Day_Snow', 'Polar_Night_Snow',
# 'Day_Snow', 'Night_Snow', 'Antarctic_Day']:
# pass
@@ -372,14 +360,14 @@ class CloudTests(object):
idx = np.nonzero((self.data[band].values >= threshold['thr'][1]) &
(self.data[self.scene_name] == 1) &
(scene_flag['sunglint'] == 0))
- test_bit[idx] = 1
+ kwargs['test_bit'][idx] = 1
idx = np.nonzero((self.data[self.scene_name] == 1) &
(scene_flag['sunglint'] == 0))
- confidence[idx] = conf.conf_test_new(self.data[band].values[idx], thr)
+ kwargs['confidence'][idx] = conf.conf_test_new(self.data[band].values[idx], thr)
- cmin = np.fmin(cmin, confidence)
+ cmin = np.fmin(cmin, kwargs['confidence'])
- return cmin, test_bit
+ return cmin, kwargs['test_bit']
@run_if_test_exists_for_scene
def nir_reflectance_test(self,
diff --git a/tests/test_spectral_tests.py b/tests/test_spectral_tests.py
index 74b6ccc..814f539 100644
--- a/tests/test_spectral_tests.py
+++ b/tests/test_spectral_tests.py
@@ -91,3 +91,41 @@ def test_sst_test(data, thresholds, ref_confidence):
assert np.allclose(cmin, ref_confidence.sst_confidence.values)
+def test_bt_diff_86_11um(data, thresholds, ref_confidence):
+ cmin = np.ones(data.latitude.shape)
+ for scene_name in ['Ocean_Day', 'Ocean_Night', 'Polar_Day_Ocean', 'Polar_Night_Ocean']:
+ SceneType = tst.CloudTests(data=data,
+ scene_name=scene_name,
+ thresholds=thresholds)
+
+ cmin, bit = SceneType.bt_diff_86_11um('M14-M15', cmin)
+
+ assert np.allclose(cmin, ref_confidence.diff86_11um_confidence.values)
+
+
+def test_11_12um_diff(data, thresholds, ref_confidence):
+ cmin = np.ones(data.latitude.shape)
+ for scene_name in ['Land_Day', 'Land_Day_Coast', 'Land_Day_Desert', 'Land_Day_Desert_Coast',
+ 'Ocean_Day', 'Ocean_Night', 'Polar_Day_Ocean', 'Polar_Night_Ocean',
+ 'Polar_Day_Land', 'Polar_Day_Coast', 'Polar_Day_Desert',
+ 'Polar_Day_Desert_Coast', 'Polar_Day_Snow', 'Land_Night', 'Polar_Night_Land',
+ 'Polar_Night_Snow', 'Day_Snow', 'Night_Snow']:
+ SceneType = tst.CloudTests(data=data,
+ scene_name=scene_name,
+ thresholds=thresholds)
+
+ cmin, bit = SceneType.test_11_12um_diff('M15-M16', cmin)
+
+ assert np.allclose(cmin, ref_confidence.diff11_12um_confidence.values)
+
+
+def test_variability_11um_test(data, thresholds, ref_confidence):
+ cmin = np.ones(data.latitude.shape)
+ for scene_name in ['Polar_Day_Ocean', 'Polar_Night_Ocean']:
+ SceneType = tst.CloudTests(data=data,
+ scene_name=scene_name,
+ thresholds=thresholds)
+
+ cmin, bit = SceneType.variability_11um_test('M15', cmin)
+
+ assert np.allclose(cmin, ref_confidence.var11um_confidence.values)
--
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