Rm interactive

README.md

@@ -47,6 +47,8 @@ Animated plots are great tools for science communication and outreach. We will d
 
 Dynamically rendering, animating, panning & zooming over a plot can be great to increase data fidelity. We will showcase how to use Holoviz technologies with Bokeh backend to create interactive plots, utilizing an unstructured grid data in the Model for Prediction Across Scales (MPAS) format.
 
+Due to environment configuration limitations, interactive plotting will be temporarily be moved to a separate Cookbook.
+
 ## Running the Notebooks
 
 You can either run the notebook using [Binder](https://binder.projectpythia.org/) or on your local machine.

environment.yml

@@ -5,18 +5,12 @@ dependencies:
   - sphinx-pythia-theme
   - jupyter-book
   - jupyterlab
-  - jupyter_bokeh
   - pandas
   - matplotlib
   - cartopy
   - metpy
   - geocat-viz>=2023.10.0
   - colorcet
-  - hvplot
-  - plotly
-  - seaborn
   - uxarray
-  - datashader
   - geocat-datafiles
-  - geoviews
   - tropycal

notebooks/1-comparison.ipynb

@@ -260,119 +260,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Plotly\n",
-    "\n",
-    "<img src=\"images/logos/plotly.png\" width=250 alt=\"Plotly Logo\"></img>\n",
-    "\n",
-    "Plotly is solid choice for interactive plotting. Plotly has functionality in several languags. Here is the [Plotly Python documentation](https://plotly.com/python/).\n",
-    "\n",
-    "Here is an example using their \"Express\" functionality:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import plotly.express as px\n",
-    "\n",
-    "fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Seaborn\n",
-    "\n",
-    "<img src=\"images/logos/seaborn.svg\" width=250 alt=\"Seaborn Logo\"></img>\n",
-    "\n",
-    "Seaborn is a high level interactive interface for creating statistical visualizations built on matplotlib. Check out the [Seaborn documentation](https://seaborn.pydata.org/).\n",
-    "\n",
-    "Here is their [heatmap example](https://seaborn.pydata.org/examples/spreadsheet_heatmap.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import seaborn as sns\n",
-    "sns.set_theme()\n",
-    "\n",
-    "# Load the example flights dataset and convert to long-form\n",
-    "flights_long = sns.load_dataset(\"flights\")\n",
-    "flights = flights_long.pivot(index=\"month\", columns=\"year\", values=\"passengers\")\n",
-    "\n",
-    "# Draw a heatmap with the numeric values in each cell\n",
-    "f, ax = plt.subplots(figsize=(9, 6))\n",
-    "sns.heatmap(flights, annot=True, fmt=\"d\", linewidths=.5, ax=ax)\n",
-    "\n",
-    "plt.show();"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Bokeh\n",
-    "\n",
-    "<img src=\"images/logos/bokeh.jpeg\" width=250 alt=\"Bokeh Logo\"></img>\n",
-    "\n",
-    "Bokeh is a Javascript-powered tool for creating interactive visualizations in modern web browsers. Check out the [Bokeh documentation](https://bokeh.org/)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## UXarray\n",
-    "\n",
-    "<img src=\"images/logos/uxarray.png\" width=250 alt=\"UXarray Logo\"></img>\n",
-    "\n",
-    "UXarray specializes in unstructured grids, built around [UGRID conventions](https://ugrid-conventions.github.io/ugrid-conventions/) and Xarray syntax. See the [UXarray documentation](https://uxarray.readthedocs.io/en/latest/) and check out the the [UXarray Cookbook](https://projectpythia.org/unstructured-grid-viz-cookbook)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## hvPlot\n",
-    "\n",
-    "<img src=\"images/logos/hvplot.svg\" width=250 alt=\"Datashader Logo\"></img>\n",
-    "\n",
-    "hvPlot wraps both [Datashader](https://datashader.org/), a graphics pipeline, and [Holoviews](https://holoviews.org/), a tool for bundling data and metadata for intuitive interactive plotting, at a higher level. All 3 tools are by [Holoviz](https://holoviz.org/). Reference the [hvPlot documentation](https://hvplot.holoviz.org/).\n",
-    "\n",
-    "Here is a simple example from their [user guide](https://hvplot.holoviz.org/user_guide/Introduction.html):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import hvplot.pandas\n",
-    "\n",
-    "pd.options.plotting.backend = 'holoviews'\n",
-    "\n",
-    "index = pd.date_range('1/1/2000', periods=1000)\n",
-    "df = pd.DataFrame(np.random.randn(1000, 4), index=index, columns=list('ABCD')).cumsum()\n",
-    "\n",
-    "df.plot()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This useful diagram from [hvPlot's documentation](https://hvplot.holoviz.org/index.html) details how different high-level tools for data visualization interact.\n",
-    "\n",
-    "<img src=\"images/hvplot_diagram.svg\" alt=\"Datashader Logo\"></img>"
+    "Interactive visualization libraries such as Plotly, UXarray, seaborn, bokeh, and hvplot will be explored in a separate interactive plotting Cookbook."
    ]
   },
   {