diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml
index 2458f1d9..5e83a6f5 100644
--- a/.github/workflows/main.yaml
+++ b/.github/workflows/main.yaml
@@ -43,12 +43,8 @@ jobs:
# NOTE: login shell activates conda environment
shell: bash -el {0}
run: |
- jupyter-book build ./ --warningiserror --keep-going
-
- - name: Dump Build Logs
- if: always()
- run: |
- if (test -a _build/html/reports/*log); then cat _build/html/reports/*log ; fi
+ jupyter-book clean ./
+ jupyter-book build ./
- name: Save Build Folder
if: always()
diff --git a/.github/workflows/pull_request.yaml b/.github/workflows/pull_request.yaml
index 2e4a8f43..9c4f92d4 100644
--- a/.github/workflows/pull_request.yaml
+++ b/.github/workflows/pull_request.yaml
@@ -40,12 +40,8 @@ jobs:
- name: Build JupyterBook
if: github.event.action != 'closed'
run: |
- jupyter-book build ./ --warningiserror --keep-going
-
- - name: Dump Build Logs
- if: github.event.action != 'closed'
- run: |
- if (test -a _build/html/reports/*log); then cat _build/html/reports/*log ; fi
+ jupyter-book clean ./
+ jupyter-book build ./
- name: Upload artifact
if: github.event.action != 'closed'
diff --git a/.github/workflows/qaqc.yaml b/.github/workflows/qaqc.yaml
index ea4d17e0..b6fc2c80 100644
--- a/.github/workflows/qaqc.yaml
+++ b/.github/workflows/qaqc.yaml
@@ -54,4 +54,5 @@ jobs:
timeout-minutes: 5
if: always()
run: |
+ jupyter-book clean ./
jupyter-book build ./ --builder linkcheck
diff --git a/_config.yml b/_config.yml
index d77f58ec..f500f1d4 100644
--- a/_config.yml
+++ b/_config.yml
@@ -76,6 +76,28 @@ sphinx:
# application/vnd.holoviews_load.v0+json, application/vnd.holoviews_exec.v0+json
suppress_warnings: ["mystnb.unknown_mime_type", "misc.highlighting_failure"]
codeautolink_concat_default: True
+ codeautolink_warn_on_missing_inventory: True
+ codeautolink_warn_on_failed_resolve: True
+ codeautolink_inventory_map: {
+ # unfortunately mapping the top level doesn't work, need all methods
+ #"xarray.core.dataarray.DataArray": "xarray.DataArray",
+ "xarray.core.accessor_dt.DatetimeAccessor": "xarray.DataArray.dt",
+ "xarray.core.dataarray.DataArray.coarsen": "xarray.DataArray.coarsen",
+ "xarray.core.dataarray.DataArray.groupby": "xarray.DataArray.groupby",
+ "xarray.core.dataarray.DataArray.groupby_bins": "xarray.DataArray.groupby_bins",
+ "xarray.core.dataarray.DataArray.isel": "xarray.DataArray.isel",
+ #"xarray.core.dataarray.DataArray.plot": "xarray.DataArray.plot",
+ "xarray.plot.accessor.DataArrayPlotAccessor": "xarray.DataArray.plot",
+ "xarray.core.dataarray.DataArray.redindex": "xarray.DataArray.reindex",
+ "xarray.core.dataarray.DataArray.reduce": "xarray.DataArray.reduce",
+ "xarray.core.dataarray.DataArray.resample": "xarray.DataArray.resample",
+ "xarray.core.dataarray.DataArray.rolling": "xarray.DataArray.rolling",
+ "xarray.core.dataarray.DataArray.sel": "xarray.DataArray.sel",
+ "xarray.core.dataarray.DataArray.weighted": "xarray.DataArray.weighted",
+ #"Same for dataset :(
+ "xarray.core.dataset.Dataset.groupby": "xarray.Dataset.groupby",
+ "xarray.core.dataset.Dataset.isel": "xarray.Dataset.sel",
+ }
notfound_context:
body: "<h1>Whoops! 404 Page Not Found</h1>\n\n<p>Sorry, this page doesn't exist. Many sections of this book have been updated recently.</p><p> Try the search box 🔎 to find what you're looking for!</p>"
notfound_urls_prefix: /
diff --git a/_static/style.css b/_static/style.css
index b1024666..1e2145b3 100644
--- a/_static/style.css
+++ b/_static/style.css
@@ -1,3 +1,15 @@
.bd-header-announcement {
background-color: var(--pst-color-info-bg);
}
+
+.sphinx-codeautolink-a:link {
+ border-bottom-color: lightgray;
+ border-bottom-style: dotted;
+ border-bottom-width: 1px;
+}
+
+.sphinx-codeautolink-a:hover {
+ border-bottom-color: rgb(255, 139, 139);
+ border-bottom-style: dotted;
+ border-bottom-width: 1px;
+}
diff --git a/intermediate/01-high-level-computation-patterns.ipynb b/intermediate/01-high-level-computation-patterns.ipynb
index e5f17c1c..77fe829e 100644
--- a/intermediate/01-high-level-computation-patterns.ipynb
+++ b/intermediate/01-high-level-computation-patterns.ipynb
@@ -97,39 +97,36 @@
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": null,
"id": "3",
"metadata": {
- "slideshow": {
- "slide_type": "slide"
- },
"tags": []
},
+ "outputs": [],
"source": [
- "### Load example dataset\n"
+ "import numpy as np\n",
+ "import xarray as xr\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# reduce figure filesize (default=100)\n",
+ "plt.rcParams[\"figure.dpi\"] = 90\n",
+ "xr.set_options(keep_attrs=True, display_expand_data=False);"
]
},
{
- "cell_type": "code",
- "execution_count": null,
+ "cell_type": "markdown",
"id": "4",
"metadata": {
+ "slideshow": {
+ "slide_type": "slide"
+ },
"tags": []
},
- "outputs": [],
"source": [
- "import numpy as np\n",
- "import xarray as xr\n",
- "import matplotlib.pyplot as plt\n",
- "\n",
- "# reduce figure size\n",
- "plt.rcParams[\"figure.dpi\"] = 90\n",
- "xr.set_options(keep_attrs=True, display_expand_data=False)\n",
+ "### Load example dataset\n",
"\n",
- "da = xr.tutorial.load_dataset(\"air_temperature\", engine=\"netcdf4\").air\n",
- "monthly = da.resample(time=\"ME\").mean()\n",
- "data = da.isel(time=0)\n",
- "data.plot();"
+ "We will use the Xarray Tutorial Air Temperature dataset in this notebook. In the following cells we quickly load, look at the data structure and make a plot. We use [Python Type Hints](https://docs.python.org/3/library/typing.html) to be explicit that `da` and `data` variables used throughout this notebook are xarray DataArrays (this is optional!).\n"
]
},
{
@@ -141,13 +138,26 @@
},
"outputs": [],
"source": [
+ "da: xr.DataArray = xr.tutorial.load_dataset(\"air_temperature\", engine=\"netcdf4\").air\n",
"da"
]
},
{
- "cell_type": "markdown",
+ "cell_type": "code",
+ "execution_count": null,
"id": "6",
"metadata": {},
+ "outputs": [],
+ "source": [
+ "monthly = da.resample(time=\"ME\").mean()\n",
+ "data: xr.DataArray = da.isel(time=0)\n",
+ "data.plot();"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7",
+ "metadata": {},
"source": [
"***\n",
"\n",
@@ -163,7 +173,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "7",
+ "id": "8",
"metadata": {
"tags": []
},
@@ -186,7 +196,7 @@
},
{
"cell_type": "markdown",
- "id": "8",
+ "id": "9",
"metadata": {},
"source": [
"This pattern is the GroupBy pattern.\n",
@@ -198,7 +208,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "9",
+ "id": "10",
"metadata": {
"tags": []
},
@@ -214,7 +224,7 @@
},
{
"cell_type": "markdown",
- "id": "10",
+ "id": "11",
"metadata": {},
"source": [
"Writing a for-loop here is not wrong, but it can quickly become cumbersome if you have a complex function to apply and it will take a while to compute on a large dataset (you may even run out of memory). Parallelizing the computation would take a lot of additional work.\n",
@@ -225,7 +235,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "11",
+ "id": "12",
"metadata": {},
"outputs": [],
"source": [
@@ -237,7 +247,7 @@
},
{
"cell_type": "markdown",
- "id": "12",
+ "id": "13",
"metadata": {},
"source": [
"```{note}\n",
@@ -258,7 +268,7 @@
},
{
"cell_type": "markdown",
- "id": "13",
+ "id": "14",
"metadata": {
"slideshow": {
"slide_type": "slide"
@@ -274,7 +284,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "14",
+ "id": "15",
"metadata": {},
"outputs": [],
"source": [
@@ -284,7 +294,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "15",
+ "id": "16",
"metadata": {
"tags": []
},
@@ -297,7 +307,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "16",
+ "id": "17",
"metadata": {
"tags": []
},
@@ -310,7 +320,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "17",
+ "id": "18",
"metadata": {
"tags": []
},
@@ -323,7 +333,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "18",
+ "id": "19",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -341,7 +351,7 @@
},
{
"cell_type": "markdown",
- "id": "19",
+ "id": "20",
"metadata": {},
"source": [
"***\n",
@@ -371,7 +381,7 @@
},
{
"cell_type": "markdown",
- "id": "20",
+ "id": "21",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -398,7 +408,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "21",
+ "id": "22",
"metadata": {},
"outputs": [],
"source": [
@@ -407,7 +417,7 @@
},
{
"cell_type": "markdown",
- "id": "22",
+ "id": "23",
"metadata": {},
"source": [
"And now smoothed 5 point running mean in lat and lon\n"
@@ -416,7 +426,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "23",
+ "id": "24",
"metadata": {},
"outputs": [],
"source": [
@@ -425,7 +435,7 @@
},
{
"cell_type": "markdown",
- "id": "24",
+ "id": "25",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -445,7 +455,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "25",
+ "id": "26",
"metadata": {},
"outputs": [],
"source": [
@@ -454,7 +464,7 @@
},
{
"cell_type": "markdown",
- "id": "26",
+ "id": "27",
"metadata": {},
"source": [
"::::{admonition} Exercise\n",
@@ -476,7 +486,7 @@
},
{
"cell_type": "markdown",
- "id": "27",
+ "id": "28",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -492,7 +502,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "28",
+ "id": "29",
"metadata": {},
"outputs": [],
"source": [
@@ -503,7 +513,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "29",
+ "id": "30",
"metadata": {},
"outputs": [],
"source": [
@@ -513,7 +523,7 @@
},
{
"cell_type": "markdown",
- "id": "30",
+ "id": "31",
"metadata": {},
"source": [
"```{note}\n",
@@ -523,7 +533,7 @@
},
{
"cell_type": "markdown",
- "id": "31",
+ "id": "32",
"metadata": {},
"source": [
"::::{admonition} Exercise\n",
@@ -542,7 +552,7 @@
},
{
"cell_type": "markdown",
- "id": "32",
+ "id": "33",
"metadata": {},
"source": [
"`construct` is clever.\n",
@@ -554,7 +564,7 @@
},
{
"cell_type": "markdown",
- "id": "33",
+ "id": "34",
"metadata": {
"tags": []
},
@@ -567,7 +577,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "34",
+ "id": "35",
"metadata": {},
"outputs": [],
"source": [
@@ -576,7 +586,7 @@
},
{
"cell_type": "markdown",
- "id": "35",
+ "id": "36",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -600,7 +610,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "36",
+ "id": "37",
"metadata": {},
"outputs": [],
"source": [
@@ -610,7 +620,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "37",
+ "id": "38",
"metadata": {},
"outputs": [],
"source": [
@@ -620,7 +630,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "38",
+ "id": "39",
"metadata": {},
"outputs": [],
"source": [
@@ -630,16 +640,16 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "39",
+ "id": "40",
"metadata": {},
"outputs": [],
"source": [
- "(data.coarsen(lat=5, lon=5, boundary=\"trim\").mean().plot())"
+ "(data.coarsen(lat=5, lon=5, boundary=\"trim\").mean().plot());"
]
},
{
"cell_type": "markdown",
- "id": "40",
+ "id": "41",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -665,7 +675,7 @@
},
{
"cell_type": "markdown",
- "id": "41",
+ "id": "42",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -685,7 +695,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "42",
+ "id": "43",
"metadata": {},
"outputs": [],
"source": [
@@ -700,7 +710,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "43",
+ "id": "44",
"metadata": {},
"outputs": [],
"source": [
@@ -710,7 +720,7 @@
},
{
"cell_type": "markdown",
- "id": "44",
+ "id": "45",
"metadata": {},
"source": [
"Note two things:\n",
@@ -725,7 +735,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "45",
+ "id": "46",
"metadata": {},
"outputs": [],
"source": [
@@ -734,7 +744,7 @@
},
{
"cell_type": "markdown",
- "id": "46",
+ "id": "47",
"metadata": {},
"source": [
"This adds values at the end of the array (see the 'nan' at the end of the time coordinate?), which is not so sensible for this\n",
@@ -744,7 +754,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "47",
+ "id": "48",
"metadata": {},
"outputs": [],
"source": [
@@ -755,7 +765,7 @@
},
{
"cell_type": "markdown",
- "id": "48",
+ "id": "49",
"metadata": {},
"source": [
"Note that `coarsen` pads with NaNs. For more control over padding, use\n",
@@ -765,7 +775,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "49",
+ "id": "50",
"metadata": {},
"outputs": [],
"source": [
@@ -779,7 +789,7 @@
},
{
"cell_type": "markdown",
- "id": "50",
+ "id": "51",
"metadata": {},
"source": [
"```{note}\n",
@@ -790,7 +800,7 @@
},
{
"cell_type": "markdown",
- "id": "51",
+ "id": "52",
"metadata": {},
"source": [
":::{admonition} Exercise\n",
@@ -834,7 +844,7 @@
},
{
"cell_type": "markdown",
- "id": "52",
+ "id": "53",
"metadata": {},
"source": [
"::::{admonition} Exercise\n",
@@ -859,7 +869,7 @@
},
{
"cell_type": "markdown",
- "id": "53",
+ "id": "54",
"metadata": {},
"source": [
"### Summary\n",
@@ -875,7 +885,7 @@
},
{
"cell_type": "markdown",
- "id": "54",
+ "id": "55",
"metadata": {
"slideshow": {
"slide_type": "slide"
@@ -921,7 +931,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "55",
+ "id": "56",
"metadata": {},
"outputs": [],
"source": [
@@ -932,7 +942,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "56",
+ "id": "57",
"metadata": {},
"outputs": [],
"source": [
@@ -945,7 +955,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "57",
+ "id": "58",
"metadata": {},
"outputs": [],
"source": [
@@ -956,7 +966,7 @@
},
{
"cell_type": "markdown",
- "id": "58",
+ "id": "59",
"metadata": {},
"source": [
"Notice that since we have averaged over all the years for each month, our resulting DataArray no longer has a \"year\" coordinate.\n",
@@ -967,7 +977,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "59",
+ "id": "60",
"metadata": {},
"outputs": [],
"source": [
@@ -976,7 +986,7 @@
},
{
"cell_type": "markdown",
- "id": "60",
+ "id": "61",
"metadata": {},
"source": [
"Similarly for binning (remember this is useful when the parameter you are binning over is not \"exact\", like a float),\n"
@@ -985,7 +995,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "61",
+ "id": "62",
"metadata": {},
"outputs": [],
"source": [
@@ -994,7 +1004,7 @@
},
{
"cell_type": "markdown",
- "id": "62",
+ "id": "63",
"metadata": {},
"source": [
"and resampling...\n"
@@ -1003,7 +1013,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "63",
+ "id": "64",
"metadata": {},
"outputs": [],
"source": [
@@ -1012,7 +1022,7 @@
},
{
"cell_type": "markdown",
- "id": "64",
+ "id": "65",
"metadata": {},
"source": [
"```{note}\n",
@@ -1024,7 +1034,7 @@
},
{
"cell_type": "markdown",
- "id": "65",
+ "id": "66",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -1058,7 +1068,7 @@
},
{
"cell_type": "markdown",
- "id": "66",
+ "id": "67",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -1077,7 +1087,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "67",
+ "id": "68",
"metadata": {
"tags": []
},
@@ -1089,7 +1099,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "68",
+ "id": "69",
"metadata": {},
"outputs": [],
"source": [
@@ -1099,7 +1109,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "69",
+ "id": "70",
"metadata": {},
"outputs": [],
"source": [
@@ -1109,7 +1119,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "70",
+ "id": "71",
"metadata": {},
"outputs": [],
"source": [
@@ -1118,7 +1128,7 @@
},
{
"cell_type": "markdown",
- "id": "71",
+ "id": "72",
"metadata": {},
"source": [
"#### Construct and use custom labels\n",
@@ -1133,7 +1143,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "72",
+ "id": "73",
"metadata": {},
"outputs": [],
"source": [
@@ -1143,7 +1153,7 @@
},
{
"cell_type": "markdown",
- "id": "73",
+ "id": "74",
"metadata": {},
"source": [
"Create a new empty array\n"
@@ -1152,7 +1162,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "74",
+ "id": "75",
"metadata": {},
"outputs": [],
"source": [
@@ -1162,7 +1172,7 @@
},
{
"cell_type": "markdown",
- "id": "75",
+ "id": "76",
"metadata": {},
"source": [
"Use `isin` to assign custom seasons,\n"
@@ -1171,7 +1181,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "76",
+ "id": "77",
"metadata": {},
"outputs": [],
"source": [
@@ -1183,7 +1193,7 @@
},
{
"cell_type": "markdown",
- "id": "77",
+ "id": "78",
"metadata": {},
"source": [
"Turn our new seasonal group array into a DataArray."
@@ -1192,7 +1202,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "78",
+ "id": "79",
"metadata": {},
"outputs": [],
"source": [
@@ -1203,7 +1213,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "79",
+ "id": "80",
"metadata": {},
"outputs": [],
"source": [
@@ -1219,7 +1229,7 @@
},
{
"cell_type": "markdown",
- "id": "80",
+ "id": "81",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -1235,7 +1245,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "81",
+ "id": "82",
"metadata": {},
"outputs": [],
"source": [
@@ -1245,7 +1255,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "82",
+ "id": "83",
"metadata": {},
"outputs": [],
"source": [
@@ -1256,7 +1266,7 @@
},
{
"cell_type": "markdown",
- "id": "83",
+ "id": "84",
"metadata": {
"slideshow": {
"slide_type": "subslide"
@@ -1274,7 +1284,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "84",
+ "id": "85",
"metadata": {},
"outputs": [],
"source": [
@@ -1283,7 +1293,7 @@
},
{
"cell_type": "markdown",
- "id": "85",
+ "id": "86",
"metadata": {
"tags": []
},
@@ -1296,7 +1306,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "86",
+ "id": "87",
"metadata": {},
"outputs": [],
"source": [
@@ -1305,7 +1315,7 @@
},
{
"cell_type": "markdown",
- "id": "87",
+ "id": "88",
"metadata": {},
"source": [
"```{tip}\n",
@@ -1315,7 +1325,7 @@
},
{
"cell_type": "markdown",
- "id": "88",
+ "id": "89",
"metadata": {
"tags": []
},
@@ -1334,7 +1344,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "89",
+ "id": "90",
"metadata": {},
"outputs": [],
"source": [
@@ -1344,7 +1354,7 @@
},
{
"cell_type": "markdown",
- "id": "90",
+ "id": "91",
"metadata": {},
"source": [
"`group` is a DataArray containing data for all December days (because the last printed `label` value is `12`, so the last `group` value is for December)."
@@ -1353,7 +1363,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "91",
+ "id": "92",
"metadata": {
"tags": []
},
@@ -1364,7 +1374,7 @@
},
{
"cell_type": "markdown",
- "id": "92",
+ "id": "93",
"metadata": {},
"source": [
"Maybe you want a histogram of December temperatures?\n"
@@ -1373,7 +1383,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "93",
+ "id": "94",
"metadata": {},
"outputs": [],
"source": [
@@ -1382,7 +1392,7 @@
},
{
"cell_type": "markdown",
- "id": "94",
+ "id": "95",
"metadata": {},
"source": [
"Remember, this example is just to show how you could operate on each group object in a groupby operation. If we wanted to just explore the December (or March) data, we should just filter for it directly:"
@@ -1391,7 +1401,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "95",
+ "id": "96",
"metadata": {},
"outputs": [],
"source": [
@@ -1400,7 +1410,7 @@
},
{
"cell_type": "markdown",
- "id": "96",
+ "id": "97",
"metadata": {
"tags": []
},
@@ -1418,7 +1428,7 @@
{
"cell_type": "code",
"execution_count": null,
- "id": "97",
+ "id": "98",
"metadata": {
"tags": []
},
@@ -1434,7 +1444,7 @@
},
{
"cell_type": "markdown",
- "id": "98",
+ "id": "99",
"metadata": {},
"source": [
"***"
@@ -1442,7 +1452,7 @@
},
{
"cell_type": "markdown",
- "id": "99",
+ "id": "100",
"metadata": {},
"source": [
"## Summary\n",