diff --git a/msaexp/data/msa_sky/jw01180025001_sky.csv b/msaexp/data/msa_sky/jw01180025001_sky.csv
new file mode 100644
index 0000000..5e1be22
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01180025001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01180026001_sky.csv b/msaexp/data/msa_sky/jw01180026001_sky.csv
new file mode 100644
index 0000000..4c1be86
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01180026001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01180027001_sky.csv b/msaexp/data/msa_sky/jw01180027001_sky.csv
new file mode 100644
index 0000000..28fe64d
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01180027001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01180029001_sky.csv b/msaexp/data/msa_sky/jw01180029001_sky.csv
new file mode 100644
index 0000000..4156e64
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01180029001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01180134001_sky.csv b/msaexp/data/msa_sky/jw01180134001_sky.csv
new file mode 100644
index 0000000..a32be63
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01180134001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01180135001_sky.csv b/msaexp/data/msa_sky/jw01180135001_sky.csv
new file mode 100644
index 0000000..1411ed1
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01180135001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01181004001_sky.csv b/msaexp/data/msa_sky/jw01181004001_sky.csv
new file mode 100644
index 0000000..eb1df4f
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01181004001_sky.csv
@@ -0,0 +1,1209 @@
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diff --git a/msaexp/data/msa_sky/jw01181005001_sky.csv b/msaexp/data/msa_sky/jw01181005001_sky.csv
new file mode 100644
index 0000000..29bf176
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01181005001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01181006001_sky.csv b/msaexp/data/msa_sky/jw01181006001_sky.csv
new file mode 100644
index 0000000..35aa09a
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01181006001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01181007001_sky.csv b/msaexp/data/msa_sky/jw01181007001_sky.csv
new file mode 100644
index 0000000..b07385a
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01181007001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01181009001_sky.csv b/msaexp/data/msa_sky/jw01181009001_sky.csv
new file mode 100644
index 0000000..e1d7563
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01181009001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01181010001_sky.csv b/msaexp/data/msa_sky/jw01181010001_sky.csv
new file mode 100644
index 0000000..716c1ca
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01181010001_sky.csv
@@ -0,0 +1,1209 @@
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diff --git a/msaexp/data/msa_sky/jw01181011001_sky.csv b/msaexp/data/msa_sky/jw01181011001_sky.csv
new file mode 100644
index 0000000..6cdc5ec
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01181011001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01181098001_sky.csv b/msaexp/data/msa_sky/jw01181098001_sky.csv
new file mode 100644
index 0000000..1564782
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01181098001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01208046001_sky.csv b/msaexp/data/msa_sky/jw01208046001_sky.csv
new file mode 100644
index 0000000..b7d74b3
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01208046001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw01208047001_sky.csv b/msaexp/data/msa_sky/jw01208047001_sky.csv
new file mode 100644
index 0000000..29bd403
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01208047001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw01208048001_sky.csv b/msaexp/data/msa_sky/jw01208048001_sky.csv
new file mode 100644
index 0000000..ce97d00
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01208048001_sky.csv
@@ -0,0 +1,1207 @@
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diff --git a/msaexp/data/msa_sky/jw01208049001_sky.csv b/msaexp/data/msa_sky/jw01208049001_sky.csv
new file mode 100644
index 0000000..83117e8
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01208049001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw01208050001_sky.csv b/msaexp/data/msa_sky/jw01208050001_sky.csv
new file mode 100644
index 0000000..fb39a99
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01208050001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw01208064001_sky.csv b/msaexp/data/msa_sky/jw01208064001_sky.csv
new file mode 100644
index 0000000..6ecd904
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01208064001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01210001001_sky.csv b/msaexp/data/msa_sky/jw01210001001_sky.csv
new file mode 100644
index 0000000..4532824
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01210001001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01210001002_sky.csv b/msaexp/data/msa_sky/jw01210001002_sky.csv
new file mode 100644
index 0000000..427120e
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01210001002_sky.csv
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diff --git a/msaexp/data/msa_sky/jw01210001003_sky.csv b/msaexp/data/msa_sky/jw01210001003_sky.csv
new file mode 100644
index 0000000..b869b07
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01210001003_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01211013001_sky.csv b/msaexp/data/msa_sky/jw01211013001_sky.csv
new file mode 100644
index 0000000..91a6413
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01211013001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01211014001_sky.csv b/msaexp/data/msa_sky/jw01211014001_sky.csv
new file mode 100644
index 0000000..68c2bdb
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01211014001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01211015001_sky.csv b/msaexp/data/msa_sky/jw01211015001_sky.csv
new file mode 100644
index 0000000..604c5b5
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01211015001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01211017001_sky.csv b/msaexp/data/msa_sky/jw01211017001_sky.csv
new file mode 100644
index 0000000..953653b
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01211017001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01211018001_sky.csv b/msaexp/data/msa_sky/jw01211018001_sky.csv
new file mode 100644
index 0000000..a397c7d
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01211018001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01211019001_sky.csv b/msaexp/data/msa_sky/jw01211019001_sky.csv
new file mode 100644
index 0000000..be767b2
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01211019001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01211020001_sky.csv b/msaexp/data/msa_sky/jw01211020001_sky.csv
new file mode 100644
index 0000000..e13f150
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01211020001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01211021001_sky.csv b/msaexp/data/msa_sky/jw01211021001_sky.csv
new file mode 100644
index 0000000..1d7520d
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01211021001_sky.csv
@@ -0,0 +1,1209 @@
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diff --git a/msaexp/data/msa_sky/jw01213001001_sky.csv b/msaexp/data/msa_sky/jw01213001001_sky.csv
new file mode 100644
index 0000000..aaffff6
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01213001001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01213002001_sky.csv b/msaexp/data/msa_sky/jw01213002001_sky.csv
new file mode 100644
index 0000000..0a66296
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01213002001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01213003001_sky.csv b/msaexp/data/msa_sky/jw01213003001_sky.csv
new file mode 100644
index 0000000..fba87c4
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01213003001_sky.csv
@@ -0,0 +1,1209 @@
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diff --git a/msaexp/data/msa_sky/jw01213004001_sky.csv b/msaexp/data/msa_sky/jw01213004001_sky.csv
new file mode 100644
index 0000000..625d85c
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01213004001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw01286001001_sky.csv b/msaexp/data/msa_sky/jw01286001001_sky.csv
new file mode 100644
index 0000000..a0606be
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01286001001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01345062001_sky.csv b/msaexp/data/msa_sky/jw01345062001_sky.csv
new file mode 100644
index 0000000..7c41ab0
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01345062001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01345063001_sky.csv b/msaexp/data/msa_sky/jw01345063001_sky.csv
new file mode 100644
index 0000000..16263f0
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01345063001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01345065001_sky.csv b/msaexp/data/msa_sky/jw01345065001_sky.csv
new file mode 100644
index 0000000..cbeac55
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01345065001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01345067001_sky.csv b/msaexp/data/msa_sky/jw01345067001_sky.csv
new file mode 100644
index 0000000..55174fd
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01345067001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01345100001_sky.csv b/msaexp/data/msa_sky/jw01345100001_sky.csv
new file mode 100644
index 0000000..9722252
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01345100001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw01345102001_sky.csv b/msaexp/data/msa_sky/jw01345102001_sky.csv
new file mode 100644
index 0000000..f2693c2
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01345102001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01433021001_sky.csv b/msaexp/data/msa_sky/jw01433021001_sky.csv
new file mode 100644
index 0000000..c60c64c
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01433021001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01433023001_sky.csv b/msaexp/data/msa_sky/jw01433023001_sky.csv
new file mode 100644
index 0000000..0bc6452
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01433023001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01747004001_sky.csv b/msaexp/data/msa_sky/jw01747004001_sky.csv
new file mode 100644
index 0000000..40b02db
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01747004001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01747007001_sky.csv b/msaexp/data/msa_sky/jw01747007001_sky.csv
new file mode 100644
index 0000000..51fba0b
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01747007001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01747012001_sky.csv b/msaexp/data/msa_sky/jw01747012001_sky.csv
new file mode 100644
index 0000000..b8c4fca
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01747012001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw01747014001_sky.csv b/msaexp/data/msa_sky/jw01747014001_sky.csv
new file mode 100644
index 0000000..85c6c64
--- /dev/null
+++ b/msaexp/data/msa_sky/jw01747014001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02073006001_sky.csv b/msaexp/data/msa_sky/jw02073006001_sky.csv
new file mode 100644
index 0000000..37a8d42
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02073006001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02073008001_sky.csv b/msaexp/data/msa_sky/jw02073008001_sky.csv
new file mode 100644
index 0000000..3f797fb
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02073008001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw02198003001_sky.csv b/msaexp/data/msa_sky/jw02198003001_sky.csv
new file mode 100644
index 0000000..79e881b
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02198003001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw02198004001_sky.csv b/msaexp/data/msa_sky/jw02198004001_sky.csv
new file mode 100644
index 0000000..a79bb55
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02198004001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02282021001_sky.csv b/msaexp/data/msa_sky/jw02282021001_sky.csv
new file mode 100644
index 0000000..e584708
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02282021001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw02282022001_sky.csv b/msaexp/data/msa_sky/jw02282022001_sky.csv
new file mode 100644
index 0000000..ce0658a
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02282022001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02282023001_sky.csv b/msaexp/data/msa_sky/jw02282023001_sky.csv
new file mode 100644
index 0000000..470e103
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02282023001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02561002002_sky.csv b/msaexp/data/msa_sky/jw02561002002_sky.csv
new file mode 100644
index 0000000..c2ca00c
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02561002002_sky.csv
@@ -0,0 +1,1209 @@
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diff --git a/msaexp/data/msa_sky/jw02561002003_sky.csv b/msaexp/data/msa_sky/jw02561002003_sky.csv
new file mode 100644
index 0000000..69bc48c
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02561002003_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02561002004_sky.csv b/msaexp/data/msa_sky/jw02561002004_sky.csv
new file mode 100644
index 0000000..addde84
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02561002004_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02561002005_sky.csv b/msaexp/data/msa_sky/jw02561002005_sky.csv
new file mode 100644
index 0000000..08182c1
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02561002005_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02565002001_sky.csv b/msaexp/data/msa_sky/jw02565002001_sky.csv
new file mode 100644
index 0000000..8ac34fd
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02565002001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02565006001_sky.csv b/msaexp/data/msa_sky/jw02565006001_sky.csv
new file mode 100644
index 0000000..9d5246e
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02565006001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02565007001_sky.csv b/msaexp/data/msa_sky/jw02565007001_sky.csv
new file mode 100644
index 0000000..9e581f0
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02565007001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02565100001_sky.csv b/msaexp/data/msa_sky/jw02565100001_sky.csv
new file mode 100644
index 0000000..5f5a0b3
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02565100001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02565200001_sky.csv b/msaexp/data/msa_sky/jw02565200001_sky.csv
new file mode 100644
index 0000000..42c94a3
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02565200001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw02565300001_sky.csv b/msaexp/data/msa_sky/jw02565300001_sky.csv
new file mode 100644
index 0000000..bbcc5a5
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02565300001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw02750002001_sky.csv b/msaexp/data/msa_sky/jw02750002001_sky.csv
new file mode 100644
index 0000000..9c77c22
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02750002001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02756001001_sky.csv b/msaexp/data/msa_sky/jw02756001001_sky.csv
new file mode 100644
index 0000000..d08651d
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02756001001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw02767005001_sky.csv b/msaexp/data/msa_sky/jw02767005001_sky.csv
new file mode 100644
index 0000000..bc6ffa3
--- /dev/null
+++ b/msaexp/data/msa_sky/jw02767005001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw03215001001_sky.csv b/msaexp/data/msa_sky/jw03215001001_sky.csv
new file mode 100644
index 0000000..e69d6d8
--- /dev/null
+++ b/msaexp/data/msa_sky/jw03215001001_sky.csv
@@ -0,0 +1,1208 @@
+wave,flux
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diff --git a/msaexp/data/msa_sky/jw03215001002_sky.csv b/msaexp/data/msa_sky/jw03215001002_sky.csv
new file mode 100644
index 0000000..5d5a873
--- /dev/null
+++ b/msaexp/data/msa_sky/jw03215001002_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw03215001003_sky.csv b/msaexp/data/msa_sky/jw03215001003_sky.csv
new file mode 100644
index 0000000..6676a26
--- /dev/null
+++ b/msaexp/data/msa_sky/jw03215001003_sky.csv
@@ -0,0 +1,1209 @@
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diff --git a/msaexp/data/msa_sky/jw03215001004_sky.csv b/msaexp/data/msa_sky/jw03215001004_sky.csv
new file mode 100644
index 0000000..6f792aa
--- /dev/null
+++ b/msaexp/data/msa_sky/jw03215001004_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw03215001005_sky.csv b/msaexp/data/msa_sky/jw03215001005_sky.csv
new file mode 100644
index 0000000..50a9ac7
--- /dev/null
+++ b/msaexp/data/msa_sky/jw03215001005_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw04106006001_sky.csv b/msaexp/data/msa_sky/jw04106006001_sky.csv
new file mode 100644
index 0000000..3f08c3e
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04106006001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw04233001001_sky.csv b/msaexp/data/msa_sky/jw04233001001_sky.csv
new file mode 100644
index 0000000..2d095d3
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04233001001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw04233001002_sky.csv b/msaexp/data/msa_sky/jw04233001002_sky.csv
new file mode 100644
index 0000000..d955a97
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04233001002_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw04233001003_sky.csv b/msaexp/data/msa_sky/jw04233001003_sky.csv
new file mode 100644
index 0000000..f47444d
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04233001003_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw04233005001_sky.csv b/msaexp/data/msa_sky/jw04233005001_sky.csv
new file mode 100644
index 0000000..82c8c5c
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04233005001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw04233005002_sky.csv b/msaexp/data/msa_sky/jw04233005002_sky.csv
new file mode 100644
index 0000000..82c8c5c
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04233005002_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw04233005003_sky.csv b/msaexp/data/msa_sky/jw04233005003_sky.csv
new file mode 100644
index 0000000..82c8c5c
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04233005003_sky.csv
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diff --git a/msaexp/data/msa_sky/jw04233006001_sky.csv b/msaexp/data/msa_sky/jw04233006001_sky.csv
new file mode 100644
index 0000000..7fee292
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04233006001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw04233006002_sky.csv b/msaexp/data/msa_sky/jw04233006002_sky.csv
new file mode 100644
index 0000000..7fee292
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04233006002_sky.csv
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diff --git a/msaexp/data/msa_sky/jw04233006003_sky.csv b/msaexp/data/msa_sky/jw04233006003_sky.csv
new file mode 100644
index 0000000..9039100
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04233006003_sky.csv
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diff --git a/msaexp/data/msa_sky/jw04318100001_sky.csv b/msaexp/data/msa_sky/jw04318100001_sky.csv
new file mode 100644
index 0000000..5d02ab3
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04318100001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw04446001001_sky.csv b/msaexp/data/msa_sky/jw04446001001_sky.csv
new file mode 100644
index 0000000..ee1f5ab
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04446001001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw04557025001_sky.csv b/msaexp/data/msa_sky/jw04557025001_sky.csv
new file mode 100644
index 0000000..38e213e
--- /dev/null
+++ b/msaexp/data/msa_sky/jw04557025001_sky.csv
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diff --git a/msaexp/data/msa_sky/jw06541001001_sky.csv b/msaexp/data/msa_sky/jw06541001001_sky.csv
new file mode 100644
index 0000000..f591a2e
--- /dev/null
+++ b/msaexp/data/msa_sky/jw06541001001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/jw06585004001_sky.csv b/msaexp/data/msa_sky/jw06585004001_sky.csv
new file mode 100644
index 0000000..5d02ab3
--- /dev/null
+++ b/msaexp/data/msa_sky/jw06585004001_sky.csv
@@ -0,0 +1,1208 @@
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diff --git a/msaexp/data/msa_sky/nirspec-global-sky.ipynb b/msaexp/data/msa_sky/nirspec-global-sky.ipynb
new file mode 100644
index 0000000..9570e2a
--- /dev/null
+++ b/msaexp/data/msa_sky/nirspec-global-sky.ipynb
@@ -0,0 +1,842 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bb1e1cb2-18f7-4e61-9833-c838de26f095",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Create global sky from MSA cutouts"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5f10917e-8dc7-4dd2-8c85-59b89a0bf0d4",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "grizli:  1.11.7.dev5+ga1d229d.d20240522\n",
+      "msaexp:  0.9.dev4+g8ed8647.d20240510\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import glob\n",
+    "import astropy.io.fits as pyfits\n",
+    "\n",
+    "from tqdm import tqdm\n",
+    "import scipy.ndimage as nd\n",
+    "\n",
+    "import eazy\n",
+    "\n",
+    "import grizli\n",
+    "from grizli import utils\n",
+    "\n",
+    "import msaexp\n",
+    "import msaexp.slit_combine\n",
+    "import msaexp.utils\n",
+    "\n",
+    "print('grizli: ', grizli.__version__)\n",
+    "print('msaexp: ', msaexp.__version__)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49bb01af-9e94-4778-a707-15104c06070d",
+   "metadata": {},
+   "source": [
+    "# Exposure prefix\n",
+    "\n",
+    "The global background is determined seperately for each observation \"visit\", indicated by the exposure filename prefix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3ea28ba-3662-4f16-afb5-e163f8e55a54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# rubies-egs61\n",
+    "visit = 'jw04233006001'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efd600c0-cd66-406a-9994-8580f1bf05d9",
+   "metadata": {},
+   "source": [
+    "# Find files\n",
+    "\n",
+    "In a directory with a set of ``*phot.*fits`` 2D cutouts, try to find all of the background slitlets.\n",
+    "\n",
+    "1. Start with ``{visit}*phot*.b*fits`` shutters that should truly be background\n",
+    "2. If not enough found, try ``{visit}*phot*_m*fits`` that are usually user-specified background shutters (but sometimes these are sources)\n",
+    "3. If still not enough found, use all files ``{visit}*phot*_*fits``\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a64e7dff-ad47-45c4-aae3-436197b37631",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": [
+     "parameters"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "# Find files\n",
+    "\n",
+    "files = glob.glob(f'{visit}*phot.*.b*.fits')\n",
+    "\n",
+    "if len(files) < 5:\n",
+    "    print('Try to add \"m\" files')\n",
+    "    files += glob.glob(f'{visit}*phot.*_m*.fits')\n",
+    "    print(len(files))\n",
+    "else:\n",
+    "    print(f\"found {len(files)} \\\".b\\\" files\")\n",
+    "    \n",
+    "if len(files) < 5:\n",
+    "    print(\"add all files\")\n",
+    "    files += glob.glob(f'{visit}*phot.*_*.fits')\n",
+    "    \n",
+    "files.sort()\n",
+    "files = np.array(files)\n",
+    "# files = files[:900]\n",
+    "keys = ['.'.join(f.split('_')[:2]) + '.' + f.split('_')[3] + '.' + f.split('.')[1] for f in files]\n",
+    "\n",
+    "files.sort()\n",
+    "len(files)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ed7a83a-98db-4e7b-9bed-aa97285629c4",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# files.sort()\n",
+    "un = utils.Unique(keys)\n",
+    "print(un.N)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "556cd2ce-9d1f-403d-b4bf-22bb1d1a8f3f",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Initialize `msaexp.slit_combine.SlitGroup` objects"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6431661-35a0-447c-b980-8b80d95f8ef6",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "bobj = {}\n",
+    "\n",
+    "num_shutters = 3\n",
+    "# num_shutters = 2\n",
+    "# num_shutters = 1\n",
+    "\n",
+    "num_shutters = 0 # Any shutter combination for sky\n",
+    "\n",
+    "do_grating = ['PRISM']\n",
+    "\n",
+    "total_count = 0\n",
+    "\n",
+    "for ik, k in enumerate(un.values):\n",
+    "    # if k not in ['nrs2.110']:\n",
+    "    #     continue\n",
+    "\n",
+    "    print(f'{ik+1} / {un.N} {k}\\n\\n')\n",
+    "    \n",
+    "    # if un[k].sum() == 3:\n",
+    "    if un[k].sum() >= 0:\n",
+    "        \n",
+    "        with pyfits.open(files[un[k]][0]) as im:\n",
+    "            if (len(im[1].header['SHUTSTA']) != num_shutters) & (num_shutters > 0):\n",
+    "                print('skip', k, im[1].header['SHUTSTA'])\n",
+    "                continue\n",
+    "\n",
+    "            if im[1].header['NAXIS1'] < 200:\n",
+    "                print('skip', k, im[1].header['NAXIS1'])\n",
+    "                continue\n",
+    "\n",
+    "            if im[0].header['GRATING'] not in do_grating:\n",
+    "                print('skip', k, im[0].header['GRATING'])\n",
+    "                continue                \n",
+    "        \n",
+    "        _obj = msaexp.slit_combine.SlitGroup(files[un[k]], 'test', position_key='y_index',\n",
+    "                                            undo_pathloss=True,\n",
+    "                                            trace_with_ypos=False, \n",
+    "                                            trace_from_yoffset=True,\n",
+    "                                            stuck_min_sn=-100,\n",
+    "                                             undo_barshadow=False,\n",
+    "                                             fix_prism_norm=True,\n",
+    "                                             pad_border=2,\n",
+    "                                             flag_profile_kwargs=None,\n",
+    "                                             hot_cold_kwargs=None,\n",
+    "                                             # bad_shutter_names=[],\n",
+    "                                             sky_arrays=None, #(fixed_slit_wave, fixed_slit_sky),\n",
+    "                                            nod_offset=None)\n",
+    "\n",
+    "        # only 3-shutters\n",
+    "        if (len(_obj.info['shutter_state'][0]) != num_shutters) & (num_shutters > 0):\n",
+    "            continue\n",
+    "\n",
+    "        if len(_obj.meta['bad_shutter_names']) > 0:\n",
+    "            continue\n",
+    "\n",
+    "        # tfit = obj.get_trace_sn()\n",
+    "        # sn_value = np.nanpercentile(tfit[\"sn\"], 86)\n",
+    "        # if sn_value > 3:\n",
+    "        #     print('skip', k, 'S/N', sn_value)\n",
+    "        #     continue\n",
+    "            \n",
+    "        # Only mostly complete spectra\n",
+    "        if _obj.sh[1] > 200:\n",
+    "            bobj[k] = _obj\n",
+    "            obj = _obj\n",
+    "            total_count += _obj.N\n",
+    "            # break\n",
+    "\n",
+    "        if total_count > 500:\n",
+    "            break\n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6b80a3e4-593b-473b-83e4-0ab6ef40cd41",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "# Compute the average sky across all shutters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "46570a68-a065-4f95-bf3f-23656757cbd6",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "fixed_slit_wave = np.arange(5000, 5.5e4) / 1.e4\n",
+    "fixed_slit_sky = fixed_slit_wave**0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "be29f3df-1bb7-4c98-91a6-1fcb0a2be45e",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from tqdm import tqdm\n",
+    "import scipy.ndimage as nd\n",
+    "import msaexp.utils as msautils\n",
+    "\n",
+    "fig, ax = plt.subplots(1,1,figsize=(8, 5))\n",
+    "\n",
+    "ydata = []\n",
+    "fdata = []\n",
+    "wdata = []\n",
+    "edata = []\n",
+    "rndata = []\n",
+    "\n",
+    "for k in tqdm(bobj):\n",
+    "    obj = bobj[k]\n",
+    "    if len(obj.meta[\"bad_shutter_names\"]) > 0:\n",
+    "        continue\n",
+    "\n",
+    "    # Just compute average from NRS2\n",
+    "    if 'xnrs1' in k:\n",
+    "        continue\n",
+    "    \n",
+    "    sn = obj.sci*1\n",
+    "    sne = np.sqrt(obj.var_total)\n",
+    "    rn = np.sqrt(obj.var_rnoise)\n",
+    "\n",
+    "    shutter_y = obj.fixed_yshutter\n",
+    "    \n",
+    "    y_shutter_index = (obj.yslit + 1)/5\n",
+    "\n",
+    "    oky = np.isfinite(shutter_y + obj.bar + sn) & (sn > -0.1) & (sn < 1)\n",
+    "\n",
+    "    shutter_mid = obj.bar > 0.95\n",
+    "    shutter_mid &= np.abs(y_shutter_index - np.round(y_shutter_index)) < 0.2\n",
+    "    shutter_mid &= np.isfinite(obj.wave + obj.sci)\n",
+    "\n",
+    "    if (obj.source_ypixel_position != 0.) & (len(obj.info['shutter_state'][0]) > 1):\n",
+    "        shutter_mid &= np.abs(obj.yslit - obj.source_ypixel_position) > 5\n",
+    "        \n",
+    "    oky &= shutter_mid\n",
+    "    # print(oky.sum())\n",
+    "    \n",
+    "    wsub = oky & (np.abs(obj.wave - 1.7) < 10.3) & np.isfinite(sn)\n",
+    "    if wsub.sum() < 256:\n",
+    "        continue\n",
+    "        \n",
+    "    # ax.scatter(obj.wave[oky], sn[oky], alpha=0.1)\n",
+    "    \n",
+    "    ydata.append(shutter_y[oky])\n",
+    "    wdata.append(obj.wave[oky])\n",
+    "    fdata.append(sn[oky])\n",
+    "    edata.append(sne[oky])\n",
+    "    rndata.append(rn[oky])\n",
+    "\n",
+    "if 0:\n",
+    "    # Scale\n",
+    "    N = len(wdata)\n",
+    "    rescale = np.zeros(N)\n",
+    "    \n",
+    "    for i in range(N):\n",
+    "        wsub = np.abs(wdata[i] - 1.7) < 0.3\n",
+    "        rescale[i] = np.nanmedian(fdata[i][wsub])\n",
+    "    \n",
+    "    rescale /= np.median(rescale)\n",
+    "    for i in range(N):\n",
+    "        fdata[i] /= rescale[i]\n",
+    "        edata[i] /= rescale[i]\n",
+    "        rndata[i] /= rescale[i]\n",
+    "\n",
+    "    keep = np.where((rescale > 0.9) & (rescale < 1.1))[0]\n",
+    "else:\n",
+    "    keep = np.arange(len(wdata), dtype=int)\n",
+    "    \n",
+    "ydata =  np.hstack([ydata[i] for i in keep])\n",
+    "wdata =  np.hstack([wdata[i] for i in keep])\n",
+    "fdata =  np.hstack([fdata[i] for i in keep])\n",
+    "edata =  np.hstack([edata[i] for i in keep])\n",
+    "rndata = np.hstack([rndata[i] for i in keep])\n",
+    "\n",
+    "so = np.argsort(wdata)\n",
+    "\n",
+    "sky_wave, sky_flux = wdata[so], nd.median_filter(fdata[so], 60)\n",
+    "okx = np.abs(fdata[so] - sky_flux) < 2*edata[so]\n",
+    "sky_wave, sky_flux = wdata[so][okx], nd.gaussian_filter(fdata[so][okx], 60)\n",
+    "\n",
+    "# Fit cubic splines to sky\n",
+    "sky_df = 81\n",
+    "\n",
+    "# sky_df = 31\n",
+    "\n",
+    "if 0:\n",
+    "    wrange = (0.6, 5.4)\n",
+    "    bspl = utils.bspline_templates(wdata, df=sky_df, minmax=wrange, get_matrix=True)\n",
+    "    splx = np.linspace(*wrange, 1024)\n",
+    "    wgrid = splx\n",
+    "    bsplm = utils.bspline_templates(splx, df=sky_df, minmax=wrange, get_matrix=True)\n",
+    "else:\n",
+    "    \n",
+    "    wgrid = msautils.get_standard_wavelength_grid('PRISM', sample=3.0)\n",
+    "    ngrid = len(wgrid)\n",
+    "    splx = np.arange(ngrid)/ngrid\n",
+    "    xdata = np.interp(wdata, wgrid, splx)\n",
+    "    wrange = (0,1)\n",
+    "    \n",
+    "    bspl = utils.bspline_templates(xdata, df=sky_df, minmax=wrange, get_matrix=True)\n",
+    "    bsplm = utils.bspline_templates(splx, df=sky_df, minmax=wrange, get_matrix=True)\n",
+    "\n",
+    "    if 1:\n",
+    "        full_interp = np.interp(wdata, fixed_slit_wave, fixed_slit_sky, left=np.nan, right=np.nan)\n",
+    "        okx &= np.isfinite(full_interp[so])\n",
+    "        bspl = (bspl.T * full_interp).T\n",
+    "        bsplm = (bsplm.T * np.interp(wgrid, fixed_slit_wave, fixed_slit_sky)).T\n",
+    "\n",
+    "_csky = np.linalg.lstsq((bspl.T/rndata).T[so[okx],:], (fdata/rndata)[so][okx], rcond=None)\n",
+    "sky0 = bspl.dot(_csky[0])\n",
+    "print('iter0', okx.sum())\n",
+    "okx = np.abs(fdata[so] - sky0[so]) < 3*edata[so]\n",
+    "print('iter1', okx.sum())\n",
+    "_csky = np.linalg.lstsq((bspl.T/rndata).T[so[okx],:], (fdata/rndata)[so][okx], rcond=None)\n",
+    "sky1 = bspl.dot(_csky[0])\n",
+    "\n",
+    "ax.scatter(wdata[so], (fdata[so] - sky1[so]) / edata[so], color='k', alpha=0.01)\n",
+    "\n",
+    "sply = bsplm.dot(_csky[0])\n",
+    "\n",
+    "ax.plot(wgrid, sply*0, color='magenta')\n",
+    "ax.set_xlabel('wavelength')\n",
+    "# ax.set_ylabel('sky flux density, uJy')\n",
+    "\n",
+    "sply[(wgrid < wdata.min()) | (wgrid > wdata.max())] = np.nan\n",
+    "\n",
+    "average_sky_wave = wgrid\n",
+    "average_sky_flux = sply*1.\n",
+    "\n",
+    "ax.set_ylim(-5, 5)\n",
+    "ax.grid()\n",
+    "\n",
+    "# plt.ylim(-0.1, 0.6)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d93ecd81-755d-4009-917a-63ea418c9f9e",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "source": [
+    "## Fit the background components\n",
+    "\n",
+    "1. Sun spectrum for zodi, modified with spline templates\n",
+    "2. HeI-1083 emission line for solar system\n",
+    "3. Flexible splines for diffuse galactic\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ced50a1b-6f38-4bee-a766-9c17b1a0d2e0",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "poly = utils.polynomial_templates(xdata, ref_wave=0.5, order=3)\n",
+    "spl_poly = np.array([poly[k].flux for k in poly])\n",
+    "spl_poly.shape\n",
+    "\n",
+    "# utils.cheb_templates?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a63dda20-da96-46ae-b907-9a4e006d7f20",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from msaexp.resample_numba import resample_template_numba as resample_func\n",
+    "import msaexp\n",
+    "from grizli import utils\n",
+    "\n",
+    "sun = utils.read_catalog('https://archive.stsci.edu/hlsps/reference-atlases/cdbs/current_calspec/sun_mod_001.fits')\n",
+    "sun['wave'] = sun['WAVELENGTH'] / 1.e4\n",
+    "sun = sun[(sun['wave'] > 0.5) & (sun['wave'] < 5.5)]\n",
+    "\n",
+    "sun['fnu'] = sun['FLUX'] / 3.e18 * sun['WAVELENGTH']**2 * 1.e8\n",
+    "\n",
+    "dl = 100./3.e5*1.083\n",
+    "sun['he_flam'] = 1./np.sqrt(2*np.pi*dl**2)*np.exp(-(sun['wave']-1.083)**2/2/dl**2)\n",
+    "sun['line He1083'] = sun['he_flam'] / (sun['wave']/1.083)**2\n",
+    "\n",
+    "pah = utils.pah33(sun['wave']*1.e4)\n",
+    "for k in pah:\n",
+    "    sun[k] = pah[k].flux_fnu\n",
+    "\n",
+    "df_sun = 5\n",
+    "df = 81\n",
+    "\n",
+    "spl_func, spl_kws, df_key, spl_off = utils.bspline_templates, dict(minmax=(0,1), get_matrix=True), 'df', 0.\n",
+    "# spl_func, spl_kws, df_key, spl_off = utils.cheb_templates, dict(minmax=(0, 1), get_matrix=True), 'order', 0. #-0.5\n",
+    "\n",
+    "spl_cont = spl_func(xdata + spl_off, **spl_kws, **{df_key: df})\n",
+    "spl_sun = spl_func(xdata + spl_off, **spl_kws, **{df_key: df_sun})\n",
+    "\n",
+    "########\n",
+    "scale_disp = 1.3\n",
+    "\n",
+    "g = 'PRISM'\n",
+    "\n",
+    "_data_path = os.path.dirname(msaexp.__file__)\n",
+    "disp = utils.read_catalog(f'{_data_path}/data/jwst_nirspec_{g}_disp.fits'.lower())\n",
+    "\n",
+    "scale_he = 1.\n",
+    "\n",
+    "spec_R_fwhm = np.interp(wdata, disp['WAVELENGTH'],\n",
+    "                        disp['R'],\n",
+    "                        left=disp['R'][0], right=disp['R'][-1])\n",
+    "\n",
+    "sun_full = wdata*0.\n",
+    "\n",
+    "sun_full[so] = resample_func(wdata[so].astype(float),\n",
+    "                        spec_R_fwhm[so]*scale_disp,\n",
+    "                        sun['wave'],\n",
+    "                        sun['fnu'],\n",
+    "                        velocity_sigma=0,\n",
+    "                        nsig=5)\n",
+    "\n",
+    "spl_arrays = [(spl_sun.T*sun_full).T, spl_cont]\n",
+    "\n",
+    "for c in sun.colnames:\n",
+    "    if c.startswith('line '):\n",
+    "        print(c)\n",
+    "        line_full = wdata*0.\n",
+    "        line_full[so] = resample_func(wdata[so].astype(float),\n",
+    "                            spec_R_fwhm[so]*scale_disp,\n",
+    "                            sun['wave'],\n",
+    "                            sun[c],\n",
+    "                            velocity_sigma=0,\n",
+    "                            nsig=5)*scale_he\n",
+    "    \n",
+    "        \n",
+    "        spl_arrays.append(line_full[:,None]/40.)\n",
+    "\n",
+    "spl = np.hstack(spl_arrays)\n",
+    "# spl = np.hstack([spl, sun_full[:,None]])\n",
+    "spltot = spl.sum(axis=0)\n",
+    "trim = spltot > 0 #0.01*spltot.max()\n",
+    "spl = spl[:, trim]\n",
+    "df_trim = trim.sum()\n",
+    "\n",
+    "# Fit model\n",
+    "\n",
+    "_csky = np.linalg.lstsq((spl.T/rndata).T[so[okx],:], (fdata/rndata)[so][okx], rcond=None)\n",
+    "sky0 = spl.dot(_csky[0])\n",
+    "print('iter0', okx.sum())\n",
+    "okx = np.abs(fdata[so] - sky0[so]) < 3*edata[so]\n",
+    "print('iter1', okx.sum())\n",
+    "\n",
+    "_csky = np.linalg.lstsq((spl.T/rndata).T[so[okx],:], (fdata/rndata)[so][okx], rcond=None)\n",
+    "sky1 = spl.dot(_csky[0])\n",
+    "_csky[0].shape, trim.sum()\n",
+    "\n",
+    "# spl_spec_cont = utils.bspline_templates(splx, get_matrix=True, df=df, minmax=(0,1))\n",
+    "# spl_spec_sun= utils.bspline_templates(splx, get_matrix=True, df=df_sun, minmax=(0,1))\n",
+    "\n",
+    "spl_spec_cont = spl_func(splx + spl_off, **spl_kws, **{df_key: df})\n",
+    "spl_spec_sun = spl_func(splx + spl_off, **spl_kws, **{df_key: df_sun})\n",
+    "\n",
+    "spec_R_fwhm = np.interp(wgrid, disp['WAVELENGTH'],\n",
+    "                        disp['R'],\n",
+    "                        left=disp['R'][0], right=disp['R'][-1])\n",
+    "\n",
+    "sun_spec = resample_func(wgrid.astype(float),\n",
+    "                        spec_R_fwhm*scale_disp,\n",
+    "                        sun['wave'],\n",
+    "                        sun['fnu'],\n",
+    "                        velocity_sigma=0,\n",
+    "                        nsig=5)\n",
+    "\n",
+    "spl_arrays = [(spl_spec_sun.T*sun_spec).T, spl_spec_cont]\n",
+    "\n",
+    "for c in sun.colnames:\n",
+    "    if c.startswith('line '):\n",
+    "        print(c)\n",
+    "\n",
+    "        line_spec = resample_func(wgrid.astype(float),\n",
+    "                                spec_R_fwhm*scale_disp,\n",
+    "                                sun['wave'],\n",
+    "                                sun[c],\n",
+    "                                velocity_sigma=0,\n",
+    "                                nsig=5)*scale_he\n",
+    "        \n",
+    "        spl_arrays.append(line_spec[:,None]/40.)\n",
+    "\n",
+    "spl_spec = np.hstack(spl_arrays)\n",
+    "print(spl_spec.shape)\n",
+    "spl_spec = spl_spec[:, trim]\n",
+    "print(spl_spec.shape)\n",
+    "\n",
+    "sp_model = spl_spec.dot(_csky[0])\n",
+    "sp_model[(wgrid < wdata.min()) | (wgrid > wdata.max())] = np.nan\n",
+    "\n",
+    "plt.plot(wgrid, sp_model)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2af47aa4-25c3-4268-ab76-3f93098d12c7",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "comp_sp_model = spl_spec * _csky[0]\n",
+    "comp_sp_model[(wgrid < wdata.min()) | (wgrid > wdata.max()),:] = np.nan\n",
+    "plt.plot(wgrid, sp_model)\n",
+    "_ = plt.plot(wgrid, comp_sp_model, color='tomato', alpha=0.3)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9567aeb7-94a1-4a18-bde0-2dcfc1c64c59",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "os.getcwd(), visit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9511244c-0035-4a91-a342-9ace6344cb92",
+   "metadata": {},
+   "source": [
+    "# Write output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "539d2905-9684-4c98-89aa-542cfd02e000",
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "ok = np.isfinite(sp_model)\n",
+    "tab = utils.GTable()\n",
+    "tab['wave'] = wgrid[ok]\n",
+    "tab['flux'] = sp_model[ok]\n",
+    "tab['wave'].format = '.6f'\n",
+    "tab['flux'].format = '.6f'\n",
+    "\n",
+    "outroot = os.getcwd().split('/')[-1]\n",
+    "\n",
+    "sky_file = f'{visit}_sky.csv'\n",
+    "\n",
+    "print(sky_file)\n",
+    "\n",
+    "tab.write(sky_file, overwrite=True)\n",
+    "\n",
+    "os.getcwd()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "209e978a-1da6-4a17-8918-2a457eb2ddd7",
+   "metadata": {},
+   "source": [
+    "# Compare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "e436087e-ab72-4b0b-a84d-024d53b4433c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['jw01208047001_sky.csv',\n",
+       " 'jw01345062001_sky.csv',\n",
+       " 'jw01345063001_sky.csv',\n",
+       " 'jw01345065001_sky.csv',\n",
+       " 'jw01345067001_sky.csv',\n",
+       " 'jw01345100001_sky.csv',\n",
+       " 'jw01345102001_sky.csv',\n",
+       " 'jw01433023001_sky.csv',\n",
+       " 'jw04233005001_sky.csv',\n",
+       " 'jw04233005002_sky.csv',\n",
+       " 'jw04233005003_sky.csv',\n",
+       " 'jw04233006001_sky.csv',\n",
+       " 'jw04233006002_sky.csv',\n",
+       " 'jw04233006003_sky.csv']"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "files = glob.glob('jw01345*csv') # CEERS\n",
+    "files += glob.glob('jw0423300[56]*csv') # RUBIES EGS\n",
+    "files += glob.glob('jw01208047*csv') # CANUCS\n",
+    "files += glob.glob('jw01433023001*csv') # D. Coe\n",
+    "\n",
+    "#files = glob.glob('*v3/jw*csv')\n",
+    "#files = glob.glob('unco*v3/jw*csv')\n",
+    "\n",
+    "files.sort()\n",
+    "files"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "d1c074c7-d209-420a-a4a7-86ef3f2822ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axes = plt.subplots(2,1,figsize=(8,8), sharex=True)\n",
+    "\n",
+    "spec_data = []\n",
+    "\n",
+    "for file in files:\n",
+    "    b = utils.read_catalog(file)\n",
+    "    \n",
+    "    if file.startswith('jw01345'):\n",
+    "        color = 'tomato'\n",
+    "    elif file.startswith('jw01208'):\n",
+    "        color = 'purple'\n",
+    "    elif file.startswith('jw01433'):\n",
+    "        color = 'olive'\n",
+    "    else:\n",
+    "        color = 'steelblue'\n",
+    "\n",
+    "    for i, ax in enumerate(axes):\n",
+    "        ax.plot(b['wave'], b['flux'] / np.interp(2., b['wave'], b['flux'])**i, alpha=0.2, color=color)\n",
+    "    \n",
+    "for ax in axes:\n",
+    "    ax.grid()\n",
+    "    ax.semilogy()\n",
+    "\n",
+    "ax.set_xlabel('wavelength, microns')\n",
+    "\n",
+    "axes[0].set_ylim(0.03, 1.2)\n",
+    "axes[1].set_ylim(0.3, 12)\n",
+    "\n",
+    "ax = axes[0]\n",
+    "\n",
+    "ax.plot(b['wave'], b['wave']*0+0.01, color='purple', label='CANUCS - MACS0416')\n",
+    "ax.plot(b['wave'], b['wave']*0+0.01, color='olive', label='Coe - MACSj0647')\n",
+    "ax.plot(b['wave'], b['wave']*0+0.01, color='tomato', label='CEERS - EGS')\n",
+    "ax.plot(b['wave'], b['wave']*0+0.01, color='steelblue', label='RUBIES - EGS')\n",
+    "ax.legend(loc='upper left')\n",
+    "\n",
+    "axes[0].set_ylabel('flux density, uJy / pix')\n",
+    "axes[1].set_ylabel('Normalized @ 2 um')\n",
+    "\n",
+    "axes[1].text(1.5, 0.7, 'Zodi\\n(Solar spectrum)', ha='center', va='bottom', color='0.5')\n",
+    "axes[1].text(4.8, 1.2, 'Diffuse MW', ha='center', va='bottom', color='0.5')\n",
+    "axes[1].text(1.083, 1.8, 'HeI-1083\\n(Solar System)', ha='center', va='bottom', color='0.5')\n",
+    "axes[1].plot([1.083, 1.083], [1.55, 1.7], color='0.5', alpha=0.3)\n",
+    "\n",
+    "axes[1].text(3.29, 1.0, '3.3um PAH\\n(Galactic)', ha='center', va='bottom', color='0.5')\n",
+    "axes[1].plot([3.29, 3.29], [0.8, 0.9], color='0.5', alpha=0.3)\n",
+    "\n",
+    "axes[0].set_title('NIRSpec PRISM background')\n",
+    "fig.tight_layout(pad=1)\n",
+    "\n",
+    "fig.savefig('nirspec_prism_background.png')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6de9fea8-3eda-4852-acd8-10008cc96aa2",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/msaexp/data/msa_sky/nirspec_prism_background.png b/msaexp/data/msa_sky/nirspec_prism_background.png
new file mode 100644
index 0000000..48272ab
Binary files /dev/null and b/msaexp/data/msa_sky/nirspec_prism_background.png differ
diff --git a/msaexp/slit_combine.py b/msaexp/slit_combine.py
index 15e3a59..906dc41 100644
--- a/msaexp/slit_combine.py
+++ b/msaexp/slit_combine.py
@@ -524,6 +524,8 @@ def __init__(
         fix_prism_norm=True,
         slit_hotpix_kwargs={},
         sky_arrays=None,
+        sky_file="read",
+        global_sky_df=7,
         estimate_sky_kwargs=None,
         flag_profile_kwargs=None,
         flag_trace_kwargs={},
@@ -596,6 +598,16 @@ def __init__(
         sky_arrays : array-like
             Optional sky data (in progress)
 
+        sky_file : str, None
+            - Filename of a tabulated global sky
+            - `"read"`: try to find a file provided with `msaexp` in
+              ``msaexp/data/sky_data``
+            - None: ignore
+
+        global_sky_df : int
+            If a ``sky_file`` is available and ``estimate_sky_kwargs`` are specified,
+            use this as the degrees of freedom in ``estimate_sky_kwargs``
+
         estimate_sky_kwargs : None, dict
             Arguments to pass to `~msaexp.slit_combine.SlitGroup.estimate_sky` to
             estimate sky directly from the slit data
@@ -745,6 +757,8 @@ def __init__(
             "has_sky_arrays": (sky_arrays is not None),
             "weight_type": weight_type,
             "percentile_outliers": 0,
+            "sky_file": "N/A",
+            "global_sky_df": global_sky_df,
         }
 
         # Comments on meta for header keywords
@@ -773,6 +787,8 @@ def __init__(
             "has_sky_arrays": "sky arrays specified",
             "weight_type": "Weighting scheme for 2D combination",
             "percentile_outliers": "Masked pixels from flag_percentile_outliers",
+            "sky_file": "Filename of a global sky background table",
+            "global_sky_df": "Degrees of freedom of fit with global sky",
         }
 
         self.shapes = []
@@ -795,6 +811,9 @@ def __init__(
 
         self.parse_data()
 
+        if sky_file is not None:
+            self.get_global_sky(sky_file=sky_file)
+
         if self.grating.startswith("G"):
             self.meta["diffs"] |= self.meta["grating_diffs"]
             msg = " Disperser {grating} is a grating.  diffs={diffs}"
@@ -1155,6 +1174,27 @@ def parse_metadata(self):
 
         return info
 
+    def get_global_sky(self, sky_file=None):
+        """
+        Try to read a global sky file from ``msaexp/data/msa_sky``
+        """
+        if sky_file in [None, "read"]:
+            visit = os.path.basename(self.files[0]).split("_")[0]
+            sky_file = os.path.join(
+                os.path.dirname(__file__),
+                "data",
+                "msa_sky",
+                f"{visit}_sky.csv",
+            )
+
+        if os.path.exists(sky_file):
+            sky_data = utils.read_catalog(sky_file)
+            msg = f" {'get_global_sky':<28}: {os.path.basename(sky_file)} to sky_arrays"
+            utils.log_comment(utils.LOGFILE, msg, verbose=VERBOSE_LOG)
+            self.sky_arrays = (sky_data["wave"], sky_data["flux"])
+            self.meta["sky_file"] = os.path.basename(sky_file)
+            self.meta["has_sky_arrays"] = True
+
     def calculate_slices(self):
         """
         Calculate slices to handle unequal cutout sizes
@@ -1730,12 +1770,17 @@ def estimate_sky(
         nbin = sky_wave.shape[0]
         xbin = np.interp(self.wave, sky_wave, np.arange(nbin) / nbin)
 
-        if df <= 0:  # | (self.sky_arrays is not None):
+        if self.meta["sky_file"] not in [None, "N/A"]:
+            df_use = self.meta["global_sky_df"]
+        else:
+            df_use = df
+
+        if df_use <= 0:  # | (self.sky_arrays is not None):
             spl_full = np.ones(xbin.size)[:, None]
         else:
             spl_full = utils.bspline_templates(
                 xbin.flatten(),
-                df=df,
+                df=df_use,
                 minmax=(0, 1),
                 get_matrix=True,
             )
@@ -1762,7 +1807,7 @@ def estimate_sky(
 
         AxT = (spl_full[ok_skyf, :].T / np.sqrt(self.var_rnoise[ok_sky])).T
         yx = (self.sci / np.sqrt(self.var_rnoise))[ok_sky]
-        if df < 0:  # | (self.sky_arrays is not None):
+        if df_use < 0:  # | (self.sky_arrays is not None):
             sky_coeffs = np.array([1.0])
         else:
             sky_coeffs = np.linalg.lstsq(AxT, yx, rcond=None)[0]
@@ -1775,12 +1820,12 @@ def estimate_sky(
             ok_sky &= ~sky_bad
             ok_skyf = ok_sky.flatten()
 
-            if df <= 0:
+            if df_use <= 0:
                 spl_full = np.ones(xbin.size)[:, None]
             else:
                 spl_full = utils.bspline_templates(
                     xbin.flatten(),
-                    df=df,
+                    df=df_use,
                     minmax=(0, 1),
                     get_matrix=True,
                 )
@@ -1793,12 +1838,12 @@ def estimate_sky(
 
             AxT = (spl_full[ok_skyf, :].T / np.sqrt(self.var_rnoise[ok_sky])).T
             yx = (self.sci / np.sqrt(self.var_rnoise))[ok_sky]
-            if df < 0:
+            if df_use < 0:
                 sky_coeffs = np.array([1.0])
             else:
                 sky_coeffs = np.linalg.lstsq(AxT, yx, rcond=None)[0]
 
-        if df <= 0:
+        if df_use <= 0:
             spl_array = np.ones(nbin)[:, None]
             sky_covar = np.array([1.0])
         else:
@@ -1806,7 +1851,7 @@ def estimate_sky(
 
             spl_array = utils.bspline_templates(
                 np.arange(nbin) / nbin,
-                df=df,
+                df=df_use,
                 minmax=(0, 1),
                 get_matrix=True,
             )
@@ -1828,7 +1873,7 @@ def estimate_sky(
             "sky_coeffs": sky_coeffs,
             "sky_covar": sky_covar,
             "spl_trim": spl_trim,
-            "df": df,
+            "df": df_use,
             "sky2d": sky2d,
             "use": use,
             "npix": ok_sky.sum(),
diff --git a/msaexp/tests/test_combine.py b/msaexp/tests/test_combine.py
index ef11e60..f359554 100644
--- a/msaexp/tests/test_combine.py
+++ b/msaexp/tests/test_combine.py
@@ -12,6 +12,7 @@
 
 TARGETS = ["1345_933"]
 
+
 def data_path():
     return os.path.join(os.path.dirname(__file__), "data")
 
@@ -113,6 +114,7 @@ def test_slit_group():
         bar_corr_mode="wave",
         fix_prism_norm=True,
         sky_arrays=None,
+        sky_file=None,
         estimate_sky_kwargs=None,
         flag_profile_kwargs={},
         flag_percentile_kwargs={},
@@ -234,6 +236,7 @@ def test_slit_group():
 
     assert np.nanmin(obj.bar[obj.mask]) > default_kwargs["min_bar"]
 
+    # These are the same if there is no sky model
     assert np.allclose(obj.data[obj.mask], obj.sci[obj.mask])
 
     assert np.allclose(np.nanmin(obj.fixed_yshutter), -1.13236, rtol=1.0e4)
@@ -308,6 +311,10 @@ def test_slit_group():
         make_plot=False,
     )
 
+    # Read global sky
+    obj.get_global_sky(sky_file="read")
+    assert obj.meta["sky_file"] == "jw01345062001_sky.csv"
+
     obj.apply_normalization_correction()
 
     obj.meta["bar_corr_mode"] = "slit"
diff --git a/setup.cfg b/setup.cfg
index d21f241..ec2748d 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -53,4 +53,5 @@ docs =
 
 [options.package_data]
 msaexp.data = 
-    *
\ No newline at end of file
+    *
+    msa_sky/*
\ No newline at end of file