From 7d8c2175052a764a731e7480414289747afefb19 Mon Sep 17 00:00:00 2001 From: Andrew <15331990+ahuang11@users.noreply.github.com> Date: Thu, 10 Aug 2023 18:17:41 -0400 Subject: [PATCH] Add viz on the fly (#40) --- _toc.yml | 1 + environment.yml | 2 + ...isualizations_with_Hvplot_Datashader.ipynb | 239 ++++++++++++++++++ notebooks/images/kerchunk.gif | Bin 0 -> 2640249 bytes notebooks/images/thredds.gif | Bin 0 -> 3485621 bytes 5 files changed, 242 insertions(+) create mode 100644 notebooks/case_studies/Streaming_Visualizations_with_Hvplot_Datashader.ipynb create mode 100644 notebooks/images/kerchunk.gif create mode 100644 notebooks/images/thredds.gif diff --git a/_toc.yml b/_toc.yml index b6ce37a4..ff3a88d3 100644 --- a/_toc.yml +++ b/_toc.yml @@ -16,3 +16,4 @@ parts: - file: notebooks/case_studies/GRIB2_HRRR - file: notebooks/case_studies/GeoTIFF_FMI - file: notebooks/case_studies/NetCDF_Pangeo_Forge_gridMET + - file: notebooks/case_studies/Streaming_Visualizations_with_Hvplot_Datashader \ No newline at end of file diff --git a/environment.yml b/environment.yml index f9478930..2ae8e165 100644 --- a/environment.yml +++ b/environment.yml @@ -34,6 +34,8 @@ dependencies: - ujson - xarray - zarr + - hvplot + - datashader - pip: - sphinx-pythia-theme diff --git a/notebooks/case_studies/Streaming_Visualizations_with_Hvplot_Datashader.ipynb b/notebooks/case_studies/Streaming_Visualizations_with_Hvplot_Datashader.ipynb new file mode 100644 index 00000000..b65ef6e7 --- /dev/null +++ b/notebooks/case_studies/Streaming_Visualizations_with_Hvplot_Datashader.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f6c39c84", + "metadata": {}, + "source": [ + "# Kerchunk, hvPlot, and Datashader: Visualizing datasets on-the-fly" + ] + }, + { + "cell_type": "markdown", + "id": "9eced552", + "metadata": {}, + "source": [ + "## Overview\n", + " \n", + "This notebook will demonstrate how to use Kerchunk with hvPlot and Datashader to lazily visualize a reference dataset in a streaming fashion.\n", + "\n", + "We will be building off content from [Kerchunk and Pangeo-Forge](../case_studies/NetCDF_Pangeo_Forge_gridMET.ipynb), so it's encouraged you first go through that.\n", + "\n", + "## Prerequisites\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Kerchunk Basics](../foundations/kerchunk_basics) | Required | Core |\n", + "| [Multiple Files and Kerchunk](../foundations/kerchunk_multi_file) | Required | Core |\n", + "| [Introduction to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Required | IO |\n", + "| [Introduction to hvPlot](https://hvplot.holoviz.org/) | Required | Data Visualization |\n", + "| [Introduction to Datashader](https://datashader.org/index.html) | Required | Big Data Visualization |\n", + "- **Time to learn**: 10 minutes\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "4043365b", + "metadata": {}, + "source": [ + "## Motivation\n", + "\n", + "Using Kerchunk, we don't have to create a copy of the data--instead we create a collection of reference files, so that the original data files can be read as if they were Zarr.\n", + "\n", + "This enables visualization on-the-fly; simply pass in the URL to the dataset and use hvplot." + ] + }, + { + "cell_type": "markdown", + "id": "8e2c4765", + "metadata": {}, + "source": [ + "## Getting to Know The Data\n", + "\n", + "`gridMET` is a high-resolution daily meteorological dataset covering CONUS from 1979-2023. It is produced by the Climatology Lab at UC Merced. In this example, we are going to look create a virtual Zarr dataset of a derived variable, Burn Index. " + ] + }, + { + "cell_type": "markdown", + "id": "f8d0f8f1", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "706c1b95", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import time\n", + "\n", + "import apache_beam as beam\n", + "import fsspec\n", + "import hvplot.xarray\n", + "import xarray as xr\n", + "from pangeo_forge_recipes.patterns import ConcatDim, FilePattern\n", + "from pangeo_forge_recipes.transforms import (\n", + " CombineReferences,\n", + " OpenWithKerchunk,\n", + " WriteCombinedReference,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "858399ce", + "metadata": {}, + "source": [ + "## Preprocess Dataset\n", + "\n", + "Here we will be preparing the Kerchunk reference files by using the recipe described in [Kerchunk and Pangeo-Forge](../case_studies/NetCDF_Pangeo_Forge_gridMET.ipynb).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "06fb2af3", + "metadata": {}, + "outputs": [], + "source": [ + "# Constants\n", + "target_root = \"references\"\n", + "store_name = \"Pangeo_Forge\"\n", + "full_path = os.path.join(target_root, store_name, \"reference.json\")\n", + "years = list(range(1979, 1980))\n", + "time_dim = ConcatDim(\"time\", keys=years)\n", + "\n", + "\n", + "# Functions\n", + "def format_function(time):\n", + " return f\"http://www.northwestknowledge.net/metdata/data/bi_{time}.nc\"\n", + "\n", + "# Patterns\n", + "pattern = FilePattern(format_function, time_dim, file_type=\"netcdf4\")\n", + "pattern = pattern.prune()\n", + "\n", + "# Apache Beam transforms\n", + "transforms = (\n", + " beam.Create(pattern.items())\n", + " | OpenWithKerchunk(file_type=pattern.file_type)\n", + " | CombineReferences(\n", + " concat_dims=[\"day\"],\n", + " identical_dims=[\"lat\", \"lon\", \"crs\"],\n", + " )\n", + " | WriteCombinedReference(target_root=target_root, store_name=store_name)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7818440e", + "metadata": {}, + "source": [ + "## Opening the Kerchunk Dataset\n", + "\n", + "Now, it's a matter of opening the Kerchunk dataset and calling `hvplot` with the `rasterize=True` keyword argument.\n", + "\n", + "If you're running this notebook locally, try zooming around the map by hovering over the plot and scrolling; it should update fairly quickly. Note, it will **not** update if you're viewing this on the docs page online as there is no backend server, but don't fret because there's a demo GIF below!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce0f1766", + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit -r 1 -n 1\n", + "\n", + "mapper = fsspec.get_mapper(\n", + " \"reference://\",\n", + " fo=full_path,\n", + " remote_protocol=\"http\",\n", + ")\n", + "ds_kerchunk = xr.open_dataset(\n", + " mapper, engine=\"zarr\", decode_coords=\"all\", backend_kwargs={\"consolidated\": False}\n", + ")\n", + "display(ds_kerchunk.hvplot(\"lon\", \"lat\", rasterize=True))" + ] + }, + { + "cell_type": "markdown", + "id": "e53531df", + "metadata": {}, + "source": [ + "\"Kerchunk" + ] + }, + { + "cell_type": "markdown", + "id": "f2c56945", + "metadata": {}, + "source": [ + "## Comparing Against THREDDS\n", + "\n", + "Now, we will be repeating the previous cell, but with THREDDS.\n", + "\n", + "Note how the initial load is longer.\n", + "\n", + "If you're running the notebook locally (or a demo GIF below), zooming in/out also takes longer to finish buffering as well." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "368ac51d", + "metadata": {}, + "outputs": [], + "source": [ + "%%timeit -r 1 -n 1\n", + "\n", + "def url_gen(year):\n", + " return (\n", + " f\"http://thredds.northwestknowledge.net:8080/thredds/dodsC/MET/bi/bi_{year}.nc\"\n", + " )\n", + "\n", + "urls_list = [url_gen(year) for year in years]\n", + "netcdf_ds = xr.open_mfdataset(urls_list, engine=\"netcdf4\")\n", + "display(netcdf_ds.hvplot(\"lon\", \"lat\", rasterize=True))" + ] + }, + { + "cell_type": "markdown", + "id": "6c0dd0cd", + "metadata": {}, + "source": [ + "\"THREDDS" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + 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