From b473756abe830612bac4e047a52ed8779872e753 Mon Sep 17 00:00:00 2001
From: "github-actions[bot]"
 <41898282+github-actions[bot]@users.noreply.github.com>
Date: Fri, 21 Jun 2024 17:03:22 +0200
Subject: [PATCH] [Sync to prerelease] Add statsmodels as a python dependency
 and rebuild file (#1178)

Co-authored-by: Christophe Dervieux <christophe.dervieux@gmail.com>
---
 Pipfile                                       |   1 +
 Pipfile.lock                                  | 243 +++++++++++-------
 .../widgets/jupyter/execute-results/html.json |   7 +-
 3 files changed, 150 insertions(+), 101 deletions(-)

diff --git a/Pipfile b/Pipfile
index a786f7d042..399c69428e 100644
--- a/Pipfile
+++ b/Pipfile
@@ -26,6 +26,7 @@ shiny = "*"
 plotly = "*"
 pathlib = "*"
 nbformat = "*"
+statsmodels = "*"
 
 [dev-packages]
 
diff --git a/Pipfile.lock b/Pipfile.lock
index 7ac6323b9c..14f88bb60a 100644
--- a/Pipfile.lock
+++ b/Pipfile.lock
@@ -1,7 +1,7 @@
 {
     "_meta": {
         "hash": {
-            "sha256": "90a4d5d79890135ef92a09e1bf657e3cb2224e85e85e452c9abb24f749cf7bcb"
+            "sha256": "89550e4b55d2e7f6712e56775c44da890b46e10856735e329b78dc50a39760ff"
         },
         "pipfile-spec": 6,
         "requires": {
@@ -1092,46 +1092,55 @@
         },
         "numpy": {
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             "markers": "python_version >= '3.9'",
-            "version": "==1.26.4"
+            "version": "==2.0.0"
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         "overrides": {
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@@ -1143,47 +1152,47 @@
         },
         "packaging": {
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-            "markers": "python_version >= '3.7'",
-            "version": "==24.0"
+            "markers": "python_version >= '3.8'",
+            "version": "==24.1"
         },
         "pandas": {
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             "markers": "python_version >= '3.9'",
-            "version": "==2.2.1"
+            "version": "==2.2.2"
         },
         "pandocfilters": {
             "hashes": [
@@ -1209,6 +1218,13 @@
             "index": "pypi",
             "version": "==1.0.1"
         },
+        "patsy": {
+            "hashes": [
+                "sha256:19056886fd8fa71863fa32f0eb090267f21fb74be00f19f5c70b2e9d76c883c6",
+                "sha256:95c6d47a7222535f84bff7f63d7303f2e297747a598db89cf5c67f0c0c7d2cdb"
+            ],
+            "version": "==0.5.6"
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                 "sha256:7236d1e080e4936be2dc3e326cec0af72acf9212a7e1d060210e70a47e253523",
@@ -1766,34 +1782,34 @@
         },
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+                "sha256:cfa31f1def5c819b19ecc3a8b52d28ffdcc7ed52bb20c9a7589669dd3c250989",
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+                "sha256:d605e9c23906d1994f55ace80e0125c587f96c020037ea6aa98d01b4bd2e222f",
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+                "sha256:e89369d27f9e7b0884ae559a3a956e77c02114cc60a6058b4e5011572eea9299",
+                "sha256:eccfa1906eacc02de42d70ef4aecea45415f5be17e72b61bafcfd329bdc52e94",
+                "sha256:f26264b282b9da0952a024ae34710c2aff7d27480ee91a2e82b7b7073c24722f"
             ],
             "markers": "python_version >= '3.9'",
-            "version": "==1.12.0"
+            "version": "==1.13.1"
         },
         "seaborn": {
             "hashes": [
@@ -1860,6 +1876,37 @@
             "markers": "python_version >= '3.8'",
             "version": "==0.37.2"
         },
+        "statsmodels": {
+            "hashes": [
+                "sha256:0e46e9d59293c1af4cc1f4e5248f17e7e7bc596bfce44d327c789ac27f09111b",
+                "sha256:10f2b7611a61adb7d596a6d239abdf1a4d5492b931b00d5ed23d32844d40e48e",
+                "sha256:201c3d00929c4a67cda1fe05b098c8dcf1b1eeefa88e80a8f963a844801ed59f",
+                "sha256:29c78a7601fdae1aa32104c5ebff2e0b72c26f33e870e2f94ab1bcfd927ece9b",
+                "sha256:4864a1c4615c5ea5f2e3b078a75bdedc90dd9da210a37e0738e064b419eccee2",
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+                "sha256:f870d14a587ea58a3b596aa994c2ed889cc051f9e450e887d2c83656fc6a64bf"
+            ],
+            "index": "pypi",
+            "markers": "python_version >= '3.9'",
+            "version": "==0.14.2"
+        },
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             "hashes": [
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diff --git a/_freeze/docs/interactive/widgets/jupyter/execute-results/html.json b/_freeze/docs/interactive/widgets/jupyter/execute-results/html.json
index 4e56512fe3..fb7b5301c7 100644
--- a/_freeze/docs/interactive/widgets/jupyter/execute-results/html.json
+++ b/_freeze/docs/interactive/widgets/jupyter/execute-results/html.json
@@ -1,17 +1,18 @@
 {
   "hash": "0de2ab874e64ad7135e40225596e9722",
   "result": {
-    "markdown": "---\ntitle: \"Jupyter Widgets\"\ntoc: false\n---\n\n## Overview\n\n[Jupyter Widgets](https://jupyter.org/widgets) enable you to use JavaScript visualization libraries like [Leaflet](https://ipyleaflet.readthedocs.io/en/latest/), [Plotly](https://plotly.com/python/), and [threejs](https://pythreejs.readthedocs.io/) directly from Python.\n\nIf you are using the Jupyter engine with Quarto this is a great way to incorporate interactivity without learning JavaScript.\n\n## Leaflet Example\n\nIncluding Jupyter Widgets within a Quarto document is as easy as including a plot. For example, here is how we embed a [Leaflet](https://ipyleaflet.readthedocs.io/en/latest/) map:\n\n::: {.cell execution_count=1}\n``` {.python .cell-code}\nfrom ipyleaflet import Map, Marker, basemaps, basemap_to_tiles\nm = Map(\n  basemap=basemap_to_tiles(\n    basemaps.NASAGIBS.ModisTerraTrueColorCR, \"2017-04-08\"\n  ),\n  center=(52.204793, 360.121558),\n  zoom=4\n)\nm.add_layer(Marker(location=(52.204793, 360.121558)))\nm\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n<script type=\"application/vnd.jupyter.widget-view+json\">\n{\"model_id\":\"75d590be7ed74e66a2eb05a31fbfc982\",\"version_major\":2,\"version_minor\":0}\n</script>\n```\n:::\n:::\n\n\nTo learn about available Jupyter Widgets visit <https://jupyter.org/widgets>.\n\n## Plotly\n\nPlotly is an interactive graphics library that can also be used with the Jupyter engine. Here's an example of using [Plotly](https://plotly.com/python/):\n\n::: {.cell execution_count=2}\n``` {.python .cell-code}\nimport plotly.express as px\nimport plotly.io as pio\ndf = px.data.iris()\nfig = px.scatter(df, x=\"sepal_width\", y=\"sepal_length\", \n                 color=\"species\", \n                 marginal_y=\"violin\", marginal_x=\"box\", \n                 trendline=\"ols\", template=\"simple_white\")\nfig.show()\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n<div>                            <div id=\"b9883908-2bed-48a8-ab05-fc373eccaaa2\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"b9883908-2bed-48a8-ab05-fc373eccaaa2\")) {                    Plotly.newPlot(                        \"b9883908-2bed-48a8-ab05-fc373eccaaa2\",                        [{\"hovertemplate\":\"species=setosa<br>sepal_width=%{x}<br>sepal_length=%{y}<extra></extra>\",\"legendgroup\":\"setosa\",\"marker\":{\"color\":\"#1F77B4\",\"symbol\":\"circle\"},\"mode\":\"markers\",\"name\":\"setosa\",\"orientation\":\"v\",\"showlegend\":true,\"x\":[3.5,3.0,3.2,3.1,3.6,3.9,3.4,3.4,2.9,3.1,3.7,3.4,3.0,3.0,4.0,4.4,3.9,3.5,3.8,3.8,3.4,3.7,3.6,3.3,3.4,3.0,3.4,3.5,3.4,3.2,3.1,3.4,4.1,4.2,3.1,3.2,3.5,3.1,3.0,3.4,3.5,2.3,3.2,3.5,3.8,3.0,3.8,3.2,3.7,3.3],\"xaxis\":\"x\",\"y\":[5.1,4.9,4.7,4.6,5.0,5.4,4.6,5.0,4.4,4.9,5.4,4.8,4.8,4.3,5.8,5.7,5.4,5.1,5.7,5.1,5.4,5.1,4.6,5.1,4.8,5.0,5.0,5.2,5.2,4.7,4.8,5.4,5.2,5.5,4.9,5.0,5.5,4.9,4.4,5.1,5.0,4.5,4.4,5.0,5.1,4.8,5.1,4.6,5.3,5.0],\"yaxis\":\"y\",\"type\":\"scatter\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"species=setosa<br>sepal_width=%{x}<extra></extra>\",\"legendgroup\":\"setosa\",\"marker\":{\"color\":\"#1F77B4\",\"symbol\":\"circle\"},\"name\":\"setosa\",\"notched\":true,\"offsetgroup\":\"setosa\",\"showlegend\":false,\"x\":[3.5,3.0,3.2,3.1,3.6,3.9,3.4,3.4,2.9,3.1,3.7,3.4,3.0,3.0,4.0,4.4,3.9,3.5,3.8,3.8,3.4,3.7,3.6,3.3,3.4,3.0,3.4,3.5,3.4,3.2,3.1,3.4,4.1,4.2,3.1,3.2,3.5,3.1,3.0,3.4,3.5,2.3,3.2,3.5,3.8,3.0,3.8,3.2,3.7,3.3],\"xaxis\":\"x3\",\"yaxis\":\"y3\",\"type\":\"box\"},{\"alignmentgroup\":\"True\",\"hovertemplate\":\"species=setosa<br>sepal_length=%{y}<extra></extra>\",\"legendgroup\":\"setosa\",\"marker\":{\"color\":\"#1F77B4\",\"symbol\":\"circle\"},\"name\":\"setosa\",\"offsetgroup\":\"setosa\",\"scalegroup\":\"y\",\"showlegend\":false,\"xaxis\":\"x2\",\"y\":[5.1,4.9,4.7,4.6,5.0,5.4,4.6,5.0,4.4,4.9,5.4,4.8,4.8,4.3,5.8,5.7,5.4,5.1,5.7,5.1,5.4,5.1,4.6,5.1,4.8,5.0,5.0,5.2,5.2,4.7,4.8,5.4,5.2,5.5,4.9,5.0,5.5,4.9,4.4,5.1,5.0,4.5,4.4,5.0,5.1,4.8,5.1,4.6,5.3,5.0],\"yaxis\":\"y2\",\"type\":\"violin\"},{\"hovertemplate\":\"<b>OLS trendline</b><br>sepal_length = 0.690854 * sepal_width + 2.64466<br>R<sup>2</sup>=0.557681<br><br>species=setosa<br>sepal_width=%{x}<br>sepal_length=%{y} 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                       {\"responsive\": true}                    ).then(function(){\n                            \nvar gd = document.getElementById('b9883908-2bed-48a8-ab05-fc373eccaaa2');\nvar x = new MutationObserver(function (mutations, observer) {{\n        var display = window.getComputedStyle(gd).display;\n        if (!display || display === 'none') {{\n            console.log([gd, 'removed!']);\n            Plotly.purge(gd);\n            observer.disconnect();\n        }}\n}});\n\n// Listen for the removal of the full notebook cells\nvar notebookContainer = gd.closest('#notebook-container');\nif (notebookContainer) {{\n    x.observe(notebookContainer, {childList: true});\n}}\n\n// Listen for the clearing of the current output cell\nvar outputEl = gd.closest('.output');\nif (outputEl) {{\n    x.observe(outputEl, {childList: true});\n}}\n\n                        })                };                });            </script>        </div>\n```\n:::\n:::\n\n\n::: {.callout-note}\nIf you are using Plotly within the VS Code Notebook Editor you will need to add a line of code to ensure that your plots can be seen both within VS Code and when rendered to HTML by Quarto. You can do this by configuring the Plotly [default renderer](https://plotly.com/python/renderers/#setting-the-default-renderer) as follows:\n\n```{{python}}\nimport plotly.io as pio\npio.renderers.default = \"plotly_mimetype+notebook_connected\"\n```\n\nThis workaround is required because when running within VS Code, Plotly chooses a default rendering that can't be easily exported to HTML (for more background, see this [GitHub Issue](https://github.com/microsoft/vscode-jupyter/issues/6999) and related discussion). Note that this workaround is only required for the VS Code Notebook Editor (it is not required if you are using Jupyter Lab or if you are editing a plain-text `.qmd` file).\n:::\n\n",
+    "engine": "jupyter",
+    "markdown": "---\ntitle: \"Jupyter Widgets\"\ntoc: false\n---\n\n\n\n\n## Overview\n\n[Jupyter Widgets](https://jupyter.org/widgets) enable you to use JavaScript visualization libraries like [Leaflet](https://ipyleaflet.readthedocs.io/en/latest/), [Plotly](https://plotly.com/python/), and [threejs](https://pythreejs.readthedocs.io/) directly from Python.\n\nIf you are using the Jupyter engine with Quarto this is a great way to incorporate interactivity without learning JavaScript.\n\n## Leaflet Example\n\nIncluding Jupyter Widgets within a Quarto document is as easy as including a plot. For example, here is how we embed a [Leaflet](https://ipyleaflet.readthedocs.io/en/latest/) map:\n\n::: {#756d6f3d .cell execution_count=1}\n``` {.python .cell-code}\nfrom ipyleaflet import Map, Marker, basemaps, basemap_to_tiles\nm = Map(\n  basemap=basemap_to_tiles(\n    basemaps.NASAGIBS.ModisTerraTrueColorCR, \"2017-04-08\"\n  ),\n  center=(52.204793, 360.121558),\n  zoom=4\n)\nm.add_layer(Marker(location=(52.204793, 360.121558)))\nm\n```\n\n::: {.cell-output .cell-output-display execution_count=1}\n```{=html}\n<script type=\"application/vnd.jupyter.widget-view+json\">\n{\"model_id\":\"5fc6f52c422044dfb8d3792a15286c5d\",\"version_major\":2,\"version_minor\":0,\"quarto_mimetype\":\"application/vnd.jupyter.widget-view+json\"}\n</script>\n```\n:::\n:::\n\n\nTo learn about available Jupyter Widgets visit <https://jupyter.org/widgets>.\n\n## Plotly\n\nPlotly is an interactive graphics library that can also be used with the Jupyter engine. Here's an example of using [Plotly](https://plotly.com/python/):\n\n::: {#474a2d34 .cell execution_count=2}\n``` {.python .cell-code}\nimport plotly.express as px\nimport plotly.io as pio\ndf = px.data.iris()\nfig = px.scatter(df, x=\"sepal_width\", y=\"sepal_length\", \n                 color=\"species\", \n                 marginal_y=\"violin\", marginal_x=\"box\", \n                 trendline=\"ols\", template=\"simple_white\")\nfig.show()\n```\n\n::: {.cell-output .cell-output-display}\n```{=html}\n<div>                            <div id=\"e038729e-08a1-4d40-b791-636d4ad35a60\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"e038729e-08a1-4d40-b791-636d4ad35a60\")) {                    Plotly.newPlot(                        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