From c0b76a489a5ce84bd2cfedf7fec9401285b45771 Mon Sep 17 00:00:00 2001
From: Coda Phillips <cphillips@sean.ssec.wisc.edu>
Date: Mon, 11 Jul 2016 13:50:32 -0500
Subject: [PATCH] Add covariance check
---
thermal_checks.py | 21 ++++++++++++++++++++-
1 file changed, 20 insertions(+), 1 deletion(-)
diff --git a/thermal_checks.py b/thermal_checks.py
index d0080fa..ce87a35 100644
--- a/thermal_checks.py
+++ b/thermal_checks.py
@@ -1,7 +1,26 @@
from util import BaseCheckList
+import sklearn.covariance
+import scipy.stats
+import numpy as np
def thermal_correlation_check(frame, parameters):
return frame
+def hbb_covariance_check(frame, parameters):
+ hbbb_mean = parameters.get('HBBbottomTemp_mean', 333.101)
+ hbbt_mean = parameters.get('HBBtopTemp_mean', 332.993)
+ hbba_mean = parameters.get('HBBapexTemp_mean', 332.818)
+
+ hbb_contamination = parameters.get('hbb_contamination', .01)
+ envelope = sklearn.covariance.EllipticEnvelope(store_precision=False, contamination=hbb_contamination)
+ envelope.location_ = np.array([hbbb_mean, hbbt_mean, hbba_mean])
+ envelope.covariance_ = np.array([[ 0.0179306 , 0.0164418 , 0.01817402],
+ [ 0.0164418 , 0.01898899, 0.02326341],
+ [ 0.01817402, 0.02326341, 0.02968953]])
+ distances = envelope.mahalanobis(frame[['HBBbottomTemp','HBBtopTemp','HBBapexTemp']]) ** .5
+ frame['hbb_covariance_check'] = (distances > 6) * 1
+ return frame
+
+
class CheckList(BaseCheckList):
- checks = [ thermal_correlation_check ]
+ checks = [ thermal_correlation_check , hbb_covariance_check]
--
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