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Merge pull request #128 from cssjessica/preprocessed-notebooks
Add notebooks for preprocessed datasets
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| Original file line number | Diff line number | Diff line change |
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| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "<img src=\"https://unidata.ucar.edu/images/logos/badges/badge_unidata_100.jpg\" alt=\"Unidata Logo\" style=\"float: right; height: 98px;\">\n", | ||
| "\n", | ||
| "# Siphon THREDDS Jupyter Notebook - Visualizing Preprocessed Data - Normalized\n", | ||
| "\n", | ||
| "## Dataset: {{datasetName}}\n", | ||
| "___" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Dependencies:\n", | ||
| "* *Siphon*: `pip install siphon`\n", | ||
| "* *xarray*: `pip install xarray` or 'conda install -c conda-forge xarray dask netCDF4 bottleneck'\n", | ||
| "* *ipywidgets*:`pip install ipywidgets` or `conda install -c conda-forge ipywidgets` " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import xarray as xr\n", | ||
| "from siphon.catalog import TDSCatalog\n", | ||
| "import ipywidgets as widgets\n", | ||
| "from ipywidgets import interact" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Access a dataset\n", | ||
| "\n", | ||
| "With the TDS catalog url, we can use Siphon to get the dataset named `datasetName`.\\\n", | ||
| "Siphon's `remote-access` returns a `Dataset` object, which opens the remote dataset and provides access to its metadata." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "catUrl = \"{{catUrl}}\"\n", | ||
| "datasetName = \"{{datasetName}}\"\n", | ||
| "catalog = TDSCatalog(catUrl)\n", | ||
| "ds = catalog.datasets[datasetName]\n", | ||
| "dataset = ds.remote_access(use_xarray=True)" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Normalized Data\n", | ||
| "\n", | ||
| "$z$:   Data point \\\n", | ||
| "$z_{\\text{min}}$:   Minimum data point value in the variable \\\n", | ||
| "$z_{\\text{max}}$:   Maximum data point value in the variable \\\n", | ||
| "$n$:   Normalized data point\n", | ||
| "\n", | ||
| "$n = \\cfrac{z - z_{\\text{min}}}{z_{\\text{max}} - z_{\\text{min}}}$" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### List of variables" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "list(dataset.data_vars)" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Display a variable:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "dropdown = widgets.Dropdown(options = list(dataset.data_vars), description = 'Select a variable')\n", | ||
| "\n", | ||
| "def plot_variable(data_var):\n", | ||
| "\tif len(dataset[data_var].shape) > 2:\n", | ||
| "\t\tdataset[data_var].plot()\n", | ||
| "\t\n", | ||
| "interact(plot_variable, data_var = dropdown)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "base", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.16 | packaged by conda-forge | (main, Feb 1 2023, 21:39:03) \n[GCC 11.3.0]" | ||
| }, | ||
| "orig_nbformat": 4, | ||
| "viewer_info": { | ||
| "accepts": { | ||
| "accept_datasetIDs": [ | ||
| "normalized.*" | ||
| ] | ||
| }, | ||
| "description": "Preprocessed Dataset Visualization - Normalized" | ||
| }, | ||
| "vscode": { | ||
| "interpreter": { | ||
| "hash": "b5cfa023891fceef02537f80a4c6e95b77988fb973cdb16a51cdb785092210be" | ||
| } | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,154 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "<img src=\"https://unidata.ucar.edu/images/logos/badges/badge_unidata_100.jpg\" alt=\"Unidata Logo\" style=\"float: right; height: 98px;\">\n", | ||
| "\n", | ||
| "# Siphon THREDDS Jupyter Notebook - Visualizing Preprocessed Data - Standardized\n", | ||
| "\n", | ||
| "## Dataset: {{datasetName}}\n", | ||
| "___" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Dependencies:\n", | ||
| "* *Siphon*: `pip install siphon`\n", | ||
| "* *xarray*: `pip install xarray` or 'conda install -c conda-forge xarray dask netCDF4 bottleneck'\n", | ||
| "* *ipywidgets*:`pip install ipywidgets` or `conda install -c conda-forge ipywidgets` " | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "import xarray as xr\n", | ||
| "from siphon.catalog import TDSCatalog\n", | ||
| "import ipywidgets as widgets\n", | ||
| "from ipywidgets import interact" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "#### Access a dataset\n", | ||
| "\n", | ||
| "With the TDS catalog url, we can use Siphon to get the dataset named `datasetName`.\\\n", | ||
| "Siphon's `remote-access` returns a `Dataset` object, which opens the remote dataset and provides access to its metadata." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "catUrl = \"{{catUrl}}\"\n", | ||
| "datasetName = \"{{datasetName}}\"\n", | ||
| "catalog = TDSCatalog(catUrl)\n", | ||
| "ds = catalog.datasets[datasetName]\n", | ||
| "dataset = ds.remote_access(use_xarray=True)" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Standardized Data\n", | ||
| "\n", | ||
| "$z$:   Data point \\\n", | ||
| "$\\mu$:   Mean value in the variable \\\n", | ||
| "$\\sigma$:   Standard deviation value in the variable \\\n", | ||
| "$s$:   Standardized data point\n", | ||
| "\n", | ||
| "$s = \\cfrac{z - \\mu}{\\sigma}$" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### List of variables" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "list(dataset.data_vars)" | ||
| ] | ||
| }, | ||
| { | ||
| "attachments": {}, | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "### Display a variable:" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": null, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "dd = widgets.Dropdown(options = list(dataset.data_vars), description = 'Select a variable')\n", | ||
| "\n", | ||
| "def plot_variable(column):\n", | ||
| "\tif len(dataset[column].shape) > 2:\n", | ||
| "\t\tdataset[column].plot()\n", | ||
| "\t\n", | ||
| "interact(plot_variable, column = dd)" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": "base", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.9.16 | packaged by conda-forge | (main, Feb 1 2023, 21:39:03) \n[GCC 11.3.0]" | ||
| }, | ||
| "orig_nbformat": 4, | ||
| "viewer_info": { | ||
| "accepts": { | ||
| "accept_datasetIDs": [ | ||
| "standardized.*" | ||
| ] | ||
| }, | ||
| "description": "Preprocessed Dataset Visualization - Standardized" | ||
| }, | ||
| "vscode": { | ||
| "interpreter": { | ||
| "hash": "b5cfa023891fceef02537f80a4c6e95b77988fb973cdb16a51cdb785092210be" | ||
| } | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |