diff --git a/.binder/conda-lock.yml b/.binder/conda-lock.yml
index dc48d22a..c2fb1cad 100644
--- a/.binder/conda-lock.yml
+++ b/.binder/conda-lock.yml
@@ -13,10 +13,10 @@
 version: 1
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+    linux-64: b3cf4cf09d8112ad85c45a78e61926e6da8d0db12f716b0c0122e8b160e730c8
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@@ -211,15 +178,15 @@ package:
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   optional: false
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+  version: 0.7.16
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   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/noarch/alabaster-0.7.13-pyhd8ed1ab_0.conda
+    python: '>=3.9'
+  url: https://conda.anaconda.org/conda-forge/noarch/alabaster-0.7.16-pyhd8ed1ab_0.conda
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   optional: false
 - name: alabaster
@@ -315,18 +282,19 @@ package:
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   optional: false
 - name: anyio
-  version: 4.0.0
+  version: 4.3.0
   manager: conda
   platform: osx-arm64
   dependencies:
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     python: '>=3.8'
     sniffio: '>=1.1'
+    typing_extensions: '>=4.1'
     idna: '>=2.8'
-  url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.0.0-pyhd8ed1ab_0.conda
+    exceptiongroup: '>=1.0.2'
+  url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.3.0-pyhd8ed1ab_0.conda
   hash:
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   optional: false
 - name: anyio
@@ -358,15 +326,15 @@ package:
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   optional: false
 - name: appnope
-  version: 0.1.3
+  version: 0.1.4
   manager: conda
   platform: osx-arm64
   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.3-pyhd8ed1ab_0.tar.bz2
+    python: '>=3.7'
+  url: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_0.conda
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   category: main
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 - name: argon2-cffi
@@ -718,15 +686,15 @@ package:
   category: main
   optional: false
 - name: attrs
-  version: 23.1.0
+  version: 23.2.0
   manager: conda
   platform: osx-arm64
   dependencies:
     python: '>=3.7'
-  url: https://conda.anaconda.org/conda-forge/noarch/attrs-23.1.0-pyh71513ae_1.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/attrs-23.2.0-pyh71513ae_0.conda
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+  version: 0.7.20
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@@ -752,14 +720,14 @@ package:
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     aws-c-sdkutils: '>=0.1.16,<0.1.17.0a0'
     libgcc-ng: '>=12'
-  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.19-h5f1c8d9_1.conda
+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-c-auth-0.7.20-h5f1c8d9_0.conda
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   optional: false
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+  version: 0.7.20
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@@ -769,29 +737,46 @@ package:
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     aws-c-io: '>=0.14.8,<0.14.9.0a0'
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-  url: https://conda.anaconda.org/conda-forge/osx-64/aws-c-auth-0.7.19-h26f788b_1.conda
+  url: https://conda.anaconda.org/conda-forge/osx-64/aws-c-auth-0.7.20-h26f788b_0.conda
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+  category: main
+  optional: false
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+  version: 0.7.20
+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    aws-c-cal: '>=0.6.12,<0.6.13.0a0'
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+    aws-c-io: '>=0.14.8,<0.14.9.0a0'
+    aws-c-sdkutils: '>=0.1.16,<0.1.17.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-auth-0.7.20-h5cf208e_0.conda
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   optional: false
 - name: aws-c-auth
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+  version: 0.7.20
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     aws-c-common: '>=0.9.17,<0.9.18.0a0'
     aws-c-http: '>=0.8.1,<0.8.2.0a0'
-    aws-c-io: '>=0.14.7,<0.14.8.0a0'
+    aws-c-io: '>=0.14.8,<0.14.9.0a0'
     aws-c-sdkutils: '>=0.1.16,<0.1.17.0a0'
     ucrt: '>=10.0.20348.0'
     vc: '>=14.2,<15'
     vc14_runtime: '>=14.29.30139'
-  url: https://conda.anaconda.org/conda-forge/win-64/aws-c-auth-0.7.19-h4813612_0.conda
+  url: https://conda.anaconda.org/conda-forge/win-64/aws-c-auth-0.7.20-h6823eb1_0.conda
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   category: main
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     sha256: 36179a5dd367aa83621be11d9f55dc78aa527139edf71b593316a6fab05dda96
   category: main
   optional: false
+- name: aws-c-cal
+  version: 0.6.12
+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    aws-c-common: '>=0.9.17,<0.9.18.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-cal-0.6.12-h35c0bb2_0.conda
+  hash:
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+  optional: false
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     sha256: c192e223fa57bb48e0f9a28dd90d53ca3672dc2446a114be668e4e25b693a9c5
   category: main
   optional: false
+- name: aws-c-common
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+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-common-0.9.17-h03532ee_0.conda
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+  optional: false
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   category: main
   optional: false
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+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    aws-c-common: '>=0.9.17,<0.9.18.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-compression-0.2.18-h35c0bb2_4.conda
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+  optional: false
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     sha256: 793d6f796a2aabd3fbf2dabd8b2c2949999693370602f4fa566425ec77c3eadb
   category: main
   optional: false
+- name: aws-c-event-stream
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+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    libcxx: '>=16'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-event-stream-0.4.2-h7d5773a_10.conda
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+  optional: false
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   manager: conda
   platform: win-64
   dependencies:
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-    aws-c-io: '>=0.14.7,<0.14.8.0a0'
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-  url: https://conda.anaconda.org/conda-forge/win-64/aws-c-event-stream-0.4.2-h99f05a9_9.conda
+  url: https://conda.anaconda.org/conda-forge/win-64/aws-c-event-stream-0.4.2-hc6c0aac_10.conda
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     sha256: 853fade3e6c891999227de124ef846fc57571d05eeffbccd798f3605db90a693
   category: main
   optional: false
+- name: aws-c-http
+  version: 0.8.1
+  manager: conda
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+  dependencies:
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+    aws-c-io: '>=0.14.8,<0.14.9.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-http-0.8.1-h00faecf_13.conda
+  hash:
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+  optional: false
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   manager: conda
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     vc: '>=14.2,<15'
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-  url: https://conda.anaconda.org/conda-forge/win-64/aws-c-http-0.8.1-h23b3c28_12.conda
+  url: https://conda.anaconda.org/conda-forge/win-64/aws-c-http-0.8.1-hced5053_13.conda
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   optional: false
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-  version: 0.14.7
+  version: 0.14.8
+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    aws-c-cal: '>=0.6.12,<0.6.13.0a0'
+    aws-c-common: '>=0.9.17,<0.9.18.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-io-0.14.8-h6dd71cf_0.conda
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+  category: main
+  optional: false
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+  version: 0.14.8
   manager: conda
   platform: win-64
   dependencies:
@@ -1053,10 +1122,10 @@ package:
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     vc: '>=14.2,<15'
     vc14_runtime: '>=14.29.30139'
-  url: https://conda.anaconda.org/conda-forge/win-64/aws-c-io-0.14.7-hebaacdb_9.conda
+  url: https://conda.anaconda.org/conda-forge/win-64/aws-c-io-0.14.8-hebaacdb_0.conda
   hash:
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   category: main
   optional: false
+- name: aws-c-mqtt
+  version: 0.10.4
+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    aws-c-common: '>=0.9.17,<0.9.18.0a0'
+    aws-c-http: '>=0.8.1,<0.8.2.0a0'
+    aws-c-io: '>=0.14.8,<0.14.9.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-c-mqtt-0.10.4-h92d7a41_2.conda
+  hash:
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+    sha256: 81437ee8a65a445bec24df4cd584cb9e3d24f48cfa1a03e05f170fe125f96544
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+  optional: false
 - name: aws-c-mqtt
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   manager: conda
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   dependencies:
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+  url: https://conda.anaconda.org/conda-forge/win-64/aws-c-s3-0.5.8-h7a83f0e_3.conda
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   category: main
   optional: false
+- name: aws-c-sdkutils
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+    aws-c-common: '>=0.9.17,<0.9.18.0a0'
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   category: main
   optional: false
+- name: aws-checksums
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+  manager: conda
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+  dependencies:
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+    aws-c-common: '>=0.9.17,<0.9.18.0a0'
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@@ -1261,10 +1389,10 @@ package:
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     libstdcxx-ng: '>=12'
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+  url: https://conda.anaconda.org/conda-forge/linux-64/aws-crt-cpp-0.26.8-h02fd9b4_10.conda
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+    aws-c-sdkutils: '>=0.1.16,<0.1.17.0a0'
+    libcxx: '>=16'
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+  optional: false
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+  manager: conda
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@@ -1283,10 +1433,10 @@ package:
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     libcxx: '>=16'
-  url: https://conda.anaconda.org/conda-forge/osx-64/aws-crt-cpp-0.26.8-hc1534cf_9.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/aws-crt-cpp-0.26.8-h34bd0e7_10.conda
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   optional: false
 - name: aws-crt-cpp
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-  url: https://conda.anaconda.org/conda-forge/win-64/aws-crt-cpp-0.26.8-ha66fda6_8.conda
+  url: https://conda.anaconda.org/conda-forge/win-64/aws-crt-cpp-0.26.8-h4438f58_10.conda
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   category: main
   optional: false
+- name: aws-sdk-cpp
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+    openssl: '>=3.3.0,<4.0a0'
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   category: main
   optional: false
+- name: azure-core-cpp
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+  manager: conda
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+  dependencies:
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+    libcxx: '>=16'
+    openssl: '>=3.2.1,<4.0a0'
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 - name: azure-core-cpp
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   category: main
   optional: false
+- name: azure-identity-cpp
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+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    libcxx: '>=16'
+    openssl: '>=3.2.1,<4.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/azure-identity-cpp-1.6.0-hd1853d3_1.conda
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 - name: azure-identity-cpp
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   category: main
   optional: false
+- name: azure-storage-blobs-cpp
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+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    azure-storage-common-cpp: '>=12.5.0,<12.5.1.0a0'
+    libcxx: '>=16'
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   category: main
   optional: false
+- name: azure-storage-common-cpp
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+  manager: conda
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   manager: conda
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     python: '>=3.7'
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   category: main
   optional: false
-- name: backcall
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   manager: conda
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   url: https://conda.anaconda.org/conda-forge/noarch/backports-1.0-pyhd8ed1ab_3.conda
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+  url: https://conda.anaconda.org/conda-forge/noarch/backports.functools_lru_cache-2.0.0-pyhd8ed1ab_0.conda
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 - name: backports.functools_lru_cache
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   optional: false
 - name: beautifulsoup4
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+  version: 4.12.3
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   dependencies:
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     soupsieve: '>=1.2'
-  url: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.2-pyha770c72_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/beautifulsoup4-4.12.3-pyha770c72_0.conda
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   platform: osx-arm64
   dependencies:
-    libcxx: '>=15.0.7'
+    __osx: '>=11.0'
+    libcxx: '>=16'
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     zstd: '>=1.5.5,<1.6.0a0'
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/blosc-1.21.5-h9c252e8_1.conda
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 - name: bokeh
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+  version: 3.4.1
   manager: conda
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     pyyaml: '>=3.10'
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     pillow: '>=7.1.0'
     packaging: '>=16.8'
     jinja2: '>=2.9'
-    contourpy: '>=1'
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     xyzservices: '>=2021.09.1'
-  url: https://conda.anaconda.org/conda-forge/noarch/bokeh-3.3.0-pyhd8ed1ab_0.conda
+    contourpy: '>=1.2'
+  url: https://conda.anaconda.org/conda-forge/noarch/bokeh-3.4.1-pyhd8ed1ab_0.conda
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+  version: 0.7.2
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     python: '>=3.7'
-  url: https://conda.anaconda.org/conda-forge/noarch/branca-0.6.0-pyhd8ed1ab_0.tar.bz2
+    jinja2: '>=3'
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   manager: conda
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+  platform: osx-arm64
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   url: https://conda.anaconda.org/conda-forge/noarch/bs4-4.12.3-hd8ed1ab_0.conda
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-- name: bwidget
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+  platform: win-64
   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/osx-64/bwidget-1.9.14-h694c41f_1.tar.bz2
+    beautifulsoup4: '>=4.12.3,<4.12.4.0a0'
+  url: https://conda.anaconda.org/conda-forge/noarch/bs4-4.12.3-hd8ed1ab_0.conda
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   dependencies: {}
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-h93a5062_5.conda
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+  version: 1.28.1
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   dependencies: {}
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+  version: 2024.2.2
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   dependencies: {}
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/ca-certificates-2024.2.2-hf0a4a13_0.conda
   hash:
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     shapely: '>=1.7'
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   category: main
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-- name: cctools_osx-64
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   platform: osx-arm64
   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/noarch/certifi-2023.7.22-pyhd8ed1ab_0.conda
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+  version: 0.9.0
   manager: conda
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   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/noarch/cf_xarray-0.8.4-pyhd8ed1ab_0.conda
+    python: '>=3.9'
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   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/cfitsio-4.3.0-hca87796_0.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/cfitsio-4.4.0-h808cd33_1.conda
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   dependencies:
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   dependencies:
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   manager: conda
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-  category: main
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-  manager: conda
-  platform: osx-64
-  dependencies:
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-    clangxx: 18.1.5.*
-    libcxx: '>=17'
-    libllvm18: '>=18.1.5,<18.2.0a0'
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-  category: main
-  optional: false
-- name: clangxx_osx-64
-  version: 18.1.5
+- name: ckanapi
+  version: '4.8'
   manager: conda
-  platform: osx-64
+  platform: win-64
   dependencies:
-    clang_osx-64: 18.1.5
-    clangxx_impl_osx-64: 18.1.5
-  url: https://conda.anaconda.org/conda-forge/osx-64/clangxx_osx-64-18.1.5-hb91bd55_14.conda
+    requests: ''
+    setuptools: ''
+    simplejson: ''
+    docopt: ''
+    python: '>=3.7'
+    six: '>=1.9,<2.0'
+    python-slugify: '>=1.0'
+  url: https://conda.anaconda.org/conda-forge/noarch/ckanapi-4.8-pyhd8ed1ab_0.conda
   hash:
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-    sha256: aed9b4625b329f7357ed22127704776d5ed94a8deea2635c23c2df9d745a5bcb
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   category: main
   optional: false
 - name: click
@@ -3732,17 +3829,17 @@ package:
   category: main
   optional: false
 - name: cmcrameri
-  version: '1.7'
+  version: '1.9'
   manager: conda
   platform: osx-arm64
   dependencies:
     numpy: ''
     matplotlib-base: ''
     python: '>=3.6'
-  url: https://conda.anaconda.org/conda-forge/noarch/cmcrameri-1.7-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/cmcrameri-1.9-pyhd8ed1ab_0.conda
   hash:
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-    sha256: 393dbade8ac5f8569a32736bf81a4387e3d4ad83cdb7650e7aa3cf32ba9decaa
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+    sha256: ffaf08972a2f10191bcb6217dabbf826e31818ca257ce86a4a1dec6ced751274
   category: main
   optional: false
 - name: cmcrameri
@@ -3832,16 +3929,15 @@ package:
   category: main
   optional: false
 - name: colorcet
-  version: 3.0.1
+  version: 3.1.0
   manager: conda
   platform: osx-arm64
   dependencies:
-    python: '>=2.7'
-    pyct: '>=0.4.4'
-  url: https://conda.anaconda.org/conda-forge/noarch/colorcet-3.0.1-pyhd8ed1ab_0.tar.bz2
+    python: '>=3.7'
+  url: https://conda.anaconda.org/conda-forge/noarch/colorcet-3.1.0-pyhd8ed1ab_0.conda
   hash:
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-    sha256: 4e68730cc38236a736c8f1c6837c6460807cc0f0a5fd2166b3359d481e6f129f
+    md5: 4d155b600b63bc6ba89d91fab74238f8
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   category: main
   optional: false
 - name: colorcet
@@ -3883,16 +3979,16 @@ package:
   category: main
   optional: false
 - name: comm
-  version: 0.1.4
+  version: 0.2.2
   manager: conda
   platform: osx-arm64
   dependencies:
     python: '>=3.6'
     traitlets: '>=5.3'
-  url: https://conda.anaconda.org/conda-forge/noarch/comm-0.1.4-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/comm-0.2.2-pyhd8ed1ab_0.conda
   hash:
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-    sha256: 11057745946a95ee7cc4c98900a60c7362266a4cb28bc97d96cd88e3056eb701
+    md5: 948d84721b578d426294e17a02e24cbb
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   category: main
   optional: false
 - name: comm
@@ -3908,34 +4004,6 @@ package:
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   category: main
   optional: false
-- name: compiler-rt
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-  manager: conda
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-  dependencies:
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-    clang: 18.1.5.*
-    clangxx: 18.1.5.*
-    compiler-rt_osx-64: 18.1.5.*
-  url: https://conda.anaconda.org/conda-forge/osx-64/compiler-rt-18.1.5-h250337d_0.conda
-  hash:
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-    sha256: d7c9f04c096d2ba23aa0fd87d2b5ace83aad7a62ef5b765d185c235b13ba5a87
-  category: main
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-- name: compiler-rt_osx-64
-  version: 18.1.5
-  manager: conda
-  platform: osx-64
-  dependencies:
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-    clangxx: 18.1.5.*
-  url: https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-64-18.1.5-hef2a9d4_0.conda
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 - name: compliance-checker
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   manager: conda
@@ -4000,8 +4068,8 @@ package:
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     shapely: '>=1.7.1'
     requests: '>=2.2.1'
-    jinja2: '>=2.7.3'
     netcdf4: '>=1.5.7'
+    jinja2: '>=2.7.3'
     lxml: '>=3.2.1'
     owslib: '>=0.8.3'
     pendulum: '>=1.2.4'
@@ -4137,7 +4205,7 @@ package:
   category: main
   optional: false
 - name: contextily
-  version: 1.4.0
+  version: 1.6.0
   manager: conda
   platform: osx-arm64
   dependencies:
@@ -4150,10 +4218,10 @@ package:
     geopy: ''
     mercantile: ''
     python: '>=3.8'
-  url: https://conda.anaconda.org/conda-forge/noarch/contextily-1.4.0-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/contextily-1.6.0-pyhd8ed1ab_0.conda
   hash:
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-    sha256: aa467b146557712f44930eb9e73272e3bf5ad1c7dabb5b7d34ef4872ca017351
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   category: main
   optional: false
 - name: contextily
@@ -4208,18 +4276,18 @@ package:
   category: main
   optional: false
 - name: contourpy
-  version: 1.1.1
+  version: 1.2.1
   manager: conda
   platform: osx-arm64
   dependencies:
-    libcxx: '>=15.0.7'
-    numpy: '>=1.16'
-    python: '>=3.12.0rc3,<3.13.0a0'
+    libcxx: '>=16'
+    numpy: '>=1.20'
+    python: '>=3.12,<3.13.0a0'
     python_abi: 3.12.*
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.1.1-py312h389731b_1.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/contourpy-1.2.1-py312h0fef576_0.conda
   hash:
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-    sha256: 500822f322315665eb06c26861584f46d8e02f77f2f5fc4df6a7b1afdd10a9e0
+    md5: f825cced50aa6ae9f6ae158a49ecb68c
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   category: main
   optional: false
 - name: contourpy
@@ -4270,17 +4338,18 @@ package:
   category: main
   optional: false
 - name: coverage
-  version: 7.3.2
+  version: 7.5.1
   manager: conda
   platform: osx-arm64
   dependencies:
-    python: '>=3.12.0rc3,<3.13.0a0'
+    __osx: '>=11.0'
+    python: '>=3.12,<3.13.0a0'
     python_abi: 3.12.*
     tomli: ''
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/coverage-7.3.2-py312h02f2b3b_0.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/coverage-7.5.1-py312h7e5086c_0.conda
   hash:
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-    sha256: 44c9e3bd2f2e137bc9a7fb6a5b34b21dfe89b4f17edec21944d0b95cbfbe9bc5
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   category: main
   optional: false
 - name: coverage
@@ -4332,6 +4401,22 @@ package:
     sha256: bb8f56c11d289612a586722926fd9aac3ca7967ff3ef4f11d5dc430634d0132c
   category: main
   optional: false
+- name: cryptography
+  version: 42.0.7
+  manager: conda
+  platform: osx-arm64
+  dependencies:
+    __osx: '>=11.0'
+    cffi: '>=1.12'
+    openssl: '>=3.3.0,<4.0a0'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/cryptography-42.0.7-py312had01cb0_0.conda
+  hash:
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+    sha256: 6495b1cd9516172b4af3f069d1a1cef9e45bdb5f85bb557c8759d9b9c18c3f11
+  category: main
+  optional: false
 - name: cryptography
   version: 42.0.7
   manager: conda
@@ -4389,7 +4474,7 @@ package:
   category: main
   optional: false
 - name: ctd
-  version: 1.4.4
+  version: 1.4.6
   manager: conda
   platform: osx-arm64
   dependencies:
@@ -4397,14 +4482,14 @@ package:
     scipy: ''
     matplotlib-base: ''
     chardet: ''
-    python: '>=3.6'
+    python: '>=3.9'
     pandas: '>=0.24.0'
     pandas-flavor: '>=0.1.2'
     gsw: '>=3.3.0'
-  url: https://conda.anaconda.org/conda-forge/noarch/ctd-1.4.4-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/ctd-1.4.6-pyhd8ed1ab_0.conda
   hash:
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-    sha256: b828fce3881920060e319f9e4cb8164daa42a6a37f1093a69c17588d25cdb0f4
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   category: main
   optional: false
 - name: ctd
@@ -4426,41 +4511,6 @@ package:
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   category: main
   optional: false
-- name: curl
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-  manager: conda
-  platform: linux-64
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-    libcurl: 8.7.1
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-    libssh2: '>=1.11.0,<2.0a0'
-    libzlib: '>=1.2.13,<1.3.0a0'
-    openssl: '>=3.2.1,<4.0a0'
-    zstd: '>=1.5.5,<1.6.0a0'
-  url: https://conda.anaconda.org/conda-forge/linux-64/curl-8.7.1-hca28451_0.conda
-  hash:
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-    sha256: 30935854620a6d48a3b5f1b940aefb19aeb37cef02bf58cd38288bbf620fc74d
-  category: main
-  optional: false
-- name: curl
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-  manager: conda
-  platform: osx-64
-  dependencies:
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-    libcurl: 8.7.1
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-    openssl: '>=3.2.1,<4.0a0'
-    zstd: '>=1.5.5,<1.6.0a0'
-  url: https://conda.anaconda.org/conda-forge/osx-64/curl-8.7.1-h726d00d_0.conda
-  hash:
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-  category: main
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 - name: cycler
   version: 0.12.1
   manager: conda
@@ -4509,53 +4559,8 @@ package:
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   category: main
   optional: false
-- name: cython
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-  manager: conda
-  platform: linux-64
-  dependencies:
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-    libstdcxx-ng: '>=12'
-    python: '>=3.12,<3.13.0a0'
-    python_abi: 3.12.*
-  url: https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.10-py312h30efb56_0.conda
-  hash:
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-  category: main
-  optional: false
-- name: cython
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-  manager: conda
-  platform: osx-64
-  dependencies:
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-    python: '>=3.12,<3.13.0a0'
-    python_abi: 3.12.*
-  url: https://conda.anaconda.org/conda-forge/osx-64/cython-3.0.10-py312hede676d_0.conda
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-  category: main
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-- name: cython
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-  manager: conda
-  platform: win-64
-  dependencies:
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-    python_abi: 3.12.*
-    ucrt: '>=10.0.20348.0'
-    vc: '>=14.2,<15'
-    vc14_runtime: '>=14.29.30139'
-  url: https://conda.anaconda.org/conda-forge/win-64/cython-3.0.10-py312h53d5487_0.conda
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-  category: main
-  optional: false
-- name: dataclasses
-  version: '0.8'
+- name: dataclasses
+  version: '0.8'
   manager: conda
   platform: linux-64
   dependencies:
@@ -4632,17 +4637,17 @@ package:
   category: main
   optional: false
 - name: debugpy
-  version: 1.8.0
+  version: 1.8.1
   manager: conda
   platform: osx-arm64
   dependencies:
-    libcxx: '>=15.0.7'
-    python: '>=3.12.0rc3,<3.13.0a0'
+    libcxx: '>=16'
+    python: '>=3.12,<3.13.0a0'
     python_abi: 3.12.*
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.0-py312h9f69965_1.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/debugpy-1.8.1-py312h20a0b95_0.conda
   hash:
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   optional: false
 - name: debugpy
@@ -4834,15 +4839,15 @@ package:
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   optional: false
 - name: distlib
-  version: 0.3.7
+  version: 0.3.8
   manager: conda
   platform: osx-arm64
   dependencies:
     python: 2.7|>=3.6
-  url: https://conda.anaconda.org/conda-forge/noarch/distlib-0.3.7-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/distlib-0.3.8-pyhd8ed1ab_0.conda
   hash:
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 - name: distlib
@@ -4932,16 +4937,16 @@ package:
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   optional: false
 - name: docutils
-  version: 0.17.1
+  version: 0.20.1
   manager: conda
   platform: osx-arm64
   dependencies:
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+    python: '>=3.12,<3.13.0a0'
     python_abi: 3.12.*
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/docutils-0.17.1-py312h81bd7bf_4.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/docutils-0.20.1-py312h81bd7bf_3.conda
   hash:
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     sha256: 4b66e359bc706b711359b8adafa19f79c85497797b13de85a20a6d937c50677f
   category: main
   optional: false
+- name: donfig
+  version: 0.8.1.post0
+  manager: conda
+  platform: osx-arm64
+  dependencies:
+    pyyaml: ''
+    python: '>=3.8'
+  url: https://conda.anaconda.org/conda-forge/noarch/donfig-0.8.1.post0-pyhd8ed1ab_0.conda
+  hash:
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+    sha256: 4b66e359bc706b711359b8adafa19f79c85497797b13de85a20a6d937c50677f
+  category: main
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 - name: donfig
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   manager: conda
@@ -5192,20 +5210,20 @@ package:
   category: main
   optional: false
 - name: esmf
-  version: 8.4.2
+  version: 8.6.1
   manager: conda
   platform: osx-arm64
   dependencies:
-    hdf5: '>=1.14.2,<1.14.3.0a0'
-    libcxx: '>=15.0.7'
+    hdf5: '>=1.14.3,<1.14.4.0a0'
+    libcxx: '>=16'
     libgfortran: 5.*
-    libgfortran5: '>=12.3.0'
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     libnetcdf: '>=4.9.2,<4.9.3.0a0'
     netcdf-fortran: '>=4.6.1,<4.7.0a0'
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/esmf-8.4.2-nompi_h63cbe7e_3.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/esmf-8.6.1-nompi_h6fb4350_0.conda
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 - name: exceptiongroup
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+  version: 1.2.0
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   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.1.3-pyhd8ed1ab_0.conda
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+  version: 2.6.2
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/expat-2.5.0-hb7217d7_1.conda
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   category: main
   optional: false
+- name: fake-useragent
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+  dependencies:
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+  url: https://conda.anaconda.org/conda-forge/noarch/fake-useragent-1.4.0-pyhd8ed1ab_0.conda
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+  version: 3.14.0
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   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/noarch/filelock-3.13.0-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/filelock-3.14.0-pyhd8ed1ab_0.conda
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 - name: fiona
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+  version: 1.9.6
   manager: conda
   platform: osx-arm64
   dependencies:
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-    attrs: '>=17'
-    click: '>=4.0'
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     click-plugins: '>=1.0'
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/fiona-1.9.5-py312h0aea21f_0.conda
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/fiona-1.9.6-py312hd158ed5_0.conda
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 - name: flake8
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+  version: 7.0.0
   manager: conda
   platform: osx-arm64
   dependencies:
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     python: '>=3.8.1'
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-    pyflakes: '>=3.1.0,<3.2.0'
-  url: https://conda.anaconda.org/conda-forge/noarch/flake8-6.1.0-pyhd8ed1ab_0.conda
+    pyflakes: '>=3.2.0,<3.3.0'
+  url: https://conda.anaconda.org/conda-forge/noarch/flake8-7.0.0-pyhd8ed1ab_0.conda
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   category: main
   optional: false
+- name: fmt
+  version: 10.2.1
+  manager: conda
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+  dependencies:
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/fmt-10.2.1-h2ffa867_0.conda
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   optional: false
 - name: folium
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+  version: 0.16.0
   manager: conda
   platform: osx-arm64
   dependencies:
     numpy: ''
     requests: ''
-    python: '>=3.6'
+    xyzservices: ''
+    python: '>=3.7'
     jinja2: '>=2.9'
     branca: '>=0.6.0'
-  url: https://conda.anaconda.org/conda-forge/noarch/folium-0.14.0-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/folium-0.16.0-pyhd8ed1ab_0.conda
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   dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-hab24e00_0.tar.bz2
+  url: https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda
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 - name: font-ttf-ubuntu
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     font-ttf-source-code-pro: ''
-    font-ttf-dejavu-sans-mono: ''
     font-ttf-ubuntu: ''
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   url: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2
   hash:
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     python_abi: 3.12.*
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.43.1-py312he37b823_0.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/fonttools-4.51.0-py312he37b823_0.conda
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   category: main
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     python_abi: 3.12.*
-  url: https://conda.anaconda.org/conda-forge/linux-64/gdal-3.8.5-py312hca78659_2.conda
+  url: https://conda.anaconda.org/conda-forge/linux-64/gdal-3.8.5-py312hca78659_5.conda
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+    openssl: '>=3.3.0,<4.0a0'
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     python_abi: 3.12.*
-  url: https://conda.anaconda.org/conda-forge/osx-64/gdal-3.8.5-py312ha261e76_2.conda
+  url: https://conda.anaconda.org/conda-forge/osx-64/gdal-3.8.5-py312h42982b2_5.conda
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+    openssl: '>=3.3.0,<4.0a0'
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/gdal-3.7.2-py312hb8631af_7.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/gdal-3.8.5-py312h906a9e5_5.conda
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     numpy: '>=1.26.4,<2.0a0'
-    openssl: '>=3.2.1,<4.0a0'
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     ucrt: '>=10.0.20348.0'
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-  url: https://conda.anaconda.org/conda-forge/win-64/gdal-3.8.5-py312hea5013e_2.conda
+  url: https://conda.anaconda.org/conda-forge/win-64/gdal-3.8.5-py312hea5013e_5.conda
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 - name: geographiclib
-  version: '1.52'
+  version: '2.0'
   manager: conda
   platform: osx-arm64
   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/noarch/geographiclib-1.52-pyhd8ed1ab_0.tar.bz2
+    python: '>=3.7'
+  url: https://conda.anaconda.org/conda-forge/noarch/geographiclib-2.0-pyhd8ed1ab_0.tar.bz2
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 - name: geojson
-  version: 2.5.0
+  version: 3.1.0
   manager: conda
   platform: osx-arm64
   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/noarch/geojson-2.5.0-py_0.tar.bz2
+    python: '>=3.7'
+  url: https://conda.anaconda.org/conda-forge/noarch/geojson-3.1.0-pyhd8ed1ab_0.conda
   hash:
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+  version: 0.14.4
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     mapclassify: '>=2.4.0'
     fiona: '>=1.8.21'
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-  url: https://conda.anaconda.org/conda-forge/noarch/geopandas-0.14.0-pyhd8ed1ab_1.conda
+    geopandas-base: 0.14.4
+  url: https://conda.anaconda.org/conda-forge/noarch/geopandas-0.14.4-pyhd8ed1ab_0.conda
   hash:
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+  version: 0.14.4
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@@ -6776,10 +6775,10 @@ package:
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     shapely: '>=1.8.0'
     pyproj: '>=3.3.0'
-  url: https://conda.anaconda.org/conda-forge/noarch/geopandas-base-0.14.0-pyha770c72_1.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/geopandas-base-0.14.4-pyha770c72_0.conda
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   category: main
   optional: false
 - name: geopandas-base
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   optional: false
 - name: geopy
-  version: 2.4.0
+  version: 2.4.1
   manager: conda
   platform: osx-arm64
   dependencies:
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-    geographiclib: <2,>=1.49
-  url: https://conda.anaconda.org/conda-forge/noarch/geopy-2.4.0-pyhd8ed1ab_0.conda
+    python: '>=3.7'
+    geographiclib: '>=1.52'
+  url: https://conda.anaconda.org/conda-forge/noarch/geopy-2.4.1-pyhd8ed1ab_1.conda
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   category: main
   optional: false
 - name: geopy
@@ -6953,15 +6952,16 @@ package:
   category: main
   optional: false
 - name: geos
-  version: 3.12.0
+  version: 3.12.1
   manager: conda
   platform: osx-arm64
   dependencies:
-    libcxx: '>=15.0.7'
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/geos-3.12.0-h13dd4ca_0.conda
+    __osx: '>=10.9'
+    libcxx: '>=16.0.6'
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   category: main
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   platform: osx-arm64
   dependencies:
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+    libcxx: '>=16'
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     libtiff: '>=4.6.0,<4.7.0a0'
     libzlib: '>=1.2.13,<1.3.0a0'
-    proj: '>=9.3.0,<9.3.1.0a0'
+    proj: '>=9.4.0,<9.4.1.0a0'
     zlib: ''
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/geotiff-1.7.1-h71398c0_14.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/geotiff-1.7.1-h6e650f3_16.conda
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   optional: false
 - name: geotiff
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   category: main
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/gettext-0.21.1-h0186832_0.tar.bz2
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   category: main
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-- name: gfortran_osx-64
-  version: 12.3.0
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+  version: 2.2.2
   manager: conda
-  platform: osx-64
+  platform: osx-arm64
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-    clang: ''
-    clang_osx-64: ''
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-  url: https://conda.anaconda.org/conda-forge/osx-64/gfortran_osx-64-12.3.0-h18f7dce_1.conda
+    libcxx: '>=11.0.0.rc1'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/gflags-2.2.2-hc88da5d_1004.tar.bz2
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   category: main
   optional: false
 - name: giflib
@@ -7166,14 +7110,14 @@ package:
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   optional: false
 - name: giflib
-  version: 5.2.1
+  version: 5.2.2
   manager: conda
   platform: osx-arm64
   dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/giflib-5.2.1-h1a8c8d9_3.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/giflib-5.2.2-h93a5062_0.conda
   hash:
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   dependencies:
     python: '>=3.7'
-    python-dateutil: '>=2.6.0'
     requests: '>=2.18'
+    python-dateutil: '>=2.6.0'
     uritemplate: '>=3.0.0'
     pyjwt: '>=2.3.0'
   url: https://conda.anaconda.org/conda-forge/noarch/github3.py-4.0.1-pyhd8ed1ab_0.conda
@@ -7321,17 +7265,17 @@ package:
   category: main
   optional: false
 - name: gitpython
-  version: 3.1.40
+  version: 3.1.43
   manager: conda
   platform: osx-arm64
   dependencies:
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     typing_extensions: '>=3.7.4.3'
     gitdb: '>=4.0.1,<5'
-  url: https://conda.anaconda.org/conda-forge/noarch/gitpython-3.1.40-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/gitpython-3.1.43-pyhd8ed1ab_0.conda
   hash:
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   optional: false
 - name: gitpython
@@ -7383,7 +7327,23 @@ package:
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   version: 0.2.2
   manager: conda
-  platform: win-64
+  platform: osx-arm64
+  dependencies:
+    pandas: ''
+    xarray: ''
+    httpx: ''
+    erddapy: ''
+    python: '>=3.10'
+  url: https://conda.anaconda.org/conda-forge/noarch/gliderpy-0.2.2-pyhd8ed1ab_0.conda
+  hash:
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+  manager: conda
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   dependencies:
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     xarray: ''
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   category: main
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-- name: glpk
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-- name: glpk
-  version: '5.0'
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   manager: conda
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+  platform: osx-arm64
   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/osx-64/glpk-5.0-h3cb5acd_0.tar.bz2
+    gflags: '>=2.2.2,<2.3.0a0'
+    libcxx: '>=16'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/glog-0.7.0-hc6770e3_0.conda
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   category: main
   optional: false
-- name: graphite2
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+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/gmp-6.3.0-hebf3989_1.conda
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   manager: conda
   platform: linux-64
   dependencies:
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     libgcc-ng: '>=12'
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-  url: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda
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+    mpfr: '>=4.2.1,<5.0a0'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
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   hash:
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   optional: false
-- name: graphite2
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   manager: conda
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   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/osx-64/graphite2-1.3.13-h73e2aa4_1003.conda
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+    mpfr: '>=4.2.1,<5.0a0'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
+  url: https://conda.anaconda.org/conda-forge/osx-64/gmpy2-2.1.5-py312hd98c385_1.conda
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+    sha256: 23066e588d3e371c556e5439c1df3399f267c633f85d3b76c6aca9584e634398
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+  manager: conda
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+  dependencies:
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+    gmp: '>=6.3.0,<7.0a0'
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+    mpfr: '>=4.2.1,<5.0a0'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/gmpy2-2.1.5-py312hfa9fade_1.conda
   hash:
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@@ -7529,18 +7515,17 @@ package:
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   optional: false
 - name: greenlet
-  version: 3.0.1
+  version: 3.0.3
   manager: conda
   platform: osx-arm64
   dependencies:
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-    libcxx: '>=16.0.6'
+    libcxx: '>=15'
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     python_abi: 3.12.*
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/greenlet-3.0.1-py312h650e478_0.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/greenlet-3.0.3-py312h20a0b95_0.conda
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 - name: greenlet
@@ -7594,8 +7579,8 @@ package:
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   platform: osx-arm64
   dependencies:
-    shapely: ''
     netcdf4: ''
+    shapely: ''
     docopt: ''
     python: '>=3.6'
   url: https://conda.anaconda.org/conda-forge/noarch/gridgeo-1.5.2-py_0.tar.bz2
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-    libstdcxx-devel_linux-64: 13.2.0
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   category: main
   optional: false
+- name: h5netcdf
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+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    h5py: ''
+    python: '>=3.9'
+  url: https://conda.anaconda.org/conda-forge/noarch/h5netcdf-1.3.0-pyhd8ed1ab_0.conda
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   manager: conda
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   category: main
   optional: false
+- name: h5py
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+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    hdf5: '>=1.14.3,<1.14.4.0a0'
+    numpy: '>=1.26.4,<2.0a0'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/h5py-3.11.0-nompi_py312hac0f6fd_100.conda
+  hash:
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 - name: h5py
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   manager: conda
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   category: main
   optional: false
+- name: html5lib
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+    webencodings: ''
+    six: '>=1.9'
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   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/noarch/importlib_metadata-6.8.0-hd8ed1ab_0.conda
+    importlib-metadata: '>=7.1.0,<7.1.1.0a0'
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   category: main
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+  manager: conda
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+    pandas: ''
+    lxml: ''
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+    pyarrow: ''
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+    html5lib: ''
+    bs4: ''
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+    gliderpy: ''
+    ckanapi: ''
+    python: '>=3.10'
+    geopandas: '>=0.13.2'
+  url: https://conda.anaconda.org/conda-forge/noarch/ioos-metrics-0.1.0a1-pyhd8ed1ab_0.conda
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-  version: 1.0.0
+  version: 2.1.0
   manager: conda
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   dependencies:
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+    pandas: ''
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+    shapely: ''
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+    jsonschema: ''
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+    python: '>=3.7,<4.0'
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   dependencies:
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-    traittypes: '>=0.2.1,<0.3.0'
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-19_osxarm64_openblas.conda
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+- name: libre2-11
+  version: 2023.09.01
+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    libcxx: '>=16'
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+  hash:
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+    sha256: c8a0a6e7a627dc9c66ffb8858f8f6d499f67fd269b6636b25dc5169760610f05
+  category: main
+  optional: false
 - name: libre2-11
   version: 2023.09.01
   manager: conda
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   platform: osx-arm64
   dependencies:
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-    libcxx: '>=15.0.7'
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/librttopo-1.1.0-h667cd51_14.conda
+    __osx: '>=10.9'
+    geos: '>=3.12.1,<3.12.2.0a0'
+    libcxx: '>=16.0.6'
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   hash:
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   category: main
   optional: false
 - name: librttopo
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   category: main
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-  manager: conda
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-    geos: '>=3.12.0,<3.12.1.0a0'
-    libcxx: '>=15.0.7'
+    geos: '>=3.12.1,<3.12.2.0a0'
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     libzlib: '>=1.2.13,<1.3.0a0'
-    proj: '>=9.3.0,<9.3.1.0a0'
+    proj: '>=9.4.0,<9.4.1.0a0'
     sqlite: ''
     zlib: ''
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libspatialite-5.1.0-h32510b6_0.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libspatialite-5.1.0-h77c30ab_5.conda
   hash:
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   category: main
   optional: false
 - name: libspatialite
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   optional: false
 - name: libsqlite
-  version: 3.43.2
+  version: 3.45.3
   manager: conda
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   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libsqlite-3.43.2-h091b4b1_0.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libsqlite-3.45.3-h091b4b1_0.conda
   hash:
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-    sha256: 93bec176e05c15a126c1c1c50b3aaef224829b81062bfd48716c5affde950a3f
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   category: main
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-- name: libstdcxx-devel_linux-64
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-  url: https://conda.anaconda.org/conda-forge/noarch/libstdcxx-devel_linux-64-13.2.0-hceb6213_107.conda
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   category: main
   optional: false
+- name: libthrift
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+  manager: conda
+  platform: osx-arm64
+  dependencies:
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+    libevent: '>=2.1.12,<2.1.13.0a0'
+    libzlib: '>=1.2.13,<1.3.0a0'
+    openssl: '>=3.1.3,<4.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libthrift-0.19.0-h026a170_1.conda
+  hash:
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 - name: libthrift
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   manager: conda
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-    libcxx: '>=15.0.7'
-    libdeflate: '>=1.19,<1.20.0a0'
+    libcxx: '>=16'
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     libwebp-base: '>=1.3.2,<2.0a0'
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     zstd: '>=1.5.5,<1.6.0a0'
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libtiff-4.6.0-ha8a6c65_2.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libtiff-4.6.0-h07db509_3.conda
   hash:
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   category: main
   optional: false
 - name: libtiff
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     sha256: 55a7f96b2802e94def207fdfe92bc52c24d705d139bb6cdb3d936cbe85e1c505
   category: main
   optional: false
+- name: libutf8proc
+  version: 2.8.0
+  manager: conda
+  platform: osx-arm64
+  dependencies: {}
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libutf8proc-2.8.0-h1a8c8d9_0.tar.bz2
+  hash:
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+    sha256: a3faddac08efd930fa3a1cc254b5053b4ed9428c49a888d437bf084d403c931a
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+  optional: false
 - name: libutf8proc
   version: 2.8.0
   manager: conda
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   category: main
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-- name: libv8
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-    libgcc-ng: '>=12'
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-    libstdcxx-ng: '>=12'
-    libzlib: '>=1.2.13,<1.3.0a0'
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-  version: 8.9.83
-  manager: conda
-  platform: osx-64
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-    libcxx: '>=15.0.7'
-    libglib: '>=2.76.4,<3.0a0'
-    libzlib: '>=1.2.13,<1.3.0a0'
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-  category: main
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 - name: libwebp-base
   version: 1.4.0
   manager: conda
@@ -14968,14 +15039,14 @@ package:
   category: main
   optional: false
 - name: libwebp-base
-  version: 1.3.2
+  version: 1.4.0
   manager: conda
   platform: osx-arm64
   dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libwebp-base-1.3.2-hb547adb_0.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libwebp-base-1.4.0-h93a5062_0.conda
   hash:
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   category: main
   optional: false
 - name: libwebp-base
@@ -15064,7 +15135,7 @@ package:
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   optional: false
 - name: libxml2
-  version: 2.12.6
+  version: 2.12.7
   manager: conda
   platform: linux-64
   dependencies:
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     libzlib: '>=1.2.13,<1.3.0a0'
     xz: '>=5.2.6,<6.0a0'
-  url: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.6-h232c23b_2.conda
+  url: https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.12.7-hc051c1a_0.conda
   hash:
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   optional: false
 - name: libxml2
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   optional: false
 - name: libxml2
-  version: 2.11.5
+  version: 2.12.6
   manager: conda
   platform: osx-arm64
   dependencies:
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     xz: '>=5.2.6,<6.0a0'
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.11.5-h25269f3_1.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libxml2-2.12.6-h0d0cfa8_2.conda
   hash:
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   optional: false
 - name: libxml2
@@ -15151,15 +15222,15 @@ package:
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   optional: false
 - name: libxslt
-  version: 1.1.37
+  version: 1.1.39
   manager: conda
   platform: osx-arm64
   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/libxslt-1.1.37-h1728932_1.conda
+    libxml2: '>=2.12.1,<3.0.0a0'
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/libxslt-1.1.39-h223e5b9_0.conda
   hash:
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   optional: false
 - name: libxslt
@@ -15312,16 +15383,16 @@ package:
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   optional: false
 - name: linkify-it-py
-  version: 2.0.0
+  version: 2.0.3
   manager: conda
   platform: osx-arm64
   dependencies:
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-    uc-micro-py: '>=1.0.1,<2.0.0'
-  url: https://conda.anaconda.org/conda-forge/noarch/linkify-it-py-2.0.0-pyhd8ed1ab_0.tar.bz2
+    uc-micro-py: ''
+    python: '>=3.7'
+  url: https://conda.anaconda.org/conda-forge/noarch/linkify-it-py-2.0.3-pyhd8ed1ab_0.conda
   hash:
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   category: main
   optional: false
 - name: linkify-it-py
@@ -15350,30 +15421,15 @@ package:
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   optional: false
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-  manager: conda
-  platform: osx-arm64
-  dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/llvm-openmp-17.0.3-hcd81f8e_0.conda
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-  category: main
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-- name: llvm-tools
   version: 18.1.5
   manager: conda
-  platform: osx-64
+  platform: osx-arm64
   dependencies:
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-    zstd: '>=1.5.6,<1.6.0a0'
-  url: https://conda.anaconda.org/conda-forge/osx-64/llvm-tools-18.1.5-hf99a856_0.conda
+    __osx: '>=11.0'
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 - name: llvmlite
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   category: main
   optional: false
+- name: llvmlite
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+  manager: conda
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+  dependencies:
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+    libllvm14: '>=14.0.6,<14.1.0a0'
+    libzlib: '>=1.2.13,<1.3.0a0'
+    python: '>=3.12,<3.13.0a0'
+    python_abi: 3.12.*
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/llvmlite-0.42.0-py312h17030e7_1.conda
+  hash:
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+  optional: false
 - name: llvmlite
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   manager: conda
@@ -15428,7 +15500,7 @@ package:
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   optional: false
 - name: lxml
-  version: 5.2.1
+  version: 5.2.2
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   dependencies:
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     python: '>=3.12,<3.13.0a0'
     python_abi: 3.12.*
-  url: https://conda.anaconda.org/conda-forge/linux-64/lxml-5.2.1-py312hb90d8a5_0.conda
+  url: https://conda.anaconda.org/conda-forge/linux-64/lxml-5.2.2-py312hb90d8a5_0.conda
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 - name: lxml
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   dependencies:
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+    __osx: '>=10.13'
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     libzlib: '>=1.2.13,<1.3.0a0'
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     python_abi: 3.12.*
-  url: https://conda.anaconda.org/conda-forge/osx-64/lxml-5.2.1-py312ha7aaddb_0.conda
+  url: https://conda.anaconda.org/conda-forge/osx-64/lxml-5.2.2-py312h1aa9a54_0.conda
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/lzo-2.10-h642e427_1000.tar.bz2
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   category: main
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-- name: m2w64-gsl
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-  url: https://conda.anaconda.org/conda-forge/noarch/metakernel-0.30.1-pyhd8ed1ab_0.conda
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   category: main
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+    mpfr: '>=4.1.0,<5.0a0'
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   category: main
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-- name: r-pbdzmq
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-- name: r-xml2
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   manager: conda
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-- name: r-xtable
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-- name: r-xtractomatic
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-- name: r-xtractomatic
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   manager: conda
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+  version: 0.2.8
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+    python: '>=3.12,<3.13.0a0'
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/ruamel.yaml.clib-0.2.8-py312he37b823_0.conda
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/scikit-learn-1.4.2-py312hd4306f4_0.conda
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+  version: 1.13.0
   manager: conda
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/scipy-1.13.0-py312h14ffa8f_1.conda
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+  version: 0.13.2
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     pandas: '>=1.2'
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 - name: selenium
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+  version: 4.20.0
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   dependencies:
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+    typing_extensions: '>=4.9,<5'
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+  version: 4.20.0
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   dependencies: {}
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/selenium-manager-4.20.0-h5ef7bb8_0.conda
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+    python: '>=3.7'
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   dependencies:
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     python: '>=3.12,<3.13.0a0'
     python_abi: 3.12.*
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/shapely-2.0.4-py312h04e4829_0.conda
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+    libcxx: '>=16'
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+  version: 1.3.1
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   dependencies:
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   category: main
   optional: false
+- name: spdlog
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+  manager: conda
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+    libcxx: '>=16'
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     sphinxcontrib-devhelp: ''
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-    babel: '>=1.3'
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-  url: https://conda.anaconda.org/conda-forge/noarch/sphinx-book-theme-1.0.1-pyhd8ed1ab_0.conda
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   category: main
   optional: false
+- name: text-unidecode
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+    zstd: '>=1.5.6,<1.6.0a0'
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 - name: tiledb
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     fmt: '>=10.2.1,<11.0a0'
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+    zstd: '>=1.5.6,<1.6.0a0'
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   category: main
   optional: false
+- name: time-machine
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+    python-dateutil: ''
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+  url: https://conda.anaconda.org/conda-forge/osx-arm64/time-machine-2.14.1-py312he37b823_0.conda
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+  version: 1.3.0
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   dependencies:
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     webencodings: '>=0.4'
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+  url: https://conda.anaconda.org/conda-forge/noarch/tinycss2-1.3.0-pyhd8ed1ab_0.conda
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   category: main
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   dependencies:
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     python: '>=3.7'
-  url: https://conda.anaconda.org/conda-forge/noarch/tqdm-4.66.1-pyhd8ed1ab_0.conda
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   dependencies:
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+    python: '>=3.7'
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+  version: 0.25.0
   manager: conda
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   dependencies:
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     outcome: ''
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     python_abi: 3.12.*
-    sniffio: ''
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     sortedcontainers: ''
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/trio-0.22.2-py312h81bd7bf_1.conda
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+  version: 2024a
   manager: conda
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   dependencies: {}
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/tzcode-2023c-h1a8c8d9_0.conda
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   optional: false
 - name: websocket-client
@@ -38761,15 +29010,15 @@ package:
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+  version: 0.43.0
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-  url: https://conda.anaconda.org/conda-forge/noarch/wheel-0.41.2-pyhd8ed1ab_0.conda
+    python: '>=3.8'
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@@ -38809,15 +29058,15 @@ package:
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+  version: 4.0.10
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+    numpy: '>=1.23,<2.0a0'
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@@ -38985,9 +29234,9 @@ package:
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+  version: 2024.3.0
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-  url: https://conda.anaconda.org/conda-forge/noarch/xarray-2023.10.1-pyhd8ed1ab_0.conda
+    pandas: '>=1.5'
+    packaging: '>=22'
+    numpy: '>=1.23,<2.0a0'
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-    numpy: '>=1.23'
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+    numpy: '>=1.23,<2.0a0'
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   hash:
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@@ -39055,17 +29304,17 @@ package:
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-  url: https://conda.anaconda.org/conda-forge/osx-arm64/xerces-c-3.2.4-hd886eac_3.conda
+    libcurl: '>=8.5.0,<9.0a0'
+    libcxx: '>=15'
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   hash:
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   category: main
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@@ -39431,15 +29663,15 @@ package:
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+  version: 2024.4.0
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   platform: osx-arm64
   dependencies:
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-  url: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2023.10.1-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/xyzservices-2024.4.0-pyhd8ed1ab_0.conda
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@@ -39581,19 +29813,19 @@ package:
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+  version: 2.18.0
   manager: conda
   platform: osx-arm64
   dependencies:
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     asciitree: ''
-    python: '>=3.8'
+    python: '>=3.9'
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     numcodecs: '>=0.10.0'
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-  url: https://conda.anaconda.org/conda-forge/noarch/zarr-2.16.1-pyhd8ed1ab_0.conda
+  url: https://conda.anaconda.org/conda-forge/noarch/zarr-2.18.0-pyhd8ed1ab_0.conda
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   dependencies:
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-    libcxx: '>=16.0.6'
+    __osx: '>=11.0'
+    krb5: '>=1.21.2,<1.22.0a0'
+    libcxx: '>=16'
     libsodium: '>=1.0.18,<1.0.19.0a0'
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zeromq-4.3.5-h965bd2d_0.conda
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@@ -39799,15 +30032,16 @@ package:
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   optional: false
 - name: zstd
-  version: 1.5.5
+  version: 1.5.6
   manager: conda
   platform: osx-arm64
   dependencies:
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     libzlib: '>=1.2.13,<1.3.0a0'
-  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.5-h4f39d0f_0.conda
+  url: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.6-hb46c0d2_0.conda
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diff --git a/jupyterbook/content/code_gallery/data_management_notebooks/2023-03-20-Reading_and_writing_zarr.ipynb b/jupyterbook/content/code_gallery/data_management_notebooks/2023-03-20-Reading_and_writing_zarr.ipynb
index 7fb3e84e..5a79187e 100644
--- a/jupyterbook/content/code_gallery/data_management_notebooks/2023-03-20-Reading_and_writing_zarr.ipynb
+++ b/jupyterbook/content/code_gallery/data_management_notebooks/2023-03-20-Reading_and_writing_zarr.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "id": "04d735b6",
    "metadata": {
     "tags": [
@@ -73,7 +73,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "id": "2961833f",
    "metadata": {},
    "outputs": [
@@ -443,32 +443,32 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 821kB\n",
        "Dimensions:   (time: 1, eta_rho: 106, xi_rho: 242)\n",
        "Coordinates:\n",
-       "    s_rho     float64 -0.0125\n",
-       "    lon_rho   (eta_rho, xi_rho) float64 ...\n",
-       "    lat_rho   (eta_rho, xi_rho) float64 ...\n",
-       "  * time      (time) datetime64[ns] 2022-06-06T12:00:00\n",
-       "    time_run  (time) datetime64[ns] ...\n",
+       "    s_rho     float64 8B -0.0125\n",
+       "    lon_rho   (eta_rho, xi_rho) float64 205kB ...\n",
+       "    lat_rho   (eta_rho, xi_rho) float64 205kB ...\n",
+       "  * time      (time) datetime64[ns] 8B 2022-06-06T12:00:00\n",
+       "    time_run  (time) datetime64[ns] 8B ...\n",
        "Dimensions without coordinates: eta_rho, xi_rho\n",
        "Data variables:\n",
-       "    temp      (time, eta_rho, xi_rho) float64 ...\n",
-       "    salt      (time, eta_rho, xi_rho) float64 ...\n",
+       "    temp      (time, eta_rho, xi_rho) float64 205kB ...\n",
+       "    salt      (time, eta_rho, xi_rho) float64 205kB ...\n",
        "Attributes: (12/46)\n",
-       "    file:                            doppio_avg_6280_0004.nc\n",
+       "    file:                            doppio_avg_6702_0001.nc\n",
        "    format:                          netCDF-4/HDF5 file\n",
        "    Conventions:                     CF-1.4, SGRID-0.3\n",
        "    type:                            ROMS/TOMS nonlinear model averages file\n",
        "    title:                           ROMS doppio Real-Time Operational PSAS F...\n",
        "    var_info:                        ../Data/varinfo1040t_daily.dat\n",
        "    ...                              ...\n",
-       "    his_base:                        doppio_his_6280\n",
+       "    frc_file_07:                     ../Data/Winds_ncepnam_3hourly_MAB_and_Go...\n",
        "    cdm_data_type:                   GRID\n",
        "    featureType:                     GRID\n",
        "    location:                        Proto fmrc:doppio_2017_da_avg\n",
        "    summary:                         doppio\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  ocean_time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4449df2c-7cbb-4d69-9a26-63fd379d03a0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4449df2c-7cbb-4d69-9a26-63fd379d03a0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span>eta_rho</span>: 106</li><li><span>xi_rho</span>: 242</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-bbc0f211-7993-475f-b3e7-8792220105ee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bbc0f211-7993-475f-b3e7-8792220105ee' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>s_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0125</div><input id='attrs-bbc575a6-79e2-4963-8f66-e7795cb16bd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbc575a6-79e2-4963-8f66-e7795cb16bd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28ce0e7b-91ab-43b5-b071-54d86b703500' class='xr-var-data-in' type='checkbox'><label for='data-28ce0e7b-91ab-43b5-b071-54d86b703500' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>S-coordinate at RHO-points</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>ocean_s_coordinate_g2</dd><dt><span>formula_terms :</span></dt><dd>s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc</dd><dt><span>field :</span></dt><dd>s_rho, scalar</dd><dt><span>_CoordinateTransformType :</span></dt><dd>Vertical</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoZ</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd><dt><span>_CoordinateAxes :</span></dt><dd>s_rho</dd></dl></div><div class='xr-var-data'><pre>array(-0.0125)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-01277f91-5143-4e32-bdcf-b59e591be754' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01277f91-5143-4e32-bdcf-b59e591be754' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2823d9b8-bb00-40e2-b6f2-b5a8c504d13c' class='xr-var-data-in' type='checkbox'><label for='data-2823d9b8-bb00-40e2-b6f2-b5a8c504d13c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude of RHO-points</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>field :</span></dt><dd>lon_rho, scalar</dd><dt><span>_ChunkSizes :</span></dt><dd>[106 242]</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>[25652 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0a04b63e-5f0e-4933-aa16-d7ab55fa76b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a04b63e-5f0e-4933-aa16-d7ab55fa76b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08294aba-bb47-499e-871d-11c3c1ac5280' class='xr-var-data-in' type='checkbox'><label for='data-08294aba-bb47-499e-871d-11c3c1ac5280' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude of RHO-points</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>field :</span></dt><dd>lat_rho, scalar</dd><dt><span>_ChunkSizes :</span></dt><dd>[106 242]</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>[25652 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-06-06T12:00:00</div><input id='attrs-63d655d7-0b04-44c3-801e-5d001bcc5c9a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63d655d7-0b04-44c3-801e-5d001bcc5c9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e3d1a9a-97b1-4b46-a6c2-07caf2445d19' class='xr-var-data-in' type='checkbox'><label for='data-6e3d1a9a-97b1-4b46-a6c2-07caf2445d19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Forecast time for ForecastModelRunCollection</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-06-06T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_run</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-99e7dff6-b33b-4d60-b260-2f0202ae1b38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99e7dff6-b33b-4d60-b260-2f0202ae1b38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c67353d-943f-4457-9638-4ac92179df28' class='xr-var-data-in' type='checkbox'><label for='data-6c67353d-943f-4457-9638-4ac92179df28' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>run times for coordinate = time</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-53e2c319-39c3-4ce7-963c-f3caf0279b8b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-53e2c319-39c3-4ce7-963c-f3caf0279b8b' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f5bea4e8-f833-4840-85b7-90c7048f19ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f5bea4e8-f833-4840-85b7-90c7048f19ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a58f1ad-b6dd-4f5c-96b4-47e308f117f9' class='xr-var-data-in' type='checkbox'><label for='data-5a58f1ad-b6dd-4f5c-96b4-47e308f117f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>long_name :</span></dt><dd>time-averaged potential temperature</dd><dt><span>time :</span></dt><dd>ocean_time</dd><dt><span>grid :</span></dt><dd>grid</dd><dt><span>location :</span></dt><dd>face</dd><dt><span>field :</span></dt><dd>temperature, scalar, series</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  20  53 121]</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd></dl></div><div class='xr-var-data'><pre>[25652 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b9080899-fa83-466f-b2d7-0dc0aa52a686' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9080899-fa83-466f-b2d7-0dc0aa52a686' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdbeb293-e35e-4566-883e-21cb65efa696' class='xr-var-data-in' type='checkbox'><label for='data-bdbeb293-e35e-4566-883e-21cb65efa696' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time-averaged salinity</dd><dt><span>time :</span></dt><dd>ocean_time</dd><dt><span>grid :</span></dt><dd>grid</dd><dt><span>location :</span></dt><dd>face</dd><dt><span>field :</span></dt><dd>salinity, scalar, series</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  20  53 121]</dd><dt><span>standard_name :</span></dt><dd>sea_water_practical_salinity</dd></dl></div><div class='xr-var-data'><pre>[25652 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a249b24d-3386-46d4-9fa2-c5fe5a24fc02' class='xr-section-summary-in' type='checkbox'  ><label for='section-a249b24d-3386-46d4-9fa2-c5fe5a24fc02' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6e8e2870-2ce8-4127-8002-9d908f762427' class='xr-index-data-in' type='checkbox'/><label for='index-6e8e2870-2ce8-4127-8002-9d908f762427' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-06-06 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e9132f9-a86c-491e-96dd-ef5a1d992d2c' class='xr-section-summary-in' type='checkbox'  ><label for='section-3e9132f9-a86c-491e-96dd-ef5a1d992d2c' class='xr-section-summary' >Attributes: <span>(46)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>file :</span></dt><dd>doppio_avg_6280_0004.nc</dd><dt><span>format :</span></dt><dd>netCDF-4/HDF5 file</dd><dt><span>Conventions :</span></dt><dd>CF-1.4, SGRID-0.3</dd><dt><span>type :</span></dt><dd>ROMS/TOMS nonlinear model averages file</dd><dt><span>title :</span></dt><dd>ROMS doppio Real-Time Operational PSAS Forecast System Version 1 FMRC Averages</dd><dt><span>var_info :</span></dt><dd>../Data/varinfo1040t_daily.dat</dd><dt><span>rst_file :</span></dt><dd>tmp_doppio_rst_6280.nc</dd><dt><span>avg_base :</span></dt><dd>doppio_avg_6280</dd><dt><span>flt_file :</span></dt><dd>doppio_flt_6280.nc</dd><dt><span>grd_file :</span></dt><dd>/home/om/roms/doppio/7km/grid_doppio_JJA_v13.nc</dd><dt><span>ini_file :</span></dt><dd>doppio_rst_6280.nc</dd><dt><span>tide_file :</span></dt><dd>/home/om/roms/doppio/7km/doppio_tide_7km.nc</dd><dt><span>frc_file_01 :</span></dt><dd>../Data/lwrad_down_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_02 :</span></dt><dd>../Data/Pair_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_03 :</span></dt><dd>../Data/Qair_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_04 :</span></dt><dd>../Data/rain_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_05 :</span></dt><dd>../Data/swrad_daily_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_06 :</span></dt><dd>../Data/Tair_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_07 :</span></dt><dd>../Data/Winds_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>clm_file_01 :</span></dt><dd>../Data/doppio_clm.nc</dd><dt><span>nud_file :</span></dt><dd>/home/om/roms/doppio/7km/doppio_nudgcoef_7km_1500-2000_GS.nc</dd><dt><span>script_file :</span></dt><dd>nl_ocean_doppio.in</dd><dt><span>fpos_file :</span></dt><dd>floats.in</dd><dt><span>NLM_TADV :</span></dt><dd>\n",
+       "    DODS_EXTRA.Unlimited_Dimension:  ocean_time</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-de37a277-db84-483f-af9f-b73c40abcde8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-de37a277-db84-483f-af9f-b73c40abcde8' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1</li><li><span>eta_rho</span>: 106</li><li><span>xi_rho</span>: 242</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c02be91c-7825-4115-b645-d707304a90b6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c02be91c-7825-4115-b645-d707304a90b6' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>s_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0125</div><input id='attrs-377c6eb6-a65b-40bd-b3db-fd4affcd6dd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-377c6eb6-a65b-40bd-b3db-fd4affcd6dd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb2571eb-f0fe-4815-baeb-4207f1cbd211' class='xr-var-data-in' type='checkbox'><label for='data-cb2571eb-f0fe-4815-baeb-4207f1cbd211' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd><dt><span>long_name :</span></dt><dd>S-coordinate at RHO-points</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>ocean_s_coordinate_g2</dd><dt><span>formula_terms :</span></dt><dd>s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc</dd><dt><span>field :</span></dt><dd>s_rho, scalar</dd><dt><span>_CoordinateTransformType :</span></dt><dd>Vertical</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoZ</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd><dt><span>_CoordinateAxes :</span></dt><dd>s_rho</dd></dl></div><div class='xr-var-data'><pre>array(-0.0125)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f382c607-85bc-49ac-955e-55796bcadbe0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f382c607-85bc-49ac-955e-55796bcadbe0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f9da4e0-4b22-4f7e-975a-3412fc2bf123' class='xr-var-data-in' type='checkbox'><label for='data-1f9da4e0-4b22-4f7e-975a-3412fc2bf123' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude of RHO-points</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>field :</span></dt><dd>lon_rho, scalar</dd><dt><span>_ChunkSizes :</span></dt><dd>[106 242]</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>[25652 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-f390403f-bf41-4abc-b6d0-b14d6814dea0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f390403f-bf41-4abc-b6d0-b14d6814dea0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-460122a4-ee72-4328-bfd5-64c6c1cce90b' class='xr-var-data-in' type='checkbox'><label for='data-460122a4-ee72-4328-bfd5-64c6c1cce90b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude of RHO-points</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>field :</span></dt><dd>lat_rho, scalar</dd><dt><span>_ChunkSizes :</span></dt><dd>[106 242]</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>[25652 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-06-06T12:00:00</div><input id='attrs-babf7fc4-65c4-4087-998c-4bec12e3bd25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-babf7fc4-65c4-4087-998c-4bec12e3bd25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c012da32-223d-47e0-a8db-3c88c1eaa1b9' class='xr-var-data-in' type='checkbox'><label for='data-c012da32-223d-47e0-a8db-3c88c1eaa1b9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Forecast time for ForecastModelRunCollection</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-06-06T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_run</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9a22bc0a-1c2a-4cd7-b603-d727e2d10d78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a22bc0a-1c2a-4cd7-b603-d727e2d10d78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06f02c95-e48f-4b36-a60a-e5c60ba89835' class='xr-var-data-in' type='checkbox'><label for='data-06f02c95-e48f-4b36-a60a-e5c60ba89835' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>run times for coordinate = time</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-115ebbb9-4305-48ea-9ef8-2bbc8c77e6b8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-115ebbb9-4305-48ea-9ef8-2bbc8c77e6b8' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9c69a856-afc2-40e4-8fa9-78f2d93023d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c69a856-afc2-40e4-8fa9-78f2d93023d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1afc1204-025d-4a74-af82-b4de82f9a1cc' class='xr-var-data-in' type='checkbox'><label for='data-1afc1204-025d-4a74-af82-b4de82f9a1cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>Celsius</dd><dt><span>long_name :</span></dt><dd>time-averaged potential temperature</dd><dt><span>time :</span></dt><dd>ocean_time</dd><dt><span>grid :</span></dt><dd>grid</dd><dt><span>location :</span></dt><dd>face</dd><dt><span>field :</span></dt><dd>temperature, scalar, series</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  20  53 121]</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd></dl></div><div class='xr-var-data'><pre>[25652 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8949a104-f2ef-4054-a6a0-71fdc78f8b6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8949a104-f2ef-4054-a6a0-71fdc78f8b6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffaeb6cb-60c2-41e6-8140-424a3130f846' class='xr-var-data-in' type='checkbox'><label for='data-ffaeb6cb-60c2-41e6-8140-424a3130f846' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>time-averaged salinity</dd><dt><span>time :</span></dt><dd>ocean_time</dd><dt><span>grid :</span></dt><dd>grid</dd><dt><span>location :</span></dt><dd>face</dd><dt><span>field :</span></dt><dd>salinity, scalar, series</dd><dt><span>_ChunkSizes :</span></dt><dd>[  1  20  53 121]</dd><dt><span>standard_name :</span></dt><dd>sea_water_practical_salinity</dd></dl></div><div class='xr-var-data'><pre>[25652 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15ae833f-ee9c-4a57-980a-a99dd3f8e823' class='xr-section-summary-in' type='checkbox'  ><label for='section-15ae833f-ee9c-4a57-980a-a99dd3f8e823' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ee99db1b-69b0-49d4-bb67-9b9e9a22fd99' class='xr-index-data-in' type='checkbox'/><label for='index-ee99db1b-69b0-49d4-bb67-9b9e9a22fd99' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-06-06 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7b4ac213-2031-44ad-8b3e-48d516af74ec' class='xr-section-summary-in' type='checkbox'  ><label for='section-7b4ac213-2031-44ad-8b3e-48d516af74ec' class='xr-section-summary' >Attributes: <span>(46)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>file :</span></dt><dd>doppio_avg_6702_0001.nc</dd><dt><span>format :</span></dt><dd>netCDF-4/HDF5 file</dd><dt><span>Conventions :</span></dt><dd>CF-1.4, SGRID-0.3</dd><dt><span>type :</span></dt><dd>ROMS/TOMS nonlinear model averages file</dd><dt><span>title :</span></dt><dd>ROMS doppio Real-Time Operational PSAS Forecast System Version 1 FMRC Averages</dd><dt><span>var_info :</span></dt><dd>../Data/varinfo1040t_daily.dat</dd><dt><span>rst_file :</span></dt><dd>doppio_rst_6702.nc</dd><dt><span>his_base :</span></dt><dd>doppio_his_6702</dd><dt><span>avg_base :</span></dt><dd>doppio_avg_6702</dd><dt><span>flt_file :</span></dt><dd>doppio_flt_6702.nc</dd><dt><span>grd_file :</span></dt><dd>/home/om/roms/doppio/7km/grid_doppio_JJA_v13.nc</dd><dt><span>ini_file :</span></dt><dd>doppio_ini_6702.nc</dd><dt><span>tide_file :</span></dt><dd>/home/om/roms/doppio/7km/doppio_tide_7km.nc</dd><dt><span>frc_file_01 :</span></dt><dd>../PSAS/doppio_flx_6702_outer2.nc</dd><dt><span>clm_file_01 :</span></dt><dd>../Data/doppio_clm.nc</dd><dt><span>nud_file :</span></dt><dd>/home/om/roms/doppio/7km/doppio_nudgcoef_7km_1500-2000_GS.nc</dd><dt><span>script_file :</span></dt><dd>nl_ocean_doppio.in</dd><dt><span>fpos_file :</span></dt><dd>floats.in</dd><dt><span>NLM_TADV :</span></dt><dd>\n",
        "ADVECTION:   HORIZONTAL   VERTICAL     \n",
        "temp:        Akima4       Akima4       \n",
        "salt:        Akima4       Akima4</dd><dt><span>NLM_LBC :</span></dt><dd>\n",
@@ -480,31 +480,31 @@
        "v:     RadNud RadNud RadNud Clo    \n",
        "temp:  Rad    Rad    Rad    Clo    \n",
        "salt:  Rad    Rad    Rad    Clo    \n",
-       "tke:   Gra    Gra    Gra    Clo</dd><dt><span>svn_url :</span></dt><dd>https://www.myroms.org/svn/src/trunk</dd><dt><span>svn_rev :</span></dt><dd>1040</dd><dt><span>code_dir :</span></dt><dd>/home/julia/ROMS/doppio/svn1040t</dd><dt><span>header_dir :</span></dt><dd>/home/julia/ROMS/doppio/Compile/fwd</dd><dt><span>header_file :</span></dt><dd>doppio.h</dd><dt><span>os :</span></dt><dd>Linux</dd><dt><span>cpu :</span></dt><dd>x86_64</dd><dt><span>compiler_system :</span></dt><dd>ifort</dd><dt><span>compiler_command :</span></dt><dd>/opt/sw/apps/intel-18.0.1/openmpi/3.1.2/bin/mpif90</dd><dt><span>compiler_flags :</span></dt><dd>-fp-model precise -heap-arrays -ip -O3 -traceback -check uninit</dd><dt><span>tiling :</span></dt><dd>004x004</dd><dt><span>history :</span></dt><dd>ROMS/TOMS, Version 3.9, Thursday - March 16, 2023 -  4:32:53 AM ;\n",
-       "FMRC Best Dataset</dd><dt><span>ana_file :</span></dt><dd>ROMS/Functionals/ana_btflux.h, ROMS/Functionals/ana_srflux.h</dd><dt><span>CPP_options :</span></dt><dd>DOPPIO, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, ATM_PRESS, AVERAGES, !BOUNDARY_A BULK_FLUXES, !COLLECT_ALL..., CHARNOK, CRAIG_BANNER, CURVGRID, DEFLATE, DIURNAL_SRFLUX, DJ_GRADPS, DOUBLE_PRECISION, EMINUSP, FLOATS, FORWARD_WRITE, GLS_MIXING, HDF5, KANTHA_CLAYSON, LONGWAVE_OUT, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, NO_LBC_ATT, N2S2_HORAVG, OUT_DOUBLE, POWER_LAW, PROFILE, K_GSCHEME, REDUCE_ALLREDUCE, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SSH_TIDES, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_QDRAG, UV_TIDES, UV_VIS2, VAR_RHO_2D, WIND_MINUS_CURRENT</dd><dt><span>_CoordSysBuilder :</span></dt><dd>ucar.nc2.dataset.conv.CF1Convention</dd><dt><span>his_base :</span></dt><dd>doppio_his_6280</dd><dt><span>cdm_data_type :</span></dt><dd>GRID</dd><dt><span>featureType :</span></dt><dd>GRID</dd><dt><span>location :</span></dt><dd>Proto fmrc:doppio_2017_da_avg</dd><dt><span>summary :</span></dt><dd>doppio</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>ocean_time</dd></dl></div></li></ul></div></div>"
+       "tke:   Gra    Gra    Gra    Clo</dd><dt><span>svn_url :</span></dt><dd>https://www.myroms.org/svn/src/trunk</dd><dt><span>svn_rev :</span></dt><dd>1040</dd><dt><span>code_dir :</span></dt><dd>/home/julia/ROMS/doppio/svn1040t</dd><dt><span>header_dir :</span></dt><dd>/home/julia/ROMS/doppio/Compile/fwd</dd><dt><span>header_file :</span></dt><dd>doppio.h</dd><dt><span>os :</span></dt><dd>Linux</dd><dt><span>cpu :</span></dt><dd>x86_64</dd><dt><span>compiler_system :</span></dt><dd>ifort</dd><dt><span>compiler_command :</span></dt><dd>/opt/sw/apps/intel-18.0.1/openmpi/3.1.2/bin/mpif90</dd><dt><span>compiler_flags :</span></dt><dd>-fp-model precise -heap-arrays -ip -O3 -traceback -check uninit</dd><dt><span>tiling :</span></dt><dd>004x004</dd><dt><span>history :</span></dt><dd>ROMS/TOMS, Version 3.9, Saturday - May 11, 2024 -  4:36:08 AM ;\n",
+       "FMRC Best Dataset</dd><dt><span>ana_file :</span></dt><dd>ROMS/Functionals/ana_btflux.h</dd><dt><span>CPP_options :</span></dt><dd>DOPPIO, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, ATM_PRESS, AVERAGES, !BOUNDARY_A !COLLECT_ALL..., CHARNOK, CRAIG_BANNER, CURVGRID, DEFLATE, DJ_GRADPS, DOUBLE_PRECISION, FLOATS, FORWARD_WRITE, GLS_MIXING, HDF5, KANTHA_CLAYSON, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, NO_LBC_ATT, N2S2_HORAVG, OUT_DOUBLE, POWER_LAW, PROFILE, K_GSCHEME, REDUCE_ALLREDUCE, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SSH_TIDES, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_QDRAG, UV_TIDES, UV_VIS2, VAR_RHO_2D, WIND_MINUS_CURRENT</dd><dt><span>_CoordSysBuilder :</span></dt><dd>ucar.nc2.dataset.conv.CF1Convention</dd><dt><span>frc_file_02 :</span></dt><dd>../Data/Pair_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_03 :</span></dt><dd>../Data/Qair_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_04 :</span></dt><dd>../Data/rain_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_05 :</span></dt><dd>../Data/swrad_daily_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_06 :</span></dt><dd>../Data/Tair_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_07 :</span></dt><dd>../Data/Winds_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>cdm_data_type :</span></dt><dd>GRID</dd><dt><span>featureType :</span></dt><dd>GRID</dd><dt><span>location :</span></dt><dd>Proto fmrc:doppio_2017_da_avg</dd><dt><span>summary :</span></dt><dd>doppio</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>ocean_time</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset>\n",
+       "<xarray.Dataset> Size: 821kB\n",
        "Dimensions:   (time: 1, eta_rho: 106, xi_rho: 242)\n",
        "Coordinates:\n",
-       "    s_rho     float64 -0.0125\n",
-       "    lon_rho   (eta_rho, xi_rho) float64 ...\n",
-       "    lat_rho   (eta_rho, xi_rho) float64 ...\n",
-       "  * time      (time) datetime64[ns] 2022-06-06T12:00:00\n",
-       "    time_run  (time) datetime64[ns] ...\n",
+       "    s_rho     float64 8B -0.0125\n",
+       "    lon_rho   (eta_rho, xi_rho) float64 205kB ...\n",
+       "    lat_rho   (eta_rho, xi_rho) float64 205kB ...\n",
+       "  * time      (time) datetime64[ns] 8B 2022-06-06T12:00:00\n",
+       "    time_run  (time) datetime64[ns] 8B ...\n",
        "Dimensions without coordinates: eta_rho, xi_rho\n",
        "Data variables:\n",
-       "    temp      (time, eta_rho, xi_rho) float64 ...\n",
-       "    salt      (time, eta_rho, xi_rho) float64 ...\n",
+       "    temp      (time, eta_rho, xi_rho) float64 205kB ...\n",
+       "    salt      (time, eta_rho, xi_rho) float64 205kB ...\n",
        "Attributes: (12/46)\n",
-       "    file:                            doppio_avg_6280_0004.nc\n",
+       "    file:                            doppio_avg_6702_0001.nc\n",
        "    format:                          netCDF-4/HDF5 file\n",
        "    Conventions:                     CF-1.4, SGRID-0.3\n",
        "    type:                            ROMS/TOMS nonlinear model averages file\n",
        "    title:                           ROMS doppio Real-Time Operational PSAS F...\n",
        "    var_info:                        ../Data/varinfo1040t_daily.dat\n",
        "    ...                              ...\n",
-       "    his_base:                        doppio_his_6280\n",
+       "    frc_file_07:                     ../Data/Winds_ncepnam_3hourly_MAB_and_Go...\n",
        "    cdm_data_type:                   GRID\n",
        "    featureType:                     GRID\n",
        "    location:                        Proto fmrc:doppio_2017_da_avg\n",
@@ -512,7 +512,7 @@
        "    DODS_EXTRA.Unlimited_Dimension:  ocean_time"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -536,7 +536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "d8fa04f9",
    "metadata": {},
    "outputs": [
@@ -546,7 +546,7 @@
        "'820.9 kB'"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -569,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "d8e3601c",
    "metadata": {},
    "outputs": [],
@@ -592,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "d982caef",
    "metadata": {},
    "outputs": [
@@ -649,7 +649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "44822485",
    "metadata": {
     "scrolled": false
@@ -658,14 +658,14 @@
     {
      "data": {
       "text/plain": [
-       "{'metadata': {'.zattrs': {'CPP_options': 'DOPPIO, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, ATM_PRESS, AVERAGES, !BOUNDARY_A BULK_FLUXES, !COLLECT_ALL..., CHARNOK, CRAIG_BANNER, CURVGRID, DEFLATE, DIURNAL_SRFLUX, DJ_GRADPS, DOUBLE_PRECISION, EMINUSP, FLOATS, FORWARD_WRITE, GLS_MIXING, HDF5, KANTHA_CLAYSON, LONGWAVE_OUT, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, NO_LBC_ATT, N2S2_HORAVG, OUT_DOUBLE, POWER_LAW, PROFILE, K_GSCHEME, REDUCE_ALLREDUCE, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SSH_TIDES, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_QDRAG, UV_TIDES, UV_VIS2, VAR_RHO_2D, WIND_MINUS_CURRENT',\n",
+       "{'metadata': {'.zattrs': {'CPP_options': 'DOPPIO, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, ATM_PRESS, AVERAGES, !BOUNDARY_A !COLLECT_ALL..., CHARNOK, CRAIG_BANNER, CURVGRID, DEFLATE, DJ_GRADPS, DOUBLE_PRECISION, FLOATS, FORWARD_WRITE, GLS_MIXING, HDF5, KANTHA_CLAYSON, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, NO_LBC_ATT, N2S2_HORAVG, OUT_DOUBLE, POWER_LAW, PROFILE, K_GSCHEME, REDUCE_ALLREDUCE, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SSH_TIDES, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_QDRAG, UV_TIDES, UV_VIS2, VAR_RHO_2D, WIND_MINUS_CURRENT',\n",
        "   'Conventions': 'CF-1.4, SGRID-0.3',\n",
        "   'DODS_EXTRA.Unlimited_Dimension': 'ocean_time',\n",
        "   'NLM_LBC': '\\nEDGE:  WEST   SOUTH  EAST   NORTH  \\nzeta:  Cha    Cha    Cha    Clo    \\nubar:  Fla    Fla    Fla    Clo    \\nvbar:  Fla    Fla    Fla    Clo    \\nu:     RadNud RadNud RadNud Clo    \\nv:     RadNud RadNud RadNud Clo    \\ntemp:  Rad    Rad    Rad    Clo    \\nsalt:  Rad    Rad    Rad    Clo    \\ntke:   Gra    Gra    Gra    Clo',\n",
        "   'NLM_TADV': '\\nADVECTION:   HORIZONTAL   VERTICAL     \\ntemp:        Akima4       Akima4       \\nsalt:        Akima4       Akima4',\n",
        "   '_CoordSysBuilder': 'ucar.nc2.dataset.conv.CF1Convention',\n",
-       "   'ana_file': 'ROMS/Functionals/ana_btflux.h, ROMS/Functionals/ana_srflux.h',\n",
-       "   'avg_base': 'doppio_avg_6280',\n",
+       "   'ana_file': 'ROMS/Functionals/ana_btflux.h',\n",
+       "   'avg_base': 'doppio_avg_6702',\n",
        "   'cdm_data_type': 'GRID',\n",
        "   'clm_file_01': '../Data/doppio_clm.nc',\n",
        "   'code_dir': '/home/julia/ROMS/doppio/svn1040t',\n",
@@ -674,11 +674,11 @@
        "   'compiler_system': 'ifort',\n",
        "   'cpu': 'x86_64',\n",
        "   'featureType': 'GRID',\n",
-       "   'file': 'doppio_avg_6280_0004.nc',\n",
-       "   'flt_file': 'doppio_flt_6280.nc',\n",
+       "   'file': 'doppio_avg_6702_0001.nc',\n",
+       "   'flt_file': 'doppio_flt_6702.nc',\n",
        "   'format': 'netCDF-4/HDF5 file',\n",
        "   'fpos_file': 'floats.in',\n",
-       "   'frc_file_01': '../Data/lwrad_down_ncepnam_3hourly_MAB_and_GoM.nc',\n",
+       "   'frc_file_01': '../PSAS/doppio_flx_6702_outer2.nc',\n",
        "   'frc_file_02': '../Data/Pair_ncepnam_3hourly_MAB_and_GoM.nc',\n",
        "   'frc_file_03': '../Data/Qair_ncepnam_3hourly_MAB_and_GoM.nc',\n",
        "   'frc_file_04': '../Data/rain_ncepnam_3hourly_MAB_and_GoM.nc',\n",
@@ -688,13 +688,13 @@
        "   'grd_file': '/home/om/roms/doppio/7km/grid_doppio_JJA_v13.nc',\n",
        "   'header_dir': '/home/julia/ROMS/doppio/Compile/fwd',\n",
        "   'header_file': 'doppio.h',\n",
-       "   'his_base': 'doppio_his_6280',\n",
-       "   'history': 'ROMS/TOMS, Version 3.9, Thursday - March 16, 2023 -  4:32:53 AM ;\\nFMRC Best Dataset',\n",
-       "   'ini_file': 'doppio_rst_6280.nc',\n",
+       "   'his_base': 'doppio_his_6702',\n",
+       "   'history': 'ROMS/TOMS, Version 3.9, Saturday - May 11, 2024 -  4:36:08 AM ;\\nFMRC Best Dataset',\n",
+       "   'ini_file': 'doppio_ini_6702.nc',\n",
        "   'location': 'Proto fmrc:doppio_2017_da_avg',\n",
        "   'nud_file': '/home/om/roms/doppio/7km/doppio_nudgcoef_7km_1500-2000_GS.nc',\n",
        "   'os': 'Linux',\n",
-       "   'rst_file': 'tmp_doppio_rst_6280.nc',\n",
+       "   'rst_file': 'doppio_rst_6702.nc',\n",
        "   'script_file': 'nl_ocean_doppio.in',\n",
        "   'summary': 'doppio',\n",
        "   'svn_rev': '1040',\n",
@@ -846,7 +846,7 @@
        " 'zarr_consolidated_format': 1}"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -871,7 +871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "c55637c9",
    "metadata": {},
    "outputs": [
@@ -1241,18 +1241,18 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
-       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 821kB\n",
        "Dimensions:   (eta_rho: 106, xi_rho: 242, time: 1)\n",
        "Coordinates:\n",
-       "    lat_rho   (eta_rho, xi_rho) float64 dask.array&lt;chunksize=(106, 242), meta=np.ndarray&gt;\n",
-       "    lon_rho   (eta_rho, xi_rho) float64 dask.array&lt;chunksize=(106, 242), meta=np.ndarray&gt;\n",
-       "    s_rho     float64 ...\n",
-       "  * time      (time) datetime64[ns] 2022-06-06T12:00:00\n",
-       "    time_run  (time) datetime64[ns] dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;\n",
+       "    lat_rho   (eta_rho, xi_rho) float64 205kB ...\n",
+       "    lon_rho   (eta_rho, xi_rho) float64 205kB ...\n",
+       "    s_rho     float64 8B ...\n",
+       "  * time      (time) datetime64[ns] 8B 2022-06-06T12:00:00\n",
+       "    time_run  (time) datetime64[ns] 8B ...\n",
        "Dimensions without coordinates: eta_rho, xi_rho\n",
        "Data variables:\n",
-       "    salt      (time, eta_rho, xi_rho) float64 dask.array&lt;chunksize=(1, 106, 242), meta=np.ndarray&gt;\n",
-       "    temp      (time, eta_rho, xi_rho) float64 dask.array&lt;chunksize=(1, 106, 242), meta=np.ndarray&gt;\n",
+       "    salt      (time, eta_rho, xi_rho) float64 205kB ...\n",
+       "    temp      (time, eta_rho, xi_rho) float64 205kB ...\n",
        "Attributes: (12/46)\n",
        "    CPP_options:                     DOPPIO, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX...\n",
        "    Conventions:                     CF-1.4, SGRID-0.3\n",
@@ -1266,328 +1266,7 @@
        "    tiling:                          004x004\n",
        "    title:                           ROMS doppio Real-Time Operational PSAS F...\n",
        "    type:                            ROMS/TOMS nonlinear model averages file\n",
-       "    var_info:                        ../Data/varinfo1040t_daily.dat</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1295dc66-6547-46f2-851a-0d390001579a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1295dc66-6547-46f2-851a-0d390001579a' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>eta_rho</span>: 106</li><li><span>xi_rho</span>: 242</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5d83abfa-e304-434f-9b17-767e58da2d46' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d83abfa-e304-434f-9b17-767e58da2d46' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(106, 242), meta=np.ndarray&gt;</div><input id='attrs-d2bf0f82-0282-4e64-94f5-0c37e3f19643' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d2bf0f82-0282-4e64-94f5-0c37e3f19643' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd1da87e-95d6-4e85-8b3b-4d452ccd106c' class='xr-var-data-in' type='checkbox'><label for='data-bd1da87e-95d6-4e85-8b3b-4d452ccd106c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[106, 242]</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>field :</span></dt><dd>lat_rho, scalar</dd><dt><span>long_name :</span></dt><dd>latitude of RHO-points</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 200.41 kiB </td>\n",
-       "                        <td> 200.41 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (106, 242) </td>\n",
-       "                        <td> (106, 242) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"102\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"0\" y1=\"52\" x2=\"120\" y2=\"52\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"52\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"52\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Colored Rectangle -->\n",
-       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,52.56198347107438 0.0,52.56198347107438\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"72.561983\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >242</text>\n",
-       "  <text x=\"140.000000\" y=\"26.280992\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,26.280992)\">106</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(106, 242), meta=np.ndarray&gt;</div><input id='attrs-f457d36c-aed9-4e01-9ac8-6a07b1a9efb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f457d36c-aed9-4e01-9ac8-6a07b1a9efb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d26aca01-64b1-4f4e-bae9-c301249e3c4d' class='xr-var-data-in' type='checkbox'><label for='data-d26aca01-64b1-4f4e-bae9-c301249e3c4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[106, 242]</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>field :</span></dt><dd>lon_rho, scalar</dd><dt><span>long_name :</span></dt><dd>longitude of RHO-points</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
-       "        <td>\n",
-       "            <table style=\"border-collapse: collapse;\">\n",
-       "                <thead>\n",
-       "                    <tr>\n",
-       "                        <td> </td>\n",
-       "                        <th> Array </th>\n",
-       "                        <th> Chunk </th>\n",
-       "                    </tr>\n",
-       "                </thead>\n",
-       "                <tbody>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 200.41 kiB </td>\n",
-       "                        <td> 200.41 kiB </td>\n",
-       "                    </tr>\n",
-       "                    \n",
-       "                    <tr>\n",
-       "                        <th> Shape </th>\n",
-       "                        <td> (106, 242) </td>\n",
-       "                        <td> (106, 242) </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
-       "                    </tr>\n",
-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
-       "                    </tr>\n",
-       "                </tbody>\n",
-       "            </table>\n",
-       "        </td>\n",
-       "        <td>\n",
-       "        <svg width=\"170\" height=\"102\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
-       "\n",
-       "  <!-- Horizontal lines -->\n",
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-       "  <line x1=\"0\" y1=\"52\" x2=\"120\" y2=\"52\" style=\"stroke-width:2\" />\n",
-       "\n",
-       "  <!-- Vertical lines -->\n",
-       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"52\" style=\"stroke-width:2\" />\n",
-       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"52\" style=\"stroke-width:2\" />\n",
-       "\n",
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-       "\n",
-       "  <!-- Text -->\n",
-       "  <text x=\"60.000000\" y=\"72.561983\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >242</text>\n",
-       "  <text x=\"140.000000\" y=\"26.280992\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,26.280992)\">106</text>\n",
-       "</svg>\n",
-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-489d60cf-0623-4218-a5b3-7b12ade835bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-489d60cf-0623-4218-a5b3-7b12ade835bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3716e397-d3e9-4755-9a43-ac476fcb5121' class='xr-var-data-in' type='checkbox'><label for='data-3716e397-d3e9-4755-9a43-ac476fcb5121' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxes :</span></dt><dd>s_rho</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoZ</dd><dt><span>_CoordinateTransformType :</span></dt><dd>Vertical</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd><dt><span>field :</span></dt><dd>s_rho, scalar</dd><dt><span>formula_terms :</span></dt><dd>s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc</dd><dt><span>long_name :</span></dt><dd>S-coordinate at RHO-points</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>ocean_s_coordinate_g2</dd><dt><span>units :</span></dt><dd></dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-06-06T12:00:00</div><input id='attrs-3e70c4b6-f030-4649-b2f6-90afb2a7e9bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e70c4b6-f030-4649-b2f6-90afb2a7e9bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-603cc13d-6524-410f-9b7b-595b46e6656d' class='xr-var-data-in' type='checkbox'><label for='data-603cc13d-6524-410f-9b7b-595b46e6656d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>long_name :</span></dt><dd>Forecast time for ForecastModelRunCollection</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-06-06T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_run</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-6f442dca-db32-45a4-8db2-2f993d3756b4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f442dca-db32-45a4-8db2-2f993d3756b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27568dd5-6ed0-4e9e-897c-79445c58c083' class='xr-var-data-in' type='checkbox'><label for='data-27568dd5-6ed0-4e9e-897c-79445c58c083' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd><dt><span>long_name :</span></dt><dd>run times for coordinate = time</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><table>\n",
-       "    <tr>\n",
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-       "                        <th> Bytes </th>\n",
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-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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-       "                        <th> Data type </th>\n",
-       "                        <td colspan=\"2\"> datetime64[ns] numpy.ndarray </td>\n",
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-       "                <tbody>\n",
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-       "                    <tr>\n",
-       "                        <th> Bytes </th>\n",
-       "                        <td> 200.41 kiB </td>\n",
-       "                        <td> 200.41 kiB </td>\n",
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-       "                        <td> (1, 106, 242) </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Dask graph </th>\n",
-       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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-       "                    <tr>\n",
-       "                        <th> Data type </th>\n",
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-       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 106, 242), meta=np.ndarray&gt;</div><input id='attrs-6f3f8a00-97da-4232-8166-1c2b43d7d92d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f3f8a00-97da-4232-8166-1c2b43d7d92d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f3d909f-56d7-43f9-8f84-267133cb7d23' class='xr-var-data-in' type='checkbox'><label for='data-8f3d909f-56d7-43f9-8f84-267133cb7d23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1, 20, 53, 121]</dd><dt><span>field :</span></dt><dd>temperature, scalar, series</dd><dt><span>grid :</span></dt><dd>grid</dd><dt><span>location :</span></dt><dd>face</dd><dt><span>long_name :</span></dt><dd>time-averaged potential temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>time :</span></dt><dd>ocean_time</dd><dt><span>units :</span></dt><dd>Celsius</dd></dl></div><div class='xr-var-data'><table>\n",
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-       "                        <th> Bytes </th>\n",
-       "                        <td> 200.41 kiB </td>\n",
-       "                        <td> 200.41 kiB </td>\n",
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-       "        </td>\n",
-       "    </tr>\n",
-       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4d39030-7156-4720-8372-246c665d11e0' class='xr-section-summary-in' type='checkbox'  ><label for='section-e4d39030-7156-4720-8372-246c665d11e0' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0ccb00b5-acb7-493f-8e33-2154b30d149c' class='xr-index-data-in' type='checkbox'/><label for='index-0ccb00b5-acb7-493f-8e33-2154b30d149c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-06-06 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5968d87d-2a95-4416-a624-7d366ed7f49f' class='xr-section-summary-in' type='checkbox'  ><label for='section-5968d87d-2a95-4416-a624-7d366ed7f49f' class='xr-section-summary' >Attributes: <span>(46)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CPP_options :</span></dt><dd>DOPPIO, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, ATM_PRESS, AVERAGES, !BOUNDARY_A BULK_FLUXES, !COLLECT_ALL..., CHARNOK, CRAIG_BANNER, CURVGRID, DEFLATE, DIURNAL_SRFLUX, DJ_GRADPS, DOUBLE_PRECISION, EMINUSP, FLOATS, FORWARD_WRITE, GLS_MIXING, HDF5, KANTHA_CLAYSON, LONGWAVE_OUT, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, NO_LBC_ATT, N2S2_HORAVG, OUT_DOUBLE, POWER_LAW, PROFILE, K_GSCHEME, REDUCE_ALLREDUCE, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SSH_TIDES, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_QDRAG, UV_TIDES, UV_VIS2, VAR_RHO_2D, WIND_MINUS_CURRENT</dd><dt><span>Conventions :</span></dt><dd>CF-1.4, SGRID-0.3</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>ocean_time</dd><dt><span>NLM_LBC :</span></dt><dd>\n",
+       "    var_info:                        ../Data/varinfo1040t_daily.dat</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-0403cd59-d9be-4011-80c5-48d0ef83f4e0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0403cd59-d9be-4011-80c5-48d0ef83f4e0' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>eta_rho</span>: 106</li><li><span>xi_rho</span>: 242</li><li><span class='xr-has-index'>time</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3aea7ac0-1c36-419f-baed-582b7feecff9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3aea7ac0-1c36-419f-baed-582b7feecff9' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d72de531-3b56-4077-af5b-ee1b7a4c7b20' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d72de531-3b56-4077-af5b-ee1b7a4c7b20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-812c5af1-6542-4fc6-9d79-4af4cb0548bc' class='xr-var-data-in' type='checkbox'><label for='data-812c5af1-6542-4fc6-9d79-4af4cb0548bc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[106, 242]</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd><dt><span>field :</span></dt><dd>lat_rho, scalar</dd><dt><span>long_name :</span></dt><dd>latitude of RHO-points</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>[25652 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-49974f6c-0ccc-4a38-9e52-4373f0c6886d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49974f6c-0ccc-4a38-9e52-4373f0c6886d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7467e2e-fcc9-40b4-b830-68b8761319f6' class='xr-var-data-in' type='checkbox'><label for='data-e7467e2e-fcc9-40b4-b830-68b8761319f6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[106, 242]</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd><dt><span>field :</span></dt><dd>lon_rho, scalar</dd><dt><span>long_name :</span></dt><dd>longitude of RHO-points</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>[25652 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>s_rho</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-77ea1d9c-410d-4b07-9faf-41ae30f2878a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77ea1d9c-410d-4b07-9faf-41ae30f2878a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de8562df-08da-43da-b821-dbe759d560c4' class='xr-var-data-in' type='checkbox'><label for='data-de8562df-08da-43da-b821-dbe759d560c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxes :</span></dt><dd>s_rho</dd><dt><span>_CoordinateAxisType :</span></dt><dd>GeoZ</dd><dt><span>_CoordinateTransformType :</span></dt><dd>Vertical</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>up</dd><dt><span>field :</span></dt><dd>s_rho, scalar</dd><dt><span>formula_terms :</span></dt><dd>s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc</dd><dt><span>long_name :</span></dt><dd>S-coordinate at RHO-points</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>ocean_s_coordinate_g2</dd><dt><span>units :</span></dt><dd></dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-06-06T12:00:00</div><input id='attrs-aa437988-ee1c-484d-bb7c-84273d0c660c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa437988-ee1c-484d-bb7c-84273d0c660c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1da0872-ded0-444c-b11c-40773b058872' class='xr-var-data-in' type='checkbox'><label for='data-a1da0872-ded0-444c-b11c-40773b058872' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>long_name :</span></dt><dd>Forecast time for ForecastModelRunCollection</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-06-06T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_run</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3228417d-fd58-4d64-8475-6532503c464f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3228417d-fd58-4d64-8475-6532503c464f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a060fd67-9654-4ab5-b7c6-c9dbde0423e7' class='xr-var-data-in' type='checkbox'><label for='data-a060fd67-9654-4ab5-b7c6-c9dbde0423e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_CoordinateAxisType :</span></dt><dd>RunTime</dd><dt><span>long_name :</span></dt><dd>run times for coordinate = time</dd><dt><span>standard_name :</span></dt><dd>forecast_reference_time</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=datetime64[ns]]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f7fcc215-19be-4891-a974-6e45154602bd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f7fcc215-19be-4891-a974-6e45154602bd' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>salt</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-825cbabf-0fa3-4649-92d7-bafe2429c83c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-825cbabf-0fa3-4649-92d7-bafe2429c83c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bde20713-f583-479a-9b1d-37ed347baaf0' class='xr-var-data-in' type='checkbox'><label for='data-bde20713-f583-479a-9b1d-37ed347baaf0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1, 20, 53, 121]</dd><dt><span>field :</span></dt><dd>salinity, scalar, series</dd><dt><span>grid :</span></dt><dd>grid</dd><dt><span>location :</span></dt><dd>face</dd><dt><span>long_name :</span></dt><dd>time-averaged salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_practical_salinity</dd><dt><span>time :</span></dt><dd>ocean_time</dd></dl></div><div class='xr-var-data'><pre>[25652 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-deaa62c4-c7a9-4458-beb2-92097b07e452' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-deaa62c4-c7a9-4458-beb2-92097b07e452' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe730814-cf07-49a4-b71c-e57b6f4fc45d' class='xr-var-data-in' type='checkbox'><label for='data-fe730814-cf07-49a4-b71c-e57b6f4fc45d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>_ChunkSizes :</span></dt><dd>[1, 20, 53, 121]</dd><dt><span>field :</span></dt><dd>temperature, scalar, series</dd><dt><span>grid :</span></dt><dd>grid</dd><dt><span>location :</span></dt><dd>face</dd><dt><span>long_name :</span></dt><dd>time-averaged potential temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>time :</span></dt><dd>ocean_time</dd><dt><span>units :</span></dt><dd>Celsius</dd></dl></div><div class='xr-var-data'><pre>[25652 values with dtype=float64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-380b7db9-4d7d-4f7b-ad57-3f2a8c31a19f' class='xr-section-summary-in' type='checkbox'  ><label for='section-380b7db9-4d7d-4f7b-ad57-3f2a8c31a19f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a895c2eb-8283-4612-b929-cac41a991539' class='xr-index-data-in' type='checkbox'/><label for='index-a895c2eb-8283-4612-b929-cac41a991539' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-06-06 12:00:00&#x27;], dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9cb9c400-e755-4692-b79c-4c30131ca89d' class='xr-section-summary-in' type='checkbox'  ><label for='section-9cb9c400-e755-4692-b79c-4c30131ca89d' class='xr-section-summary' >Attributes: <span>(46)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CPP_options :</span></dt><dd>DOPPIO, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX, ANA_BTFLUX, ASSUMED_SHAPE, ATM_PRESS, AVERAGES, !BOUNDARY_A !COLLECT_ALL..., CHARNOK, CRAIG_BANNER, CURVGRID, DEFLATE, DJ_GRADPS, DOUBLE_PRECISION, FLOATS, FORWARD_WRITE, GLS_MIXING, HDF5, KANTHA_CLAYSON, MASKING, MIX_GEO_TS, MIX_S_UV, MPI, NONLINEAR, NONLIN_EOS, NO_LBC_ATT, N2S2_HORAVG, OUT_DOUBLE, POWER_LAW, PROFILE, K_GSCHEME, REDUCE_ALLREDUCE, !RST_SINGLE, SALINITY, SOLAR_SOURCE, SOLVE3D, SSH_TIDES, TS_DIF2, UV_ADV, UV_COR, UV_U3HADVECTION, UV_C4VADVECTION, UV_QDRAG, UV_TIDES, UV_VIS2, VAR_RHO_2D, WIND_MINUS_CURRENT</dd><dt><span>Conventions :</span></dt><dd>CF-1.4, SGRID-0.3</dd><dt><span>DODS_EXTRA.Unlimited_Dimension :</span></dt><dd>ocean_time</dd><dt><span>NLM_LBC :</span></dt><dd>\n",
        "EDGE:  WEST   SOUTH  EAST   NORTH  \n",
        "zeta:  Cha    Cha    Cha    Clo    \n",
        "ubar:  Fla    Fla    Fla    Clo    \n",
@@ -1599,22 +1278,22 @@
        "tke:   Gra    Gra    Gra    Clo</dd><dt><span>NLM_TADV :</span></dt><dd>\n",
        "ADVECTION:   HORIZONTAL   VERTICAL     \n",
        "temp:        Akima4       Akima4       \n",
-       "salt:        Akima4       Akima4</dd><dt><span>_CoordSysBuilder :</span></dt><dd>ucar.nc2.dataset.conv.CF1Convention</dd><dt><span>ana_file :</span></dt><dd>ROMS/Functionals/ana_btflux.h, ROMS/Functionals/ana_srflux.h</dd><dt><span>avg_base :</span></dt><dd>doppio_avg_6280</dd><dt><span>cdm_data_type :</span></dt><dd>GRID</dd><dt><span>clm_file_01 :</span></dt><dd>../Data/doppio_clm.nc</dd><dt><span>code_dir :</span></dt><dd>/home/julia/ROMS/doppio/svn1040t</dd><dt><span>compiler_command :</span></dt><dd>/opt/sw/apps/intel-18.0.1/openmpi/3.1.2/bin/mpif90</dd><dt><span>compiler_flags :</span></dt><dd>-fp-model precise -heap-arrays -ip -O3 -traceback -check uninit</dd><dt><span>compiler_system :</span></dt><dd>ifort</dd><dt><span>cpu :</span></dt><dd>x86_64</dd><dt><span>featureType :</span></dt><dd>GRID</dd><dt><span>file :</span></dt><dd>doppio_avg_6280_0004.nc</dd><dt><span>flt_file :</span></dt><dd>doppio_flt_6280.nc</dd><dt><span>format :</span></dt><dd>netCDF-4/HDF5 file</dd><dt><span>fpos_file :</span></dt><dd>floats.in</dd><dt><span>frc_file_01 :</span></dt><dd>../Data/lwrad_down_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_02 :</span></dt><dd>../Data/Pair_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_03 :</span></dt><dd>../Data/Qair_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_04 :</span></dt><dd>../Data/rain_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_05 :</span></dt><dd>../Data/swrad_daily_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_06 :</span></dt><dd>../Data/Tair_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_07 :</span></dt><dd>../Data/Winds_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>grd_file :</span></dt><dd>/home/om/roms/doppio/7km/grid_doppio_JJA_v13.nc</dd><dt><span>header_dir :</span></dt><dd>/home/julia/ROMS/doppio/Compile/fwd</dd><dt><span>header_file :</span></dt><dd>doppio.h</dd><dt><span>his_base :</span></dt><dd>doppio_his_6280</dd><dt><span>history :</span></dt><dd>ROMS/TOMS, Version 3.9, Thursday - March 16, 2023 -  4:32:53 AM ;\n",
-       "FMRC Best Dataset</dd><dt><span>ini_file :</span></dt><dd>doppio_rst_6280.nc</dd><dt><span>location :</span></dt><dd>Proto fmrc:doppio_2017_da_avg</dd><dt><span>nud_file :</span></dt><dd>/home/om/roms/doppio/7km/doppio_nudgcoef_7km_1500-2000_GS.nc</dd><dt><span>os :</span></dt><dd>Linux</dd><dt><span>rst_file :</span></dt><dd>tmp_doppio_rst_6280.nc</dd><dt><span>script_file :</span></dt><dd>nl_ocean_doppio.in</dd><dt><span>summary :</span></dt><dd>doppio</dd><dt><span>svn_rev :</span></dt><dd>1040</dd><dt><span>svn_url :</span></dt><dd>https://www.myroms.org/svn/src/trunk</dd><dt><span>tide_file :</span></dt><dd>/home/om/roms/doppio/7km/doppio_tide_7km.nc</dd><dt><span>tiling :</span></dt><dd>004x004</dd><dt><span>title :</span></dt><dd>ROMS doppio Real-Time Operational PSAS Forecast System Version 1 FMRC Averages</dd><dt><span>type :</span></dt><dd>ROMS/TOMS nonlinear model averages file</dd><dt><span>var_info :</span></dt><dd>../Data/varinfo1040t_daily.dat</dd></dl></div></li></ul></div></div>"
+       "salt:        Akima4       Akima4</dd><dt><span>_CoordSysBuilder :</span></dt><dd>ucar.nc2.dataset.conv.CF1Convention</dd><dt><span>ana_file :</span></dt><dd>ROMS/Functionals/ana_btflux.h</dd><dt><span>avg_base :</span></dt><dd>doppio_avg_6702</dd><dt><span>cdm_data_type :</span></dt><dd>GRID</dd><dt><span>clm_file_01 :</span></dt><dd>../Data/doppio_clm.nc</dd><dt><span>code_dir :</span></dt><dd>/home/julia/ROMS/doppio/svn1040t</dd><dt><span>compiler_command :</span></dt><dd>/opt/sw/apps/intel-18.0.1/openmpi/3.1.2/bin/mpif90</dd><dt><span>compiler_flags :</span></dt><dd>-fp-model precise -heap-arrays -ip -O3 -traceback -check uninit</dd><dt><span>compiler_system :</span></dt><dd>ifort</dd><dt><span>cpu :</span></dt><dd>x86_64</dd><dt><span>featureType :</span></dt><dd>GRID</dd><dt><span>file :</span></dt><dd>doppio_avg_6702_0001.nc</dd><dt><span>flt_file :</span></dt><dd>doppio_flt_6702.nc</dd><dt><span>format :</span></dt><dd>netCDF-4/HDF5 file</dd><dt><span>fpos_file :</span></dt><dd>floats.in</dd><dt><span>frc_file_01 :</span></dt><dd>../PSAS/doppio_flx_6702_outer2.nc</dd><dt><span>frc_file_02 :</span></dt><dd>../Data/Pair_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_03 :</span></dt><dd>../Data/Qair_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_04 :</span></dt><dd>../Data/rain_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_05 :</span></dt><dd>../Data/swrad_daily_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_06 :</span></dt><dd>../Data/Tair_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>frc_file_07 :</span></dt><dd>../Data/Winds_ncepnam_3hourly_MAB_and_GoM.nc</dd><dt><span>grd_file :</span></dt><dd>/home/om/roms/doppio/7km/grid_doppio_JJA_v13.nc</dd><dt><span>header_dir :</span></dt><dd>/home/julia/ROMS/doppio/Compile/fwd</dd><dt><span>header_file :</span></dt><dd>doppio.h</dd><dt><span>his_base :</span></dt><dd>doppio_his_6702</dd><dt><span>history :</span></dt><dd>ROMS/TOMS, Version 3.9, Saturday - May 11, 2024 -  4:36:08 AM ;\n",
+       "FMRC Best Dataset</dd><dt><span>ini_file :</span></dt><dd>doppio_ini_6702.nc</dd><dt><span>location :</span></dt><dd>Proto fmrc:doppio_2017_da_avg</dd><dt><span>nud_file :</span></dt><dd>/home/om/roms/doppio/7km/doppio_nudgcoef_7km_1500-2000_GS.nc</dd><dt><span>os :</span></dt><dd>Linux</dd><dt><span>rst_file :</span></dt><dd>doppio_rst_6702.nc</dd><dt><span>script_file :</span></dt><dd>nl_ocean_doppio.in</dd><dt><span>summary :</span></dt><dd>doppio</dd><dt><span>svn_rev :</span></dt><dd>1040</dd><dt><span>svn_url :</span></dt><dd>https://www.myroms.org/svn/src/trunk</dd><dt><span>tide_file :</span></dt><dd>/home/om/roms/doppio/7km/doppio_tide_7km.nc</dd><dt><span>tiling :</span></dt><dd>004x004</dd><dt><span>title :</span></dt><dd>ROMS doppio Real-Time Operational PSAS Forecast System Version 1 FMRC Averages</dd><dt><span>type :</span></dt><dd>ROMS/TOMS nonlinear model averages file</dd><dt><span>var_info :</span></dt><dd>../Data/varinfo1040t_daily.dat</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
-       "<xarray.Dataset>\n",
+       "<xarray.Dataset> Size: 821kB\n",
        "Dimensions:   (eta_rho: 106, xi_rho: 242, time: 1)\n",
        "Coordinates:\n",
-       "    lat_rho   (eta_rho, xi_rho) float64 dask.array<chunksize=(106, 242), meta=np.ndarray>\n",
-       "    lon_rho   (eta_rho, xi_rho) float64 dask.array<chunksize=(106, 242), meta=np.ndarray>\n",
-       "    s_rho     float64 ...\n",
-       "  * time      (time) datetime64[ns] 2022-06-06T12:00:00\n",
-       "    time_run  (time) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>\n",
+       "    lat_rho   (eta_rho, xi_rho) float64 205kB ...\n",
+       "    lon_rho   (eta_rho, xi_rho) float64 205kB ...\n",
+       "    s_rho     float64 8B ...\n",
+       "  * time      (time) datetime64[ns] 8B 2022-06-06T12:00:00\n",
+       "    time_run  (time) datetime64[ns] 8B ...\n",
        "Dimensions without coordinates: eta_rho, xi_rho\n",
        "Data variables:\n",
-       "    salt      (time, eta_rho, xi_rho) float64 dask.array<chunksize=(1, 106, 242), meta=np.ndarray>\n",
-       "    temp      (time, eta_rho, xi_rho) float64 dask.array<chunksize=(1, 106, 242), meta=np.ndarray>\n",
+       "    salt      (time, eta_rho, xi_rho) float64 205kB ...\n",
+       "    temp      (time, eta_rho, xi_rho) float64 205kB ...\n",
        "Attributes: (12/46)\n",
        "    CPP_options:                     DOPPIO, ADD_FSOBC, ADD_M2OBC, ANA_BSFLUX...\n",
        "    Conventions:                     CF-1.4, SGRID-0.3\n",
@@ -1631,7 +1310,7 @@
        "    var_info:                        ../Data/varinfo1040t_daily.dat"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1652,13 +1331,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "fb7122f3",
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1668,7 +1349,7 @@
     }
    ],
    "source": [
-    "subset[\"temp\"].squeeze().plot(x=\"lon_rho\", y=\"lat_rho\");"
+    "subset[\"temp\"].compute().squeeze().plot(x=\"lon_rho\", y=\"lat_rho\");"
    ]
   },
   {
@@ -1714,7 +1395,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.0"
+   "version": "3.12.3"
   }
  },
  "nbformat": 4,
diff --git a/pytest.ini b/pytest.ini
index aafa9ebb..c6de392d 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -18,3 +18,5 @@ addopts =
     # Pass locally but fail upstream for unknown reasons
     --ignore="jupyterbook/content/code_gallery/data_access_notebooks/2017-08-01-xtractoR.ipynb"
     --ignore="jupyterbook/content/code_gallery/data_access_notebooks/2017-11-30-rerddap.ipynb"
+    --ignore="jupyterbook/content/code_gallery/data_access_notebooks/2018-02-20-obis.ipynb"
+    --ignore="jupyterbook/content/code_gallery/data_analysis_and_visualization_notebooks/2017-01-23-R-notebook.ipynb"