{"diffoscope-json-version": 1, "source1": "/srv/reproducible-results/rbuild-debian/r-b-build.fJv1aePw/b1/python-xarray_2023.01.0-1.1_armhf.changes", "source2": "/srv/reproducible-results/rbuild-debian/r-b-build.fJv1aePw/b2/python-xarray_2023.01.0-1.1_armhf.changes", "unified_diff": null, "details": [{"source1": "Files", "source2": "Files", "unified_diff": "@@ -1,3 +1,3 @@\n \n- f2c747a67f8200ce5498424acef0c8e1 5277120 doc optional python-xarray-doc_2023.01.0-1.1_all.deb\n+ 44e33ac1c4d118fd91e800b7932faaee 5277408 doc optional python-xarray-doc_2023.01.0-1.1_all.deb\n 6e5f6af35de770365644ec5792cfe64e 630312 python optional python3-xarray_2023.01.0-1.1_all.deb\n"}, {"source1": "python-xarray-doc_2023.01.0-1.1_all.deb", "source2": "python-xarray-doc_2023.01.0-1.1_all.deb", "unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -1,3 +1,3 @@\n -rw-r--r-- 0 0 0 4 2023-02-19 00:50:57.000000 debian-binary\n -rw-r--r-- 0 0 0 6248 2023-02-19 00:50:57.000000 control.tar.xz\n--rw-r--r-- 0 0 0 5270680 2023-02-19 00:50:57.000000 data.tar.xz\n+-rw-r--r-- 0 0 0 5270968 2023-02-19 00:50:57.000000 data.tar.xz\n"}, {"source1": "control.tar.xz", "source2": "control.tar.xz", "unified_diff": null, "details": [{"source1": "control.tar", "source2": "control.tar", "unified_diff": null, "details": [{"source1": "./control", "source2": "./control", "unified_diff": "@@ -1,13 +1,13 @@\n Package: python-xarray-doc\n Source: python-xarray\n Version: 2023.01.0-1.1\n Architecture: all\n Maintainer: Debian Science Maintainers \n-Installed-Size: 13025\n+Installed-Size: 13026\n Depends: libjs-sphinxdoc (>= 5.2), libjs-mathjax, libjs-requirejs\n Built-Using: alabaster (= 0.7.12-1), sphinx (= 5.3.0-4)\n Section: doc\n Priority: optional\n Homepage: http://xarray.pydata.org/\n Description: documentation for xarray\n xarray (formerly xray) is an open source project and Python package that aims\n"}, {"source1": "./md5sums", "source2": "./md5sums", "unified_diff": null, "details": [{"source1": "./md5sums", "source2": "./md5sums", "comments": ["Files differ"], "unified_diff": null}]}]}]}, {"source1": "data.tar.xz", "source2": "data.tar.xz", "unified_diff": null, "details": [{"source1": "data.tar", "source2": "data.tar", "unified_diff": null, "details": [{"source1": "file list", "source2": "file list", "unified_diff": "@@ -233,31 +233,31 @@\n -rw-r--r-- 0 root (0) root (0) 5097 2023-02-19 00:50:57.000000 ./usr/share/doc/python-xarray-doc/html/api-hidden.html\n -rw-r--r-- 0 root (0) root (0) 17503 2023-02-19 00:50:57.000000 ./usr/share/doc/python-xarray-doc/html/api.html\n -rw-r--r-- 0 root (0) root (0) 81125 2023-02-19 00:50:57.000000 ./usr/share/doc/python-xarray-doc/html/contributing.html\n -rw-r--r-- 0 root (0) root (0) 7040 2023-02-19 00:50:57.000000 ./usr/share/doc/python-xarray-doc/html/developers-meeting.html\n -rw-r--r-- 0 root (0) root (0) 19841 2023-02-19 00:50:57.000000 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./usr/share/doc/python-xarray-doc/html/examples/weather-data.ipynb.gz\n -rw-r--r-- 0 root (0) root (0) 6546 2023-02-19 00:50:57.000000 ./usr/share/doc/python-xarray-doc/html/gallery.html\n -rw-r--r-- 0 root (0) root (0) 8063 2023-02-19 00:50:57.000000 ./usr/share/doc/python-xarray-doc/html/genindex.html\n drwxr-xr-x 0 root (0) root (0) 0 2023-02-19 00:50:57.000000 ./usr/share/doc/python-xarray-doc/html/getting-started-guide/\n -rw-r--r-- 0 root (0) root (0) 28859 2023-02-19 00:50:57.000000 ./usr/share/doc/python-xarray-doc/html/getting-started-guide/faq.html\n -rw-r--r-- 0 root (0) root (0) 6381 2023-02-19 00:50:57.000000 ./usr/share/doc/python-xarray-doc/html/getting-started-guide/index.html\n -rw-r--r-- 0 root (0) root (0) 20999 2023-02-19 00:50:57.000000 ./usr/share/doc/python-xarray-doc/html/getting-started-guide/installing.html\n -rw-r--r-- 0 root (0) root (0) 42869 2023-02-19 00:50:57.000000 ./usr/share/doc/python-xarray-doc/html/getting-started-guide/quick-overview.html\n@@ 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"./usr/share/doc/python-xarray-doc/html/examples/ERA5-GRIB-example.html", "unified_diff": "@@ -433,15 +433,15 @@\n \n \n
\n
\n
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\n 
\n-Error in callback <function _draw_all_if_interactive at 0xab993398> (for post_execute):\n+Error in callback <function _draw_all_if_interactive at 0xec4c83e8> (for post_execute):\n
\n
\n
\n
\n
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\n 
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -99,15 +99,15 @@\n ----> 7 plot = ds.t2m[0].plot(\n       8     cmap=plt.cm.coolwarm, transform=ccrs.PlateCarree(), cbar_kwargs=\n {\"shrink\": 0.6}\n       9 )\n      10 plt.title(\"ERA5 - 2m temperature British Isles March 2019\")\n \n NameError: name 'ds' is not defined\n-Error in callback  (for\n+Error in callback  (for\n post_execute):\n ---------------------------------------------------------------------------\n PermissionError                           Traceback (most recent call last)\n File /usr/lib/python3/dist-packages/matplotlib/pyplot.py:119, in\n _draw_all_if_interactive()\n     117 def _draw_all_if_interactive():\n     118     if matplotlib.is_interactive():\n"}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/examples/ERA5-GRIB-example.ipynb.gz", "source2": "./usr/share/doc/python-xarray-doc/html/examples/ERA5-GRIB-example.ipynb.gz", "unified_diff": null, "details": [{"source1": "ERA5-GRIB-example.ipynb", "source2": "ERA5-GRIB-example.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9985416666666667%", "Differences: {\"'cells'\": \"{2: {'metadata': {'execution': {'iopub.execute_input': '2024-01-12T07:11:32.555303Z', \"", "            \"'iopub.status.busy': '2024-01-12T07:11:32.544632Z', 'iopub.status.idle': \"", "            \"'2024-01-12T07:12:00.141414Z', 'shell.execute_reply': \"", "            \"'2024-01-12T07:12:00.137464Z'}}}, 4: {'metadata': {'execution': \"", "            \"{'iopub.execute_input': '2024-01-12T07:12:00.186716Z', 'iopub.status.busy': \"", "            \"'2024-01-12T07:12:00.172439Z', 'iopub.status.idle': '2024-01-12T07:12:0 [\u2026]"], "unified_diff": "@@ -15,18 +15,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 1,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:27:45.659634Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:27:45.658603Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:27:56.981312Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:27:56.974026Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:11:32.555303Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:11:32.544632Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:12:00.141414Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:12:00.137464Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"name\": \"stderr\",\n                     \"output_type\": \"stream\",\n                     \"text\": [\n@@ -54,18 +54,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 2,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:27:56.995943Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:27:56.994799Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:27:58.504791Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:27:58.493195Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:12:00.186716Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:12:00.172439Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:12:03.490813Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:12:03.481047Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"ImportError\",\n                     \"evalue\": \"tutorial.open_dataset depends on pooch to download and manage datasets. To proceed please install pooch.\",\n                     \"output_type\": \"error\",\n@@ -95,18 +95,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 3,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:27:58.531900Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:27:58.530912Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:27:58.668475Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:27:58.655996Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:12:03.530528Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:12:03.516818Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:12:03.758827Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:12:03.752318Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n@@ -131,18 +131,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 4,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:27:58.688901Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:27:58.682330Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:07.904844Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:07.900757Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:12:03.802179Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:12:03.788211Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:12:43.422803Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:12:43.409443Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n@@ -153,15 +153,15 @@\n                         \"\\u001b[0;31mNameError\\u001b[0m: name 'ds' is not defined\"\n                     ]\n                 },\n                 {\n                     \"name\": \"stdout\",\n                     \"output_type\": \"stream\",\n                     \"text\": [\n-                        \"Error in callback  (for post_execute):\\n\"\n+                        \"Error in callback  (for post_execute):\\n\"\n                     ]\n                 },\n                 {\n                     \"ename\": \"PermissionError\",\n                     \"evalue\": \"[Errno 13] Permission denied: '/nonexistent'\",\n                     \"output_type\": \"error\",\n                     \"traceback\": [\n@@ -262,18 +262,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 5,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:07.934908Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:07.933593Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:08.016535Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:08.014220Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:12:43.491677Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:12:43.476951Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:12:43.841505Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:12:43.833471Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n"}]}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/examples/ROMS_ocean_model.ipynb.gz", "source2": "./usr/share/doc/python-xarray-doc/html/examples/ROMS_ocean_model.ipynb.gz", "unified_diff": null, "details": [{"source1": "ROMS_ocean_model.ipynb", "source2": "ROMS_ocean_model.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9988051470588235%", "Differences: {\"'cells'\": \"{2: {'metadata': {'execution': {'iopub.execute_input': '2024-01-12T07:13:28.895559Z', \"", "            \"'iopub.status.busy': '2024-01-12T07:13:28.884752Z', 'iopub.status.idle': \"", "            \"'2024-01-12T07:13:50.738928Z', 'shell.execute_reply': \"", "            \"'2024-01-12T07:13:50.725448Z'}}}, 5: {'metadata': {'execution': \"", "            \"{'iopub.execute_input': '2024-01-12T07:13:50.800417Z', 'iopub.status.busy': \"", "            \"'2024-01-12T07:13:50.785000Z', 'iopub.status.idle': '2024-01-12T07:13:5 [\u2026]"], "unified_diff": "@@ -17,18 +17,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 1,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:20.016913Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:20.015742Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:26.407406Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:26.400924Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:13:28.895559Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:13:28.884752Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:13:50.738928Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:13:50.725448Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"import numpy as np\\n\",\n                 \"import cartopy.crs as ccrs\\n\",\n                 \"import cartopy.feature as cfeature\\n\",\n@@ -75,18 +75,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 2,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:26.451149Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:26.444092Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:27.880056Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:27.876540Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:13:50.800417Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:13:50.785000Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:13:55.779942Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:13:55.773502Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"ImportError\",\n                     \"evalue\": \"tutorial.open_dataset depends on pooch to download and manage datasets. To proceed please install pooch.\",\n                     \"output_type\": \"error\",\n@@ -130,18 +130,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 3,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:27.905009Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:27.902910Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:28.022945Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:28.020466Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:13:55.848873Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:13:55.846611Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:13:56.399557Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:13:56.393503Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n@@ -175,18 +175,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 4,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:28.034440Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:28.033593Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:28.152468Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:28.147071Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:13:56.460876Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:13:56.458607Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:13:56.836022Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:13:56.829521Z\"\n                 },\n                 \"scrolled\": false\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n@@ -211,18 +211,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 5,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:28.166343Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:28.165553Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:28.233905Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:28.231522Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:13:56.888719Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:13:56.886430Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:13:57.131933Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:13:57.113508Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n@@ -250,18 +250,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 6,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:28.245275Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:28.244359Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:28.313242Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:28.309503Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:13:57.192900Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:13:57.190651Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:13:57.571773Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:13:57.565505Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n@@ -285,18 +285,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 7,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:28.326428Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:28.325587Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:29.224496Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:29.212455Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:13:57.638944Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:13:57.636493Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:14:01.305608Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:14:01.289508Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n"}]}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/examples/apply_ufunc_vectorize_1d.ipynb.gz", "source2": "./usr/share/doc/python-xarray-doc/html/examples/apply_ufunc_vectorize_1d.ipynb.gz", "unified_diff": null, "details": [{"source1": "apply_ufunc_vectorize_1d.ipynb", "source2": "apply_ufunc_vectorize_1d.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9994283536585367%", "Differences: {\"'cells'\": \"{2: {'metadata': {'execution': {'iopub.execute_input': '2024-01-12T07:14:40.539598Z', \"", "            \"'iopub.status.busy': '2024-01-12T07:14:40.524738Z', 'iopub.status.idle': \"", "            \"'2024-01-12T07:14:57.157626Z', 'shell.execute_reply': \"", "            \"'2024-01-12T07:14:57.141523Z'}}}, 4: {'metadata': {'execution': \"", "            \"{'iopub.execute_input': '2024-01-12T07:14:57.233942Z', 'iopub.status.busy': \"", "            \"'2024-01-12T07:14:57.215378Z', 'iopub.status.idle': '2024-01-12T07:14:5 [\u2026]"], "unified_diff": "@@ -36,18 +36,18 @@\n             \"execution_count\": 1,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:45:51.659160Z\",\n                     \"start_time\": \"2020-01-15T14:45:50.528742Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:39.751599Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:39.750572Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:44.092490Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:44.076476Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:14:40.539598Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:14:40.524738Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:14:57.157626Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:14:57.141523Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"ImportError\",\n                     \"evalue\": \"tutorial.open_dataset depends on pooch to download and manage datasets. To proceed please install pooch.\",\n                     \"output_type\": \"error\",\n@@ -91,18 +91,18 @@\n             \"execution_count\": 2,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:45:55.431708Z\",\n                     \"start_time\": \"2020-01-15T14:45:55.104701Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:44.119492Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:44.118634Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:44.247113Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:44.236806Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:14:57.233942Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:14:57.215378Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:14:57.633622Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:14:57.617543Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -131,18 +131,18 @@\n             \"execution_count\": 3,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:45:57.889496Z\",\n                     \"start_time\": \"2020-01-15T14:45:57.792269Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:44.270162Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:44.269283Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:44.363112Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:44.359855Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:14:57.701391Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:14:57.683097Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:14:58.175688Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:14:58.165509Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -190,18 +190,18 @@\n             \"execution_count\": 4,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:45:59.768626Z\",\n                     \"start_time\": \"2020-01-15T14:45:59.543808Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:44.385668Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:44.384804Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:44.477476Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:44.471236Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:14:58.242575Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:14:58.227957Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:14:58.631494Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:14:58.625504Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -256,18 +256,18 @@\n             \"execution_count\": 5,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:46:02.187012Z\",\n                     \"start_time\": \"2020-01-15T14:46:02.105563Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:44.501794Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:44.500890Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:44.575515Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:44.571338Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:14:58.665407Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:14:58.663024Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:14:58.929278Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:14:58.923146Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -334,18 +334,18 @@\n             \"execution_count\": 6,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:46:05.031672Z\",\n                     \"start_time\": \"2020-01-15T14:46:04.947588Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:44.587475Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:44.586623Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:44.680573Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:44.678336Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:14:58.960788Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:14:58.958714Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:14:59.180814Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:14:59.173256Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -380,18 +380,18 @@\n             \"execution_count\": 7,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:46:09.325218Z\",\n                     \"start_time\": \"2020-01-15T14:46:09.303020Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:44.694533Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:44.691814Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:44.777794Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:44.775113Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:14:59.217092Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:14:59.214657Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:14:59.435088Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:14:59.428350Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -428,18 +428,18 @@\n             \"execution_count\": 8,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:46:11.295440Z\",\n                     \"start_time\": \"2020-01-15T14:46:11.226553Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:44.795684Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:44.794854Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:44.897428Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:44.888151Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:14:59.472598Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:14:59.471148Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:14:59.933490Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:14:59.917457Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -492,18 +492,18 @@\n             \"execution_count\": 9,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:46:13.808646Z\",\n                     \"start_time\": \"2020-01-15T14:46:13.680098Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:44.910341Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:44.909495Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:45.005826Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:45.000788Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:14:59.983592Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:14:59.972847Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:15:00.513602Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:15:00.497499Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -565,18 +565,18 @@\n             \"execution_count\": 10,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:46:26.633233Z\",\n                     \"start_time\": \"2020-01-15T14:46:26.515209Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:45.018983Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:45.018177Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:45.107067Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:45.104461Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:15:00.574621Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:15:00.566662Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:15:01.105590Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:15:01.089483Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -622,18 +622,18 @@\n             \"execution_count\": 11,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:46:30.026663Z\",\n                     \"start_time\": \"2020-01-15T14:46:29.893267Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:45.120392Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:45.119478Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:45.227144Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:45.223091Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:15:01.164850Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:15:01.162606Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:15:01.760563Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:15:01.753507Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -710,18 +710,18 @@\n             \"execution_count\": 12,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:48:42.469341Z\",\n                     \"start_time\": \"2020-01-15T14:48:42.344209Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:45.246384Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:45.244835Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:45.353060Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:45.350688Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:15:01.829950Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:15:01.823260Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:15:02.491388Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:15:02.478134Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -796,18 +796,18 @@\n             \"execution_count\": 13,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:48:45.267633Z\",\n                     \"start_time\": \"2020-01-15T14:48:44.943939Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:45.369914Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:45.369028Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:45.516508Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:45.500424Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:15:02.565343Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:15:02.551111Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:15:02.875263Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:15:02.865548Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"ModuleNotFoundError\",\n                     \"evalue\": \"No module named 'numba'\",\n                     \"output_type\": \"error\",\n@@ -848,18 +848,18 @@\n             \"execution_count\": 14,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:48:54.755405Z\",\n                     \"start_time\": \"2020-01-15T14:48:54.634724Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:45.539248Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:45.538415Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:45.715421Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:45.700429Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:15:02.946494Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:15:02.944188Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:15:03.484073Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:15:03.477510Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'interp1d_np_gufunc' is not defined\",\n                     \"output_type\": \"error\",\n@@ -902,18 +902,18 @@\n             \"execution_count\": 15,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-01-15T14:49:28.667528Z\",\n                     \"start_time\": \"2020-01-15T14:49:28.103914Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:45.735359Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:45.734494Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:28:45.932517Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:28:45.916452Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:15:03.551647Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:15:03.548128Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:15:04.161635Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:15:04.145617Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"ModuleNotFoundError\",\n                     \"evalue\": \"No module named 'numba'\",\n                     \"output_type\": \"error\",\n"}]}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/examples/area_weighted_temperature.ipynb.gz", "source2": "./usr/share/doc/python-xarray-doc/html/examples/area_weighted_temperature.ipynb.gz", "unified_diff": null, "details": [{"source1": "area_weighted_temperature.ipynb", "source2": "area_weighted_temperature.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.99921875%", "Differences: {\"'cells'\": \"{2: {'metadata': {'execution': {'iopub.execute_input': '2024-01-12T07:16:10.459385Z', \"", "            \"'iopub.status.busy': '2024-01-12T07:16:10.448667Z', 'iopub.status.idle': \"", "            \"'2024-01-12T07:16:35.914804Z', 'shell.execute_reply': \"", "            \"'2024-01-12T07:16:35.905449Z'}}}, 4: {'metadata': {'execution': \"", "            \"{'iopub.execute_input': '2024-01-12T07:16:35.971728Z', 'iopub.status.busy': \"", "            \"'2024-01-12T07:16:35.948614Z', 'iopub.status.idle': '2024-01-12T07:16:4 [\u2026]"], "unified_diff": "@@ -28,18 +28,18 @@\n             \"execution_count\": 1,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-03-17T14:43:57.222351Z\",\n                     \"start_time\": \"2020-03-17T14:43:56.147541Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:28:59.089378Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:28:59.088185Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:03.777284Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:03.770050Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:16:10.459385Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:16:10.448667Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:16:35.914804Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:16:35.905449Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"%matplotlib inline\\n\",\n                 \"\\n\",\n                 \"import cartopy.crs as ccrs\\n\",\n@@ -63,18 +63,18 @@\n             \"execution_count\": 2,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-03-17T14:43:57.831734Z\",\n                     \"start_time\": \"2020-03-17T14:43:57.651845Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:03.793926Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:03.792657Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:04.470927Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:04.468775Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:16:35.971728Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:16:35.948614Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:16:41.299922Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:16:41.293026Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"ImportError\",\n                     \"evalue\": \"tutorial.open_dataset depends on pooch to download and manage datasets. To proceed please install pooch.\",\n                     \"output_type\": \"error\",\n@@ -116,18 +116,18 @@\n             \"execution_count\": 3,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-03-17T14:43:59.887120Z\",\n                     \"start_time\": \"2020-03-17T14:43:59.582894Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:04.481977Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:04.481186Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:04.892126Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:04.889926Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:16:41.332536Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:16:41.330116Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:16:43.533614Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:16:43.517573Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -172,18 +172,18 @@\n             \"execution_count\": 4,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-03-17T14:44:18.777092Z\",\n                     \"start_time\": \"2020-03-17T14:44:18.736587Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:04.907607Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:04.906468Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:04.969062Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:04.966736Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:16:43.597108Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:16:43.594791Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:16:43.997593Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:16:43.981513Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -213,18 +213,18 @@\n             \"execution_count\": 5,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-03-17T14:44:52.607120Z\",\n                     \"start_time\": \"2020-03-17T14:44:52.564674Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:04.985138Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:04.979887Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:05.038553Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:05.036398Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:16:44.049159Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:16:44.046839Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:16:44.425609Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:16:44.409506Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air' is not defined\",\n                     \"output_type\": \"error\",\n@@ -246,18 +246,18 @@\n             \"execution_count\": 6,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-03-17T14:44:54.334279Z\",\n                     \"start_time\": \"2020-03-17T14:44:54.280022Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:05.049916Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:05.049035Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:05.617684Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:05.612425Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:16:44.492680Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:16:44.490532Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:16:45.869600Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:16:45.853509Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'air_weighted' is not defined\",\n                     \"output_type\": \"error\",\n@@ -288,18 +288,18 @@\n             \"execution_count\": 7,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2020-03-17T14:45:08.877307Z\",\n                     \"start_time\": \"2020-03-17T14:45:08.673383Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:05.639390Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:05.638558Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:05.768472Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:05.752415Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:16:45.949040Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:16:45.946820Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:16:46.335853Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:16:46.329502Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'weighted_mean' is not defined\",\n                     \"output_type\": \"error\",\n"}]}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/examples/blank_template.ipynb.gz", "source2": "./usr/share/doc/python-xarray-doc/html/examples/blank_template.ipynb.gz", "unified_diff": null, "details": [{"source1": "blank_template.ipynb", "source2": "blank_template.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9991319444444444%", "Differences: {\"'cells'\": \"{1: {'metadata': {'execution': {'iopub.execute_input': '2024-01-12T07:17:25.863843Z', \"", "            \"'iopub.status.busy': '2024-01-12T07:17:25.852207Z', 'iopub.status.idle': \"", "            \"'2024-01-12T07:17:40.713613Z', 'shell.execute_reply': \"", "            \"'2024-01-12T07:17:40.697514Z'}}}}\"}"], "unified_diff": "@@ -12,18 +12,18 @@\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 1,\n             \"id\": \"41b90ede\",\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:16.543369Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:16.542402Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:19.845511Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:19.842875Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:17:25.863843Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:17:25.852207Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:17:40.713613Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:17:40.697514Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"ImportError\",\n                     \"evalue\": \"tutorial.open_dataset depends on pooch to download and manage datasets. To proceed please install pooch.\",\n                     \"output_type\": \"error\",\n"}]}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/examples/monthly-means.ipynb.gz", "source2": "./usr/share/doc/python-xarray-doc/html/examples/monthly-means.ipynb.gz", "unified_diff": null, "details": [{"source1": "monthly-means.ipynb", "source2": "monthly-means.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.998721590909091%", "Differences: {\"'cells'\": \"{1: {'metadata': {'execution': {'iopub.execute_input': '2024-01-12T07:18:12.287447Z', \"", "            \"'iopub.status.busy': '2024-01-12T07:18:12.272781Z', 'iopub.status.idle': \"", "            \"'2024-01-12T07:18:31.081477Z', 'shell.execute_reply': \"", "            \"'2024-01-12T07:18:31.069451Z'}}}, 3: {'metadata': {'execution': \"", "            \"{'iopub.execute_input': '2024-01-12T07:18:31.156308Z', 'iopub.status.busy': \"", "            \"'2024-01-12T07:18:31.123862Z', 'iopub.status.idle': '2024-01-12T07:18:3 [\u2026]"], "unified_diff": "@@ -19,18 +19,18 @@\n             \"execution_count\": 1,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:51:35.958210Z\",\n                     \"start_time\": \"2018-11-28T20:51:35.936966Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:27.943292Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:27.942410Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:34.497605Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:34.487860Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:18:12.287447Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:18:12.272781Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:18:31.081477Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:18:31.069451Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"%matplotlib inline\\n\",\n                 \"import numpy as np\\n\",\n                 \"import pandas as pd\\n\",\n@@ -50,18 +50,18 @@\n             \"execution_count\": 2,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:51:36.072316Z\",\n                     \"start_time\": \"2018-11-28T20:51:36.016594Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:34.522932Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:34.521641Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:35.553068Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:35.549732Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:18:31.156308Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:18:31.123862Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:18:36.917030Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:18:36.909529Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"ImportError\",\n                     \"evalue\": \"tutorial.open_dataset depends on pooch to download and manage datasets. To proceed please install pooch.\",\n                     \"output_type\": \"error\",\n@@ -96,18 +96,18 @@\n             ]\n         },\n         {\n             \"cell_type\": \"code\",\n             \"execution_count\": 3,\n             \"metadata\": {\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:35.572064Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:35.571119Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:35.675460Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:35.672569Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:18:36.985552Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:18:36.983104Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:18:37.327003Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:18:37.313494Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n@@ -129,18 +129,18 @@\n             \"execution_count\": 4,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:51:36.132413Z\",\n                     \"start_time\": \"2018-11-28T20:51:36.073708Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:35.694581Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:35.693520Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:35.832101Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:35.828504Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:18:37.388660Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:18:37.386307Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:18:38.021607Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:18:38.005517Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'month_length' is not defined\",\n                     \"output_type\": \"error\",\n@@ -170,18 +170,18 @@\n             \"execution_count\": 5,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:51:36.152913Z\",\n                     \"start_time\": \"2018-11-28T20:51:36.133997Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:35.855147Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:35.854238Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:35.949907Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:35.947536Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:18:38.085071Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:18:38.082778Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:18:38.392220Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:18:38.385525Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds_weighted' is not defined\",\n                     \"output_type\": \"error\",\n@@ -202,18 +202,18 @@\n             \"execution_count\": 6,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:51:36.190765Z\",\n                     \"start_time\": \"2018-11-28T20:51:36.154416Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:35.974016Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:35.973111Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:36.065118Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:36.060560Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:18:38.422894Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:18:38.420485Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:18:38.791928Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:18:38.779568Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n@@ -236,18 +236,18 @@\n             \"execution_count\": 7,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:51:40.264871Z\",\n                     \"start_time\": \"2018-11-28T20:51:36.192467Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:36.087792Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:36.086913Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:36.266815Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:36.263477Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:18:38.850160Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:18:38.847484Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:18:39.162649Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:18:39.158596Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds_unweighted' is not defined\",\n                     \"output_type\": \"error\",\n@@ -316,18 +316,18 @@\n             \"execution_count\": 8,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:51:40.284898Z\",\n                     \"start_time\": \"2018-11-28T20:51:40.266406Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:36.282748Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:36.281797Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:36.314928Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:36.312589Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:18:39.184050Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:18:39.180812Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:18:39.218316Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:18:39.214314Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"# Wrap it into a simple function\\n\",\n                 \"def season_mean(ds, calendar=\\\"standard\\\"):\\n\",\n                 \"    # Make a DataArray with the number of days in each month, size = len(time)\\n\",\n"}]}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/examples/multidimensional-coords.ipynb.gz", "source2": "./usr/share/doc/python-xarray-doc/html/examples/multidimensional-coords.ipynb.gz", "unified_diff": null, "details": [{"source1": "multidimensional-coords.ipynb", "source2": "multidimensional-coords.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.99931640625%", "Differences: {\"'cells'\": \"{1: {'metadata': {'execution': {'iopub.execute_input': '2024-01-12T07:19:20.722371Z', \"", "            \"'iopub.status.busy': '2024-01-12T07:19:20.704404Z', 'iopub.status.idle': \"", "            \"'2024-01-12T07:19:45.302904Z', 'shell.execute_reply': \"", "            \"'2024-01-12T07:19:45.293458Z'}}}, 3: {'metadata': {'execution': \"", "            \"{'iopub.execute_input': '2024-01-12T07:19:45.352248Z', 'iopub.status.busy': \"", "            \"'2024-01-12T07:19:45.340961Z', 'iopub.status.idle': '2024-01-12T07:19:5 [\u2026]"], "unified_diff": "@@ -16,18 +16,18 @@\n             \"execution_count\": 1,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:49:56.068395Z\",\n                     \"start_time\": \"2018-11-28T20:49:56.035349Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:47.355494Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:47.354550Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:53.928633Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:53.915953Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:19:20.722371Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:19:20.704404Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:19:45.302904Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:19:45.293458Z\"\n                 }\n             },\n             \"outputs\": [],\n             \"source\": [\n                 \"%matplotlib inline\\n\",\n                 \"import numpy as np\\n\",\n                 \"import pandas as pd\\n\",\n@@ -48,18 +48,18 @@\n             \"execution_count\": 2,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:50:13.629720Z\",\n                     \"start_time\": \"2018-11-28T20:50:13.484542Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:53.973019Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:53.947963Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:55.161211Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:55.158670Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:19:45.352248Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:19:45.340961Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:19:50.661593Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:19:50.657443Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"ImportError\",\n                     \"evalue\": \"tutorial.open_dataset depends on pooch to download and manage datasets. To proceed please install pooch.\",\n                     \"output_type\": \"error\",\n@@ -93,18 +93,18 @@\n             \"execution_count\": 3,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:50:15.836061Z\",\n                     \"start_time\": \"2018-11-28T20:50:15.768376Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:55.177573Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:55.176609Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:55.258326Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:55.255684Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:19:50.719814Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:19:50.709021Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:19:51.034801Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:19:51.025439Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n@@ -135,18 +135,18 @@\n             \"execution_count\": 4,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:50:17.928556Z\",\n                     \"start_time\": \"2018-11-28T20:50:17.031211Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:55.273177Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:55.272234Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:58.083899Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:58.080445Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:19:51.091503Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:19:51.080825Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:19:59.137504Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:19:59.121453Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n@@ -188,18 +188,18 @@\n             \"execution_count\": 5,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:50:20.567749Z\",\n                     \"start_time\": \"2018-11-28T20:50:19.999393Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:58.095045Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:58.094177Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:58.204989Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:58.200508Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:19:59.191190Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:19:59.168612Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:19:59.521503Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:19:59.509440Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n@@ -227,18 +227,18 @@\n             \"execution_count\": 6,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:50:31.131708Z\",\n                     \"start_time\": \"2018-11-28T20:50:30.444697Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:58.235602Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:58.234715Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:58.989925Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:58.984481Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:19:59.574631Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:19:59.559927Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:20:01.221657Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:20:01.213507Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n@@ -285,18 +285,18 @@\n             \"execution_count\": 7,\n             \"metadata\": {\n                 \"ExecuteTime\": {\n                     \"end_time\": \"2018-11-28T20:50:43.670463Z\",\n                     \"start_time\": \"2018-11-28T20:50:43.245501Z\"\n                 },\n                 \"execution\": {\n-                    \"iopub.execute_input\": \"2024-01-12T06:29:59.027577Z\",\n-                    \"iopub.status.busy\": \"2024-01-12T06:29:59.026706Z\",\n-                    \"iopub.status.idle\": \"2024-01-12T06:29:59.267594Z\",\n-                    \"shell.execute_reply\": \"2024-01-12T06:29:59.252453Z\"\n+                    \"iopub.execute_input\": \"2024-01-12T07:20:01.302125Z\",\n+                    \"iopub.status.busy\": \"2024-01-12T07:20:01.280434Z\",\n+                    \"iopub.status.idle\": \"2024-01-12T07:20:01.811581Z\",\n+                    \"shell.execute_reply\": \"2024-01-12T07:20:01.796696Z\"\n                 }\n             },\n             \"outputs\": [\n                 {\n                     \"ename\": \"NameError\",\n                     \"evalue\": \"name 'ds' is not defined\",\n                     \"output_type\": \"error\",\n"}]}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/examples/visualization_gallery.html", "source2": "./usr/share/doc/python-xarray-doc/html/examples/visualization_gallery.html", "unified_diff": "@@ -574,15 +574,15 @@\n
\n
\n
\n
\n
\n
\n 
\n-/tmp/ipykernel_24969/2946363816.py:1: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n+/tmp/ipykernel_27594/2946363816.py:1: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n   da = xr.tutorial.open_rasterio("RGB.byte")\n
\n
\n
\n
\n
\n
\n@@ -657,15 +657,15 @@\n
\n
\n
\n
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\n 
\n-/tmp/ipykernel_24969/3653941964.py:4: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n+/tmp/ipykernel_27594/3653941964.py:4: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n   da = xr.tutorial.open_rasterio("RGB.byte")\n
\n
\n
\n
\n
\n
\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -210,15 +210,15 @@\n # https://github.com/SciTools/cartopy/issues/813 is implemented\n crs = ccrs.UTM(\"18\")\n \n # Plot on a map\n ax = plt.subplot(projection=crs)\n da.plot.imshow(ax=ax, rgb=\"band\", transform=crs)\n ax.coastlines(\"10m\", color=\"r\")\n-/tmp/ipykernel_24969/2946363816.py:1: DeprecationWarning: open_rasterio is\n+/tmp/ipykernel_27594/2946363816.py:1: DeprecationWarning: open_rasterio is\n Deprecated in favor of rioxarray. For information about transitioning, see:\n https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n da = xr.tutorial.open_rasterio(\"RGB.byte\")\n ---------------------------------------------------------------------------\n ModuleNotFoundError Traceback (most recent call last)\n File /build/reproducible-path/python-xarray-2023.01.0/xarray/tutorial.py:222,\n in open_rasterio(name, engine, cache, cache_dir, **kws)\n@@ -282,15 +282,15 @@\n y=\"lat\",\n transform=ccrs.PlateCarree(),\n cmap=\"Greys_r\",\n shading=\"auto\",\n add_colorbar=False,\n )\n ax.coastlines(\"10m\", color=\"r\")\n-/tmp/ipykernel_24969/3653941964.py:4: DeprecationWarning: open_rasterio is\n+/tmp/ipykernel_27594/3653941964.py:4: DeprecationWarning: open_rasterio is\n Deprecated in favor of rioxarray. For information about transitioning, see:\n https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\n da = xr.tutorial.open_rasterio(\"RGB.byte\")\n ---------------------------------------------------------------------------\n ModuleNotFoundError Traceback (most recent call last)\n File /build/reproducible-path/python-xarray-2023.01.0/xarray/tutorial.py:222,\n in open_rasterio(name, engine, cache, cache_dir, **kws)\n"}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/examples/visualization_gallery.ipynb.gz", "source2": "./usr/share/doc/python-xarray-doc/html/examples/visualization_gallery.ipynb.gz", "unified_diff": null, "details": [{"source1": "visualization_gallery.ipynb", "source2": "visualization_gallery.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9983506944444445%", "Differences: {\"'cells'\": \"{1: {'metadata': {'execution': {'iopub.execute_input': '2024-01-12T07:20:43.365334Z', \"", " \"'iopub.status.busy': '2024-01-12T07:20:43.347185Z', 'iopub.status.idle': \"", " \"'2024-01-12T07:21:06.292541Z', 'shell.execute_reply': \"", " \"'2024-01-12T07:21:06.285518Z'}}}, 3: {'metadata': {'execution': \"", " \"{'iopub.execute_input': '2024-01-12T07:21:06.363581Z', 'iopub.status.busy': \"", " \"'2024-01-12T07:21:06.350619Z', 'iopub.status.idle': '2024-01-12T07:21:1 [\u2026]"], "unified_diff": "@@ -10,18 +10,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:14.697103Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:14.691355Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:30:21.728694Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:30:21.712830Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:20:43.365334Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:20:43.347185Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:21:06.292541Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:21:06.285518Z\"\n }\n },\n \"outputs\": [],\n \"source\": [\n \"import cartopy.crs as ccrs\\n\",\n \"import matplotlib.pyplot as plt\\n\",\n \"import xarray as xr\\n\",\n@@ -37,18 +37,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 2,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:21.748048Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:21.746789Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:30:23.136524Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:30:23.124463Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:21:06.363581Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:21:06.350619Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:21:11.617507Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:21:11.601451Z\"\n }\n },\n \"outputs\": [\n {\n \"ename\": \"ImportError\",\n \"evalue\": \"tutorial.open_dataset depends on pooch to download and manage datasets. To proceed please install pooch.\",\n \"output_type\": \"error\",\n@@ -85,18 +85,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 3,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:23.171852Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:23.170939Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:30:23.400504Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:30:23.384452Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:21:11.707459Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:21:11.688591Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:21:12.284334Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:21:12.277438Z\"\n }\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'ds' is not defined\",\n \"output_type\": \"error\",\n@@ -138,18 +138,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 4,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:23.419572Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:23.418707Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:30:23.672523Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:30:23.656467Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:21:12.343388Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:21:12.337746Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:21:12.931854Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:21:12.921498Z\"\n }\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'ds' is not defined\",\n \"output_type\": \"error\",\n@@ -202,18 +202,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 5,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:23.691397Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:23.690506Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:30:23.892487Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:30:23.884455Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:21:13.012538Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:21:13.011027Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:21:13.604006Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:21:13.593503Z\"\n }\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'ds' is not defined\",\n \"output_type\": \"error\",\n@@ -258,18 +258,18 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 6,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:23.911269Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:23.910393Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:30:24.189202Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:30:24.120436Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:21:13.669045Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:21:13.660834Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:21:14.281499Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:21:14.265434Z\"\n }\n },\n \"outputs\": [\n {\n \"ename\": \"NameError\",\n \"evalue\": \"name 'ds' is not defined\",\n \"output_type\": \"error\",\n@@ -320,26 +320,26 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 7,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:24.207107Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:24.206238Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:30:25.300489Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:30:25.286057Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:21:14.350194Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:21:14.327961Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:21:16.866809Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:21:16.853428Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n- \"/tmp/ipykernel_24969/2946363816.py:1: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\\n\",\n+ \"/tmp/ipykernel_27594/2946363816.py:1: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\\n\",\n \" da = xr.tutorial.open_rasterio(\\\"RGB.byte\\\")\\n\"\n ]\n },\n {\n \"ename\": \"ImportError\",\n \"evalue\": \"tutorial.open_rasterio depends on pooch to download and manage datasets. To proceed please install pooch.\",\n \"output_type\": \"error\",\n@@ -385,26 +385,26 @@\n ]\n },\n {\n \"cell_type\": \"code\",\n \"execution_count\": 8,\n \"metadata\": {\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:25.323536Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:25.322663Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:30:25.737645Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:30:25.728452Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:21:16.939860Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:21:16.925067Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:21:18.313509Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:21:18.297438Z\"\n }\n },\n \"outputs\": [\n {\n \"name\": \"stderr\",\n \"output_type\": \"stream\",\n \"text\": [\n- \"/tmp/ipykernel_24969/3653941964.py:4: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\\n\",\n+ \"/tmp/ipykernel_27594/3653941964.py:4: DeprecationWarning: open_rasterio is Deprecated in favor of rioxarray. For information about transitioning, see: https://corteva.github.io/rioxarray/stable/getting_started/getting_started.html\\n\",\n \" da = xr.tutorial.open_rasterio(\\\"RGB.byte\\\")\\n\"\n ]\n },\n {\n \"ename\": \"ImportError\",\n \"evalue\": \"tutorial.open_rasterio depends on pooch to download and manage datasets. To proceed please install pooch.\",\n \"output_type\": \"error\",\n"}]}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/examples/weather-data.html", "source2": "./usr/share/doc/python-xarray-doc/html/examples/weather-data.html", "unified_diff": "@@ -705,37 +705,37 @@\n 
<xarray.Dataset>\n Dimensions:   (time: 731, location: 3)\n Coordinates:\n   * time      (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2001-12-31\n   * location  (location) <U2 'IA' 'IN' 'IL'\n Data variables:\n     tmin      (time, location) float64 -8.037 -1.788 -3.932 ... -1.346 -4.544\n-    tmax      (time, location) float64 12.98 3.31 6.779 ... 6.636 3.343 3.805
  • location
    PandasIndex
    PandasIndex(Index(['IA', 'IN', 'IL'], dtype='object', name='location'))
    • \n \n
    \n

    Examine a dataset with pandas and seaborn\u00b6

    \n
    \n

    Convert to a pandas DataFrame\u00b6

    \n
    \n 
    [2]:\n@@ -932,15 +932,15 @@\n
    \n
    \n 
    [5]:\n
    \n
    \n
    \n 
    \n-<seaborn.axisgrid.PairGrid at 0xaa1915f0>\n+<seaborn.axisgrid.PairGrid at 0xeab28fd0>\n
    \n
    \n
    \n
    \n
    \n
    \n \"../_images/examples_weather-data_9_1.png\"\n@@ -1338,26 +1338,26 @@\n [0. , 0. , 0. ],\n [0. , 0. , 0. ],\n [0. , 0.01612903, 0. ],\n [0.33333333, 0.35 , 0.23333333],\n [0.93548387, 0.85483871, 0.82258065]])\n Coordinates:\n * location (location) <U2 'IA' 'IN' 'IL'\n- * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
    • location
      (location)
      <U2
      'IA' 'IN' 'IL'
      array(['IA', 'IN', 'IL'], dtype='<U2')
    • month
      (month)
      int64
      1 2 3 4 5 6 7 8 9 10 11 12
      array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int64)
    • location
      PandasIndex
      PandasIndex(Index(['IA', 'IN', 'IL'], dtype='object', name='location'))
    • month
      PandasIndex
      PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))
    • \n \n
    \n 
    [7]:\n
    \n
    \n 
    freeze.to_pandas().plot()\n
    \n@@ -1863,18 +1863,18 @@\n Dimensions: (time: 731, location: 3)\n Coordinates:\n * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2001-12-31\n * location (location) <U2 'IA' 'IN' 'IL'\n month (time) int64 1 1 1 1 1 1 1 1 1 ... 12 12 12 12 12 12 12 12 12\n Data variables:\n some_missing (time, location) float64 nan nan nan ... 2.063 -1.346 -4.544\n- filled (time, location) float64 -5.163 -4.216 ... -1.346 -4.544
  • location
    PandasIndex
    PandasIndex(Index(['IA', 'IN', 'IL'], dtype='object', name='location'))
    • \n \n
    \n 
    [12]:\n
    \n
    \n 
    df = both.sel(time="2000").mean("location").reset_coords(drop=True).to_dataframe()\n df.head()\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -157,15 +157,15 @@\n \n [../_images/examples_weather-data_7_1.png]\n \n **** Visualize using seaborn\u00c2\u00b6 ****\n [5]:\n sns.pairplot(df.reset_index(), vars=ds.data_vars)\n [5]:\n-\n+\n [../_images/examples_weather-data_9_1.png]\n \n ***** Probability of freeze by calendar month\u00c2\u00b6 *****\n [6]:\n freeze = (ds[\"tmin\"] <= 0).groupby(\"time.month\").mean(\"time\")\n freeze\n [6]:\n"}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/examples/weather-data.ipynb.gz", "source2": "./usr/share/doc/python-xarray-doc/html/examples/weather-data.ipynb.gz", "unified_diff": null, "details": [{"source1": "weather-data.ipynb", "source2": "weather-data.ipynb", "unified_diff": null, "details": [{"source1": "Pretty-printed", "source2": "Pretty-printed", "comments": ["Similarity: 0.9992323118860381%", "Differences: {\"'cells'\": \"{1: {'metadata': {'execution': {'iopub.execute_input': '2024-01-12T07:22:02.370334Z', \"", "            \"'iopub.status.busy': '2024-01-12T07:22:02.352558Z', 'iopub.status.idle': \"", "            \"'2024-01-12T07:22:37.843692Z', 'shell.execute_reply': \"", "            \"'2024-01-12T07:22:37.837513Z'}}, 'outputs': {0: {'data': {'text/html': {insert: \"", "            '[(370, \"    tmax      (time, location) float64 12.98 3.31 6.779 ... 6.636 3.343 '", "            \"3.805
    <xarray.Dataset>\\n\",\n \"Dimensions: (time: 731, location: 3)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2001-12-31\\n\",\n \" * location (location) <U2 'IA' 'IN' 'IL'\\n\",\n \"Data variables:\\n\",\n \" tmin (time, location) float64 -8.037 -1.788 -3.932 ... -1.346 -4.544\\n\",\n- \" tmax (time, location) float64 12.98 3.31 6.779 ... 6.636 3.343 3.805
    • \"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (time: 731, location: 3)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2001-12-31\\n\",\n \" * location (location) \\n\",\n@@ -587,18 +587,18 @@\n \"execution_count\": 3,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:47:32.682065Z\",\n \"start_time\": \"2020-01-27T15:47:32.652629Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:48.698455Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:48.697628Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:30:48.760511Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:30:48.757951Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:22:38.209636Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:22:38.203179Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:22:38.507621Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:22:38.501523Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
    \\n\",\n@@ -701,18 +701,18 @@\n \"execution_count\": 4,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:47:34.617042Z\",\n \"start_time\": \"2020-01-27T15:47:34.282605Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:48.771307Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:48.770489Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:30:51.584494Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:30:51.576455Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:22:38.586282Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:22:38.583834Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:22:43.759932Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:22:43.753496Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n@@ -749,25 +749,25 @@\n \"execution_count\": 5,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:47:37.643175Z\",\n \"start_time\": \"2020-01-27T15:47:37.202479Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:51.603471Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:51.602541Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:30:59.208552Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:30:59.192447Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:22:43.838564Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:22:43.826437Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:23:12.597486Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:23:12.583334Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n- \"\"\n+ \"\"\n ]\n },\n \"execution_count\": 5,\n \"metadata\": {},\n \"output_type\": \"execute_result\"\n },\n {\n@@ -797,18 +797,18 @@\n \"execution_count\": 6,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:48:11.241224Z\",\n \"start_time\": \"2020-01-27T15:48:11.211156Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:59.239391Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:59.238519Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:30:59.421224Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:30:59.418929Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:23:12.680098Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:23:12.677010Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:23:13.251286Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:23:13.244533Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
    \\n\",\n@@ -1185,26 +1185,26 @@\n \" [0. , 0. , 0. ],\\n\",\n \" [0. , 0. , 0. ],\\n\",\n \" [0. , 0.01612903, 0. ],\\n\",\n \" [0.33333333, 0.35 , 0.23333333],\\n\",\n \" [0.93548387, 0.85483871, 0.82258065]])\\n\",\n \"Coordinates:\\n\",\n \" * location (location) <U2 'IA' 'IN' 'IL'\\n\",\n- \" * month (month) int64 1 2 3 4 5 6 7 8 9 10 11 12
    • location
      (location)
      <U2
      'IA' 'IN' 'IL'
      array(['IA', 'IN', 'IL'], dtype='<U2')
    • month
      (month)
      int64
      1 2 3 4 5 6 7 8 9 10 11 12
      array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12], dtype=int64)
    • location
      PandasIndex
      PandasIndex(Index(['IA', 'IN', 'IL'], dtype='object', name='location'))
    • month
      PandasIndex
      PandasIndex(Int64Index([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], dtype='int64', name='month'))
    • \"\n ],\n \"text/plain\": [\n \"\\n\",\n \"array([[0.9516129 , 0.88709677, 0.93548387],\\n\",\n \" [0.84210526, 0.71929825, 0.77192982],\\n\",\n \" [0.24193548, 0.12903226, 0.16129032],\\n\",\n \" [0. , 0. , 0. ],\\n\",\n@@ -1236,18 +1236,18 @@\n \"execution_count\": 7,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:48:13.131247Z\",\n \"start_time\": \"2020-01-27T15:48:12.924985Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:30:59.430870Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:30:59.430044Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:31:00.244500Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:31:00.228469Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:23:13.316697Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:23:13.303180Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:23:17.793493Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:23:17.781432Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n@@ -1284,18 +1284,18 @@\n \"execution_count\": 8,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:48:08.498259Z\",\n \"start_time\": \"2020-01-27T15:48:08.210890Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:31:00.259194Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:31:00.258366Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:31:02.540507Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:31:02.524450Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:23:17.812981Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:23:17.811409Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:23:23.831001Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:23:23.821482Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n@@ -1349,18 +1349,18 @@\n \"execution_count\": 9,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:49:34.855086Z\",\n \"start_time\": \"2020-01-27T15:49:34.406439Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:31:02.563511Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:31:02.562632Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:31:04.667100Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:31:04.664822Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:23:23.869460Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:23:23.863488Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:23:29.481132Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:23:29.477434Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n@@ -1408,18 +1408,18 @@\n \"execution_count\": 10,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:50:09.144586Z\",\n \"start_time\": \"2020-01-27T15:50:08.734682Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:31:04.684627Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:31:04.683713Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:31:06.868989Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:31:06.856443Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:23:29.526170Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:23:29.516454Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:23:36.589466Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:23:36.585442Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n@@ -1477,18 +1477,18 @@\n \"execution_count\": 11,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:51:40.279299Z\",\n \"start_time\": \"2020-01-27T15:51:40.220342Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:31:06.883616Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:31:06.882874Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:31:07.196495Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:31:07.180450Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:23:36.646578Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:23:36.640712Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:23:37.593497Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:23:37.585398Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/html\": [\n \"
    \\n\",\n@@ -1858,18 +1858,18 @@\n \"Dimensions: (time: 731, location: 3)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2001-12-31\\n\",\n \" * location (location) <U2 'IA' 'IN' 'IL'\\n\",\n \" month (time) int64 1 1 1 1 1 1 1 1 1 ... 12 12 12 12 12 12 12 12 12\\n\",\n \"Data variables:\\n\",\n \" some_missing (time, location) float64 nan nan nan ... 2.063 -1.346 -4.544\\n\",\n- \" filled (time, location) float64 -5.163 -4.216 ... -1.346 -4.544
    • \"\n ],\n \"text/plain\": [\n \"\\n\",\n \"Dimensions: (time: 731, location: 3)\\n\",\n \"Coordinates:\\n\",\n \" * time (time) datetime64[ns] 2000-01-01 2000-01-02 ... 2001-12-31\\n\",\n \" * location (location) \\n\",\n@@ -2052,18 +2052,18 @@\n \"execution_count\": 13,\n \"metadata\": {\n \"ExecuteTime\": {\n \"end_time\": \"2020-01-27T15:52:14.867866Z\",\n \"start_time\": \"2020-01-27T15:52:14.449684Z\"\n },\n \"execution\": {\n- \"iopub.execute_input\": \"2024-01-12T06:31:07.363542Z\",\n- \"iopub.status.busy\": \"2024-01-12T06:31:07.362670Z\",\n- \"iopub.status.idle\": \"2024-01-12T06:31:09.737512Z\",\n- \"shell.execute_reply\": \"2024-01-12T06:31:09.734980Z\"\n+ \"iopub.execute_input\": \"2024-01-12T07:23:37.822135Z\",\n+ \"iopub.status.busy\": \"2024-01-12T07:23:37.808143Z\",\n+ \"iopub.status.idle\": \"2024-01-12T07:23:44.161314Z\",\n+ \"shell.execute_reply\": \"2024-01-12T07:23:44.157451Z\"\n }\n },\n \"outputs\": [\n {\n \"data\": {\n \"text/plain\": [\n \"\"\n"}]}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/getting-started-guide/quick-overview.html", "source2": "./usr/share/doc/python-xarray-doc/html/getting-started-guide/quick-overview.html", "unified_diff": "@@ -310,15 +310,15 @@\n
    \n \n
    \n
    \n

    Plotting\u00b6

    \n

    Visualizing your datasets is quick and convenient:

    \n 
    In [37]: data.plot()\n-Out[37]: <matplotlib.collections.QuadMesh at 0xab597710>\n+Out[37]: <matplotlib.collections.QuadMesh at 0xee94efb0>\n
    \n
    \n \"../_images/plotting_quick_overview.png\"\n

    Note the automatic labeling with names and units. Our effort in adding metadata attributes has paid off! Many aspects of these figures are customizable: see Plotting.

    \n
    \n
    \n

    pandas\u00b6

    \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -269,15 +269,15 @@\n Coordinates:\n * x (x) int32 10 20\n Dimensions without coordinates: y\n \n ***** Plotting\u00c2\u00b6 *****\n Visualizing your datasets is quick and convenient:\n In [37]: data.plot()\n-Out[37]: \n+Out[37]: \n [../_images/plotting_quick_overview.png]\n Note the automatic labeling with names and units. Our effort in adding metadata\n attributes has paid off! Many aspects of these figures are customizable: see\n Plotting.\n \n ***** pandas\u00c2\u00b6 *****\n Xarray objects can be easily converted to and from pandas objects using the\n"}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/searchindex.js", "source2": "./usr/share/doc/python-xarray-doc/html/searchindex.js", "unified_diff": null, "details": [{"source1": "js-beautify {}", "source2": "js-beautify {}", "unified_diff": "@@ -1124,15 +1124,15 @@\n \"british\": 6,\n \"isl\": 6,\n \"march\": [6, 46],\n \"7\": [6, 7, 8, 9, 11, 12, 13, 14, 19, 23, 25, 28, 29, 31, 32, 33, 34, 36, 38, 39, 40, 42, 43, 44, 46, 47],\n \"callback\": 6,\n \"lt\": [6, 14],\n \"_draw_all_if_interact\": 6,\n- \"0xab993398\": 6,\n+ \"0xec4c83e8\": 6,\n \"post_execut\": 6,\n \"permissionerror\": 6,\n \"usr\": 6,\n \"lib\": 6,\n \"dist\": 6,\n \"119\": [6, 32, 38, 39, 43, 46],\n \"117\": [6, 32, 38, 43, 46],\n@@ -1281,15 +1281,15 @@\n \"108\": [6, 32, 38, 43, 46],\n \"105\": [6, 31, 32, 38, 42, 43, 48],\n \"isnan\": 6,\n \"106\": [6, 32, 38, 43, 48],\n \"extent_geom\": 6,\n \"sgeom\": 6,\n \"box\": 6,\n- \"107\": [6, 32, 38, 43],\n+ \"107\": [6, 32, 33, 38, 43],\n \"109\": [6, 32, 38, 43],\n \"110\": [6, 32, 38, 43],\n \"111\": [6, 32, 38, 43],\n \"287\": 6,\n \"categori\": [6, 29, 34],\n \"286\": 6,\n \"_natural_earth_geom_cach\": 6,\n@@ -1813,21 +1813,21 @@\n \"until\": [13, 33, 36, 40, 43],\n \"scitool\": [13, 18],\n \"813\": 13,\n \"band\": [13, 34, 40, 44],\n \"color\": [13, 16, 18, 43, 48],\n \"r\": [13, 28, 32, 33, 34, 38, 39, 40, 48],\n \"tmp\": 13,\n- \"ipykernel_24969\": 13,\n+ \"ipykernel_27594\": 13,\n \"2946363816\": 13,\n \"favor\": [13, 16, 40, 42, 48],\n \"transit\": [13, 40, 43, 48],\n \"corteva\": [13, 40],\n \"getting_start\": [13, 40],\n- \"222\": [13, 33],\n+ \"222\": 13,\n \"221\": 13,\n \"223\": 13,\n \"224\": 13,\n \"226\": 13,\n \"227\": 13,\n \"229\": [13, 48],\n \"230\": 13,\n@@ -1936,15 +1936,15 @@\n \"80527925\": 14,\n \"timepandasindexpandasindex\": 14,\n \"datetimeindex\": [14, 34, 38, 46, 47, 48],\n \"04\": [14, 33, 34, 38, 40, 42, 46],\n \"05\": [14, 32, 34, 40, 43, 46, 47, 48],\n \"06\": [14, 32, 34, 40, 46],\n \"07\": [14, 34, 46, 47],\n- \"08\": [14, 34, 46, 48],\n+ \"08\": [14, 33, 34, 46, 48],\n \"09\": [14, 32, 34, 46],\n \"22\": [14, 19, 29, 31, 32, 33, 34, 36, 38, 39, 40, 42, 43, 44, 46, 47],\n \"26\": [14, 19, 31, 32, 33, 34, 36, 38, 39, 40, 42, 43, 44, 46, 47],\n \"27\": [14, 19, 31, 32, 33, 34, 36, 38, 39, 40, 43, 44, 46, 47],\n \"freq\": [14, 34, 38, 46, 47, 48],\n \"locationpandasindexpandasindex\": 14,\n \"df\": [14, 33, 34, 42, 48],\n@@ -1983,15 +1983,15 @@\n \"xlabel\": [14, 43],\n \"pairplot\": 14,\n \"reset_index\": [14, 42, 44, 48],\n \"var\": [14, 25, 31, 32, 38, 43, 48],\n \"data_var\": [14, 32, 34, 38, 40, 44, 48],\n \"axisgrid\": 14,\n \"pairgrid\": 14,\n- \"0xaa1915f0\": 14,\n+ \"0xeab28fd0\": 14,\n \"9516129\": 14,\n \"88709677\": 14,\n \"93548387\": 14,\n \"84210526\": 14,\n \"71929825\": 14,\n \"77192982\": 14,\n \"24193548\": 14,\n@@ -2321,15 +2321,15 @@\n \"36\": [19, 31, 32, 33, 34, 38, 39, 40, 43, 44, 46, 48],\n \"9781708\": 19,\n \"37342613\": 19,\n \"49497537\": 19,\n \"33584385\": 19,\n \"37\": [19, 31, 32, 33, 34, 36, 38, 39, 40, 43, 44, 48],\n \"quadmesh\": [19, 43, 48],\n- \"0xab597710\": 19,\n+ \"0xee94efb0\": 19,\n \"paid\": 19,\n \"customiz\": 19,\n \"to_seri\": [19, 42, 48],\n \"to_xarrai\": [19, 42],\n \"38\": [19, 31, 32, 33, 34, 38, 39, 40, 43, 44, 48],\n \"469112\": [19, 42],\n \"282863\": [19, 42],\n@@ -2993,15 +2993,15 @@\n \"64\": [32, 34, 38, 39, 40, 43, 48],\n \"65\": [32, 34, 38, 40, 43, 48],\n \"66\": [32, 34, 38, 39, 40, 43, 48],\n \"67\": [32, 34, 36, 38, 40, 43, 48],\n \"1999\": 32,\n \"364\": [32, 46],\n \"68\": [32, 34, 38, 40, 43, 48],\n- \"69\": [32, 34, 38, 40, 43, 48],\n+ \"69\": [32, 33, 34, 38, 40, 43, 48],\n \"03343858\": 32,\n \"06683976\": 32,\n \"48672119\": 32,\n \"51565952\": 32,\n \"54402111\": 32,\n \"03343845\": 32,\n \"06683951\": 32,\n@@ -3183,15 +3183,15 @@\n \"gaussian_2d\": 32,\n \"xalpha\": 32,\n \"yalpha\": 32,\n \"94\": [32, 38, 43, 48],\n \"multi_peak\": 32,\n \"zero\": [32, 38, 40, 43, 48],\n \"96\": [32, 38, 43, 48],\n- \"97\": [32, 38, 43, 48],\n+ \"97\": [32, 33, 38, 43, 48],\n \"n_peak\": 32,\n \"99\": [32, 34, 38, 39, 43, 44, 48],\n \"101\": [32, 38, 43, 48],\n \"102\": [32, 38, 43, 48],\n \"normal\": [32, 38, 43, 44, 48],\n \"103\": [32, 38, 43, 48],\n \"param_nam\": 32,\n@@ -3213,15 +3213,15 @@\n \"466e\": 32,\n \"parlanc\": 32,\n \"expand\": [32, 40, 42, 48],\n \"reorder\": [32, 48],\n \"112\": [32, 38, 43],\n \"subtract\": [32, 33, 38, 39, 48],\n \"113\": [32, 38, 43],\n- \"114\": [32, 33, 38, 43],\n+ \"114\": [32, 38, 43],\n \"a2\": [32, 48],\n \"b2\": [32, 48],\n \"115\": [32, 38, 43],\n \"116\": [32, 38, 43, 46],\n \"inner\": [32, 38, 48],\n \"120\": [32, 38, 40, 43, 46],\n \"arithmetic_join\": [32, 41, 48],\n@@ -3308,19 +3308,22 @@\n \"178\": 33,\n \"179\": [33, 38],\n \"concaten\": [33, 36, 40, 44, 48],\n \"disclaim\": 33,\n \"execut\": [33, 48],\n \"ineffect\": 33,\n \"reveal\": 33,\n- \"86fd385b8a6f7c00d182c4a804b7fb4ctemperatur\": 33,\n+ \"66d334fc2f4611bff21c1218002c3d06temperatur\": 33,\n \"progressbar\": 33,\n \"progress\": [33, 48],\n \"schedul\": [33, 48],\n \"delayed_obj\": 33,\n+ \"211\": 33,\n+ \"315\": 33,\n+ \"420\": 33,\n \"hdf5_use_file_lock\": 33,\n \"compet\": 33,\n \"hdf5\": [33, 40, 48],\n \"swmr\": 33,\n \"scheme\": [33, 46],\n \"to_dask_datafram\": [33, 42, 48],\n \"npartit\": 33,\n@@ -3667,16 +3670,16 @@\n \"__delitem__\": [34, 48],\n \"shallow\": 34,\n \"modif\": [34, 40],\n \"temperature2\": 34,\n \"chain\": [34, 38, 48],\n \"flow\": 34,\n \"line2d\": [34, 39, 43],\n- \"0xa447e770\": 34,\n- \"0xa44b4750\": 34,\n+ \"0xe5332a10\": 34,\n+ \"0xe4e7c970\": 34,\n \"penalti\": 34,\n \"mutat\": [34, 48],\n \"swap_dim\": [34, 48],\n \"swap\": [34, 48],\n \"ancillari\": 34,\n \"sole\": [34, 48],\n \"otherwis\": [34, 38, 44, 47, 48],\n@@ -3963,19 +3966,19 @@\n \"911\": 39,\n \"912\": 39,\n \"789\": 39,\n \"069\": 39,\n \"interp1d\": [39, 46, 48],\n \"decomposit\": 39,\n \"interpn\": 39,\n- \"0xade47150\": 39,\n- \"0xab58eb10\": 39,\n+ \"0xe50483b0\": 39,\n+ \"0xee971150\": 39,\n \"cubic\": [39, 48],\n- \"0xadf9fbb0\": 39,\n- \"0xadf927b0\": 39,\n+ \"0xee84da30\": 39,\n+ \"0xee849310\": 39,\n \"814\": [39, 40],\n \"604\": 39,\n \"2778\": 39,\n \"05556\": 39,\n \"1667\": 39,\n \"8333\": [39, 40],\n \"056\": 39,\n@@ -4440,15 +4443,15 @@\n \"dataarraycoordin\": [43, 48],\n \"385\": 43,\n \"t_dataarrai\": 43,\n \"819\": 43,\n \"818\": 43,\n \"zip\": [43, 48],\n \"_replace_maybe_drop_dim\": 43,\n- \"0xa15581b0\": 43,\n+ \"0xe1f76bb0\": 43,\n \"contour\": [43, 48],\n \"prove\": 43,\n \"america\": 43,\n \"nha\": 43,\n \"fallen\": 43,\n \"ylabel\": 43,\n \"d_ylog\": 43,\n@@ -4557,80 +4560,80 @@\n \"373e\": 43,\n \"072e\": 43,\n \"667e\": 43,\n \"453e\": 43,\n \"906e\": 43,\n \"aunit\": 43,\n \"pathcollect\": 43,\n- \"0xa14a82f0\": 43,\n- \"0xa14964f0\": 43,\n- \"0xa12db170\": 43,\n- \"0xa1222e50\": 43,\n+ \"0xe4e5ecf0\": 43,\n+ \"0xe5bd3a30\": 43,\n+ \"0xe1f76f70\": 43,\n+ \"0xe1d6cbd0\": 43,\n \"colorbar\": [43, 48],\n- \"0xa11c4930\": 43,\n- \"0xa12d7d50\": 43,\n+ \"0xe238c2d0\": 43,\n+ \"0xe1c2c190\": 43,\n \"markers\": 43,\n \"size_norm\": 43,\n- \"0xa118b0d0\": 43,\n+ \"0xe1c322f0\": 43,\n \"mpl_toolkit\": 43,\n \"mplot3d\": 43,\n \"art3d\": 43,\n \"path3dcollect\": 43,\n- \"0xa11de4f0\": 43,\n- \"0xa11cabf0\": 43,\n- \"0xa0ad3a50\": 43,\n- \"0xa03b8d70\": 43,\n+ \"0xe1c9c3d0\": 43,\n+ \"0xe1bcbc10\": 43,\n+ \"0xe2381df0\": 43,\n+ \"0xe15358d0\": 43,\n \"denot\": 43,\n- \"0xa048eb10\": 43,\n+ \"0xe195c2d0\": 43,\n \"streamlin\": 43,\n \"linecollect\": 43,\n- \"0xa04dfd30\": 43,\n+ \"0xe16a0bf0\": 43,\n \"tangent\": 43,\n- \"0xa0e44a90\": 43,\n+ \"0xe1785410\": 43,\n \"script\": [43, 48],\n \"orthograph\": 43,\n \"grai\": [43, 48],\n \"transfer\": 43,\n \"gridlin\": 43,\n \"submodul\": 43,\n \"xplt\": 43,\n- \"0xa0a65130\": 43,\n- \"0xa0ce4270\": 43,\n- \"0xa0f6e6b0\": 43,\n- \"0x9edaf030\": 43,\n+ \"0xdf809df0\": 43,\n+ \"0xdf85def0\": 43,\n+ \"0xe1979130\": 43,\n+ \"0xe0ecc390\": 43,\n \"dispatch\": [43, 48],\n \"uniformli\": 43,\n \"pixel\": [43, 48],\n \"carefulli\": 43,\n- \"0x9ee399b0\": 43,\n+ \"0xdf791b50\": 43,\n \"strang\": 43,\n- \"0x9ede9990\": 43,\n+ \"0xdfe82ef0\": 43,\n \"polar\": [43, 48],\n \"gh781\": [43, 48],\n \"geocollect\": 43,\n \"geoquadmesh\": 43,\n- \"0x9ede9bb0\": 43,\n- \"0x9ed308b0\": 43,\n- \"0x9f46b2d0\": 43,\n+ \"0xe1705f10\": 43,\n+ \"0xe18e3290\": 43,\n+ \"0xdf834510\": 43,\n \"draw_label\": 43,\n- \"0x9ed21a90\": 43,\n+ \"0xdf7630f0\": 43,\n \"infer_interv\": [43, 48],\n- \"0x9daa3b30\": 43,\n- \"0x9daab230\": 43,\n- \"0x9ed10cb0\": 43,\n- \"0x9db0e0d0\": 43,\n- \"0x9db14c10\": 43,\n- \"0x9da1d6f0\": 43,\n- \"0x9da1d850\": 43,\n- \"0x9da1d9b0\": 43,\n- \"0x9da94e30\": 43,\n- \"0x9da2a7b0\": 43,\n- \"0x9da2a910\": 43,\n- \"0x9da2aa70\": 43,\n- \"0x9da2abf0\": 43,\n+ \"0xde527730\": 43,\n+ \"0xde527d90\": 43,\n+ \"0xde52ec90\": 43,\n+ \"0xde504250\": 43,\n+ \"0xde523250\": 43,\n+ \"0xde49ba70\": 43,\n+ \"0xde4f5390\": 43,\n+ \"0xde49bbd0\": 43,\n+ \"0xde49b130\": 43,\n+ \"0xde4ac9f0\": 43,\n+ \"0xde4acb10\": 43,\n+ \"0xde4acc70\": 43,\n+ \"0xde4acdf0\": 43,\n \"revers\": 44,\n \"nascent\": [44, 48],\n \"unlist\": [44, 48],\n \"stacked2\": 44,\n \"depart\": 44,\n \"complic\": 44,\n \"sample_dim\": 44,\n@@ -6982,15 +6985,15 @@\n \"pete\": 48,\n \"cabl\": 48,\n \"sinclair\": 48,\n \"gh185\": 48,\n \"gh479\": 48,\n \"gh475\": 48,\n \"abcdefg\": 48,\n- \"0x9d68cfd0\": 48,\n+ \"0xde251f10\": 48,\n \"ma\": 48,\n \"maskedarrai\": 48,\n \"random_sampl\": 48,\n \"352\": 48,\n \"masked_arrai\": 48,\n \"12696983303810094\": 48,\n \"26047600586578334\": 48,\n"}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/user-guide/dask.html", "source2": "./usr/share/doc/python-xarray-doc/html/user-guide/dask.html", "unified_diff": "@@ -114,15 +114,15 @@\n

    You\u2019ll notice that printing a dataset still shows a preview of array values,\n even if they are actually Dask arrays. We can do this quickly with Dask because\n we only need to compute the first few values (typically from the first block).\n To reveal the true nature of an array, print a DataArray:

    \n 
    In [3]: ds.temperature\n Out[3]: \n <xarray.DataArray 'temperature' (time: 30, latitude: 180, longitude: 180)>\n-dask.array<open_dataset-86fd385b8a6f7c00d182c4a804b7fb4ctemperature, shape=(30, 180, 180), dtype=float64, chunksize=(10, 180, 180), chunktype=numpy.ndarray>\n+dask.array<open_dataset-66d334fc2f4611bff21c1218002c3d06temperature, shape=(30, 180, 180), dtype=float64, chunksize=(10, 180, 180), chunktype=numpy.ndarray>\n Coordinates:\n   * time       (time) datetime64[ns] 2015-01-01 2015-01-02 ... 2015-01-30\n   * longitude  (longitude) int32 0 1 2 3 4 5 6 7 ... 173 174 175 176 177 178 179\n   * latitude   (latitude) float64 89.5 88.5 87.5 86.5 ... -87.5 -88.5 -89.5\n
    \n
    \n

    Once you\u2019ve manipulated a Dask array, you can still write a dataset too big to\n@@ -138,17 +138,19 @@\n # or distributed.progress when using the distributed scheduler\n In [6]: delayed_obj = ds.to_netcdf("manipulated-example-data.nc", compute=False)\n \n In [7]: with ProgressBar():\n ...: results = delayed_obj.compute()\n ...: \n \n-[ ] | 0% Completed | 11.74 ms\n-[########## ] | 25% Completed | 114.86 ms\n-[########################################] | 100% Completed | 222.89 ms\n+[ ] | 0% Completed | 3.97 ms\n+[############### ] | 37% Completed | 107.69 ms\n+[############### ] | 37% Completed | 211.69 ms\n+[################################### ] | 87% Completed | 315.69 ms\n+[########################################] | 100% Completed | 420.08 ms\n \n \n

    \n

    Note

    \n

    When using Dask\u2019s distributed scheduler to write NETCDF4 files,\n it may be necessary to set the environment variable HDF5_USE_FILE_LOCKING=FALSE\n to avoid competing locks within the HDF5 SWMR file locking scheme. Note that\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -76,15 +76,15 @@\n You\u00e2\u0080\u0099ll notice that printing a dataset still shows a preview of array values,\n even if they are actually Dask arrays. We can do this quickly with Dask because\n we only need to compute the first few values (typically from the first block).\n To reveal the true nature of an array, print a DataArray:\n In [3]: ds.temperature\n Out[3]:\n \n-dask.array\n Coordinates:\n * time (time) datetime64[ns] 2015-01-01 2015-01-02 ... 2015-01-30\n * longitude (longitude) int32 0 1 2 3 4 5 6 7 ... 173 174 175 176 177 178\n 179\n * latitude (latitude) float64 89.5 88.5 87.5 86.5 ... -87.5 -88.5 -89.5\n Once you\u00e2\u0080\u0099ve manipulated a Dask array, you can still write a dataset too big\n@@ -98,17 +98,19 @@\n In [6]: delayed_obj = ds.to_netcdf(\"manipulated-example-data.nc\",\n compute=False)\n \n In [7]: with ProgressBar():\n ...: results = delayed_obj.compute()\n ...:\n \n-[ ] | 0% Completed | 11.74 ms\n-[########## ] | 25% Completed | 114.86 ms\n-[########################################] | 100% Completed | 222.89 ms\n+[ ] | 0% Completed | 3.97 ms\n+[############### ] | 37% Completed | 107.69 ms\n+[############### ] | 37% Completed | 211.69 ms\n+[################################### ] | 87% Completed | 315.69 ms\n+[########################################] | 100% Completed | 420.08 ms\n Note\n When using Dask\u00e2\u0080\u0099s distributed scheduler to write NETCDF4 files, it may be\n necessary to set the environment variableHDF5_USE_FILE_LOCKING=FALSEto avoid\n competing locks within the HDF5 SWMR file locking scheme. Note that writing\n netCDF files with Dask\u00e2\u0080\u0099s distributed scheduler is only supported for\n thenetcdf4backend.\n A dataset can also be converted to a Dask DataFrame using to_dask_dataframe().\n"}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/user-guide/data-structures.html", "source2": "./usr/share/doc/python-xarray-doc/html/user-guide/data-structures.html", "unified_diff": "@@ -687,18 +687,18 @@\n a method call with an external function (e.g., ds.pipe(func)) instead of\n simply calling it (e.g., func(ds)). This allows you to write pipelines for\n transforming your data (using \u201cmethod chaining\u201d) instead of writing hard to\n follow nested function calls:

    \n 
    # these lines are equivalent, but with pipe we can make the logic flow\n # entirely from left to right\n In [60]: plt.plot((2 * ds.temperature.sel(x=0)).mean("y"))\n-Out[60]: [<matplotlib.lines.Line2D at 0xa447e770>]\n+Out[60]: [<matplotlib.lines.Line2D at 0xe5332a10>]\n \n In [61]: (ds.temperature.sel(x=0).pipe(lambda x: 2 * x).mean("y").pipe(plt.plot))\n-Out[61]: [<matplotlib.lines.Line2D at 0xa44b4750>]\n+Out[61]: [<matplotlib.lines.Line2D at 0xe4e7c970>]\n
    \n
    \n

    Both pipe and assign replicate the pandas methods of the same names\n (DataFrame.pipe and\n DataFrame.assign).

    \n

    With xarray, there is no performance penalty for creating new datasets, even if\n variables are lazily loaded from a file on disk. Creating new objects instead\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -574,19 +574,19 @@\n There is also the pipe() method that allows you to use a method call with an\n external function (e.g., ds.pipe(func)) instead of simply calling it (e.g.,\n func(ds)). This allows you to write pipelines for transforming your data (using\n \u00e2\u0080\u009cmethod chaining\u00e2\u0080\u009d) instead of writing hard to follow nested function calls:\n # these lines are equivalent, but with pipe we can make the logic flow\n # entirely from left to right\n In [60]: plt.plot((2 * ds.temperature.sel(x=0)).mean(\"y\"))\n-Out[60]: []\n+Out[60]: []\n \n In [61]: (ds.temperature.sel(x=0).pipe(lambda x: 2 * x).mean(\"y\").pipe\n (plt.plot))\n-Out[61]: []\n+Out[61]: []\n Both pipe and assign replicate the pandas methods of the same names\n (DataFrame.pipe and DataFrame.assign).\n With xarray, there is no performance penalty for creating new datasets, even if\n variables are lazily loaded from a file on disk. Creating new objects instead\n of mutating existing objects often results in easier to understand code, so we\n encourage using this approach.\n \n"}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/user-guide/indexing.html", "source2": "./usr/share/doc/python-xarray-doc/html/user-guide/indexing.html", "unified_diff": "@@ -1277,15 +1277,15 @@\n File /build/reproducible-path/python-xarray-2023.01.0/xarray/core/utils.py:857, in drop_dims_from_indexers(indexers, dims, missing_dims)\n 856 if invalid:\n --> 857 raise ValueError(\n 858 f"Dimensions {invalid} do not exist. Expected one or more of {dims}"\n 859 )\n 861 return indexers\n \n-ValueError: Dimensions {'latitude', 'longitude'} do not exist. Expected one or more of ('x', 'y')\n+ValueError: Dimensions {'longitude', 'latitude'} do not exist. Expected one or more of ('x', 'y')\n \n The above exception was the direct cause of the following exception:\n \n ValueError Traceback (most recent call last)\n Cell In [87], line 1\n ----> 1 ds[dict(latitude=2, longitude=2)] = 1\n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -1165,15 +1165,15 @@\n 856 if invalid:\n --> 857 raise ValueError(\n 858 f\"Dimensions {invalid} do not exist. Expected one or more of\n {dims}\"\n 859 )\n 861 return indexers\n \n-ValueError: Dimensions {'latitude', 'longitude'} do not exist. Expected one or\n+ValueError: Dimensions {'longitude', 'latitude'} do not exist. Expected one or\n more of ('x', 'y')\n \n The above exception was the direct cause of the following exception:\n \n ValueError Traceback (most recent call last)\n Cell In [87], line 1\n ----> 1 ds[dict(latitude=2, longitude=2)] = 1\n"}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/user-guide/interpolation.html", "source2": "./usr/share/doc/python-xarray-doc/html/user-guide/interpolation.html", "unified_diff": "@@ -222,24 +222,24 @@\n ....: np.sin(np.linspace(0, 2 * np.pi, 10)),\n ....: dims="x",\n ....: coords={"x": np.linspace(0, 1, 10)},\n ....: )\n ....: \n \n In [17]: da.plot.line("o", label="original")\n-Out[17]: [<matplotlib.lines.Line2D at 0xade47150>]\n+Out[17]: [<matplotlib.lines.Line2D at 0xe50483b0>]\n \n In [18]: da.interp(x=np.linspace(0, 1, 100)).plot.line(label="linear (default)")\n-Out[18]: [<matplotlib.lines.Line2D at 0xab58eb10>]\n+Out[18]: [<matplotlib.lines.Line2D at 0xee971150>]\n \n In [19]: da.interp(x=np.linspace(0, 1, 100), method="cubic").plot.line(label="cubic")\n-Out[19]: [<matplotlib.lines.Line2D at 0xadf9fbb0>]\n+Out[19]: [<matplotlib.lines.Line2D at 0xee84da30>]\n \n In [20]: plt.legend()\n-Out[20]: <matplotlib.legend.Legend at 0xadf927b0>\n+Out[20]: <matplotlib.legend.Legend at 0xee849310>\n

    \n \n \"../_images/interpolation_sample1.png\"\n

    Additional keyword arguments can be passed to scipy\u2019s functions.

    \n 
    # fill 0 for the outside of the original coordinates.\n In [21]: da.interp(x=np.linspace(-0.5, 1.5, 10), kwargs={"fill_value": 0.0})\n Out[21]: \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -165,26 +165,26 @@\n    ....:     np.sin(np.linspace(0, 2 * np.pi, 10)),\n    ....:     dims=\"x\",\n    ....:     coords={\"x\": np.linspace(0, 1, 10)},\n    ....: )\n    ....:\n \n In [17]: da.plot.line(\"o\", label=\"original\")\n-Out[17]: []\n+Out[17]: []\n \n In [18]: da.interp(x=np.linspace(0, 1, 100)).plot.line(label=\"linear\n (default)\")\n-Out[18]: []\n+Out[18]: []\n \n In [19]: da.interp(x=np.linspace(0, 1, 100), method=\"cubic\").plot.line\n (label=\"cubic\")\n-Out[19]: []\n+Out[19]: []\n \n In [20]: plt.legend()\n-Out[20]: \n+Out[20]: \n [../_images/interpolation_sample1.png]\n Additional keyword arguments can be passed to scipy\u00e2\u0080\u0099s functions.\n # fill 0 for the outside of the original coordinates.\n In [21]: da.interp(x=np.linspace(-0.5, 1.5, 10), kwargs={\"fill_value\": 0.0})\n Out[21]:\n \n array([ 0.   ,  0.   ,  0.   ,  0.814,  0.604, -0.604, -0.814,  0.   ,  0.   ,\n"}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/user-guide/plotting.html", "source2": "./usr/share/doc/python-xarray-doc/html/user-guide/plotting.html", "unified_diff": "@@ -643,15 +643,15 @@\n --> 186     raise KeyError(key)\n     188 ref_name, var_name = split_key\n     189 ref_var = variables[ref_name]\n \n KeyError: 'lat'\n \n In [51]: b.plot()\n-Out[51]: [<matplotlib.lines.Line2D at 0xa15581b0>]\n+Out[51]: [<matplotlib.lines.Line2D at 0xe1f76bb0>]\n
    \n
    \n \"../_images/plotting_nonuniform_coords.png\"\n
    \n
    \n

    Other types of plot\u00b6

    \n

    There are several other options for plotting 2D data.

    \n@@ -1205,104 +1205,104 @@\n * y (y) float64 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0\n * z (z) int32 0 1 2 3\n * w (w) <U5 'one' 'two' 'three' 'five'\n Attributes:\n units: Aunits\n \n In [99]: ds.A.plot.scatter(x="y")\n-Out[99]: <matplotlib.collections.PathCollection at 0xa14a82f0>\n+Out[99]: <matplotlib.collections.PathCollection at 0xe4e5ecf0>\n \n \n \"../_images/da_A_y.png\"\n

    Same plot can be displayed using the dataset:

    \n 
    In [100]: ds.plot.scatter(x="y", y="A")\n-Out[100]: <matplotlib.collections.PathCollection at 0xa14964f0>\n+Out[100]: <matplotlib.collections.PathCollection at 0xe5bd3a30>\n
    \n
    \n \"../_images/ds_A_y.png\"\n

    Now suppose we want to scatter the A DataArray against the B DataArray

    \n 
    In [101]: ds.plot.scatter(x="A", y="B")\n-Out[101]: <matplotlib.collections.PathCollection at 0xa12db170>\n+Out[101]: <matplotlib.collections.PathCollection at 0xe1f76f70>\n
    \n
    \n \"../_images/ds_simple_scatter.png\"\n

    The hue kwarg lets you vary the color by variable value

    \n 
    In [102]: ds.plot.scatter(x="A", y="B", hue="w")\n-Out[102]: <matplotlib.collections.PathCollection at 0xa1222e50>\n+Out[102]: <matplotlib.collections.PathCollection at 0xe1d6cbd0>\n
    \n
    \n \"../_images/ds_hue_scatter.png\"\n

    You can force a legend instead of a colorbar by setting add_legend=True, add_colorbar=False.

    \n 
    In [103]: ds.plot.scatter(x="A", y="B", hue="w", add_legend=True, add_colorbar=False)\n-Out[103]: <matplotlib.collections.PathCollection at 0xa11c4930>\n+Out[103]: <matplotlib.collections.PathCollection at 0xe238c2d0>\n
    \n
    \n \"../_images/ds_discrete_legend_hue_scatter.png\"\n 
    In [104]: ds.plot.scatter(x="A", y="B", hue="w", add_legend=False, add_colorbar=True)\n-Out[104]: <matplotlib.collections.PathCollection at 0xa12d7d50>\n+Out[104]: <matplotlib.collections.PathCollection at 0xe1c2c190>\n
    \n
    \n \"../_images/ds_discrete_colorbar_hue_scatter.png\"\n

    The markersize kwarg lets you vary the point\u2019s size by variable value.\n You can additionally pass size_norm to control how the variable\u2019s values are mapped to point sizes.

    \n 
    In [105]: ds.plot.scatter(x="A", y="B", hue="y", markersize="z")\n-Out[105]: <matplotlib.collections.PathCollection at 0xa118b0d0>\n+Out[105]: <matplotlib.collections.PathCollection at 0xe1c322f0>\n
    \n
    \n \"../_images/ds_hue_size_scatter.png\"\n

    The z kwarg lets you plot the data along the z-axis as well.

    \n 
    In [106]: ds.plot.scatter(x="A", y="B", z="z", hue="y", markersize="x")\n-Out[106]: <mpl_toolkits.mplot3d.art3d.Path3DCollection at 0xa11de4f0>\n+Out[106]: <mpl_toolkits.mplot3d.art3d.Path3DCollection at 0xe1c9c3d0>\n
    \n
    \n \"../_images/ds_hue_size_scatter_z.png\"\n

    Faceting is also possible

    \n 
    In [107]: ds.plot.scatter(x="A", y="B", hue="y", markersize="x", row="x", col="w")\n-Out[107]: <xarray.plot.facetgrid.FacetGrid at 0xa11cabf0>\n+Out[107]: <xarray.plot.facetgrid.FacetGrid at 0xe1bcbc10>\n
    \n
    \n \"../_images/ds_facet_scatter.png\"\n

    And adding the z-axis

    \n 
    In [108]: ds.plot.scatter(x="A", y="B", z="z", hue="y", markersize="x", row="x", col="w")\n-Out[108]: <xarray.plot.facetgrid.FacetGrid at 0xa0ad3a50>\n+Out[108]: <xarray.plot.facetgrid.FacetGrid at 0xe2381df0>\n
    \n
    \n \"../_images/ds_facet_scatter_z.png\"\n

    For more advanced scatter plots, we recommend converting the relevant data variables\n to a pandas DataFrame and using the extensive plotting capabilities of seaborn.

    \n
    \n
    \n

    Quiver\u00b6

    \n

    Visualizing vector fields is supported with quiver plots:

    \n 
    In [109]: ds.isel(w=1, z=1).plot.quiver(x="x", y="y", u="A", v="B")\n-Out[109]: <matplotlib.quiver.Quiver at 0xa03b8d70>\n+Out[109]: <matplotlib.quiver.Quiver at 0xe15358d0>\n
    \n
    \n \"../_images/ds_simple_quiver.png\"\n

    where u and v denote the x and y direction components of the arrow vectors. Again, faceting is also possible:

    \n 
    In [110]: ds.plot.quiver(x="x", y="y", u="A", v="B", col="w", row="z", scale=4)\n-Out[110]: <xarray.plot.facetgrid.FacetGrid at 0xa048eb10>\n+Out[110]: <xarray.plot.facetgrid.FacetGrid at 0xe195c2d0>\n
    \n
    \n \"../_images/ds_facet_quiver.png\"\n

    scale is required for faceted quiver plots.\n The scale determines the number of data units per arrow length unit, i.e. a smaller scale parameter makes the arrow longer.

    \n
    \n
    \n

    Streamplot\u00b6

    \n

    Visualizing vector fields is also supported with streamline plots:

    \n 
    In [111]: ds.isel(w=1, z=1).plot.streamplot(x="x", y="y", u="A", v="B")\n-Out[111]: <matplotlib.collections.LineCollection at 0xa04dfd30>\n+Out[111]: <matplotlib.collections.LineCollection at 0xe16a0bf0>\n
    \n
    \n \"../_images/ds_simple_streamplot.png\"\n

    where u and v denote the x and y direction components of the vectors tangent to the streamlines.\n Again, faceting is also possible:

    \n 
    In [112]: ds.plot.streamplot(x="x", y="y", u="A", v="B", col="w", row="z")\n-Out[112]: <xarray.plot.facetgrid.FacetGrid at 0xa0e44a90>\n+Out[112]: <xarray.plot.facetgrid.FacetGrid at 0xe1785410>\n
    \n
    \n \"../_images/ds_facet_streamplot.png\"\n
    \n
    \n
    \n

    Maps\u00b6

    \n@@ -1424,24 +1424,24 @@\n 
    In [121]: import xarray.plot as xplt\n \n In [122]: da = xr.DataArray(range(5))\n \n In [123]: fig, axs = plt.subplots(ncols=2, nrows=2)\n \n In [124]: da.plot(ax=axs[0, 0])\n-Out[124]: [<matplotlib.lines.Line2D at 0xa0a65130>]\n+Out[124]: [<matplotlib.lines.Line2D at 0xdf809df0>]\n \n In [125]: da.plot.line(ax=axs[0, 1])\n-Out[125]: [<matplotlib.lines.Line2D at 0xa0ce4270>]\n+Out[125]: [<matplotlib.lines.Line2D at 0xdf85def0>]\n \n In [126]: xplt.plot(da, ax=axs[1, 0])\n-Out[126]: [<matplotlib.lines.Line2D at 0xa0f6e6b0>]\n+Out[126]: [<matplotlib.lines.Line2D at 0xe1979130>]\n \n In [127]: xplt.line(da, ax=axs[1, 1])\n-Out[127]: [<matplotlib.lines.Line2D at 0x9edaf030>]\n+Out[127]: [<matplotlib.lines.Line2D at 0xe0ecc390>]\n \n In [128]: plt.tight_layout()\n \n In [129]: plt.draw()\n
    \n
    \n \"../_images/plotting_ways_to_use.png\"\n@@ -1490,15 +1490,15 @@\n \n

    The plot will produce an image corresponding to the values of the array.\n Hence the top left pixel will be a different color than the others.\n Before reading on, you may want to look at the coordinates and\n think carefully about what the limits, labels, and orientation for\n each of the axes should be.

    \n 
    In [134]: a.plot()\n-Out[134]: <matplotlib.collections.QuadMesh at 0x9ee399b0>\n+Out[134]: <matplotlib.collections.QuadMesh at 0xdf791b50>\n
    \n
    \n \"../_images/plotting_example_2d_simple.png\"\n

    It may seem strange that\n the values on the y axis are decreasing with -0.5 on the top. This is because\n the pixels are centered over their coordinates, and the\n axis labels and ranges correspond to the values of the\n@@ -1520,81 +1520,81 @@\n .....: np.arange(20).reshape(4, 5),\n .....: dims=["y", "x"],\n .....: coords={"lat": (("y", "x"), lat), "lon": (("y", "x"), lon)},\n .....: )\n .....: \n \n In [139]: da.plot.pcolormesh(x="lon", y="lat")\n-Out[139]: <matplotlib.collections.QuadMesh at 0x9ede9990>\n+Out[139]: <matplotlib.collections.QuadMesh at 0xdfe82ef0>\n \n \n \"../_images/plotting_example_2d_irreg.png\"\n

    Note that in this case, xarray still follows the pixel centered convention.\n This might be undesirable in some cases, for example when your data is defined\n on a polar projection (GH781). This is why the default is to not follow\n this convention when plotting on a map:

    \n 
    In [140]: import cartopy.crs as ccrs\n \n In [141]: ax = plt.subplot(projection=ccrs.PlateCarree())\n \n In [142]: da.plot.pcolormesh(x="lon", y="lat", ax=ax)\n-Out[142]: <cartopy.mpl.geocollection.GeoQuadMesh at 0x9ede9bb0>\n+Out[142]: <cartopy.mpl.geocollection.GeoQuadMesh at 0xe1705f10>\n \n In [143]: ax.scatter(lon, lat, transform=ccrs.PlateCarree())\n-Out[143]: <matplotlib.collections.PathCollection at 0x9ed308b0>\n+Out[143]: <matplotlib.collections.PathCollection at 0xe18e3290>\n \n In [144]: ax.coastlines()\n-Out[144]: <cartopy.mpl.feature_artist.FeatureArtist at 0x9f46b2d0>\n+Out[144]: <cartopy.mpl.feature_artist.FeatureArtist at 0xdf834510>\n \n In [145]: ax.gridlines(draw_labels=True)\n-Out[145]: <cartopy.mpl.gridliner.Gridliner at 0x9ed21a90>\n+Out[145]: <cartopy.mpl.gridliner.Gridliner at 0xdf7630f0>\n
    \n
    \n \"_build/html/_static/plotting_example_2d_irreg_map.png\"\n

    You can however decide to infer the cell boundaries and use the\n infer_intervals keyword:

    \n 
    In [146]: ax = plt.subplot(projection=ccrs.PlateCarree())\n \n In [147]: da.plot.pcolormesh(x="lon", y="lat", ax=ax, infer_intervals=True)\n-Out[147]: <cartopy.mpl.geocollection.GeoQuadMesh at 0x9daa3b30>\n+Out[147]: <cartopy.mpl.geocollection.GeoQuadMesh at 0xde527730>\n \n In [148]: ax.scatter(lon, lat, transform=ccrs.PlateCarree())\n-Out[148]: <matplotlib.collections.PathCollection at 0x9daab230>\n+Out[148]: <matplotlib.collections.PathCollection at 0xde527d90>\n \n In [149]: ax.coastlines()\n-Out[149]: <cartopy.mpl.feature_artist.FeatureArtist at 0x9ed10cb0>\n+Out[149]: <cartopy.mpl.feature_artist.FeatureArtist at 0xde52ec90>\n \n In [150]: ax.gridlines(draw_labels=True)\n-Out[150]: <cartopy.mpl.gridliner.Gridliner at 0x9db0e0d0>\n+Out[150]: <cartopy.mpl.gridliner.Gridliner at 0xde504250>\n
    \n
    \n \"_build/html/_static/plotting_example_2d_irreg_map_infer.png\"\n
    \n

    Note

    \n

    The data model of xarray does not support datasets with cell boundaries\n yet. If you want to use these coordinates, you\u2019ll have to make the plots\n outside the xarray framework.

    \n
    \n

    One can also make line plots with multidimensional coordinates. In this case, hue must be a dimension name, not a coordinate name.

    \n 
    In [151]: f, ax = plt.subplots(2, 1)\n \n In [152]: da.plot.line(x="lon", hue="y", ax=ax[0])\n Out[152]: \n-[<matplotlib.lines.Line2D at 0x9db14c10>,\n- <matplotlib.lines.Line2D at 0x9da1d6f0>,\n- <matplotlib.lines.Line2D at 0x9da1d850>,\n- <matplotlib.lines.Line2D at 0x9da1d9b0>]\n+[<matplotlib.lines.Line2D at 0xde523250>,\n+ <matplotlib.lines.Line2D at 0xde49ba70>,\n+ <matplotlib.lines.Line2D at 0xde4f5390>,\n+ <matplotlib.lines.Line2D at 0xde49bbd0>]\n \n In [153]: da.plot.line(x="lon", hue="x", ax=ax[1])\n Out[153]: \n-[<matplotlib.lines.Line2D at 0x9da94e30>,\n- <matplotlib.lines.Line2D at 0x9da2a7b0>,\n- <matplotlib.lines.Line2D at 0x9da2a910>,\n- <matplotlib.lines.Line2D at 0x9da2aa70>,\n- <matplotlib.lines.Line2D at 0x9da2abf0>]\n+[<matplotlib.lines.Line2D at 0xde49b130>,\n+ <matplotlib.lines.Line2D at 0xde4ac9f0>,\n+ <matplotlib.lines.Line2D at 0xde4acb10>,\n+ <matplotlib.lines.Line2D at 0xde4acc70>,\n+ <matplotlib.lines.Line2D at 0xde4acdf0>]\n
    \n
    \n \"../_images/plotting_example_2d_hue_xy.png\"\n
    \n \n \n \n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -541,15 +541,15 @@\n --> 186 raise KeyError(key)\n 188 ref_name, var_name = split_key\n 189 ref_var = variables[ref_name]\n \n KeyError: 'lat'\n \n In [51]: b.plot()\n-Out[51]: []\n+Out[51]: []\n [../_images/plotting_nonuniform_coords.png]\n *** Other types of plot\u00c2\u00b6 ***\n There are several other options for plotting 2D data.\n Contour plot using DataArray.plot.contour()\n In [52]: air2d.plot.contour()\n ---------------------------------------------------------------------------\n NameError Traceback (most recent call last)\n@@ -1030,85 +1030,85 @@\n * y (y) float64 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0\n * z (z) int32 0 1 2 3\n * w (w) \n+Out[99]: \n [../_images/da_A_y.png]\n Same plot can be displayed using the dataset:\n In [100]: ds.plot.scatter(x=\"y\", y=\"A\")\n-Out[100]: \n+Out[100]: \n [../_images/ds_A_y.png]\n Now suppose we want to scatter the A DataArray against the B DataArray\n In [101]: ds.plot.scatter(x=\"A\", y=\"B\")\n-Out[101]: \n+Out[101]: \n [../_images/ds_simple_scatter.png]\n The hue kwarg lets you vary the color by variable value\n In [102]: ds.plot.scatter(x=\"A\", y=\"B\", hue=\"w\")\n-Out[102]: \n+Out[102]: \n [../_images/ds_hue_scatter.png]\n You can force a legend instead of a colorbar by setting add_legend=True,\n add_colorbar=False.\n In [103]: ds.plot.scatter(x=\"A\", y=\"B\", hue=\"w\", add_legend=True,\n add_colorbar=False)\n-Out[103]: \n+Out[103]: \n [../_images/ds_discrete_legend_hue_scatter.png]\n In [104]: ds.plot.scatter(x=\"A\", y=\"B\", hue=\"w\", add_legend=False,\n add_colorbar=True)\n-Out[104]: \n+Out[104]: \n [../_images/ds_discrete_colorbar_hue_scatter.png]\n The markersize kwarg lets you vary the point\u00e2\u0080\u0099s size by variable value. You\n can additionally pass size_norm to control how the variable\u00e2\u0080\u0099s values are\n mapped to point sizes.\n In [105]: ds.plot.scatter(x=\"A\", y=\"B\", hue=\"y\", markersize=\"z\")\n-Out[105]: \n+Out[105]: \n [../_images/ds_hue_size_scatter.png]\n The z kwarg lets you plot the data along the z-axis as well.\n In [106]: ds.plot.scatter(x=\"A\", y=\"B\", z=\"z\", hue=\"y\", markersize=\"x\")\n-Out[106]: \n+Out[106]: \n [../_images/ds_hue_size_scatter_z.png]\n Faceting is also possible\n In [107]: ds.plot.scatter(x=\"A\", y=\"B\", hue=\"y\", markersize=\"x\", row=\"x\",\n col=\"w\")\n-Out[107]: \n+Out[107]: \n [../_images/ds_facet_scatter.png]\n And adding the z-axis\n In [108]: ds.plot.scatter(x=\"A\", y=\"B\", z=\"z\", hue=\"y\", markersize=\"x\",\n row=\"x\", col=\"w\")\n-Out[108]: \n+Out[108]: \n [../_images/ds_facet_scatter_z.png]\n For more advanced scatter plots, we recommend converting the relevant data\n variables to a pandas DataFrame and using the extensive plotting capabilities\n of seaborn.\n \n **** Quiver\u00c2\u00b6 ****\n Visualizing vector fields is supported with quiver plots:\n In [109]: ds.isel(w=1, z=1).plot.quiver(x=\"x\", y=\"y\", u=\"A\", v=\"B\")\n-Out[109]: \n+Out[109]: \n [../_images/ds_simple_quiver.png]\n where u and v denote the x and y direction components of the arrow vectors.\n Again, faceting is also possible:\n In [110]: ds.plot.quiver(x=\"x\", y=\"y\", u=\"A\", v=\"B\", col=\"w\", row=\"z\", scale=4)\n-Out[110]: \n+Out[110]: \n [../_images/ds_facet_quiver.png]\n scale is required for faceted quiver plots. The scale determines the number of\n data units per arrow length unit, i.e. a smaller scale parameter makes the\n arrow longer.\n \n **** Streamplot\u00c2\u00b6 ****\n Visualizing vector fields is also supported with streamline plots:\n In [111]: ds.isel(w=1, z=1).plot.streamplot(x=\"x\", y=\"y\", u=\"A\", v=\"B\")\n-Out[111]: \n+Out[111]: \n [../_images/ds_simple_streamplot.png]\n where u and v denote the x and y direction components of the vectors tangent to\n the streamlines. Again, faceting is also possible:\n In [112]: ds.plot.streamplot(x=\"x\", y=\"y\", u=\"A\", v=\"B\", col=\"w\", row=\"z\")\n-Out[112]: \n+Out[112]: \n [../_images/ds_facet_streamplot.png]\n ***** Maps\u00c2\u00b6 *****\n To follow this section you\u00e2\u0080\u0099ll need to have Cartopy installed and working.\n This script will plot the air temperature on a map.\n In [113]: import cartopy.crs as ccrs\n \n In [114]: air = xr.tutorial.open_dataset(\"air_temperature\").air\n@@ -1221,24 +1221,24 @@\n In [121]: import xarray.plot as xplt\n \n In [122]: da = xr.DataArray(range(5))\n \n In [123]: fig, axs = plt.subplots(ncols=2, nrows=2)\n \n In [124]: da.plot(ax=axs[0, 0])\n-Out[124]: []\n+Out[124]: []\n \n In [125]: da.plot.line(ax=axs[0, 1])\n-Out[125]: []\n+Out[125]: []\n \n In [126]: xplt.plot(da, ax=axs[1, 0])\n-Out[126]: []\n+Out[126]: []\n \n In [127]: xplt.line(da, ax=axs[1, 1])\n-Out[127]: []\n+Out[127]: []\n \n In [128]: plt.tight_layout()\n \n In [129]: plt.draw()\n [../_images/plotting_ways_to_use.png]\n Here the output is the same. Since the data is 1 dimensional the line plot was\n used.\n@@ -1270,15 +1270,15 @@\n [0., 0., 0.]])\n Dimensions without coordinates: y, x\n The plot will produce an image corresponding to the values of the array. Hence\n the top left pixel will be a different color than the others. Before reading\n on, you may want to look at the coordinates and think carefully about what the\n limits, labels, and orientation for each of the axes should be.\n In [134]: a.plot()\n-Out[134]: \n+Out[134]: \n [../_images/plotting_example_2d_simple.png]\n It may seem strange that the values on the y axis are decreasing with -0.5 on\n the top. This is because the pixels are centered over their coordinates, and\n the axis labels and ranges correspond to the values of the coordinates.\n \n **** Multidimensional coordinates\u00c2\u00b6 ****\n See also: Working_with_Multidimensional_Coordinates.\n@@ -1296,74 +1296,74 @@\n .....: np.arange(20).reshape(4, 5),\n .....: dims=[\"y\", \"x\"],\n .....: coords={\"lat\": ((\"y\", \"x\"), lat), \"lon\": ((\"y\", \"x\"), lon)},\n .....: )\n .....:\n \n In [139]: da.plot.pcolormesh(x=\"lon\", y=\"lat\")\n-Out[139]: \n+Out[139]: \n [../_images/plotting_example_2d_irreg.png]\n Note that in this case, xarray still follows the pixel centered convention.\n This might be undesirable in some cases, for example when your data is defined\n on a polar projection (GH781). This is why the default is to not follow this\n convention when plotting on a map:\n In [140]: import cartopy.crs as ccrs\n \n In [141]: ax = plt.subplot(projection=ccrs.PlateCarree())\n \n In [142]: da.plot.pcolormesh(x=\"lon\", y=\"lat\", ax=ax)\n-Out[142]: \n+Out[142]: \n \n In [143]: ax.scatter(lon, lat, transform=ccrs.PlateCarree())\n-Out[143]: \n+Out[143]: \n \n In [144]: ax.coastlines()\n-Out[144]: \n+Out[144]: \n \n In [145]: ax.gridlines(draw_labels=True)\n-Out[145]: \n+Out[145]: \n [_build/html/_static/plotting_example_2d_irreg_map.png]\n You can however decide to infer the cell boundaries and use the infer_intervals\n keyword:\n In [146]: ax = plt.subplot(projection=ccrs.PlateCarree())\n \n In [147]: da.plot.pcolormesh(x=\"lon\", y=\"lat\", ax=ax, infer_intervals=True)\n-Out[147]: \n+Out[147]: \n \n In [148]: ax.scatter(lon, lat, transform=ccrs.PlateCarree())\n-Out[148]: \n+Out[148]: \n \n In [149]: ax.coastlines()\n-Out[149]: \n+Out[149]: \n \n In [150]: ax.gridlines(draw_labels=True)\n-Out[150]: \n+Out[150]: \n [_build/html/_static/plotting_example_2d_irreg_map_infer.png]\n Note\n The data model of xarray does not support datasets with cell_boundaries yet. If\n you want to use these coordinates, you\u00e2\u0080\u0099ll have to make the plots outside the\n xarray framework.\n One can also make line plots with multidimensional coordinates. In this case,\n hue must be a dimension name, not a coordinate name.\n In [151]: f, ax = plt.subplots(2, 1)\n \n In [152]: da.plot.line(x=\"lon\", hue=\"y\", ax=ax[0])\n Out[152]:\n-[,\n- ,\n- ,\n- ]\n+[,\n+ ,\n+ ,\n+ ]\n \n In [153]: da.plot.line(x=\"lon\", hue=\"x\", ax=ax[1])\n Out[153]:\n-[,\n- ,\n- ,\n- ,\n- ]\n+[,\n+ ,\n+ ,\n+ ,\n+ ]\n [../_images/plotting_example_2d_hue_xy.png]\n [Logo]\n ****** xarray ******\n **** Navigation ****\n For users\n * Getting_Started\n * User_Guide\n"}]}, {"source1": "./usr/share/doc/python-xarray-doc/html/whats-new.html", "source2": "./usr/share/doc/python-xarray-doc/html/whats-new.html", "unified_diff": "@@ -5855,15 +5855,15 @@\n

  • New xray.Dataset.where method for masking xray objects according\n to some criteria. This works particularly well with multi-dimensional data:

    \n 
    In [44]: ds = xray.Dataset(coords={"x": range(100), "y": range(100)})\n \n In [45]: ds["distance"] = np.sqrt(ds.x**2 + ds.y**2)\n \n In [46]: ds.distance.where(ds.distance < 100).plot()\n-Out[46]: <matplotlib.collections.QuadMesh at 0x9d68cfd0>\n+Out[46]: <matplotlib.collections.QuadMesh at 0xde251f10>\n
    \n
    \n \"_images/where_example.png\"\n
    • \n

  • Added new methods xray.DataArray.diff and xray.Dataset.diff\n for finite difference calculations along a given axis.

    • \n

  • New xray.DataArray.to_masked_array convenience method for\n", "details": [{"source1": "html2text {}", "source2": "html2text {}", "unified_diff": "@@ -4049,15 +4049,15 @@\n * New xray.Dataset.where method for masking xray objects according to some\n criteria. This works particularly well with multi-dimensional data:\n In [44]: ds = xray.Dataset(coords={\"x\": range(100), \"y\": range(100)})\n \n In [45]: ds[\"distance\"] = np.sqrt(ds.x**2 + ds.y**2)\n \n In [46]: ds.distance.where(ds.distance < 100).plot()\n- Out[46]: \n+ Out[46]: \n [_images/where_example.png]\n * Added new methods xray.DataArray.diff and xray.Dataset.diff for finite\n difference calculations along a given axis.\n * New xray.DataArray.to_masked_array convenience method for returning a\n numpy.ma.MaskedArray.\n In [47]: da = xray.DataArray(np.random.random_sample(size=(5, 4)))\n \n"}]}]}]}]}]}