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When it is not found, a full rebuild will be done. -config: b710a7e1a3097c92db574946ba7bfd2f -tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/_preview/22/README.html b/_preview/22/README.html deleted file mode 100644 index e2c90ad..0000000 --- a/_preview/22/README.html +++ /dev/null @@ -1,583 +0,0 @@ - - - - - - - - Landsat ML Cookbook — Landsat ML Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Landsat 8

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Landsat ML Cookbook

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nightly-build -Binder -DOI

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This Project Pythia Cookbook covers the essential materials for working with Landsat data in the context of machine learning workflows.

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Motivation

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Once you complete this cookbook, you will have the skills to access, resample, regrid, reshape, and rescale satellite data, as well as the foundation for applying machine learning to it. You will also learn how to interactively visualize your data at every step in the process.

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Authors

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Demetris Roumis

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Contributors

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This cookbook was initially inspired by the EarthML . See a list of the EarthML contributors here: - -https://contrib.rocks/image?repo=pyviz-topics/EarthML -

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Structure

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This cookbook is broken up into two main sections - “Foundations” and “Example Workflows.”

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Foundations

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The foundational content includes:

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  • Start Here - Introduction to Landsat data

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  • Data Ingestion - Planetary Computer - Demonstrating a method for loading and accessing Landsat data from Microsoft’s platform

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  • Data Ingestion - Intake - Demonstrating approaches for data access using Intake

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  • Preprocessing - Regrid - Demonstrating common preprocessing approaches such as regridding

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Example Workflows

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Example workflows include:

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  • Spectral Clustering - Demonstrating a machine learning approach to cluster pixels of satellite data and comparing cluster results across time

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Running the Notebooks

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You can either run the notebook using Binder or on your local machine.

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Running on Binder

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The simplest way to interact with a Jupyter Notebook is through -Binder, which enables the execution of a -Jupyter Book in the cloud. The details of how this works are not -important for now. All you need to know is how to launch a Pythia -Cookbooks chapter via Binder. Simply navigate your mouse to -the top right corner of the book chapter you are viewing and click -on the rocket ship icon, (see figure below), and be sure to select -“launch Binder”. After a moment you should be presented with a -notebook that you can interact with. I.e. you’ll be able to execute -and even change the example programs. You’ll see that the code cells -have no output at first, until you execute them by pressing -Shift+Enter. Complete details on how to interact with -a live Jupyter notebook are described in Getting Started with -Jupyter.

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Running on Your Own Machine

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If you are interested in running this material locally on your computer, you will need to follow this workflow:

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  1. Clone the Landsat ML Cookbook repository:

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     git clone https://github.com/ProjectPythia/landsat-ml-cookbook.git
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  3. Move into the landsat-ml-cookbook directory

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    cd landsat-ml-cookbook
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  5. Create and activate your conda environment from the environment.yml file

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    conda env create -f environment.yml
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  7. Move into the notebooks directory and start up Jupyterlab

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    cd notebooks/
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zvXGthUG8N>K=-EcX(-G(h65}bkh<)t3AWU4<%&nwS=Z`wSs|^|?F~-U*6e?s&!B-T x!H`w&C7zH3%hsi}bnhN-Rzl7QiMA*0{eQnxixz&pI;j8v002ovPDHLkV1l_Q-4y@; diff --git a/_preview/22/_sources/README.md b/_preview/22/_sources/README.md deleted file mode 100644 index a15d713..0000000 --- a/_preview/22/_sources/README.md +++ /dev/null @@ -1,86 +0,0 @@ -![Landsat 8](./notebooks/images/nasa_landsat8.jpg "Landsat 8") - -# Landsat ML Cookbook - -[![nightly-build](https://github.com/ProjectPythia/landsat-ml-cookbook/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/ProjectPythia/landsat-ml-cookbook/actions/workflows/nightly-build.yaml) -[![Binder](http://binder.projectpythia.org/badge_logo.svg)](http://binder.projectpythia.org/v2/gh/ProjectPythia/landsat-ml-cookbook/main?labpath=notebooks) -[![DOI](https://zenodo.org/badge/563445694.svg)](https://zenodo.org/badge/latestdoi/563445694) - -This Project Pythia Cookbook covers the essential materials for working with Landsat data in the context of machine learning workflows. - -## Motivation - -Once you complete this cookbook, you will have the skills to access, resample, regrid, reshape, and rescale satellite data, as well as the foundation for applying machine learning to it. You will also learn how to interactively visualize your data at every step in the process. - -## Authors - -[Demetris Roumis](https://github.com/droumis) - -### Contributors - - - - - - -This cookbook was initially inspired by the [EarthML](https://github.com/pyviz-topics/EarthML) . See a list of the EarthML contributors [here:](https://github.com/pyviz-topics/EarthML/graphs/contributors) - - - - -## Structure -This cookbook is broken up into two main sections - "Foundations" and "Example Workflows." - -### Foundations -The foundational content includes: -- Start Here - Introduction to Landsat data -- Data Ingestion - Planetary Computer - Demonstrating a method for loading and accessing Landsat data from Microsoft's platform -- Data Ingestion - Intake - Demonstrating approaches for data access using Intake -- Preprocessing - Regrid - Demonstrating common preprocessing approaches such as regridding - -### Example Workflows -Example workflows include: -- Spectral Clustering - Demonstrating a machine learning approach to cluster pixels of satellite data and comparing cluster results across time - -## Running the Notebooks -You can either run the notebook using [Binder](https://binder.projectpythia.org/) or on your local machine. - -### Running on Binder - -The simplest way to interact with a Jupyter Notebook is through -[Binder](https://binder.projectpythia.org/), which enables the execution of a -[Jupyter Book](https://jupyterbook.org) in the cloud. The details of how this works are not -important for now. All you need to know is how to launch a Pythia -Cookbooks chapter via Binder. Simply navigate your mouse to -the top right corner of the book chapter you are viewing and click -on the rocket ship icon, (see figure below), and be sure to select -“launch Binder”. After a moment you should be presented with a -notebook that you can interact with. I.e. you’ll be able to execute -and even change the example programs. You’ll see that the code cells -have no output at first, until you execute them by pressing -{kbd}`Shift`\+{kbd}`Enter`. Complete details on how to interact with -a live Jupyter notebook are described in [Getting Started with -Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter.html). - -### Running on Your Own Machine -If you are interested in running this material locally on your computer, you will need to follow this workflow: - -1. Clone the Landsat ML Cookbook repository: - - ```bash - git clone https://github.com/ProjectPythia/landsat-ml-cookbook.git - ``` -1. Move into the `landsat-ml-cookbook` directory - ```bash - cd landsat-ml-cookbook - ``` -1. Create and activate your conda environment from the `environment.yml` file - ```bash - conda env create -f environment.yml - conda activate landsat-ml-cookbook - ``` -1. Move into the `notebooks` directory and start up Jupyterlab - ```bash - cd notebooks/ - jupyter lab - ``` diff --git a/_preview/22/_sources/notebooks/0.0_Intro_Landsat.ipynb b/_preview/22/_sources/notebooks/0.0_Intro_Landsat.ipynb deleted file mode 100644 index e9ea8aa..0000000 --- a/_preview/22/_sources/notebooks/0.0_Intro_Landsat.ipynb +++ /dev/null @@ -1,131 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "477b00c6-6565-4a16-b302-46dac2fff9de", - "metadata": {}, - "source": [ - "![Landsat8](./images/nasa_landsat8.jpg \"Landsat8\")" - ] - }, - { - "cell_type": "markdown", - "id": "e9a66402-d146-40a2-a013-ef1078026efa", - "metadata": {}, - "source": [ - "# Start Here - Intro to Landsat Data" - ] - }, - { - "cell_type": "markdown", - "id": "8a8416c4-c56b-4b53-bc3f-8eb6285dfcac", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "a6587d1b-8e22-4404-abf0-0da0bfde096e", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "In this cookbook, you will access, process, analyze, and visualize satellite data in the context of machine learning workflows. This particular cookbook notebook will provide an introduction to Landsat data to build our intuition as we move toward data ingestion, processing, and analysis." - ] - }, - { - "cell_type": "markdown", - "id": "a8cb6848-e976-4390-9195-64ffb9049d81", - "metadata": {}, - "source": [ - "- **Time to learn**: 5 minutes" - ] - }, - { - "cell_type": "markdown", - "id": "5ecd112d-3666-4412-acce-05a6dd770de9", - "metadata": {}, - "source": [ - "## Landsat Data" - ] - }, - { - "cell_type": "markdown", - "id": "3b8e2b5e-9a96-4161-96b6-f0bbe6b17f47", - "metadata": {}, - "source": [ - "The data in this cookbook originally come from the [Landsat](https://en.wikipedia.org/wiki/Landsat_program) program, which is the longest record of moderate resolution multispectral data of the Earth’s surface. This program has launched several different satellites spanning many years which are designated as Landsat 1-9.\n", - "\n", - "![USGS Landsat Timeline](images/landsat_timeline.png \"USGS Landsat Timeline\")\n", - "\n", - "\n", - "When accessing the data, it's important to keep in mind a couple key points. First, the instruments on different Landsat missions (1-9) varied in certain aspects. Second, Landsat data is available from multiple providers (USGS, NASA, Google, Microsoft, AWS, etc) but may vary in completeness and the level of processing applied. For the dataset that you end up using, it is crucial to review to relevant information from the particular data provider and the specific Landsat mission to understand the details, especially if you are comparing data across providers or missions.\n", - "\n", - "In general, a common aspect of Landsat data is the use of different wavelength-bands to capture multiple images of the same area - together providing much more information about different features on the ground than a single image alone. This provides us with a stack of images for each spatial region that we might be interested.\n", - "\n", - "![Spectral Bands](images/nasa_bands.png \"Spectral Bands\")\n", - "\n", - "Additionally, whenever we are looking at changes in satellite images over time, we will have an additional time dimension. For example, we will consider two stacks of images from different years to look at the change in the water level around a lake.\n" - ] - }, - { - "cell_type": "markdown", - "id": "e7b29113-9cf8-40d8-b03b-0b7f0a6a6604", - "metadata": {}, - "source": [ - "___" - ] - }, - { - "cell_type": "markdown", - "id": "eb228292-bbda-437e-a6e8-2133cbb8e18b", - "metadata": {}, - "source": [ - "## Summary\n", - "Before accessing any data, it's a good idea to start by learning about the context and details of the dataset. This will give you the intuition to make informed decisions as you form a processing and analysis pipeline.\n", - "\n", - "### What's next?\n", - "Next, we'll learn about loading the data using the Microsoft Planetary Computer platform." - ] - }, - { - "cell_type": "markdown", - "id": "6e54a0ab-93e4-4299-a036-0fa7c3e23ec5", - "metadata": {}, - "source": [ - "## Resources and references\n", - "- The Landsat timeline image is originally from [USGS](https://www.usgs.gov/landsat-missions/landsat-satellite-missions?qt-science_support_page_related_con=2) but discovered through [earthsciencedata.org](https://www.earthdatascience.org/courses/use-data-open-source-python/multispectral-remote-sensing/landsat-in-Python/)\n", - "- The Landsat 8 banner image is from NASA\n", - "- The Landsat spectral bands is from [NASA](https://landsat.gsfc.nasa.gov/satellites/landsat-next/)\n", - "- This page was authored by Demetris Roumis circa Jan, 2023" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.8" - }, - "vscode": { - "interpreter": { - "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_preview/22/_sources/notebooks/1.0_Data_Ingestion-Planetary_Computer.ipynb b/_preview/22/_sources/notebooks/1.0_Data_Ingestion-Planetary_Computer.ipynb deleted file mode 100644 index 29651f0..0000000 --- a/_preview/22/_sources/notebooks/1.0_Data_Ingestion-Planetary_Computer.ipynb +++ /dev/null @@ -1,637 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "53b39a59-2225-4406-9136-65b0a4956a6c", - "metadata": {}, - "source": [ - "![Planetary Computer](images/planetary_computer_header_800w.png \"Planetary Computer\")" - ] - }, - { - "cell_type": "markdown", - "id": "65c844ec-98e2-40e0-9312-9d6bcd30e4a4", - "metadata": {}, - "source": [ - "# Data Ingestion - Microsoft Planetary Computer" - ] - }, - { - "cell_type": "markdown", - "id": "27bd740d-8c47-4843-9899-40a282eb5a18", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "091f838a-f459-4c41-957d-5f04083f95da", - "metadata": {}, - "source": [ - "## Overview" - ] - }, - { - "cell_type": "markdown", - "id": "488398aa-5d16-4a7a-b074-c2de5f6fac24", - "metadata": {}, - "source": [ - "In this notebook, you will ingest Landsat data for use in machine learning. Machine learning tasks often involve a lot of data, and in Python, data is typically stored in memory as simple [NumPy](https://foundations.projectpythia.org/core/numpy.html) arrays. However, higher-level containers built on top of NumPy arrays provide more functionality for multidimensional gridded data ([xarray](http://xarray.pydata.org)) or out-of-core and distributed data ([Dask](http://dask.pydata.org)). Our goal for data ingestion will be to load specific Landsat data of interest into one of these higher-level containers.\n", - "\n", - "[Microsoft Plantery Computer](https://planetarycomputer.microsoft.com/docs/overview/about) is one of several providers of [Landsat Data](https://planetarycomputer.microsoft.com/dataset/group/landsat). We are using it together with [pystac-client](https://pystac-client.readthedocs.io/en/stable/index.html) and [odc-stac](https://odc-stac.readthedocs.io/en/latest/index.html) because together they provide a nice Python API for searching and loading with specific criteria such as spatial area, datetime, Landsat mission, and cloud coverage.\n", - "\n", - "Earth science datasets are often stored on remote servers that may be too large to download locally. Therefore, in this cookbook, we will focus primarily on ingestion approaches that load small portions of data from a remote source, as needed. However, the approach for your own work will depend not only on data size and location but also the intended analysis, so in a follow up notebook, you will see alternative approaches to loading data." - ] - }, - { - "cell_type": "markdown", - "id": "836bb190-f0a1-4cb4-b599-386ed72a63ff", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Intro to Landsat](./0.0_Intro_Landsat.ipynb) | Necessary | Background |\n", - "| [About the Microsoft Planetary Computer](https://planetarycomputer.microsoft.com/docs/overview/about) | Helpful | Background |\n", - "| [pystac-client Usage](https://pystac-client.readthedocs.io/en/stable/usage.html) | Helpful | Consult as needed |\n", - "| [odc.stac.load Reference](https://odc-stac.readthedocs.io/en/latest/_api/odc.stac.load.html) | Helpful | Consult as needed |\n", - "| [xarray](https://foundations.projectpythia.org/core/xarray.html) | Necessary | |\n", - "| [Intro to Dask Array](https://docs.dask.org/en/stable/array.html) | Helpful | |\n", - "| [Panel Getting Started Guide](https://panel.holoviz.org/getting_started/build_app.html) | Helpful | |\n", - "\n", - "- **Time to learn**: 10 minutes" - ] - }, - { - "cell_type": "markdown", - "id": "70f621c3-8cdc-401b-a21c-79f31abd7bbf", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "28f179f7-1dbc-4127-b284-8ebabe3eff72", - "metadata": {}, - "outputs": [], - "source": [ - "# Data\n", - "import odc.stac\n", - "import pandas as pd\n", - "import planetary_computer\n", - "import pystac_client\n", - "import xarray as xr\n", - "from pystac.extensions.eo import EOExtension as eo\n", - "\n", - "# Viz\n", - "import hvplot.xarray\n", - "import panel as pn\n", - "\n", - "pn.extension()" - ] - }, - { - "cell_type": "markdown", - "id": "ec00475e-01fa-48f4-9323-0deaff92086b", - "metadata": {}, - "source": [ - "## Open and read the root of the STAC catalog" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a4007807-b3b1-40fe-8aae-a01a0d01b03b", - "metadata": {}, - "outputs": [], - "source": [ - "catalog = pystac_client.Client.open(\n", - " \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n", - " modifier=planetary_computer.sign_inplace,\n", - ")\n", - "catalog.title" - ] - }, - { - "cell_type": "markdown", - "id": "3ec64999-76f4-41db-b89b-b8c55c1ba788", - "metadata": {}, - "source": [ - "Microsoft Planetary Computer has a public STAC metadata but the actual data assets are in private Azure Blob Storage containers and require authentication. `pystac-client` provides a `modifier` keyword that we can use to manually sign the item. Otherwise, we'd get an error when trying to access the asset." - ] - }, - { - "cell_type": "markdown", - "id": "21cb2e57-3fd5-4fae-8fa3-beefc415478e", - "metadata": {}, - "source": [ - "# Search for Landsat Data" - ] - }, - { - "cell_type": "markdown", - "id": "8444b837-8826-45f7-841e-4b053a4bea86", - "metadata": {}, - "source": [ - "Let's say that an analysis we want to run requires landsat data over a specific region and from a specific time period. We can use our catalog to search for assets that fit our search criteria." - ] - }, - { - "cell_type": "markdown", - "id": "42052634-9c2f-4073-9080-a346703b2081", - "metadata": {}, - "source": [ - "First, let's find the name of the landsat dataset. [This page](https://planetarycomputer.microsoft.com/catalog) is a nice resource for browsing the available collections, but we can also just search the catalog for 'landsat':" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba583769-59d7-49bd-b506-79c744430a42", - "metadata": {}, - "outputs": [], - "source": [ - "all_collections = [i.id for i in catalog.get_collections()]\n", - "landsat_collections = [\n", - " collection for collection in all_collections if \"landsat\" in collection\n", - "]\n", - "landsat_collections" - ] - }, - { - "cell_type": "markdown", - "id": "a15872e0-c837-48a0-833b-9815ef4262f6", - "metadata": {}, - "source": [ - "We'll use the `landsat-c2-l2` dataset, which stands for Collection 2 Level-2. It contains data from several landsat missions and has better data quality than Level 1 (`landsat-c2-l1`). Microsoft Planetary Computer has descriptions of [Level 1](https://planetarycomputer.microsoft.com/dataset/landsat-c2-l1) and [Level 2](https://planetarycomputer.microsoft.com/dataset/landsat-c2-l2), but a direct and succinct comparison can be found in [this community post](https://gis.stackexchange.com/questions/439767/landsat-collections), and the information can be verified with [USGS](https://www.usgs.gov/landsat-missions/landsat-collection-2)." - ] - }, - { - "cell_type": "markdown", - "id": "a05e403e-92ae-46b4-b925-28c4f9628aa2", - "metadata": {}, - "source": [ - "Now, let's set our search parameters. You may already know the bounding box (region/area of interest) coordinates, but if you don't, there are many useful tools like [bboxfinder.com](http://bboxfinder.com/) that can help." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "77b40b78-c7f5-493d-b6a2-e5c765d2aaae", - "metadata": {}, - "outputs": [], - "source": [ - "bbox = [-118.89, 38.54, -118.57, 38.84] # Region over a lake in Nevada, USA\n", - "datetime = \"2017-06-01/2017-09-30\" # Summer months of 2017\n", - "collection = \"landsat-c2-l2\"" - ] - }, - { - "cell_type": "markdown", - "id": "1f3f7c56-4cfb-4764-b7ee-c01f58667d13", - "metadata": {}, - "source": [ - "We can also specify other parameters in the query, such as a specific landsat mission and the max percent of cloud cover:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ce29099d-1a57-447b-83dc-957f3b9d0096", - "metadata": {}, - "outputs": [], - "source": [ - "platform = \"landsat-8\"\n", - "cloudy_less_than = 1 # percent" - ] - }, - { - "cell_type": "markdown", - "id": "e44b0124-aaeb-461a-a48c-9e576da3bc54", - "metadata": {}, - "source": [ - "Now we run the search and list the results:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4d40e83c-d293-43d6-8f54-75ee67f66b99", - "metadata": {}, - "outputs": [], - "source": [ - "search = catalog.search(\n", - " collections=[\"landsat-c2-l2\"],\n", - " bbox=bbox,\n", - " datetime=datetime,\n", - " query={\"eo:cloud_cover\": {\"lt\": cloudy_less_than}, \"platform\": {\"in\": [platform]}},\n", - ")\n", - "items = search.get_all_items()\n", - "print(f\"Returned {len(items)} Items:\")\n", - "item_id = {(i, item.id): i for i, item in enumerate(items)}\n", - "item_id" - ] - }, - { - "cell_type": "markdown", - "id": "c246504f-f36c-4e9c-a740-8041a4d13612", - "metadata": {}, - "source": [ - "It looks like there were three image stacks taken by Landsat 8 over this spatial region during the summer months of 2017 that has less than 1 percent cloud cover." - ] - }, - { - "cell_type": "markdown", - "id": "1ec703af-b574-42eb-b97c-756fd6f8f909", - "metadata": {}, - "source": [ - "## Preview Results and Select a Dataset" - ] - }, - { - "cell_type": "markdown", - "id": "478b863e-e32c-499c-8a39-79c66ca1fb33", - "metadata": {}, - "source": [ - "Before loading one of the available image stacks, it would be useful to get a visual check of the results. Many datasets have a rendered preview or thumbnail image that can be accessed without having to load the full resolution data.\n", - "\n", - "We can create a simple interactive application using the [Panel](https://panel.holoviz.org/index.html) library to access and display rendered PNG previews of the our search results. Note that these pre-rendered images are of large tiles that span beyond our bounding box of interest. In the next steps, we will only be loading in a small area around the lake." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1f8e05bf-1c36-446b-8bc2-58bbecf6b68a", - "metadata": {}, - "outputs": [], - "source": [ - "item_sel = pn.widgets.Select(value=1, options=item_id, name=\"item\")\n", - "\n", - "def get_preview(i):\n", - " return pn.panel(items[i].assets[\"rendered_preview\"].href, height=300)\n", - "\n", - "\n", - "pn.Row(item_sel, pn.bind(get_preview, item_sel))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18f2d557-f274-4076-b328-39a06dc066c9", - "metadata": {}, - "outputs": [], - "source": [ - "selected_item = items[1]\n", - "selected_item" - ] - }, - { - "cell_type": "markdown", - "id": "311bb64c-230c-4a09-acd0-1c6d48dc927e", - "metadata": {}, - "source": [ - "## Access the Data" - ] - }, - { - "cell_type": "markdown", - "id": "7e253896-b4cb-4111-8094-8572e2c6d2ff", - "metadata": {}, - "source": [ - "Now that we have selected a dataset from our catalog, we can procede to access the data. We want to be very selective about the data that we read and when we read it because the amount of downloaded data can quickly get out of hand. Therefore, let's select only a subset of images.\n", - "\n", - "First, we'll preview the different image assets (or [Bands](https://github.com/stac-extensions/eo)) available in the Landsat item." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d49a164f-3d5d-4a7d-a5cb-029d6bede1db", - "metadata": {}, - "outputs": [], - "source": [ - "assets = []\n", - "for _, asset in selected_item.assets.items():\n", - " try:\n", - " assets.append(asset.extra_fields[\"eo:bands\"][0])\n", - " except:\n", - " pass\n", - "\n", - "cols_ordered = [\n", - " \"common_name\",\n", - " \"description\",\n", - " \"name\",\n", - " \"center_wavelength\",\n", - " \"full_width_half_max\",\n", - "]\n", - "bands = pd.DataFrame.from_dict(assets)[cols_ordered]\n", - "bands" - ] - }, - { - "cell_type": "markdown", - "id": "f8cbc4e1-7754-47b2-8477-e9ed3fef00f7", - "metadata": {}, - "source": [ - "Then we will select a few bands (images) of interest:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1831d5f4-f068-476e-80fe-de32a851d12a", - "metadata": {}, - "outputs": [], - "source": [ - "bands_of_interest = [\"red\", \"green\", \"blue\"]" - ] - }, - { - "cell_type": "markdown", - "id": "764ecd52-b893-4aed-b021-02d25b257c12", - "metadata": {}, - "source": [ - "Finally, we lazily load the selected data. We will use the package called `odc` which allows us to load only a specific region of interest (bounding box or 'bbox') and specific bands (images) of interest. We will also use the `chunks` argument to load the data as dask arrays; this will load the metadata now and delay the loading until we actually use the data, or until we force the data to be loaded by using `.compute()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "eca1a4a4-c0bc-4c81-960f-fb3550048a33", - "metadata": {}, - "outputs": [], - "source": [ - "ds = odc.stac.stac_load(\n", - " [selected_item],\n", - " bands=bands_of_interest,\n", - " bbox=bbox,\n", - " chunks={}, # <-- use Dask\n", - ").isel(time=0)\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "id": "1e410e59-8f71-4b0c-b2db-177b7ce00278", - "metadata": {}, - "source": [ - "Let's combine the bands of the dataset into a single DataArray that has the band names as coordinates of a new 'band' dimension, and also call `.compute()` to finally load the data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e63d16a2-a36d-4967-808c-ffcb1479088b", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "da = ds.to_array(dim=\"band\").compute()\n", - "da" - ] - }, - { - "cell_type": "markdown", - "id": "4ac0f3c1-3ff8-46c5-aa51-b06c73d96c66", - "metadata": {}, - "source": [ - "## Visualize the data" - ] - }, - { - "cell_type": "markdown", - "id": "ce573b8c-815d-439b-a49f-fa258d0e07a7", - "metadata": {}, - "source": [ - "Often, data ingestion involves quickly visualizing your raw data to get a sense that things are proceeding accordingly. As we have created an array with red, blue, and green bands, we can quickly display a natural color image of the lake using the `.plot.imshow()` function of `xarray`. We'll use the `robust=True` argument because the data values are outside the range of typical RGB images." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "30c9eec0-6975-4b1e-9a7c-318b98c332b2", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "da.plot.imshow(robust=True, size=3)" - ] - }, - { - "cell_type": "markdown", - "id": "c188d2c2-9eb9-4030-8fd0-54f88bb252c3", - "metadata": {}, - "source": [ - "Now, let's use `hvplot` to provide an interactive visualization of the inividual bands in our array." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31d88fa3-dbb9-4c14-b086-51ee20b52602", - "metadata": {}, - "outputs": [], - "source": [ - "ds" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c0cc8aa2-2878-4d2e-8bf3-6d9f1fcf9a58", - "metadata": {}, - "outputs": [], - "source": [ - "da.hvplot.image(x=\"x\", y=\"y\", cmap=\"viridis\", aspect=1)" - ] - }, - { - "cell_type": "markdown", - "id": "a9724bab-42c1-4275-a6dc-562c16b907f7", - "metadata": {}, - "source": [ - "Let's plot the bands as seperate columns by specifying a dimension to expand with `col='band'`. We can also set `rasterize=True` to use [Datashader](https://datashader.org/) (another HoloViz tool) to render large data into a 2D histogram, where every array cell counts the data points falling into that pixel, as set by the resolution of your screen. This is especially important for large and high resolution images that would otherwise cause issues when attempting to render in a browser." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2ef8c6d8-3717-44ca-8096-216e00116d45", - "metadata": {}, - "outputs": [], - "source": [ - "da.hvplot.image(\n", - " x=\"x\", y=\"y\", col=\"band\", cmap=\"viridis\", xaxis=False, yaxis=False, colorbar=False, rasterize=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "651f855c-0b9b-49d8-a851-5717a08532e7", - "metadata": {}, - "source": [ - "Select the zoom tool and zoom in on of the plots to see that all the images are all automatically linked!" - ] - }, - { - "cell_type": "markdown", - "id": "bbb72422-b9d6-4eee-a070-7f5ae5bca468", - "metadata": {}, - "source": [ - "## Retain Attributes" - ] - }, - { - "cell_type": "markdown", - "id": "32708bb1-adf1-423c-b76b-d2c2e0ced5cd", - "metadata": {}, - "source": [ - "When working with many image arrays, it's critical to retain the data properties as xarray attributes:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1c746f5e-c68b-40ac-9672-8c184c6b2d30", - "metadata": {}, - "outputs": [], - "source": [ - "da.attrs = selected_item.properties\n", - "da" - ] - }, - { - "cell_type": "markdown", - "id": "146c063b-563a-46fb-b964-676adb4a09c2", - "metadata": { - "jupyter": { - "outputs_hidden": true - }, - "tags": [] - }, - "source": [ - "Notice that you can now expand the `Attributes: ` dropdown to see the properties of this data." - ] - }, - { - "cell_type": "markdown", - "id": "63747da5-e126-4104-a27c-0501da98121b", - "metadata": {}, - "source": [ - "## Set the `crs` attribute" - ] - }, - { - "cell_type": "markdown", - "id": "8f315ec7-c868-4946-afe8-0a9fb5632aea", - "metadata": {}, - "source": [ - "As the data is in 'meter' units from a reference point, we can plot in commonly used longitude, latitude coordinates with `.hvplot(geo=True)` if our array has a valid coordinate reference system (CRS) attribute. This value is provided from Microsoft Planetary Computer as the `proj:epsg` property, so we just need to copy it to a new attribute `crs` so that hvPlot can automatically find it, without us having to further specify anything in our plotting code\n", - "\n", - "Note, this CRS is referenced by an EPSG code that can be accessed from the metadata of our selected catalog search result. We can see more about this dataset's specific code at [EPSG.io/32611](https://epsg.io/32611). You can also read more about EPSG codes in general in this [Coordinate Reference Systems: EPSG codes](https://pygis.io/docs/d_understand_crs_codes.html#epsg-codes) online book chapter. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cfb957fb-94e7-458f-a4dd-cff04d6331ec", - "metadata": {}, - "outputs": [], - "source": [ - "da.attrs[\"crs\"] = f\"epsg:{selected_item.properties['proj:epsg']}\"\n", - "da.attrs[\"crs\"]" - ] - }, - { - "cell_type": "markdown", - "id": "36637826-b5de-41bf-86fc-5d8d659a8217", - "metadata": {}, - "source": [ - "Now we can use `.hvplot(geo=True)` to plot in longitude and latitude coordinates. Informing `hvPlot` that this is geographic data also allows us to overlay data on aligned geographic tiles using the `tiles` parameter." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "87dbdd45-6a65-48c1-b0b0-559f31cb873f", - "metadata": {}, - "outputs": [], - "source": [ - "da.hvplot.image(\n", - " x=\"x\", y=\"y\", cmap=\"viridis\", geo=True, alpha=.9, tiles=\"ESRI\", xlabel=\"Longitude\", ylabel=\"Latitude\", colorbar=False, aspect=1,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "54214e63-420e-436c-aa95-c806c74c4c02", - "metadata": {}, - "source": [ - "___" - ] - }, - { - "cell_type": "markdown", - "id": "356f1670-ffea-4e9c-a7df-632864aecff0", - "metadata": {}, - "source": [ - "## Summary\n", - "The data access approach should adapt to features of the data and your intended analysis. As Landsat data is large and multidimensional, a good approach is to use [Microsoft Plantery Computer](https://planetarycomputer.microsoft.com/docs/overview/about), [pystac-client](https://pystac-client.readthedocs.io/en/stable/index.html), and [odc-stac](https://odc-stac.readthedocs.io/en/latest/index.html) together for searching the metadata catalog and lazily loading specific data chunks. Once you have accessed data, visualize it with hvPlot to ensure that it matches your expectations.\n", - "\n", - "### What's next?\n", - "Before we proceed to workflow examples, we can explore alternate ways of accessing data and the processing steps that are often utilized." - ] - }, - { - "cell_type": "markdown", - "id": "d200eed4-b3be-4a09-ae4a-671fa6f22e23", - "metadata": {}, - "source": [ - "## Resources and References\n", - "- Authored by Demetris Roumis circa Jan, 2023\n", - "- Guidance for parts of this notebook was provided by Microsoft in ['Reading Data from the STAC API'](https://planetarycomputer.microsoft.com/docs/quickstarts/reading-stac/)\n", - "- The image used in the banner is from [Microsoft](https://ai4edatasetspublicassets.blob.core.windows.net/assets/aod_images/planetary_computer_header_800w.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "22affb0b-715d-4520-9d5b-2631bf1105c3", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.8" - }, - "vscode": { - "interpreter": { - "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_preview/22/_sources/notebooks/1.1_Data_Ingestion-Intake.ipynb b/_preview/22/_sources/notebooks/1.1_Data_Ingestion-Intake.ipynb deleted file mode 100644 index d328054..0000000 --- a/_preview/22/_sources/notebooks/1.1_Data_Ingestion-Intake.ipynb +++ /dev/null @@ -1,1873 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Landsat8](./images/nasa_landsat8.jpg \"Landsat8\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Data Ingestion - Intake" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "In the last notebook, you learned how to efficiently load data from the Microsoft Planetary Computer platform. If that approach works for you, please proceed to a workflow example. In this notebook we will demonstrate common alternative approaches and techniques for general data access, centered around [Intake](https://intake.readthedocs.io)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "

\n", - "

Info

\n", - " A great way to contribute to this cookbook is to create a notebook that focuses on data access from a specific provider.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Intro to Landsat](./0.0_Intro_Landsat.ipynb) | Necessary | Background |\n", - "| [Data Ingestion - Planetary Computer](1.0_Data_Ingestion-Planetary_Computer.ipynb) | Helpful | |\n", - "| [Pandas Cookbook](https://foundations.projectpythia.org/core/pandas.html) | Helpful | |\n", - "| [xarray Cookbook](https://foundations.projectpythia.org/core/xarray.html) | Necessary | |\n", - "| [Intake Quickstart](https://intake.readthedocs.io/en/latest/index.html) | Helpful | |\n", - "|[Intake Cookbook](https://projectpythia.org/intake-cookbook/README.html)| Necessary | |\n", - "\n", - "- **Time to learn**: 20 minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.3.4'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = false;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks;\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n 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(OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n", - "application/vnd.holoviews_load.v0+json": "" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
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\n", - "
\n", - "" - ] - }, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "p1002" - } - }, - "output_type": "display_data" - } - ], - "source": [ - "import intake\n", - "import hvplot.xarray\n", - "import planetary_computer\n", - "\n", - "import warnings\n", - "warnings.simplefilter('ignore', FutureWarning) # Ignore warning about the format of epsg codes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get started, we need to provide a STAC URL (or any other data source URL) to intake, and we can ask intake to recommend some suitable datatypes." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[, , , , ]\n" - ] - } - ], - "source": [ - "url = \"https://planetarycomputer.microsoft.com/api/stac/v1\"\n", - "data_types = intake.readers.datatypes.recommend(url)\n", - "print(data_types)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will use STACJSON to read the URL." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "STACJSON, {'url': 'https://planetarycomputer.microsoft.com/api/stac/v1', 'storage_options': None, 'metadata': {}}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "data_type = intake.datatypes.STACJSON(url)\n", - "data_type" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Similarly, we can check out the possible readers to use with the STACJSON datatype." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'importable': [, , , , , , ], 'not_importable': [, , , , ]}\n" - ] - } - ], - "source": [ - "readers = data_type.possible_readers\n", - "print(readers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The StacCatalogReader is probably the most suitable for our use case. We can use it to read the STAC catalog and explore the available contents." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "StacCatalogReader reader producing intake.readers.entry:Catalog" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "reader = intake.catalogs.StacCatalogReader(\n", - " data_type, signer=planetary_computer.sign_inplace\n", - ")\n", - "reader" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can read the catalog and see what's available:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "stac_cat = reader.read()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "['daymet-annual-pr',\n", - " 'daymet-daily-hi',\n", - " '3dep-seamless',\n", - " '3dep-lidar-dsm',\n", - " 'fia',\n", - " 'sentinel-1-rtc',\n", - " 'gridmet',\n", - " 'daymet-annual-na',\n", - " 'daymet-monthly-na',\n", - " 'daymet-annual-hi',\n", - " 'daymet-monthly-hi',\n", - " 'daymet-monthly-pr',\n", - " 'gnatsgo-tables',\n", - " 'hgb',\n", - " 'cop-dem-glo-30',\n", - " 'cop-dem-glo-90',\n", - " 'goes-cmi',\n", - " 'terraclimate',\n", - " 'nasa-nex-gddp-cmip6',\n", - " 'gpm-imerg-hhr',\n", - " 'gnatsgo-rasters',\n", - " '3dep-lidar-hag',\n", - " 'io-lulc-annual-v02',\n", - " '3dep-lidar-intensity',\n", - " '3dep-lidar-pointsourceid',\n", - " 'mtbs',\n", - " 'noaa-c-cap',\n", - " '3dep-lidar-copc',\n", - " 'modis-64A1-061',\n", - " 'alos-fnf-mosaic',\n", - " '3dep-lidar-returns',\n", - " 'mobi',\n", - " 'landsat-c2-l2',\n", - " 'era5-pds',\n", - " 'chloris-biomass',\n", - " 'kaza-hydroforecast',\n", - " 'planet-nicfi-analytic',\n", - " 'modis-17A2H-061',\n", - " 'modis-11A2-061',\n", - " 'daymet-daily-pr',\n", - " '3dep-lidar-dtm-native',\n", - " '3dep-lidar-classification',\n", - " '3dep-lidar-dtm',\n", - " 'gap',\n", - " 'modis-17A2HGF-061',\n", - " 'planet-nicfi-visual',\n", - " 'gbif',\n", - " 'modis-17A3HGF-061',\n", - " 'modis-09A1-061',\n", - " 'alos-dem',\n", - " 'alos-palsar-mosaic',\n", - " 'deltares-water-availability',\n", - " 'modis-16A3GF-061',\n", - " 'modis-21A2-061',\n", - " 'us-census',\n", - " 'jrc-gsw',\n", - " 'deltares-floods',\n", - " 'modis-43A4-061',\n", - " 'modis-09Q1-061',\n", - " 'modis-14A1-061',\n", - " 'hrea',\n", - " 'modis-13Q1-061',\n", - " 'modis-14A2-061',\n", - " 'sentinel-2-l2a',\n", - " 'modis-15A2H-061',\n", - " 'modis-11A1-061',\n", - " 'modis-15A3H-061',\n", - " 'modis-13A1-061',\n", - " 'daymet-daily-na',\n", - " 'nrcan-landcover',\n", - " 'modis-10A2-061',\n", - " 'ecmwf-forecast',\n", - " 'noaa-mrms-qpe-24h-pass2',\n", - " 'sentinel-1-grd',\n", - " 'nasadem',\n", - " 'io-lulc',\n", - " 'landsat-c2-l1',\n", - " 'drcog-lulc',\n", - " 'chesapeake-lc-7',\n", - " 'chesapeake-lc-13',\n", - " 'chesapeake-lu',\n", - " 'noaa-mrms-qpe-1h-pass1',\n", - " 'noaa-mrms-qpe-1h-pass2',\n", - " 'noaa-nclimgrid-monthly',\n", - " 'goes-glm',\n", - " 'usda-cdl',\n", - " 'eclipse',\n", - " 'esa-cci-lc',\n", - " 'esa-cci-lc-netcdf',\n", - " 'fws-nwi',\n", - " 'usgs-lcmap-conus-v13',\n", - " 'usgs-lcmap-hawaii-v10',\n", - " 'noaa-climate-normals-tabular',\n", - " 'noaa-climate-normals-netcdf',\n", - " 'noaa-climate-normals-gridded',\n", - " 'aster-l1t',\n", - " 'cil-gdpcir-cc-by-sa',\n", - " 'naip',\n", - " 'io-lulc-9-class',\n", - " 'io-biodiversity',\n", - " 'noaa-cdr-sea-surface-temperature-whoi',\n", - " 'noaa-cdr-ocean-heat-content',\n", - " 'cil-gdpcir-cc0',\n", - " 'cil-gdpcir-cc-by',\n", - " 'noaa-cdr-sea-surface-temperature-whoi-netcdf',\n", - " 'noaa-cdr-sea-surface-temperature-optimum-interpolation',\n", - " 'modis-10A1-061',\n", - " 'sentinel-5p-l2-netcdf',\n", - " 'sentinel-3-olci-wfr-l2-netcdf',\n", - " 'noaa-cdr-ocean-heat-content-netcdf',\n", - " 'sentinel-3-synergy-aod-l2-netcdf',\n", - " 'sentinel-3-synergy-v10-l2-netcdf',\n", - " 'sentinel-3-olci-lfr-l2-netcdf',\n", - " 'sentinel-3-sral-lan-l2-netcdf',\n", - " 'sentinel-3-slstr-lst-l2-netcdf',\n", - " 'sentinel-3-slstr-wst-l2-netcdf',\n", - " 'sentinel-3-sral-wat-l2-netcdf',\n", - " 'ms-buildings',\n", - " 'sentinel-3-slstr-frp-l2-netcdf',\n", - " 'sentinel-3-synergy-syn-l2-netcdf',\n", - " 'sentinel-3-synergy-vgp-l2-netcdf',\n", - " 'sentinel-3-synergy-vg1-l2-netcdf',\n", - " 'esa-worldcover']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "metadata = {}\n", - "for data_description in stac_cat.data.values():\n", - " data = data_description.kwargs[\"data\"]\n", - " metadata[data[\"id\"]] = data[\"description\"]\n", - "list(metadata.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can print the description of the desired IDs." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1: Landsat Collection 2 Level-1 data, consisting of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data. These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.\n", - "\n", - "This dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", - "\n", - "2: Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\n", - "\n", - "This dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n", - "\n" - ] - } - ], - "source": [ - "print(\"1:\", metadata[\"landsat-c2-l1\"])\n", - "print(\"2:\", metadata[\"landsat-c2-l2\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Specifically, we want landsat-c2-l2." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "landsat_reader = stac_cat[\"landsat-c2-l2\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see the metadata below." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "{'type': 'Collection',\n", - " 'id': 'landsat-c2-l2',\n", - " 'stac_version': '1.0.0',\n", - " 'description': 'Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\\n\\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\\n',\n", - " 'conformsTo': ['http://www.opengis.net/spec/cql2/1.0/conf/basic-cql2',\n", - " 'http://www.opengis.net/spec/cql2/1.0/conf/cql2-json',\n", - " 'http://www.opengis.net/spec/cql2/1.0/conf/cql2-text',\n", - " 'http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/core',\n", - " 'http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/geojson',\n", - " 'http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/oas30',\n", - " 'http://www.opengis.net/spec/ogcapi-features-3/1.0/conf/filter',\n", - " 'https://api.stacspec.org/v1.0.0-rc.1/collections',\n", - " 'https://api.stacspec.org/v1.0.0-rc.1/core',\n", - " 'https://api.stacspec.org/v1.0.0-rc.1/item-search',\n", - " 'https://api.stacspec.org/v1.0.0-rc.1/item-search#fields',\n", - " 'https://api.stacspec.org/v1.0.0-rc.1/item-search#filter',\n", - " 'https://api.stacspec.org/v1.0.0-rc.1/item-search#query',\n", - " 'https://api.stacspec.org/v1.0.0-rc.1/item-search#sort',\n", - " 'https://api.stacspec.org/v1.0.0-rc.1/ogcapi-features'],\n", - " 'title': 'Landsat Collection 2 Level-2',\n", - " 'stac_extensions': ['https://stac-extensions.github.io/item-assets/v1.0.0/schema.json',\n", - " 'https://stac-extensions.github.io/view/v1.0.0/schema.json',\n", - " 'https://stac-extensions.github.io/scientific/v1.0.0/schema.json',\n", - " 'https://stac-extensions.github.io/raster/v1.1.0/schema.json',\n", - " 'https://stac-extensions.github.io/eo/v1.0.0/schema.json',\n", - " 'https://stac-extensions.github.io/table/v1.2.0/schema.json'],\n", - " 'item_assets': {'qa': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data'],\n", - " 'title': 'Surface Temperature Quality Assessment Band',\n", - " 'description': 'Collection 2 Level-2 Quality Assessment Band (ST_QA) Surface Temperature Product',\n", - " 'raster:bands': [{'unit': 'kelvin',\n", - " 'scale': 0.01,\n", - " 'nodata': -9999,\n", - " 'data_type': 'int16',\n", - " 'spatial_resolution': 30}]},\n", - " 'ang': {'type': 'text/plain',\n", - " 'roles': ['metadata'],\n", - " 'title': 'Angle Coefficients File',\n", - " 'description': 'Collection 2 Level-1 Angle Coefficients File'},\n", - " 'red': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'reflectance'],\n", - " 'title': 'Red Band',\n", - " 'eo:bands': [{'common_name': 'red', 'description': 'Visible red'}],\n", - " 'raster:bands': [{'scale': 2.75e-05,\n", - " 'nodata': 0,\n", - " 'offset': -0.2,\n", - " 'data_type': 'uint16',\n", - " 'spatial_resolution': 30}]},\n", - " 'blue': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'reflectance'],\n", - " 'title': 'Blue Band',\n", - " 'eo:bands': [{'common_name': 'blue', 'description': 'Visible blue'}],\n", - " 'raster:bands': [{'scale': 2.75e-05,\n", - " 'nodata': 0,\n", - " 'offset': -0.2,\n", - " 'data_type': 'uint16',\n", - " 'spatial_resolution': 30}]},\n", - " 'drad': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data'],\n", - " 'title': 'Downwelled Radiance Band',\n", - " 'description': 'Collection 2 Level-2 Downwelled Radiance Band (ST_DRAD) Surface Temperature Product',\n", - " 'raster:bands': [{'unit': 'watt/steradian/square_meter/micrometer',\n", - " 'scale': 0.001,\n", - " 'nodata': -9999,\n", - " 'data_type': 'int16',\n", - " 'spatial_resolution': 30}]},\n", - " 'emis': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data'],\n", - " 'title': 'Emissivity Band',\n", - " 'description': 'Collection 2 Level-2 Emissivity Band (ST_EMIS) Surface Temperature Product',\n", - " 'raster:bands': [{'unit': 'emissivity coefficient',\n", - " 'scale': 0.0001,\n", - " 'nodata': -9999,\n", - " 'data_type': 'int16',\n", - " 'spatial_resolution': 30}]},\n", - " 'emsd': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data'],\n", - " 'title': 'Emissivity Standard Deviation Band',\n", - " 'description': 'Collection 2 Level-2 Emissivity Standard Deviation Band (ST_EMSD) Surface Temperature Product',\n", - " 'raster:bands': [{'unit': 'emissivity coefficient',\n", - " 'scale': 0.0001,\n", - " 'nodata': -9999,\n", - " 'data_type': 'int16',\n", - " 'spatial_resolution': 30}]},\n", - " 'lwir': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'temperature'],\n", - " 'title': 'Surface Temperature Band',\n", - " 'eo:bands': [{'common_name': 'lwir', 'description': 'Long-wave infrared'}],\n", - " 'description': 'Collection 2 Level-2 Thermal Infrared Band (ST_B6) Surface Temperature',\n", - " 'raster:bands': [{'unit': 'kelvin',\n", - " 'scale': 0.00341802,\n", - " 'nodata': 0,\n", - " 'offset': 149.0,\n", - " 'data_type': 'uint16',\n", - " 'spatial_resolution': 30}]},\n", - " 'trad': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data'],\n", - " 'title': 'Thermal Radiance Band',\n", - " 'description': 'Collection 2 Level-2 Thermal Radiance Band (ST_TRAD) Surface Temperature Product',\n", - " 'raster:bands': [{'unit': 'watt/steradian/square_meter/micrometer',\n", - " 'scale': 0.001,\n", - " 'nodata': -9999,\n", - " 'data_type': 'int16',\n", - " 'spatial_resolution': 30}]},\n", - " 'urad': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data'],\n", - " 'title': 'Upwelled Radiance Band',\n", - " 'description': 'Collection 2 Level-2 Upwelled Radiance Band (ST_URAD) Surface Temperature Product',\n", - " 'raster:bands': [{'unit': 'watt/steradian/square_meter/micrometer',\n", - " 'scale': 0.001,\n", - " 'nodata': -9999,\n", - " 'data_type': 'int16',\n", - " 'spatial_resolution': 30}]},\n", - " 'atran': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data'],\n", - " 'title': 'Atmospheric Transmittance Band',\n", - " 'description': 'Collection 2 Level-2 Atmospheric Transmittance Band (ST_ATRAN) Surface Temperature Product',\n", - " 'raster:bands': [{'scale': 0.0001,\n", - " 'nodata': -9999,\n", - " 'data_type': 'int16',\n", - " 'spatial_resolution': 30}]},\n", - " 'cdist': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data'],\n", - " 'title': 'Cloud Distance Band',\n", - " 'description': 'Collection 2 Level-2 Cloud Distance Band (ST_CDIST) Surface Temperature Product',\n", - " 'raster:bands': [{'unit': 'kilometer',\n", - " 'scale': 0.01,\n", - " 'nodata': -9999,\n", - " 'data_type': 'int16',\n", - " 'spatial_resolution': 30}]},\n", - " 'green': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'reflectance'],\n", - " 'title': 'Green Band',\n", - " 'eo:bands': [{'common_name': 'green',\n", - " 'description': 'Visible green',\n", - " 'center_wavelength': 0.56}],\n", - " 'raster:bands': [{'scale': 2.75e-05,\n", - " 'nodata': 0,\n", - " 'offset': -0.2,\n", - " 'data_type': 'uint16',\n", - " 'spatial_resolution': 30}]},\n", - " 'nir08': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'reflectance'],\n", - " 'title': 'Near Infrared Band 0.8',\n", - " 'eo:bands': [{'common_name': 'nir08', 'description': 'Near infrared'}],\n", - " 'raster:bands': [{'scale': 2.75e-05,\n", - " 'nodata': 0,\n", - " 'offset': -0.2,\n", - " 'data_type': 'uint16',\n", - " 'spatial_resolution': 30}]},\n", - " 'lwir11': {'gsd': 100,\n", - " 'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'temperature'],\n", - " 'title': 'Surface Temperature Band',\n", - " 'eo:bands': [{'name': 'TIRS_B10',\n", - " 'common_name': 'lwir11',\n", - " 'description': 'Long-wave infrared',\n", - " 'center_wavelength': 10.9,\n", - " 'full_width_half_max': 0.59}],\n", - " 'description': 'Collection 2 Level-2 Thermal Infrared Band (ST_B10) Surface Temperature',\n", - " 'raster:bands': [{'unit': 'kelvin',\n", - " 'scale': 0.00341802,\n", - " 'nodata': 0,\n", - " 'offset': 149.0,\n", - " 'data_type': 'uint16',\n", - " 'spatial_resolution': 30}]},\n", - " 'swir16': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'reflectance'],\n", - " 'title': 'Short-wave Infrared Band 1.6',\n", - " 'eo:bands': [{'common_name': 'swir16',\n", - " 'description': 'Short-wave infrared'}],\n", - " 'raster:bands': [{'scale': 2.75e-05,\n", - " 'nodata': 0,\n", - " 'offset': -0.2,\n", - " 'data_type': 'uint16',\n", - " 'spatial_resolution': 30}]},\n", - " 'swir22': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'reflectance'],\n", - " 'title': 'Short-wave Infrared Band 2.2',\n", - " 'eo:bands': [{'common_name': 'swir22',\n", - " 'description': 'Short-wave infrared'}],\n", - " 'description': 'Collection 2 Level-2 Short-wave Infrared Band 2.2 (SR_B7) Surface Reflectance',\n", - " 'raster:bands': [{'scale': 2.75e-05,\n", - " 'nodata': 0,\n", - " 'offset': -0.2,\n", - " 'data_type': 'uint16',\n", - " 'spatial_resolution': 30}]},\n", - " 'coastal': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data', 'reflectance'],\n", - " 'title': 'Coastal/Aerosol Band',\n", - " 'eo:bands': [{'name': 'OLI_B1',\n", - " 'common_name': 'coastal',\n", - " 'description': 'Coastal/Aerosol',\n", - " 'center_wavelength': 0.44,\n", - " 'full_width_half_max': 0.02}],\n", - " 'description': 'Collection 2 Level-2 Coastal/Aerosol Band (SR_B1) Surface Reflectance',\n", - " 'raster:bands': [{'scale': 2.75e-05,\n", - " 'nodata': 0,\n", - " 'offset': -0.2,\n", - " 'data_type': 'uint16',\n", - " 'spatial_resolution': 30}]},\n", - " 'mtl.txt': {'type': 'text/plain',\n", - " 'roles': ['metadata'],\n", - " 'title': 'Product Metadata File (txt)',\n", - " 'description': 'Collection 2 Level-2 Product Metadata File (txt)'},\n", - " 'mtl.xml': {'type': 'application/xml',\n", - " 'roles': ['metadata'],\n", - " 'title': 'Product Metadata File (xml)',\n", - " 'description': 'Collection 2 Level-2 Product Metadata File (xml)'},\n", - " 'cloud_qa': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['cloud', 'cloud-shadow', 'snow-ice', 'water-mask'],\n", - " 'title': 'Cloud Quality Assessment Band',\n", - " 'description': 'Collection 2 Level-2 Cloud Quality Assessment Band (SR_CLOUD_QA) Surface Reflectance Product',\n", - " 'raster:bands': [{'unit': 'bit index',\n", - " 'data_type': 'uint8',\n", - " 'spatial_resolution': 30}],\n", - " 'classification:bitfields': [{'name': 'ddv',\n", - " 'length': 1,\n", - " 'offset': 0,\n", - " 'classes': [{'name': 'not_ddv',\n", - " 'value': 0,\n", - " 'description': 'Pixel has no DDV'},\n", - " {'name': 'ddv', 'value': 1, 'description': 'Pixel has DDV'}],\n", - " 'description': 'Dense Dark Vegetation (DDV)'},\n", - " {'name': 'cloud',\n", - " 'length': 1,\n", - " 'offset': 1,\n", - " 'classes': [{'name': 'not_cloud',\n", - " 'value': 0,\n", - " 'description': 'Pixel has no cloud'},\n", - " {'name': 'cloud', 'value': 1, 'description': 'Pixel has cloud'}],\n", - " 'description': 'Cloud mask'},\n", - " {'name': 'cloud_shadow',\n", - " 'length': 1,\n", - " 'offset': 2,\n", - " 'classes': [{'name': 'not_shadow',\n", - " 'value': 0,\n", - " 'description': 'Pixel has no cloud shadow'},\n", - " {'name': 'shadow', 'value': 1, 'description': 'Pixel has cloud shadow'}],\n", - " 'description': 'Cloud shadow mask'},\n", - " {'name': 'cloud_adjacent',\n", - " 'length': 1,\n", - " 'offset': 3,\n", - " 'classes': [{'name': 'not_adjacent',\n", - " 'value': 0,\n", - " 'description': 'Pixel is not adjacent to cloud'},\n", - " {'name': 'adjacent',\n", - " 'value': 1,\n", - " 'description': 'Pixel is adjacent to cloud'}],\n", - " 'description': 'Cloud adjacency'},\n", - " {'name': 'snow',\n", - " 'length': 1,\n", - " 'offset': 4,\n", - " 'classes': [{'name': 'not_snow',\n", - " 'value': 0,\n", - " 'description': 'Pixel is not snow'},\n", - " {'name': 'shadow', 'value': 1, 'description': 'Pixel is snow'}],\n", - " 'description': 'Snow mask'},\n", - " {'name': 'water',\n", - " 'length': 1,\n", - " 'offset': 5,\n", - " 'classes': [{'name': 'not_water',\n", - " 'value': 0,\n", - " 'description': 'Pixel is not water'},\n", - " {'name': 'water', 'value': 1, 'description': 'Pixel is water'}],\n", - " 'description': 'Water mask'}]},\n", - " 'mtl.json': {'type': 'application/json',\n", - " 'roles': ['metadata'],\n", - " 'title': 'Product Metadata File (json)',\n", - " 'description': 'Collection 2 Level-2 Product Metadata File (json)'},\n", - " 'qa_pixel': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['cloud', 'cloud-shadow', 'snow-ice', 'water-mask'],\n", - " 'title': 'Pixel Quality Assessment Band',\n", - " 'description': 'Collection 2 Level-1 Pixel Quality Assessment Band (QA_PIXEL)',\n", - " 'raster:bands': [{'unit': 'bit index',\n", - " 'nodata': 1,\n", - " 'data_type': 'uint16',\n", - " 'spatial_resolution': 30}]},\n", - " 'qa_radsat': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['saturation'],\n", - " 'raster:bands': [{'unit': 'bit index',\n", - " 'data_type': 'uint16',\n", - " 'spatial_resolution': 30}]},\n", - " 'qa_aerosol': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data-mask', 'water-mask'],\n", - " 'title': 'Aerosol Quality Assessment Band',\n", - " 'description': 'Collection 2 Level-2 Aerosol Quality Assessment Band (SR_QA_AEROSOL) Surface Reflectance Product',\n", - " 'raster:bands': [{'unit': 'bit index',\n", - " 'nodata': 1,\n", - " 'data_type': 'uint8',\n", - " 'spatial_resolution': 30}],\n", - " 'classification:bitfields': [{'name': 'fill',\n", - " 'length': 1,\n", - " 'offset': 0,\n", - " 'classes': [{'name': 'not_fill',\n", - " 'value': 0,\n", - " 'description': 'Pixel is not fill'},\n", - " {'name': 'fill', 'value': 1, 'description': 'Pixel is fill'}],\n", - " 'description': 'Image or fill data'},\n", - " {'name': 'retrieval',\n", - " 'length': 1,\n", - " 'offset': 1,\n", - " 'classes': [{'name': 'not_valid',\n", - " 'value': 0,\n", - " 'description': 'Pixel retrieval is not valid'},\n", - " {'name': 'valid',\n", - " 'value': 1,\n", - " 'description': 'Pixel retrieval is valid'}],\n", - " 'description': 'Valid aerosol retrieval'},\n", - " {'name': 'water',\n", - " 'length': 1,\n", - " 'offset': 2,\n", - " 'classes': [{'name': 'not_water',\n", - " 'value': 0,\n", - " 'description': 'Pixel is not water'},\n", - " {'name': 'water', 'value': 1, 'description': 'Pixel is water'}],\n", - " 'description': 'Water mask'},\n", - " {'name': 'interpolated',\n", - " 'length': 1,\n", - " 'offset': 5,\n", - " 'classes': [{'name': 'not_interpolated',\n", - " 'value': 0,\n", - " 'description': 'Pixel is not interpolated aerosol'},\n", - " {'name': 'interpolated',\n", - " 'value': 1,\n", - " 'description': 'Pixel is interpolated aerosol'}],\n", - " 'description': 'Aerosol interpolation'},\n", - " {'name': 'level',\n", - " 'length': 2,\n", - " 'offset': 6,\n", - " 'classes': [{'name': 'climatology',\n", - " 'value': 0,\n", - " 'description': 'No aerosol correction applied'},\n", - " {'name': 'low', 'value': 1, 'description': 'Low aerosol level'},\n", - " {'name': 'medium', 'value': 2, 'description': 'Medium aerosol level'},\n", - " {'name': 'high', 'value': 3, 'description': 'High aerosol level'}],\n", - " 'description': 'Aerosol level'}]},\n", - " 'atmos_opacity': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',\n", - " 'roles': ['data'],\n", - " 'title': 'Atmospheric Opacity Band',\n", - " 'description': 'Collection 2 Level-2 Atmospheric Opacity Band (SR_ATMOS_OPACITY) Surface Reflectance Product',\n", - " 'raster:bands': [{'scale': 0.001,\n", - " 'nodata': -9999,\n", - " 'data_type': 'int16',\n", - " 'spatial_resolution': 30}]}},\n", - " 'msft:group_id': 'landsat',\n", - " 'msft:container': 'landsat-c2',\n", - " 'msft:storage_account': 'landsateuwest',\n", - " 'msft:short_description': 'Landsat Collection 2 Level-2 data from the Thematic Mapper (TM) onboard Landsat 4 and 5, the Enhanced Thematic Mapper Plus (ETM+) onboard Landsat 7, and the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard Landsat 8 and 9.',\n", - " 'msft:region': 'westeurope',\n", - " 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},\n", - " 'temporal': {'interval': [['1982-08-22T00:00:00Z', None]]}},\n", - " 'license': 'proprietary',\n", - " 'keywords': ['Landsat',\n", - " 'USGS',\n", - " 'NASA',\n", - " 'Satellite',\n", - " 'Global',\n", - " 'Imagery',\n", - " 'Reflectance',\n", - " 'Temperature'],\n", - " 'providers': [{'name': 'NASA',\n", - " 'roles': ['producer', 'licensor'],\n", - " 'url': 'https://landsat.gsfc.nasa.gov/'},\n", - " {'name': 'USGS',\n", - " 'roles': ['producer', 'processor', 'licensor'],\n", - " 'url': 'https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products'},\n", - " {'name': 'Microsoft',\n", - " 'roles': ['host'],\n", - " 'url': 'https://planetarycomputer.microsoft.com'}],\n", - " 'summaries': {'gsd': [30, 60, 100, 120],\n", - " 'sci:doi': ['10.5066/P9IAXOVV', '10.5066/P9C7I13B', '10.5066/P9OGBGM6'],\n", - " 'eo:bands': [{'name': 'TM_B1',\n", - " 'common_name': 'blue',\n", - " 'description': 'Visible blue (Thematic Mapper)',\n", - " 'center_wavelength': 0.49,\n", - " 'full_width_half_max': 0.07},\n", - " {'name': 'TM_B2',\n", - " 'common_name': 'green',\n", - " 'description': 'Visible green (Thematic Mapper)',\n", - " 'center_wavelength': 0.56,\n", - " 'full_width_half_max': 0.08},\n", - " {'name': 'TM_B3',\n", - " 'common_name': 'red',\n", - " 'description': 'Visible red (Thematic Mapper)',\n", - " 'center_wavelength': 0.66,\n", - " 'full_width_half_max': 0.06},\n", - " {'name': 'TM_B4',\n", - " 'common_name': 'nir08',\n", - " 'description': 'Near infrared (Thematic Mapper)',\n", - " 'center_wavelength': 0.83,\n", - " 'full_width_half_max': 0.14},\n", - " {'name': 'TM_B5',\n", - " 'common_name': 'swir16',\n", - " 'description': 'Short-wave infrared (Thematic Mapper)',\n", - " 'center_wavelength': 1.65,\n", - " 'full_width_half_max': 0.2},\n", - " {'name': 'TM_B6',\n", - " 'common_name': 'lwir',\n", - " 'description': 'Long-wave infrared (Thematic Mapper)',\n", - " 'center_wavelength': 11.45,\n", - " 'full_width_half_max': 2.1},\n", - " {'name': 'TM_B7',\n", - " 'common_name': 'swir22',\n", - " 'description': 'Short-wave infrared (Thematic Mapper)',\n", - " 'center_wavelength': 2.22,\n", - " 'full_width_half_max': 0.27},\n", - " {'name': 'ETM_B1',\n", - " 'common_name': 'blue',\n", - " 'description': 'Visible blue (Enhanced Thematic Mapper Plus)',\n", - " 'center_wavelength': 0.48,\n", - " 'full_width_half_max': 0.07},\n", - " {'name': 'ETM_B2',\n", - " 'common_name': 'green',\n", - " 'description': 'Visible green (Enhanced Thematic Mapper Plus)',\n", - " 'center_wavelength': 0.56,\n", - " 'full_width_half_max': 0.08},\n", - " {'name': 'ETM_B3',\n", - " 'common_name': 'red',\n", - " 'description': 'Visible red (Enhanced Thematic Mapper Plus)',\n", - " 'center_wavelength': 0.66,\n", - " 'full_width_half_max': 0.06},\n", - " {'name': 'ETM_B4',\n", - " 'common_name': 'nir08',\n", - " 'description': 'Near infrared (Enhanced Thematic Mapper Plus)',\n", - " 'center_wavelength': 0.84,\n", - " 'full_width_half_max': 0.13},\n", - " {'name': 'ETM_B5',\n", - " 'common_name': 'swir16',\n", - " 'description': 'Short-wave infrared (Enhanced Thematic Mapper Plus)',\n", - " 'center_wavelength': 1.65,\n", - " 'full_width_half_max': 0.2},\n", - " {'name': 'ETM_B6',\n", - " 'common_name': 'lwir',\n", - " 'description': 'Long-wave infrared (Enhanced Thematic Mapper Plus)',\n", - " 'center_wavelength': 11.34,\n", - " 'full_width_half_max': 2.05},\n", - " {'name': 'ETM_B7',\n", - " 'common_name': 'swir22',\n", - " 'description': 'Short-wave infrared (Enhanced Thematic Mapper Plus)',\n", - " 'center_wavelength': 2.2,\n", - " 'full_width_half_max': 0.28},\n", - " {'name': 'OLI_B1',\n", - " 'common_name': 'coastal',\n", - " 'description': 'Coastal/Aerosol (Operational Land Imager)',\n", - " 'center_wavelength': 0.44,\n", - " 'full_width_half_max': 0.02},\n", - " {'name': 'OLI_B2',\n", - " 'common_name': 'blue',\n", - " 'description': 'Visible blue (Operational Land Imager)',\n", - " 'center_wavelength': 0.48,\n", - " 'full_width_half_max': 0.06},\n", - " {'name': 'OLI_B3',\n", - " 'common_name': 'green',\n", - " 'description': 'Visible green (Operational Land Imager)',\n", - " 'center_wavelength': 0.56,\n", - " 'full_width_half_max': 0.06},\n", - " {'name': 'OLI_B4',\n", - " 'common_name': 'red',\n", - " 'description': 'Visible red (Operational Land Imager)',\n", - " 'center_wavelength': 0.65,\n", - " 'full_width_half_max': 0.04},\n", - " {'name': 'OLI_B5',\n", - " 'common_name': 'nir08',\n", - " 'description': 'Near infrared (Operational Land Imager)',\n", - " 'center_wavelength': 0.87,\n", - " 'full_width_half_max': 0.03},\n", - " {'name': 'OLI_B6',\n", - " 'common_name': 'swir16',\n", - " 'description': 'Short-wave infrared (Operational Land Imager)',\n", - " 'center_wavelength': 1.61,\n", - " 'full_width_half_max': 0.09},\n", - " {'name': 'OLI_B7',\n", - " 'common_name': 'swir22',\n", - " 'description': 'Short-wave infrared (Operational Land Imager)',\n", - " 'center_wavelength': 2.2,\n", - " 'full_width_half_max': 0.19},\n", - " {'name': 'TIRS_B10',\n", - " 'common_name': 'lwir11',\n", - " 'description': 'Long-wave infrared (Thermal Infrared Sensor)',\n", - " 'center_wavelength': 10.9,\n", - " 'full_width_half_max': 0.59}],\n", - " 'platform': ['landsat-4',\n", - " 'landsat-5',\n", - " 'landsat-7',\n", - " 'landsat-8',\n", - " 'landsat-9'],\n", - " 'instruments': ['tm', 'etm+', 'oli', 'tirs'],\n", - " 'view:off_nadir': {'minimum': 0, 'maximum': 15}},\n", - " 'assets': {'thumbnail': {'href': 'https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/landsat-c2-l2-thumb.png',\n", - " 'type': 'image/png',\n", - " 'title': 'Landsat Collection 2 Level-2 thumbnail',\n", - " 'roles': ['thumbnail']},\n", - " 'geoparquet-items': {'href': 'abfs://items/landsat-c2-l2.parquet',\n", - " 'type': 'application/x-parquet',\n", - " 'title': 'GeoParquet STAC items',\n", - " 'description': \"Snapshot of the collection's STAC items exported to GeoParquet format.\",\n", - " 'msft:partition_info': {'is_partitioned': True,\n", - " 'partition_frequency': 'MS'},\n", - " 'table:storage_options': {'account_name': 'pcstacitems',\n", - " 'credential': 'st=2024-03-14T19%3A50%3A37Z&se=2024-03-15T20%3A35%3A37Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2024-03-15T09%3A31%3A17Z&ske=2024-03-22T09%3A31%3A17Z&sks=b&skv=2021-06-08&sig=EB9FLj2HzN7t2ByMCBdGf5GtVY%2BaJZrmaPCDuUj5YvE%3D',\n", - " 'sas_token': 'st=2024-03-14T19%3A50%3A37Z&se=2024-03-15T20%3A35%3A37Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2024-03-15T09%3A31%3A17Z&ske=2024-03-22T09%3A31%3A17Z&sks=b&skv=2021-06-08&sig=EB9FLj2HzN7t2ByMCBdGf5GtVY%2BaJZrmaPCDuUj5YvE%3D'},\n", - " 'roles': ['stac-items']}}}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "landsat_reader.read().metadata" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can get a preview of the dataset by looking at the thumbnail." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "b4a5c77b5cee457f9f117ac442782239", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "BokehModel(combine_events=True, render_bundle={'docs_json': {'2919f699-f842-4f2a-b30e-79e5fac128d6': {'version…" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# data as array\n", - "landsat_reader[\"thumbnail\"].read()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "PanelImageViewer reader producing panel.pane:Image" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# render with panel\n", - "landsat_reader[\"thumbnail\"].to_reader(\"panel\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If that's desired, we can move on to get the items in the catalog.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "DaskGeoParquet reader producing dask_geopandas.core:GeoDataFrame" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "landsat_items = landsat_reader[\"geoparquet-items\"]\n", - "landsat_items" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/ahuang/miniconda3/envs/landsat-ml-cookbook/lib/python3.10/site-packages/geopandas/_compat.py:153: UserWarning: The Shapely GEOS version (3.11.1-CAPI-1.17.1) is incompatible with the GEOS version PyGEOS was compiled with (3.10.4-CAPI-1.16.2). Conversions between both will be slow.\n", - " set_use_pygeos()\n" - ] - } - ], - "source": [ - "# note `output_instance`: this is because .tail() makes a pandas from a dask dataframe. GeoDataFrameToSTACCatalog\n", - "# works specifically with geopandas only\n", - "cat = landsat_items.tail(output_instance=\"geopandas:GeoDataFrame\").GeoDataFrameToSTACCatalog.read()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Catalog\n", - " named datasets: ['LC09_L2SP_105013_20240301_02_T1', 'LC09_L2SP_105014_20240301_02_T1', 'LC09_L2SP_105015_20240301_02_T1', 'LC09_L2SP_105016_20240301_02_T2', 'LC09_L2SP_105017_20240301_02_T2']" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# this is an \"item collection\"; each item is a set of assets (many levels here)\n", - "cat" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Repeat the process aforementioned." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Catalog\n", - " named datasets: ['ang', 'atran', 'blue', 'cdist', 'coastal', 'drad', 'emis', 'emsd', 'green', 'lwir11', 'mtl.json', 'mtl.txt', 'mtl.xml', 'nir08', 'qa', 'qa_aerosol', 'qa_pixel', 'qa_radsat', 'red', 'rendered_preview', 'swir16', 'swir22', 'tilejson', 'trad', 'urad']" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "item_key = list(cat.entries.keys())[0]\n", - "subcat = cat[item_key].read()\n", - "subcat" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[\n", - " [66437901 values with dtype=uint16]\n", - " Coordinates:\n", - " * band (band) int64 1\n", - " * x (x) float64 4.455e+05 4.455e+05 ... 6.891e+05 6.891e+05\n", - " * y (y) float64 6.939e+06 6.939e+06 ... 6.693e+06 6.693e+06\n", - " spatial_ref int64 0\n", - " Attributes:\n", - " AREA_OR_POINT: Point\n", - " _FillValue: 0\n", - " scale_factor: 1.0\n", - " add_offset: 0.0]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# single image in one band\n", - "subcat.red.read()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [], - "source": [ - "# unfortunately, the \"signer\" didn't make it through\n", - "catbands = cat[item_key].to_reader(reader=\"StackBands\", bands=[\"red\", \"green\", \"blue\"], signer=planetary_computer.sign_inplace)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then, we can load the actual assets." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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-       "array([[[0, 0, 0, ..., 0, 0, 0],\n",
-       "        [0, 0, 0, ..., 0, 0, 0],\n",
-       "        [0, 0, 0, ..., 0, 0, 0],\n",
-       "        ...,\n",
-       "        [0, 0, 0, ..., 0, 0, 0],\n",
-       "        [0, 0, 0, ..., 0, 0, 0],\n",
-       "        [0, 0, 0, ..., 0, 0, 0]],\n",
-       "\n",
-       "       [[0, 0, 0, ..., 0, 0, 0],\n",
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-       "        ...,\n",
-       "        [0, 0, 0, ..., 0, 0, 0],\n",
-       "        [0, 0, 0, ..., 0, 0, 0],\n",
-       "        [0, 0, 0, ..., 0, 0, 0]],\n",
-       "\n",
-       "       [[0, 0, 0, ..., 0, 0, 0],\n",
-       "        [0, 0, 0, ..., 0, 0, 0],\n",
-       "        [0, 0, 0, ..., 0, 0, 0],\n",
-       "        ...,\n",
-       "        [0, 0, 0, ..., 0, 0, 0],\n",
-       "        [0, 0, 0, ..., 0, 0, 0],\n",
-       "        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint16)\n",
-       "Coordinates:\n",
-       "  * band         (band) int64 1 1 1\n",
-       "  * x            (x) float64 4.455e+05 4.455e+05 ... 6.891e+05 6.891e+05\n",
-       "  * y            (y) float64 6.939e+06 6.939e+06 ... 6.693e+06 6.693e+06\n",
-       "    spatial_ref  int64 0\n",
-       "Attributes:\n",
-       "    AREA_OR_POINT:  Point\n",
-       "    _FillValue:     0\n",
-       "    scale_factor:   1.0\n",
-       "    add_offset:     0.0
" - ], - "text/plain": [ - "\n", - "array([[[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]],\n", - "\n", - " [[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]],\n", - "\n", - " [[0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " ...,\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0],\n", - " [0, 0, 0, ..., 0, 0, 0]]], dtype=uint16)\n", - "Coordinates:\n", - " * band (band) int64 1 1 1\n", - " * x (x) float64 4.455e+05 4.455e+05 ... 6.891e+05 6.891e+05\n", - " * y (y) float64 6.939e+06 6.939e+06 ... 6.693e+06 6.693e+06\n", - " spatial_ref int64 0\n", - "Attributes:\n", - " AREA_OR_POINT: Point\n", - " _FillValue: 0\n", - " scale_factor: 1.0\n", - " add_offset: 0.0" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# multiband image. Unfortunately, the value of the \"band\" variable in each input is 1, not the real\n", - "# value; they could be relabelled here\n", - "data = catbands.read(dim=\"band\")\n", - "data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can plot the true color imagery with the extracted bands." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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The next step is to ensure that the data has been appropriately prepared for whatever analysis we intend to run. \n", - "\n", - "For example, as we may be directly comparing data across time points, it would be very useful if these images were on the same underlying coordinate grid. In this notebook, we will preprocess the data to reshape and align our images for efficient consumption by analysis steps. We will also see how to use interactive visualization to assist in our processing pipeline. Note, if you are adopting the data access approach in the [Planetary Computer notebook](./1.0_Data_Ingestion-Planetary_Computer.ipynb), you may not need to regrid the data and can proceed to an analysis workflow." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Data Ingestion - Intake](1.1_Data_Ingestion-Intake.ipynb) | Necessary | |\n", - "| [Coordinate Reference Systems and EPSG](https://pygis.io/docs/d_understand_crs_codes.html#epsg-codes)| Helpful | |\n", - "| [GeoViews resampling grids](http://geoviews.org/user_guide/Resampling_Grids.html)| Helpful | |\n", - "\n", - "- **Time to learn**: 20 minutes." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import intake\n", - "import numpy as np\n", - "import xarray as xr\n", - "import cartopy.crs as ccrs\n", - "import geoviews as gv\n", - "import hvplot.xarray\n", - "import holoviews as hv\n", - "\n", - "import warnings\n", - "warnings.simplefilter('ignore', FutureWarning) # Ignore a warning about the format of epsg codes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Loading data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You have already read through the previous tutorial on loading data with Intake, so now we'll jump right to using Intake to read in chunks of small versions of the Landsat 5 and Landsat 8 data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cat = intake.open_catalog('./data/catalog.yml')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cat.landsat_8_small.container" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We know that our Landsat data will ultimately be an xarray object. Let's load the metadata with Dask:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "landsat_5_da = cat.landsat_5_small.to_dask()\n", - "landsat_8_da = cat.landsat_8_small.to_dask()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "landsat_8_da" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Calculate the Vegetation Index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To motivate our preprocessing, let's conduct a simple analysis on the Normalized Difference Vegetation Index [NDVI](https://en.wikipedia.org/wiki/Normalized_difference_vegetation_index) for each of these image sets as follows:\n", - "\n", - "\\begin{align}\n", - "{NDVI} & = \\frac{NIR-Red}{NIR+Red}\n", - "\\end{align}\n", - "\n", - "where Red and NIR stand for the spectral reflectance measurements acquired in the Red (visible) and near-infrared regions, respectively. Note that in Landsat 5 the Red and NIR bands are stored in bands 3 and 4 respectively whereas in Landsat 8 the Red and NIR are bands 4 and 5." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with xr.set_options(keep_attrs=True):\n", - " NDVI_1988 = (landsat_5_da.sel(band=4) - landsat_5_da.sel(band=3)) / (landsat_5_da.sel(band=4) + landsat_5_da.sel(band=3))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "

Info

\n", - " Above, we are using a context manager \"with xr.set_options(keep_attrs=True):\" to retain the array's attributes through the operations. That is, we want all the metadata like 'crs' to stay with our result so we can use 'geo=True' in our plotting.\n", - "
" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NDVI_1988" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NDVI_1988_plot = NDVI_1988.hvplot.image(x='x', y='y', geo=True, clim=(-1,1), title='NDVI 1988', rot=45, cmap='viridis')\n", - "NDVI_1988_plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with xr.set_options(keep_attrs=True):\n", - " NDVI_2017 = (landsat_8_da.sel(band=5) - landsat_8_da.sel(band=4)) / (landsat_8_da.sel(band=5) + landsat_8_da.sel(band=4))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NDVI_2017_plot = NDVI_2017.hvplot.image(x='x', y='y', geo=True, clim=(-1,1), title='NDVI 2017', rot=45, cmap='viridis')\n", - "NDVI_2017_plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can calculate the difference between these two years by subtracting one from the other." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with xr.set_options(keep_attrs=True):\n", - " NDVI_diff = NDVI_2017 - NDVI_1988" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NDVI_diff_plot = NDVI_diff.hvplot.image(x='x', y='y', geo=True, cmap='coolwarm', clim=(-1,1), title='NDVI 2017 - 1988', rot=45)\n", - "NDVI_diff_plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice how pixelated that image looks. What is going on here? To figure it out, let's take a look at the shape of `diff`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NDVI_diff.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's a lot smaller than our NDVI for each year. What is happening is that when we compute the difference on the data we only get values where there are values for each year in the same grid cell. Since the cells are on a different resolution this only happens once every so often. What we'd rather do is interpolate to the same grid and then do our computations on that.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Combine data from overlapping grids" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These two sets of Landsat bands cover roughly the same area but were taken in 1988 and 2017. They have different resolutions, different numbers of grid cells per image, and different x and y offsets. We can see the offset by printing the first `x` value for each year and seeing that they are not equivalent." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print(NDVI_1988.x[0].values)\n", - "print(NDVI_2017.x[0].values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can also do a quick check of resolution by subtracting the second x value from the first x value for each year." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "print((NDVI_1988.x[1] - NDVI_1988.x[0]).values)\n", - "print((NDVI_2017.x[1] - NDVI_2017.x[0]).values)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To align the grid, resolution, and offset of these images, we will select a region of interest, create a new grid within this region, and then interpolate our images onto this common grid." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Select region of interest" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The first step to selecting a Region of Interest (ROI) is to define its center point.\n", - "\n", - "There are multiple ways to define this central point, and we will start with a case in which we know the coordinates in latitude, longitude and need to convert it into the CRS of our data. Alternatively, we will see how easy it is to use interactive visualization to estimate the center point without knowing any prior coordinates.\n", - "\n", - "For the first approach, the first step is getting the CRS of our data. This information is stored in the attributes of our original landsat data (either `.crs` or `.rio.crs` depending on package versions). Let's take a look at it:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "try:\n", - " print(landsat_8_da.crs)\n", - "except:\n", - " print(landsat_8_da.rio.crs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This CRS is referenced by an EPSG code. We can see more about this specific code at [EPSG.io](https://epsg.io/32611). Read more about EPSG codes in general in this [Coordinate Reference Systems: EPSG codes](https://pygis.io/docs/d_understand_crs_codes.html#epsg-codes) online book chapter. Note, the `+init=:` syntax [is being deprecated](https://pyproj4.github.io/pyproj/stable/gotchas.html#init-auth-auth-code-should-be-replaced-with-auth-auth-code), and `:` is the preferred initialization method." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can convert this EPSG number into a `cartopy.crs` object using the `cartopy.crs.epsg` method. This will allow us to transform any point from latitude, longitude into our data's CRS so that we can define a center for our ROI." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crs = ccrs.epsg(32611)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crs.__repr__()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crs.area_of_use" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crs_box = [(crs.x_limits[0], crs.y_limits[0]), (crs.x_limits[0], crs.y_limits[1]),\n", - " (crs.x_limits[1], crs.y_limits[1]), (crs.x_limits[1], crs.y_limits[0])]\n", - "\n", - "crs_extent = gv.Polygons(crs_box, crs=crs).options(alpha=0.4, color='blue', line_alpha=0)\n", - "\n", - "(gv.feature.coastline * crs_extent).options(title='EPSG:32611', global_extent=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just for comparison, let's preview the coverage of a totally different EPSG; perhaps one in the southern hemisphere." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crs2 = ccrs.epsg(32735)\n", - "crs2.area_of_use" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crs_box2 = [(crs2.x_limits[0], crs2.y_limits[0]), (crs2.x_limits[0], crs2.y_limits[1]),\n", - " (crs2.x_limits[1], crs2.y_limits[1]), (crs2.x_limits[1], crs2.y_limits[0])]\n", - "\n", - "crs_extent2 = gv.Polygons(crs_box, crs=crs2).options(alpha=0.4, color='red', line_alpha=0)\n", - "\n", - "(gv.feature.coastline * crs_extent2).options(title='EPSG:32735', global_extent=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hopefully, this gives you some intuition for the data that we are looking at and that it comes from a small North American region within the blue highlighted area on the 'EPSG:32611' plot." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, if we know already know our center point in terms of a latitude, longitude point (from a `cartopy.crs.PlateCarree()` projection), we can transform this into the CRS of our data using the `transform_point` method:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "center={'x':np.nan, 'y':np.nan}\n", - "center['x'], center['y'] = crs.transform_point(-118.7081, 38.6942, ccrs.PlateCarree())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "center['x']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But what if we didn't happen to know the center point in terms of latitude and longitude? Since we are using interactive plots with `hvplot`, we can look at one of our previous plots of the lake and hover over the region that we want to use as a center point to reveal the coordinates in latitude, longitude (created by plotting with `geo=True`). \n", - "\n", - "Alternatively, we could just hover over a plot of our data that is still in our data `crs` projection (by using the default `geo=False`) to directly get our `x_center` and `y_center`. Try it below - hover over the center of the lake to reveal the x and y points:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NDVI_2017.hvplot.image(x='x', y='y', width=400, clim=(-1,1), rot=45, cmap='viridis')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A more advanced approach would be to update automatically assign the coordinates to a variable when we click on the image. For this, we will reach to HoloViews, the library that powers hvPlot. For more information about this functionality of HoloViews, check out the [HoloViews Tap stream](https://holoviews.org/reference/streams/bokeh/Tap.html) page." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we'll create function to update our `center` dictionary based on clicks." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def onclick(x,y):\n", - " center['x'] = x\n", - " center['y'] = y" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Then we'll connect a HoloViews Tap stream to our plot" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "crs_plot = NDVI_2017.hvplot.image(x='x', y='y', width=400, clim=(-1,1), rot=45, cmap='viridis')\n", - "\n", - "tap_stream = hv.streams.Tap(source=crs_plot)\n", - "\n", - "tap_stream.add_subscriber(onclick)\n", - "\n", - "crs_plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now our `center` variable will change each time we click on the plot above. Go ahead and click near the center of the lake on the plot above and then run the cell below to see the update. Every time you click a new spot on the plot, you can rerun the cell to confirm that the value has changed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "center" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Don't worry too much about capturing the exact center of the lake, a rough estimate is sufficient." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we just need to define the area that we are interested in around this point. In this case we'll use a 30 km box around the center point." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "buffer = 1.5e4\n", - "\n", - "xmin = center['x'] - buffer\n", - "xmax = center['x'] + buffer\n", - "ymin = center['y'] - buffer\n", - "ymax = center['y'] + buffer" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "bounding_box = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's just check that the bounding box captures the lake:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "roi = gv.Polygons(bounding_box, crs=crs)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "gv.tile_sources.EsriImagery * roi.options(alpha=0.3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Regrid" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can use this region to define a new grid onto which we will interpolate our data. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note, we will be regridding with simple linear interpolation which treats the space as flat. This is fine for our demonstration purposes at a relatively small spatial scale. However, despite what you may have heard, the earth is not flat, and treating it as such can give less accurate results when working with a larger spherical space. For more information about spherical resampling methods, check out the [GeoViews resampling grids](http://geoviews.org/user_guide/Resampling_Grids.html) page, as it describes using different grid types including rectilinear, curvilinear grids and trimeshes. In particular, the conservative regridding approach using the [xESMF](https://xesmf.readthedocs.io/en/latest/) library has become a common choice." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's set the resolution of our new grid within the bounding box to roughly match the resolution of our original images." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res = 200\n", - "x = np.arange(xmin, xmax, res)\n", - "y = np.arange(ymin, ymax, res)\n", - "x.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will use xarray's linear interpolation to calculate the values for each grid cell." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "NDVI_2017_regridded = NDVI_2017.interp(x=x, y=y)\n", - "NDVI_1988_regridded = NDVI_1988.interp(x=x, y=y)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's compare our original data to this regridded form." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NDVI_2017_regridded_plot = NDVI_2017_regridded.hvplot.image(x='x', y='y', clim=(-1,1), title='NDVI 2017 regridded', geo=True, rot=45, cmap='viridis')\n", - "NDVI_1988_regridded_plot = NDVI_1988_regridded.hvplot.image(x='x', y='y', clim=(-1,1), title='NDVI 1988 regridded', geo=True, rot=45, cmap='viridis')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
\n", - "

Note

\n", - " With hvPlot, you can render multiple plots using the `+` operator, and stack them into rows by defining the number of columns with `.cols`. When possible, hvPlot will automatically link the plots so you can zoom and pan on one image and keep them in sync.\n", - "
\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Zoom into one of the images below to see the result of interpolating on this new common grid. The regridded plots (bottom row) will have a consistent pixelation compared to the original NDVI data (top row)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "(NDVI_1988_plot + NDVI_2017_plot + NDVI_1988_regridded_plot + NDVI_2017_regridded_plot).cols(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Combining the data \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have our data on the same grid we can combine our two years into one `xarray` object. We will treat the years as names and create an `xarray.Dataset` - a group of named `xarray.DataArray`s that share some of the same coordinates." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds_regridded = xr.Dataset({'NDVI_1988': NDVI_1988_regridded, 'NDVI_2017': NDVI_2017_regridded})\n", - "ds_regridded" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Visualizing output\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can now reference each year from the same object, and plot the arrays side by side:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "ds_regridded.NDVI_1988.hvplot.image(x='x', y='y', geo=True, clim=(-1, 1), title='NDVI 1988', cmap='viridis') +\\\n", - "ds_regridded.NDVI_2017.hvplot.image(x='x', y='y', geo=True, clim=(-1, 1), title='NDVI 2017', cmap='viridis')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Or we can calculate and plot the difference between the two years:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "with xr.set_options(keep_attrs=True):\n", - " diff_regridded = ds_regridded['NDVI_2017'] - ds_regridded['NDVI_1988']\n", - "diff_regridded" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "NDVI_diff_regridded_plot = diff_regridded.hvplot.image(x='x', y='y', geo=True, rot=45, cmap='coolwarm', clim=(-1,1), title='NDVI 2017 - 1988')\n", - "NDVI_diff_regridded_plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As the Vegetation Index will generally give a lower value where water is present, you can clearly see a large positive change along the edge of the lake indicating a reduction in the size of the lake over this time period." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Side-note: Resampling\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Depending on your analysis, it may be beneficial to downsample your data. Especially early on during analysis development, running computations on full resolution data may eat into valuable time that you could otherwise use iterating, debugging, and improving your workflow. Luckily, downsampling xarray data is made pretty easy by grouping the values into bins based on the desired resolution and taking some aggregation (like the mean) on each of those bins." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "res_1000 = 1e3\n", - "x_1000 = np.arange(xmin, xmax, res_1000)\n", - "y_1000 = np.arange(ymin, ymax, res_1000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We'll use the left edge as the label for now." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "diff_res_1000 = (diff_regridded\n", - " .groupby_bins('x', x_1000, labels=x_1000[:-1]).mean(dim='x')\n", - " .groupby_bins('y', y_1000, labels=y_1000[:-1]).mean(dim='y')\n", - " .rename(x_bins='x', y_bins='y')\n", - ")\n", - "diff_res_1000" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "diff_res_1000.hvplot.image(x='x', y='y', geo=True, rot=45, cmap='coolwarm', clim=(-1,1), title='Downsampled')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And there you go, nicely downsampled data ready for some fast computations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "With the target of preparing our data for further analysis, we've learned how to ensure that our data are on a consistent coordinate grid. Again, this is especially important if we are performing operations across the datasets, like taking the difference between their images. Although preprocessing can sometimes be tedious, it's a very critical part of the workflow, and often only needs to be done once (or a small number of times) before being able to apply the processed data to a large number of different analyses.\n", - "\n", - "### What's next?\n", - "Now that we know how to prepare data, it's time to proceed to analysis, where we will explore a some simple machine learning approaches.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resources and references\n", - "- Authored/adapted by Demetris Roumis circa Dec, 2022\n", - "- The Landsat 8 banner image is from NASA" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - }, - "nbdime-conflicts": { - "local_diff": [ - { - "diff": [ - { - "diff": [ - { - "key": 0, - "op": "addrange", - "valuelist": [ - "Python 3" - ] - }, - { - "key": 0, - "length": 1, - "op": "removerange" - } - ], - "key": "display_name", - "op": "patch" - } - ], - "key": "kernelspec", - "op": "patch" - } - ], - "remote_diff": [ - { - "diff": [ - { - "diff": [ - { - "key": 0, - "op": "addrange", - "valuelist": [ - "Python3" - ] - }, - { - "key": 0, - "length": 1, - "op": "removerange" - } - ], - "key": "display_name", - "op": "patch" - } - ], - "key": "kernelspec", - "op": "patch" - } - ] - }, - "toc-autonumbering": false, - "vscode": { - "interpreter": { - "hash": "d2ed0a8e3e051554a0b51e3917f81e884b169a97835ad70210b3681eb3cb39c7" - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_preview/22/_sources/notebooks/3.0_Spectral_Clustering_PC.ipynb b/_preview/22/_sources/notebooks/3.0_Spectral_Clustering_PC.ipynb deleted file mode 100644 index 7612a9a..0000000 --- a/_preview/22/_sources/notebooks/3.0_Spectral_Clustering_PC.ipynb +++ /dev/null @@ -1,972 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "95d6fc25-545a-4bcc-97ff-a17df7f6082e", - "metadata": {}, - "source": [ - "![Spectral Clustering](images/spectral_clustering_lake.png \"Spectral Clustering\")" - ] - }, - { - "cell_type": "markdown", - "id": "4257bf18-ba7c-42a6-81e3-2b5e48b3bc8b", - "metadata": {}, - "source": [ - "# Spectral Clustering" - ] - }, - { - "cell_type": "markdown", - "id": "796926b4-a134-4daf-ae19-46978acf5e89", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "id": "9e7946b7-d7c6-4d60-bfb2-474188cfeb54", - "metadata": {}, - "source": [ - "## Overview" - ] - }, - { - "cell_type": "markdown", - "id": "09b5645f-ea45-4f49-9811-18bff4192034", - "metadata": {}, - "source": [ - "The current notebook will demonstrate a simplified machine learning approach to observe the change in a lake water's extent across time. In order to identify the water, we can use spectral clustering to classify each grid cell into a category based on the similarity of the combined set of pixels across [wavelength-bands](./0.0_Intro_Landsat) in our image stacks.\n", - "\n", - "Our example approach uses a version of spectral clustering from [dask_ml](http://ml.dask.org/clustering.html#spectral-clustering) that is a scalable equivalent of what is available in [scikit-learn](https://scikit-learn.org/stable/modules/clustering.html#spectral-clustering). We will begin this approach with a single image stack and then conduct a direct comparison on the results from different time points.\n", - "\n", - "This workflow uses data from Microsoft Planetary Computer but it can be adapted to work with any data ingestion approach from this cookbook." - ] - }, - { - "cell_type": "markdown", - "id": "f2d4e405-956d-4400-86f5-219664ef7b79", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Data Ingestion - Planetary Computer](1.0_Data_Ingestion-Planetary_Computer.ipynb) | Necessary | |\n", - "|[scikit-learn](https://scikit-learn.org/stable/modules/clustering.html#spectral-clustering) | Helpful | Spectral clustering |\n", - "| [dask_ml](http://ml.dask.org/clustering.html#spectral-clustering) | Helpful | Spectral clustering at scale | \n", - "\n", - "\n", - "- **Time to learn**: 20 minutes." - ] - }, - { - "cell_type": "markdown", - "id": "dd99cc55-fb0f-4bdf-bc7a-82044188a2f2", - "metadata": {}, - "source": [ - "## Imports" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e106b4ce-e682-4a71-817b-966bbf926989", - "metadata": {}, - "outputs": [], - "source": [ - "# Data\n", - "import numpy as np\n", - "import odc.stac\n", - "import pandas as pd\n", - "import planetary_computer\n", - "import pystac_client\n", - "import xarray as xr\n", - "from dask.distributed import Client\n", - "from pystac.extensions.eo import EOExtension as eo\n", - "\n", - "# Analysis\n", - "from dask_ml.cluster import SpectralClustering\n", - "\n", - "# Viz\n", - "import hvplot.xarray" - ] - }, - { - "cell_type": "markdown", - "id": "2eef62e1-2df6-4e67-9e10-92c9bf74c136", - "metadata": {}, - "source": [ - "## Loading Data" - ] - }, - { - "cell_type": "markdown", - "id": "41855f82-661f-4c02-9c48-6893a694bfe4", - "metadata": {}, - "source": [ - "Let's start by loading some Landsat data. These steps are covered in the [Data Ingestion - Planetary Computer](1.0_Data_Ingestion-Planetary_Computer.ipynb) prerequisite." - ] - }, - { - "cell_type": "markdown", - "id": "5dbc14ea-a560-4b01-81c0-4fc01f767de9", - "metadata": {}, - "source": [ - "### Search the catalog" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5059488-8f7e-446f-97d4-992eab1e7928", - "metadata": {}, - "outputs": [], - "source": [ - "catalog = pystac_client.Client.open(\n", - " \"https://planetarycomputer.microsoft.com/api/stac/v1\",\n", - " modifier=planetary_computer.sign_inplace,\n", - ")\n", - "\n", - "bbox = [-118.89, 38.54, -118.57, 38.84] # Region over a lake in Nevada, USA\n", - "datetime = \"2017-06-01/2017-09-30\" # Summer months of 2017\n", - "collection = \"landsat-c2-l2\"\n", - "platform = \"landsat-8\"\n", - "cloudy_less_than = 1 # percent\n", - "\n", - "search = catalog.search(\n", - " collections=[\"landsat-c2-l2\"],\n", - " bbox=bbox,\n", - " datetime=datetime,\n", - " query={\"eo:cloud_cover\": {\"lt\": cloudy_less_than}, \"platform\": {\"in\": [platform]}},\n", - ")\n", - "items = search.get_all_items()\n", - "print(f\"Returned {len(items)} Items:\")\n", - "[[i, item.id] for i, item in enumerate(items)]" - ] - }, - { - "cell_type": "markdown", - "id": "e89ef2a4-8b5d-4799-a529-3d9adbc61a89", - "metadata": {}, - "source": [ - "### Load a dataset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4fbc1c60-ab38-49b8-beb2-167dc4b6f298", - "metadata": {}, - "outputs": [], - "source": [ - "item = items[1] # select one of the results" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d5f6b4f4-80aa-4e82-bc9b-bd70b6d63d49", - "metadata": {}, - "outputs": [], - "source": [ - "assets = []\n", - "for _, asset in item.assets.items():\n", - " try:\n", - " assets.append(asset.extra_fields[\"eo:bands\"][0])\n", - " except:\n", - " pass\n", - "\n", - "cols_ordered = [\n", - " \"common_name\",\n", - " \"description\",\n", - " \"name\",\n", - " \"center_wavelength\",\n", - " \"full_width_half_max\",\n", - "]\n", - "bands = pd.DataFrame.from_dict(assets)[cols_ordered]\n", - "bands" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d17bcf9e-116c-4471-90ca-754a3b3fb305", - "metadata": {}, - "outputs": [], - "source": [ - "ds_2017 = odc.stac.stac_load(\n", - " [item],\n", - " bands=bands.common_name.values,\n", - " bbox=bbox,\n", - " chunks={}, # <-- use Dask\n", - ").isel(time=0)" - ] - }, - { - "cell_type": "markdown", - "id": "03e8f2cb-31ef-4e42-a16f-ffa7f2e79d78", - "metadata": {}, - "source": [ - "### Retain CRS Attribute" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a8f8e88f-a517-4a13-b823-e8dff53b0e47", - "metadata": {}, - "outputs": [], - "source": [ - "epsg = item.properties[\"proj:epsg\"]\n", - "ds_2017.attrs[\"crs\"] = f\"epsg:{epsg}\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "7b7d3201-bb41-48d0-90b7-565c2e454f9c", - "metadata": {}, - "outputs": [], - "source": [ - "da_2017 = ds_2017.to_array(dim=\"band\")\n", - "da_2017" - ] - }, - { - "cell_type": "markdown", - "id": "865eb3d1-59a9-401a-b1f1-7588bb5c1700", - "metadata": {}, - "source": [ - "## Reshaping Data\n", - "\n", - "The shape of our data is currently `n_bands`, `n_y`, `n_x`. In order for dask-ml / scikit-learn to consume our data, we'll need to reshape our image stacks into `n_samples, n_features`, where `n_features` is the number of wavelength-bands and `n_samples` is the total number of pixels in each wavelength-band image. Essentially, we'll be creating a vector of pixels out of each image, where each pixel has multiple features (bands), but the ordering of the pixels is no longer relevant to the computation. " - ] - }, - { - "cell_type": "markdown", - "id": "042bfffb-c979-4958-9086-646a83918d61", - "metadata": {}, - "source": [ - "By using xarray methods to flatten the data, we can keep track of the coordinate labels 'x' and 'y' along the way. This means that we have the ability to reshape back to our original array at any time with no information loss!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "653adf8b-da16-4eb6-ae64-d172c8eae75f", - "metadata": {}, - "outputs": [], - "source": [ - "flattened_xda = da_2017.stack(z=(\"x\", \"y\")) # flatten each band\n", - "flattened_t_xda = flattened_xda.transpose(\"z\", \"band\")\n", - "flattened_t_xda" - ] - }, - { - "cell_type": "markdown", - "id": "e427b1dd-08c3-4657-84e7-09b0269edad9", - "metadata": {}, - "source": [ - "## Standardize Data\n", - "\n", - "Now that we have the data in the correct shape, let's standardize (or rescale) the values of the data. We do this to get all the flattened image vectors onto a common scale while preserving the differences in the ranges of values. Again, we'll demonstrate doing this first in NumPy and then xarray." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d8cd5c3c-1256-400a-9cea-32debd51ec4d", - "metadata": {}, - "outputs": [], - "source": [ - "with xr.set_options(keep_attrs=True):\n", - " rescaled_xda = (flattened_t_xda - flattened_t_xda.mean()) / flattened_t_xda.std()\n", - "rescaled_xda" - ] - }, - { - "cell_type": "markdown", - "id": "37a47e0b-7f74-404f-8f16-bf80c3bb1995", - "metadata": {}, - "source": [ - "
\n", - "

Info

\n", - " Above, we are using a context manager \"with xr.set_options(keep_attrs=True):\" to retain the array's attributes through the operations. That is, we want any metadata like 'crs' to stay with our result so we can use 'geo=True' in our plotting.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "id": "27d69080-eff4-471e-9536-02f99420bd0a", - "metadata": {}, - "source": [ - "As `rescaled_xda` is still a Dask object, if we wanted to actually run the rescaling at this point (provided that all the data can fit into memory), we would use `rescaled_xda.compute()`." - ] - }, - { - "cell_type": "markdown", - "id": "07b01093-da37-419c-93f2-9bba60be1578", - "metadata": {}, - "source": [ - "\n", - "## ML pipeline\n", - "Now that our data is in the proper shape and value range, we are ready to conduct spectral clustering. Here we will use a version of [spectral clustering from dask_ml](https://ml.dask.org/modules/generated/dask_ml.cluster.SpectralClustering.html) that is a scalable equivalent to operations from Scikit-learn that cluster pixels based on similarity (across all wavelength-bands, which makes it spectral clustering by spectra!)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "62075e17-55d1-470c-8df6-55be6ab895bd", - "metadata": {}, - "outputs": [], - "source": [ - "client = Client(processes=False)\n", - "client" - ] - }, - { - "cell_type": "markdown", - "id": "2d68c9d8-9b46-4123-8ab1-cef625ad9ea6", - "metadata": {}, - "source": [ - "Now we will compute and persist the rescaled data to feed into the ML pipeline. Notice that our `X` matrix below has the shape: `n_samples, n_features` as discussed earlier. " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "083a3ea0-1ae2-42d0-ac83-31d66a01b11d", - "metadata": {}, - "outputs": [], - "source": [ - "X = client.persist(rescaled_xda)\n", - "X.shape" - ] - }, - { - "cell_type": "markdown", - "id": "c0f22024-87fd-49d2-8e06-6e6eff714708", - "metadata": {}, - "source": [ - "First we will set up the model with the number of clusters, and other options." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4afcf563-8d46-4b35-9936-3d330e226d26", - "metadata": {}, - "outputs": [], - "source": [ - "clf = SpectralClustering(\n", - " n_clusters=4,\n", - " random_state=0,\n", - " gamma=None,\n", - " kmeans_params={\"init_max_iter\": 5},\n", - " persist_embedding=True,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "0666c6af-b948-41ed-a789-7f7cae9459f0", - "metadata": {}, - "source": [ - "**This next step is the slow part.** We'll fit the model to our matrix `X`. Depending on your setup, it could take seconds to minutes to run depending on the size of our data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "372c890c-8ce7-4ce8-958b-43909c3de0e9", - "metadata": {}, - "outputs": [], - "source": [ - "%time clf.fit(X)" - ] - }, - { - "cell_type": "markdown", - "id": "60e746bd-5aa6-436a-84de-3b93e1bed1dd", - "metadata": {}, - "source": [ - "Let's check the shape of the result:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "73e9af39-dadf-4956-b23e-5dc3feb65c56", - "metadata": {}, - "outputs": [], - "source": [ - "labels = clf.assign_labels_.labels_.compute()\n", - "labels.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f9e1a56c-0d6e-4c89-bbe8-0c8d9d0dd05f", - "metadata": {}, - "outputs": [], - "source": [ - "labels" - ] - }, - { - "cell_type": "markdown", - "id": "e8e59036-e8c0-4a0e-8d78-9037509b8c90", - "metadata": {}, - "source": [ - "The result is a single vector of cluster labels." - ] - }, - { - "cell_type": "markdown", - "id": "084207cc-3e00-419e-a61d-300ad0868a6e", - "metadata": { - "tags": [] - }, - "source": [ - "## Un-flattening\n", - "\n", - "Once the computation is done, we can use the coordinates of our input array to restack our output array back into an image. Again, one of the main benefits of using `xarray` for this stacking and unstacking is that it keeps track of the coordinate information for us. " - ] - }, - { - "cell_type": "markdown", - "id": "625ae4c4-64cd-46fd-b6ec-43810c8be359", - "metadata": {}, - "source": [ - "Since the original array is n_samples by n_features (90000, 6) and the cluster label output is (90000,), we just need the coordinates from one of the original features in the shape of n_samples. We can just copy the coordinates from the first input feature and populate is with our output data:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "642cf283-64d8-4c9e-91c3-32be8ec35c4b", - "metadata": {}, - "outputs": [], - "source": [ - "template = flattened_t_xda[:, 0]\n", - "output_array = template.copy(data=labels)\n", - "output_array" - ] - }, - { - "cell_type": "markdown", - "id": "d321a58f-9617-4540-98c7-8a7bccd414ae", - "metadata": {}, - "source": [ - "With this new output array with coordinates copied from the input array, we can unstack back to the original `x` and `y` image dimensions by just using `.unstack()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d16ec3f0-c6dc-470b-9e56-7c3540066af7", - "metadata": {}, - "outputs": [], - "source": [ - "unstacked_2017 = output_array.unstack()\n", - "unstacked_2017" - ] - }, - { - "cell_type": "markdown", - "id": "588814fe-3776-4ef5-9392-dd60e6e6776c", - "metadata": {}, - "source": [ - "Finally, we can visualize the results! By hovering over the resulting imge, we can see that the lake water has been clustered with a certain label or 'value'." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a9e6863f-0cec-436b-bdcc-9aa533e6df9f", - "metadata": {}, - "outputs": [], - "source": [ - "raw_plot_2017 = da_2017.sel(band=\"red\").hvplot.image(\n", - " x=\"x\", y=\"y\", geo=True, xlabel=\"lon\", ylabel=\"lat\", datashade=True, cmap=\"greys\", title=\"Raw Image 2017\",\n", - ")\n", - "\n", - "result_plot_2017 = unstacked_2017.hvplot(\n", - " x=\"x\", y=\"y\", cmap=\"Set3\", geo=True, xlabel=\"lon\", ylabel=\"lat\", colorbar=False, title=\"Spectral Clustering 2017\",\n", - ")\n", - "\n", - "raw_plot_2017 + result_plot_2017" - ] - }, - { - "cell_type": "markdown", - "id": "ba47bdd3-995b-4b98-8cf0-efeea65688da", - "metadata": {}, - "source": [ - "## Spectral Clustering for 1988" - ] - }, - { - "cell_type": "markdown", - "id": "bea4b2b5-5e74-4f8c-b171-89a24e57b8ae", - "metadata": {}, - "source": [ - "We have conducted the spectral clustering for 2017 and now we want to compare this result to the lake in 1988. Let's load data from 1988 and run the same analysis as above." - ] - }, - { - "cell_type": "markdown", - "id": "f4d284d4-23b9-4c77-9275-58aeb9c6e563", - "metadata": {}, - "source": [ - "We will use the same catalog, but we will search it for a different point in time and different Landsat mission" - ] - }, - { - "cell_type": "markdown", - "id": "dcb18049-fb4c-44e4-9d72-90bd24094bd7", - "metadata": {}, - "source": [ - "### Load the data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e00ab9e6-b36b-47ec-a5a3-4b6bbf6a9ca6", - "metadata": {}, - "outputs": [], - "source": [ - "bbox = [-118.89, 38.54, -118.57, 38.84] # Region over a lake in Nevada, USA\n", - "datetime = \"1988-06-01/1988-09-30\" # Summer months of 1988\n", - "collection = \"landsat-c2-l2\"\n", - "platform = \"landsat-5\" # Searching through an earlier landsat mission\n", - "cloudy_less_than = 1 # percent\n", - "\n", - "search = catalog.search(\n", - " collections=[\"landsat-c2-l2\"],\n", - " bbox=bbox,\n", - " datetime=datetime,\n", - " query={\"eo:cloud_cover\": {\"lt\": cloudy_less_than}, \"platform\": {\"in\": [platform]}},\n", - ")\n", - "\n", - "items = search.get_all_items()\n", - "item = items[1] # select one of the results" - ] - }, - { - "cell_type": "markdown", - "id": "1428fff4-fc72-4692-9c20-e9e168337fc2", - "metadata": {}, - "source": [ - "Notice that Landsat 5 data from 1988 has slightly different spectra than Landsat 8 from 2017. Details like this are important to keep in mind when performing analyses that directly compare across missions." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "665e250d-6790-402d-b877-bbe967556089", - "metadata": {}, - "outputs": [], - "source": [ - "assets = []\n", - "for _, asset in item.assets.items():\n", - " try:\n", - " assets.append(asset.extra_fields[\"eo:bands\"][0])\n", - " except:\n", - " pass\n", - "\n", - "cols_ordered = [\n", - " \"common_name\",\n", - " \"description\",\n", - " \"name\",\n", - " \"center_wavelength\",\n", - " \"full_width_half_max\",\n", - "]\n", - "bands = pd.DataFrame.from_dict(assets)[cols_ordered]\n", - "bands" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cef800e2-55f5-4777-90a5-63d466ce5f6f", - "metadata": {}, - "outputs": [], - "source": [ - "ds_1988 = odc.stac.stac_load(\n", - " [item],\n", - " bands=bands.common_name.values,\n", - " bbox=bbox,\n", - " chunks={}, # <-- use Dask\n", - ").isel(time=0)\n", - "\n", - "epsg = item.properties[\"proj:epsg\"]\n", - "ds_1988.attrs[\"crs\"] = f\"epsg:{epsg}\"\n", - "\n", - "da_1988 = ds_1988.to_array(dim=\"band\")\n", - "da_1988" - ] - }, - { - "cell_type": "markdown", - "id": "057bd00b-193a-45b1-97ac-62a77fa0a322", - "metadata": {}, - "source": [ - "### Reshape and Standardize" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ceb4ee87-9a8b-4278-9721-c2af8a56f6b8", - "metadata": {}, - "outputs": [], - "source": [ - "flattened_xda = da_1988.stack(z=(\"x\", \"y\"))\n", - "flattened_t_xda = flattened_xda.transpose(\"z\", \"band\")\n", - "with xr.set_options(keep_attrs=True):\n", - " rescaled_xda = (flattened_t_xda - flattened_t_xda.mean()) / flattened_t_xda.std()\n", - "rescaled_xda" - ] - }, - { - "cell_type": "markdown", - "id": "61bf4b05-3266-4bbd-b618-f4c01b56eccd", - "metadata": {}, - "source": [ - "### Spectral Clustering" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4a4a50c6-f6dd-46c2-afb3-95196b341e21", - "metadata": {}, - "outputs": [], - "source": [ - "X = client.persist(rescaled_xda)\n", - "clf = SpectralClustering(\n", - " n_clusters=4,\n", - " random_state=0,\n", - " gamma=None,\n", - " kmeans_params={\"init_max_iter\": 5},\n", - " persist_embedding=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "229368ab-ed3a-4fc1-b328-d84b7e7ebebb", - "metadata": {}, - "outputs": [], - "source": [ - "%time clf.fit(X)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ec353001-4e0e-44af-b38d-f602f5a1dab8", - "metadata": {}, - "outputs": [], - "source": [ - "labels = clf.assign_labels_.labels_.compute()\n", - "labels.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "937d7db4-b848-499b-8da2-8d00287b5d81", - "metadata": {}, - "outputs": [], - "source": [ - "labels" - ] - }, - { - "cell_type": "markdown", - "id": "1c690262-bb13-48cd-9e24-d3be8ed8fb91", - "metadata": {}, - "source": [ - "### Unstack and Visualize" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "ba40cfc1-2da7-4be2-80f8-4bd0838dd64a", - "metadata": {}, - "outputs": [], - "source": [ - "template = flattened_t_xda[:, 0]\n", - "output_array = template.copy(data=labels)\n", - "unstacked_1988 = output_array.unstack()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3be0319-7f51-4384-befe-f0afcd2fecff", - "metadata": {}, - "outputs": [], - "source": [ - "unstacked_1988" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d41ed729-af6c-4817-aad1-154671e743c5", - "metadata": {}, - "outputs": [], - "source": [ - "raw_plot_1988 = da_1988.sel(band=\"red\").hvplot.image(\n", - " x=\"x\", y=\"y\", geo=True, xlabel=\"lon\", ylabel=\"lat\", datashade=True, cmap=\"greys\", title=\"Raw 1988\"\n", - ")\n", - "\n", - "result_plot_1988 = unstacked_1988.hvplot(\n", - " x=\"x\", y=\"y\", cmap=\"Set3\", geo=True, xlabel=\"lon\", ylabel=\"lat\", colorbar=False, title=\"Spectral Clustering 1988\",\n", - ")\n", - "\n", - "raw_plot_1988 + result_plot_1988" - ] - }, - { - "cell_type": "markdown", - "id": "6ff424f8-b243-4a20-a069-ae45f2490370", - "metadata": {}, - "source": [ - "## Spectral Clustering Over Time" - ] - }, - { - "cell_type": "markdown", - "id": "c872fea9-55bc-4c60-8979-91796ef649a9", - "metadata": {}, - "source": [ - "Our hypothesis is that the lake's area is receding over time and so we want to visualize the potential change. Let's first visually compare the plot of the clustering results from the different time points." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5b3f7a6d-3651-4324-8bed-a044034e3e67", - "metadata": {}, - "outputs": [], - "source": [ - "result_plot_1988 + result_plot_2017" - ] - }, - { - "cell_type": "markdown", - "id": "4b170f41-3442-4fce-91be-40a41317f38d", - "metadata": {}, - "source": [ - "By hovering over the lake in the center of each image, we can see that the water was labeled with a cluster label `value` in both images. Let's programmatically grab the cluster label at the center for each image." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "896cc4a9-d84b-46e6-8cc2-e04e1820b898", - "metadata": {}, - "outputs": [], - "source": [ - "def get_center_value(arr):\n", - " center_y = arr.shape[0] // 2\n", - " center_x = arr.shape[1] // 2\n", - " center_value = arr[center_y, center_x]\n", - " return int(center_value.values)\n", - "\n", - "water_cluster_1988_label = get_center_value(result_plot_1988.data.value)\n", - "water_cluster_2017_label = get_center_value(result_plot_2017.data.value)" - ] - }, - { - "cell_type": "markdown", - "id": "0b51d984-1b83-408b-9758-c843e9d8c58e", - "metadata": {}, - "source": [ - "Now, we want to align the cluster label for water. Let's try to set anything that is water to `1` and otherwise `0`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f20df2e1-b66d-4857-a39d-9a16ee50ad22", - "metadata": {}, - "outputs": [], - "source": [ - "water_1988 = xr.where(unstacked_1988 == water_cluster_1988_label, 1, 0)\n", - "water_2017 = xr.where(unstacked_2017 == water_cluster_2017_label, 1, 0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "57e6b497-ab7b-4641-a2af-1e81a45da7de", - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "water_1988.hvplot(geo=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b90cabc5-cac6-4e53-8730-9b808c688104", - "metadata": {}, - "outputs": [], - "source": [ - "water_1988_plot = water_1988.hvplot(\n", - " x=\"x\", y=\"y\", cmap=\"greys\", geo=True, colorbar=False, title=\"1988 Water\"\n", - ")\n", - "\n", - "water_2017_plot = water_2017.hvplot(\n", - " x=\"x\", y=\"y\", cmap=\"greys\", geo=True, colorbar=False, title=\"2017 Water\"\n", - ")\n", - "\n", - "water_1988_plot + water_2017_plot" - ] - }, - { - "cell_type": "markdown", - "id": "313e08b0-bbec-43cf-94fa-a4b8a84e1177", - "metadata": {}, - "source": [ - "Now we can take the difference of these water label arrays to see exactly where the water levels has changed." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4af122d1-3044-48cc-a0be-3922839474c1", - "metadata": {}, - "outputs": [], - "source": [ - "with xr.set_options(keep_attrs=True):\n", - " water_diff = water_1988 - water_2017" - ] - }, - { - "cell_type": "markdown", - "id": "d5517bf1-bced-455e-a053-528b47809e0a", - "metadata": {}, - "source": [ - "Red pixels (array value '1') of our image below are where water was lost from 1988 to 2017." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b9ac483-0240-4ffc-b167-f84eac832a32", - "metadata": {}, - "outputs": [], - "source": [ - "water_diff.hvplot(\n", - " x=\"x\", y=\"y\", cmap='coolwarm', geo=True, xlabel=\"long\", ylabel=\"lat\", colorbar=False, title=\"Water Change 1988-2017\",\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "1466477f-19b5-4866-940c-5cc52c979b83", - "metadata": {}, - "source": [ - "We did it! We are observing the change in the lake shoreline over time using a simple spectral clustering approach." - ] - }, - { - "cell_type": "markdown", - "id": "d14ad11a-18df-4082-a596-a0be684e75a7", - "metadata": {}, - "source": [ - "Let's finish things off by adding some geo tiles as a background. To only display the colored pixels overlaid on geo tiles, we could either set the array's background value ('0') to 'Not a Number' (NaN), or we could just inform hvPlot that we want the background valued pixels to be transparent with `.redim.nodata(value=0)`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17ad0e8a-c880-4c67-9994-4e1425da2829", - "metadata": {}, - "outputs": [], - "source": [ - "water_diff.hvplot(\n", - " x=\"x\", y=\"y\", width=400, height=400, cmap='coolwarm', geo=True, xlabel=\"lon\", ylabel=\"lat\", alpha=1, colorbar=False, title=\"Water Loss from 1988 to 2017\", tiles=\"ESRI\",\n", - ").redim.nodata(value=0)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "46b24b86-d262-4ce3-8cae-5b4185574dc7", - "metadata": {}, - "source": [ - "___" - ] - }, - { - "cell_type": "markdown", - "id": "072e479f-7b69-495c-83a3-9b77352be007", - "metadata": {}, - "source": [ - "## Summary\n", - "Starting from raw Landsat data, we have used a simple spectral clustering approach to observe the change in a lake water's extent across time.\n", - "\n", - "### What's next?\n", - "Adapt this notebook for your own use case or select another workflow example notebook." - ] - }, - { - "cell_type": "markdown", - "id": "ebee9cbd-ed88-4827-809c-c820b8af509a", - "metadata": {}, - "source": [ - "## Resources and References\n", - "- Authored by Demetris Roumis circa Jan, 2023" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3993488c-4dcf-4613-8c21-b02431112866", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.12" - }, - "vscode": { - "interpreter": { - "hash": "b0fa6594d8f4cbf19f97940f81e996739fb7646882a419484c72d19e05852a7e" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_preview/22/_sources/notebooks/Untitled.ipynb b/_preview/22/_sources/notebooks/Untitled.ipynb deleted file mode 100644 index 363fcab..0000000 --- a/_preview/22/_sources/notebooks/Untitled.ipynb +++ /dev/null @@ -1,6 +0,0 @@ -{ - "cells": [], - "metadata": {}, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_preview/22/_sources/notebooks/Untitled1.ipynb b/_preview/22/_sources/notebooks/Untitled1.ipynb deleted file mode 100644 index d3556d1..0000000 --- a/_preview/22/_sources/notebooks/Untitled1.ipynb +++ /dev/null @@ -1,146 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "28afd690-fe4b-46d9-952d-c2f2a619c4bf", - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: why doesn't `.interp` work when using the bounds of the diff as the ROI bbox?" - ] - }, - { - "cell_type": "markdown", - "id": "e3d96599-5152-4aae-8fb2-cc7c35043192", - "metadata": {}, - "source": [ - "One last approach to selecting a region of interest - rather than starting from a center point and using a region around it, it may make sense to just use the bounds of the entire overlapping region between our images. Let's try it:" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "64172cbd-1316-482f-9360-71c9421e7727", - "metadata": {}, - "outputs": [], - "source": [ - "xmin = NDVI_diff.x.values[0]\n", - "xmax = NDVI_diff.x.values[-1]\n", - "ymin = NDVI_diff.y.values[0]\n", - "ymax = NDVI_diff.y.values[-1]\n", - "bounding_box = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)]" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "id": "abe64df8-0e98-4cf1-bc98-ba9f7c883263", - "metadata": {}, - "outputs": [], - "source": [ - "roi = gv.Polygons(bounding_box, crs=crs)" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "id": "3b65b002-27b5-4372-b404-2fcc12ed0c75", - "metadata": {}, - "outputs": [ - { - "data": {}, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.holoviews_exec.v0+json": "", - "text/html": [ - "
\n", - "
\n", - "
\n", - "" - ], - "text/plain": [ - ":Overlay\n", - " .WMTS.I :WMTS [Longitude,Latitude]\n", - " .Polygons.I :Polygons [Longitude,Latitude]" - ] - }, - "execution_count": 44, - "metadata": { - "application/vnd.holoviews_exec.v0+json": { - "id": "2354" - } - }, - "output_type": "execute_result" - } - ], - "source": [ - "gv.tile_sources.EsriImagery * roi.options(alpha=0.3)" - ] - }, - { - "cell_type": "markdown", - "id": "5ea9382c-7c68-4018-9e7a-d48bca2d4a36", - "metadata": {}, - "source": [ - "That looks good! As you can see, there are many ways to select a region of interest. The next step is to put our data on a common grid within this region." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "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": "3.10.8" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/_preview/22/_sources/notebooks/how-to-cite.md b/_preview/22/_sources/notebooks/how-to-cite.md deleted file mode 100644 index ea36ba7..0000000 --- a/_preview/22/_sources/notebooks/how-to-cite.md +++ /dev/null @@ -1,7 +0,0 @@ -# How to Cite This Cookbook - -The material in Project Pythia's Landsat ML Cookbook is licensed for free and open consumption and reuse. All code is served under [Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0), while all non-code content is licensed under [Creative Commons BY 4.0 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/). Effectively, this means you are free to share and adapt this material so long as you give appropriate credit to the Cookbook authors and the Project Pythia community. - -The source code for the book is [released on GitHub](https://github.com/ProjectPythia/landsat-ml-cookbook) and archived on Zenodo. This DOI will always resolve to the latest release of the book source: - -[![DOI](https://zenodo.org/badge/563445694.svg)](https://zenodo.org/badge/latestdoi/563445694) \ No newline at end of file diff --git a/_preview/22/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_preview/22/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css deleted file mode 100644 index 3225661..0000000 --- a/_preview/22/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css +++ /dev/null @@ -1 +0,0 @@ -.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) 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user-select: none; - padding: 0; - border: none; - outline: none; - border-radius: 0.4em; - /* The colors that GitHub uses */ - border: #1b1f2426 1px solid; - background-color: #f6f8fa; - color: #57606a; -} - -button.copybtn.success { - border-color: #22863a; - color: #22863a; -} - -button.copybtn svg { - stroke: currentColor; - width: 1.5em; - height: 1.5em; - padding: 0.1em; -} - -div.highlight { - position: relative; -} - -/* Show the copybutton */ -.highlight:hover button.copybtn, button.copybtn.success { - opacity: 1; -} - -.highlight button.copybtn:hover { - background-color: rgb(235, 235, 235); -} - -.highlight button.copybtn:active { - background-color: rgb(187, 187, 187); -} - -/** - * A minimal CSS-only tooltip copied from: - * https://codepen.io/mildrenben/pen/rVBrpK - * - * To use, write HTML like the following: - * - *

Short

- */ - .o-tooltip--left { - position: relative; - } - - .o-tooltip--left:after { - opacity: 0; - visibility: hidden; - position: absolute; - content: attr(data-tooltip); - padding: .2em; - font-size: .8em; - left: -.2em; - background: grey; - color: white; - white-space: nowrap; - z-index: 2; - border-radius: 2px; - transform: translateX(-102%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); -} - -.o-tooltip--left:hover:after { - display: block; - opacity: 1; - visibility: visible; - transform: translateX(-100%) translateY(0); - transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1); - transition-delay: .5s; -} - -/* By default the copy button shouldn't show up when printing a page */ -@media print { - button.copybtn { - display: none; - } -} diff --git a/_preview/22/_static/copybutton.js b/_preview/22/_static/copybutton.js deleted file mode 100644 index 2ea7ff3..0000000 --- a/_preview/22/_static/copybutton.js +++ /dev/null @@ -1,248 +0,0 @@ -// Localization support -const messages = { - 'en': { - 'copy': 'Copy', - 'copy_to_clipboard': 'Copy to clipboard', - 'copy_success': 'Copied!', - 'copy_failure': 'Failed to copy', - }, - 'es' : { - 'copy': 'Copiar', - 'copy_to_clipboard': 'Copiar al portapapeles', - 'copy_success': '¡Copiado!', - 'copy_failure': 'Error al copiar', - }, - 'de' : { - 'copy': 'Kopieren', - 'copy_to_clipboard': 'In die Zwischenablage kopieren', - 'copy_success': 'Kopiert!', - 'copy_failure': 'Fehler beim Kopieren', - }, - 'fr' : { - 'copy': 'Copier', - 'copy_to_clipboard': 'Copier dans le presse-papier', - 'copy_success': 'Copié !', - 'copy_failure': 'Échec de la copie', - }, - 'ru': { - 'copy': 'Скопировать', - 'copy_to_clipboard': 'Скопировать в буфер', - 'copy_success': 'Скопировано!', - 'copy_failure': 'Не удалось скопировать', - }, - 'zh-CN': { - 'copy': '复制', - 'copy_to_clipboard': '复制到剪贴板', - 'copy_success': '复制成功!', - 'copy_failure': '复制失败', - }, - 'it' : { - 'copy': 'Copiare', - 'copy_to_clipboard': 'Copiato negli appunti', - 'copy_success': 'Copiato!', - 'copy_failure': 'Errore durante la copia', - } -} - -let locale = 'en' -if( document.documentElement.lang !== undefined - && messages[document.documentElement.lang] !== undefined ) { - locale = document.documentElement.lang -} - -let doc_url_root = DOCUMENTATION_OPTIONS.URL_ROOT; -if (doc_url_root == '#') { - doc_url_root = ''; -} - -/** - * SVG files for our copy buttons - */ -let iconCheck = ` - ${messages[locale]['copy_success']} - - -` - -// If the user specified their own SVG use that, otherwise use the default -let iconCopy = ``; -if (!iconCopy) { - iconCopy = ` - ${messages[locale]['copy_to_clipboard']} - - - -` -} - -/** - * Set up copy/paste for code blocks - */ - -const runWhenDOMLoaded = cb => { - if (document.readyState != 'loading') { - cb() - } else if (document.addEventListener) { - document.addEventListener('DOMContentLoaded', cb) - } else { - document.attachEvent('onreadystatechange', function() { - if (document.readyState == 'complete') cb() - }) - } -} - -const codeCellId = index => `codecell${index}` - -// Clears selected text since ClipboardJS will select the text when copying -const clearSelection = () => { - if (window.getSelection) { - window.getSelection().removeAllRanges() - } else if (document.selection) { - document.selection.empty() - } -} - -// Changes tooltip text for a moment, then changes it back -// We want the timeout of our `success` class to be a bit shorter than the -// tooltip and icon change, so that we can hide the icon before changing back. -var timeoutIcon = 2000; -var timeoutSuccessClass = 1500; - -const temporarilyChangeTooltip = (el, oldText, newText) => { - el.setAttribute('data-tooltip', newText) - el.classList.add('success') - // Remove success a little bit sooner than we change the tooltip - // So that we can use CSS to hide the copybutton first - setTimeout(() => el.classList.remove('success'), timeoutSuccessClass) - setTimeout(() => el.setAttribute('data-tooltip', oldText), timeoutIcon) -} - -// Changes the copy button icon for two seconds, then changes it back -const temporarilyChangeIcon = (el) => { - el.innerHTML = iconCheck; - setTimeout(() => {el.innerHTML = iconCopy}, timeoutIcon) -} - -const addCopyButtonToCodeCells = () => { - // If ClipboardJS hasn't loaded, wait a bit and try again. This - // happens because we load ClipboardJS asynchronously. - if (window.ClipboardJS === undefined) { - setTimeout(addCopyButtonToCodeCells, 250) - return - } - - // Add copybuttons to all of our code cells - const COPYBUTTON_SELECTOR = 'div.highlight pre'; - const codeCells = document.querySelectorAll(COPYBUTTON_SELECTOR) - codeCells.forEach((codeCell, index) => { - const id = codeCellId(index) - codeCell.setAttribute('id', id) - - const clipboardButton = id => - `` - codeCell.insertAdjacentHTML('afterend', clipboardButton(id)) - }) - -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} - - -var copyTargetText = (trigger) => { - var target = document.querySelector(trigger.attributes['data-clipboard-target'].value); - - // get filtered text - let exclude = '.linenos'; - - let text = filterText(target, exclude); - return formatCopyText(text, '', false, true, true, true, '', '') -} - - // Initialize with a callback so we can modify the text before copy - const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText}) - - // Update UI with error/success messages - clipboard.on('success', event => { - clearSelection() - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_success']) - temporarilyChangeIcon(event.trigger) - }) - - clipboard.on('error', event => { - temporarilyChangeTooltip(event.trigger, messages[locale]['copy'], messages[locale]['copy_failure']) - }) -} - -runWhenDOMLoaded(addCopyButtonToCodeCells) \ No newline at end of file diff --git a/_preview/22/_static/copybutton_funcs.js b/_preview/22/_static/copybutton_funcs.js deleted file mode 100644 index dbe1aaa..0000000 --- a/_preview/22/_static/copybutton_funcs.js +++ /dev/null @@ -1,73 +0,0 @@ -function escapeRegExp(string) { - return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string -} - -/** - * Removes excluded text from a Node. - * - * @param {Node} target Node to filter. - * @param {string} exclude CSS selector of nodes to exclude. - * @returns {DOMString} Text from `target` with text removed. - */ -export function filterText(target, exclude) { - const clone = target.cloneNode(true); // clone as to not modify the live DOM - if (exclude) { - // remove excluded nodes - clone.querySelectorAll(exclude).forEach(node => node.remove()); - } - return clone.innerText; -} - -// Callback when a copy button is clicked. Will be passed the node that was clicked -// should then grab the text and replace pieces of text that shouldn't be used in output -export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true, copyEmptyLines = true, lineContinuationChar = "", hereDocDelim = "") { - var regexp; - var match; - - // Do we check for line continuation characters and "HERE-documents"? - var useLineCont = !!lineContinuationChar - var useHereDoc = !!hereDocDelim - - // create regexp to capture prompt and remaining line - if (isRegexp) { - regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)') - } else { - regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)') - } - - const outputLines = []; - var promptFound = false; - var gotLineCont = false; - var gotHereDoc = false; - const lineGotPrompt = []; - for (const line of textContent.split('\n')) { - match = line.match(regexp) - if (match || gotLineCont || gotHereDoc) { - promptFound = regexp.test(line) - lineGotPrompt.push(promptFound) - if (removePrompts && promptFound) { - outputLines.push(match[2]) - } else { - outputLines.push(line) - } - gotLineCont = line.endsWith(lineContinuationChar) & useLineCont - if (line.includes(hereDocDelim) & useHereDoc) - gotHereDoc = !gotHereDoc - } else if (!onlyCopyPromptLines) { - outputLines.push(line) - } else if (copyEmptyLines && line.trim() === '') { - outputLines.push(line) - } - } - - // If no lines with the prompt were found then just use original lines - if (lineGotPrompt.some(v => v === true)) { - textContent = outputLines.join('\n'); - } - - // Remove a trailing newline to avoid auto-running when pasting - if (textContent.endsWith("\n")) { - textContent = textContent.slice(0, -1) - } - return textContent -} diff --git a/_preview/22/_static/css/blank.css b/_preview/22/_static/css/blank.css deleted file mode 100644 index 8a686ec..0000000 --- a/_preview/22/_static/css/blank.css +++ /dev/null @@ -1,2 +0,0 @@ -/* This file is intentionally left blank to override the stylesheet of the -parent theme via theme.conf. The parent style we import directly in theme.css */ \ No newline at end of file diff --git a/_preview/22/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css b/_preview/22/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css deleted file mode 100644 index 9b1c5d7..0000000 --- a/_preview/22/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css +++ /dev/null @@ -1,6 +0,0 @@ -/*! - * Bootstrap v4.5.0 (https://getbootstrap.com/) - * Copyright 2011-2020 The Bootstrap Authors - * Copyright 2011-2020 Twitter, Inc. - * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) - 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ul{list-style:none;padding:0 0 0 1.5rem}.bd-sidebar .nav li>a{display:block;padding:.25rem 1.5rem;color:rgba(var(--pst-color-sidebar-link),1)}.bd-sidebar .nav li>a:hover{color:rgba(var(--pst-color-sidebar-link-hover),1);text-decoration:none;background-color:transparent}.bd-sidebar .nav li>a.reference.external:after{font-family:Font Awesome\ 5 Free;font-weight:900;content:"\f35d";font-size:.75em;margin-left:.3em}.bd-sidebar .nav .active:hover>a,.bd-sidebar .nav .active>a{font-weight:600;color:rgba(var(--pst-color-sidebar-link-active),1)}.toc-h2{font-size:.85rem}.toc-h3{font-size:.75rem}.toc-h4{font-size:.65rem}.toc-entry>.nav-link.active{font-weight:600;color:#130654;color:rgba(var(--pst-color-toc-link-active),1);background-color:transparent;border-left:2px solid rgba(var(--pst-color-toc-link-active),1)}.nav-link:hover{border-style:none}#navbar-main-elements li.nav-item i{font-size:.7rem;padding-left:2px;vertical-align:middle}.bd-toc .nav .nav{display:none}.bd-toc .nav .nav.visible,.bd-toc .nav>.active>ul{display:block}.prev-next-area{margin:20px 0}.prev-next-area p{margin:0 .3em;line-height:1.3em}.prev-next-area i{font-size:1.2em}.prev-next-area a{display:flex;align-items:center;border:none;padding:10px;max-width:45%;overflow-x:hidden;color:rgba(0,0,0,.65);text-decoration:none}.prev-next-area a p.prev-next-title{color:rgba(var(--pst-color-link),1);font-weight:600;font-size:1.1em}.prev-next-area a:hover p.prev-next-title{text-decoration:underline}.prev-next-area a .prev-next-info{flex-direction:column;margin:0 .5em}.prev-next-area a .prev-next-info .prev-next-subtitle{text-transform:capitalize}.prev-next-area a.left-prev{float:left}.prev-next-area a.right-next{float:right}.prev-next-area a.right-next div.prev-next-info{text-align:right}.alert{padding-bottom:0}.alert-info a{color:#e83e8c}#navbar-icon-links i.fa,#navbar-icon-links i.fab,#navbar-icon-links i.far,#navbar-icon-links i.fas{vertical-align:middle;font-style:normal;font-size:1.5rem;line-height:1.25}#navbar-icon-links i.fa-github-square:before{color:#333}#navbar-icon-links i.fa-twitter-square:before{color:#55acee}#navbar-icon-links i.fa-gitlab:before{color:#548}#navbar-icon-links i.fa-bitbucket:before{color:#0052cc}.tocsection{border-left:1px solid #eee;padding:.3rem 1.5rem}.tocsection i{padding-right:.5rem}.editthispage{padding-top:2rem}.editthispage a{color:var(--pst-color-sidebar-link-active)}.xr-wrap[hidden]{display:block!important}.toctree-checkbox{position:absolute;display:none}.toctree-checkbox~ul{display:none}.toctree-checkbox~label i{transform:rotate(0deg)}.toctree-checkbox:checked~ul{display:block}.toctree-checkbox:checked~label i{transform:rotate(180deg)}.bd-sidebar li{position:relative}.bd-sidebar label{position:absolute;top:0;right:0;height:30px;width:30px;cursor:pointer;display:flex;justify-content:center;align-items:center}.bd-sidebar label:hover{background:rgba(var(--pst-color-sidebar-expander-background-hover),1)}.bd-sidebar label i{display:inline-block;font-size:.75rem;text-align:center}.bd-sidebar label i:hover{color:rgba(var(--pst-color-sidebar-link-hover),1)}.bd-sidebar li.has-children>.reference{padding-right:30px}div.doctest>div.highlight span.gp,span.linenos,table.highlighttable td.linenos{user-select:none;-webkit-user-select:text;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none}.docutils.container{padding-left:unset;padding-right:unset} \ No newline at end of file diff --git a/_preview/22/_static/css/theme.css b/_preview/22/_static/css/theme.css deleted file mode 100644 index 2e03fe3..0000000 --- a/_preview/22/_static/css/theme.css +++ /dev/null @@ -1,120 +0,0 @@ -/* Provided by the Sphinx base theme template at build time */ -@import "../basic.css"; - -:root { - /***************************************************************************** - * Theme config - **/ - --pst-header-height: 60px; - - /***************************************************************************** - * Font size - **/ - --pst-font-size-base: 15px; /* base font size - applied at body / html level */ - - /* heading font sizes */ - --pst-font-size-h1: 36px; - --pst-font-size-h2: 32px; - --pst-font-size-h3: 26px; - --pst-font-size-h4: 21px; - --pst-font-size-h5: 18px; - --pst-font-size-h6: 16px; - - /* smaller then heading font sizes*/ - --pst-font-size-milli: 12px; - - --pst-sidebar-font-size: .9em; - --pst-sidebar-caption-font-size: .9em; - - /***************************************************************************** - * Font family - **/ - /* These are adapted from https://systemfontstack.com/ */ - --pst-font-family-base-system: -apple-system, BlinkMacSystemFont, Segoe UI, "Helvetica Neue", - Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol; - --pst-font-family-monospace-system: "SFMono-Regular", Menlo, Consolas, Monaco, - Liberation Mono, Lucida Console, monospace; - - --pst-font-family-base: var(--pst-font-family-base-system); - --pst-font-family-heading: var(--pst-font-family-base); - --pst-font-family-monospace: var(--pst-font-family-monospace-system); - - /***************************************************************************** - * Color - * - * Colors are defined in rgb string way, "red, green, blue" - **/ - --pst-color-primary: 19, 6, 84; - --pst-color-success: 40, 167, 69; - --pst-color-info: 0, 123, 255; /*23, 162, 184;*/ - --pst-color-warning: 255, 193, 7; - --pst-color-danger: 220, 53, 69; - --pst-color-text-base: 51, 51, 51; - - --pst-color-h1: var(--pst-color-primary); - --pst-color-h2: var(--pst-color-primary); - --pst-color-h3: var(--pst-color-text-base); - --pst-color-h4: var(--pst-color-text-base); - --pst-color-h5: var(--pst-color-text-base); - --pst-color-h6: var(--pst-color-text-base); - --pst-color-paragraph: var(--pst-color-text-base); - --pst-color-link: 0, 91, 129; - --pst-color-link-hover: 227, 46, 0; - --pst-color-headerlink: 198, 15, 15; - --pst-color-headerlink-hover: 255, 255, 255; - --pst-color-preformatted-text: 34, 34, 34; - --pst-color-preformatted-background: 250, 250, 250; - --pst-color-inline-code: 232, 62, 140; - - --pst-color-active-navigation: 19, 6, 84; - --pst-color-navbar-link: 77, 77, 77; - --pst-color-navbar-link-hover: var(--pst-color-active-navigation); - --pst-color-navbar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-link: 77, 77, 77; - --pst-color-sidebar-link-hover: var(--pst-color-active-navigation); - --pst-color-sidebar-link-active: var(--pst-color-active-navigation); - --pst-color-sidebar-expander-background-hover: 244, 244, 244; - --pst-color-sidebar-caption: 77, 77, 77; - --pst-color-toc-link: 119, 117, 122; - --pst-color-toc-link-hover: var(--pst-color-active-navigation); - --pst-color-toc-link-active: var(--pst-color-active-navigation); - - /***************************************************************************** - * Icon - **/ - - /* font awesome icons*/ - --pst-icon-check-circle: '\f058'; - --pst-icon-info-circle: '\f05a'; - --pst-icon-exclamation-triangle: '\f071'; - --pst-icon-exclamation-circle: '\f06a'; - --pst-icon-times-circle: '\f057'; - --pst-icon-lightbulb: '\f0eb'; - - /***************************************************************************** - * Admonitions - **/ - - --pst-color-admonition-default: var(--pst-color-info); - --pst-color-admonition-note: var(--pst-color-info); - --pst-color-admonition-attention: var(--pst-color-warning); - --pst-color-admonition-caution: var(--pst-color-warning); - --pst-color-admonition-warning: var(--pst-color-warning); - --pst-color-admonition-danger: var(--pst-color-danger); - --pst-color-admonition-error: var(--pst-color-danger); - --pst-color-admonition-hint: var(--pst-color-success); - --pst-color-admonition-tip: var(--pst-color-success); - --pst-color-admonition-important: var(--pst-color-success); - - --pst-icon-admonition-default: var(--pst-icon-info-circle); - --pst-icon-admonition-note: var(--pst-icon-info-circle); - --pst-icon-admonition-attention: var(--pst-icon-exclamation-circle); - --pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle); - --pst-icon-admonition-error: var(--pst-icon-times-circle); - --pst-icon-admonition-hint: var(--pst-icon-lightbulb); - --pst-icon-admonition-tip: var(--pst-icon-lightbulb); - --pst-icon-admonition-important: var(--pst-icon-exclamation-circle); - -} diff --git a/_preview/22/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_preview/22/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css deleted file mode 100644 index 3225661..0000000 --- a/_preview/22/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css +++ /dev/null @@ -1 +0,0 @@ -.sd-bg-primary{background-color:var(--sd-color-primary) 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label.previousElementSibling.checked = true; - } - window.localStorage.setItem("sphinx-design-last-tab", syncId); -} - -document.addEventListener("DOMContentLoaded", ready, false); diff --git a/_preview/22/_static/doctools.js b/_preview/22/_static/doctools.js deleted file mode 100644 index e1bfd70..0000000 --- a/_preview/22/_static/doctools.js +++ /dev/null @@ -1,358 +0,0 @@ -/* - * doctools.js - * ~~~~~~~~~~~ - * - * Sphinx JavaScript utilities for all documentation. - * - * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -/** - * select a different prefix for underscore - */ -$u = _.noConflict(); - -/** - * make the code below compatible with browsers without - * an installed firebug like debugger -if (!window.console || !console.firebug) { - var names = ["log", "debug", "info", "warn", "error", "assert", "dir", - "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", - "profile", "profileEnd"]; - window.console = {}; - for (var i = 0; i < names.length; ++i) - window.console[names[i]] = function() {}; -} - */ - -/** - * small helper function to urldecode strings - * - * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL - */ -jQuery.urldecode = function(x) { - if (!x) { - return x - } - return decodeURIComponent(x.replace(/\+/g, ' ')); -}; - -/** - * small helper function to urlencode strings - */ -jQuery.urlencode = encodeURIComponent; - -/** - * This function returns the parsed url parameters of the - * current request. Multiple values per key are supported, - * it will always return arrays of strings for the value parts. - */ -jQuery.getQueryParameters = function(s) { - if (typeof s === 'undefined') - s = document.location.search; - var parts = s.substr(s.indexOf('?') + 1).split('&'); - var result = {}; - for (var i = 0; i < parts.length; i++) { - var tmp = parts[i].split('=', 2); - var key = jQuery.urldecode(tmp[0]); - var value = jQuery.urldecode(tmp[1]); - if (key in result) - result[key].push(value); - else - result[key] = [value]; - } - return result; -}; - -/** - * highlight a given string on a jquery object by wrapping it in - * span elements with the given class name. - */ -jQuery.fn.highlightText = function(text, className) { - function highlight(node, addItems) { - if (node.nodeType === 3) { - var val = node.nodeValue; - var pos = val.toLowerCase().indexOf(text); - if (pos >= 0 && - !jQuery(node.parentNode).hasClass(className) && - !jQuery(node.parentNode).hasClass("nohighlight")) { - var span; - var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); - if (isInSVG) { - span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); - } else { - span = document.createElement("span"); - span.className = className; - } - span.appendChild(document.createTextNode(val.substr(pos, text.length))); - node.parentNode.insertBefore(span, node.parentNode.insertBefore( - document.createTextNode(val.substr(pos + text.length)), - node.nextSibling)); - node.nodeValue = val.substr(0, pos); - if (isInSVG) { - var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); - var bbox = node.parentElement.getBBox(); - rect.x.baseVal.value = bbox.x; - rect.y.baseVal.value = bbox.y; - rect.width.baseVal.value = bbox.width; - rect.height.baseVal.value = bbox.height; - rect.setAttribute('class', className); - addItems.push({ - "parent": node.parentNode, - "target": rect}); - } - } - } - else if (!jQuery(node).is("button, select, textarea")) { - jQuery.each(node.childNodes, function() { - highlight(this, addItems); - }); - } - } - var addItems = []; - var result = this.each(function() { - highlight(this, addItems); - }); - for (var i = 0; i < addItems.length; ++i) { - jQuery(addItems[i].parent).before(addItems[i].target); - } - return result; -}; - -/* - * backward compatibility for jQuery.browser - * This will be supported until firefox bug is fixed. - */ -if (!jQuery.browser) { - jQuery.uaMatch = function(ua) { - ua = ua.toLowerCase(); - - var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || - /(webkit)[ \/]([\w.]+)/.exec(ua) || - /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || - /(msie) ([\w.]+)/.exec(ua) || - ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || - []; - - return { - browser: match[ 1 ] || "", - version: match[ 2 ] || "0" - }; - }; - jQuery.browser = {}; - jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; -} - -/** - * Small JavaScript module for the documentation. - */ -var Documentation = { - - init : function() { - this.fixFirefoxAnchorBug(); - this.highlightSearchWords(); - this.initIndexTable(); - this.initOnKeyListeners(); - }, - - /** - * i18n support - */ - TRANSLATIONS : {}, - PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, - LOCALE : 'unknown', - - // gettext and ngettext don't access this so that the functions - // can safely bound to a different name (_ = Documentation.gettext) - gettext : function(string) { - var translated = Documentation.TRANSLATIONS[string]; - if (typeof translated === 'undefined') - return string; - return (typeof translated === 'string') ? translated : translated[0]; - }, - - ngettext : function(singular, plural, n) { - var translated = Documentation.TRANSLATIONS[singular]; - if (typeof translated === 'undefined') - return (n == 1) ? singular : plural; - return translated[Documentation.PLURALEXPR(n)]; - }, - - addTranslations : function(catalog) { - for (var key in catalog.messages) - this.TRANSLATIONS[key] = catalog.messages[key]; - this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); - this.LOCALE = catalog.locale; - }, - - /** - * add context elements like header anchor links - */ - addContextElements : function() { - $('div[id] > :header:first').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this headline')). - appendTo(this); - }); - $('dt[id]').each(function() { - $('\u00B6'). - attr('href', '#' + this.id). - attr('title', _('Permalink to this definition')). - appendTo(this); - }); - }, - - /** - * workaround a firefox stupidity - * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 - */ - fixFirefoxAnchorBug : function() { - if (document.location.hash && $.browser.mozilla) - window.setTimeout(function() { - document.location.href += ''; - }, 10); - }, - - /** - * highlight the search words provided in the url in the text - */ - highlightSearchWords : function() { - var params = $.getQueryParameters(); - var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : []; - if (terms.length) { - var body = $('div.body'); - if (!body.length) { - body = $('body'); - } - window.setTimeout(function() { - $.each(terms, function() { - body.highlightText(this.toLowerCase(), 'highlighted'); - }); - }, 10); - $('') - .appendTo($('#searchbox')); - } - }, - - /** - * init the domain index toggle buttons - */ - initIndexTable : function() { - var togglers = $('img.toggler').click(function() { - var src = $(this).attr('src'); - var idnum = $(this).attr('id').substr(7); - $('tr.cg-' + idnum).toggle(); - if (src.substr(-9) === 'minus.png') - $(this).attr('src', src.substr(0, src.length-9) + 'plus.png'); - else - $(this).attr('src', src.substr(0, src.length-8) + 'minus.png'); - }).css('display', ''); - if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) { - togglers.click(); - } - }, - - /** - * helper function to hide the search marks again - */ - hideSearchWords : function() { - $('#searchbox .highlight-link').fadeOut(300); - $('span.highlighted').removeClass('highlighted'); - var url = new URL(window.location); - url.searchParams.delete('highlight'); - window.history.replaceState({}, '', url); - }, - - /** - * helper function to focus on search bar - */ - focusSearchBar : function() { - $('input[name=q]').first().focus(); - }, - - /** - * make the url absolute - */ - makeURL : function(relativeURL) { - return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL; - }, - - /** - * get the current relative url - */ - getCurrentURL : function() { - var path = document.location.pathname; - var parts = path.split(/\//); - $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() { - if (this === '..') - parts.pop(); - }); - var url = parts.join('/'); - return path.substring(url.lastIndexOf('/') + 1, path.length - 1); - }, - - initOnKeyListeners: function() { - // only install a listener if it is really needed - if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS && - 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@@ -1,10872 +0,0 @@ -/*! - * jQuery JavaScript Library v3.5.1 - * https://jquery.com/ - * - * Includes Sizzle.js - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://jquery.org/license - * - * Date: 2020-05-04T22:49Z - */ -( function( global, factory ) { - - "use strict"; - - if ( typeof module === "object" && typeof module.exports === "object" ) { - - // For CommonJS and CommonJS-like environments where a proper `window` - // is present, execute the factory and get jQuery. - // For environments that do not have a `window` with a `document` - // (such as Node.js), expose a factory as module.exports. - // This accentuates the need for the creation of a real `window`. - // e.g. var jQuery = require("jquery")(window); - // See ticket #14549 for more info. - module.exports = global.document ? - factory( global, true ) : - function( w ) { - if ( !w.document ) { - throw new Error( "jQuery requires a window with a document" ); - } - return factory( w ); - }; - } else { - factory( global ); - } - -// Pass this if window is not defined yet -} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { - -// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 -// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode -// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common -// enough that all such attempts are guarded in a try block. -"use strict"; - -var arr = []; - -var getProto = Object.getPrototypeOf; - -var slice = arr.slice; - -var flat = arr.flat ? function( array ) { - return arr.flat.call( array ); -} : function( array ) { - return arr.concat.apply( [], array ); -}; - - -var push = arr.push; - -var indexOf = arr.indexOf; - -var class2type = {}; - -var toString = class2type.toString; - -var hasOwn = class2type.hasOwnProperty; - -var fnToString = hasOwn.toString; - -var ObjectFunctionString = fnToString.call( Object ); - -var support = {}; - -var isFunction = function isFunction( obj ) { - - // Support: Chrome <=57, Firefox <=52 - // In some browsers, typeof returns "function" for HTML elements - // (i.e., `typeof document.createElement( "object" ) === "function"`). - // We don't want to classify *any* DOM node as a function. - return typeof obj === "function" && typeof obj.nodeType !== "number"; - }; - - -var isWindow = function isWindow( obj ) { - return obj != null && obj === obj.window; - }; - - -var document = window.document; - - - - var preservedScriptAttributes = { - type: true, - src: true, - nonce: true, - noModule: true - }; - - function DOMEval( code, node, doc ) { - doc = doc || document; - - var i, val, - script = doc.createElement( "script" ); - - script.text = code; - if ( node ) { - for ( i in preservedScriptAttributes ) { - - // Support: Firefox 64+, Edge 18+ - // Some browsers don't support the "nonce" property on scripts. - // On the other hand, just using `getAttribute` is not enough as - // the `nonce` attribute is reset to an empty string whenever it - // becomes browsing-context connected. - // See https://github.com/whatwg/html/issues/2369 - // See https://html.spec.whatwg.org/#nonce-attributes - // The `node.getAttribute` check was added for the sake of - // `jQuery.globalEval` so that it can fake a nonce-containing node - // via an object. - val = node[ i ] || node.getAttribute && node.getAttribute( i ); - if ( val ) { - script.setAttribute( i, val ); - } - } - } - doc.head.appendChild( script ).parentNode.removeChild( script ); - } - - -function toType( obj ) { - if ( obj == null ) { - return obj + ""; - } - - // Support: Android <=2.3 only (functionish RegExp) - return typeof obj === "object" || typeof obj === "function" ? - class2type[ toString.call( obj ) ] || "object" : - typeof obj; -} -/* global Symbol */ -// Defining this global in .eslintrc.json would create a danger of using the global -// unguarded in another place, it seems safer to define global only for this module - - - -var - version = "3.5.1", - - // Define a local copy of jQuery - jQuery = function( selector, context ) { - - // The jQuery object is actually just the init constructor 'enhanced' - // Need init if jQuery is called (just allow error to be thrown if not included) - return new jQuery.fn.init( selector, context ); - }; - -jQuery.fn = jQuery.prototype = { - - // The current version of jQuery being used - jquery: version, - - constructor: jQuery, - - // The default length of a jQuery object is 0 - length: 0, - - toArray: function() { - return slice.call( this ); - }, - - // Get the Nth element in the matched element set OR - // Get the whole matched element set as a clean array - get: function( num ) { - - // Return all the elements in a clean array - if ( num == null ) { - return slice.call( this ); - } - - // Return just the one element from the set - return num < 0 ? this[ num + this.length ] : this[ num ]; - }, - - // Take an array of elements and push it onto the stack - // (returning the new matched element set) - pushStack: function( elems ) { - - // Build a new jQuery matched element set - var ret = jQuery.merge( this.constructor(), elems ); - - // Add the old object onto the stack (as a reference) - ret.prevObject = this; - - // Return the newly-formed element set - return ret; - }, - - // Execute a callback for every element in the matched set. - each: function( callback ) { - return jQuery.each( this, callback ); - }, - - map: function( callback ) { - return this.pushStack( jQuery.map( this, function( elem, i ) { - return callback.call( elem, i, elem ); - } ) ); - }, - - slice: function() { - return this.pushStack( slice.apply( this, arguments ) ); - }, - - first: function() { - return this.eq( 0 ); - }, - - last: function() { - return this.eq( -1 ); - }, - - even: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return ( i + 1 ) % 2; - } ) ); - }, - - odd: function() { - return this.pushStack( jQuery.grep( this, function( _elem, i ) { - return i % 2; - } ) ); - }, - - eq: function( i ) { - var len = this.length, - j = +i + ( i < 0 ? len : 0 ); - return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); - }, - - end: function() { - return this.prevObject || this.constructor(); - }, - - // For internal use only. - // Behaves like an Array's method, not like a jQuery method. - push: push, - sort: arr.sort, - splice: arr.splice -}; - -jQuery.extend = jQuery.fn.extend = function() { - var options, name, src, copy, copyIsArray, clone, - target = arguments[ 0 ] || {}, - i = 1, - length = arguments.length, - deep = false; - - // Handle a deep copy situation - if ( typeof target === "boolean" ) { - deep = target; - - // Skip the boolean and the target - target = arguments[ i ] || {}; - i++; - } - - // Handle case when target is a string or something (possible in deep copy) - if ( typeof target !== "object" && !isFunction( target ) ) { - target = {}; - } - - // Extend jQuery itself if only one argument is passed - if ( i === length ) { - target = this; - i--; - } - - for ( ; i < length; i++ ) { - - // Only deal with non-null/undefined values - if ( ( options = arguments[ i ] ) != null ) { - - // Extend the base object - for ( name in options ) { - copy = options[ name ]; - - // Prevent Object.prototype pollution - // Prevent never-ending loop - if ( name === "__proto__" || target === copy ) { - continue; - } - - // Recurse if we're merging plain objects or arrays - if ( deep && copy && ( jQuery.isPlainObject( copy ) || - ( copyIsArray = Array.isArray( copy ) ) ) ) { - src = target[ name ]; - - // Ensure proper type for the source value - if ( copyIsArray && !Array.isArray( src ) ) { - clone = []; - } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { - clone = {}; - } else { - clone = src; - } - copyIsArray = false; - - // Never move original objects, clone them - target[ name ] = jQuery.extend( deep, clone, copy ); - - // Don't bring in undefined values - } else if ( copy !== undefined ) { - target[ name ] = copy; - } - } - } - } - - // Return the modified object - return target; -}; - -jQuery.extend( { - - // Unique for each copy of jQuery on the page - expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), - - // Assume jQuery is ready without the ready module - isReady: true, - - error: function( msg ) { - throw new Error( msg ); - }, - - noop: function() {}, - - isPlainObject: function( obj ) { - var proto, Ctor; - - // Detect obvious negatives - // Use toString instead of jQuery.type to catch host objects - if ( !obj || toString.call( obj ) !== "[object Object]" ) { - return false; - } - - proto = getProto( obj ); - - // Objects with no prototype (e.g., `Object.create( null )`) are plain - if ( !proto ) { - return true; - } - - // Objects with prototype are plain iff they were constructed by a global Object function - Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; - return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; - }, - - isEmptyObject: function( obj ) { - var name; - - for ( name in obj ) { - return false; - } - return true; - }, - - // Evaluates a script in a provided context; falls back to the global one - // if not specified. - globalEval: function( code, options, doc ) { - DOMEval( code, { nonce: options && options.nonce }, doc ); - }, - - each: function( obj, callback ) { - var length, i = 0; - - if ( isArrayLike( obj ) ) { - length = obj.length; - for ( ; i < length; i++ ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } else { - for ( i in obj ) { - if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { - break; - } - } - } - - return obj; - }, - - // results is for internal usage only - makeArray: function( arr, results ) { - var ret = results || []; - - if ( arr != null ) { - if ( isArrayLike( Object( arr ) ) ) { - jQuery.merge( ret, - typeof arr === "string" ? - [ arr ] : arr - ); - } else { - push.call( ret, arr ); - } - } - - return ret; - }, - - inArray: function( elem, arr, i ) { - return arr == null ? -1 : indexOf.call( arr, elem, i ); - }, - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - merge: function( first, second ) { - var len = +second.length, - j = 0, - i = first.length; - - for ( ; j < len; j++ ) { - first[ i++ ] = second[ j ]; - } - - first.length = i; - - return first; - }, - - grep: function( elems, callback, invert ) { - var callbackInverse, - matches = [], - i = 0, - length = elems.length, - callbackExpect = !invert; - - // Go through the array, only saving the items - // that pass the validator function - for ( ; i < length; i++ ) { - callbackInverse = !callback( elems[ i ], i ); - if ( callbackInverse !== callbackExpect ) { - matches.push( elems[ i ] ); - } - } - - return matches; - }, - - // arg is for internal usage only - map: function( elems, callback, arg ) { - var length, value, - i = 0, - ret = []; - - // Go through the array, translating each of the items to their new values - if ( isArrayLike( elems ) ) { - length = elems.length; - for ( ; i < length; i++ ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - - // Go through every key on the object, - } else { - for ( i in elems ) { - value = callback( elems[ i ], i, arg ); - - if ( value != null ) { - ret.push( value ); - } - } - } - - // Flatten any nested arrays - return flat( ret ); - }, - - // A global GUID counter for objects - guid: 1, - - // jQuery.support is not used in Core but other projects attach their - // properties to it so it needs to exist. - support: support -} ); - -if ( typeof Symbol === "function" ) { - jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; -} - -// Populate the class2type map -jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), -function( _i, name ) { - class2type[ "[object " + name + "]" ] = name.toLowerCase(); -} ); - -function isArrayLike( obj ) { - - // Support: real iOS 8.2 only (not reproducible in simulator) - // `in` check used to prevent JIT error (gh-2145) - // hasOwn isn't used here due to false negatives - // regarding Nodelist length in IE - var length = !!obj && "length" in obj && obj.length, - type = toType( obj ); - - if ( isFunction( obj ) || isWindow( obj ) ) { - return false; - } - - return type === "array" || length === 0 || - typeof length === "number" && length > 0 && ( length - 1 ) in obj; -} -var Sizzle = -/*! - * Sizzle CSS Selector Engine v2.3.5 - * https://sizzlejs.com/ - * - * Copyright JS Foundation and other contributors - * Released under the MIT license - * https://js.foundation/ - * - * Date: 2020-03-14 - */ -( function( window ) { -var i, - support, - Expr, - getText, - isXML, - tokenize, - compile, - select, - outermostContext, - sortInput, - hasDuplicate, - - // Local document vars - setDocument, - document, - docElem, - documentIsHTML, - rbuggyQSA, - rbuggyMatches, - matches, - contains, - - // Instance-specific data - expando = "sizzle" + 1 * new Date(), - preferredDoc = window.document, - dirruns = 0, - done = 0, - classCache = createCache(), - tokenCache = createCache(), - compilerCache = createCache(), - nonnativeSelectorCache = createCache(), - sortOrder = function( a, b ) { - if ( a === b ) { - hasDuplicate = true; - } - return 0; - }, - - // Instance methods - hasOwn = ( {} ).hasOwnProperty, - arr = [], - pop = arr.pop, - pushNative = arr.push, - push = arr.push, - slice = arr.slice, - - // Use a stripped-down indexOf as it's faster than native - // https://jsperf.com/thor-indexof-vs-for/5 - indexOf = function( list, elem ) { - var i = 0, - len = list.length; - for ( ; i < len; i++ ) { - if ( list[ i ] === elem ) { - return i; - } - } - return -1; - }, - - booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + - "ismap|loop|multiple|open|readonly|required|scoped", - - // Regular expressions - - // http://www.w3.org/TR/css3-selectors/#whitespace - whitespace = "[\\x20\\t\\r\\n\\f]", - - // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram - identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + - "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", - - // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors - attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + - - // Operator (capture 2) - "*([*^$|!~]?=)" + whitespace + - - // "Attribute values must be CSS identifiers [capture 5] - // or strings [capture 3 or capture 4]" - "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + - whitespace + "*\\]", - - pseudos = ":(" + identifier + ")(?:\\((" + - - // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: - // 1. quoted (capture 3; capture 4 or capture 5) - "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + - - // 2. simple (capture 6) - "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + - - // 3. anything else (capture 2) - ".*" + - ")\\)|)", - - // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter - rwhitespace = new RegExp( whitespace + "+", "g" ), - rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + - whitespace + "+$", "g" ), - - rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), - rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + - "*" ), - rdescend = new RegExp( whitespace + "|>" ), - - rpseudo = new RegExp( pseudos ), - ridentifier = new RegExp( "^" + identifier + "$" ), - - matchExpr = { - "ID": new RegExp( "^#(" + identifier + ")" ), - "CLASS": new RegExp( "^\\.(" + identifier + ")" ), - "TAG": new RegExp( "^(" + identifier + "|[*])" ), - "ATTR": new RegExp( "^" + attributes ), - "PSEUDO": new RegExp( "^" + pseudos ), - "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + - whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + - whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), - "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), - - // For use in libraries implementing .is() - // We use this for POS matching in `select` - "needsContext": new RegExp( "^" + whitespace + - "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + - "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) - }, - - rhtml = /HTML$/i, - rinputs = /^(?:input|select|textarea|button)$/i, - rheader = /^h\d$/i, - - rnative = /^[^{]+\{\s*\[native \w/, - - // Easily-parseable/retrievable ID or TAG or CLASS selectors - rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, - - rsibling = /[+~]/, - - // CSS escapes - // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters - runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), - funescape = function( escape, nonHex ) { - var high = "0x" + escape.slice( 1 ) - 0x10000; - - return nonHex ? - - // Strip the backslash prefix from a non-hex escape sequence - nonHex : - - // Replace a hexadecimal escape sequence with the encoded Unicode code point - // Support: IE <=11+ - // For values outside the Basic Multilingual Plane (BMP), manually construct a - // surrogate pair - high < 0 ? - String.fromCharCode( high + 0x10000 ) : - String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); - }, - - // CSS string/identifier serialization - // https://drafts.csswg.org/cssom/#common-serializing-idioms - rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, - fcssescape = function( ch, asCodePoint ) { - if ( asCodePoint ) { - - // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER - if ( ch === "\0" ) { - return "\uFFFD"; - } - - // Control characters and (dependent upon position) numbers get escaped as code points - return ch.slice( 0, -1 ) + "\\" + - ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; - } - - // Other potentially-special ASCII characters get backslash-escaped - return "\\" + ch; - }, - - // Used for iframes - // See setDocument() - // Removing the function wrapper causes a "Permission Denied" - // error in IE - unloadHandler = function() { - setDocument(); - }, - - inDisabledFieldset = addCombinator( - function( elem ) { - return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; - }, - { dir: "parentNode", next: "legend" } - ); - -// Optimize for push.apply( _, NodeList ) -try { - push.apply( - ( arr = slice.call( preferredDoc.childNodes ) ), - preferredDoc.childNodes - ); - - // Support: Android<4.0 - // Detect silently failing push.apply - // eslint-disable-next-line no-unused-expressions - arr[ preferredDoc.childNodes.length ].nodeType; -} catch ( e ) { - push = { apply: arr.length ? - - // Leverage slice if possible - function( target, els ) { - pushNative.apply( target, slice.call( els ) ); - } : - - // Support: IE<9 - // Otherwise append directly - function( target, els ) { - var j = target.length, - i = 0; - - // Can't trust NodeList.length - while ( ( target[ j++ ] = els[ i++ ] ) ) {} - target.length = j - 1; - } - }; -} - -function Sizzle( selector, context, results, seed ) { - var m, i, elem, nid, match, groups, newSelector, - newContext = context && context.ownerDocument, - - // nodeType defaults to 9, since context defaults to document - nodeType = context ? context.nodeType : 9; - - results = results || []; - - // Return early from calls with invalid selector or context - if ( typeof selector !== "string" || !selector || - nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { - - return results; - } - - // Try to shortcut find operations (as opposed to filters) in HTML documents - if ( !seed ) { - setDocument( context ); - context = context || document; - - if ( documentIsHTML ) { - - // If the selector is sufficiently simple, try using a "get*By*" DOM method - // (excepting DocumentFragment context, where the methods don't exist) - if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { - - // ID selector - if ( ( m = match[ 1 ] ) ) { - - // Document context - if ( nodeType === 9 ) { - if ( ( elem = context.getElementById( m ) ) ) { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( elem.id === m ) { - results.push( elem ); - return results; - } - } else { - return results; - } - - // Element context - } else { - - // Support: IE, Opera, Webkit - // TODO: identify versions - // getElementById can match elements by name instead of ID - if ( newContext && ( elem = newContext.getElementById( m ) ) && - contains( context, elem ) && - elem.id === m ) { - - results.push( elem ); - return results; - } - } - - // Type selector - } else if ( match[ 2 ] ) { - push.apply( results, context.getElementsByTagName( selector ) ); - return results; - - // Class selector - } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && - context.getElementsByClassName ) { - - push.apply( results, context.getElementsByClassName( m ) ); - return results; - } - } - - // Take advantage of querySelectorAll - if ( support.qsa && - !nonnativeSelectorCache[ selector + " " ] && - ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && - - // Support: IE 8 only - // Exclude object elements - ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { - - newSelector = selector; - newContext = context; - - // qSA considers elements outside a scoping root when evaluating child or - // descendant combinators, which is not what we want. - // In such cases, we work around the behavior by prefixing every selector in the - // list with an ID selector referencing the scope context. - // The technique has to be used as well when a leading combinator is used - // as such selectors are not recognized by querySelectorAll. - // Thanks to Andrew Dupont for this technique. - if ( nodeType === 1 && - ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { - - // Expand context for sibling selectors - newContext = rsibling.test( selector ) && testContext( context.parentNode ) || - context; - - // We can use :scope instead of the ID hack if the browser - // supports it & if we're not changing the context. - if ( newContext !== context || !support.scope ) { - - // Capture the context ID, setting it first if necessary - if ( ( nid = context.getAttribute( "id" ) ) ) { - nid = nid.replace( rcssescape, fcssescape ); - } else { - context.setAttribute( "id", ( nid = expando ) ); - } - } - - // Prefix every selector in the list - groups = tokenize( selector ); - i = groups.length; - while ( i-- ) { - groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + - toSelector( groups[ i ] ); - } - newSelector = groups.join( "," ); - } - - try { - push.apply( results, - newContext.querySelectorAll( newSelector ) - ); - return results; - } catch ( qsaError ) { - nonnativeSelectorCache( selector, true ); - } finally { - if ( nid === expando ) { - context.removeAttribute( "id" ); - } - } - } - } - } - - // All others - return select( selector.replace( rtrim, "$1" ), context, results, seed ); -} - -/** - * Create key-value caches of limited size - * @returns {function(string, object)} Returns the Object data after storing it on itself with - * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) - * deleting the oldest entry - */ -function createCache() { - var keys = []; - - function cache( key, value ) { - - // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) - if ( keys.push( key + " " ) > Expr.cacheLength ) { - - // Only keep the most recent entries - delete cache[ keys.shift() ]; - } - return ( cache[ key + " " ] = value ); - } - return cache; -} - -/** - * Mark a function for special use by Sizzle - * @param {Function} fn The function to mark - */ -function markFunction( fn ) { - fn[ expando ] = true; - return fn; -} - -/** - * Support testing using an element - * @param {Function} fn Passed the created element and returns a boolean result - */ -function assert( fn ) { - var el = document.createElement( "fieldset" ); - - try { - return !!fn( el ); - } catch ( e ) { - return false; - } finally { - - // Remove from its parent by default - if ( el.parentNode ) { - el.parentNode.removeChild( el ); - } - - // release memory in IE - el = null; - } -} - -/** - * Adds the same handler for all of the specified attrs - * @param {String} attrs Pipe-separated list of attributes - * @param {Function} handler The method that will be applied - */ -function addHandle( attrs, handler ) { - var arr = attrs.split( "|" ), - i = arr.length; - - while ( i-- ) { - Expr.attrHandle[ arr[ i ] ] = handler; - } -} - -/** - * Checks document order of two siblings - * @param {Element} a - * @param {Element} b - * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b - */ -function siblingCheck( a, b ) { - var cur = b && a, - diff = cur && a.nodeType === 1 && b.nodeType === 1 && - a.sourceIndex - b.sourceIndex; - - // Use IE sourceIndex if available on both nodes - if ( diff ) { - return diff; - } - - // Check if b follows a - if ( cur ) { - while ( ( cur = cur.nextSibling ) ) { - if ( cur === b ) { - return -1; - } - } - } - - return a ? 1 : -1; -} - -/** - * Returns a function to use in pseudos for input types - * @param {String} type - */ -function createInputPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for buttons - * @param {String} type - */ -function createButtonPseudo( type ) { - return function( elem ) { - var name = elem.nodeName.toLowerCase(); - return ( name === "input" || name === "button" ) && elem.type === type; - }; -} - -/** - * Returns a function to use in pseudos for :enabled/:disabled - * @param {Boolean} disabled true for :disabled; false for :enabled - */ -function createDisabledPseudo( disabled ) { - - // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable - return function( elem ) { - - // Only certain elements can match :enabled or :disabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled - // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled - if ( "form" in elem ) { - - // Check for inherited disabledness on relevant non-disabled elements: - // * listed form-associated elements in a disabled fieldset - // https://html.spec.whatwg.org/multipage/forms.html#category-listed - // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled - // * option elements in a disabled optgroup - // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled - // All such elements have a "form" property. - if ( elem.parentNode && elem.disabled === false ) { - - // Option elements defer to a parent optgroup if present - if ( "label" in elem ) { - if ( "label" in elem.parentNode ) { - return elem.parentNode.disabled === disabled; - } else { - return elem.disabled === disabled; - } - } - - // Support: IE 6 - 11 - // Use the isDisabled shortcut property to check for disabled fieldset ancestors - return elem.isDisabled === disabled || - - // Where there is no isDisabled, check manually - /* jshint -W018 */ - elem.isDisabled !== !disabled && - inDisabledFieldset( elem ) === disabled; - } - - return elem.disabled === disabled; - - // Try to winnow out elements that can't be disabled before trusting the disabled property. - // Some victims get caught in our net (label, legend, menu, track), but it shouldn't - // even exist on them, let alone have a boolean value. - } else if ( "label" in elem ) { - return elem.disabled === disabled; - } - - // Remaining elements are neither :enabled nor :disabled - return false; - }; -} - -/** - * Returns a function to use in pseudos for positionals - * @param {Function} fn - */ -function createPositionalPseudo( fn ) { - return markFunction( function( argument ) { - argument = +argument; - return markFunction( function( seed, matches ) { - var j, - matchIndexes = fn( [], seed.length, argument ), - i = matchIndexes.length; - - // Match elements found at the specified indexes - while ( i-- ) { - if ( seed[ ( j = matchIndexes[ i ] ) ] ) { - seed[ j ] = !( matches[ j ] = seed[ j ] ); - } - } - } ); - } ); -} - -/** - * Checks a node for validity as a Sizzle context - * @param {Element|Object=} context - * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value - */ -function testContext( context ) { - return context && typeof context.getElementsByTagName !== "undefined" && context; -} - -// Expose support vars for convenience -support = Sizzle.support = {}; - -/** - * Detects XML nodes - * @param {Element|Object} elem An element or a document - * @returns {Boolean} True iff elem is a non-HTML XML node - */ -isXML = Sizzle.isXML = function( elem ) { - var namespace = elem.namespaceURI, - docElem = ( elem.ownerDocument || elem ).documentElement; - - // Support: IE <=8 - // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes - // https://bugs.jquery.com/ticket/4833 - return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); -}; - -/** - * Sets document-related variables once based on the current document - * @param {Element|Object} [doc] An element or document object to use to set the document - * @returns {Object} Returns the current document - */ -setDocument = Sizzle.setDocument = function( node ) { - var hasCompare, subWindow, - doc = node ? node.ownerDocument || node : preferredDoc; - - // Return early if doc is invalid or already selected - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { - return document; - } - - // Update global variables - document = doc; - docElem = document.documentElement; - documentIsHTML = !isXML( document ); - - // Support: IE 9 - 11+, Edge 12 - 18+ - // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( preferredDoc != document && - ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { - - // Support: IE 11, Edge - if ( subWindow.addEventListener ) { - subWindow.addEventListener( "unload", unloadHandler, false ); - - // Support: IE 9 - 10 only - } else if ( subWindow.attachEvent ) { - subWindow.attachEvent( "onunload", unloadHandler ); - } - } - - // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, - // Safari 4 - 5 only, Opera <=11.6 - 12.x only - // IE/Edge & older browsers don't support the :scope pseudo-class. - // Support: Safari 6.0 only - // Safari 6.0 supports :scope but it's an alias of :root there. - support.scope = assert( function( el ) { - docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); - return typeof el.querySelectorAll !== "undefined" && - !el.querySelectorAll( ":scope fieldset div" ).length; - } ); - - /* Attributes - ---------------------------------------------------------------------- */ - - // Support: IE<8 - // Verify that getAttribute really returns attributes and not properties - // (excepting IE8 booleans) - support.attributes = assert( function( el ) { - el.className = "i"; - return !el.getAttribute( "className" ); - } ); - - /* getElement(s)By* - ---------------------------------------------------------------------- */ - - // Check if getElementsByTagName("*") returns only elements - support.getElementsByTagName = assert( function( el ) { - el.appendChild( document.createComment( "" ) ); - return !el.getElementsByTagName( "*" ).length; - } ); - - // Support: IE<9 - support.getElementsByClassName = rnative.test( document.getElementsByClassName ); - - // Support: IE<10 - // Check if getElementById returns elements by name - // The broken getElementById methods don't pick up programmatically-set names, - // so use a roundabout getElementsByName test - support.getById = assert( function( el ) { - docElem.appendChild( el ).id = expando; - return !document.getElementsByName || !document.getElementsByName( expando ).length; - } ); - - // ID filter and find - if ( support.getById ) { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - return elem.getAttribute( "id" ) === attrId; - }; - }; - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var elem = context.getElementById( id ); - return elem ? [ elem ] : []; - } - }; - } else { - Expr.filter[ "ID" ] = function( id ) { - var attrId = id.replace( runescape, funescape ); - return function( elem ) { - var node = typeof elem.getAttributeNode !== "undefined" && - elem.getAttributeNode( "id" ); - return node && node.value === attrId; - }; - }; - - // Support: IE 6 - 7 only - // getElementById is not reliable as a find shortcut - Expr.find[ "ID" ] = function( id, context ) { - if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { - var node, i, elems, - elem = context.getElementById( id ); - - if ( elem ) { - - // Verify the id attribute - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - - // Fall back on getElementsByName - elems = context.getElementsByName( id ); - i = 0; - while ( ( elem = elems[ i++ ] ) ) { - node = elem.getAttributeNode( "id" ); - if ( node && node.value === id ) { - return [ elem ]; - } - } - } - - return []; - } - }; - } - - // Tag - Expr.find[ "TAG" ] = support.getElementsByTagName ? - function( tag, context ) { - if ( typeof context.getElementsByTagName !== "undefined" ) { - return context.getElementsByTagName( tag ); - - // DocumentFragment nodes don't have gEBTN - } else if ( support.qsa ) { - return context.querySelectorAll( tag ); - } - } : - - function( tag, context ) { - var elem, - tmp = [], - i = 0, - - // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too - results = context.getElementsByTagName( tag ); - - // Filter out possible comments - if ( tag === "*" ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem.nodeType === 1 ) { - tmp.push( elem ); - } - } - - return tmp; - } - return results; - }; - - // Class - Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { - if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { - return context.getElementsByClassName( className ); - } - }; - - /* QSA/matchesSelector - ---------------------------------------------------------------------- */ - - // QSA and matchesSelector support - - // matchesSelector(:active) reports false when true (IE9/Opera 11.5) - rbuggyMatches = []; - - // qSa(:focus) reports false when true (Chrome 21) - // We allow this because of a bug in IE8/9 that throws an error - // whenever `document.activeElement` is accessed on an iframe - // So, we allow :focus to pass through QSA all the time to avoid the IE error - // See https://bugs.jquery.com/ticket/13378 - rbuggyQSA = []; - - if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { - - // Build QSA regex - // Regex strategy adopted from Diego Perini - assert( function( el ) { - - var input; - - // Select is set to empty string on purpose - // This is to test IE's treatment of not explicitly - // setting a boolean content attribute, - // since its presence should be enough - // https://bugs.jquery.com/ticket/12359 - docElem.appendChild( el ).innerHTML = "" + - ""; - - // Support: IE8, Opera 11-12.16 - // Nothing should be selected when empty strings follow ^= or $= or *= - // The test attribute must be unknown in Opera but "safe" for WinRT - // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section - if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { - rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); - } - - // Support: IE8 - // Boolean attributes and "value" are not treated correctly - if ( !el.querySelectorAll( "[selected]" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); - } - - // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ - if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { - rbuggyQSA.push( "~=" ); - } - - // Support: IE 11+, Edge 15 - 18+ - // IE 11/Edge don't find elements on a `[name='']` query in some cases. - // Adding a temporary attribute to the document before the selection works - // around the issue. - // Interestingly, IE 10 & older don't seem to have the issue. - input = document.createElement( "input" ); - input.setAttribute( "name", "" ); - el.appendChild( input ); - if ( !el.querySelectorAll( "[name='']" ).length ) { - rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + - whitespace + "*(?:''|\"\")" ); - } - - // Webkit/Opera - :checked should return selected option elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - // IE8 throws error here and will not see later tests - if ( !el.querySelectorAll( ":checked" ).length ) { - rbuggyQSA.push( ":checked" ); - } - - // Support: Safari 8+, iOS 8+ - // https://bugs.webkit.org/show_bug.cgi?id=136851 - // In-page `selector#id sibling-combinator selector` fails - if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { - rbuggyQSA.push( ".#.+[+~]" ); - } - - // Support: Firefox <=3.6 - 5 only - // Old Firefox doesn't throw on a badly-escaped identifier. - el.querySelectorAll( "\\\f" ); - rbuggyQSA.push( "[\\r\\n\\f]" ); - } ); - - assert( function( el ) { - el.innerHTML = "" + - ""; - - // Support: Windows 8 Native Apps - // The type and name attributes are restricted during .innerHTML assignment - var input = document.createElement( "input" ); - input.setAttribute( "type", "hidden" ); - el.appendChild( input ).setAttribute( "name", "D" ); - - // Support: IE8 - // Enforce case-sensitivity of name attribute - if ( el.querySelectorAll( "[name=d]" ).length ) { - rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); - } - - // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) - // IE8 throws error here and will not see later tests - if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: IE9-11+ - // IE's :disabled selector does not pick up the children of disabled fieldsets - docElem.appendChild( el ).disabled = true; - if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { - rbuggyQSA.push( ":enabled", ":disabled" ); - } - - // Support: Opera 10 - 11 only - // Opera 10-11 does not throw on post-comma invalid pseudos - el.querySelectorAll( "*,:x" ); - rbuggyQSA.push( ",.*:" ); - } ); - } - - if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || - docElem.webkitMatchesSelector || - docElem.mozMatchesSelector || - docElem.oMatchesSelector || - docElem.msMatchesSelector ) ) ) ) { - - assert( function( el ) { - - // Check to see if it's possible to do matchesSelector - // on a disconnected node (IE 9) - support.disconnectedMatch = matches.call( el, "*" ); - - // This should fail with an exception - // Gecko does not error, returns false instead - matches.call( el, "[s!='']:x" ); - rbuggyMatches.push( "!=", pseudos ); - } ); - } - - rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); - rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); - - /* Contains - ---------------------------------------------------------------------- */ - hasCompare = rnative.test( docElem.compareDocumentPosition ); - - // Element contains another - // Purposefully self-exclusive - // As in, an element does not contain itself - contains = hasCompare || rnative.test( docElem.contains ) ? - function( a, b ) { - var adown = a.nodeType === 9 ? a.documentElement : a, - bup = b && b.parentNode; - return a === bup || !!( bup && bup.nodeType === 1 && ( - adown.contains ? - adown.contains( bup ) : - a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 - ) ); - } : - function( a, b ) { - if ( b ) { - while ( ( b = b.parentNode ) ) { - if ( b === a ) { - return true; - } - } - } - return false; - }; - - /* Sorting - ---------------------------------------------------------------------- */ - - // Document order sorting - sortOrder = hasCompare ? - function( a, b ) { - - // Flag for duplicate removal - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - // Sort on method existence if only one input has compareDocumentPosition - var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; - if ( compare ) { - return compare; - } - - // Calculate position if both inputs belong to the same document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? - a.compareDocumentPosition( b ) : - - // Otherwise we know they are disconnected - 1; - - // Disconnected nodes - if ( compare & 1 || - ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { - - // Choose the first element that is related to our preferred document - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( a == document || a.ownerDocument == preferredDoc && - contains( preferredDoc, a ) ) { - return -1; - } - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( b == document || b.ownerDocument == preferredDoc && - contains( preferredDoc, b ) ) { - return 1; - } - - // Maintain original order - return sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - } - - return compare & 4 ? -1 : 1; - } : - function( a, b ) { - - // Exit early if the nodes are identical - if ( a === b ) { - hasDuplicate = true; - return 0; - } - - var cur, - i = 0, - aup = a.parentNode, - bup = b.parentNode, - ap = [ a ], - bp = [ b ]; - - // Parentless nodes are either documents or disconnected - if ( !aup || !bup ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - return a == document ? -1 : - b == document ? 1 : - /* eslint-enable eqeqeq */ - aup ? -1 : - bup ? 1 : - sortInput ? - ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : - 0; - - // If the nodes are siblings, we can do a quick check - } else if ( aup === bup ) { - return siblingCheck( a, b ); - } - - // Otherwise we need full lists of their ancestors for comparison - cur = a; - while ( ( cur = cur.parentNode ) ) { - ap.unshift( cur ); - } - cur = b; - while ( ( cur = cur.parentNode ) ) { - bp.unshift( cur ); - } - - // Walk down the tree looking for a discrepancy - while ( ap[ i ] === bp[ i ] ) { - i++; - } - - return i ? - - // Do a sibling check if the nodes have a common ancestor - siblingCheck( ap[ i ], bp[ i ] ) : - - // Otherwise nodes in our document sort first - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - /* eslint-disable eqeqeq */ - ap[ i ] == preferredDoc ? -1 : - bp[ i ] == preferredDoc ? 1 : - /* eslint-enable eqeqeq */ - 0; - }; - - return document; -}; - -Sizzle.matches = function( expr, elements ) { - return Sizzle( expr, null, null, elements ); -}; - -Sizzle.matchesSelector = function( elem, expr ) { - setDocument( elem ); - - if ( support.matchesSelector && documentIsHTML && - !nonnativeSelectorCache[ expr + " " ] && - ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && - ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { - - try { - var ret = matches.call( elem, expr ); - - // IE 9's matchesSelector returns false on disconnected nodes - if ( ret || support.disconnectedMatch || - - // As well, disconnected nodes are said to be in a document - // fragment in IE 9 - elem.document && elem.document.nodeType !== 11 ) { - return ret; - } - } catch ( e ) { - nonnativeSelectorCache( expr, true ); - } - } - - return Sizzle( expr, document, null, [ elem ] ).length > 0; -}; - -Sizzle.contains = function( context, elem ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( context.ownerDocument || context ) != document ) { - setDocument( context ); - } - return contains( context, elem ); -}; - -Sizzle.attr = function( elem, name ) { - - // Set document vars if needed - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( ( elem.ownerDocument || elem ) != document ) { - setDocument( elem ); - } - - var fn = Expr.attrHandle[ name.toLowerCase() ], - - // Don't get fooled by Object.prototype properties (jQuery #13807) - val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? - fn( elem, name, !documentIsHTML ) : - undefined; - - return val !== undefined ? - val : - support.attributes || !documentIsHTML ? - elem.getAttribute( name ) : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; -}; - -Sizzle.escape = function( sel ) { - return ( sel + "" ).replace( rcssescape, fcssescape ); -}; - -Sizzle.error = function( msg ) { - throw new Error( "Syntax error, unrecognized expression: " + msg ); -}; - -/** - * Document sorting and removing duplicates - * @param {ArrayLike} results - */ -Sizzle.uniqueSort = function( results ) { - var elem, - duplicates = [], - j = 0, - i = 0; - - // Unless we *know* we can detect duplicates, assume their presence - hasDuplicate = !support.detectDuplicates; - sortInput = !support.sortStable && results.slice( 0 ); - results.sort( sortOrder ); - - if ( hasDuplicate ) { - while ( ( elem = results[ i++ ] ) ) { - if ( elem === results[ i ] ) { - j = duplicates.push( i ); - } - } - while ( j-- ) { - results.splice( duplicates[ j ], 1 ); - } - } - - // Clear input after sorting to release objects - // See https://github.com/jquery/sizzle/pull/225 - sortInput = null; - - return results; -}; - -/** - * Utility function for retrieving the text value of an array of DOM nodes - * @param {Array|Element} elem - */ -getText = Sizzle.getText = function( elem ) { - var node, - ret = "", - i = 0, - nodeType = elem.nodeType; - - if ( !nodeType ) { - - // If no nodeType, this is expected to be an array - while ( ( node = elem[ i++ ] ) ) { - - // Do not traverse comment nodes - ret += getText( node ); - } - } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { - - // Use textContent for elements - // innerText usage removed for consistency of new lines (jQuery #11153) - if ( typeof elem.textContent === "string" ) { - return elem.textContent; - } else { - - // Traverse its children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - ret += getText( elem ); - } - } - } else if ( nodeType === 3 || nodeType === 4 ) { - return elem.nodeValue; - } - - // Do not include comment or processing instruction nodes - - return ret; -}; - -Expr = Sizzle.selectors = { - - // Can be adjusted by the user - cacheLength: 50, - - createPseudo: markFunction, - - match: matchExpr, - - attrHandle: {}, - - find: {}, - - relative: { - ">": { dir: "parentNode", first: true }, - " ": { dir: "parentNode" }, - "+": { dir: "previousSibling", first: true }, - "~": { dir: "previousSibling" } - }, - - preFilter: { - "ATTR": function( match ) { - match[ 1 ] = match[ 1 ].replace( runescape, funescape ); - - // Move the given value to match[3] whether quoted or unquoted - match[ 3 ] = ( match[ 3 ] || match[ 4 ] || - match[ 5 ] || "" ).replace( runescape, funescape ); - - if ( match[ 2 ] === "~=" ) { - match[ 3 ] = " " + match[ 3 ] + " "; - } - - return match.slice( 0, 4 ); - }, - - "CHILD": function( match ) { - - /* matches from matchExpr["CHILD"] - 1 type (only|nth|...) - 2 what (child|of-type) - 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) - 4 xn-component of xn+y argument ([+-]?\d*n|) - 5 sign of xn-component - 6 x of xn-component - 7 sign of y-component - 8 y of y-component - */ - match[ 1 ] = match[ 1 ].toLowerCase(); - - if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { - - // nth-* requires argument - if ( !match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - // numeric x and y parameters for Expr.filter.CHILD - // remember that false/true cast respectively to 0/1 - match[ 4 ] = +( match[ 4 ] ? - match[ 5 ] + ( match[ 6 ] || 1 ) : - 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); - match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); - - // other types prohibit arguments - } else if ( match[ 3 ] ) { - Sizzle.error( match[ 0 ] ); - } - - return match; - }, - - "PSEUDO": function( match ) { - var excess, - unquoted = !match[ 6 ] && match[ 2 ]; - - if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { - return null; - } - - // Accept quoted arguments as-is - if ( match[ 3 ] ) { - match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; - - // Strip excess characters from unquoted arguments - } else if ( unquoted && rpseudo.test( unquoted ) && - - // Get excess from tokenize (recursively) - ( excess = tokenize( unquoted, true ) ) && - - // advance to the next closing parenthesis - ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { - - // excess is a negative index - match[ 0 ] = match[ 0 ].slice( 0, excess ); - match[ 2 ] = unquoted.slice( 0, excess ); - } - - // Return only captures needed by the pseudo filter method (type and argument) - return match.slice( 0, 3 ); - } - }, - - filter: { - - "TAG": function( nodeNameSelector ) { - var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); - return nodeNameSelector === "*" ? - function() { - return true; - } : - function( elem ) { - return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; - }; - }, - - "CLASS": function( className ) { - var pattern = classCache[ className + " " ]; - - return pattern || - ( pattern = new RegExp( "(^|" + whitespace + - ")" + className + "(" + whitespace + "|$)" ) ) && classCache( - className, function( elem ) { - return pattern.test( - typeof elem.className === "string" && elem.className || - typeof elem.getAttribute !== "undefined" && - elem.getAttribute( "class" ) || - "" - ); - } ); - }, - - "ATTR": function( name, operator, check ) { - return function( elem ) { - var result = Sizzle.attr( elem, name ); - - if ( result == null ) { - return operator === "!="; - } - if ( !operator ) { - return true; - } - - result += ""; - - /* eslint-disable max-len */ - - return operator === "=" ? result === check : - operator === "!=" ? result !== check : - operator === "^=" ? check && result.indexOf( check ) === 0 : - operator === "*=" ? check && result.indexOf( check ) > -1 : - operator === "$=" ? check && result.slice( -check.length ) === check : - operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : - operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : - false; - /* eslint-enable max-len */ - - }; - }, - - "CHILD": function( type, what, _argument, first, last ) { - var simple = type.slice( 0, 3 ) !== "nth", - forward = type.slice( -4 ) !== "last", - ofType = what === "of-type"; - - return first === 1 && last === 0 ? - - // Shortcut for :nth-*(n) - function( elem ) { - return !!elem.parentNode; - } : - - function( elem, _context, xml ) { - var cache, uniqueCache, outerCache, node, nodeIndex, start, - dir = simple !== forward ? "nextSibling" : "previousSibling", - parent = elem.parentNode, - name = ofType && elem.nodeName.toLowerCase(), - useCache = !xml && !ofType, - diff = false; - - if ( parent ) { - - // :(first|last|only)-(child|of-type) - if ( simple ) { - while ( dir ) { - node = elem; - while ( ( node = node[ dir ] ) ) { - if ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) { - - return false; - } - } - - // Reverse direction for :only-* (if we haven't yet done so) - start = dir = type === "only" && !start && "nextSibling"; - } - return true; - } - - start = [ forward ? parent.firstChild : parent.lastChild ]; - - // non-xml :nth-child(...) stores cache data on `parent` - if ( forward && useCache ) { - - // Seek `elem` from a previously-cached index - - // ...in a gzip-friendly way - node = parent; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex && cache[ 2 ]; - node = nodeIndex && parent.childNodes[ nodeIndex ]; - - while ( ( node = ++nodeIndex && node && node[ dir ] || - - // Fallback to seeking `elem` from the start - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - // When found, cache indexes on `parent` and break - if ( node.nodeType === 1 && ++diff && node === elem ) { - uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; - break; - } - } - - } else { - - // Use previously-cached element index if available - if ( useCache ) { - - // ...in a gzip-friendly way - node = elem; - outerCache = node[ expando ] || ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - cache = uniqueCache[ type ] || []; - nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; - diff = nodeIndex; - } - - // xml :nth-child(...) - // or :nth-last-child(...) or :nth(-last)?-of-type(...) - if ( diff === false ) { - - // Use the same loop as above to seek `elem` from the start - while ( ( node = ++nodeIndex && node && node[ dir ] || - ( diff = nodeIndex = 0 ) || start.pop() ) ) { - - if ( ( ofType ? - node.nodeName.toLowerCase() === name : - node.nodeType === 1 ) && - ++diff ) { - - // Cache the index of each encountered element - if ( useCache ) { - outerCache = node[ expando ] || - ( node[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ node.uniqueID ] || - ( outerCache[ node.uniqueID ] = {} ); - - uniqueCache[ type ] = [ dirruns, diff ]; - } - - if ( node === elem ) { - break; - } - } - } - } - } - - // Incorporate the offset, then check against cycle size - diff -= last; - return diff === first || ( diff % first === 0 && diff / first >= 0 ); - } - }; - }, - - "PSEUDO": function( pseudo, argument ) { - - // pseudo-class names are case-insensitive - // http://www.w3.org/TR/selectors/#pseudo-classes - // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters - // Remember that setFilters inherits from pseudos - var args, - fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || - Sizzle.error( "unsupported pseudo: " + pseudo ); - - // The user may use createPseudo to indicate that - // arguments are needed to create the filter function - // just as Sizzle does - if ( fn[ expando ] ) { - return fn( argument ); - } - - // But maintain support for old signatures - if ( fn.length > 1 ) { - args = [ pseudo, pseudo, "", argument ]; - return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? - markFunction( function( seed, matches ) { - var idx, - matched = fn( seed, argument ), - i = matched.length; - while ( i-- ) { - idx = indexOf( seed, matched[ i ] ); - seed[ idx ] = !( matches[ idx ] = matched[ i ] ); - } - } ) : - function( elem ) { - return fn( elem, 0, args ); - }; - } - - return fn; - } - }, - - pseudos: { - - // Potentially complex pseudos - "not": markFunction( function( selector ) { - - // Trim the selector passed to compile - // to avoid treating leading and trailing - // spaces as combinators - var input = [], - results = [], - matcher = compile( selector.replace( rtrim, "$1" ) ); - - return matcher[ expando ] ? - markFunction( function( seed, matches, _context, xml ) { - var elem, - unmatched = matcher( seed, null, xml, [] ), - i = seed.length; - - // Match elements unmatched by `matcher` - while ( i-- ) { - if ( ( elem = unmatched[ i ] ) ) { - seed[ i ] = !( matches[ i ] = elem ); - } - } - } ) : - function( elem, _context, xml ) { - input[ 0 ] = elem; - matcher( input, null, xml, results ); - - // Don't keep the element (issue #299) - input[ 0 ] = null; - return !results.pop(); - }; - } ), - - "has": markFunction( function( selector ) { - return function( elem ) { - return Sizzle( selector, elem ).length > 0; - }; - } ), - - "contains": markFunction( function( text ) { - text = text.replace( runescape, funescape ); - return function( elem ) { - return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; - }; - } ), - - // "Whether an element is represented by a :lang() selector - // is based solely on the element's language value - // being equal to the identifier C, - // or beginning with the identifier C immediately followed by "-". - // The matching of C against the element's language value is performed case-insensitively. - // The identifier C does not have to be a valid language name." - // http://www.w3.org/TR/selectors/#lang-pseudo - "lang": markFunction( function( lang ) { - - // lang value must be a valid identifier - if ( !ridentifier.test( lang || "" ) ) { - Sizzle.error( "unsupported lang: " + lang ); - } - lang = lang.replace( runescape, funescape ).toLowerCase(); - return function( elem ) { - var elemLang; - do { - if ( ( elemLang = documentIsHTML ? - elem.lang : - elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { - - elemLang = elemLang.toLowerCase(); - return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; - } - } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); - return false; - }; - } ), - - // Miscellaneous - "target": function( elem ) { - var hash = window.location && window.location.hash; - return hash && hash.slice( 1 ) === elem.id; - }, - - "root": function( elem ) { - return elem === docElem; - }, - - "focus": function( elem ) { - return elem === document.activeElement && - ( !document.hasFocus || document.hasFocus() ) && - !!( elem.type || elem.href || ~elem.tabIndex ); - }, - - // Boolean properties - "enabled": createDisabledPseudo( false ), - "disabled": createDisabledPseudo( true ), - - "checked": function( elem ) { - - // In CSS3, :checked should return both checked and selected elements - // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked - var nodeName = elem.nodeName.toLowerCase(); - return ( nodeName === "input" && !!elem.checked ) || - ( nodeName === "option" && !!elem.selected ); - }, - - "selected": function( elem ) { - - // Accessing this property makes selected-by-default - // options in Safari work properly - if ( elem.parentNode ) { - // eslint-disable-next-line no-unused-expressions - elem.parentNode.selectedIndex; - } - - return elem.selected === true; - }, - - // Contents - "empty": function( elem ) { - - // http://www.w3.org/TR/selectors/#empty-pseudo - // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), - // but not by others (comment: 8; processing instruction: 7; etc.) - // nodeType < 6 works because attributes (2) do not appear as children - for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { - if ( elem.nodeType < 6 ) { - return false; - } - } - return true; - }, - - "parent": function( elem ) { - return !Expr.pseudos[ "empty" ]( elem ); - }, - - // Element/input types - "header": function( elem ) { - return rheader.test( elem.nodeName ); - }, - - "input": function( elem ) { - return rinputs.test( elem.nodeName ); - }, - - "button": function( elem ) { - var name = elem.nodeName.toLowerCase(); - return name === "input" && elem.type === "button" || name === "button"; - }, - - "text": function( elem ) { - var attr; - return elem.nodeName.toLowerCase() === "input" && - elem.type === "text" && - - // Support: IE<8 - // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" - ( ( attr = elem.getAttribute( "type" ) ) == null || - attr.toLowerCase() === "text" ); - }, - - // Position-in-collection - "first": createPositionalPseudo( function() { - return [ 0 ]; - } ), - - "last": createPositionalPseudo( function( _matchIndexes, length ) { - return [ length - 1 ]; - } ), - - "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { - return [ argument < 0 ? argument + length : argument ]; - } ), - - "even": createPositionalPseudo( function( matchIndexes, length ) { - var i = 0; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "odd": createPositionalPseudo( function( matchIndexes, length ) { - var i = 1; - for ( ; i < length; i += 2 ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? - argument + length : - argument > length ? - length : - argument; - for ( ; --i >= 0; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ), - - "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { - var i = argument < 0 ? argument + length : argument; - for ( ; ++i < length; ) { - matchIndexes.push( i ); - } - return matchIndexes; - } ) - } -}; - -Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; - -// Add button/input type pseudos -for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { - Expr.pseudos[ i ] = createInputPseudo( i ); -} -for ( i in { submit: true, reset: true } ) { - Expr.pseudos[ i ] = createButtonPseudo( i ); -} - -// Easy API for creating new setFilters -function setFilters() {} -setFilters.prototype = Expr.filters = Expr.pseudos; -Expr.setFilters = new setFilters(); - -tokenize = Sizzle.tokenize = function( selector, parseOnly ) { - var matched, match, tokens, type, - soFar, groups, preFilters, - cached = tokenCache[ selector + " " ]; - - if ( cached ) { - return parseOnly ? 0 : cached.slice( 0 ); - } - - soFar = selector; - groups = []; - preFilters = Expr.preFilter; - - while ( soFar ) { - - // Comma and first run - if ( !matched || ( match = rcomma.exec( soFar ) ) ) { - if ( match ) { - - // Don't consume trailing commas as valid - soFar = soFar.slice( match[ 0 ].length ) || soFar; - } - groups.push( ( tokens = [] ) ); - } - - matched = false; - - // Combinators - if ( ( match = rcombinators.exec( soFar ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - - // Cast descendant combinators to space - type: match[ 0 ].replace( rtrim, " " ) - } ); - soFar = soFar.slice( matched.length ); - } - - // Filters - for ( type in Expr.filter ) { - if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || - ( match = preFilters[ type ]( match ) ) ) ) { - matched = match.shift(); - tokens.push( { - value: matched, - type: type, - matches: match - } ); - soFar = soFar.slice( matched.length ); - } - } - - if ( !matched ) { - break; - } - } - - // Return the length of the invalid excess - // if we're just parsing - // Otherwise, throw an error or return tokens - return parseOnly ? - soFar.length : - soFar ? - Sizzle.error( selector ) : - - // Cache the tokens - tokenCache( selector, groups ).slice( 0 ); -}; - -function toSelector( tokens ) { - var i = 0, - len = tokens.length, - selector = ""; - for ( ; i < len; i++ ) { - selector += tokens[ i ].value; - } - return selector; -} - -function addCombinator( matcher, combinator, base ) { - var dir = combinator.dir, - skip = combinator.next, - key = skip || dir, - checkNonElements = base && key === "parentNode", - doneName = done++; - - return combinator.first ? - - // Check against closest ancestor/preceding element - function( elem, context, xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - return matcher( elem, context, xml ); - } - } - return false; - } : - - // Check against all ancestor/preceding elements - function( elem, context, xml ) { - var oldCache, uniqueCache, outerCache, - newCache = [ dirruns, doneName ]; - - // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching - if ( xml ) { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - if ( matcher( elem, context, xml ) ) { - return true; - } - } - } - } else { - while ( ( elem = elem[ dir ] ) ) { - if ( elem.nodeType === 1 || checkNonElements ) { - outerCache = elem[ expando ] || ( elem[ expando ] = {} ); - - // Support: IE <9 only - // Defend against cloned attroperties (jQuery gh-1709) - uniqueCache = outerCache[ elem.uniqueID ] || - ( outerCache[ elem.uniqueID ] = {} ); - - if ( skip && skip === elem.nodeName.toLowerCase() ) { - elem = elem[ dir ] || elem; - } else if ( ( oldCache = uniqueCache[ key ] ) && - oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { - - // Assign to newCache so results back-propagate to previous elements - return ( newCache[ 2 ] = oldCache[ 2 ] ); - } else { - - // Reuse newcache so results back-propagate to previous elements - uniqueCache[ key ] = newCache; - - // A match means we're done; a fail means we have to keep checking - if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { - return true; - } - } - } - } - } - return false; - }; -} - -function elementMatcher( matchers ) { - return matchers.length > 1 ? - function( elem, context, xml ) { - var i = matchers.length; - while ( i-- ) { - if ( !matchers[ i ]( elem, context, xml ) ) { - return false; - } - } - return true; - } : - matchers[ 0 ]; -} - -function multipleContexts( selector, contexts, results ) { - var i = 0, - len = contexts.length; - for ( ; i < len; i++ ) { - Sizzle( selector, contexts[ i ], results ); - } - return results; -} - -function condense( unmatched, map, filter, context, xml ) { - var elem, - newUnmatched = [], - i = 0, - len = unmatched.length, - mapped = map != null; - - for ( ; i < len; i++ ) { - if ( ( elem = unmatched[ i ] ) ) { - if ( !filter || filter( elem, context, xml ) ) { - newUnmatched.push( elem ); - if ( mapped ) { - map.push( i ); - } - } - } - } - - return newUnmatched; -} - -function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { - if ( postFilter && !postFilter[ expando ] ) { - postFilter = setMatcher( postFilter ); - } - if ( postFinder && !postFinder[ expando ] ) { - postFinder = setMatcher( postFinder, postSelector ); - } - return markFunction( function( seed, results, context, xml ) { - var temp, i, elem, - preMap = [], - postMap = [], - preexisting = results.length, - - // Get initial elements from seed or context - elems = seed || multipleContexts( - selector || "*", - context.nodeType ? [ context ] : context, - [] - ), - - // Prefilter to get matcher input, preserving a map for seed-results synchronization - matcherIn = preFilter && ( seed || !selector ) ? - condense( elems, preMap, preFilter, context, xml ) : - elems, - - matcherOut = matcher ? - - // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, - postFinder || ( seed ? preFilter : preexisting || postFilter ) ? - - // ...intermediate processing is necessary - [] : - - // ...otherwise use results directly - results : - matcherIn; - - // Find primary matches - if ( matcher ) { - matcher( matcherIn, matcherOut, context, xml ); - } - - // Apply postFilter - if ( postFilter ) { - temp = condense( matcherOut, postMap ); - postFilter( temp, [], context, xml ); - - // Un-match failing elements by moving them back to matcherIn - i = temp.length; - while ( i-- ) { - if ( ( elem = temp[ i ] ) ) { - matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); - } - } - } - - if ( seed ) { - if ( postFinder || preFilter ) { - if ( postFinder ) { - - // Get the final matcherOut by condensing this intermediate into postFinder contexts - temp = []; - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) ) { - - // Restore matcherIn since elem is not yet a final match - temp.push( ( matcherIn[ i ] = elem ) ); - } - } - postFinder( null, ( matcherOut = [] ), temp, xml ); - } - - // Move matched elements from seed to results to keep them synchronized - i = matcherOut.length; - while ( i-- ) { - if ( ( elem = matcherOut[ i ] ) && - ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { - - seed[ temp ] = !( results[ temp ] = elem ); - } - } - } - - // Add elements to results, through postFinder if defined - } else { - matcherOut = condense( - matcherOut === results ? - matcherOut.splice( preexisting, matcherOut.length ) : - matcherOut - ); - if ( postFinder ) { - postFinder( null, results, matcherOut, xml ); - } else { - push.apply( results, matcherOut ); - } - } - } ); -} - -function matcherFromTokens( tokens ) { - var checkContext, matcher, j, - len = tokens.length, - leadingRelative = Expr.relative[ tokens[ 0 ].type ], - implicitRelative = leadingRelative || Expr.relative[ " " ], - i = leadingRelative ? 1 : 0, - - // The foundational matcher ensures that elements are reachable from top-level context(s) - matchContext = addCombinator( function( elem ) { - return elem === checkContext; - }, implicitRelative, true ), - matchAnyContext = addCombinator( function( elem ) { - return indexOf( checkContext, elem ) > -1; - }, implicitRelative, true ), - matchers = [ function( elem, context, xml ) { - var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( - ( checkContext = context ).nodeType ? - matchContext( elem, context, xml ) : - matchAnyContext( elem, context, xml ) ); - - // Avoid hanging onto element (issue #299) - checkContext = null; - return ret; - } ]; - - for ( ; i < len; i++ ) { - if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { - matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; - } else { - matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); - - // Return special upon seeing a positional matcher - if ( matcher[ expando ] ) { - - // Find the next relative operator (if any) for proper handling - j = ++i; - for ( ; j < len; j++ ) { - if ( Expr.relative[ tokens[ j ].type ] ) { - break; - } - } - return setMatcher( - i > 1 && elementMatcher( matchers ), - i > 1 && toSelector( - - // If the preceding token was a descendant combinator, insert an implicit any-element `*` - tokens - .slice( 0, i - 1 ) - .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) - ).replace( rtrim, "$1" ), - matcher, - i < j && matcherFromTokens( tokens.slice( i, j ) ), - j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), - j < len && toSelector( tokens ) - ); - } - matchers.push( matcher ); - } - } - - return elementMatcher( matchers ); -} - -function matcherFromGroupMatchers( elementMatchers, setMatchers ) { - var bySet = setMatchers.length > 0, - byElement = elementMatchers.length > 0, - superMatcher = function( seed, context, xml, results, outermost ) { - var elem, j, matcher, - matchedCount = 0, - i = "0", - unmatched = seed && [], - setMatched = [], - contextBackup = outermostContext, - - // We must always have either seed elements or outermost context - elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), - - // Use integer dirruns iff this is the outermost matcher - dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), - len = elems.length; - - if ( outermost ) { - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - outermostContext = context == document || context || outermost; - } - - // Add elements passing elementMatchers directly to results - // Support: IE<9, Safari - // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id - for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { - if ( byElement && elem ) { - j = 0; - - // Support: IE 11+, Edge 17 - 18+ - // IE/Edge sometimes throw a "Permission denied" error when strict-comparing - // two documents; shallow comparisons work. - // eslint-disable-next-line eqeqeq - if ( !context && elem.ownerDocument != document ) { - setDocument( elem ); - xml = !documentIsHTML; - } - while ( ( matcher = elementMatchers[ j++ ] ) ) { - if ( matcher( elem, context || document, xml ) ) { - results.push( elem ); - break; - } - } - if ( outermost ) { - dirruns = dirrunsUnique; - } - } - - // Track unmatched elements for set filters - if ( bySet ) { - - // They will have gone through all possible matchers - if ( ( elem = !matcher && elem ) ) { - matchedCount--; - } - - // Lengthen the array for every element, matched or not - if ( seed ) { - unmatched.push( elem ); - } - } - } - - // `i` is now the count of elements visited above, and adding it to `matchedCount` - // makes the latter nonnegative. - matchedCount += i; - - // Apply set filters to unmatched elements - // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` - // equals `i`), unless we didn't visit _any_ elements in the above loop because we have - // no element matchers and no seed. - // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that - // case, which will result in a "00" `matchedCount` that differs from `i` but is also - // numerically zero. - if ( bySet && i !== matchedCount ) { - j = 0; - while ( ( matcher = setMatchers[ j++ ] ) ) { - matcher( unmatched, setMatched, context, xml ); - } - - if ( seed ) { - - // Reintegrate element matches to eliminate the need for sorting - if ( matchedCount > 0 ) { - while ( i-- ) { - if ( !( unmatched[ i ] || setMatched[ i ] ) ) { - setMatched[ i ] = pop.call( results ); - } - } - } - - // Discard index placeholder values to get only actual matches - setMatched = condense( setMatched ); - } - - // Add matches to results - push.apply( results, setMatched ); - - // Seedless set matches succeeding multiple successful matchers stipulate sorting - if ( outermost && !seed && setMatched.length > 0 && - ( matchedCount + setMatchers.length ) > 1 ) { - - Sizzle.uniqueSort( results ); - } - } - - // Override manipulation of globals by nested matchers - if ( outermost ) { - dirruns = dirrunsUnique; - outermostContext = contextBackup; - } - - return unmatched; - }; - - return bySet ? - markFunction( superMatcher ) : - superMatcher; -} - -compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { - var i, - setMatchers = [], - elementMatchers = [], - cached = compilerCache[ selector + " " ]; - - if ( !cached ) { - - // Generate a function of recursive functions that can be used to check each element - if ( !match ) { - match = tokenize( selector ); - } - i = match.length; - while ( i-- ) { - cached = matcherFromTokens( match[ i ] ); - if ( cached[ expando ] ) { - setMatchers.push( cached ); - } else { - elementMatchers.push( cached ); - } - } - - // Cache the compiled function - cached = compilerCache( - selector, - matcherFromGroupMatchers( elementMatchers, setMatchers ) - ); - - // Save selector and tokenization - cached.selector = selector; - } - return cached; -}; - -/** - * A low-level selection function that works with Sizzle's compiled - * selector functions - * @param {String|Function} selector A selector or a pre-compiled - * selector function built with Sizzle.compile - * @param {Element} context - * @param {Array} [results] - * @param {Array} [seed] A set of elements to match against - */ -select = Sizzle.select = function( selector, context, results, seed ) { - var i, tokens, token, type, find, - compiled = typeof selector === "function" && selector, - match = !seed && tokenize( ( selector = compiled.selector || selector ) ); - - results = results || []; - - // Try to minimize operations if there is only one selector in the list and no seed - // (the latter of which guarantees us context) - if ( match.length === 1 ) { - - // Reduce context if the leading compound selector is an ID - tokens = match[ 0 ] = match[ 0 ].slice( 0 ); - if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && - context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { - - context = ( Expr.find[ "ID" ]( token.matches[ 0 ] - .replace( runescape, funescape ), context ) || [] )[ 0 ]; - if ( !context ) { - return results; - - // Precompiled matchers will still verify ancestry, so step up a level - } else if ( compiled ) { - context = context.parentNode; - } - - selector = selector.slice( tokens.shift().value.length ); - } - - // Fetch a seed set for right-to-left matching - i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; - while ( i-- ) { - token = tokens[ i ]; - - // Abort if we hit a combinator - if ( Expr.relative[ ( type = token.type ) ] ) { - break; - } - if ( ( find = Expr.find[ type ] ) ) { - - // Search, expanding context for leading sibling combinators - if ( ( seed = find( - token.matches[ 0 ].replace( runescape, funescape ), - rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || - context - ) ) ) { - - // If seed is empty or no tokens remain, we can return early - tokens.splice( i, 1 ); - selector = seed.length && toSelector( tokens ); - if ( !selector ) { - push.apply( results, seed ); - return results; - } - - break; - } - } - } - } - - // Compile and execute a filtering function if one is not provided - // Provide `match` to avoid retokenization if we modified the selector above - ( compiled || compile( selector, match ) )( - seed, - context, - !documentIsHTML, - results, - !context || rsibling.test( selector ) && testContext( context.parentNode ) || context - ); - return results; -}; - -// One-time assignments - -// Sort stability -support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; - -// Support: Chrome 14-35+ -// Always assume duplicates if they aren't passed to the comparison function -support.detectDuplicates = !!hasDuplicate; - -// Initialize against the default document -setDocument(); - -// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) -// Detached nodes confoundingly follow *each other* -support.sortDetached = assert( function( el ) { - - // Should return 1, but returns 4 (following) - return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; -} ); - -// Support: IE<8 -// Prevent attribute/property "interpolation" -// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx -if ( !assert( function( el ) { - el.innerHTML = ""; - return el.firstChild.getAttribute( "href" ) === "#"; -} ) ) { - addHandle( "type|href|height|width", function( elem, name, isXML ) { - if ( !isXML ) { - return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); - } - } ); -} - -// Support: IE<9 -// Use defaultValue in place of getAttribute("value") -if ( !support.attributes || !assert( function( el ) { - el.innerHTML = ""; - el.firstChild.setAttribute( "value", "" ); - return el.firstChild.getAttribute( "value" ) === ""; -} ) ) { - addHandle( "value", function( elem, _name, isXML ) { - if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { - return elem.defaultValue; - } - } ); -} - -// Support: IE<9 -// Use getAttributeNode to fetch booleans when getAttribute lies -if ( !assert( function( el ) { - return el.getAttribute( "disabled" ) == null; -} ) ) { - addHandle( booleans, function( elem, name, isXML ) { - var val; - if ( !isXML ) { - return elem[ name ] === true ? name.toLowerCase() : - ( val = elem.getAttributeNode( name ) ) && val.specified ? - val.value : - null; - } - } ); -} - -return Sizzle; - -} )( window ); - - - -jQuery.find = Sizzle; -jQuery.expr = Sizzle.selectors; - -// Deprecated -jQuery.expr[ ":" ] = jQuery.expr.pseudos; -jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; -jQuery.text = Sizzle.getText; -jQuery.isXMLDoc = Sizzle.isXML; -jQuery.contains = Sizzle.contains; -jQuery.escapeSelector = Sizzle.escape; - - - - -var dir = function( elem, dir, until ) { - var matched = [], - truncate = until !== undefined; - - while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { - if ( elem.nodeType === 1 ) { - if ( truncate && jQuery( elem ).is( until ) ) { - break; - } - matched.push( elem ); - } - } - return matched; -}; - - -var siblings = function( n, elem ) { - var matched = []; - - for ( ; n; n = n.nextSibling ) { - if ( n.nodeType === 1 && n !== elem ) { - matched.push( n ); - } - } - - return matched; -}; - - -var rneedsContext = jQuery.expr.match.needsContext; - - - -function nodeName( elem, name ) { - - return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); - -}; -var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); - - - -// Implement the identical functionality for filter and not -function winnow( elements, qualifier, not ) { - if ( isFunction( qualifier ) ) { - return jQuery.grep( elements, function( elem, i ) { - return !!qualifier.call( elem, i, elem ) !== not; - } ); - } - - // Single element - if ( qualifier.nodeType ) { - return jQuery.grep( elements, function( elem ) { - return ( elem === qualifier ) !== not; - } ); - } - - // Arraylike of elements (jQuery, arguments, Array) - if ( typeof qualifier !== "string" ) { - return jQuery.grep( elements, function( elem ) { - return ( indexOf.call( qualifier, elem ) > -1 ) !== not; - } ); - } - - // Filtered directly for both simple and complex selectors - return jQuery.filter( qualifier, elements, not ); -} - -jQuery.filter = function( expr, elems, not ) { - var elem = elems[ 0 ]; - - if ( not ) { - expr = ":not(" + expr + ")"; - } - - if ( elems.length === 1 && elem.nodeType === 1 ) { - return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; - } - - return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { - return elem.nodeType === 1; - } ) ); -}; - -jQuery.fn.extend( { - find: function( selector ) { - var i, ret, - len = this.length, - self = this; - - if ( typeof selector !== "string" ) { - return this.pushStack( jQuery( selector ).filter( function() { - for ( i = 0; i < len; i++ ) { - if ( jQuery.contains( self[ i ], this ) ) { - return true; - } - } - } ) ); - } - - ret = this.pushStack( [] ); - - for ( i = 0; i < len; i++ ) { - jQuery.find( selector, self[ i ], ret ); - } - - return len > 1 ? jQuery.uniqueSort( ret ) : ret; - }, - filter: function( selector ) { - return this.pushStack( winnow( this, selector || [], false ) ); - }, - not: function( selector ) { - return this.pushStack( winnow( this, selector || [], true ) ); - }, - is: function( selector ) { - return !!winnow( - this, - - // If this is a positional/relative selector, check membership in the returned set - // so $("p:first").is("p:last") won't return true for a doc with two "p". - typeof selector === "string" && rneedsContext.test( selector ) ? - jQuery( selector ) : - selector || [], - false - ).length; - } -} ); - - -// Initialize a jQuery object - - -// A central reference to the root jQuery(document) -var rootjQuery, - - // A simple way to check for HTML strings - // Prioritize #id over to avoid XSS via location.hash (#9521) - // Strict HTML recognition (#11290: must start with <) - // Shortcut simple #id case for speed - rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, - - init = jQuery.fn.init = function( selector, context, root ) { - var match, elem; - - // HANDLE: $(""), $(null), $(undefined), $(false) - if ( !selector ) { - return this; - } - - // Method init() accepts an alternate rootjQuery - // so migrate can support jQuery.sub (gh-2101) - root = root || rootjQuery; - - // Handle HTML strings - if ( typeof selector === "string" ) { - if ( selector[ 0 ] === "<" && - selector[ selector.length - 1 ] === ">" && - selector.length >= 3 ) { - - // Assume that strings that start and end with <> are HTML and skip the regex check - match = [ null, selector, null ]; - - } else { - match = rquickExpr.exec( selector ); - } - - // Match html or make sure no context is specified for #id - if ( match && ( match[ 1 ] || !context ) ) { - - // HANDLE: $(html) -> $(array) - if ( match[ 1 ] ) { - context = context instanceof jQuery ? context[ 0 ] : context; - - // Option to run scripts is true for back-compat - // Intentionally let the error be thrown if parseHTML is not present - jQuery.merge( this, jQuery.parseHTML( - match[ 1 ], - context && context.nodeType ? context.ownerDocument || context : document, - true - ) ); - - // HANDLE: $(html, props) - if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { - for ( match in context ) { - - // Properties of context are called as methods if possible - if ( isFunction( this[ match ] ) ) { - this[ match ]( context[ match ] ); - - // ...and otherwise set as attributes - } else { - this.attr( match, context[ match ] ); - } - } - } - - return this; - - // HANDLE: $(#id) - } else { - elem = document.getElementById( match[ 2 ] ); - - if ( elem ) { - - // Inject the element directly into the jQuery object - this[ 0 ] = elem; - this.length = 1; - } - return this; - } - - // HANDLE: $(expr, $(...)) - } else if ( !context || context.jquery ) { - return ( context || root ).find( selector ); - - // HANDLE: $(expr, context) - // (which is just equivalent to: $(context).find(expr) - } else { - return this.constructor( context ).find( selector ); - } - - // HANDLE: $(DOMElement) - } else if ( selector.nodeType ) { - this[ 0 ] = selector; - this.length = 1; - return this; - - // HANDLE: $(function) - // Shortcut for document ready - } else if ( isFunction( selector ) ) { - return root.ready !== undefined ? - root.ready( selector ) : - - // Execute immediately if ready is not present - selector( jQuery ); - } - - return jQuery.makeArray( selector, this ); - }; - -// Give the init function the jQuery prototype for later instantiation -init.prototype = jQuery.fn; - -// Initialize central reference -rootjQuery = jQuery( document ); - - -var rparentsprev = /^(?:parents|prev(?:Until|All))/, - - // Methods guaranteed to produce a unique set when starting from a unique set - guaranteedUnique = { - children: true, - contents: true, - next: true, - prev: true - }; - -jQuery.fn.extend( { - has: function( target ) { - var targets = jQuery( target, this ), - l = targets.length; - - return this.filter( function() { - var i = 0; - for ( ; i < l; i++ ) { - if ( jQuery.contains( this, targets[ i ] ) ) { - return true; - } - } - } ); - }, - - closest: function( selectors, context ) { - var cur, - i = 0, - l = this.length, - matched = [], - targets = typeof selectors !== "string" && jQuery( selectors ); - - // Positional selectors never match, since there's no _selection_ context - if ( !rneedsContext.test( selectors ) ) { - for ( ; i < l; i++ ) { - for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { - - // Always skip document fragments - if ( cur.nodeType < 11 && ( targets ? - targets.index( cur ) > -1 : - - // Don't pass non-elements to Sizzle - cur.nodeType === 1 && - jQuery.find.matchesSelector( cur, selectors ) ) ) { - - matched.push( cur ); - break; - } - } - } - } - - return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); - }, - - // Determine the position of an element within the set - index: function( elem ) { - - // No argument, return index in parent - if ( !elem ) { - return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; - } - - // Index in selector - if ( typeof elem === "string" ) { - return indexOf.call( jQuery( elem ), this[ 0 ] ); - } - - // Locate the position of the desired element - return indexOf.call( this, - - // If it receives a jQuery object, the first element is used - elem.jquery ? elem[ 0 ] : elem - ); - }, - - add: function( selector, context ) { - return this.pushStack( - jQuery.uniqueSort( - jQuery.merge( this.get(), jQuery( selector, context ) ) - ) - ); - }, - - addBack: function( selector ) { - return this.add( selector == null ? - this.prevObject : this.prevObject.filter( selector ) - ); - } -} ); - -function sibling( cur, dir ) { - while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} - return cur; -} - -jQuery.each( { - parent: function( elem ) { - var parent = elem.parentNode; - return parent && parent.nodeType !== 11 ? parent : null; - }, - parents: function( elem ) { - return dir( elem, "parentNode" ); - }, - parentsUntil: function( elem, _i, until ) { - return dir( elem, "parentNode", until ); - }, - next: function( elem ) { - return sibling( elem, "nextSibling" ); - }, - prev: function( elem ) { - return sibling( elem, "previousSibling" ); - }, - nextAll: function( elem ) { - return dir( elem, "nextSibling" ); - }, - prevAll: function( elem ) { - return dir( elem, "previousSibling" ); - }, - nextUntil: function( elem, _i, until ) { - return dir( elem, "nextSibling", until ); - }, - prevUntil: function( elem, _i, until ) { - return dir( elem, "previousSibling", until ); - }, - siblings: function( elem ) { - return siblings( ( elem.parentNode || {} ).firstChild, elem ); - }, - children: function( elem ) { - return siblings( elem.firstChild ); - }, - contents: function( elem ) { - if ( elem.contentDocument != null && - - // Support: IE 11+ - // elements with no `data` attribute has an object - // `contentDocument` with a `null` prototype. - getProto( elem.contentDocument ) ) { - - return elem.contentDocument; - } - - // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only - // Treat the template element as a regular one in browsers that - // don't support it. - if ( nodeName( elem, "template" ) ) { - elem = elem.content || elem; - } - - return jQuery.merge( [], elem.childNodes ); - } -}, function( name, fn ) { - jQuery.fn[ name ] = function( until, selector ) { - var matched = jQuery.map( this, fn, until ); - - if ( name.slice( -5 ) !== "Until" ) { - selector = until; - } - - if ( selector && typeof selector === "string" ) { - matched = jQuery.filter( selector, matched ); - } - - if ( this.length > 1 ) { - - // Remove duplicates - if ( !guaranteedUnique[ name ] ) { - jQuery.uniqueSort( matched ); - } - - // Reverse order for parents* and prev-derivatives - if ( rparentsprev.test( name ) ) { - matched.reverse(); - } - } - - return this.pushStack( matched ); - }; -} ); -var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); - - - -// Convert String-formatted options into Object-formatted ones -function createOptions( options ) { - var object = {}; - jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { - object[ flag ] = true; - } ); - return object; -} - -/* - * Create a callback list using the following parameters: - * - * options: an optional list of space-separated options that will change how - * the callback list behaves or a more traditional option object - * - * By default a callback list will act like an event callback list and can be - * "fired" multiple times. - * - * Possible options: - * - * once: will ensure the callback list can only be fired once (like a Deferred) - * - * memory: will keep track of previous values and will call any callback added - * after the list has been fired right away with the latest "memorized" - * values (like a Deferred) - * - * unique: will ensure a callback can only be added once (no duplicate in the list) - * - * stopOnFalse: interrupt callings when a callback returns false - * - */ -jQuery.Callbacks = function( options ) { - - // Convert options from String-formatted to Object-formatted if needed - // (we check in cache first) - options = typeof options === "string" ? - createOptions( options ) : - jQuery.extend( {}, options ); - - var // Flag to know if list is currently firing - firing, - - // Last fire value for non-forgettable lists - memory, - - // Flag to know if list was already fired - fired, - - // Flag to prevent firing - locked, - - // Actual callback list - list = [], - - // Queue of execution data for repeatable lists - queue = [], - - // Index of currently firing callback (modified by add/remove as needed) - firingIndex = -1, - - // Fire callbacks - fire = function() { - - // Enforce single-firing - locked = locked || options.once; - - // Execute callbacks for all pending executions, - // respecting firingIndex overrides and runtime changes - fired = firing = true; - for ( ; queue.length; firingIndex = -1 ) { - memory = queue.shift(); - while ( ++firingIndex < list.length ) { - - // Run callback and check for early termination - if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && - options.stopOnFalse ) { - - // Jump to end and forget the data so .add doesn't re-fire - firingIndex = list.length; - memory = false; - } - } - } - - // Forget the data if we're done with it - if ( !options.memory ) { - memory = false; - } - - firing = false; - - // Clean up if we're done firing for good - if ( locked ) { - - // Keep an empty list if we have data for future add calls - if ( memory ) { - list = []; - - // Otherwise, this object is spent - } else { - list = ""; - } - } - }, - - // Actual Callbacks object - self = { - - // Add a callback or a collection of callbacks to the list - add: function() { - if ( list ) { - - // If we have memory from a past run, we should fire after adding - if ( memory && !firing ) { - firingIndex = list.length - 1; - queue.push( memory ); - } - - ( function add( args ) { - jQuery.each( args, function( _, arg ) { - if ( isFunction( arg ) ) { - if ( !options.unique || !self.has( arg ) ) { - list.push( arg ); - } - } else if ( arg && arg.length && toType( arg ) !== "string" ) { - - // Inspect recursively - add( arg ); - } - } ); - } )( arguments ); - - if ( memory && !firing ) { - fire(); - } - } - return this; - }, - - // Remove a callback from the list - remove: function() { - jQuery.each( arguments, function( _, arg ) { - var index; - while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { - list.splice( index, 1 ); - - // Handle firing indexes - if ( index <= firingIndex ) { - firingIndex--; - } - } - } ); - return this; - }, - - // Check if a given callback is in the list. - // If no argument is given, return whether or not list has callbacks attached. - has: function( fn ) { - return fn ? - jQuery.inArray( fn, list ) > -1 : - list.length > 0; - }, - - // Remove all callbacks from the list - empty: function() { - if ( list ) { - list = []; - } - return this; - }, - - // Disable .fire and .add - // Abort any current/pending executions - // Clear all callbacks and values - disable: function() { - locked = queue = []; - list = memory = ""; - return this; - }, - disabled: function() { - return !list; - }, - - // Disable .fire - // Also disable .add unless we have memory (since it would have no effect) - // Abort any pending executions - lock: function() { - locked = queue = []; - if ( !memory && !firing ) { - list = memory = ""; - } - return this; - }, - locked: function() { - return !!locked; - }, - - // Call all callbacks with the given context and arguments - fireWith: function( context, args ) { - if ( !locked ) { - args = args || []; - args = [ context, args.slice ? args.slice() : args ]; - queue.push( args ); - if ( !firing ) { - fire(); - } - } - return this; - }, - - // Call all the callbacks with the given arguments - fire: function() { - self.fireWith( this, arguments ); - return this; - }, - - // To know if the callbacks have already been called at least once - fired: function() { - return !!fired; - } - }; - - return self; -}; - - -function Identity( v ) { - return v; -} -function Thrower( ex ) { - throw ex; -} - -function adoptValue( value, resolve, reject, noValue ) { - var method; - - try { - - // Check for promise aspect first to privilege synchronous behavior - if ( value && isFunction( ( method = value.promise ) ) ) { - method.call( value ).done( resolve ).fail( reject ); - - // Other thenables - } else if ( value && isFunction( ( method = value.then ) ) ) { - method.call( value, resolve, reject ); - - // Other non-thenables - } else { - - // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: - // * false: [ value ].slice( 0 ) => resolve( value ) - // * true: [ value ].slice( 1 ) => resolve() - resolve.apply( undefined, [ value ].slice( noValue ) ); - } - - // For Promises/A+, convert exceptions into rejections - // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in - // Deferred#then to conditionally suppress rejection. - } catch ( value ) { - - // Support: Android 4.0 only - // Strict mode functions invoked without .call/.apply get global-object context - reject.apply( undefined, [ value ] ); - } -} - -jQuery.extend( { - - Deferred: function( func ) { - var tuples = [ - - // action, add listener, callbacks, - // ... .then handlers, argument index, [final state] - [ "notify", "progress", jQuery.Callbacks( "memory" ), - jQuery.Callbacks( "memory" ), 2 ], - [ "resolve", "done", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 0, "resolved" ], - [ "reject", "fail", jQuery.Callbacks( "once memory" ), - jQuery.Callbacks( "once memory" ), 1, "rejected" ] - ], - state = "pending", - promise = { - state: function() { - return state; - }, - always: function() { - deferred.done( arguments ).fail( arguments ); - return this; - }, - "catch": function( fn ) { - return promise.then( null, fn ); - }, - - // Keep pipe for back-compat - pipe: function( /* fnDone, fnFail, fnProgress */ ) { - var fns = arguments; - - return jQuery.Deferred( function( newDefer ) { - jQuery.each( tuples, function( _i, tuple ) { - - // Map tuples (progress, done, fail) to arguments (done, fail, progress) - var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; - - // deferred.progress(function() { bind to newDefer or newDefer.notify }) - // deferred.done(function() { bind to newDefer or newDefer.resolve }) - // deferred.fail(function() { bind to newDefer or newDefer.reject }) - deferred[ tuple[ 1 ] ]( function() { - var returned = fn && fn.apply( this, arguments ); - if ( returned && isFunction( returned.promise ) ) { - returned.promise() - .progress( newDefer.notify ) - .done( newDefer.resolve ) - .fail( newDefer.reject ); - } else { - newDefer[ tuple[ 0 ] + "With" ]( - this, - fn ? [ returned ] : arguments - ); - } - } ); - } ); - fns = null; - } ).promise(); - }, - then: function( onFulfilled, onRejected, onProgress ) { - var maxDepth = 0; - function resolve( depth, deferred, handler, special ) { - return function() { - var that = this, - args = arguments, - mightThrow = function() { - var returned, then; - - // Support: Promises/A+ section 2.3.3.3.3 - // https://promisesaplus.com/#point-59 - // Ignore double-resolution attempts - if ( depth < maxDepth ) { - return; - } - - returned = handler.apply( that, args ); - - // Support: Promises/A+ section 2.3.1 - // https://promisesaplus.com/#point-48 - if ( returned === deferred.promise() ) { - throw new TypeError( "Thenable self-resolution" ); - } - - // Support: Promises/A+ sections 2.3.3.1, 3.5 - // https://promisesaplus.com/#point-54 - // https://promisesaplus.com/#point-75 - // Retrieve `then` only once - then = returned && - - // Support: Promises/A+ section 2.3.4 - // https://promisesaplus.com/#point-64 - // Only check objects and functions for thenability - ( typeof returned === "object" || - typeof returned === "function" ) && - returned.then; - - // Handle a returned thenable - if ( isFunction( then ) ) { - - // Special processors (notify) just wait for resolution - if ( special ) { - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ) - ); - - // Normal processors (resolve) also hook into progress - } else { - - // ...and disregard older resolution values - maxDepth++; - - then.call( - returned, - resolve( maxDepth, deferred, Identity, special ), - resolve( maxDepth, deferred, Thrower, special ), - resolve( maxDepth, deferred, Identity, - deferred.notifyWith ) - ); - } - - // Handle all other returned values - } else { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Identity ) { - that = undefined; - args = [ returned ]; - } - - // Process the value(s) - // Default process is resolve - ( special || deferred.resolveWith )( that, args ); - } - }, - - // Only normal processors (resolve) catch and reject exceptions - process = special ? - mightThrow : - function() { - try { - mightThrow(); - } catch ( e ) { - - if ( jQuery.Deferred.exceptionHook ) { - jQuery.Deferred.exceptionHook( e, - process.stackTrace ); - } - - // Support: Promises/A+ section 2.3.3.3.4.1 - // https://promisesaplus.com/#point-61 - // Ignore post-resolution exceptions - if ( depth + 1 >= maxDepth ) { - - // Only substitute handlers pass on context - // and multiple values (non-spec behavior) - if ( handler !== Thrower ) { - that = undefined; - args = [ e ]; - } - - deferred.rejectWith( that, args ); - } - } - }; - - // Support: Promises/A+ section 2.3.3.3.1 - // https://promisesaplus.com/#point-57 - // Re-resolve promises immediately to dodge false rejection from - // subsequent errors - if ( depth ) { - process(); - } else { - - // Call an optional hook to record the stack, in case of exception - // since it's otherwise lost when execution goes async - if ( jQuery.Deferred.getStackHook ) { - process.stackTrace = jQuery.Deferred.getStackHook(); - } - window.setTimeout( process ); - } - }; - } - - return jQuery.Deferred( function( newDefer ) { - - // progress_handlers.add( ... ) - tuples[ 0 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onProgress ) ? - onProgress : - Identity, - newDefer.notifyWith - ) - ); - - // fulfilled_handlers.add( ... ) - tuples[ 1 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onFulfilled ) ? - onFulfilled : - Identity - ) - ); - - // rejected_handlers.add( ... ) - tuples[ 2 ][ 3 ].add( - resolve( - 0, - newDefer, - isFunction( onRejected ) ? - onRejected : - Thrower - ) - ); - } ).promise(); - }, - - // Get a promise for this deferred - // If obj is provided, the promise aspect is added to the object - promise: function( obj ) { - return obj != null ? jQuery.extend( obj, promise ) : promise; - } - }, - deferred = {}; - - // Add list-specific methods - jQuery.each( tuples, function( i, tuple ) { - var list = tuple[ 2 ], - stateString = tuple[ 5 ]; - - // promise.progress = list.add - // promise.done = list.add - // promise.fail = list.add - promise[ tuple[ 1 ] ] = list.add; - - // Handle state - if ( stateString ) { - list.add( - function() { - - // state = "resolved" (i.e., fulfilled) - // state = "rejected" - state = stateString; - }, - - // rejected_callbacks.disable - // fulfilled_callbacks.disable - tuples[ 3 - i ][ 2 ].disable, - - // rejected_handlers.disable - // fulfilled_handlers.disable - tuples[ 3 - i ][ 3 ].disable, - - // progress_callbacks.lock - tuples[ 0 ][ 2 ].lock, - - // progress_handlers.lock - tuples[ 0 ][ 3 ].lock - ); - } - - // progress_handlers.fire - // fulfilled_handlers.fire - // rejected_handlers.fire - list.add( tuple[ 3 ].fire ); - - // deferred.notify = function() { deferred.notifyWith(...) } - // deferred.resolve = function() { deferred.resolveWith(...) } - // deferred.reject = function() { deferred.rejectWith(...) } - deferred[ tuple[ 0 ] ] = function() { - deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); - return this; - }; - - // deferred.notifyWith = list.fireWith - // deferred.resolveWith = list.fireWith - // deferred.rejectWith = list.fireWith - deferred[ tuple[ 0 ] + "With" ] = list.fireWith; - } ); - - // Make the deferred a promise - promise.promise( deferred ); - - // Call given func if any - if ( func ) { - func.call( deferred, deferred ); - } - - // All done! - return deferred; - }, - - // Deferred helper - when: function( singleValue ) { - var - - // count of uncompleted subordinates - remaining = arguments.length, - - // count of unprocessed arguments - i = remaining, - - // subordinate fulfillment data - resolveContexts = Array( i ), - resolveValues = slice.call( arguments ), - - // the master Deferred - master = jQuery.Deferred(), - - // subordinate callback factory - updateFunc = function( i ) { - return function( value ) { - resolveContexts[ i ] = this; - resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; - if ( !( --remaining ) ) { - master.resolveWith( resolveContexts, resolveValues ); - } - }; - }; - - // Single- and empty arguments are adopted like Promise.resolve - if ( remaining <= 1 ) { - adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject, - !remaining ); - - // Use .then() to unwrap secondary thenables (cf. gh-3000) - if ( master.state() === "pending" || - isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { - - return master.then(); - } - } - - // Multiple arguments are aggregated like Promise.all array elements - while ( i-- ) { - adoptValue( resolveValues[ i ], updateFunc( i ), master.reject ); - } - - return master.promise(); - } -} ); - - -// These usually indicate a programmer mistake during development, -// warn about them ASAP rather than swallowing them by default. -var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; - -jQuery.Deferred.exceptionHook = function( error, stack ) { - - // Support: IE 8 - 9 only - // Console exists when dev tools are open, which can happen at any time - if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { - window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); - } -}; - - - - -jQuery.readyException = function( error ) { - window.setTimeout( function() { - throw error; - } ); -}; - - - - -// The deferred used on DOM ready -var readyList = jQuery.Deferred(); - -jQuery.fn.ready = function( fn ) { - - readyList - .then( fn ) - - // Wrap jQuery.readyException in a function so that the lookup - // happens at the time of error handling instead of callback - // registration. - .catch( function( error ) { - jQuery.readyException( error ); - } ); - - return this; -}; - -jQuery.extend( { - - // Is the DOM ready to be used? Set to true once it occurs. - isReady: false, - - // A counter to track how many items to wait for before - // the ready event fires. See #6781 - readyWait: 1, - - // Handle when the DOM is ready - ready: function( wait ) { - - // Abort if there are pending holds or we're already ready - if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { - return; - } - - // Remember that the DOM is ready - jQuery.isReady = true; - - // If a normal DOM Ready event fired, decrement, and wait if need be - if ( wait !== true && --jQuery.readyWait > 0 ) { - return; - } - - // If there are functions bound, to execute - readyList.resolveWith( document, [ jQuery ] ); - } -} ); - -jQuery.ready.then = readyList.then; - -// The ready event handler and self cleanup method -function completed() { - document.removeEventListener( "DOMContentLoaded", completed ); - window.removeEventListener( "load", completed ); - jQuery.ready(); -} - -// Catch cases where $(document).ready() is called -// after the browser event has already occurred. -// Support: IE <=9 - 10 only -// Older IE sometimes signals "interactive" too soon -if ( document.readyState === "complete" || - ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { - - // Handle it asynchronously to allow scripts the opportunity to delay ready - window.setTimeout( jQuery.ready ); - -} else { - - // Use the handy event callback - document.addEventListener( "DOMContentLoaded", completed ); - - // A fallback to window.onload, that will always work - window.addEventListener( "load", completed ); -} - - - - -// Multifunctional method to get and set values of a collection -// The value/s can optionally be executed if it's a function -var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { - var i = 0, - len = elems.length, - bulk = key == null; - - // Sets many values - if ( toType( key ) === "object" ) { - chainable = true; - for ( i in key ) { - access( elems, fn, i, key[ i ], true, emptyGet, raw ); - } - - // Sets one value - } else if ( value !== undefined ) { - chainable = true; - - if ( !isFunction( value ) ) { - raw = true; - } - - if ( bulk ) { - - // Bulk operations run against the entire set - if ( raw ) { - fn.call( elems, value ); - fn = null; - - // ...except when executing function values - } else { - bulk = fn; - fn = function( elem, _key, value ) { - return bulk.call( jQuery( elem ), value ); - }; - } - } - - if ( fn ) { - for ( ; i < len; i++ ) { - fn( - elems[ i ], key, raw ? - value : - value.call( elems[ i ], i, fn( elems[ i ], key ) ) - ); - } - } - } - - if ( chainable ) { - return elems; - } - - // Gets - if ( bulk ) { - return fn.call( elems ); - } - - return len ? fn( elems[ 0 ], key ) : emptyGet; -}; - - -// Matches dashed string for camelizing -var rmsPrefix = /^-ms-/, - rdashAlpha = /-([a-z])/g; - -// Used by camelCase as callback to replace() -function fcamelCase( _all, letter ) { - return letter.toUpperCase(); -} - -// Convert dashed to camelCase; used by the css and data modules -// Support: IE <=9 - 11, Edge 12 - 15 -// Microsoft forgot to hump their vendor prefix (#9572) -function camelCase( string ) { - return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); -} -var acceptData = function( owner ) { - - // Accepts only: - // - Node - // - Node.ELEMENT_NODE - // - Node.DOCUMENT_NODE - // - Object - // - Any - return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); -}; - - - - -function Data() { - this.expando = jQuery.expando + Data.uid++; -} - -Data.uid = 1; - -Data.prototype = { - - cache: function( owner ) { - - // Check if the owner object already has a cache - var value = owner[ this.expando ]; - - // If not, create one - if ( !value ) { - value = {}; - - // We can accept data for non-element nodes in modern browsers, - // but we should not, see #8335. - // Always return an empty object. - if ( acceptData( owner ) ) { - - // If it is a node unlikely to be stringify-ed or looped over - // use plain assignment - if ( owner.nodeType ) { - owner[ this.expando ] = value; - - // Otherwise secure it in a non-enumerable property - // configurable must be true to allow the property to be - // deleted when data is removed - } else { - Object.defineProperty( owner, this.expando, { - value: value, - configurable: true - } ); - } - } - } - - return value; - }, - set: function( owner, data, value ) { - var prop, - cache = this.cache( owner ); - - // Handle: [ owner, key, value ] args - // Always use camelCase key (gh-2257) - if ( typeof data === "string" ) { - cache[ camelCase( data ) ] = value; - - // Handle: [ owner, { properties } ] args - } else { - - // Copy the properties one-by-one to the cache object - for ( prop in data ) { - cache[ camelCase( prop ) ] = data[ prop ]; - } - } - return cache; - }, - get: function( owner, key ) { - return key === undefined ? - this.cache( owner ) : - - // Always use camelCase key (gh-2257) - owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; - }, - access: function( owner, key, value ) { - - // In cases where either: - // - // 1. No key was specified - // 2. A string key was specified, but no value provided - // - // Take the "read" path and allow the get method to determine - // which value to return, respectively either: - // - // 1. The entire cache object - // 2. The data stored at the key - // - if ( key === undefined || - ( ( key && typeof key === "string" ) && value === undefined ) ) { - - return this.get( owner, key ); - } - - // When the key is not a string, or both a key and value - // are specified, set or extend (existing objects) with either: - // - // 1. An object of properties - // 2. A key and value - // - this.set( owner, key, value ); - - // Since the "set" path can have two possible entry points - // return the expected data based on which path was taken[*] - return value !== undefined ? value : key; - }, - remove: function( owner, key ) { - var i, - cache = owner[ this.expando ]; - - if ( cache === undefined ) { - return; - } - - if ( key !== undefined ) { - - // Support array or space separated string of keys - if ( Array.isArray( key ) ) { - - // If key is an array of keys... - // We always set camelCase keys, so remove that. - key = key.map( camelCase ); - } else { - key = camelCase( key ); - - // If a key with the spaces exists, use it. - // Otherwise, create an array by matching non-whitespace - key = key in cache ? - [ key ] : - ( key.match( rnothtmlwhite ) || [] ); - } - - i = key.length; - - while ( i-- ) { - delete cache[ key[ i ] ]; - } - } - - // Remove the expando if there's no more data - if ( key === undefined || jQuery.isEmptyObject( cache ) ) { - - // Support: Chrome <=35 - 45 - // Webkit & Blink performance suffers when deleting properties - // from DOM nodes, so set to undefined instead - // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) - if ( owner.nodeType ) { - owner[ this.expando ] = undefined; - } else { - delete owner[ this.expando ]; - } - } - }, - hasData: function( owner ) { - var cache = owner[ this.expando ]; - return cache !== undefined && !jQuery.isEmptyObject( cache ); - } -}; -var dataPriv = new Data(); - -var dataUser = new Data(); - - - -// Implementation Summary -// -// 1. Enforce API surface and semantic compatibility with 1.9.x branch -// 2. Improve the module's maintainability by reducing the storage -// paths to a single mechanism. -// 3. Use the same single mechanism to support "private" and "user" data. -// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) -// 5. Avoid exposing implementation details on user objects (eg. expando properties) -// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 - -var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, - rmultiDash = /[A-Z]/g; - -function getData( data ) { - if ( data === "true" ) { - return true; - } - - if ( data === "false" ) { - return false; - } - - if ( data === "null" ) { - return null; - } - - // Only convert to a number if it doesn't change the string - if ( data === +data + "" ) { - return +data; - } - - if ( rbrace.test( data ) ) { - return JSON.parse( data ); - } - - return data; -} - -function dataAttr( elem, key, data ) { - var name; - - // If nothing was found internally, try to fetch any - // data from the HTML5 data-* attribute - if ( data === undefined && elem.nodeType === 1 ) { - name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); - data = elem.getAttribute( name ); - - if ( typeof data === "string" ) { - try { - data = getData( data ); - } catch ( e ) {} - - // Make sure we set the data so it isn't changed later - dataUser.set( elem, key, data ); - } else { - data = undefined; - } - } - return data; -} - -jQuery.extend( { - hasData: function( elem ) { - return dataUser.hasData( elem ) || dataPriv.hasData( elem ); - }, - - data: function( elem, name, data ) { - return dataUser.access( elem, name, data ); - }, - - removeData: function( elem, name ) { - dataUser.remove( elem, name ); - }, - - // TODO: Now that all calls to _data and _removeData have been replaced - // with direct calls to dataPriv methods, these can be deprecated. - _data: function( elem, name, data ) { - return dataPriv.access( elem, name, data ); - }, - - _removeData: function( elem, name ) { - dataPriv.remove( elem, name ); - } -} ); - -jQuery.fn.extend( { - data: function( key, value ) { - var i, name, data, - elem = this[ 0 ], - attrs = elem && elem.attributes; - - // Gets all values - if ( key === undefined ) { - if ( this.length ) { - data = dataUser.get( elem ); - - if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { - i = attrs.length; - while ( i-- ) { - - // Support: IE 11 only - // The attrs elements can be null (#14894) - if ( attrs[ i ] ) { - name = attrs[ i ].name; - if ( name.indexOf( "data-" ) === 0 ) { - name = camelCase( name.slice( 5 ) ); - dataAttr( elem, name, data[ name ] ); - } - } - } - dataPriv.set( elem, "hasDataAttrs", true ); - } - } - - return data; - } - - // Sets multiple values - if ( typeof key === "object" ) { - return this.each( function() { - dataUser.set( this, key ); - } ); - } - - return access( this, function( value ) { - var data; - - // The calling jQuery object (element matches) is not empty - // (and therefore has an element appears at this[ 0 ]) and the - // `value` parameter was not undefined. An empty jQuery object - // will result in `undefined` for elem = this[ 0 ] which will - // throw an exception if an attempt to read a data cache is made. - if ( elem && value === undefined ) { - - // Attempt to get data from the cache - // The key will always be camelCased in Data - data = dataUser.get( elem, key ); - if ( data !== undefined ) { - return data; - } - - // Attempt to "discover" the data in - // HTML5 custom data-* attrs - data = dataAttr( elem, key ); - if ( data !== undefined ) { - return data; - } - - // We tried really hard, but the data doesn't exist. - return; - } - - // Set the data... - this.each( function() { - - // We always store the camelCased key - dataUser.set( this, key, value ); - } ); - }, null, value, arguments.length > 1, null, true ); - }, - - removeData: function( key ) { - return this.each( function() { - dataUser.remove( this, key ); - } ); - } -} ); - - -jQuery.extend( { - queue: function( elem, type, data ) { - var queue; - - if ( elem ) { - type = ( type || "fx" ) + "queue"; - queue = dataPriv.get( elem, type ); - - // Speed up dequeue by getting out quickly if this is just a lookup - if ( data ) { - if ( !queue || Array.isArray( data ) ) { - queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); - } else { - queue.push( data ); - } - } - return queue || []; - } - }, - - dequeue: function( elem, type ) { - type = type || "fx"; - - var queue = jQuery.queue( elem, type ), - startLength = queue.length, - fn = queue.shift(), - hooks = jQuery._queueHooks( elem, type ), - next = function() { - jQuery.dequeue( elem, type ); - }; - - // If the fx queue is dequeued, always remove the progress sentinel - if ( fn === "inprogress" ) { - fn = queue.shift(); - startLength--; - } - - if ( fn ) { - - // Add a progress sentinel to prevent the fx queue from being - // automatically dequeued - if ( type === "fx" ) { - queue.unshift( "inprogress" ); - } - - // Clear up the last queue stop function - delete hooks.stop; - fn.call( elem, next, hooks ); - } - - if ( !startLength && hooks ) { - hooks.empty.fire(); - } - }, - - // Not public - generate a queueHooks object, or return the current one - _queueHooks: function( elem, type ) { - var key = type + "queueHooks"; - return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { - empty: jQuery.Callbacks( "once memory" ).add( function() { - dataPriv.remove( elem, [ type + "queue", key ] ); - } ) - } ); - } -} ); - -jQuery.fn.extend( { - queue: function( type, data ) { - var setter = 2; - - if ( typeof type !== "string" ) { - data = type; - type = "fx"; - setter--; - } - - if ( arguments.length < setter ) { - return jQuery.queue( this[ 0 ], type ); - } - - return data === undefined ? - this : - this.each( function() { - var queue = jQuery.queue( this, type, data ); - - // Ensure a hooks for this queue - jQuery._queueHooks( this, type ); - - if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { - jQuery.dequeue( this, type ); - } - } ); - }, - dequeue: function( type ) { - return this.each( function() { - jQuery.dequeue( this, type ); - } ); - }, - clearQueue: function( type ) { - return this.queue( type || "fx", [] ); - }, - - // Get a promise resolved when queues of a certain type - // are emptied (fx is the type by default) - promise: function( type, obj ) { - var tmp, - count = 1, - defer = jQuery.Deferred(), - elements = this, - i = this.length, - resolve = function() { - if ( !( --count ) ) { - defer.resolveWith( elements, [ elements ] ); - } - }; - - if ( typeof type !== "string" ) { - obj = type; - type = undefined; - } - type = type || "fx"; - - while ( i-- ) { - tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); - if ( tmp && tmp.empty ) { - count++; - tmp.empty.add( resolve ); - } - } - resolve(); - return defer.promise( obj ); - } -} ); -var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; - -var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); - - -var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; - -var documentElement = document.documentElement; - - - - var isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ); - }, - composed = { composed: true }; - - // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only - // Check attachment across shadow DOM boundaries when possible (gh-3504) - // Support: iOS 10.0-10.2 only - // Early iOS 10 versions support `attachShadow` but not `getRootNode`, - // leading to errors. We need to check for `getRootNode`. - if ( documentElement.getRootNode ) { - isAttached = function( elem ) { - return jQuery.contains( elem.ownerDocument, elem ) || - elem.getRootNode( composed ) === elem.ownerDocument; - }; - } -var isHiddenWithinTree = function( elem, el ) { - - // isHiddenWithinTree might be called from jQuery#filter function; - // in that case, element will be second argument - elem = el || elem; - - // Inline style trumps all - return elem.style.display === "none" || - elem.style.display === "" && - - // Otherwise, check computed style - // Support: Firefox <=43 - 45 - // Disconnected elements can have computed display: none, so first confirm that elem is - // in the document. - isAttached( elem ) && - - jQuery.css( elem, "display" ) === "none"; - }; - - - -function adjustCSS( elem, prop, valueParts, tween ) { - var adjusted, scale, - maxIterations = 20, - currentValue = tween ? - function() { - return tween.cur(); - } : - function() { - return jQuery.css( elem, prop, "" ); - }, - initial = currentValue(), - unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), - - // Starting value computation is required for potential unit mismatches - initialInUnit = elem.nodeType && - ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && - rcssNum.exec( jQuery.css( elem, prop ) ); - - if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { - - // Support: Firefox <=54 - // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) - initial = initial / 2; - - // Trust units reported by jQuery.css - unit = unit || initialInUnit[ 3 ]; - - // Iteratively approximate from a nonzero starting point - initialInUnit = +initial || 1; - - while ( maxIterations-- ) { - - // Evaluate and update our best guess (doubling guesses that zero out). - // Finish if the scale equals or crosses 1 (making the old*new product non-positive). - jQuery.style( elem, prop, initialInUnit + unit ); - if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { - maxIterations = 0; - } - initialInUnit = initialInUnit / scale; - - } - - initialInUnit = initialInUnit * 2; - jQuery.style( elem, prop, initialInUnit + unit ); - - // Make sure we update the tween properties later on - valueParts = valueParts || []; - } - - if ( valueParts ) { - initialInUnit = +initialInUnit || +initial || 0; - - // Apply relative offset (+=/-=) if specified - adjusted = valueParts[ 1 ] ? - initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : - +valueParts[ 2 ]; - if ( tween ) { - tween.unit = unit; - tween.start = initialInUnit; - tween.end = adjusted; - } - } - return adjusted; -} - - -var defaultDisplayMap = {}; - -function getDefaultDisplay( elem ) { - var temp, - doc = elem.ownerDocument, - nodeName = elem.nodeName, - display = defaultDisplayMap[ nodeName ]; - - if ( display ) { - return display; - } - - temp = doc.body.appendChild( doc.createElement( nodeName ) ); - display = jQuery.css( temp, "display" ); - - temp.parentNode.removeChild( temp ); - - if ( display === "none" ) { - display = "block"; - } - defaultDisplayMap[ nodeName ] = display; - - return display; -} - -function showHide( elements, show ) { - var display, elem, - values = [], - index = 0, - length = elements.length; - - // Determine new display value for elements that need to change - for ( ; index < length; index++ ) { - elem = elements[ index ]; - if ( !elem.style ) { - continue; - } - - display = elem.style.display; - if ( show ) { - - // Since we force visibility upon cascade-hidden elements, an immediate (and slow) - // check is required in this first loop unless we have a nonempty display value (either - // inline or about-to-be-restored) - if ( display === "none" ) { - values[ index ] = dataPriv.get( elem, "display" ) || null; - if ( !values[ index ] ) { - elem.style.display = ""; - } - } - if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { - values[ index ] = getDefaultDisplay( elem ); - } - } else { - if ( display !== "none" ) { - values[ index ] = "none"; - - // Remember what we're overwriting - dataPriv.set( elem, "display", display ); - } - } - } - - // Set the display of the elements in a second loop to avoid constant reflow - for ( index = 0; index < length; index++ ) { - if ( values[ index ] != null ) { - elements[ index ].style.display = values[ index ]; - } - } - - return elements; -} - -jQuery.fn.extend( { - show: function() { - return showHide( this, true ); - }, - hide: function() { - return showHide( this ); - }, - toggle: function( state ) { - if ( typeof state === "boolean" ) { - return state ? this.show() : this.hide(); - } - - return this.each( function() { - if ( isHiddenWithinTree( this ) ) { - jQuery( this ).show(); - } else { - jQuery( this ).hide(); - } - } ); - } -} ); -var rcheckableType = ( /^(?:checkbox|radio)$/i ); - -var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); - -var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); - - - -( function() { - var fragment = document.createDocumentFragment(), - div = fragment.appendChild( document.createElement( "div" ) ), - input = document.createElement( "input" ); - - // Support: Android 4.0 - 4.3 only - // Check state lost if the name is set (#11217) - // Support: Windows Web Apps (WWA) - // `name` and `type` must use .setAttribute for WWA (#14901) - input.setAttribute( "type", "radio" ); - input.setAttribute( "checked", "checked" ); - input.setAttribute( "name", "t" ); - - div.appendChild( input ); - - // Support: Android <=4.1 only - // Older WebKit doesn't clone checked state correctly in fragments - support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; - - // Support: IE <=11 only - // Make sure textarea (and checkbox) defaultValue is properly cloned - div.innerHTML = ""; - support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; - - // Support: IE <=9 only - // IE <=9 replaces "; - support.option = !!div.lastChild; -} )(); - - -// We have to close these tags to support XHTML (#13200) -var wrapMap = { - - // XHTML parsers do not magically insert elements in the - // same way that tag soup parsers do. So we cannot shorten - // this by omitting or other required elements. - thead: [ 1, "", "
" ], - col: [ 2, "", "
" ], - tr: [ 2, "", "
" ], - td: [ 3, "", "
" ], - - _default: [ 0, "", "" ] -}; - -wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; -wrapMap.th = wrapMap.td; - -// Support: IE <=9 only -if ( !support.option ) { - wrapMap.optgroup = wrapMap.option = [ 1, "" ]; -} - - -function getAll( context, tag ) { - - // Support: IE <=9 - 11 only - // Use typeof to avoid zero-argument method invocation on host objects (#15151) - var ret; - - if ( typeof context.getElementsByTagName !== "undefined" ) { - ret = context.getElementsByTagName( tag || "*" ); - - } else if ( typeof context.querySelectorAll !== "undefined" ) { - ret = context.querySelectorAll( tag || "*" ); - - } else { - ret = []; - } - - if ( tag === undefined || tag && nodeName( context, tag ) ) { - return jQuery.merge( [ context ], ret ); - } - - return ret; -} - - -// Mark scripts as having already been evaluated -function setGlobalEval( elems, refElements ) { - var i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - dataPriv.set( - elems[ i ], - "globalEval", - !refElements || dataPriv.get( refElements[ i ], "globalEval" ) - ); - } -} - - -var rhtml = /<|&#?\w+;/; - -function buildFragment( elems, context, scripts, selection, ignored ) { - var elem, tmp, tag, wrap, attached, j, - fragment = context.createDocumentFragment(), - nodes = [], - i = 0, - l = elems.length; - - for ( ; i < l; i++ ) { - elem = elems[ i ]; - - if ( elem || elem === 0 ) { - - // Add nodes directly - if ( toType( elem ) === "object" ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); - - // Convert non-html into a text node - } else if ( !rhtml.test( elem ) ) { - nodes.push( context.createTextNode( elem ) ); - - // Convert html into DOM nodes - } else { - tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); - - // Deserialize a standard representation - tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); - wrap = wrapMap[ tag ] || wrapMap._default; - tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; - - // Descend through wrappers to the right content - j = wrap[ 0 ]; - while ( j-- ) { - tmp = tmp.lastChild; - } - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( nodes, tmp.childNodes ); - - // Remember the top-level container - tmp = fragment.firstChild; - - // Ensure the created nodes are orphaned (#12392) - tmp.textContent = ""; - } - } - } - - // Remove wrapper from fragment - fragment.textContent = ""; - - i = 0; - while ( ( elem = nodes[ i++ ] ) ) { - - // Skip elements already in the context collection (trac-4087) - if ( selection && jQuery.inArray( elem, selection ) > -1 ) { - if ( ignored ) { - ignored.push( elem ); - } - continue; - } - - attached = isAttached( elem ); - - // Append to fragment - tmp = getAll( fragment.appendChild( elem ), "script" ); - - // Preserve script evaluation history - if ( attached ) { - setGlobalEval( tmp ); - } - - // Capture executables - if ( scripts ) { - j = 0; - while ( ( elem = tmp[ j++ ] ) ) { - if ( rscriptType.test( elem.type || "" ) ) { - scripts.push( elem ); - } - } - } - } - - return fragment; -} - - -var - rkeyEvent = /^key/, - rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/, - rtypenamespace = /^([^.]*)(?:\.(.+)|)/; - -function returnTrue() { - return true; -} - -function returnFalse() { - return false; -} - -// Support: IE <=9 - 11+ -// focus() and blur() are asynchronous, except when they are no-op. -// So expect focus to be synchronous when the element is already active, -// and blur to be synchronous when the element is not already active. -// (focus and blur are always synchronous in other supported browsers, -// this just defines when we can count on it). -function expectSync( elem, type ) { - return ( elem === safeActiveElement() ) === ( type === "focus" ); -} - -// Support: IE <=9 only -// Accessing document.activeElement can throw unexpectedly -// https://bugs.jquery.com/ticket/13393 -function safeActiveElement() { - try { - return document.activeElement; - } catch ( err ) { } -} - -function on( elem, types, selector, data, fn, one ) { - var origFn, type; - - // Types can be a map of types/handlers - if ( typeof types === "object" ) { - - // ( types-Object, selector, data ) - if ( typeof selector !== "string" ) { - - // ( types-Object, data ) - data = data || selector; - selector = undefined; - } - for ( type in types ) { - on( elem, type, selector, data, types[ type ], one ); - } - return elem; - } - - if ( data == null && fn == null ) { - - // ( types, fn ) - fn = selector; - data = selector = undefined; - } else if ( fn == null ) { - if ( typeof selector === "string" ) { - - // ( types, selector, fn ) - fn = data; - data = undefined; - } else { - - // ( types, data, fn ) - fn = data; - data = selector; - selector = undefined; - } - } - if ( fn === false ) { - fn = returnFalse; - } else if ( !fn ) { - return elem; - } - - if ( one === 1 ) { - origFn = fn; - fn = function( event ) { - - // Can use an empty set, since event contains the info - jQuery().off( event ); - return origFn.apply( this, arguments ); - }; - - // Use same guid so caller can remove using origFn - fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); - } - return elem.each( function() { - jQuery.event.add( this, types, fn, data, selector ); - } ); -} - -/* - * Helper functions for managing events -- not part of the public interface. - * Props to Dean Edwards' addEvent library for many of the ideas. - */ -jQuery.event = { - - global: {}, - - add: function( elem, types, handler, data, selector ) { - - var handleObjIn, eventHandle, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.get( elem ); - - // Only attach events to objects that accept data - if ( !acceptData( elem ) ) { - return; - } - - // Caller can pass in an object of custom data in lieu of the handler - if ( handler.handler ) { - handleObjIn = handler; - handler = handleObjIn.handler; - selector = handleObjIn.selector; - } - - // Ensure that invalid selectors throw exceptions at attach time - // Evaluate against documentElement in case elem is a non-element node (e.g., document) - if ( selector ) { - jQuery.find.matchesSelector( documentElement, selector ); - } - - // Make sure that the handler has a unique ID, used to find/remove it later - if ( !handler.guid ) { - handler.guid = jQuery.guid++; - } - - // Init the element's event structure and main handler, if this is the first - if ( !( events = elemData.events ) ) { - events = elemData.events = Object.create( null ); - } - if ( !( eventHandle = elemData.handle ) ) { - eventHandle = elemData.handle = function( e ) { - - // Discard the second event of a jQuery.event.trigger() and - // when an event is called after a page has unloaded - return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? - jQuery.event.dispatch.apply( elem, arguments ) : undefined; - }; - } - - // Handle multiple events separated by a space - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // There *must* be a type, no attaching namespace-only handlers - if ( !type ) { - continue; - } - - // If event changes its type, use the special event handlers for the changed type - special = jQuery.event.special[ type ] || {}; - - // If selector defined, determine special event api type, otherwise given type - type = ( selector ? special.delegateType : special.bindType ) || type; - - // Update special based on newly reset type - special = jQuery.event.special[ type ] || {}; - - // handleObj is passed to all event handlers - handleObj = jQuery.extend( { - type: type, - origType: origType, - data: data, - handler: handler, - guid: handler.guid, - selector: selector, - needsContext: selector && jQuery.expr.match.needsContext.test( selector ), - namespace: namespaces.join( "." ) - }, handleObjIn ); - - // Init the event handler queue if we're the first - if ( !( handlers = events[ type ] ) ) { - handlers = events[ type ] = []; - handlers.delegateCount = 0; - - // Only use addEventListener if the special events handler returns false - if ( !special.setup || - special.setup.call( elem, data, namespaces, eventHandle ) === false ) { - - if ( elem.addEventListener ) { - elem.addEventListener( type, eventHandle ); - } - } - } - - if ( special.add ) { - special.add.call( elem, handleObj ); - - if ( !handleObj.handler.guid ) { - handleObj.handler.guid = handler.guid; - } - } - - // Add to the element's handler list, delegates in front - if ( selector ) { - handlers.splice( handlers.delegateCount++, 0, handleObj ); - } else { - handlers.push( handleObj ); - } - - // Keep track of which events have ever been used, for event optimization - jQuery.event.global[ type ] = true; - } - - }, - - // Detach an event or set of events from an element - remove: function( elem, types, handler, selector, mappedTypes ) { - - var j, origCount, tmp, - events, t, handleObj, - special, handlers, type, namespaces, origType, - elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); - - if ( !elemData || !( events = elemData.events ) ) { - return; - } - - // Once for each type.namespace in types; type may be omitted - types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; - t = types.length; - while ( t-- ) { - tmp = rtypenamespace.exec( types[ t ] ) || []; - type = origType = tmp[ 1 ]; - namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); - - // Unbind all events (on this namespace, if provided) for the element - if ( !type ) { - for ( type in events ) { - jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); - } - continue; - } - - special = jQuery.event.special[ type ] || {}; - type = ( selector ? special.delegateType : special.bindType ) || type; - handlers = events[ type ] || []; - tmp = tmp[ 2 ] && - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); - - // Remove matching events - origCount = j = handlers.length; - while ( j-- ) { - handleObj = handlers[ j ]; - - if ( ( mappedTypes || origType === handleObj.origType ) && - ( !handler || handler.guid === handleObj.guid ) && - ( !tmp || tmp.test( handleObj.namespace ) ) && - ( !selector || selector === handleObj.selector || - selector === "**" && handleObj.selector ) ) { - handlers.splice( j, 1 ); - - if ( handleObj.selector ) { - handlers.delegateCount--; - } - if ( special.remove ) { - special.remove.call( elem, handleObj ); - } - } - } - - // Remove generic event handler if we removed something and no more handlers exist - // (avoids potential for endless recursion during removal of special event handlers) - if ( origCount && !handlers.length ) { - if ( !special.teardown || - special.teardown.call( elem, namespaces, elemData.handle ) === false ) { - - jQuery.removeEvent( elem, type, elemData.handle ); - } - - delete events[ type ]; - } - } - - // Remove data and the expando if it's no longer used - if ( jQuery.isEmptyObject( events ) ) { - dataPriv.remove( elem, "handle events" ); - } - }, - - dispatch: function( nativeEvent ) { - - var i, j, ret, matched, handleObj, handlerQueue, - args = new Array( arguments.length ), - - // Make a writable jQuery.Event from the native event object - event = jQuery.event.fix( nativeEvent ), - - handlers = ( - dataPriv.get( this, "events" ) || Object.create( null ) - )[ event.type ] || [], - special = jQuery.event.special[ event.type ] || {}; - - // Use the fix-ed jQuery.Event rather than the (read-only) native event - args[ 0 ] = event; - - for ( i = 1; i < arguments.length; i++ ) { - args[ i ] = arguments[ i ]; - } - - event.delegateTarget = this; - - // Call the preDispatch hook for the mapped type, and let it bail if desired - if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { - return; - } - - // Determine handlers - handlerQueue = jQuery.event.handlers.call( this, event, handlers ); - - // Run delegates first; they may want to stop propagation beneath us - i = 0; - while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { - event.currentTarget = matched.elem; - - j = 0; - while ( ( handleObj = matched.handlers[ j++ ] ) && - !event.isImmediatePropagationStopped() ) { - - // If the event is namespaced, then each handler is only invoked if it is - // specially universal or its namespaces are a superset of the event's. - if ( !event.rnamespace || handleObj.namespace === false || - event.rnamespace.test( handleObj.namespace ) ) { - - event.handleObj = handleObj; - event.data = handleObj.data; - - ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || - handleObj.handler ).apply( matched.elem, args ); - - if ( ret !== undefined ) { - if ( ( event.result = ret ) === false ) { - event.preventDefault(); - event.stopPropagation(); - } - } - } - } - } - - // Call the postDispatch hook for the mapped type - if ( special.postDispatch ) { - special.postDispatch.call( this, event ); - } - - return event.result; - }, - - handlers: function( event, handlers ) { - var i, handleObj, sel, matchedHandlers, matchedSelectors, - handlerQueue = [], - delegateCount = handlers.delegateCount, - cur = event.target; - - // Find delegate handlers - if ( delegateCount && - - // Support: IE <=9 - // Black-hole SVG instance trees (trac-13180) - cur.nodeType && - - // Support: Firefox <=42 - // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) - // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click - // Support: IE 11 only - // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) - !( event.type === "click" && event.button >= 1 ) ) { - - for ( ; cur !== this; cur = cur.parentNode || this ) { - - // Don't check non-elements (#13208) - // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) - if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { - matchedHandlers = []; - matchedSelectors = {}; - for ( i = 0; i < delegateCount; i++ ) { - handleObj = handlers[ i ]; - - // Don't conflict with Object.prototype properties (#13203) - sel = handleObj.selector + " "; - - if ( matchedSelectors[ sel ] === undefined ) { - matchedSelectors[ sel ] = handleObj.needsContext ? - jQuery( sel, this ).index( cur ) > -1 : - jQuery.find( sel, this, null, [ cur ] ).length; - } - if ( matchedSelectors[ sel ] ) { - matchedHandlers.push( handleObj ); - } - } - if ( matchedHandlers.length ) { - handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); - } - } - } - } - - // Add the remaining (directly-bound) handlers - cur = this; - if ( delegateCount < handlers.length ) { - handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); - } - - return handlerQueue; - }, - - addProp: function( name, hook ) { - Object.defineProperty( jQuery.Event.prototype, name, { - enumerable: true, - configurable: true, - - get: isFunction( hook ) ? - function() { - if ( this.originalEvent ) { - return hook( this.originalEvent ); - } - } : - function() { - if ( this.originalEvent ) { - return this.originalEvent[ name ]; - } - }, - - set: function( value ) { - Object.defineProperty( this, name, { - enumerable: true, - configurable: true, - writable: true, - value: value - } ); - } - } ); - }, - - fix: function( originalEvent ) { - return originalEvent[ jQuery.expando ] ? - originalEvent : - new jQuery.Event( originalEvent ); - }, - - special: { - load: { - - // Prevent triggered image.load events from bubbling to window.load - noBubble: true - }, - click: { - - // Utilize native event to ensure correct state for checkable inputs - setup: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Claim the first handler - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - // dataPriv.set( el, "click", ... ) - leverageNative( el, "click", returnTrue ); - } - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function( data ) { - - // For mutual compressibility with _default, replace `this` access with a local var. - // `|| data` is dead code meant only to preserve the variable through minification. - var el = this || data; - - // Force setup before triggering a click - if ( rcheckableType.test( el.type ) && - el.click && nodeName( el, "input" ) ) { - - leverageNative( el, "click" ); - } - - // Return non-false to allow normal event-path propagation - return true; - }, - - // For cross-browser consistency, suppress native .click() on links - // Also prevent it if we're currently inside a leveraged native-event stack - _default: function( event ) { - var target = event.target; - return rcheckableType.test( target.type ) && - target.click && nodeName( target, "input" ) && - dataPriv.get( target, "click" ) || - nodeName( target, "a" ); - } - }, - - beforeunload: { - postDispatch: function( event ) { - - // Support: Firefox 20+ - // Firefox doesn't alert if the returnValue field is not set. - if ( event.result !== undefined && event.originalEvent ) { - event.originalEvent.returnValue = event.result; - } - } - } - } -}; - -// Ensure the presence of an event listener that handles manually-triggered -// synthetic events by interrupting progress until reinvoked in response to -// *native* events that it fires directly, ensuring that state changes have -// already occurred before other listeners are invoked. -function leverageNative( el, type, expectSync ) { - - // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add - if ( !expectSync ) { - if ( dataPriv.get( el, type ) === undefined ) { - jQuery.event.add( el, type, returnTrue ); - } - return; - } - - // Register the controller as a special universal handler for all event namespaces - dataPriv.set( el, type, false ); - jQuery.event.add( el, type, { - namespace: false, - handler: function( event ) { - var notAsync, result, - saved = dataPriv.get( this, type ); - - if ( ( event.isTrigger & 1 ) && this[ type ] ) { - - // Interrupt processing of the outer synthetic .trigger()ed event - // Saved data should be false in such cases, but might be a leftover capture object - // from an async native handler (gh-4350) - if ( !saved.length ) { - - // Store arguments for use when handling the inner native event - // There will always be at least one argument (an event object), so this array - // will not be confused with a leftover capture object. - saved = slice.call( arguments ); - dataPriv.set( this, type, saved ); - - // Trigger the native event and capture its result - // Support: IE <=9 - 11+ - // focus() and blur() are asynchronous - notAsync = expectSync( this, type ); - this[ type ](); - result = dataPriv.get( this, type ); - if ( saved !== result || notAsync ) { - dataPriv.set( this, type, false ); - } else { - result = {}; - } - if ( saved !== result ) { - - // Cancel the outer synthetic event - event.stopImmediatePropagation(); - event.preventDefault(); - return result.value; - } - - // If this is an inner synthetic event for an event with a bubbling surrogate - // (focus or blur), assume that the surrogate already propagated from triggering the - // native event and prevent that from happening again here. - // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the - // bubbling surrogate propagates *after* the non-bubbling base), but that seems - // less bad than duplication. - } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { - event.stopPropagation(); - } - - // If this is a native event triggered above, everything is now in order - // Fire an inner synthetic event with the original arguments - } else if ( saved.length ) { - - // ...and capture the result - dataPriv.set( this, type, { - value: jQuery.event.trigger( - - // Support: IE <=9 - 11+ - // Extend with the prototype to reset the above stopImmediatePropagation() - jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), - saved.slice( 1 ), - this - ) - } ); - - // Abort handling of the native event - event.stopImmediatePropagation(); - } - } - } ); -} - -jQuery.removeEvent = function( elem, type, handle ) { - - // This "if" is needed for plain objects - if ( elem.removeEventListener ) { - elem.removeEventListener( type, handle ); - } -}; - -jQuery.Event = function( src, props ) { - - // Allow instantiation without the 'new' keyword - if ( !( this instanceof jQuery.Event ) ) { - return new jQuery.Event( src, props ); - } - - // Event object - if ( src && src.type ) { - this.originalEvent = src; - this.type = src.type; - - // Events bubbling up the document may have been marked as prevented - // by a handler lower down the tree; reflect the correct value. - this.isDefaultPrevented = src.defaultPrevented || - src.defaultPrevented === undefined && - - // Support: Android <=2.3 only - src.returnValue === false ? - returnTrue : - returnFalse; - - // Create target properties - // Support: Safari <=6 - 7 only - // Target should not be a text node (#504, #13143) - this.target = ( src.target && src.target.nodeType === 3 ) ? - src.target.parentNode : - src.target; - - this.currentTarget = src.currentTarget; - this.relatedTarget = src.relatedTarget; - - // Event type - } else { - this.type = src; - } - - // Put explicitly provided properties onto the event object - if ( props ) { - jQuery.extend( this, props ); - } - - // Create a timestamp if incoming event doesn't have one - this.timeStamp = src && src.timeStamp || Date.now(); - - // Mark it as fixed - this[ jQuery.expando ] = true; -}; - -// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding -// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html -jQuery.Event.prototype = { - constructor: jQuery.Event, - isDefaultPrevented: returnFalse, - isPropagationStopped: returnFalse, - isImmediatePropagationStopped: returnFalse, - isSimulated: false, - - preventDefault: function() { - var e = this.originalEvent; - - this.isDefaultPrevented = returnTrue; - - if ( e && !this.isSimulated ) { - e.preventDefault(); - } - }, - stopPropagation: function() { - var e = this.originalEvent; - - this.isPropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopPropagation(); - } - }, - stopImmediatePropagation: function() { - var e = this.originalEvent; - - this.isImmediatePropagationStopped = returnTrue; - - if ( e && !this.isSimulated ) { - e.stopImmediatePropagation(); - } - - this.stopPropagation(); - } -}; - -// Includes all common event props including KeyEvent and MouseEvent specific props -jQuery.each( { - altKey: true, - bubbles: true, - cancelable: true, - changedTouches: true, - ctrlKey: true, - detail: true, - eventPhase: true, - metaKey: true, - pageX: true, - pageY: true, - shiftKey: true, - view: true, - "char": true, - code: true, - charCode: true, - key: true, - keyCode: true, - button: true, - buttons: true, - clientX: true, - clientY: true, - offsetX: true, - offsetY: true, - pointerId: true, - pointerType: true, - screenX: true, - screenY: true, - targetTouches: true, - toElement: true, - touches: true, - - which: function( event ) { - var button = event.button; - - // Add which for key events - if ( event.which == null && rkeyEvent.test( event.type ) ) { - return event.charCode != null ? event.charCode : event.keyCode; - } - - // Add which for click: 1 === left; 2 === middle; 3 === right - if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) { - if ( button & 1 ) { - return 1; - } - - if ( button & 2 ) { - return 3; - } - - if ( button & 4 ) { - return 2; - } - - return 0; - } - - return event.which; - } -}, jQuery.event.addProp ); - -jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { - jQuery.event.special[ type ] = { - - // Utilize native event if possible so blur/focus sequence is correct - setup: function() { - - // Claim the first handler - // dataPriv.set( this, "focus", ... ) - // dataPriv.set( this, "blur", ... ) - leverageNative( this, type, expectSync ); - - // Return false to allow normal processing in the caller - return false; - }, - trigger: function() { - - // Force setup before trigger - leverageNative( this, type ); - - // Return non-false to allow normal event-path propagation - return true; - }, - - delegateType: delegateType - }; -} ); - -// Create mouseenter/leave events using mouseover/out and event-time checks -// so that event delegation works in jQuery. -// Do the same for pointerenter/pointerleave and pointerover/pointerout -// -// Support: Safari 7 only -// Safari sends mouseenter too often; see: -// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 -// for the description of the bug (it existed in older Chrome versions as well). -jQuery.each( { - mouseenter: "mouseover", - mouseleave: "mouseout", - pointerenter: "pointerover", - pointerleave: "pointerout" -}, function( orig, fix ) { - jQuery.event.special[ orig ] = { - delegateType: fix, - bindType: fix, - - handle: function( event ) { - var ret, - target = this, - related = event.relatedTarget, - handleObj = event.handleObj; - - // For mouseenter/leave call the handler if related is outside the target. - // NB: No relatedTarget if the mouse left/entered the browser window - if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { - event.type = handleObj.origType; - ret = handleObj.handler.apply( this, arguments ); - event.type = fix; - } - return ret; - } - }; -} ); - -jQuery.fn.extend( { - - on: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn ); - }, - one: function( types, selector, data, fn ) { - return on( this, types, selector, data, fn, 1 ); - }, - off: function( types, selector, fn ) { - var handleObj, type; - if ( types && types.preventDefault && types.handleObj ) { - - // ( event ) dispatched jQuery.Event - handleObj = types.handleObj; - jQuery( types.delegateTarget ).off( - handleObj.namespace ? - handleObj.origType + "." + handleObj.namespace : - handleObj.origType, - handleObj.selector, - handleObj.handler - ); - return this; - } - if ( typeof types === "object" ) { - - // ( types-object [, selector] ) - for ( type in types ) { - this.off( type, selector, types[ type ] ); - } - return this; - } - if ( selector === false || typeof selector === "function" ) { - - // ( types [, fn] ) - fn = selector; - selector = undefined; - } - if ( fn === false ) { - fn = returnFalse; - } - return this.each( function() { - jQuery.event.remove( this, types, fn, selector ); - } ); - } -} ); - - -var - - // Support: IE <=10 - 11, Edge 12 - 13 only - // In IE/Edge using regex groups here causes severe slowdowns. - // See https://connect.microsoft.com/IE/feedback/details/1736512/ - rnoInnerhtml = /\s*$/g; - -// Prefer a tbody over its parent table for containing new rows -function manipulationTarget( elem, content ) { - if ( nodeName( elem, "table" ) && - nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { - - return jQuery( elem ).children( "tbody" )[ 0 ] || elem; - } - - return elem; -} - -// Replace/restore the type attribute of script elements for safe DOM manipulation -function disableScript( elem ) { - elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; - return elem; -} -function restoreScript( elem ) { - if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { - elem.type = elem.type.slice( 5 ); - } else { - elem.removeAttribute( "type" ); - } - - return elem; -} - -function cloneCopyEvent( src, dest ) { - var i, l, type, pdataOld, udataOld, udataCur, events; - - if ( dest.nodeType !== 1 ) { - return; - } - - // 1. Copy private data: events, handlers, etc. - if ( dataPriv.hasData( src ) ) { - pdataOld = dataPriv.get( src ); - events = pdataOld.events; - - if ( events ) { - dataPriv.remove( dest, "handle events" ); - - for ( type in events ) { - for ( i = 0, l = events[ type ].length; i < l; i++ ) { - jQuery.event.add( dest, type, events[ type ][ i ] ); - } - } - } - } - - // 2. Copy user data - if ( dataUser.hasData( src ) ) { - udataOld = dataUser.access( src ); - udataCur = jQuery.extend( {}, udataOld ); - - dataUser.set( dest, udataCur ); - } -} - -// Fix IE bugs, see support tests -function fixInput( src, dest ) { - var nodeName = dest.nodeName.toLowerCase(); - - // Fails to persist the checked state of a cloned checkbox or radio button. - if ( nodeName === "input" && rcheckableType.test( src.type ) ) { - dest.checked = src.checked; - - // Fails to return the selected option to the default selected state when cloning options - } else if ( nodeName === "input" || nodeName === "textarea" ) { - dest.defaultValue = src.defaultValue; - } -} - -function domManip( collection, args, callback, ignored ) { - - // Flatten any nested arrays - args = flat( args ); - - var fragment, first, scripts, hasScripts, node, doc, - i = 0, - l = collection.length, - iNoClone = l - 1, - value = args[ 0 ], - valueIsFunction = isFunction( value ); - - // We can't cloneNode fragments that contain checked, in WebKit - if ( valueIsFunction || - ( l > 1 && typeof value === "string" && - !support.checkClone && rchecked.test( value ) ) ) { - return collection.each( function( index ) { - var self = collection.eq( index ); - if ( valueIsFunction ) { - args[ 0 ] = value.call( this, index, self.html() ); - } - domManip( self, args, callback, ignored ); - } ); - } - - if ( l ) { - fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); - first = fragment.firstChild; - - if ( fragment.childNodes.length === 1 ) { - fragment = first; - } - - // Require either new content or an interest in ignored elements to invoke the callback - if ( first || ignored ) { - scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); - hasScripts = scripts.length; - - // Use the original fragment for the last item - // instead of the first because it can end up - // being emptied incorrectly in certain situations (#8070). - for ( ; i < l; i++ ) { - node = fragment; - - if ( i !== iNoClone ) { - node = jQuery.clone( node, true, true ); - - // Keep references to cloned scripts for later restoration - if ( hasScripts ) { - - // Support: Android <=4.0 only, PhantomJS 1 only - // push.apply(_, arraylike) throws on ancient WebKit - jQuery.merge( scripts, getAll( node, "script" ) ); - } - } - - callback.call( collection[ i ], node, i ); - } - - if ( hasScripts ) { - doc = scripts[ scripts.length - 1 ].ownerDocument; - - // Reenable scripts - jQuery.map( scripts, restoreScript ); - - // Evaluate executable scripts on first document insertion - for ( i = 0; i < hasScripts; i++ ) { - node = scripts[ i ]; - if ( rscriptType.test( node.type || "" ) && - !dataPriv.access( node, "globalEval" ) && - jQuery.contains( doc, node ) ) { - - if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { - - // Optional AJAX dependency, but won't run scripts if not present - if ( jQuery._evalUrl && !node.noModule ) { - jQuery._evalUrl( node.src, { - nonce: node.nonce || node.getAttribute( "nonce" ) - }, doc ); - } - } else { - DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); - } - } - } - } - } - } - - return collection; -} - -function remove( elem, selector, keepData ) { - var node, - nodes = selector ? jQuery.filter( selector, elem ) : elem, - i = 0; - - for ( ; ( node = nodes[ i ] ) != null; i++ ) { - if ( !keepData && node.nodeType === 1 ) { - jQuery.cleanData( getAll( node ) ); - } - - if ( node.parentNode ) { - if ( keepData && isAttached( node ) ) { - setGlobalEval( getAll( node, "script" ) ); - } - node.parentNode.removeChild( node ); - } - } - - return elem; -} - -jQuery.extend( { - htmlPrefilter: function( html ) { - return html; - }, - - clone: function( elem, dataAndEvents, deepDataAndEvents ) { - var i, l, srcElements, destElements, - clone = elem.cloneNode( true ), - inPage = isAttached( elem ); - - // Fix IE cloning issues - if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && - !jQuery.isXMLDoc( elem ) ) { - - // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 - destElements = getAll( clone ); - srcElements = getAll( elem ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - fixInput( srcElements[ i ], destElements[ i ] ); - } - } - - // Copy the events from the original to the clone - if ( dataAndEvents ) { - if ( deepDataAndEvents ) { - srcElements = srcElements || getAll( elem ); - destElements = destElements || getAll( clone ); - - for ( i = 0, l = srcElements.length; i < l; i++ ) { - cloneCopyEvent( srcElements[ i ], destElements[ i ] ); - } - } else { - cloneCopyEvent( elem, clone ); - } - } - - // Preserve script evaluation history - destElements = getAll( clone, "script" ); - if ( destElements.length > 0 ) { - setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); - } - - // Return the cloned set - return clone; - }, - - cleanData: function( elems ) { - var data, elem, type, - special = jQuery.event.special, - i = 0; - - for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { - if ( acceptData( elem ) ) { - if ( ( data = elem[ dataPriv.expando ] ) ) { - if ( data.events ) { - for ( type in data.events ) { - if ( special[ type ] ) { - jQuery.event.remove( elem, type ); - - // This is a shortcut to avoid jQuery.event.remove's overhead - } else { - jQuery.removeEvent( elem, type, data.handle ); - } - } - } - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataPriv.expando ] = undefined; - } - if ( elem[ dataUser.expando ] ) { - - // Support: Chrome <=35 - 45+ - // Assign undefined instead of using delete, see Data#remove - elem[ dataUser.expando ] = undefined; - } - } - } - } -} ); - -jQuery.fn.extend( { - detach: function( selector ) { - return remove( this, selector, true ); - }, - - remove: function( selector ) { - return remove( this, selector ); - }, - - text: function( value ) { - return access( this, function( value ) { - return value === undefined ? - jQuery.text( this ) : - this.empty().each( function() { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - this.textContent = value; - } - } ); - }, null, value, arguments.length ); - }, - - append: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.appendChild( elem ); - } - } ); - }, - - prepend: function() { - return domManip( this, arguments, function( elem ) { - if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { - var target = manipulationTarget( this, elem ); - target.insertBefore( elem, target.firstChild ); - } - } ); - }, - - before: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this ); - } - } ); - }, - - after: function() { - return domManip( this, arguments, function( elem ) { - if ( this.parentNode ) { - this.parentNode.insertBefore( elem, this.nextSibling ); - } - } ); - }, - - empty: function() { - var elem, - i = 0; - - for ( ; ( elem = this[ i ] ) != null; i++ ) { - if ( elem.nodeType === 1 ) { - - // Prevent memory leaks - jQuery.cleanData( getAll( elem, false ) ); - - // Remove any remaining nodes - elem.textContent = ""; - } - } - - return this; - }, - - clone: function( dataAndEvents, deepDataAndEvents ) { - dataAndEvents = dataAndEvents == null ? false : dataAndEvents; - deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; - - return this.map( function() { - return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); - } ); - }, - - html: function( value ) { - return access( this, function( value ) { - var elem = this[ 0 ] || {}, - i = 0, - l = this.length; - - if ( value === undefined && elem.nodeType === 1 ) { - return elem.innerHTML; - } - - // See if we can take a shortcut and just use innerHTML - if ( typeof value === "string" && !rnoInnerhtml.test( value ) && - !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { - - value = jQuery.htmlPrefilter( value ); - - try { - for ( ; i < l; i++ ) { - elem = this[ i ] || {}; - - // Remove element nodes and prevent memory leaks - if ( elem.nodeType === 1 ) { - jQuery.cleanData( getAll( elem, false ) ); - elem.innerHTML = value; - } - } - - elem = 0; - - // If using innerHTML throws an exception, use the fallback method - } catch ( e ) {} - } - - if ( elem ) { - this.empty().append( value ); - } - }, null, value, arguments.length ); - }, - - replaceWith: function() { - var ignored = []; - - // Make the changes, replacing each non-ignored context element with the new content - return domManip( this, arguments, function( elem ) { - var parent = this.parentNode; - - if ( jQuery.inArray( this, ignored ) < 0 ) { - jQuery.cleanData( getAll( this ) ); - if ( parent ) { - parent.replaceChild( elem, this ); - } - } - - // Force callback invocation - }, ignored ); - } -} ); - -jQuery.each( { - appendTo: "append", - prependTo: "prepend", - insertBefore: "before", - insertAfter: "after", - replaceAll: "replaceWith" -}, function( name, original ) { - jQuery.fn[ name ] = function( selector ) { - var elems, - ret = [], - insert = jQuery( selector ), - last = insert.length - 1, - i = 0; - - for ( ; i <= last; i++ ) { - elems = i === last ? this : this.clone( true ); - jQuery( insert[ i ] )[ original ]( elems ); - - // Support: Android <=4.0 only, PhantomJS 1 only - // .get() because push.apply(_, arraylike) throws on ancient WebKit - push.apply( ret, elems.get() ); - } - - return this.pushStack( ret ); - }; -} ); -var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); - -var getStyles = function( elem ) { - - // Support: IE <=11 only, Firefox <=30 (#15098, #14150) - // IE throws on elements created in popups - // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" - var view = elem.ownerDocument.defaultView; - - if ( !view || !view.opener ) { - view = window; - } - - return view.getComputedStyle( elem ); - }; - -var swap = function( elem, options, callback ) { - var ret, name, - old = {}; - - // Remember the old values, and insert the new ones - for ( name in options ) { - old[ name ] = elem.style[ name ]; - elem.style[ name ] = options[ name ]; - } - - ret = callback.call( elem ); - - // Revert the old values - for ( name in options ) { - elem.style[ name ] = old[ name ]; - } - - return ret; -}; - - -var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); - - - -( function() { - - // Executing both pixelPosition & boxSizingReliable tests require only one layout - // so they're executed at the same time to save the second computation. - function computeStyleTests() { - - // This is a singleton, we need to execute it only once - if ( !div ) { - return; - } - - container.style.cssText = "position:absolute;left:-11111px;width:60px;" + - "margin-top:1px;padding:0;border:0"; - div.style.cssText = - "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + - "margin:auto;border:1px;padding:1px;" + - "width:60%;top:1%"; - documentElement.appendChild( container ).appendChild( div ); - - var divStyle = window.getComputedStyle( div ); - pixelPositionVal = divStyle.top !== "1%"; - - // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 - reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; - - // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 - // Some styles come back with percentage values, even though they shouldn't - div.style.right = "60%"; - pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; - - // Support: IE 9 - 11 only - // Detect misreporting of content dimensions for box-sizing:border-box elements - boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; - - // Support: IE 9 only - // Detect overflow:scroll screwiness (gh-3699) - // Support: Chrome <=64 - // Don't get tricked when zoom affects offsetWidth (gh-4029) - div.style.position = "absolute"; - scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; - - documentElement.removeChild( container ); - - // Nullify the div so it wouldn't be stored in the memory and - // it will also be a sign that checks already performed - div = null; - } - - function roundPixelMeasures( measure ) { - return Math.round( parseFloat( measure ) ); - } - - var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, - reliableTrDimensionsVal, reliableMarginLeftVal, - container = document.createElement( "div" ), - div = document.createElement( "div" ); - - // Finish early in limited (non-browser) environments - if ( !div.style ) { - return; - } - - // Support: IE <=9 - 11 only - // Style of cloned element affects source element cloned (#8908) - div.style.backgroundClip = "content-box"; - div.cloneNode( true ).style.backgroundClip = ""; - support.clearCloneStyle = div.style.backgroundClip === "content-box"; - - jQuery.extend( support, { - boxSizingReliable: function() { - computeStyleTests(); - return boxSizingReliableVal; - }, - pixelBoxStyles: function() { - computeStyleTests(); - return pixelBoxStylesVal; - }, - pixelPosition: function() { - computeStyleTests(); - return pixelPositionVal; - }, - reliableMarginLeft: function() { - computeStyleTests(); - return reliableMarginLeftVal; - }, - scrollboxSize: function() { - computeStyleTests(); - return scrollboxSizeVal; - }, - - // Support: IE 9 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Behavior in IE 9 is more subtle than in newer versions & it passes - // some versions of this test; make sure not to make it pass there! - reliableTrDimensions: function() { - var table, tr, trChild, trStyle; - if ( reliableTrDimensionsVal == null ) { - table = document.createElement( "table" ); - tr = document.createElement( "tr" ); - trChild = document.createElement( "div" ); - - table.style.cssText = "position:absolute;left:-11111px"; - tr.style.height = "1px"; - trChild.style.height = "9px"; - - documentElement - .appendChild( table ) - .appendChild( tr ) - .appendChild( trChild ); - - trStyle = window.getComputedStyle( tr ); - reliableTrDimensionsVal = parseInt( trStyle.height ) > 3; - - documentElement.removeChild( table ); - } - return reliableTrDimensionsVal; - } - } ); -} )(); - - -function curCSS( elem, name, computed ) { - var width, minWidth, maxWidth, ret, - - // Support: Firefox 51+ - // Retrieving style before computed somehow - // fixes an issue with getting wrong values - // on detached elements - style = elem.style; - - computed = computed || getStyles( elem ); - - // getPropertyValue is needed for: - // .css('filter') (IE 9 only, #12537) - // .css('--customProperty) (#3144) - if ( computed ) { - ret = computed.getPropertyValue( name ) || computed[ name ]; - - if ( ret === "" && !isAttached( elem ) ) { - ret = jQuery.style( elem, name ); - } - - // A tribute to the "awesome hack by Dean Edwards" - // Android Browser returns percentage for some values, - // but width seems to be reliably pixels. - // This is against the CSSOM draft spec: - // https://drafts.csswg.org/cssom/#resolved-values - if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { - - // Remember the original values - width = style.width; - minWidth = style.minWidth; - maxWidth = style.maxWidth; - - // Put in the new values to get a computed value out - style.minWidth = style.maxWidth = style.width = ret; - ret = computed.width; - - // Revert the changed values - style.width = width; - style.minWidth = minWidth; - style.maxWidth = maxWidth; - } - } - - return ret !== undefined ? - - // Support: IE <=9 - 11 only - // IE returns zIndex value as an integer. - ret + "" : - ret; -} - - -function addGetHookIf( conditionFn, hookFn ) { - - // Define the hook, we'll check on the first run if it's really needed. - return { - get: function() { - if ( conditionFn() ) { - - // Hook not needed (or it's not possible to use it due - // to missing dependency), remove it. - delete this.get; - return; - } - - // Hook needed; redefine it so that the support test is not executed again. - return ( this.get = hookFn ).apply( this, arguments ); - } - }; -} - - -var cssPrefixes = [ "Webkit", "Moz", "ms" ], - emptyStyle = document.createElement( "div" ).style, - vendorProps = {}; - -// Return a vendor-prefixed property or undefined -function vendorPropName( name ) { - - // Check for vendor prefixed names - var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), - i = cssPrefixes.length; - - while ( i-- ) { - name = cssPrefixes[ i ] + capName; - if ( name in emptyStyle ) { - return name; - } - } -} - -// Return a potentially-mapped jQuery.cssProps or vendor prefixed property -function finalPropName( name ) { - var final = jQuery.cssProps[ name ] || vendorProps[ name ]; - - if ( final ) { - return final; - } - if ( name in emptyStyle ) { - return name; - } - return vendorProps[ name ] = vendorPropName( name ) || name; -} - - -var - - // Swappable if display is none or starts with table - // except "table", "table-cell", or "table-caption" - // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display - rdisplayswap = /^(none|table(?!-c[ea]).+)/, - rcustomProp = /^--/, - cssShow = { position: "absolute", visibility: "hidden", display: "block" }, - cssNormalTransform = { - letterSpacing: "0", - fontWeight: "400" - }; - -function setPositiveNumber( _elem, value, subtract ) { - - // Any relative (+/-) values have already been - // normalized at this point - var matches = rcssNum.exec( value ); - return matches ? - - // Guard against undefined "subtract", e.g., when used as in cssHooks - Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : - value; -} - -function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { - var i = dimension === "width" ? 1 : 0, - extra = 0, - delta = 0; - - // Adjustment may not be necessary - if ( box === ( isBorderBox ? "border" : "content" ) ) { - return 0; - } - - for ( ; i < 4; i += 2 ) { - - // Both box models exclude margin - if ( box === "margin" ) { - delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); - } - - // If we get here with a content-box, we're seeking "padding" or "border" or "margin" - if ( !isBorderBox ) { - - // Add padding - delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - - // For "border" or "margin", add border - if ( box !== "padding" ) { - delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - - // But still keep track of it otherwise - } else { - extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - - // If we get here with a border-box (content + padding + border), we're seeking "content" or - // "padding" or "margin" - } else { - - // For "content", subtract padding - if ( box === "content" ) { - delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); - } - - // For "content" or "padding", subtract border - if ( box !== "margin" ) { - delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); - } - } - } - - // Account for positive content-box scroll gutter when requested by providing computedVal - if ( !isBorderBox && computedVal >= 0 ) { - - // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border - // Assuming integer scroll gutter, subtract the rest and round down - delta += Math.max( 0, Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - computedVal - - delta - - extra - - 0.5 - - // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter - // Use an explicit zero to avoid NaN (gh-3964) - ) ) || 0; - } - - return delta; -} - -function getWidthOrHeight( elem, dimension, extra ) { - - // Start with computed style - var styles = getStyles( elem ), - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). - // Fake content-box until we know it's needed to know the true value. - boxSizingNeeded = !support.boxSizingReliable() || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - valueIsBorderBox = isBorderBox, - - val = curCSS( elem, dimension, styles ), - offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); - - // Support: Firefox <=54 - // Return a confounding non-pixel value or feign ignorance, as appropriate. - if ( rnumnonpx.test( val ) ) { - if ( !extra ) { - return val; - } - val = "auto"; - } - - - // Support: IE 9 - 11 only - // Use offsetWidth/offsetHeight for when box sizing is unreliable. - // In those cases, the computed value can be trusted to be border-box. - if ( ( !support.boxSizingReliable() && isBorderBox || - - // Support: IE 10 - 11+, Edge 15 - 18+ - // IE/Edge misreport `getComputedStyle` of table rows with width/height - // set in CSS while `offset*` properties report correct values. - // Interestingly, in some cases IE 9 doesn't suffer from this issue. - !support.reliableTrDimensions() && nodeName( elem, "tr" ) || - - // Fall back to offsetWidth/offsetHeight when value is "auto" - // This happens for inline elements with no explicit setting (gh-3571) - val === "auto" || - - // Support: Android <=4.1 - 4.3 only - // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) - !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && - - // Make sure the element is visible & connected - elem.getClientRects().length ) { - - isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; - - // Where available, offsetWidth/offsetHeight approximate border box dimensions. - // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the - // retrieved value as a content box dimension. - valueIsBorderBox = offsetProp in elem; - if ( valueIsBorderBox ) { - val = elem[ offsetProp ]; - } - } - - // Normalize "" and auto - val = parseFloat( val ) || 0; - - // Adjust for the element's box model - return ( val + - boxModelAdjustment( - elem, - dimension, - extra || ( isBorderBox ? "border" : "content" ), - valueIsBorderBox, - styles, - - // Provide the current computed size to request scroll gutter calculation (gh-3589) - val - ) - ) + "px"; -} - -jQuery.extend( { - - // Add in style property hooks for overriding the default - // behavior of getting and setting a style property - cssHooks: { - opacity: { - get: function( elem, computed ) { - if ( computed ) { - - // We should always get a number back from opacity - var ret = curCSS( elem, "opacity" ); - return ret === "" ? "1" : ret; - } - } - } - }, - - // Don't automatically add "px" to these possibly-unitless properties - cssNumber: { - "animationIterationCount": true, - "columnCount": true, - "fillOpacity": true, - "flexGrow": true, - "flexShrink": true, - "fontWeight": true, - "gridArea": true, - "gridColumn": true, - "gridColumnEnd": true, - "gridColumnStart": true, - "gridRow": true, - "gridRowEnd": true, - "gridRowStart": true, - "lineHeight": true, - "opacity": true, - "order": true, - "orphans": true, - "widows": true, - "zIndex": true, - "zoom": true - }, - - // Add in properties whose names you wish to fix before - // setting or getting the value - cssProps: {}, - - // Get and set the style property on a DOM Node - style: function( elem, name, value, extra ) { - - // Don't set styles on text and comment nodes - if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { - return; - } - - // Make sure that we're working with the right name - var ret, type, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ), - style = elem.style; - - // Make sure that we're working with the right name. We don't - // want to query the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Gets hook for the prefixed version, then unprefixed version - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // Check if we're setting a value - if ( value !== undefined ) { - type = typeof value; - - // Convert "+=" or "-=" to relative numbers (#7345) - if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { - value = adjustCSS( elem, name, ret ); - - // Fixes bug #9237 - type = "number"; - } - - // Make sure that null and NaN values aren't set (#7116) - if ( value == null || value !== value ) { - return; - } - - // If a number was passed in, add the unit (except for certain CSS properties) - // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append - // "px" to a few hardcoded values. - if ( type === "number" && !isCustomProp ) { - value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); - } - - // background-* props affect original clone's values - if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { - style[ name ] = "inherit"; - } - - // If a hook was provided, use that value, otherwise just set the specified value - if ( !hooks || !( "set" in hooks ) || - ( value = hooks.set( elem, value, extra ) ) !== undefined ) { - - if ( isCustomProp ) { - style.setProperty( name, value ); - } else { - style[ name ] = value; - } - } - - } else { - - // If a hook was provided get the non-computed value from there - if ( hooks && "get" in hooks && - ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { - - return ret; - } - - // Otherwise just get the value from the style object - return style[ name ]; - } - }, - - css: function( elem, name, extra, styles ) { - var val, num, hooks, - origName = camelCase( name ), - isCustomProp = rcustomProp.test( name ); - - // Make sure that we're working with the right name. We don't - // want to modify the value if it is a CSS custom property - // since they are user-defined. - if ( !isCustomProp ) { - name = finalPropName( origName ); - } - - // Try prefixed name followed by the unprefixed name - hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; - - // If a hook was provided get the computed value from there - if ( hooks && "get" in hooks ) { - val = hooks.get( elem, true, extra ); - } - - // Otherwise, if a way to get the computed value exists, use that - if ( val === undefined ) { - val = curCSS( elem, name, styles ); - } - - // Convert "normal" to computed value - if ( val === "normal" && name in cssNormalTransform ) { - val = cssNormalTransform[ name ]; - } - - // Make numeric if forced or a qualifier was provided and val looks numeric - if ( extra === "" || extra ) { - num = parseFloat( val ); - return extra === true || isFinite( num ) ? num || 0 : val; - } - - return val; - } -} ); - -jQuery.each( [ "height", "width" ], function( _i, dimension ) { - jQuery.cssHooks[ dimension ] = { - get: function( elem, computed, extra ) { - if ( computed ) { - - // Certain elements can have dimension info if we invisibly show them - // but it must have a current display style that would benefit - return rdisplayswap.test( jQuery.css( elem, "display" ) ) && - - // Support: Safari 8+ - // Table columns in Safari have non-zero offsetWidth & zero - // getBoundingClientRect().width unless display is changed. - // Support: IE <=11 only - // Running getBoundingClientRect on a disconnected node - // in IE throws an error. - ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? - swap( elem, cssShow, function() { - return getWidthOrHeight( elem, dimension, extra ); - } ) : - getWidthOrHeight( elem, dimension, extra ); - } - }, - - set: function( elem, value, extra ) { - var matches, - styles = getStyles( elem ), - - // Only read styles.position if the test has a chance to fail - // to avoid forcing a reflow. - scrollboxSizeBuggy = !support.scrollboxSize() && - styles.position === "absolute", - - // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) - boxSizingNeeded = scrollboxSizeBuggy || extra, - isBorderBox = boxSizingNeeded && - jQuery.css( elem, "boxSizing", false, styles ) === "border-box", - subtract = extra ? - boxModelAdjustment( - elem, - dimension, - extra, - isBorderBox, - styles - ) : - 0; - - // Account for unreliable border-box dimensions by comparing offset* to computed and - // faking a content-box to get border and padding (gh-3699) - if ( isBorderBox && scrollboxSizeBuggy ) { - subtract -= Math.ceil( - elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - - parseFloat( styles[ dimension ] ) - - boxModelAdjustment( elem, dimension, "border", false, styles ) - - 0.5 - ); - } - - // Convert to pixels if value adjustment is needed - if ( subtract && ( matches = rcssNum.exec( value ) ) && - ( matches[ 3 ] || "px" ) !== "px" ) { - - elem.style[ dimension ] = value; - value = jQuery.css( elem, dimension ); - } - - return setPositiveNumber( elem, value, subtract ); - } - }; -} ); - -jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, - function( elem, computed ) { - if ( computed ) { - return ( parseFloat( curCSS( elem, "marginLeft" ) ) || - elem.getBoundingClientRect().left - - swap( elem, { marginLeft: 0 }, function() { - return elem.getBoundingClientRect().left; - } ) - ) + "px"; - } - } -); - -// These hooks are used by animate to expand properties -jQuery.each( { - margin: "", - padding: "", - border: "Width" -}, function( prefix, suffix ) { - jQuery.cssHooks[ prefix + suffix ] = { - expand: function( value ) { - var i = 0, - expanded = {}, - - // Assumes a single number if not a string - parts = typeof value === "string" ? value.split( " " ) : [ value ]; - - for ( ; i < 4; i++ ) { - expanded[ prefix + cssExpand[ i ] + suffix ] = - parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; - } - - return expanded; - } - }; - - if ( prefix !== "margin" ) { - jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; - } -} ); - -jQuery.fn.extend( { - css: function( name, value ) { - return access( this, function( elem, name, value ) { - var styles, len, - map = {}, - i = 0; - - if ( Array.isArray( name ) ) { - styles = getStyles( elem ); - len = name.length; - - for ( ; i < len; i++ ) { - map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); - } - - return map; - } - - return value !== undefined ? - jQuery.style( elem, name, value ) : - jQuery.css( elem, name ); - }, name, value, arguments.length > 1 ); - } -} ); - - -function Tween( elem, options, prop, end, easing ) { - return new Tween.prototype.init( elem, options, prop, end, easing ); -} -jQuery.Tween = Tween; - -Tween.prototype = { - constructor: Tween, - init: function( elem, options, prop, end, easing, unit ) { - this.elem = elem; - this.prop = prop; - this.easing = easing || jQuery.easing._default; - this.options = options; - this.start = this.now = this.cur(); - this.end = end; - this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); - }, - cur: function() { - var hooks = Tween.propHooks[ this.prop ]; - - return hooks && hooks.get ? - hooks.get( this ) : - Tween.propHooks._default.get( this ); - }, - run: function( percent ) { - var eased, - hooks = Tween.propHooks[ this.prop ]; - - if ( this.options.duration ) { - this.pos = eased = jQuery.easing[ this.easing ]( - percent, this.options.duration * percent, 0, 1, this.options.duration - ); - } else { - this.pos = eased = percent; - } - this.now = ( this.end - this.start ) * eased + this.start; - - if ( this.options.step ) { - this.options.step.call( this.elem, this.now, this ); - } - - if ( hooks && hooks.set ) { - hooks.set( this ); - } else { - Tween.propHooks._default.set( this ); - } - return this; - } -}; - -Tween.prototype.init.prototype = Tween.prototype; - -Tween.propHooks = { - _default: { - get: function( tween ) { - var result; - - // Use a property on the element directly when it is not a DOM element, - // or when there is no matching style property that exists. - if ( tween.elem.nodeType !== 1 || - tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { - return tween.elem[ tween.prop ]; - } - - // Passing an empty string as a 3rd parameter to .css will automatically - // attempt a parseFloat and fallback to a string if the parse fails. - // Simple values such as "10px" are parsed to Float; - // complex values such as "rotate(1rad)" are returned as-is. - result = jQuery.css( tween.elem, tween.prop, "" ); - - // Empty strings, null, undefined and "auto" are converted to 0. - return !result || result === "auto" ? 0 : result; - }, - set: function( tween ) { - - // Use step hook for back compat. - // Use cssHook if its there. - // Use .style if available and use plain properties where available. - if ( jQuery.fx.step[ tween.prop ] ) { - jQuery.fx.step[ tween.prop ]( tween ); - } else if ( tween.elem.nodeType === 1 && ( - jQuery.cssHooks[ tween.prop ] || - tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { - jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); - } else { - tween.elem[ tween.prop ] = tween.now; - } - } - } -}; - -// Support: IE <=9 only -// Panic based approach to setting things on disconnected nodes -Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { - set: function( tween ) { - if ( tween.elem.nodeType && tween.elem.parentNode ) { - tween.elem[ tween.prop ] = tween.now; - } - } -}; - -jQuery.easing = { - linear: function( p ) { - return p; - }, - swing: function( p ) { - return 0.5 - Math.cos( p * Math.PI ) / 2; - }, - _default: "swing" -}; - -jQuery.fx = Tween.prototype.init; - -// Back compat <1.8 extension point -jQuery.fx.step = {}; - - - - -var - fxNow, inProgress, - rfxtypes = /^(?:toggle|show|hide)$/, - rrun = /queueHooks$/; - -function schedule() { - if ( inProgress ) { - if ( document.hidden === false && window.requestAnimationFrame ) { - window.requestAnimationFrame( schedule ); - } else { - window.setTimeout( schedule, jQuery.fx.interval ); - } - - jQuery.fx.tick(); - } -} - -// Animations created synchronously will run synchronously -function createFxNow() { - window.setTimeout( function() { - fxNow = undefined; - } ); - return ( fxNow = Date.now() ); -} - -// Generate parameters to create a standard animation -function genFx( type, includeWidth ) { - var which, - i = 0, - attrs = { height: type }; - - // If we include width, step value is 1 to do all cssExpand values, - // otherwise step value is 2 to skip over Left and Right - includeWidth = includeWidth ? 1 : 0; - for ( ; i < 4; i += 2 - includeWidth ) { - which = cssExpand[ i ]; - attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; - } - - if ( includeWidth ) { - attrs.opacity = attrs.width = type; - } - - return attrs; -} - -function createTween( value, prop, animation ) { - var tween, - collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), - index = 0, - length = collection.length; - for ( ; index < length; index++ ) { - if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { - - // We're done with this property - return tween; - } - } -} - -function defaultPrefilter( elem, props, opts ) { - var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, - isBox = "width" in props || "height" in props, - anim = this, - orig = {}, - style = elem.style, - hidden = elem.nodeType && isHiddenWithinTree( elem ), - dataShow = dataPriv.get( elem, "fxshow" ); - - // Queue-skipping animations hijack the fx hooks - if ( !opts.queue ) { - hooks = jQuery._queueHooks( elem, "fx" ); - if ( hooks.unqueued == null ) { - hooks.unqueued = 0; - oldfire = hooks.empty.fire; - hooks.empty.fire = function() { - if ( !hooks.unqueued ) { - oldfire(); - } - }; - } - hooks.unqueued++; - - anim.always( function() { - - // Ensure the complete handler is called before this completes - anim.always( function() { - hooks.unqueued--; - if ( !jQuery.queue( elem, "fx" ).length ) { - hooks.empty.fire(); - } - } ); - } ); - } - - // Detect show/hide animations - for ( prop in props ) { - value = props[ prop ]; - if ( rfxtypes.test( value ) ) { - delete props[ prop ]; - toggle = toggle || value === "toggle"; - if ( value === ( hidden ? "hide" : "show" ) ) { - - // Pretend to be hidden if this is a "show" and - // there is still data from a stopped show/hide - if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { - hidden = true; - - // Ignore all other no-op show/hide data - } else { - continue; - } - } - orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); - } - } - - // Bail out if this is a no-op like .hide().hide() - propTween = !jQuery.isEmptyObject( props ); - if ( !propTween && jQuery.isEmptyObject( orig ) ) { - return; - } - - // Restrict "overflow" and "display" styles during box animations - if ( isBox && elem.nodeType === 1 ) { - - // Support: IE <=9 - 11, Edge 12 - 15 - // Record all 3 overflow attributes because IE does not infer the shorthand - // from identically-valued overflowX and overflowY and Edge just mirrors - // the overflowX value there. - opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; - - // Identify a display type, preferring old show/hide data over the CSS cascade - restoreDisplay = dataShow && dataShow.display; - if ( restoreDisplay == null ) { - restoreDisplay = dataPriv.get( elem, "display" ); - } - display = jQuery.css( elem, "display" ); - if ( display === "none" ) { - if ( restoreDisplay ) { - display = restoreDisplay; - } else { - - // Get nonempty value(s) by temporarily forcing visibility - showHide( [ elem ], true ); - restoreDisplay = elem.style.display || restoreDisplay; - display = jQuery.css( elem, "display" ); - showHide( [ elem ] ); - } - } - - // Animate inline elements as inline-block - if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { - if ( jQuery.css( elem, "float" ) === "none" ) { - - // Restore the original display value at the end of pure show/hide animations - if ( !propTween ) { - anim.done( function() { - style.display = restoreDisplay; - } ); - if ( restoreDisplay == null ) { - display = style.display; - restoreDisplay = display === "none" ? "" : display; - } - } - style.display = "inline-block"; - } - } - } - - if ( opts.overflow ) { - style.overflow = "hidden"; - anim.always( function() { - style.overflow = opts.overflow[ 0 ]; - style.overflowX = opts.overflow[ 1 ]; - style.overflowY = opts.overflow[ 2 ]; - } ); - } - - // Implement show/hide animations - propTween = false; - for ( prop in orig ) { - - // General show/hide setup for this element animation - if ( !propTween ) { - if ( dataShow ) { - if ( "hidden" in dataShow ) { - hidden = dataShow.hidden; - } - } else { - dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); - } - - // Store hidden/visible for toggle so `.stop().toggle()` "reverses" - if ( toggle ) { - dataShow.hidden = !hidden; - } - - // Show elements before animating them - if ( hidden ) { - showHide( [ elem ], true ); - } - - /* eslint-disable no-loop-func */ - - anim.done( function() { - - /* eslint-enable no-loop-func */ - - // The final step of a "hide" animation is actually hiding the element - if ( !hidden ) { - showHide( [ elem ] ); - } - dataPriv.remove( elem, "fxshow" ); - for ( prop in orig ) { - jQuery.style( elem, prop, orig[ prop ] ); - } - } ); - } - - // Per-property setup - propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); - if ( !( prop in dataShow ) ) { - dataShow[ prop ] = propTween.start; - if ( hidden ) { - propTween.end = propTween.start; - propTween.start = 0; - } - } - } -} - -function propFilter( props, specialEasing ) { - var index, name, easing, value, hooks; - - // camelCase, specialEasing and expand cssHook pass - for ( index in props ) { - name = camelCase( index ); - easing = specialEasing[ name ]; - value = props[ index ]; - if ( Array.isArray( value ) ) { - easing = value[ 1 ]; - value = props[ index ] = value[ 0 ]; - } - - if ( index !== name ) { - props[ name ] = value; - delete props[ index ]; - } - - hooks = jQuery.cssHooks[ name ]; - if ( hooks && "expand" in hooks ) { - value = hooks.expand( value ); - delete props[ name ]; - - // Not quite $.extend, this won't overwrite existing keys. - // Reusing 'index' because we have the correct "name" - for ( index in value ) { - if ( !( index in props ) ) { - props[ index ] = value[ index ]; - specialEasing[ index ] = easing; - } - } - } else { - specialEasing[ name ] = easing; - } - } -} - -function Animation( elem, properties, options ) { - var result, - stopped, - index = 0, - length = Animation.prefilters.length, - deferred = jQuery.Deferred().always( function() { - - // Don't match elem in the :animated selector - delete tick.elem; - } ), - tick = function() { - if ( stopped ) { - return false; - } - var currentTime = fxNow || createFxNow(), - remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), - - // Support: Android 2.3 only - // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) - temp = remaining / animation.duration || 0, - percent = 1 - temp, - index = 0, - length = animation.tweens.length; - - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( percent ); - } - - deferred.notifyWith( elem, [ animation, percent, remaining ] ); - - // If there's more to do, yield - if ( percent < 1 && length ) { - return remaining; - } - - // If this was an empty animation, synthesize a final progress notification - if ( !length ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - } - - // Resolve the animation and report its conclusion - deferred.resolveWith( elem, [ animation ] ); - return false; - }, - animation = deferred.promise( { - elem: elem, - props: jQuery.extend( {}, properties ), - opts: jQuery.extend( true, { - specialEasing: {}, - easing: jQuery.easing._default - }, options ), - originalProperties: properties, - originalOptions: options, - startTime: fxNow || createFxNow(), - duration: options.duration, - tweens: [], - createTween: function( prop, end ) { - var tween = jQuery.Tween( elem, animation.opts, prop, end, - animation.opts.specialEasing[ prop ] || animation.opts.easing ); - animation.tweens.push( tween ); - return tween; - }, - stop: function( gotoEnd ) { - var index = 0, - - // If we are going to the end, we want to run all the tweens - // otherwise we skip this part - length = gotoEnd ? animation.tweens.length : 0; - if ( stopped ) { - return this; - } - stopped = true; - for ( ; index < length; index++ ) { - animation.tweens[ index ].run( 1 ); - } - - // Resolve when we played the last frame; otherwise, reject - if ( gotoEnd ) { - deferred.notifyWith( elem, [ animation, 1, 0 ] ); - deferred.resolveWith( elem, [ animation, gotoEnd ] ); - } else { - deferred.rejectWith( elem, [ animation, gotoEnd ] ); - } - return this; - } - } ), - props = animation.props; - - propFilter( props, animation.opts.specialEasing ); - - for ( ; index < length; index++ ) { - result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); - if ( result ) { - if ( isFunction( result.stop ) ) { - jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = - result.stop.bind( result ); - } - return result; - } - } - - jQuery.map( props, createTween, animation ); - - if ( isFunction( animation.opts.start ) ) { - animation.opts.start.call( elem, animation ); - } - - // Attach callbacks from options - animation - .progress( animation.opts.progress ) - .done( animation.opts.done, animation.opts.complete ) - .fail( animation.opts.fail ) - .always( animation.opts.always ); - - jQuery.fx.timer( - jQuery.extend( tick, { - elem: elem, - anim: animation, - queue: animation.opts.queue - } ) - ); - - return animation; -} - -jQuery.Animation = jQuery.extend( Animation, { - - tweeners: { - "*": [ function( prop, value ) { - var tween = this.createTween( prop, value ); - adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); - return tween; - } ] - }, - - tweener: function( props, callback ) { - if ( isFunction( props ) ) { - callback = props; - props = [ "*" ]; - } else { - props = props.match( rnothtmlwhite ); - } - - var prop, - index = 0, - length = props.length; - - for ( ; index < length; index++ ) { - prop = props[ index ]; - Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; - Animation.tweeners[ prop ].unshift( callback ); - } - }, - - prefilters: [ defaultPrefilter ], - - prefilter: function( callback, prepend ) { - if ( prepend ) { - Animation.prefilters.unshift( callback ); - } else { - Animation.prefilters.push( callback ); - } - } -} ); - -jQuery.speed = function( speed, easing, fn ) { - var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { - complete: fn || !fn && easing || - isFunction( speed ) && speed, - duration: speed, - easing: fn && easing || easing && !isFunction( easing ) && easing - }; - - // Go to the end state if fx are off - if ( jQuery.fx.off ) { - opt.duration = 0; - - } else { - if ( typeof opt.duration !== "number" ) { - if ( opt.duration in jQuery.fx.speeds ) { - opt.duration = jQuery.fx.speeds[ opt.duration ]; - - } else { - opt.duration = jQuery.fx.speeds._default; - } - } - } - - // Normalize opt.queue - true/undefined/null -> "fx" - if ( opt.queue == null || opt.queue === true ) { - opt.queue = "fx"; - } - - // Queueing - opt.old = opt.complete; - - opt.complete = function() { - if ( isFunction( opt.old ) ) { - opt.old.call( this ); - } - - if ( opt.queue ) { - jQuery.dequeue( this, opt.queue ); - } - }; - - return opt; -}; - -jQuery.fn.extend( { - fadeTo: function( speed, to, easing, callback ) { - - // Show any hidden elements after setting opacity to 0 - return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() - - // Animate to the value specified - .end().animate( { opacity: to }, speed, easing, callback ); - }, - animate: function( prop, speed, easing, callback ) { - var empty = jQuery.isEmptyObject( prop ), - optall = jQuery.speed( speed, easing, callback ), - doAnimation = function() { - - // Operate on a copy of prop so per-property easing won't be lost - var anim = Animation( this, jQuery.extend( {}, prop ), optall ); - - // Empty animations, or finishing resolves immediately - if ( empty || dataPriv.get( this, "finish" ) ) { - anim.stop( true ); - } - }; - doAnimation.finish = doAnimation; - - return empty || optall.queue === false ? - this.each( doAnimation ) : - this.queue( optall.queue, doAnimation ); - }, - stop: function( type, clearQueue, gotoEnd ) { - var stopQueue = function( hooks ) { - var stop = hooks.stop; - delete hooks.stop; - stop( gotoEnd ); - }; - - if ( typeof type !== "string" ) { - gotoEnd = clearQueue; - clearQueue = type; - type = undefined; - } - if ( clearQueue ) { - this.queue( type || "fx", [] ); - } - - return this.each( function() { - var dequeue = true, - index = type != null && type + "queueHooks", - timers = jQuery.timers, - data = dataPriv.get( this ); - - if ( index ) { - if ( data[ index ] && data[ index ].stop ) { - stopQueue( data[ index ] ); - } - } else { - for ( index in data ) { - if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { - stopQueue( data[ index ] ); - } - } - } - - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && - ( type == null || timers[ index ].queue === type ) ) { - - timers[ index ].anim.stop( gotoEnd ); - dequeue = false; - timers.splice( index, 1 ); - } - } - - // Start the next in the queue if the last step wasn't forced. - // Timers currently will call their complete callbacks, which - // will dequeue but only if they were gotoEnd. - if ( dequeue || !gotoEnd ) { - jQuery.dequeue( this, type ); - } - } ); - }, - finish: function( type ) { - if ( type !== false ) { - type = type || "fx"; - } - return this.each( function() { - var index, - data = dataPriv.get( this ), - queue = data[ type + "queue" ], - hooks = data[ type + "queueHooks" ], - timers = jQuery.timers, - length = queue ? queue.length : 0; - - // Enable finishing flag on private data - data.finish = true; - - // Empty the queue first - jQuery.queue( this, type, [] ); - - if ( hooks && hooks.stop ) { - hooks.stop.call( this, true ); - } - - // Look for any active animations, and finish them - for ( index = timers.length; index--; ) { - if ( timers[ index ].elem === this && timers[ index ].queue === type ) { - timers[ index ].anim.stop( true ); - timers.splice( index, 1 ); - } - } - - // Look for any animations in the old queue and finish them - for ( index = 0; index < length; index++ ) { - if ( queue[ index ] && queue[ index ].finish ) { - queue[ index ].finish.call( this ); - } - } - - // Turn off finishing flag - delete data.finish; - } ); - } -} ); - -jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { - var cssFn = jQuery.fn[ name ]; - jQuery.fn[ name ] = function( speed, easing, callback ) { - return speed == null || typeof speed === "boolean" ? - cssFn.apply( this, arguments ) : - this.animate( genFx( name, true ), speed, easing, callback ); - }; -} ); - -// Generate shortcuts for custom animations -jQuery.each( { - slideDown: genFx( "show" ), - slideUp: genFx( "hide" ), - slideToggle: genFx( "toggle" ), - fadeIn: { opacity: "show" }, - fadeOut: { opacity: "hide" }, - fadeToggle: { opacity: "toggle" } -}, function( name, props ) { - jQuery.fn[ name ] = function( speed, easing, callback ) { - return this.animate( props, speed, easing, callback ); - }; -} ); - -jQuery.timers = []; -jQuery.fx.tick = function() { - var timer, - i = 0, - timers = jQuery.timers; - - fxNow = Date.now(); - - for ( ; i < timers.length; i++ ) { - timer = timers[ i ]; - - // Run the timer and safely remove it when done (allowing for external removal) - if ( !timer() && timers[ i ] === timer ) { - timers.splice( i--, 1 ); - } - } - - if ( !timers.length ) { - jQuery.fx.stop(); - } - fxNow = undefined; -}; - -jQuery.fx.timer = function( timer ) { - jQuery.timers.push( timer ); - jQuery.fx.start(); -}; - -jQuery.fx.interval = 13; -jQuery.fx.start = function() { - if ( inProgress ) { - return; - } - - inProgress = true; - schedule(); -}; - -jQuery.fx.stop = function() { - inProgress = null; -}; - -jQuery.fx.speeds = { - slow: 600, - fast: 200, - - // Default speed - _default: 400 -}; - - -// Based off of the plugin by Clint Helfers, with permission. -// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ -jQuery.fn.delay = function( time, type ) { - time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; - type = type || "fx"; - - return this.queue( type, function( next, hooks ) { - var timeout = window.setTimeout( next, time ); - hooks.stop = function() { - window.clearTimeout( timeout ); - }; - } ); -}; - - -( function() { - var input = document.createElement( "input" ), - select = document.createElement( "select" ), - opt = select.appendChild( document.createElement( "option" ) ); - - input.type = "checkbox"; - - // Support: Android <=4.3 only - // Default value for a checkbox should be "on" - support.checkOn = input.value !== ""; - - // Support: IE <=11 only - // Must access selectedIndex to make default options select - support.optSelected = opt.selected; - - // Support: IE <=11 only - // An input loses its value after becoming a radio - input = document.createElement( "input" ); - input.value = "t"; - input.type = "radio"; - support.radioValue = input.value === "t"; -} )(); - - -var boolHook, - attrHandle = jQuery.expr.attrHandle; - -jQuery.fn.extend( { - attr: function( name, value ) { - return access( this, jQuery.attr, name, value, arguments.length > 1 ); - }, - - removeAttr: function( name ) { - return this.each( function() { - jQuery.removeAttr( this, name ); - } ); - } -} ); - -jQuery.extend( { - attr: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set attributes on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - // Fallback to prop when attributes are not supported - if ( typeof elem.getAttribute === "undefined" ) { - return jQuery.prop( elem, name, value ); - } - - // Attribute hooks are determined by the lowercase version - // Grab necessary hook if one is defined - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - hooks = jQuery.attrHooks[ name.toLowerCase() ] || - ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); - } - - if ( value !== undefined ) { - if ( value === null ) { - jQuery.removeAttr( elem, name ); - return; - } - - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - elem.setAttribute( name, value + "" ); - return value; - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - ret = jQuery.find.attr( elem, name ); - - // Non-existent attributes return null, we normalize to undefined - return ret == null ? undefined : ret; - }, - - attrHooks: { - type: { - set: function( elem, value ) { - if ( !support.radioValue && value === "radio" && - nodeName( elem, "input" ) ) { - var val = elem.value; - elem.setAttribute( "type", value ); - if ( val ) { - elem.value = val; - } - return value; - } - } - } - }, - - removeAttr: function( elem, value ) { - var name, - i = 0, - - // Attribute names can contain non-HTML whitespace characters - // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 - attrNames = value && value.match( rnothtmlwhite ); - - if ( attrNames && elem.nodeType === 1 ) { - while ( ( name = attrNames[ i++ ] ) ) { - elem.removeAttribute( name ); - } - } - } -} ); - -// Hooks for boolean attributes -boolHook = { - set: function( elem, value, name ) { - if ( value === false ) { - - // Remove boolean attributes when set to false - jQuery.removeAttr( elem, name ); - } else { - elem.setAttribute( name, name ); - } - return name; - } -}; - -jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { - var getter = attrHandle[ name ] || jQuery.find.attr; - - attrHandle[ name ] = function( elem, name, isXML ) { - var ret, handle, - lowercaseName = name.toLowerCase(); - - if ( !isXML ) { - - // Avoid an infinite loop by temporarily removing this function from the getter - handle = attrHandle[ lowercaseName ]; - attrHandle[ lowercaseName ] = ret; - ret = getter( elem, name, isXML ) != null ? - lowercaseName : - null; - attrHandle[ lowercaseName ] = handle; - } - return ret; - }; -} ); - - - - -var rfocusable = /^(?:input|select|textarea|button)$/i, - rclickable = /^(?:a|area)$/i; - -jQuery.fn.extend( { - prop: function( name, value ) { - return access( this, jQuery.prop, name, value, arguments.length > 1 ); - }, - - removeProp: function( name ) { - return this.each( function() { - delete this[ jQuery.propFix[ name ] || name ]; - } ); - } -} ); - -jQuery.extend( { - prop: function( elem, name, value ) { - var ret, hooks, - nType = elem.nodeType; - - // Don't get/set properties on text, comment and attribute nodes - if ( nType === 3 || nType === 8 || nType === 2 ) { - return; - } - - if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { - - // Fix name and attach hooks - name = jQuery.propFix[ name ] || name; - hooks = jQuery.propHooks[ name ]; - } - - if ( value !== undefined ) { - if ( hooks && "set" in hooks && - ( ret = hooks.set( elem, value, name ) ) !== undefined ) { - return ret; - } - - return ( elem[ name ] = value ); - } - - if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { - return ret; - } - - return elem[ name ]; - }, - - propHooks: { - tabIndex: { - get: function( elem ) { - - // Support: IE <=9 - 11 only - // elem.tabIndex doesn't always return the - // correct value when it hasn't been explicitly set - // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ - // Use proper attribute retrieval(#12072) - var tabindex = jQuery.find.attr( elem, "tabindex" ); - - if ( tabindex ) { - return parseInt( tabindex, 10 ); - } - - if ( - rfocusable.test( elem.nodeName ) || - rclickable.test( elem.nodeName ) && - elem.href - ) { - return 0; - } - - return -1; - } - } - }, - - propFix: { - "for": "htmlFor", - "class": "className" - } -} ); - -// Support: IE <=11 only -// Accessing the selectedIndex property -// forces the browser to respect setting selected -// on the option -// The getter ensures a default option is selected -// when in an optgroup -// eslint rule "no-unused-expressions" is disabled for this code -// since it considers such accessions noop -if ( !support.optSelected ) { - jQuery.propHooks.selected = { - get: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent && parent.parentNode ) { - parent.parentNode.selectedIndex; - } - return null; - }, - set: function( elem ) { - - /* eslint no-unused-expressions: "off" */ - - var parent = elem.parentNode; - if ( parent ) { - parent.selectedIndex; - - if ( parent.parentNode ) { - parent.parentNode.selectedIndex; - } - } - } - }; -} - -jQuery.each( [ - "tabIndex", - "readOnly", - "maxLength", - "cellSpacing", - "cellPadding", - "rowSpan", - "colSpan", - "useMap", - "frameBorder", - "contentEditable" -], function() { - jQuery.propFix[ this.toLowerCase() ] = this; -} ); - - - - - // Strip and collapse whitespace according to HTML spec - // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace - function stripAndCollapse( value ) { - var tokens = value.match( rnothtmlwhite ) || []; - return tokens.join( " " ); - } - - -function getClass( elem ) { - return elem.getAttribute && elem.getAttribute( "class" ) || ""; -} - -function classesToArray( value ) { - if ( Array.isArray( value ) ) { - return value; - } - if ( typeof value === "string" ) { - return value.match( rnothtmlwhite ) || []; - } - return []; -} - -jQuery.fn.extend( { - addClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - if ( cur.indexOf( " " + clazz + " " ) < 0 ) { - cur += clazz + " "; - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - removeClass: function( value ) { - var classes, elem, cur, curValue, clazz, j, finalValue, - i = 0; - - if ( isFunction( value ) ) { - return this.each( function( j ) { - jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); - } ); - } - - if ( !arguments.length ) { - return this.attr( "class", "" ); - } - - classes = classesToArray( value ); - - if ( classes.length ) { - while ( ( elem = this[ i++ ] ) ) { - curValue = getClass( elem ); - - // This expression is here for better compressibility (see addClass) - cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); - - if ( cur ) { - j = 0; - while ( ( clazz = classes[ j++ ] ) ) { - - // Remove *all* instances - while ( cur.indexOf( " " + clazz + " " ) > -1 ) { - cur = cur.replace( " " + clazz + " ", " " ); - } - } - - // Only assign if different to avoid unneeded rendering. - finalValue = stripAndCollapse( cur ); - if ( curValue !== finalValue ) { - elem.setAttribute( "class", finalValue ); - } - } - } - } - - return this; - }, - - toggleClass: function( value, stateVal ) { - var type = typeof value, - isValidValue = type === "string" || Array.isArray( value ); - - if ( typeof stateVal === "boolean" && isValidValue ) { - return stateVal ? this.addClass( value ) : this.removeClass( value ); - } - - if ( isFunction( value ) ) { - return this.each( function( i ) { - jQuery( this ).toggleClass( - value.call( this, i, getClass( this ), stateVal ), - stateVal - ); - } ); - } - - return this.each( function() { - var className, i, self, classNames; - - if ( isValidValue ) { - - // Toggle individual class names - i = 0; - self = jQuery( this ); - classNames = classesToArray( value ); - - while ( ( className = classNames[ i++ ] ) ) { - - // Check each className given, space separated list - if ( self.hasClass( className ) ) { - self.removeClass( className ); - } else { - self.addClass( className ); - } - } - - // Toggle whole class name - } else if ( value === undefined || type === "boolean" ) { - className = getClass( this ); - if ( className ) { - - // Store className if set - dataPriv.set( this, "__className__", className ); - } - - // If the element has a class name or if we're passed `false`, - // then remove the whole classname (if there was one, the above saved it). - // Otherwise bring back whatever was previously saved (if anything), - // falling back to the empty string if nothing was stored. - if ( this.setAttribute ) { - this.setAttribute( "class", - className || value === false ? - "" : - dataPriv.get( this, "__className__" ) || "" - ); - } - } - } ); - }, - - hasClass: function( selector ) { - var className, elem, - i = 0; - - className = " " + selector + " "; - while ( ( elem = this[ i++ ] ) ) { - if ( elem.nodeType === 1 && - ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { - return true; - } - } - - return false; - } -} ); - - - - -var rreturn = /\r/g; - -jQuery.fn.extend( { - val: function( value ) { - var hooks, ret, valueIsFunction, - elem = this[ 0 ]; - - if ( !arguments.length ) { - if ( elem ) { - hooks = jQuery.valHooks[ elem.type ] || - jQuery.valHooks[ elem.nodeName.toLowerCase() ]; - - if ( hooks && - "get" in hooks && - ( ret = hooks.get( elem, "value" ) ) !== undefined - ) { - return ret; - } - - ret = elem.value; - - // Handle most common string cases - if ( typeof ret === "string" ) { - return ret.replace( rreturn, "" ); - } - - // Handle cases where value is null/undef or number - return ret == null ? "" : ret; - } - - return; - } - - valueIsFunction = isFunction( value ); - - return this.each( function( i ) { - var val; - - if ( this.nodeType !== 1 ) { - return; - } - - if ( valueIsFunction ) { - val = value.call( this, i, jQuery( this ).val() ); - } else { - val = value; - } - - // Treat null/undefined as ""; convert numbers to string - if ( val == null ) { - val = ""; - - } else if ( typeof val === "number" ) { - val += ""; - - } else if ( Array.isArray( val ) ) { - val = jQuery.map( val, function( value ) { - return value == null ? "" : value + ""; - } ); - } - - hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; - - // If set returns undefined, fall back to normal setting - if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { - this.value = val; - } - } ); - } -} ); - -jQuery.extend( { - valHooks: { - option: { - get: function( elem ) { - - var val = jQuery.find.attr( elem, "value" ); - return val != null ? - val : - - // Support: IE <=10 - 11 only - // option.text throws exceptions (#14686, #14858) - // Strip and collapse whitespace - // https://html.spec.whatwg.org/#strip-and-collapse-whitespace - stripAndCollapse( jQuery.text( elem ) ); - } - }, - select: { - get: function( elem ) { - var value, option, i, - options = elem.options, - index = elem.selectedIndex, - one = elem.type === "select-one", - values = one ? null : [], - max = one ? index + 1 : options.length; - - if ( index < 0 ) { - i = max; - - } else { - i = one ? index : 0; - } - - // Loop through all the selected options - for ( ; i < max; i++ ) { - option = options[ i ]; - - // Support: IE <=9 only - // IE8-9 doesn't update selected after form reset (#2551) - if ( ( option.selected || i === index ) && - - // Don't return options that are disabled or in a disabled optgroup - !option.disabled && - ( !option.parentNode.disabled || - !nodeName( option.parentNode, "optgroup" ) ) ) { - - // Get the specific value for the option - value = jQuery( option ).val(); - - // We don't need an array for one selects - if ( one ) { - return value; - } - - // Multi-Selects return an array - values.push( value ); - } - } - - return values; - }, - - set: function( elem, value ) { - var optionSet, option, - options = elem.options, - values = jQuery.makeArray( value ), - i = options.length; - - while ( i-- ) { - option = options[ i ]; - - /* eslint-disable no-cond-assign */ - - if ( option.selected = - jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 - ) { - optionSet = true; - } - - /* eslint-enable no-cond-assign */ - } - - // Force browsers to behave consistently when non-matching value is set - if ( !optionSet ) { - elem.selectedIndex = -1; - } - return values; - } - } - } -} ); - -// Radios and checkboxes getter/setter -jQuery.each( [ "radio", "checkbox" ], function() { - jQuery.valHooks[ this ] = { - set: function( elem, value ) { - if ( Array.isArray( value ) ) { - return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); - } - } - }; - if ( !support.checkOn ) { - jQuery.valHooks[ this ].get = function( elem ) { - return elem.getAttribute( "value" ) === null ? "on" : elem.value; - }; - } -} ); - - - - -// Return jQuery for attributes-only inclusion - - -support.focusin = "onfocusin" in window; - - -var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, - stopPropagationCallback = function( e ) { - e.stopPropagation(); - }; - -jQuery.extend( jQuery.event, { - - trigger: function( event, data, elem, onlyHandlers ) { - - var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, - eventPath = [ elem || document ], - type = hasOwn.call( event, "type" ) ? event.type : event, - namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; - - cur = lastElement = tmp = elem = elem || document; - - // Don't do events on text and comment nodes - if ( elem.nodeType === 3 || elem.nodeType === 8 ) { - return; - } - - // focus/blur morphs to focusin/out; ensure we're not firing them right now - if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { - return; - } - - if ( type.indexOf( "." ) > -1 ) { - - // Namespaced trigger; create a regexp to match event type in handle() - namespaces = type.split( "." ); - type = namespaces.shift(); - namespaces.sort(); - } - ontype = type.indexOf( ":" ) < 0 && "on" + type; - - // Caller can pass in a jQuery.Event object, Object, or just an event type string - event = event[ jQuery.expando ] ? - event : - new jQuery.Event( type, typeof event === "object" && event ); - - // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) - event.isTrigger = onlyHandlers ? 2 : 3; - event.namespace = namespaces.join( "." ); - event.rnamespace = event.namespace ? - new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : - null; - - // Clean up the event in case it is being reused - event.result = undefined; - if ( !event.target ) { - event.target = elem; - } - - // Clone any incoming data and prepend the event, creating the handler arg list - data = data == null ? - [ event ] : - jQuery.makeArray( data, [ event ] ); - - // Allow special events to draw outside the lines - special = jQuery.event.special[ type ] || {}; - if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { - return; - } - - // Determine event propagation path in advance, per W3C events spec (#9951) - // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) - if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { - - bubbleType = special.delegateType || type; - if ( !rfocusMorph.test( bubbleType + type ) ) { - cur = cur.parentNode; - } - for ( ; cur; cur = cur.parentNode ) { - eventPath.push( cur ); - tmp = cur; - } - - // Only add window if we got to document (e.g., not plain obj or detached DOM) - if ( tmp === ( elem.ownerDocument || document ) ) { - eventPath.push( tmp.defaultView || tmp.parentWindow || window ); - } - } - - // Fire handlers on the event path - i = 0; - while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { - lastElement = cur; - event.type = i > 1 ? - bubbleType : - special.bindType || type; - - // jQuery handler - handle = ( - dataPriv.get( cur, "events" ) || Object.create( null ) - )[ event.type ] && - dataPriv.get( cur, "handle" ); - if ( handle ) { - handle.apply( cur, data ); - } - - // Native handler - handle = ontype && cur[ ontype ]; - if ( handle && handle.apply && acceptData( cur ) ) { - event.result = handle.apply( cur, data ); - if ( event.result === false ) { - event.preventDefault(); - } - } - } - event.type = type; - - // If nobody prevented the default action, do it now - if ( !onlyHandlers && !event.isDefaultPrevented() ) { - - if ( ( !special._default || - special._default.apply( eventPath.pop(), data ) === false ) && - acceptData( elem ) ) { - - // Call a native DOM method on the target with the same name as the event. - // Don't do default actions on window, that's where global variables be (#6170) - if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { - - // Don't re-trigger an onFOO event when we call its FOO() method - tmp = elem[ ontype ]; - - if ( tmp ) { - elem[ ontype ] = null; - } - - // Prevent re-triggering of the same event, since we already bubbled it above - jQuery.event.triggered = type; - - if ( event.isPropagationStopped() ) { - lastElement.addEventListener( type, stopPropagationCallback ); - } - - elem[ type ](); - - if ( event.isPropagationStopped() ) { - lastElement.removeEventListener( type, stopPropagationCallback ); - } - - jQuery.event.triggered = undefined; - - if ( tmp ) { - elem[ ontype ] = tmp; - } - } - } - } - - return event.result; - }, - - // Piggyback on a donor event to simulate a different one - // Used only for `focus(in | out)` events - simulate: function( type, elem, event ) { - var e = jQuery.extend( - new jQuery.Event(), - event, - { - type: type, - isSimulated: true - } - ); - - jQuery.event.trigger( e, null, elem ); - } - -} ); - -jQuery.fn.extend( { - - trigger: function( type, data ) { - return this.each( function() { - jQuery.event.trigger( type, data, this ); - } ); - }, - triggerHandler: function( type, data ) { - var elem = this[ 0 ]; - if ( elem ) { - return jQuery.event.trigger( type, data, elem, true ); - } - } -} ); - - -// Support: Firefox <=44 -// Firefox doesn't have focus(in | out) events -// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 -// -// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 -// focus(in | out) events fire after focus & blur events, -// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order -// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 -if ( !support.focusin ) { - jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { - - // Attach a single capturing handler on the document while someone wants focusin/focusout - var handler = function( event ) { - jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); - }; - - jQuery.event.special[ fix ] = { - setup: function() { - - // Handle: regular nodes (via `this.ownerDocument`), window - // (via `this.document`) & document (via `this`). - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ); - - if ( !attaches ) { - doc.addEventListener( orig, handler, true ); - } - dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); - }, - teardown: function() { - var doc = this.ownerDocument || this.document || this, - attaches = dataPriv.access( doc, fix ) - 1; - - if ( !attaches ) { - doc.removeEventListener( orig, handler, true ); - dataPriv.remove( doc, fix ); - - } else { - dataPriv.access( doc, fix, attaches ); - } - } - }; - } ); -} -var location = window.location; - -var nonce = { guid: Date.now() }; - -var rquery = ( /\?/ ); - - - -// Cross-browser xml parsing -jQuery.parseXML = function( data ) { - var xml; - if ( !data || typeof data !== "string" ) { - return null; - } - - // Support: IE 9 - 11 only - // IE throws on parseFromString with invalid input. - try { - xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); - } catch ( e ) { - xml = undefined; - } - - if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) { - jQuery.error( "Invalid XML: " + data ); - } - return xml; -}; - - -var - rbracket = /\[\]$/, - rCRLF = /\r?\n/g, - rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, - rsubmittable = /^(?:input|select|textarea|keygen)/i; - -function buildParams( prefix, obj, traditional, add ) { - var name; - - if ( Array.isArray( obj ) ) { - - // Serialize array item. - jQuery.each( obj, function( i, v ) { - if ( traditional || rbracket.test( prefix ) ) { - - // Treat each array item as a scalar. - add( prefix, v ); - - } else { - - // Item is non-scalar (array or object), encode its numeric index. - buildParams( - prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", - v, - traditional, - add - ); - } - } ); - - } else if ( !traditional && toType( obj ) === "object" ) { - - // Serialize object item. - for ( name in obj ) { - buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); - } - - } else { - - // Serialize scalar item. - add( prefix, obj ); - } -} - -// Serialize an array of form elements or a set of -// key/values into a query string -jQuery.param = function( a, traditional ) { - var prefix, - s = [], - add = function( key, valueOrFunction ) { - - // If value is a function, invoke it and use its return value - var value = isFunction( valueOrFunction ) ? - valueOrFunction() : - valueOrFunction; - - s[ s.length ] = encodeURIComponent( key ) + "=" + - encodeURIComponent( value == null ? "" : value ); - }; - - if ( a == null ) { - return ""; - } - - // If an array was passed in, assume that it is an array of form elements. - if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { - - // Serialize the form elements - jQuery.each( a, function() { - add( this.name, this.value ); - } ); - - } else { - - // If traditional, encode the "old" way (the way 1.3.2 or older - // did it), otherwise encode params recursively. - for ( prefix in a ) { - buildParams( prefix, a[ prefix ], traditional, add ); - } - } - - // Return the resulting serialization - return s.join( "&" ); -}; - -jQuery.fn.extend( { - serialize: function() { - return jQuery.param( this.serializeArray() ); - }, - serializeArray: function() { - return this.map( function() { - - // Can add propHook for "elements" to filter or add form elements - var elements = jQuery.prop( this, "elements" ); - return elements ? jQuery.makeArray( elements ) : this; - } ) - .filter( function() { - var type = this.type; - - // Use .is( ":disabled" ) so that fieldset[disabled] works - return this.name && !jQuery( this ).is( ":disabled" ) && - rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && - ( this.checked || !rcheckableType.test( type ) ); - } ) - .map( function( _i, elem ) { - var val = jQuery( this ).val(); - - if ( val == null ) { - return null; - } - - if ( Array.isArray( val ) ) { - return jQuery.map( val, function( val ) { - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ); - } - - return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; - } ).get(); - } -} ); - - -var - r20 = /%20/g, - rhash = /#.*$/, - rantiCache = /([?&])_=[^&]*/, - rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, - - // #7653, #8125, #8152: local protocol detection - rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, - rnoContent = /^(?:GET|HEAD)$/, - rprotocol = /^\/\//, - - /* Prefilters - * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) - * 2) These are called: - * - BEFORE asking for a transport - * - AFTER param serialization (s.data is a string if s.processData is true) - * 3) key is the dataType - * 4) the catchall symbol "*" can be used - * 5) execution will start with transport dataType and THEN continue down to "*" if needed - */ - prefilters = {}, - - /* Transports bindings - * 1) key is the dataType - * 2) the catchall symbol "*" can be used - * 3) selection will start with transport dataType and THEN go to "*" if needed - */ - transports = {}, - - // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression - allTypes = "*/".concat( "*" ), - - // Anchor tag for parsing the document origin - originAnchor = document.createElement( "a" ); - originAnchor.href = location.href; - -// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport -function addToPrefiltersOrTransports( structure ) { - - // dataTypeExpression is optional and defaults to "*" - return function( dataTypeExpression, func ) { - - if ( typeof dataTypeExpression !== "string" ) { - func = dataTypeExpression; - dataTypeExpression = "*"; - } - - var dataType, - i = 0, - dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; - - if ( isFunction( func ) ) { - - // For each dataType in the dataTypeExpression - while ( ( dataType = dataTypes[ i++ ] ) ) { - - // Prepend if requested - if ( dataType[ 0 ] === "+" ) { - dataType = dataType.slice( 1 ) || "*"; - ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); - - // Otherwise append - } else { - ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); - } - } - } - }; -} - -// Base inspection function for prefilters and transports -function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { - - var inspected = {}, - seekingTransport = ( structure === transports ); - - function inspect( dataType ) { - var selected; - inspected[ dataType ] = true; - jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { - var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); - if ( typeof dataTypeOrTransport === "string" && - !seekingTransport && !inspected[ dataTypeOrTransport ] ) { - - options.dataTypes.unshift( dataTypeOrTransport ); - inspect( dataTypeOrTransport ); - return false; - } else if ( seekingTransport ) { - return !( selected = dataTypeOrTransport ); - } - } ); - return selected; - } - - return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); -} - -// A special extend for ajax options -// that takes "flat" options (not to be deep extended) -// Fixes #9887 -function ajaxExtend( target, src ) { - var key, deep, - flatOptions = jQuery.ajaxSettings.flatOptions || {}; - - for ( key in src ) { - if ( src[ key ] !== undefined ) { - ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; - } - } - if ( deep ) { - jQuery.extend( true, target, deep ); - } - - return target; -} - -/* Handles responses to an ajax request: - * - finds the right dataType (mediates between content-type and expected dataType) - * - returns the corresponding response - */ -function ajaxHandleResponses( s, jqXHR, responses ) { - - var ct, type, finalDataType, firstDataType, - contents = s.contents, - dataTypes = s.dataTypes; - - // Remove auto dataType and get content-type in the process - while ( dataTypes[ 0 ] === "*" ) { - dataTypes.shift(); - if ( ct === undefined ) { - ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); - } - } - - // Check if we're dealing with a known content-type - if ( ct ) { - for ( type in contents ) { - if ( contents[ type ] && contents[ type ].test( ct ) ) { - dataTypes.unshift( type ); - break; - } - } - } - - // Check to see if we have a response for the expected dataType - if ( dataTypes[ 0 ] in responses ) { - finalDataType = dataTypes[ 0 ]; - } else { - - // Try convertible dataTypes - for ( type in responses ) { - if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { - finalDataType = type; - break; - } - if ( !firstDataType ) { - firstDataType = type; - } - } - - // Or just use first one - finalDataType = finalDataType || firstDataType; - } - - // If we found a dataType - // We add the dataType to the list if needed - // and return the corresponding response - if ( finalDataType ) { - if ( finalDataType !== dataTypes[ 0 ] ) { - dataTypes.unshift( finalDataType ); - } - return responses[ finalDataType ]; - } -} - -/* Chain conversions given the request and the original response - * Also sets the responseXXX fields on the jqXHR instance - */ -function ajaxConvert( s, response, jqXHR, isSuccess ) { - var conv2, current, conv, tmp, prev, - converters = {}, - - // Work with a copy of dataTypes in case we need to modify it for conversion - dataTypes = s.dataTypes.slice(); - - // Create converters map with lowercased keys - if ( dataTypes[ 1 ] ) { - for ( conv in s.converters ) { - converters[ conv.toLowerCase() ] = s.converters[ conv ]; - } - } - - current = dataTypes.shift(); - - // Convert to each sequential dataType - while ( current ) { - - if ( s.responseFields[ current ] ) { - jqXHR[ s.responseFields[ current ] ] = response; - } - - // Apply the dataFilter if provided - if ( !prev && isSuccess && s.dataFilter ) { - response = s.dataFilter( response, s.dataType ); - } - - prev = current; - current = dataTypes.shift(); - - if ( current ) { - - // There's only work to do if current dataType is non-auto - if ( current === "*" ) { - - current = prev; - - // Convert response if prev dataType is non-auto and differs from current - } else if ( prev !== "*" && prev !== current ) { - - // Seek a direct converter - conv = converters[ prev + " " + current ] || converters[ "* " + current ]; - - // If none found, seek a pair - if ( !conv ) { - for ( conv2 in converters ) { - - // If conv2 outputs current - tmp = conv2.split( " " ); - if ( tmp[ 1 ] === current ) { - - // If prev can be converted to accepted input - conv = converters[ prev + " " + tmp[ 0 ] ] || - converters[ "* " + tmp[ 0 ] ]; - if ( conv ) { - - // Condense equivalence converters - if ( conv === true ) { - conv = converters[ conv2 ]; - - // Otherwise, insert the intermediate dataType - } else if ( converters[ conv2 ] !== true ) { - current = tmp[ 0 ]; - dataTypes.unshift( tmp[ 1 ] ); - } - break; - } - } - } - } - - // Apply converter (if not an equivalence) - if ( conv !== true ) { - - // Unless errors are allowed to bubble, catch and return them - if ( conv && s.throws ) { - response = conv( response ); - } else { - try { - response = conv( response ); - } catch ( e ) { - return { - state: "parsererror", - error: conv ? e : "No conversion from " + prev + " to " + current - }; - } - } - } - } - } - } - - return { state: "success", data: response }; -} - -jQuery.extend( { - - // Counter for holding the number of active queries - active: 0, - - // Last-Modified header cache for next request - lastModified: {}, - etag: {}, - - ajaxSettings: { - url: location.href, - type: "GET", - isLocal: rlocalProtocol.test( location.protocol ), - global: true, - processData: true, - async: true, - contentType: "application/x-www-form-urlencoded; charset=UTF-8", - - /* - timeout: 0, - data: null, - dataType: null, - username: null, - password: null, - cache: null, - throws: false, - traditional: false, - headers: {}, - */ - - accepts: { - "*": allTypes, - text: "text/plain", - html: "text/html", - xml: "application/xml, text/xml", - json: "application/json, text/javascript" - }, - - contents: { - xml: /\bxml\b/, - html: /\bhtml/, - json: /\bjson\b/ - }, - - responseFields: { - xml: "responseXML", - text: "responseText", - json: "responseJSON" - }, - - // Data converters - // Keys separate source (or catchall "*") and destination types with a single space - converters: { - - // Convert anything to text - "* text": String, - - // Text to html (true = no transformation) - "text html": true, - - // Evaluate text as a json expression - "text json": JSON.parse, - - // Parse text as xml - "text xml": jQuery.parseXML - }, - - // For options that shouldn't be deep extended: - // you can add your own custom options here if - // and when you create one that shouldn't be - // deep extended (see ajaxExtend) - flatOptions: { - url: true, - context: true - } - }, - - // Creates a full fledged settings object into target - // with both ajaxSettings and settings fields. - // If target is omitted, writes into ajaxSettings. - ajaxSetup: function( target, settings ) { - return settings ? - - // Building a settings object - ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : - - // Extending ajaxSettings - ajaxExtend( jQuery.ajaxSettings, target ); - }, - - ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), - ajaxTransport: addToPrefiltersOrTransports( transports ), - - // Main method - ajax: function( url, options ) { - - // If url is an object, simulate pre-1.5 signature - if ( typeof url === "object" ) { - options = url; - url = undefined; - } - - // Force options to be an object - options = options || {}; - - var transport, - - // URL without anti-cache param - cacheURL, - - // Response headers - responseHeadersString, - responseHeaders, - - // timeout handle - timeoutTimer, - - // Url cleanup var - urlAnchor, - - // Request state (becomes false upon send and true upon completion) - completed, - - // To know if global events are to be dispatched - fireGlobals, - - // Loop variable - i, - - // uncached part of the url - uncached, - - // Create the final options object - s = jQuery.ajaxSetup( {}, options ), - - // Callbacks context - callbackContext = s.context || s, - - // Context for global events is callbackContext if it is a DOM node or jQuery collection - globalEventContext = s.context && - ( callbackContext.nodeType || callbackContext.jquery ) ? - jQuery( callbackContext ) : - jQuery.event, - - // Deferreds - deferred = jQuery.Deferred(), - completeDeferred = jQuery.Callbacks( "once memory" ), - - // Status-dependent callbacks - statusCode = s.statusCode || {}, - - // Headers (they are sent all at once) - requestHeaders = {}, - requestHeadersNames = {}, - - // Default abort message - strAbort = "canceled", - - // Fake xhr - jqXHR = { - readyState: 0, - - // Builds headers hashtable if needed - getResponseHeader: function( key ) { - var match; - if ( completed ) { - if ( !responseHeaders ) { - responseHeaders = {}; - while ( ( match = rheaders.exec( responseHeadersString ) ) ) { - responseHeaders[ match[ 1 ].toLowerCase() + " " ] = - ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) - .concat( match[ 2 ] ); - } - } - match = responseHeaders[ key.toLowerCase() + " " ]; - } - return match == null ? null : match.join( ", " ); - }, - - // Raw string - getAllResponseHeaders: function() { - return completed ? responseHeadersString : null; - }, - - // Caches the header - setRequestHeader: function( name, value ) { - if ( completed == null ) { - name = requestHeadersNames[ name.toLowerCase() ] = - requestHeadersNames[ name.toLowerCase() ] || name; - requestHeaders[ name ] = value; - } - return this; - }, - - // Overrides response content-type header - overrideMimeType: function( type ) { - if ( completed == null ) { - s.mimeType = type; - } - return this; - }, - - // Status-dependent callbacks - statusCode: function( map ) { - var code; - if ( map ) { - if ( completed ) { - - // Execute the appropriate callbacks - jqXHR.always( map[ jqXHR.status ] ); - } else { - - // Lazy-add the new callbacks in a way that preserves old ones - for ( code in map ) { - statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; - } - } - } - return this; - }, - - // Cancel the request - abort: function( statusText ) { - var finalText = statusText || strAbort; - if ( transport ) { - transport.abort( finalText ); - } - done( 0, finalText ); - return this; - } - }; - - // Attach deferreds - deferred.promise( jqXHR ); - - // Add protocol if not provided (prefilters might expect it) - // Handle falsy url in the settings object (#10093: consistency with old signature) - // We also use the url parameter if available - s.url = ( ( url || s.url || location.href ) + "" ) - .replace( rprotocol, location.protocol + "//" ); - - // Alias method option to type as per ticket #12004 - s.type = options.method || options.type || s.method || s.type; - - // Extract dataTypes list - s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; - - // A cross-domain request is in order when the origin doesn't match the current origin. - if ( s.crossDomain == null ) { - urlAnchor = document.createElement( "a" ); - - // Support: IE <=8 - 11, Edge 12 - 15 - // IE throws exception on accessing the href property if url is malformed, - // e.g. http://example.com:80x/ - try { - urlAnchor.href = s.url; - - // Support: IE <=8 - 11 only - // Anchor's host property isn't correctly set when s.url is relative - urlAnchor.href = urlAnchor.href; - s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== - urlAnchor.protocol + "//" + urlAnchor.host; - } catch ( e ) { - - // If there is an error parsing the URL, assume it is crossDomain, - // it can be rejected by the transport if it is invalid - s.crossDomain = true; - } - } - - // Convert data if not already a string - if ( s.data && s.processData && typeof s.data !== "string" ) { - s.data = jQuery.param( s.data, s.traditional ); - } - - // Apply prefilters - inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); - - // If request was aborted inside a prefilter, stop there - if ( completed ) { - return jqXHR; - } - - // We can fire global events as of now if asked to - // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) - fireGlobals = jQuery.event && s.global; - - // Watch for a new set of requests - if ( fireGlobals && jQuery.active++ === 0 ) { - jQuery.event.trigger( "ajaxStart" ); - } - - // Uppercase the type - s.type = s.type.toUpperCase(); - - // Determine if request has content - s.hasContent = !rnoContent.test( s.type ); - - // Save the URL in case we're toying with the If-Modified-Since - // and/or If-None-Match header later on - // Remove hash to simplify url manipulation - cacheURL = s.url.replace( rhash, "" ); - - // More options handling for requests with no content - if ( !s.hasContent ) { - - // Remember the hash so we can put it back - uncached = s.url.slice( cacheURL.length ); - - // If data is available and should be processed, append data to url - if ( s.data && ( s.processData || typeof s.data === "string" ) ) { - cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; - - // #9682: remove data so that it's not used in an eventual retry - delete s.data; - } - - // Add or update anti-cache param if needed - if ( s.cache === false ) { - cacheURL = cacheURL.replace( rantiCache, "$1" ); - uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + - uncached; - } - - // Put hash and anti-cache on the URL that will be requested (gh-1732) - s.url = cacheURL + uncached; - - // Change '%20' to '+' if this is encoded form body content (gh-2658) - } else if ( s.data && s.processData && - ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { - s.data = s.data.replace( r20, "+" ); - } - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - if ( jQuery.lastModified[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); - } - if ( jQuery.etag[ cacheURL ] ) { - jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); - } - } - - // Set the correct header, if data is being sent - if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { - jqXHR.setRequestHeader( "Content-Type", s.contentType ); - } - - // Set the Accepts header for the server, depending on the dataType - jqXHR.setRequestHeader( - "Accept", - s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? - s.accepts[ s.dataTypes[ 0 ] ] + - ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : - s.accepts[ "*" ] - ); - - // Check for headers option - for ( i in s.headers ) { - jqXHR.setRequestHeader( i, s.headers[ i ] ); - } - - // Allow custom headers/mimetypes and early abort - if ( s.beforeSend && - ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { - - // Abort if not done already and return - return jqXHR.abort(); - } - - // Aborting is no longer a cancellation - strAbort = "abort"; - - // Install callbacks on deferreds - completeDeferred.add( s.complete ); - jqXHR.done( s.success ); - jqXHR.fail( s.error ); - - // Get transport - transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); - - // If no transport, we auto-abort - if ( !transport ) { - done( -1, "No Transport" ); - } else { - jqXHR.readyState = 1; - - // Send global event - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); - } - - // If request was aborted inside ajaxSend, stop there - if ( completed ) { - return jqXHR; - } - - // Timeout - if ( s.async && s.timeout > 0 ) { - timeoutTimer = window.setTimeout( function() { - jqXHR.abort( "timeout" ); - }, s.timeout ); - } - - try { - completed = false; - transport.send( requestHeaders, done ); - } catch ( e ) { - - // Rethrow post-completion exceptions - if ( completed ) { - throw e; - } - - // Propagate others as results - done( -1, e ); - } - } - - // Callback for when everything is done - function done( status, nativeStatusText, responses, headers ) { - var isSuccess, success, error, response, modified, - statusText = nativeStatusText; - - // Ignore repeat invocations - if ( completed ) { - return; - } - - completed = true; - - // Clear timeout if it exists - if ( timeoutTimer ) { - window.clearTimeout( timeoutTimer ); - } - - // Dereference transport for early garbage collection - // (no matter how long the jqXHR object will be used) - transport = undefined; - - // Cache response headers - responseHeadersString = headers || ""; - - // Set readyState - jqXHR.readyState = status > 0 ? 4 : 0; - - // Determine if successful - isSuccess = status >= 200 && status < 300 || status === 304; - - // Get response data - if ( responses ) { - response = ajaxHandleResponses( s, jqXHR, responses ); - } - - // Use a noop converter for missing script - if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) { - s.converters[ "text script" ] = function() {}; - } - - // Convert no matter what (that way responseXXX fields are always set) - response = ajaxConvert( s, response, jqXHR, isSuccess ); - - // If successful, handle type chaining - if ( isSuccess ) { - - // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. - if ( s.ifModified ) { - modified = jqXHR.getResponseHeader( "Last-Modified" ); - if ( modified ) { - jQuery.lastModified[ cacheURL ] = modified; - } - modified = jqXHR.getResponseHeader( "etag" ); - if ( modified ) { - jQuery.etag[ cacheURL ] = modified; - } - } - - // if no content - if ( status === 204 || s.type === "HEAD" ) { - statusText = "nocontent"; - - // if not modified - } else if ( status === 304 ) { - statusText = "notmodified"; - - // If we have data, let's convert it - } else { - statusText = response.state; - success = response.data; - error = response.error; - isSuccess = !error; - } - } else { - - // Extract error from statusText and normalize for non-aborts - error = statusText; - if ( status || !statusText ) { - statusText = "error"; - if ( status < 0 ) { - status = 0; - } - } - } - - // Set data for the fake xhr object - jqXHR.status = status; - jqXHR.statusText = ( nativeStatusText || statusText ) + ""; - - // Success/Error - if ( isSuccess ) { - deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); - } else { - deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); - } - - // Status-dependent callbacks - jqXHR.statusCode( statusCode ); - statusCode = undefined; - - if ( fireGlobals ) { - globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", - [ jqXHR, s, isSuccess ? success : error ] ); - } - - // Complete - completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); - - if ( fireGlobals ) { - globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); - - // Handle the global AJAX counter - if ( !( --jQuery.active ) ) { - jQuery.event.trigger( "ajaxStop" ); - } - } - } - - return jqXHR; - }, - - getJSON: function( url, data, callback ) { - return jQuery.get( url, data, callback, "json" ); - }, - - getScript: function( url, callback ) { - return jQuery.get( url, undefined, callback, "script" ); - } -} ); - -jQuery.each( [ "get", "post" ], function( _i, method ) { - jQuery[ method ] = function( url, data, callback, type ) { - - // Shift arguments if data argument was omitted - if ( isFunction( data ) ) { - type = type || callback; - callback = data; - data = undefined; - } - - // The url can be an options object (which then must have .url) - return jQuery.ajax( jQuery.extend( { - url: url, - type: method, - dataType: type, - data: data, - success: callback - }, jQuery.isPlainObject( url ) && url ) ); - }; -} ); - -jQuery.ajaxPrefilter( function( s ) { - var i; - for ( i in s.headers ) { - if ( i.toLowerCase() === "content-type" ) { - s.contentType = s.headers[ i ] || ""; - } - } -} ); - - -jQuery._evalUrl = function( url, options, doc ) { - return jQuery.ajax( { - url: url, - - // Make this explicit, since user can override this through ajaxSetup (#11264) - type: "GET", - dataType: "script", - cache: true, - async: false, - global: false, - - // Only evaluate the response if it is successful (gh-4126) - // dataFilter is not invoked for failure responses, so using it instead - // of the default converter is kludgy but it works. - converters: { - "text script": function() {} - }, - dataFilter: function( response ) { - jQuery.globalEval( response, options, doc ); - } - } ); -}; - - -jQuery.fn.extend( { - wrapAll: function( html ) { - var wrap; - - if ( this[ 0 ] ) { - if ( isFunction( html ) ) { - html = html.call( this[ 0 ] ); - } - - // The elements to wrap the target around - wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); - - if ( this[ 0 ].parentNode ) { - wrap.insertBefore( this[ 0 ] ); - } - - wrap.map( function() { - var elem = this; - - while ( elem.firstElementChild ) { - elem = elem.firstElementChild; - } - - return elem; - } ).append( this ); - } - - return this; - }, - - wrapInner: function( html ) { - if ( isFunction( html ) ) { - return this.each( function( i ) { - jQuery( this ).wrapInner( html.call( this, i ) ); - } ); - } - - return this.each( function() { - var self = jQuery( this ), - contents = self.contents(); - - if ( contents.length ) { - contents.wrapAll( html ); - - } else { - self.append( html ); - } - } ); - }, - - wrap: function( html ) { - var htmlIsFunction = isFunction( html ); - - return this.each( function( i ) { - jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); - } ); - }, - - unwrap: function( selector ) { - this.parent( selector ).not( "body" ).each( function() { - jQuery( this ).replaceWith( this.childNodes ); - } ); - return this; - } -} ); - - -jQuery.expr.pseudos.hidden = function( elem ) { - return !jQuery.expr.pseudos.visible( elem ); -}; -jQuery.expr.pseudos.visible = function( elem ) { - return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); -}; - - - - -jQuery.ajaxSettings.xhr = function() { - try { - return new window.XMLHttpRequest(); - } catch ( e ) {} -}; - -var xhrSuccessStatus = { - - // File protocol always yields status code 0, assume 200 - 0: 200, - - // Support: IE <=9 only - // #1450: sometimes IE returns 1223 when it should be 204 - 1223: 204 - }, - xhrSupported = jQuery.ajaxSettings.xhr(); - -support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); -support.ajax = xhrSupported = !!xhrSupported; - -jQuery.ajaxTransport( function( options ) { - var callback, errorCallback; - - // Cross domain only allowed if supported through XMLHttpRequest - if ( support.cors || xhrSupported && !options.crossDomain ) { - return { - send: function( headers, complete ) { - var i, - xhr = options.xhr(); - - xhr.open( - options.type, - options.url, - options.async, - options.username, - options.password - ); - - // Apply custom fields if provided - if ( options.xhrFields ) { - for ( i in options.xhrFields ) { - xhr[ i ] = options.xhrFields[ i ]; - } - } - - // Override mime type if needed - if ( options.mimeType && xhr.overrideMimeType ) { - xhr.overrideMimeType( options.mimeType ); - } - - // X-Requested-With header - // For cross-domain requests, seeing as conditions for a preflight are - // akin to a jigsaw puzzle, we simply never set it to be sure. - // (it can always be set on a per-request basis or even using ajaxSetup) - // For same-domain requests, won't change header if already provided. - if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { - headers[ "X-Requested-With" ] = "XMLHttpRequest"; - } - - // Set headers - for ( i in headers ) { - xhr.setRequestHeader( i, headers[ i ] ); - } - - // Callback - callback = function( type ) { - return function() { - if ( callback ) { - callback = errorCallback = xhr.onload = - xhr.onerror = xhr.onabort = xhr.ontimeout = - xhr.onreadystatechange = null; - - if ( type === "abort" ) { - xhr.abort(); - } else if ( type === "error" ) { - - // Support: IE <=9 only - // On a manual native abort, IE9 throws - // errors on any property access that is not readyState - if ( typeof xhr.status !== "number" ) { - complete( 0, "error" ); - } else { - complete( - - // File: protocol always yields status 0; see #8605, #14207 - xhr.status, - xhr.statusText - ); - } - } else { - complete( - xhrSuccessStatus[ xhr.status ] || xhr.status, - xhr.statusText, - - // Support: IE <=9 only - // IE9 has no XHR2 but throws on binary (trac-11426) - // For XHR2 non-text, let the caller handle it (gh-2498) - ( xhr.responseType || "text" ) !== "text" || - typeof xhr.responseText !== "string" ? - { binary: xhr.response } : - { text: xhr.responseText }, - xhr.getAllResponseHeaders() - ); - } - } - }; - }; - - // Listen to events - xhr.onload = callback(); - errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); - - // Support: IE 9 only - // Use onreadystatechange to replace onabort - // to handle uncaught aborts - if ( xhr.onabort !== undefined ) { - xhr.onabort = errorCallback; - } else { - xhr.onreadystatechange = function() { - - // Check readyState before timeout as it changes - if ( xhr.readyState === 4 ) { - - // Allow onerror to be called first, - // but that will not handle a native abort - // Also, save errorCallback to a variable - // as xhr.onerror cannot be accessed - window.setTimeout( function() { - if ( callback ) { - errorCallback(); - } - } ); - } - }; - } - - // Create the abort callback - callback = callback( "abort" ); - - try { - - // Do send the request (this may raise an exception) - xhr.send( options.hasContent && options.data || null ); - } catch ( e ) { - - // #14683: Only rethrow if this hasn't been notified as an error yet - if ( callback ) { - throw e; - } - } - }, - - abort: function() { - if ( callback ) { - callback(); - } - } - }; - } -} ); - - - - -// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) -jQuery.ajaxPrefilter( function( s ) { - if ( s.crossDomain ) { - s.contents.script = false; - } -} ); - -// Install script dataType -jQuery.ajaxSetup( { - accepts: { - script: "text/javascript, application/javascript, " + - "application/ecmascript, application/x-ecmascript" - }, - contents: { - script: /\b(?:java|ecma)script\b/ - }, - converters: { - "text script": function( text ) { - jQuery.globalEval( text ); - return text; - } - } -} ); - -// Handle cache's special case and crossDomain -jQuery.ajaxPrefilter( "script", function( s ) { - if ( s.cache === undefined ) { - s.cache = false; - } - if ( s.crossDomain ) { - s.type = "GET"; - } -} ); - -// Bind script tag hack transport -jQuery.ajaxTransport( "script", function( s ) { - - // This transport only deals with cross domain or forced-by-attrs requests - if ( s.crossDomain || s.scriptAttrs ) { - var script, callback; - return { - send: function( _, complete ) { - script = jQuery( " -{% endmacro %} \ No newline at end of file diff --git a/_preview/22/genindex.html b/_preview/22/genindex.html deleted file mode 100644 index f0b5d26..0000000 --- a/_preview/22/genindex.html +++ /dev/null @@ -1,382 +0,0 @@ - - - - - - - - Index — Landsat ML Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Landsat8

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Start Here - Intro to Landsat Data

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Overview

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In this cookbook, you will access, process, analyze, and visualize satellite data in the context of machine learning workflows. This particular cookbook notebook will provide an introduction to Landsat data to build our intuition as we move toward data ingestion, processing, and analysis.

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    -
  • Time to learn: 5 minutes

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Landsat Data

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The data in this cookbook originally come from the Landsat program, which is the longest record of moderate resolution multispectral data of the Earth’s surface. This program has launched several different satellites spanning many years which are designated as Landsat 1-9.

-

USGS Landsat Timeline

-

When accessing the data, it’s important to keep in mind a couple key points. First, the instruments on different Landsat missions (1-9) varied in certain aspects. Second, Landsat data is available from multiple providers (USGS, NASA, Google, Microsoft, AWS, etc) but may vary in completeness and the level of processing applied. For the dataset that you end up using, it is crucial to review to relevant information from the particular data provider and the specific Landsat mission to understand the details, especially if you are comparing data across providers or missions.

-

In general, a common aspect of Landsat data is the use of different wavelength-bands to capture multiple images of the same area - together providing much more information about different features on the ground than a single image alone. This provides us with a stack of images for each spatial region that we might be interested.

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Spectral Bands

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Additionally, whenever we are looking at changes in satellite images over time, we will have an additional time dimension. For example, we will consider two stacks of images from different years to look at the change in the water level around a lake.

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Summary

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Before accessing any data, it’s a good idea to start by learning about the context and details of the dataset. This will give you the intuition to make informed decisions as you form a processing and analysis pipeline.

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What’s next?

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Next, we’ll learn about loading the data using the Microsoft Planetary Computer platform.

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Resources and references

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  • The Landsat timeline image is originally from USGS but discovered through earthsciencedata.org

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  • The Landsat 8 banner image is from NASA

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  • The Landsat spectral bands is from NASA

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  • This page was authored by Demetris Roumis circa Jan, 2023

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Planetary Computer

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Data Ingestion - Microsoft Planetary Computer

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Overview

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In this notebook, you will ingest Landsat data for use in machine learning. Machine learning tasks often involve a lot of data, and in Python, data is typically stored in memory as simple NumPy arrays. However, higher-level containers built on top of NumPy arrays provide more functionality for multidimensional gridded data (xarray) or out-of-core and distributed data (Dask). Our goal for data ingestion will be to load specific Landsat data of interest into one of these higher-level containers.

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Microsoft Plantery Computer is one of several providers of Landsat Data. We are using it together with pystac-client and odc-stac because together they provide a nice Python API for searching and loading with specific criteria such as spatial area, datetime, Landsat mission, and cloud coverage.

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Earth science datasets are often stored on remote servers that may be too large to download locally. Therefore, in this cookbook, we will focus primarily on ingestion approaches that load small portions of data from a remote source, as needed. However, the approach for your own work will depend not only on data size and location but also the intended analysis, so in a follow up notebook, you will see alternative approaches to loading data.

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Prerequisites

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Concepts

Importance

Notes

Intro to Landsat

Necessary

Background

About the Microsoft Planetary Computer

Helpful

Background

pystac-client Usage

Helpful

Consult as needed

odc.stac.load Reference

Helpful

Consult as needed

xarray

Necessary

Intro to Dask Array

Helpful

Panel Getting Started Guide

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  • Time to learn: 10 minutes

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Imports

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# Data
-import odc.stac
-import pandas as pd
-import planetary_computer
-import pystac_client
-import xarray as xr
-from pystac.extensions.eo import EOExtension as eo
-
-# Viz
-import hvplot.xarray
-import panel as pn
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-pn.extension()
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Open and read the root of the STAC catalog

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catalog = pystac_client.Client.open(
-    "https://planetarycomputer.microsoft.com/api/stac/v1",
-    modifier=planetary_computer.sign_inplace,
-)
-catalog.title
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'Microsoft Planetary Computer STAC API'
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Microsoft Planetary Computer has a public STAC metadata but the actual data assets are in private Azure Blob Storage containers and require authentication. pystac-client provides a modifier keyword that we can use to manually sign the item. Otherwise, we’d get an error when trying to access the asset.

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Search for Landsat Data

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Let’s say that an analysis we want to run requires landsat data over a specific region and from a specific time period. We can use our catalog to search for assets that fit our search criteria.

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First, let’s find the name of the landsat dataset. This page is a nice resource for browsing the available collections, but we can also just search the catalog for ‘landsat’:

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all_collections = [i.id for i in catalog.get_collections()]
-landsat_collections = [
-    collection for collection in all_collections if "landsat" in collection
-]
-landsat_collections
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['landsat-c2-l2', 'landsat-c2-l1']
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We’ll use the landsat-c2-l2 dataset, which stands for Collection 2 Level-2. It contains data from several landsat missions and has better data quality than Level 1 (landsat-c2-l1). Microsoft Planetary Computer has descriptions of Level 1 and Level 2, but a direct and succinct comparison can be found in this community post, and the information can be verified with USGS.

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Now, let’s set our search parameters. You may already know the bounding box (region/area of interest) coordinates, but if you don’t, there are many useful tools like bboxfinder.com that can help.

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bbox = [-118.89, 38.54, -118.57, 38.84]  # Region over a lake in Nevada, USA
-datetime = "2017-06-01/2017-09-30"  # Summer months of 2017
-collection = "landsat-c2-l2"
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We can also specify other parameters in the query, such as a specific landsat mission and the max percent of cloud cover:

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platform = "landsat-8"
-cloudy_less_than = 1  # percent
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Now we run the search and list the results:

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search = catalog.search(
-    collections=["landsat-c2-l2"],
-    bbox=bbox,
-    datetime=datetime,
-    query={"eo:cloud_cover": {"lt": cloudy_less_than}, "platform": {"in": [platform]}},
-)
-items = search.get_all_items()
-print(f"Returned {len(items)} Items:")
-item_id = {(i, item.id): i for i, item in enumerate(items)}
-item_id
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/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/pystac_client/item_search.py:849: FutureWarning: get_all_items() is deprecated, use item_collection() instead.
-  warnings.warn(
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Returned 3 Items:
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{(0, 'LC08_L2SP_042033_20170718_02_T1'): 0,
- (1, 'LC08_L2SP_042033_20170702_02_T1'): 1,
- (2, 'LC08_L2SP_042033_20170616_02_T1'): 2}
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It looks like there were three image stacks taken by Landsat 8 over this spatial region during the summer months of 2017 that has less than 1 percent cloud cover.

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Preview Results and Select a Dataset

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Before loading one of the available image stacks, it would be useful to get a visual check of the results. Many datasets have a rendered preview or thumbnail image that can be accessed without having to load the full resolution data.

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We can create a simple interactive application using the Panel library to access and display rendered PNG previews of the our search results. Note that these pre-rendered images are of large tiles that span beyond our bounding box of interest. In the next steps, we will only be loading in a small area around the lake.

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item_sel = pn.widgets.Select(value=1, options=item_id, name="item")
-
-def get_preview(i):
-    return pn.panel(items[i].assets["rendered_preview"].href, height=300)
-
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-pn.Row(item_sel, pn.bind(get_preview, item_sel))
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selected_item = items[1]
-selected_item
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Access the Data

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Now that we have selected a dataset from our catalog, we can procede to access the data. We want to be very selective about the data that we read and when we read it because the amount of downloaded data can quickly get out of hand. Therefore, let’s select only a subset of images.

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First, we’ll preview the different image assets (or Bands) available in the Landsat item.

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assets = []
-for _, asset in selected_item.assets.items():
-    try:
-        assets.append(asset.extra_fields["eo:bands"][0])
-    except:
-        pass
-
-cols_ordered = [
-    "common_name",
-    "description",
-    "name",
-    "center_wavelength",
-    "full_width_half_max",
-]
-bands = pd.DataFrame.from_dict(assets)[cols_ordered]
-bands
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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
common_namedescriptionnamecenter_wavelengthfull_width_half_max
0redVisible redOLI_B40.650.04
1blueVisible blueOLI_B20.480.06
2greenVisible greenOLI_B30.560.06
3nir08Near infraredOLI_B50.870.03
4lwir11Long-wave infraredTIRS_B1010.900.59
5swir16Short-wave infraredOLI_B61.610.09
6swir22Short-wave infraredOLI_B72.200.19
7coastalCoastal/AerosolOLI_B10.440.02
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Then we will select a few bands (images) of interest:

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bands_of_interest = ["red", "green", "blue"]
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Finally, we lazily load the selected data. We will use the package called odc which allows us to load only a specific region of interest (bounding box or ‘bbox’) and specific bands (images) of interest. We will also use the chunks argument to load the data as dask arrays; this will load the metadata now and delay the loading until we actually use the data, or until we force the data to be loaded by using .compute().

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ds = odc.stac.stac_load(
-    [selected_item],
-    bands=bands_of_interest,
-    bbox=bbox,
-    chunks={},  # <-- use Dask
-).isel(time=0)
-ds
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- - - - - - - - - - - - - - -
<xarray.Dataset>
-Dimensions:      (y: 1128, x: 950)
-Coordinates:
-  * y            (y) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
-  * x            (x) float64 3.353e+05 3.353e+05 ... 3.637e+05 3.638e+05
-    spatial_ref  int32 32611
-    time         datetime64[ns] 2017-07-02T18:33:06.200763
-Data variables:
-    red          (y, x) uint16 dask.array<chunksize=(1128, 950), meta=np.ndarray>
-    green        (y, x) uint16 dask.array<chunksize=(1128, 950), meta=np.ndarray>
-    blue         (y, x) uint16 dask.array<chunksize=(1128, 950), meta=np.ndarray>
-
-

Let’s combine the bands of the dataset into a single DataArray that has the band names as coordinates of a new ‘band’ dimension, and also call .compute() to finally load the data.

-
-
-
da = ds.to_array(dim="band").compute()
-da
-
-
-
-
-
- - - - - - - - - - - - - - -
<xarray.DataArray (band: 3, y: 1128, x: 950)>
-array([[[14691, 14914, 14988, ..., 16283, 16292, 16316],
-        [14655, 14859, 14969, ..., 16272, 16185, 16079],
-        [14531, 14699, 14972, ..., 15318, 15526, 14734],
-        ...,
-        [13804, 13561, 13601, ..., 18311, 18202, 17625],
-        [13857, 13828, 13858, ..., 19400, 18942, 18551],
-        [13840, 13786, 13867, ..., 17873, 17917, 18453]],
-
-       [[13233, 13402, 13565, ..., 14553, 14658, 14657],
-        [13291, 13428, 13585, ..., 14590, 14478, 14550],
-        [13122, 13287, 13601, ..., 13987, 14220, 13571],
-        ...,
-        [12720, 12552, 12468, ..., 16580, 16411, 15899],
-        [12704, 12644, 12658, ..., 17351, 16853, 16505],
-        [12647, 12620, 12698, ..., 15990, 16211, 16686]],
-
-       [[11572, 11629, 11723, ..., 12857, 12918, 12946],
-        [11588, 11655, 11721, ..., 12848, 12792, 12715],
-        [11510, 11608, 11781, ..., 12371, 12453, 12053],
-        ...,
-        [11195, 11104, 11045, ..., 14182, 14031, 13716],
-        [11125, 11061, 11106, ..., 14652, 14284, 14062],
-        [11059, 11050, 11134, ..., 13756, 13865, 14209]]], dtype=uint16)
-Coordinates:
-  * y            (y) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
-  * x            (x) float64 3.353e+05 3.353e+05 ... 3.637e+05 3.638e+05
-    spatial_ref  int32 32611
-    time         datetime64[ns] 2017-07-02T18:33:06.200763
-  * band         (band) object 'red' 'green' 'blue'
-
-
-
-

Visualize the data

-

Often, data ingestion involves quickly visualizing your raw data to get a sense that things are proceeding accordingly. As we have created an array with red, blue, and green bands, we can quickly display a natural color image of the lake using the .plot.imshow() function of xarray. We’ll use the robust=True argument because the data values are outside the range of typical RGB images.

-
-
-
da.plot.imshow(robust=True, size=3)
-
-
-
-
-
<matplotlib.image.AxesImage at 0x7fbd283c58d0>
-
-
-../_images/1.0_Data_Ingestion-Planetary_Computer_38_1.png -
-
-

Now, let’s use hvplot to provide an interactive visualization of the inividual bands in our array.

-
-
-
ds
-
-
-
-
-
- - - - - - - - - - - - - - -
<xarray.Dataset>
-Dimensions:      (y: 1128, x: 950)
-Coordinates:
-  * y            (y) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
-  * x            (x) float64 3.353e+05 3.353e+05 ... 3.637e+05 3.638e+05
-    spatial_ref  int32 32611
-    time         datetime64[ns] 2017-07-02T18:33:06.200763
-Data variables:
-    red          (y, x) uint16 dask.array<chunksize=(1128, 950), meta=np.ndarray>
-    green        (y, x) uint16 dask.array<chunksize=(1128, 950), meta=np.ndarray>
-    blue         (y, x) uint16 dask.array<chunksize=(1128, 950), meta=np.ndarray>
-
-
-
-
da.hvplot.image(x="x", y="y", cmap="viridis", aspect=1)
-
-
-
-
-
-
-
-
-
-

Let’s plot the bands as seperate columns by specifying a dimension to expand with col='band'. We can also set rasterize=True to use Datashader (another HoloViz tool) to render large data into a 2D histogram, where every array cell counts the data points falling into that pixel, as set by the resolution of your screen. This is especially important for large and high resolution images that would otherwise cause issues when attempting to render in a browser.

-
-
-
da.hvplot.image(
-    x="x", y="y", col="band", cmap="viridis", xaxis=False, yaxis=False, colorbar=False, rasterize=True
-)
-
-
-
-
-
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 12.0.1. Please consider upgrading.
-  warnings.warn(
-
-
-
-
-
-
-
-

Select the zoom tool and zoom in on of the plots to see that all the images are all automatically linked!

-
-
-

Retain Attributes

-

When working with many image arrays, it’s critical to retain the data properties as xarray attributes:

-
-
-
da.attrs = selected_item.properties
-da
-
-
-
-
-
- - - - - - - - - - - - - - -
<xarray.DataArray (band: 3, y: 1128, x: 950)>
-array([[[14691, 14914, 14988, ..., 16283, 16292, 16316],
-        [14655, 14859, 14969, ..., 16272, 16185, 16079],
-        [14531, 14699, 14972, ..., 15318, 15526, 14734],
-        ...,
-        [13804, 13561, 13601, ..., 18311, 18202, 17625],
-        [13857, 13828, 13858, ..., 19400, 18942, 18551],
-        [13840, 13786, 13867, ..., 17873, 17917, 18453]],
-
-       [[13233, 13402, 13565, ..., 14553, 14658, 14657],
-        [13291, 13428, 13585, ..., 14590, 14478, 14550],
-        [13122, 13287, 13601, ..., 13987, 14220, 13571],
-        ...,
-        [12720, 12552, 12468, ..., 16580, 16411, 15899],
-        [12704, 12644, 12658, ..., 17351, 16853, 16505],
-        [12647, 12620, 12698, ..., 15990, 16211, 16686]],
-
-       [[11572, 11629, 11723, ..., 12857, 12918, 12946],
-        [11588, 11655, 11721, ..., 12848, 12792, 12715],
-        [11510, 11608, 11781, ..., 12371, 12453, 12053],
-        ...,
-        [11195, 11104, 11045, ..., 14182, 14031, 13716],
-        [11125, 11061, 11106, ..., 14652, 14284, 14062],
-        [11059, 11050, 11134, ..., 13756, 13865, 14209]]], dtype=uint16)
-Coordinates:
-  * y            (y) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
-  * x            (x) float64 3.353e+05 3.353e+05 ... 3.637e+05 3.638e+05
-    spatial_ref  int32 32611
-    time         datetime64[ns] 2017-07-02T18:33:06.200763
-  * band         (band) object 'red' 'green' 'blue'
-Attributes: (12/22)
-    gsd:                          30
-    created:                      2022-05-06T17:46:34.110946Z
-    sci:doi:                      10.5066/P9OGBGM6
-    datetime:                     2017-07-02T18:33:06.200763Z
-    platform:                     landsat-8
-    proj:epsg:                    32611
-    ...                           ...
-    view:sun_azimuth:             125.03739105
-    landsat:correction:           L2SP
-    view:sun_elevation:           65.85380157
-    landsat:cloud_cover_land:     0.53
-    landsat:collection_number:    02
-    landsat:collection_category:  T1
-
-

Notice that you can now expand the Attributes: dropdown to see the properties of this data.

-
-
-

Set the crs attribute

-

As the data is in ‘meter’ units from a reference point, we can plot in commonly used longitude, latitude coordinates with .hvplot(geo=True) if our array has a valid coordinate reference system (CRS) attribute. This value is provided from Microsoft Planetary Computer as the proj:epsg property, so we just need to copy it to a new attribute crs so that hvPlot can automatically find it, without us having to further specify anything in our plotting code

-

Note, this CRS is referenced by an EPSG code that can be accessed from the metadata of our selected catalog search result. We can see more about this dataset’s specific code at EPSG.io/32611. You can also read more about EPSG codes in general in this Coordinate Reference Systems: EPSG codes online book chapter.

-
-
-
da.attrs["crs"] = f"epsg:{selected_item.properties['proj:epsg']}"
-da.attrs["crs"]
-
-
-
-
-
'epsg:32611'
-
-
-
-
-

Now we can use .hvplot(geo=True) to plot in longitude and latitude coordinates. Informing hvPlot that this is geographic data also allows us to overlay data on aligned geographic tiles using the tiles parameter.

-
-
-
da.hvplot.image(
-    x="x", y="y", cmap="viridis", geo=True, alpha=.9, tiles="ESRI", xlabel="Longitude", ylabel="Latitude", colorbar=False, aspect=1,
-)
-
-
-
-
-
-
-
-
-
-
-
-
-

Summary

-

The data access approach should adapt to features of the data and your intended analysis. As Landsat data is large and multidimensional, a good approach is to use Microsoft Plantery Computer, pystac-client, and odc-stac together for searching the metadata catalog and lazily loading specific data chunks. Once you have accessed data, visualize it with hvPlot to ensure that it matches your expectations.

-
-

What’s next?

-

Before we proceed to workflow examples, we can explore alternate ways of accessing data and the processing steps that are often utilized.

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Resources and References

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- - \ No newline at end of file diff --git a/_preview/22/notebooks/1.1_Data_Ingestion-Intake.html b/_preview/22/notebooks/1.1_Data_Ingestion-Intake.html deleted file mode 100644 index 439c190..0000000 --- a/_preview/22/notebooks/1.1_Data_Ingestion-Intake.html +++ /dev/null @@ -1,2512 +0,0 @@ - - - - - - - - Data Ingestion - Intake — Landsat ML Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Landsat8

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Data Ingestion - Intake

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Overview

-

In the last notebook, you learned how to efficiently load data from the Microsoft Planetary Computer platform. If that approach works for you, please proceed to a workflow example. In this notebook we will demonstrate common alternative approaches and techniques for general data access, centered around Intake.

-
-

Info

-

A great way to contribute to this cookbook is to create a notebook that focuses on data access from a specific provider.

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Prerequisites

- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -

Concepts

Importance

Notes

Intro to Landsat

Necessary

Background

Data Ingestion - Planetary Computer

Helpful

Pandas Cookbook

Helpful

xarray Cookbook

Necessary

Intake Quickstart

Helpful

Intake Cookbook

Necessary

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    -
  • Time to learn: 20 minutes

  • -
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Imports

-
-
-
import intake
-import hvplot.xarray
-import planetary_computer
-
-import warnings
-warnings.simplefilter('ignore', FutureWarning) # Ignore warning about the format of epsg codes
-
-
-
-
-
-
-
-
-
-

To get started, we need to provide a STAC URL (or any other data source URL) to intake, and we can ask intake to recommend some suitable datatypes.

-
-
-
url = "https://planetarycomputer.microsoft.com/api/stac/v1"
-data_types = intake.readers.datatypes.recommend(url)
-print(data_types)
-
-
-
-
-
[<class 'intake.readers.datatypes.Handle'>, <class 'intake.readers.datatypes.JSONFile'>, <class 'intake.readers.datatypes.STACJSON'>, <class 'intake.readers.datatypes.TiledService'>, <class 'intake.readers.datatypes.CatalogAPI'>]
-
-
-
-
-

We will use STACJSON to read the URL.

-
-
-
data_type = intake.datatypes.STACJSON(url)
-data_type
-
-
-
-
-
STACJSON, {'url': 'https://planetarycomputer.microsoft.com/api/stac/v1', 'storage_options': None, 'metadata': {}}
-
-
-
-
-

Similarly, we can check out the possible readers to use with the STACJSON datatype.

-
-
-
readers = data_type.possible_readers
-print(readers)
-
-
-
-
-
{'importable': [<class 'intake.readers.catalogs.StackBands'>, <class 'intake.readers.catalogs.StacSearch'>, <class 'intake.readers.catalogs.StacCatalogReader'>, <class 'intake.readers.readers.DaskJSON'>, <class 'intake.readers.readers.FileByteReader'>, <class 'intake.readers.readers.FileTextReader'>, <class 'intake.readers.readers.FileExistsReader'>], 'not_importable': [<class 'intake.readers.readers.DuckJSON'>, <class 'intake.readers.readers.PolarsJSON'>, <class 'intake.readers.readers.RayJSON'>, <class 'intake.readers.readers.AwkwardJSON'>, <class 'intake.readers.readers.RayBinary'>]}
-
-
-
-
-

The StacCatalogReader is probably the most suitable for our use case. We can use it to read the STAC catalog and explore the available contents.

-
-
-
reader = intake.catalogs.StacCatalogReader(
-    data_type, signer=planetary_computer.sign_inplace
-)
-reader
-
-
-
-
-
StacCatalogReader reader producing intake.readers.entry:Catalog
-
-
-
-
-

We can read the catalog and see what’s available:

-
-
-
stac_cat = reader.read()
-
-
-
-
-
-
-
metadata = {}
-for data_description in stac_cat.data.values():
-    data = data_description.kwargs["data"]
-    metadata[data["id"]] = data["description"]
-list(metadata.keys())
-
-
-
-
-
['daymet-annual-pr',
- 'daymet-daily-hi',
- '3dep-seamless',
- '3dep-lidar-dsm',
- 'fia',
- 'sentinel-1-rtc',
- 'gridmet',
- 'daymet-annual-na',
- 'daymet-monthly-na',
- 'daymet-annual-hi',
- 'daymet-monthly-hi',
- 'daymet-monthly-pr',
- 'gnatsgo-tables',
- 'hgb',
- 'cop-dem-glo-30',
- 'cop-dem-glo-90',
- 'goes-cmi',
- 'terraclimate',
- 'nasa-nex-gddp-cmip6',
- 'gpm-imerg-hhr',
- 'gnatsgo-rasters',
- '3dep-lidar-hag',
- 'io-lulc-annual-v02',
- '3dep-lidar-intensity',
- '3dep-lidar-pointsourceid',
- 'mtbs',
- 'noaa-c-cap',
- '3dep-lidar-copc',
- 'modis-64A1-061',
- 'alos-fnf-mosaic',
- '3dep-lidar-returns',
- 'mobi',
- 'landsat-c2-l2',
- 'era5-pds',
- 'chloris-biomass',
- 'kaza-hydroforecast',
- 'planet-nicfi-analytic',
- 'modis-17A2H-061',
- 'modis-11A2-061',
- 'daymet-daily-pr',
- '3dep-lidar-dtm-native',
- '3dep-lidar-classification',
- '3dep-lidar-dtm',
- 'gap',
- 'modis-17A2HGF-061',
- 'planet-nicfi-visual',
- 'gbif',
- 'modis-17A3HGF-061',
- 'modis-09A1-061',
- 'alos-dem',
- 'alos-palsar-mosaic',
- 'deltares-water-availability',
- 'modis-16A3GF-061',
- 'modis-21A2-061',
- 'us-census',
- 'jrc-gsw',
- 'deltares-floods',
- 'modis-43A4-061',
- 'modis-09Q1-061',
- 'modis-14A1-061',
- 'hrea',
- 'modis-13Q1-061',
- 'modis-14A2-061',
- 'sentinel-2-l2a',
- 'modis-15A2H-061',
- 'modis-11A1-061',
- 'modis-15A3H-061',
- 'modis-13A1-061',
- 'daymet-daily-na',
- 'nrcan-landcover',
- 'modis-10A2-061',
- 'ecmwf-forecast',
- 'noaa-mrms-qpe-24h-pass2',
- 'sentinel-1-grd',
- 'nasadem',
- 'io-lulc',
- 'landsat-c2-l1',
- 'drcog-lulc',
- 'chesapeake-lc-7',
- 'chesapeake-lc-13',
- 'chesapeake-lu',
- 'noaa-mrms-qpe-1h-pass1',
- 'noaa-mrms-qpe-1h-pass2',
- 'noaa-nclimgrid-monthly',
- 'goes-glm',
- 'usda-cdl',
- 'eclipse',
- 'esa-cci-lc',
- 'esa-cci-lc-netcdf',
- 'fws-nwi',
- 'usgs-lcmap-conus-v13',
- 'usgs-lcmap-hawaii-v10',
- 'noaa-climate-normals-tabular',
- 'noaa-climate-normals-netcdf',
- 'noaa-climate-normals-gridded',
- 'aster-l1t',
- 'cil-gdpcir-cc-by-sa',
- 'naip',
- 'io-lulc-9-class',
- 'io-biodiversity',
- 'noaa-cdr-sea-surface-temperature-whoi',
- 'noaa-cdr-ocean-heat-content',
- 'cil-gdpcir-cc0',
- 'cil-gdpcir-cc-by',
- 'noaa-cdr-sea-surface-temperature-whoi-netcdf',
- 'noaa-cdr-sea-surface-temperature-optimum-interpolation',
- 'modis-10A1-061',
- 'sentinel-5p-l2-netcdf',
- 'sentinel-3-olci-wfr-l2-netcdf',
- 'noaa-cdr-ocean-heat-content-netcdf',
- 'sentinel-3-synergy-aod-l2-netcdf',
- 'sentinel-3-synergy-v10-l2-netcdf',
- 'sentinel-3-olci-lfr-l2-netcdf',
- 'sentinel-3-sral-lan-l2-netcdf',
- 'sentinel-3-slstr-lst-l2-netcdf',
- 'sentinel-3-slstr-wst-l2-netcdf',
- 'sentinel-3-sral-wat-l2-netcdf',
- 'ms-buildings',
- 'sentinel-3-slstr-frp-l2-netcdf',
- 'sentinel-3-synergy-syn-l2-netcdf',
- 'sentinel-3-synergy-vgp-l2-netcdf',
- 'sentinel-3-synergy-vg1-l2-netcdf',
- 'esa-worldcover']
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We can print the description of the desired IDs.

-
-
-
print("1:", metadata["landsat-c2-l1"])
-print("2:", metadata["landsat-c2-l2"])
-
-
-
-
-
1: Landsat Collection 2 Level-1 data, consisting of quantized and calibrated scaled Digital Numbers (DN) representing the multispectral image data. These [Level-1](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-1-data) data can be [rescaled](https://www.usgs.gov/landsat-missions/using-usgs-landsat-level-1-data-product) to top of atmosphere (TOA) reflectance and/or radiance. Thermal band data can be rescaled to TOA brightness temperature.
-
-This dataset represents the global archive of Level-1 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Multispectral Scanner System](https://landsat.gsfc.nasa.gov/multispectral-scanner-system/) onboard Landsat 1 through Landsat 5 from July 7, 1972 to January 7, 2013. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.
-
-2: Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.
-
-This dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.
-
-
-
-
-

Specifically, we want landsat-c2-l2.

-
-
-
landsat_reader = stac_cat["landsat-c2-l2"]
-
-
-
-
-

We can see the metadata below.

-
-
-
landsat_reader.read().metadata
-
-
-
-
-
{'type': 'Collection',
- 'id': 'landsat-c2-l2',
- 'stac_version': '1.0.0',
- 'description': 'Landsat Collection 2 Level-2 [Science Products](https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products), consisting of atmospherically corrected [surface reflectance](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-reflectance) and [surface temperature](https://www.usgs.gov/landsat-missions/landsat-collection-2-surface-temperature) image data. Collection 2 Level-2 Science Products are available from August 22, 1982 to present.\n\nThis dataset represents the global archive of Level-2 data from [Landsat Collection 2](https://www.usgs.gov/core-science-systems/nli/landsat/landsat-collection-2) acquired by the [Thematic Mapper](https://landsat.gsfc.nasa.gov/thematic-mapper/) onboard Landsat 4 and 5, the [Enhanced Thematic Mapper](https://landsat.gsfc.nasa.gov/the-enhanced-thematic-mapper-plus-etm/) onboard Landsat 7, and the [Operatational Land Imager](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/operational-land-imager/) and [Thermal Infrared Sensor](https://landsat.gsfc.nasa.gov/satellites/landsat-8/spacecraft-instruments/thermal-infrared-sensor/) onboard Landsat 8 and 9. Images are stored in [cloud-optimized GeoTIFF](https://www.cogeo.org/) format.\n',
- 'conformsTo': ['http://www.opengis.net/spec/cql2/1.0/conf/basic-cql2',
-  'http://www.opengis.net/spec/cql2/1.0/conf/cql2-json',
-  'http://www.opengis.net/spec/cql2/1.0/conf/cql2-text',
-  'http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/core',
-  'http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/geojson',
-  'http://www.opengis.net/spec/ogcapi-features-1/1.0/conf/oas30',
-  'http://www.opengis.net/spec/ogcapi-features-3/1.0/conf/filter',
-  'https://api.stacspec.org/v1.0.0-rc.1/collections',
-  'https://api.stacspec.org/v1.0.0-rc.1/core',
-  'https://api.stacspec.org/v1.0.0-rc.1/item-search',
-  'https://api.stacspec.org/v1.0.0-rc.1/item-search#fields',
-  'https://api.stacspec.org/v1.0.0-rc.1/item-search#filter',
-  'https://api.stacspec.org/v1.0.0-rc.1/item-search#query',
-  'https://api.stacspec.org/v1.0.0-rc.1/item-search#sort',
-  'https://api.stacspec.org/v1.0.0-rc.1/ogcapi-features'],
- 'title': 'Landsat Collection 2 Level-2',
- 'stac_extensions': ['https://stac-extensions.github.io/item-assets/v1.0.0/schema.json',
-  'https://stac-extensions.github.io/view/v1.0.0/schema.json',
-  'https://stac-extensions.github.io/scientific/v1.0.0/schema.json',
-  'https://stac-extensions.github.io/raster/v1.1.0/schema.json',
-  'https://stac-extensions.github.io/eo/v1.0.0/schema.json',
-  'https://stac-extensions.github.io/table/v1.2.0/schema.json'],
- 'item_assets': {'qa': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data'],
-   'title': 'Surface Temperature Quality Assessment Band',
-   'description': 'Collection 2 Level-2 Quality Assessment Band (ST_QA) Surface Temperature Product',
-   'raster:bands': [{'unit': 'kelvin',
-     'scale': 0.01,
-     'nodata': -9999,
-     'data_type': 'int16',
-     'spatial_resolution': 30}]},
-  'ang': {'type': 'text/plain',
-   'roles': ['metadata'],
-   'title': 'Angle Coefficients File',
-   'description': 'Collection 2 Level-1 Angle Coefficients File'},
-  'red': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data', 'reflectance'],
-   'title': 'Red Band',
-   'eo:bands': [{'common_name': 'red', 'description': 'Visible red'}],
-   'raster:bands': [{'scale': 2.75e-05,
-     'nodata': 0,
-     'offset': -0.2,
-     'data_type': 'uint16',
-     'spatial_resolution': 30}]},
-  'blue': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data', 'reflectance'],
-   'title': 'Blue Band',
-   'eo:bands': [{'common_name': 'blue', 'description': 'Visible blue'}],
-   'raster:bands': [{'scale': 2.75e-05,
-     'nodata': 0,
-     'offset': -0.2,
-     'data_type': 'uint16',
-     'spatial_resolution': 30}]},
-  'drad': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data'],
-   'title': 'Downwelled Radiance Band',
-   'description': 'Collection 2 Level-2 Downwelled Radiance Band (ST_DRAD) Surface Temperature Product',
-   'raster:bands': [{'unit': 'watt/steradian/square_meter/micrometer',
-     'scale': 0.001,
-     'nodata': -9999,
-     'data_type': 'int16',
-     'spatial_resolution': 30}]},
-  'emis': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data'],
-   'title': 'Emissivity Band',
-   'description': 'Collection 2 Level-2 Emissivity Band (ST_EMIS) Surface Temperature Product',
-   'raster:bands': [{'unit': 'emissivity coefficient',
-     'scale': 0.0001,
-     'nodata': -9999,
-     'data_type': 'int16',
-     'spatial_resolution': 30}]},
-  'emsd': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data'],
-   'title': 'Emissivity Standard Deviation Band',
-   'description': 'Collection 2 Level-2 Emissivity Standard Deviation Band (ST_EMSD) Surface Temperature Product',
-   'raster:bands': [{'unit': 'emissivity coefficient',
-     'scale': 0.0001,
-     'nodata': -9999,
-     'data_type': 'int16',
-     'spatial_resolution': 30}]},
-  'lwir': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data', 'temperature'],
-   'title': 'Surface Temperature Band',
-   'eo:bands': [{'common_name': 'lwir', 'description': 'Long-wave infrared'}],
-   'description': 'Collection 2 Level-2 Thermal Infrared Band (ST_B6) Surface Temperature',
-   'raster:bands': [{'unit': 'kelvin',
-     'scale': 0.00341802,
-     'nodata': 0,
-     'offset': 149.0,
-     'data_type': 'uint16',
-     'spatial_resolution': 30}]},
-  'trad': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data'],
-   'title': 'Thermal Radiance Band',
-   'description': 'Collection 2 Level-2 Thermal Radiance Band (ST_TRAD) Surface Temperature Product',
-   'raster:bands': [{'unit': 'watt/steradian/square_meter/micrometer',
-     'scale': 0.001,
-     'nodata': -9999,
-     'data_type': 'int16',
-     'spatial_resolution': 30}]},
-  'urad': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data'],
-   'title': 'Upwelled Radiance Band',
-   'description': 'Collection 2 Level-2 Upwelled Radiance Band (ST_URAD) Surface Temperature Product',
-   'raster:bands': [{'unit': 'watt/steradian/square_meter/micrometer',
-     'scale': 0.001,
-     'nodata': -9999,
-     'data_type': 'int16',
-     'spatial_resolution': 30}]},
-  'atran': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data'],
-   'title': 'Atmospheric Transmittance Band',
-   'description': 'Collection 2 Level-2 Atmospheric Transmittance Band (ST_ATRAN) Surface Temperature Product',
-   'raster:bands': [{'scale': 0.0001,
-     'nodata': -9999,
-     'data_type': 'int16',
-     'spatial_resolution': 30}]},
-  'cdist': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data'],
-   'title': 'Cloud Distance Band',
-   'description': 'Collection 2 Level-2 Cloud Distance Band (ST_CDIST) Surface Temperature Product',
-   'raster:bands': [{'unit': 'kilometer',
-     'scale': 0.01,
-     'nodata': -9999,
-     'data_type': 'int16',
-     'spatial_resolution': 30}]},
-  'green': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data', 'reflectance'],
-   'title': 'Green Band',
-   'eo:bands': [{'common_name': 'green',
-     'description': 'Visible green',
-     'center_wavelength': 0.56}],
-   'raster:bands': [{'scale': 2.75e-05,
-     'nodata': 0,
-     'offset': -0.2,
-     'data_type': 'uint16',
-     'spatial_resolution': 30}]},
-  'nir08': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data', 'reflectance'],
-   'title': 'Near Infrared Band 0.8',
-   'eo:bands': [{'common_name': 'nir08', 'description': 'Near infrared'}],
-   'raster:bands': [{'scale': 2.75e-05,
-     'nodata': 0,
-     'offset': -0.2,
-     'data_type': 'uint16',
-     'spatial_resolution': 30}]},
-  'lwir11': {'gsd': 100,
-   'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data', 'temperature'],
-   'title': 'Surface Temperature Band',
-   'eo:bands': [{'name': 'TIRS_B10',
-     'common_name': 'lwir11',
-     'description': 'Long-wave infrared',
-     'center_wavelength': 10.9,
-     'full_width_half_max': 0.59}],
-   'description': 'Collection 2 Level-2 Thermal Infrared Band (ST_B10) Surface Temperature',
-   'raster:bands': [{'unit': 'kelvin',
-     'scale': 0.00341802,
-     'nodata': 0,
-     'offset': 149.0,
-     'data_type': 'uint16',
-     'spatial_resolution': 30}]},
-  'swir16': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data', 'reflectance'],
-   'title': 'Short-wave Infrared Band 1.6',
-   'eo:bands': [{'common_name': 'swir16',
-     'description': 'Short-wave infrared'}],
-   'raster:bands': [{'scale': 2.75e-05,
-     'nodata': 0,
-     'offset': -0.2,
-     'data_type': 'uint16',
-     'spatial_resolution': 30}]},
-  'swir22': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data', 'reflectance'],
-   'title': 'Short-wave Infrared Band 2.2',
-   'eo:bands': [{'common_name': 'swir22',
-     'description': 'Short-wave infrared'}],
-   'description': 'Collection 2 Level-2 Short-wave Infrared Band 2.2 (SR_B7) Surface Reflectance',
-   'raster:bands': [{'scale': 2.75e-05,
-     'nodata': 0,
-     'offset': -0.2,
-     'data_type': 'uint16',
-     'spatial_resolution': 30}]},
-  'coastal': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data', 'reflectance'],
-   'title': 'Coastal/Aerosol Band',
-   'eo:bands': [{'name': 'OLI_B1',
-     'common_name': 'coastal',
-     'description': 'Coastal/Aerosol',
-     'center_wavelength': 0.44,
-     'full_width_half_max': 0.02}],
-   'description': 'Collection 2 Level-2 Coastal/Aerosol Band (SR_B1) Surface Reflectance',
-   'raster:bands': [{'scale': 2.75e-05,
-     'nodata': 0,
-     'offset': -0.2,
-     'data_type': 'uint16',
-     'spatial_resolution': 30}]},
-  'mtl.txt': {'type': 'text/plain',
-   'roles': ['metadata'],
-   'title': 'Product Metadata File (txt)',
-   'description': 'Collection 2 Level-2 Product Metadata File (txt)'},
-  'mtl.xml': {'type': 'application/xml',
-   'roles': ['metadata'],
-   'title': 'Product Metadata File (xml)',
-   'description': 'Collection 2 Level-2 Product Metadata File (xml)'},
-  'cloud_qa': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['cloud', 'cloud-shadow', 'snow-ice', 'water-mask'],
-   'title': 'Cloud Quality Assessment Band',
-   'description': 'Collection 2 Level-2 Cloud Quality Assessment Band (SR_CLOUD_QA) Surface Reflectance Product',
-   'raster:bands': [{'unit': 'bit index',
-     'data_type': 'uint8',
-     'spatial_resolution': 30}],
-   'classification:bitfields': [{'name': 'ddv',
-     'length': 1,
-     'offset': 0,
-     'classes': [{'name': 'not_ddv',
-       'value': 0,
-       'description': 'Pixel has no DDV'},
-      {'name': 'ddv', 'value': 1, 'description': 'Pixel has DDV'}],
-     'description': 'Dense Dark Vegetation (DDV)'},
-    {'name': 'cloud',
-     'length': 1,
-     'offset': 1,
-     'classes': [{'name': 'not_cloud',
-       'value': 0,
-       'description': 'Pixel has no cloud'},
-      {'name': 'cloud', 'value': 1, 'description': 'Pixel has cloud'}],
-     'description': 'Cloud mask'},
-    {'name': 'cloud_shadow',
-     'length': 1,
-     'offset': 2,
-     'classes': [{'name': 'not_shadow',
-       'value': 0,
-       'description': 'Pixel has no cloud shadow'},
-      {'name': 'shadow', 'value': 1, 'description': 'Pixel has cloud shadow'}],
-     'description': 'Cloud shadow mask'},
-    {'name': 'cloud_adjacent',
-     'length': 1,
-     'offset': 3,
-     'classes': [{'name': 'not_adjacent',
-       'value': 0,
-       'description': 'Pixel is not adjacent to cloud'},
-      {'name': 'adjacent',
-       'value': 1,
-       'description': 'Pixel is adjacent to cloud'}],
-     'description': 'Cloud adjacency'},
-    {'name': 'snow',
-     'length': 1,
-     'offset': 4,
-     'classes': [{'name': 'not_snow',
-       'value': 0,
-       'description': 'Pixel is not snow'},
-      {'name': 'shadow', 'value': 1, 'description': 'Pixel is snow'}],
-     'description': 'Snow mask'},
-    {'name': 'water',
-     'length': 1,
-     'offset': 5,
-     'classes': [{'name': 'not_water',
-       'value': 0,
-       'description': 'Pixel is not water'},
-      {'name': 'water', 'value': 1, 'description': 'Pixel is water'}],
-     'description': 'Water mask'}]},
-  'mtl.json': {'type': 'application/json',
-   'roles': ['metadata'],
-   'title': 'Product Metadata File (json)',
-   'description': 'Collection 2 Level-2 Product Metadata File (json)'},
-  'qa_pixel': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['cloud', 'cloud-shadow', 'snow-ice', 'water-mask'],
-   'title': 'Pixel Quality Assessment Band',
-   'description': 'Collection 2 Level-1 Pixel Quality Assessment Band (QA_PIXEL)',
-   'raster:bands': [{'unit': 'bit index',
-     'nodata': 1,
-     'data_type': 'uint16',
-     'spatial_resolution': 30}]},
-  'qa_radsat': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['saturation'],
-   'raster:bands': [{'unit': 'bit index',
-     'data_type': 'uint16',
-     'spatial_resolution': 30}]},
-  'qa_aerosol': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data-mask', 'water-mask'],
-   'title': 'Aerosol Quality Assessment Band',
-   'description': 'Collection 2 Level-2 Aerosol Quality Assessment Band (SR_QA_AEROSOL) Surface Reflectance Product',
-   'raster:bands': [{'unit': 'bit index',
-     'nodata': 1,
-     'data_type': 'uint8',
-     'spatial_resolution': 30}],
-   'classification:bitfields': [{'name': 'fill',
-     'length': 1,
-     'offset': 0,
-     'classes': [{'name': 'not_fill',
-       'value': 0,
-       'description': 'Pixel is not fill'},
-      {'name': 'fill', 'value': 1, 'description': 'Pixel is fill'}],
-     'description': 'Image or fill data'},
-    {'name': 'retrieval',
-     'length': 1,
-     'offset': 1,
-     'classes': [{'name': 'not_valid',
-       'value': 0,
-       'description': 'Pixel retrieval is not valid'},
-      {'name': 'valid',
-       'value': 1,
-       'description': 'Pixel retrieval is valid'}],
-     'description': 'Valid aerosol retrieval'},
-    {'name': 'water',
-     'length': 1,
-     'offset': 2,
-     'classes': [{'name': 'not_water',
-       'value': 0,
-       'description': 'Pixel is not water'},
-      {'name': 'water', 'value': 1, 'description': 'Pixel is water'}],
-     'description': 'Water mask'},
-    {'name': 'interpolated',
-     'length': 1,
-     'offset': 5,
-     'classes': [{'name': 'not_interpolated',
-       'value': 0,
-       'description': 'Pixel is not interpolated aerosol'},
-      {'name': 'interpolated',
-       'value': 1,
-       'description': 'Pixel is interpolated aerosol'}],
-     'description': 'Aerosol interpolation'},
-    {'name': 'level',
-     'length': 2,
-     'offset': 6,
-     'classes': [{'name': 'climatology',
-       'value': 0,
-       'description': 'No aerosol correction applied'},
-      {'name': 'low', 'value': 1, 'description': 'Low aerosol level'},
-      {'name': 'medium', 'value': 2, 'description': 'Medium aerosol level'},
-      {'name': 'high', 'value': 3, 'description': 'High aerosol level'}],
-     'description': 'Aerosol level'}]},
-  'atmos_opacity': {'type': 'image/tiff; application=geotiff; profile=cloud-optimized',
-   'roles': ['data'],
-   'title': 'Atmospheric Opacity Band',
-   'description': 'Collection 2 Level-2 Atmospheric Opacity Band (SR_ATMOS_OPACITY) Surface Reflectance Product',
-   'raster:bands': [{'scale': 0.001,
-     'nodata': -9999,
-     'data_type': 'int16',
-     'spatial_resolution': 30}]}},
- 'msft:group_id': 'landsat',
- 'msft:container': 'landsat-c2',
- 'msft:storage_account': 'landsateuwest',
- 'msft:short_description': 'Landsat Collection 2 Level-2 data from the Thematic Mapper (TM) onboard Landsat 4 and 5, the Enhanced Thematic Mapper Plus (ETM+) onboard Landsat 7, and the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) onboard Landsat 8 and 9.',
- 'msft:region': 'westeurope',
- 'extent': {'spatial': {'bbox': [[-180.0, -90.0, 180.0, 90.0]]},
-  'temporal': {'interval': [['1982-08-22T00:00:00Z', None]]}},
- 'license': 'proprietary',
- 'keywords': ['Landsat',
-  'USGS',
-  'NASA',
-  'Satellite',
-  'Global',
-  'Imagery',
-  'Reflectance',
-  'Temperature'],
- 'providers': [{'name': 'NASA',
-   'roles': ['producer', 'licensor'],
-   'url': 'https://landsat.gsfc.nasa.gov/'},
-  {'name': 'USGS',
-   'roles': ['producer', 'processor', 'licensor'],
-   'url': 'https://www.usgs.gov/landsat-missions/landsat-collection-2-level-2-science-products'},
-  {'name': 'Microsoft',
-   'roles': ['host'],
-   'url': 'https://planetarycomputer.microsoft.com'}],
- 'summaries': {'gsd': [30, 60, 100, 120],
-  'sci:doi': ['10.5066/P9IAXOVV', '10.5066/P9C7I13B', '10.5066/P9OGBGM6'],
-  'eo:bands': [{'name': 'TM_B1',
-    'common_name': 'blue',
-    'description': 'Visible blue (Thematic Mapper)',
-    'center_wavelength': 0.49,
-    'full_width_half_max': 0.07},
-   {'name': 'TM_B2',
-    'common_name': 'green',
-    'description': 'Visible green (Thematic Mapper)',
-    'center_wavelength': 0.56,
-    'full_width_half_max': 0.08},
-   {'name': 'TM_B3',
-    'common_name': 'red',
-    'description': 'Visible red (Thematic Mapper)',
-    'center_wavelength': 0.66,
-    'full_width_half_max': 0.06},
-   {'name': 'TM_B4',
-    'common_name': 'nir08',
-    'description': 'Near infrared (Thematic Mapper)',
-    'center_wavelength': 0.83,
-    'full_width_half_max': 0.14},
-   {'name': 'TM_B5',
-    'common_name': 'swir16',
-    'description': 'Short-wave infrared (Thematic Mapper)',
-    'center_wavelength': 1.65,
-    'full_width_half_max': 0.2},
-   {'name': 'TM_B6',
-    'common_name': 'lwir',
-    'description': 'Long-wave infrared (Thematic Mapper)',
-    'center_wavelength': 11.45,
-    'full_width_half_max': 2.1},
-   {'name': 'TM_B7',
-    'common_name': 'swir22',
-    'description': 'Short-wave infrared (Thematic Mapper)',
-    'center_wavelength': 2.22,
-    'full_width_half_max': 0.27},
-   {'name': 'ETM_B1',
-    'common_name': 'blue',
-    'description': 'Visible blue (Enhanced Thematic Mapper Plus)',
-    'center_wavelength': 0.48,
-    'full_width_half_max': 0.07},
-   {'name': 'ETM_B2',
-    'common_name': 'green',
-    'description': 'Visible green (Enhanced Thematic Mapper Plus)',
-    'center_wavelength': 0.56,
-    'full_width_half_max': 0.08},
-   {'name': 'ETM_B3',
-    'common_name': 'red',
-    'description': 'Visible red (Enhanced Thematic Mapper Plus)',
-    'center_wavelength': 0.66,
-    'full_width_half_max': 0.06},
-   {'name': 'ETM_B4',
-    'common_name': 'nir08',
-    'description': 'Near infrared (Enhanced Thematic Mapper Plus)',
-    'center_wavelength': 0.84,
-    'full_width_half_max': 0.13},
-   {'name': 'ETM_B5',
-    'common_name': 'swir16',
-    'description': 'Short-wave infrared (Enhanced Thematic Mapper Plus)',
-    'center_wavelength': 1.65,
-    'full_width_half_max': 0.2},
-   {'name': 'ETM_B6',
-    'common_name': 'lwir',
-    'description': 'Long-wave infrared (Enhanced Thematic Mapper Plus)',
-    'center_wavelength': 11.34,
-    'full_width_half_max': 2.05},
-   {'name': 'ETM_B7',
-    'common_name': 'swir22',
-    'description': 'Short-wave infrared (Enhanced Thematic Mapper Plus)',
-    'center_wavelength': 2.2,
-    'full_width_half_max': 0.28},
-   {'name': 'OLI_B1',
-    'common_name': 'coastal',
-    'description': 'Coastal/Aerosol (Operational Land Imager)',
-    'center_wavelength': 0.44,
-    'full_width_half_max': 0.02},
-   {'name': 'OLI_B2',
-    'common_name': 'blue',
-    'description': 'Visible blue (Operational Land Imager)',
-    'center_wavelength': 0.48,
-    'full_width_half_max': 0.06},
-   {'name': 'OLI_B3',
-    'common_name': 'green',
-    'description': 'Visible green (Operational Land Imager)',
-    'center_wavelength': 0.56,
-    'full_width_half_max': 0.06},
-   {'name': 'OLI_B4',
-    'common_name': 'red',
-    'description': 'Visible red (Operational Land Imager)',
-    'center_wavelength': 0.65,
-    'full_width_half_max': 0.04},
-   {'name': 'OLI_B5',
-    'common_name': 'nir08',
-    'description': 'Near infrared (Operational Land Imager)',
-    'center_wavelength': 0.87,
-    'full_width_half_max': 0.03},
-   {'name': 'OLI_B6',
-    'common_name': 'swir16',
-    'description': 'Short-wave infrared (Operational Land Imager)',
-    'center_wavelength': 1.61,
-    'full_width_half_max': 0.09},
-   {'name': 'OLI_B7',
-    'common_name': 'swir22',
-    'description': 'Short-wave infrared (Operational Land Imager)',
-    'center_wavelength': 2.2,
-    'full_width_half_max': 0.19},
-   {'name': 'TIRS_B10',
-    'common_name': 'lwir11',
-    'description': 'Long-wave infrared (Thermal Infrared Sensor)',
-    'center_wavelength': 10.9,
-    'full_width_half_max': 0.59}],
-  'platform': ['landsat-4',
-   'landsat-5',
-   'landsat-7',
-   'landsat-8',
-   'landsat-9'],
-  'instruments': ['tm', 'etm+', 'oli', 'tirs'],
-  'view:off_nadir': {'minimum': 0, 'maximum': 15}},
- 'assets': {'thumbnail': {'href': 'https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/landsat-c2-l2-thumb.png',
-   'type': 'image/png',
-   'title': 'Landsat Collection 2 Level-2 thumbnail',
-   'roles': ['thumbnail']},
-  'geoparquet-items': {'href': 'abfs://items/landsat-c2-l2.parquet',
-   'type': 'application/x-parquet',
-   'title': 'GeoParquet STAC items',
-   'description': "Snapshot of the collection's STAC items exported to GeoParquet format.",
-   'msft:partition_info': {'is_partitioned': True,
-    'partition_frequency': 'MS'},
-   'table:storage_options': {'account_name': 'pcstacitems',
-    'credential': 'st=2024-03-25T23%3A06%3A04Z&se=2024-03-26T23%3A51%3A04Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2024-03-26T03%3A28%3A43Z&ske=2024-04-02T03%3A28%3A43Z&sks=b&skv=2021-06-08&sig=hVf9NUwVAPLn7oML%2BFjYrW2%2Bpnhr9jkOC1C7v0zRsCg%3D',
-    'sas_token': 'st=2024-03-25T23%3A06%3A04Z&se=2024-03-26T23%3A51%3A04Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2024-03-26T03%3A28%3A43Z&ske=2024-04-02T03%3A28%3A43Z&sks=b&skv=2021-06-08&sig=hVf9NUwVAPLn7oML%2BFjYrW2%2Bpnhr9jkOC1C7v0zRsCg%3D'},
-   'roles': ['stac-items']}}}
-
-
-
-
-

We can get a preview of the dataset by looking at the thumbnail.

-
-
-
# data as array
-landsat_reader["thumbnail"].read()
-
-
-
-
-
-
-
-
-
-
-
-
# render with panel
-landsat_reader["thumbnail"].to_reader("panel")
-
-
-
-
-
PanelImageViewer reader producing panel.pane:Image
-
-
-
-
-

If that’s desired, we can move on to get the items in the catalog.

-
-
-
landsat_items = landsat_reader["geoparquet-items"]
-landsat_items
-
-
-
-
-
DaskGeoParquet reader producing dask_geopandas.core:GeoDataFrame
-
-
-
-
-
-
-
# note `output_instance`: this is because .tail() makes a pandas from a dask dataframe. GeoDataFrameToSTACCatalog
-# works specifically with geopandas only
-cat = landsat_items.tail(output_instance="geopandas:GeoDataFrame").GeoDataFrameToSTACCatalog.read()
-
-
-
-
-
-
-
# this is an "item collection"; each item is a set of assets (many levels here)
-cat
-
-
-
-
-
Catalog
- named datasets: ['LC09_L2SP_105013_20240301_02_T1', 'LC09_L2SP_105014_20240301_02_T1', 'LC09_L2SP_105015_20240301_02_T1', 'LC09_L2SP_105016_20240301_02_T2', 'LC09_L2SP_105017_20240301_02_T2']
-
-
-
-
-

Repeat the process aforementioned.

-
-
-
item_key = list(cat.entries.keys())[0]
-subcat = cat[item_key].read()
-subcat
-
-
-
-
-
Catalog
- named datasets: ['ang', 'atran', 'blue', 'cdist', 'coastal', 'drad', 'emis', 'emsd', 'green', 'lwir11', 'mtl.json', 'mtl.txt', 'mtl.xml', 'nir08', 'qa', 'qa_aerosol', 'qa_pixel', 'qa_radsat', 'red', 'rendered_preview', 'swir16', 'swir22', 'tilejson', 'trad', 'urad']
-
-
-
-
-
-
-
# single image in one band
-subcat.red.read()
-
-
-
-
-
[<xarray.DataArray (band: 1, y: 8181, x: 8121)>
- [66437901 values with dtype=uint16]
- Coordinates:
-   * band         (band) int64 1
-   * x            (x) float64 4.455e+05 4.455e+05 ... 6.891e+05 6.891e+05
-   * y            (y) float64 6.939e+06 6.939e+06 ... 6.693e+06 6.693e+06
-     spatial_ref  int64 0
- Attributes:
-     AREA_OR_POINT:  Point
-     _FillValue:     0
-     scale_factor:   1.0
-     add_offset:     0.0]
-
-
-
-
-
-
-
# unfortunately, the "signer" didn't make it through
-catbands = cat[item_key].to_reader(reader="StackBands", bands=["red", "green", "blue"], signer=planetary_computer.sign_inplace)
-
-
-
-
-

Then, we can load the actual assets.

-
-
-
# multiband image. Unfortunately, the value of the "band" variable in each input is 1, not the real
-# value; they could be relabelled here
-data = catbands.read(dim="band")
-data
-
-
-
-
-
- - - - - - - - - - - - - - -
<xarray.DataArray (band: 3, y: 8181, x: 8121)>
-array([[[0, 0, 0, ..., 0, 0, 0],
-        [0, 0, 0, ..., 0, 0, 0],
-        [0, 0, 0, ..., 0, 0, 0],
-        ...,
-        [0, 0, 0, ..., 0, 0, 0],
-        [0, 0, 0, ..., 0, 0, 0],
-        [0, 0, 0, ..., 0, 0, 0]],
-
-       [[0, 0, 0, ..., 0, 0, 0],
-        [0, 0, 0, ..., 0, 0, 0],
-        [0, 0, 0, ..., 0, 0, 0],
-        ...,
-        [0, 0, 0, ..., 0, 0, 0],
-        [0, 0, 0, ..., 0, 0, 0],
-        [0, 0, 0, ..., 0, 0, 0]],
-
-       [[0, 0, 0, ..., 0, 0, 0],
-        [0, 0, 0, ..., 0, 0, 0],
-        [0, 0, 0, ..., 0, 0, 0],
-        ...,
-        [0, 0, 0, ..., 0, 0, 0],
-        [0, 0, 0, ..., 0, 0, 0],
-        [0, 0, 0, ..., 0, 0, 0]]], dtype=uint16)
-Coordinates:
-  * band         (band) int64 1 1 1
-  * x            (x) float64 4.455e+05 4.455e+05 ... 6.891e+05 6.891e+05
-  * y            (y) float64 6.939e+06 6.939e+06 ... 6.693e+06 6.693e+06
-    spatial_ref  int64 0
-Attributes:
-    AREA_OR_POINT:  Point
-    _FillValue:     0
-    scale_factor:   1.0
-    add_offset:     0.0
-
-

Now, we can plot the true color imagery with the extracted bands.

-
-
-
data.plot.imshow(robust=True, figsize=(10, 10))
-
-
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-
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<matplotlib.image.AxesImage at 0x7f7e286bcd30>
-
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-../_images/1.1_Data_Ingestion-Intake_40_1.png -
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- - - - -
- - -
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- -
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- - - -
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- - - - - - - - - -
-
- - \ No newline at end of file diff --git a/_preview/22/notebooks/2.0_Preprocessing.html b/_preview/22/notebooks/2.0_Preprocessing.html deleted file mode 100644 index 77d782d..0000000 --- a/_preview/22/notebooks/2.0_Preprocessing.html +++ /dev/null @@ -1,2246 +0,0 @@ - - - - - - - - Preprocessing - Regrid — Landsat ML Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
- - -
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- - - - - -
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- -

Landsat8

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-

Preprocessing - Regrid

-
-
-

Overview

-

The last notebook demonstrated how to access the data with some common alternative approaches to the Planetary Computer platform. The next step is to ensure that the data has been appropriately prepared for whatever analysis we intend to run.

-

For example, as we may be directly comparing data across time points, it would be very useful if these images were on the same underlying coordinate grid. In this notebook, we will preprocess the data to reshape and align our images for efficient consumption by analysis steps. We will also see how to use interactive visualization to assist in our processing pipeline. Note, if you are adopting the data access approach in the Planetary Computer notebook, you may not need to regrid the data and can proceed to an analysis workflow.

-
-
-

Prerequisites

- - - - - - - - - - - - - - - - - - - - - -

Concepts

Importance

Notes

Data Ingestion - Intake

Necessary

Coordinate Reference Systems and EPSG

Helpful

GeoViews resampling grids

Helpful

-
    -
  • Time to learn: 20 minutes.

  • -
-
-
-
-

Imports

-
-
-
import intake
-import numpy as np
-import xarray as xr
-import cartopy.crs as ccrs
-import geoviews as gv
-import hvplot.xarray
-import holoviews as hv
-
-import warnings
-warnings.simplefilter('ignore', FutureWarning) # Ignore a warning about the format of epsg codes
-
-
-
-
-
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 12.0.1. Please consider upgrading.
-  warnings.warn(
-
-
-
-
-
-
-
-
-
-

Loading data

-

You have already read through the previous tutorial on loading data with Intake, so now we’ll jump right to using Intake to read in chunks of small versions of the Landsat 5 and Landsat 8 data.

-
-
-
cat = intake.open_catalog('./data/catalog.yml')
-
-
-
-
-
-
-
cat.landsat_8_small.container
-
-
-
-
-
'xarray'
-
-
-
-
-

We know that our Landsat data will ultimately be an xarray object. Let’s load the metadata with Dask:

-
-
-
landsat_5_da = cat.landsat_5_small.to_dask()
-landsat_8_da = cat.landsat_8_small.to_dask()
-
-
-
-
-
---------------------------------------------------------------------------
-KeyError                                  Traceback (most recent call last)
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/xarray/backends/file_manager.py:210, in CachingFileManager._acquire_with_cache_info(self, needs_lock)
-    209 try:
---> 210     file = self._cache[self._key]
-    211 except KeyError:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/xarray/backends/lru_cache.py:56, in LRUCache.__getitem__(self, key)
-     55 with self._lock:
----> 56     value = self._cache[key]
-     57     self._cache.move_to_end(key)
-
-KeyError: [<function open at 0x7fe455a016c0>, ('s3://earth-data/landsat/small/LT05_L1TP_042033_19881022_20161001_01_T1_sr_band{band:d}.tif',), 'r', (('sharing', False),), 'a2e41f23-44a7-4714-801d-c718a4562b13']
-
-During handling of the above exception, another exception occurred:
-
-CPLE_AWSInvalidCredentialsError           Traceback (most recent call last)
-File rasterio/_base.pyx:308, in rasterio._base.DatasetBase.__init__()
-
-File rasterio/_base.pyx:219, in rasterio._base.open_dataset()
-
-File rasterio/_err.pyx:221, in rasterio._err.exc_wrap_pointer()
-
-CPLE_AWSInvalidCredentialsError: AWS_SECRET_ACCESS_KEY and AWS_NO_SIGN_REQUEST configuration options not defined, and /home/runner/.aws/credentials not filled
-
-During handling of the above exception, another exception occurred:
-
-RasterioIOError                           Traceback (most recent call last)
-Cell In[4], line 1
-----> 1 landsat_5_da = cat.landsat_5_small.to_dask()
-      2 landsat_8_da = cat.landsat_8_small.to_dask()
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/intake_xarray/base.py:69, in DataSourceMixin.to_dask(self)
-     67 def to_dask(self):
-     68     """Return xarray object where variables are dask arrays"""
----> 69     return self.read_chunked()
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/intake_xarray/base.py:44, in DataSourceMixin.read_chunked(self)
-     42 def read_chunked(self):
-     43     """Return xarray object (which will have chunks)"""
----> 44     self._load_metadata()
-     45     return self._ds
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/intake/source/base.py:84, in DataSourceBase._load_metadata(self)
-     82 """load metadata only if needed"""
-     83 if self._schema is None:
----> 84     self._schema = self._get_schema()
-     85     self.dtype = self._schema.dtype
-     86     self.shape = self._schema.shape
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/intake_xarray/raster.py:103, in RasterIOSource._get_schema(self)
-    100 self.urlpath, *_ = self._get_cache(self.urlpath)
-    102 if self._ds is None:
---> 103     self._open_dataset()
-    105     ds2 = xr.Dataset({'raster': self._ds})
-    106     metadata = {
-    107         'dims': dict(ds2.dims),
-    108         'data_vars': {k: list(ds2[k].coords)
-   (...)
-    111         'array': 'raster'
-    112     }
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/intake_xarray/raster.py:91, in RasterIOSource._open_dataset(self)
-     89     self._ds = self._open_files(files)
-     90 else:
----> 91     self._ds = rio.open_rasterio(files, chunks=self.chunks,
-     92                                  **self._kwargs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/rioxarray/_io.py:1124, in open_rasterio(filename, parse_coordinates, chunks, cache, lock, masked, mask_and_scale, variable, group, default_name, decode_times, decode_timedelta, band_as_variable, **open_kwargs)
-   1122     else:
-   1123         manager = URIManager(file_opener, filename, mode="r", kwargs=open_kwargs)
--> 1124     riods = manager.acquire()
-   1125     captured_warnings = rio_warnings.copy()
-   1127 # raise the NotGeoreferencedWarning if applicable
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/xarray/backends/file_manager.py:192, in CachingFileManager.acquire(self, needs_lock)
-    177 def acquire(self, needs_lock=True):
-    178     """Acquire a file object from the manager.
-    179 
-    180     A new file is only opened if it has expired from the
-   (...)
-    190         An open file object, as returned by ``opener(*args, **kwargs)``.
-    191     """
---> 192     file, _ = self._acquire_with_cache_info(needs_lock)
-    193     return file
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/xarray/backends/file_manager.py:216, in CachingFileManager._acquire_with_cache_info(self, needs_lock)
-    214     kwargs = kwargs.copy()
-    215     kwargs["mode"] = self._mode
---> 216 file = self._opener(*self._args, **kwargs)
-    217 if self._mode == "w":
-    218     # ensure file doesn't get overridden when opened again
-    219     self._mode = "a"
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/rasterio/env.py:451, in ensure_env_with_credentials.<locals>.wrapper(*args, **kwds)
-    448     session = DummySession()
-    450 with env_ctor(session=session):
---> 451     return f(*args, **kwds)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/rasterio/__init__.py:304, in open(fp, mode, driver, width, height, count, crs, transform, dtype, nodata, sharing, **kwargs)
-    301 path = _parse_path(raw_dataset_path)
-    303 if mode == "r":
---> 304     dataset = DatasetReader(path, driver=driver, sharing=sharing, **kwargs)
-    305 elif mode == "r+":
-    306     dataset = get_writer_for_path(path, driver=driver)(
-    307         path, mode, driver=driver, sharing=sharing, **kwargs
-    308     )
-
-File rasterio/_base.pyx:310, in rasterio._base.DatasetBase.__init__()
-
-RasterioIOError: AWS_SECRET_ACCESS_KEY and AWS_NO_SIGN_REQUEST configuration options not defined, and /home/runner/.aws/credentials not filled
-
-
-
-
-
-
-
landsat_8_da
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[5], line 1
-----> 1 landsat_8_da
-
-NameError: name 'landsat_8_da' is not defined
-
-
-
-
-
-
-

Calculate the Vegetation Index

-

To motivate our preprocessing, let’s conduct a simple analysis on the Normalized Difference Vegetation Index NDVI for each of these image sets as follows:

-
-(1)\[\begin{align} -{NDVI} & = \frac{NIR-Red}{NIR+Red} -\end{align}\]
-

where Red and NIR stand for the spectral reflectance measurements acquired in the Red (visible) and near-infrared regions, respectively. Note that in Landsat 5 the Red and NIR bands are stored in bands 3 and 4 respectively whereas in Landsat 8 the Red and NIR are bands 4 and 5.

-
-
-
with xr.set_options(keep_attrs=True):
-    NDVI_1988 = (landsat_5_da.sel(band=4) - landsat_5_da.sel(band=3)) / (landsat_5_da.sel(band=4) + landsat_5_da.sel(band=3))
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[6], line 2
-      1 with xr.set_options(keep_attrs=True):
-----> 2     NDVI_1988 = (landsat_5_da.sel(band=4) - landsat_5_da.sel(band=3)) / (landsat_5_da.sel(band=4) + landsat_5_da.sel(band=3))
-
-NameError: name 'landsat_5_da' is not defined
-
-
-
-
-
-

Info

-

Above, we are using a context manager “with xr.set_options(keep_attrs=True):” to retain the array’s attributes through the operations. That is, we want all the metadata like ‘crs’ to stay with our result so we can use ‘geo=True’ in our plotting.

-
-
-
-
NDVI_1988
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[7], line 1
-----> 1 NDVI_1988
-
-NameError: name 'NDVI_1988' is not defined
-
-
-
-
-
-
-
NDVI_1988_plot = NDVI_1988.hvplot.image(x='x', y='y', geo=True, clim=(-1,1), title='NDVI 1988', rot=45, cmap='viridis')
-NDVI_1988_plot
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[8], line 1
-----> 1 NDVI_1988_plot = NDVI_1988.hvplot.image(x='x', y='y', geo=True, clim=(-1,1), title='NDVI 1988', rot=45, cmap='viridis')
-      2 NDVI_1988_plot
-
-NameError: name 'NDVI_1988' is not defined
-
-
-
-
-
-
-
with xr.set_options(keep_attrs=True):
-    NDVI_2017 = (landsat_8_da.sel(band=5) - landsat_8_da.sel(band=4)) / (landsat_8_da.sel(band=5) + landsat_8_da.sel(band=4))
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[9], line 2
-      1 with xr.set_options(keep_attrs=True):
-----> 2     NDVI_2017 = (landsat_8_da.sel(band=5) - landsat_8_da.sel(band=4)) / (landsat_8_da.sel(band=5) + landsat_8_da.sel(band=4))
-
-NameError: name 'landsat_8_da' is not defined
-
-
-
-
-
-
-
NDVI_2017_plot = NDVI_2017.hvplot.image(x='x', y='y', geo=True, clim=(-1,1), title='NDVI 2017', rot=45, cmap='viridis')
-NDVI_2017_plot
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[10], line 1
-----> 1 NDVI_2017_plot = NDVI_2017.hvplot.image(x='x', y='y', geo=True, clim=(-1,1), title='NDVI 2017', rot=45, cmap='viridis')
-      2 NDVI_2017_plot
-
-NameError: name 'NDVI_2017' is not defined
-
-
-
-
-

We can calculate the difference between these two years by subtracting one from the other.

-
-
-
with xr.set_options(keep_attrs=True):
-    NDVI_diff = NDVI_2017 - NDVI_1988
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[11], line 2
-      1 with xr.set_options(keep_attrs=True):
-----> 2     NDVI_diff = NDVI_2017 - NDVI_1988
-
-NameError: name 'NDVI_2017' is not defined
-
-
-
-
-
-
-
NDVI_diff_plot = NDVI_diff.hvplot.image(x='x', y='y', geo=True, cmap='coolwarm', clim=(-1,1), title='NDVI 2017 - 1988', rot=45)
-NDVI_diff_plot
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[12], line 1
-----> 1 NDVI_diff_plot = NDVI_diff.hvplot.image(x='x', y='y', geo=True, cmap='coolwarm', clim=(-1,1), title='NDVI 2017 - 1988', rot=45)
-      2 NDVI_diff_plot
-
-NameError: name 'NDVI_diff' is not defined
-
-
-
-
-

Notice how pixelated that image looks. What is going on here? To figure it out, let’s take a look at the shape of diff.

-
-
-
NDVI_diff.shape
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[13], line 1
-----> 1 NDVI_diff.shape
-
-NameError: name 'NDVI_diff' is not defined
-
-
-
-
-

That’s a lot smaller than our NDVI for each year. What is happening is that when we compute the difference on the data we only get values where there are values for each year in the same grid cell. Since the cells are on a different resolution this only happens once every so often. What we’d rather do is interpolate to the same grid and then do our computations on that.

-
-
-

Combine data from overlapping grids

-

These two sets of Landsat bands cover roughly the same area but were taken in 1988 and 2017. They have different resolutions, different numbers of grid cells per image, and different x and y offsets. We can see the offset by printing the first x value for each year and seeing that they are not equivalent.

-
-
-
print(NDVI_1988.x[0].values)
-print(NDVI_2017.x[0].values)
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[14], line 1
-----> 1 print(NDVI_1988.x[0].values)
-      2 print(NDVI_2017.x[0].values)
-
-NameError: name 'NDVI_1988' is not defined
-
-
-
-
-

We can also do a quick check of resolution by subtracting the second x value from the first x value for each year.

-
-
-
print((NDVI_1988.x[1] - NDVI_1988.x[0]).values)
-print((NDVI_2017.x[1] - NDVI_2017.x[0]).values)
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[15], line 1
-----> 1 print((NDVI_1988.x[1] - NDVI_1988.x[0]).values)
-      2 print((NDVI_2017.x[1] - NDVI_2017.x[0]).values)
-
-NameError: name 'NDVI_1988' is not defined
-
-
-
-
-

To align the grid, resolution, and offset of these images, we will select a region of interest, create a new grid within this region, and then interpolate our images onto this common grid.

-
-

Select region of interest

-

The first step to selecting a Region of Interest (ROI) is to define its center point.

-

There are multiple ways to define this central point, and we will start with a case in which we know the coordinates in latitude, longitude and need to convert it into the CRS of our data. Alternatively, we will see how easy it is to use interactive visualization to estimate the center point without knowing any prior coordinates.

-

For the first approach, the first step is getting the CRS of our data. This information is stored in the attributes of our original landsat data (either .crs or .rio.crs depending on package versions). Let’s take a look at it:

-
-
-
try:
-    print(landsat_8_da.crs)
-except:
-    print(landsat_8_da.rio.crs)
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[16], line 2
-      1 try:
-----> 2     print(landsat_8_da.crs)
-      3 except:
-
-NameError: name 'landsat_8_da' is not defined
-
-During handling of the above exception, another exception occurred:
-
-NameError                                 Traceback (most recent call last)
-Cell In[16], line 4
-      2     print(landsat_8_da.crs)
-      3 except:
-----> 4     print(landsat_8_da.rio.crs)
-
-NameError: name 'landsat_8_da' is not defined
-
-
-
-
-

This CRS is referenced by an EPSG code. We can see more about this specific code at EPSG.io. Read more about EPSG codes in general in this Coordinate Reference Systems: EPSG codes online book chapter. Note, the +init=<authority>:<code> syntax is being deprecated, and <authority>:<code> is the preferred initialization method.

-

We can convert this EPSG number into a cartopy.crs object using the cartopy.crs.epsg method. This will allow us to transform any point from latitude, longitude into our data’s CRS so that we can define a center for our ROI.

-
-
-
crs = ccrs.epsg(32611)
-
-
-
-
-
-
-
crs.__repr__()
-
-
-
-
-
'_EPSGProjection(32611)'
-
-
-
-
-
-
-
crs.area_of_use
-
-
-
-
-
AreaOfUse(west=-120.0, south=0.0, east=-114.0, north=84.0, name='Between 120°W and 114°W, northern hemisphere between equator and 84°N, onshore and offshore. Canada - Alberta; British Columbia (BC); Northwest Territories (NWT); Nunavut. Mexico. United States (USA).')
-
-
-
-
-
-
-
crs_box = [(crs.x_limits[0], crs.y_limits[0]), (crs.x_limits[0], crs.y_limits[1]),
-           (crs.x_limits[1], crs.y_limits[1]), (crs.x_limits[1], crs.y_limits[0])]
-
-crs_extent = gv.Polygons(crs_box, crs=crs).options(alpha=0.4, color='blue', line_alpha=0)
-
-(gv.feature.coastline * crs_extent).options(title='EPSG:32611', global_extent=True)
-
-
-
-
-
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/io/__init__.py:241: DownloadWarning: Downloading: https://naturalearth.s3.amazonaws.com/110m_physical/ne_110m_coastline.zip
-  warnings.warn(f'Downloading: {url}', DownloadWarning)
-
-
-
-
-
-
-
-

Just for comparison, let’s preview the coverage of a totally different EPSG; perhaps one in the southern hemisphere.

-
-
-
crs2 = ccrs.epsg(32735)
-crs2.area_of_use
-
-
-
-
-
AreaOfUse(west=24.0, south=-80.0, east=30.0, north=0.0, name='Between 24°E and 30°E, southern hemisphere between 80°S and equator, onshore and offshore. Botswana. Burundi. Democratic Republic of the Congo (Zaire). Rwanda. South Africa. Tanzania. Uganda. Zambia. Zimbabwe.')
-
-
-
-
-
-
-
crs_box2 = [(crs2.x_limits[0], crs2.y_limits[0]), (crs2.x_limits[0], crs2.y_limits[1]),
-           (crs2.x_limits[1], crs2.y_limits[1]), (crs2.x_limits[1], crs2.y_limits[0])]
-
-crs_extent2 = gv.Polygons(crs_box, crs=crs2).options(alpha=0.4, color='red', line_alpha=0)
-
-(gv.feature.coastline * crs_extent2).options(title='EPSG:32735', global_extent=True)
-
-
-
-
-
-
-
-
-
-

Hopefully, this gives you some intuition for the data that we are looking at and that it comes from a small North American region within the blue highlighted area on the ‘EPSG:32611’ plot.

-

Now, if we know already know our center point in terms of a latitude, longitude point (from a cartopy.crs.PlateCarree() projection), we can transform this into the CRS of our data using the transform_point method:

-
-
-
center={'x':np.nan, 'y':np.nan}
-center['x'], center['y'] = crs.transform_point(-118.7081, 38.6942, ccrs.PlateCarree())
-
-
-
-
-
-
-
center['x']
-
-
-
-
-
351453.22378616617
-
-
-
-
-

But what if we didn’t happen to know the center point in terms of latitude and longitude? Since we are using interactive plots with hvplot, we can look at one of our previous plots of the lake and hover over the region that we want to use as a center point to reveal the coordinates in latitude, longitude (created by plotting with geo=True).

-

Alternatively, we could just hover over a plot of our data that is still in our data crs projection (by using the default geo=False) to directly get our x_center and y_center. Try it below - hover over the center of the lake to reveal the x and y points:

-
-
-
NDVI_2017.hvplot.image(x='x', y='y', width=400, clim=(-1,1), rot=45, cmap='viridis')
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[25], line 1
-----> 1 NDVI_2017.hvplot.image(x='x', y='y', width=400, clim=(-1,1), rot=45, cmap='viridis')
-
-NameError: name 'NDVI_2017' is not defined
-
-
-
-
-

A more advanced approach would be to update automatically assign the coordinates to a variable when we click on the image. For this, we will reach to HoloViews, the library that powers hvPlot. For more information about this functionality of HoloViews, check out the HoloViews Tap stream page.

-

First, we’ll create function to update our center dictionary based on clicks.

-
-
-
def onclick(x,y):
-    center['x'] = x
-    center['y'] = y
-
-
-
-
-

Then we’ll connect a HoloViews Tap stream to our plot

-
-
-
crs_plot = NDVI_2017.hvplot.image(x='x', y='y', width=400, clim=(-1,1), rot=45, cmap='viridis')
-
-tap_stream = hv.streams.Tap(source=crs_plot)
-
-tap_stream.add_subscriber(onclick)
-
-crs_plot
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[27], line 1
-----> 1 crs_plot = NDVI_2017.hvplot.image(x='x', y='y', width=400, clim=(-1,1), rot=45, cmap='viridis')
-      3 tap_stream = hv.streams.Tap(source=crs_plot)
-      5 tap_stream.add_subscriber(onclick)
-
-NameError: name 'NDVI_2017' is not defined
-
-
-
-
-

Now our center variable will change each time we click on the plot above. Go ahead and click near the center of the lake on the plot above and then run the cell below to see the update. Every time you click a new spot on the plot, you can rerun the cell to confirm that the value has changed.

-
-
-
center
-
-
-
-
-
{'x': 351453.22378616617, 'y': 4284227.00873892}
-
-
-
-
-

Don’t worry too much about capturing the exact center of the lake, a rough estimate is sufficient.

-

Now we just need to define the area that we are interested in around this point. In this case we’ll use a 30 km box around the center point.

-
-
-
buffer = 1.5e4
-
-xmin = center['x'] - buffer
-xmax = center['x'] + buffer
-ymin = center['y'] - buffer
-ymax = center['y'] + buffer
-
-
-
-
-
-
-
bounding_box = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)]
-
-
-
-
-

Let’s just check that the bounding box captures the lake:

-
-
-
roi = gv.Polygons(bounding_box, crs=crs)
-
-
-
-
-
-
-
gv.tile_sources.EsriImagery * roi.options(alpha=0.3)
-
-
-
-
-
-
-
-
-
-
-
-

Regrid

-

We can use this region to define a new grid onto which we will interpolate our data.

-

Note, we will be regridding with simple linear interpolation which treats the space as flat. This is fine for our demonstration purposes at a relatively small spatial scale. However, despite what you may have heard, the earth is not flat, and treating it as such can give less accurate results when working with a larger spherical space. For more information about spherical resampling methods, check out the GeoViews resampling grids page, as it describes using different grid types including rectilinear, curvilinear grids and trimeshes. In particular, the conservative regridding approach using the xESMF library has become a common choice.

-

Let’s set the resolution of our new grid within the bounding box to roughly match the resolution of our original images.

-
-
-
res = 200
-x = np.arange(xmin, xmax, res)
-y = np.arange(ymin, ymax, res)
-x.shape
-
-
-
-
-
(150,)
-
-
-
-
-

We will use xarray’s linear interpolation to calculate the values for each grid cell.

-
-
-
NDVI_2017_regridded = NDVI_2017.interp(x=x, y=y)
-NDVI_1988_regridded = NDVI_1988.interp(x=x, y=y)
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[34], line 1
-----> 1 NDVI_2017_regridded = NDVI_2017.interp(x=x, y=y)
-      2 NDVI_1988_regridded = NDVI_1988.interp(x=x, y=y)
-
-NameError: name 'NDVI_2017' is not defined
-
-
-
-
-

Let’s compare our original data to this regridded form.

-
-
-
NDVI_2017_regridded_plot = NDVI_2017_regridded.hvplot.image(x='x', y='y', clim=(-1,1), title='NDVI 2017 regridded', geo=True, rot=45, cmap='viridis')
-NDVI_1988_regridded_plot = NDVI_1988_regridded.hvplot.image(x='x', y='y', clim=(-1,1), title='NDVI 1988 regridded', geo=True, rot=45, cmap='viridis')
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[35], line 1
-----> 1 NDVI_2017_regridded_plot = NDVI_2017_regridded.hvplot.image(x='x', y='y', clim=(-1,1), title='NDVI 2017 regridded', geo=True, rot=45, cmap='viridis')
-      2 NDVI_1988_regridded_plot = NDVI_1988_regridded.hvplot.image(x='x', y='y', clim=(-1,1), title='NDVI 1988 regridded', geo=True, rot=45, cmap='viridis')
-
-NameError: name 'NDVI_2017_regridded' is not defined
-
-
-
-
-
-

Note

-

With hvPlot, you can render multiple plots using the + operator, and stack them into rows by defining the number of columns with .cols. When possible, hvPlot will automatically link the plots so you can zoom and pan on one image and keep them in sync.

-
-

Zoom into one of the images below to see the result of interpolating on this new common grid. The regridded plots (bottom row) will have a consistent pixelation compared to the original NDVI data (top row).

-
-
-
(NDVI_1988_plot + NDVI_2017_plot + NDVI_1988_regridded_plot + NDVI_2017_regridded_plot).cols(2)
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[36], line 1
-----> 1 (NDVI_1988_plot + NDVI_2017_plot + NDVI_1988_regridded_plot + NDVI_2017_regridded_plot).cols(2)
-
-NameError: name 'NDVI_1988_plot' is not defined
-
-
-
-
-
-
-

Combining the data

-

Now that we have our data on the same grid we can combine our two years into one xarray object. We will treat the years as names and create an xarray.Dataset - a group of named xarray.DataArrays that share some of the same coordinates.

-
-
-
ds_regridded = xr.Dataset({'NDVI_1988': NDVI_1988_regridded, 'NDVI_2017': NDVI_2017_regridded})
-ds_regridded
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[37], line 1
-----> 1 ds_regridded = xr.Dataset({'NDVI_1988': NDVI_1988_regridded, 'NDVI_2017': NDVI_2017_regridded})
-      2 ds_regridded
-
-NameError: name 'NDVI_1988_regridded' is not defined
-
-
-
-
-
-
-

Visualizing output

-

We can now reference each year from the same object, and plot the arrays side by side:

-
-
-
ds_regridded.NDVI_1988.hvplot.image(x='x', y='y', geo=True, clim=(-1, 1), title='NDVI 1988', cmap='viridis')  +\
-ds_regridded.NDVI_2017.hvplot.image(x='x', y='y', geo=True, clim=(-1, 1), title='NDVI 2017', cmap='viridis')
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[38], line 1
-----> 1 ds_regridded.NDVI_1988.hvplot.image(x='x', y='y', geo=True, clim=(-1, 1), title='NDVI 1988', cmap='viridis')  +\
-      2 ds_regridded.NDVI_2017.hvplot.image(x='x', y='y', geo=True, clim=(-1, 1), title='NDVI 2017', cmap='viridis')
-
-NameError: name 'ds_regridded' is not defined
-
-
-
-
-

Or we can calculate and plot the difference between the two years:

-
-
-
with xr.set_options(keep_attrs=True):
-    diff_regridded = ds_regridded['NDVI_2017'] - ds_regridded['NDVI_1988']
-diff_regridded
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[39], line 2
-      1 with xr.set_options(keep_attrs=True):
-----> 2     diff_regridded = ds_regridded['NDVI_2017'] - ds_regridded['NDVI_1988']
-      3 diff_regridded
-
-NameError: name 'ds_regridded' is not defined
-
-
-
-
-
-
-
NDVI_diff_regridded_plot = diff_regridded.hvplot.image(x='x', y='y', geo=True, rot=45, cmap='coolwarm', clim=(-1,1), title='NDVI 2017 - 1988')
-NDVI_diff_regridded_plot
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[40], line 1
-----> 1 NDVI_diff_regridded_plot = diff_regridded.hvplot.image(x='x', y='y', geo=True, rot=45, cmap='coolwarm', clim=(-1,1), title='NDVI 2017 - 1988')
-      2 NDVI_diff_regridded_plot
-
-NameError: name 'diff_regridded' is not defined
-
-
-
-
-

As the Vegetation Index will generally give a lower value where water is present, you can clearly see a large positive change along the edge of the lake indicating a reduction in the size of the lake over this time period.

-
-
-
-

Side-note: Resampling

-

Depending on your analysis, it may be beneficial to downsample your data. Especially early on during analysis development, running computations on full resolution data may eat into valuable time that you could otherwise use iterating, debugging, and improving your workflow. Luckily, downsampling xarray data is made pretty easy by grouping the values into bins based on the desired resolution and taking some aggregation (like the mean) on each of those bins.

-
-
-
res_1000 = 1e3
-x_1000 = np.arange(xmin, xmax, res_1000)
-y_1000 = np.arange(ymin, ymax, res_1000)
-
-
-
-
-

We’ll use the left edge as the label for now.

-
-
-
diff_res_1000 = (diff_regridded
-    .groupby_bins('x', x_1000, labels=x_1000[:-1]).mean(dim='x')
-    .groupby_bins('y', y_1000, labels=y_1000[:-1]).mean(dim='y')
-    .rename(x_bins='x', y_bins='y')
-)
-diff_res_1000
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[42], line 1
-----> 1 diff_res_1000 = (diff_regridded
-      2     .groupby_bins('x', x_1000, labels=x_1000[:-1]).mean(dim='x')
-      3     .groupby_bins('y', y_1000, labels=y_1000[:-1]).mean(dim='y')
-      4     .rename(x_bins='x', y_bins='y')
-      5 )
-      6 diff_res_1000
-
-NameError: name 'diff_regridded' is not defined
-
-
-
-
-
-
-
diff_res_1000.hvplot.image(x='x', y='y', geo=True, rot=45, cmap='coolwarm', clim=(-1,1), title='Downsampled')
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[43], line 1
-----> 1 diff_res_1000.hvplot.image(x='x', y='y', geo=True, rot=45, cmap='coolwarm', clim=(-1,1), title='Downsampled')
-
-NameError: name 'diff_res_1000' is not defined
-
-
-
-
-

And there you go, nicely downsampled data ready for some fast computations.

-
-
-
-

Summary

-

With the target of preparing our data for further analysis, we’ve learned how to ensure that our data are on a consistent coordinate grid. Again, this is especially important if we are performing operations across the datasets, like taking the difference between their images. Although preprocessing can sometimes be tedious, it’s a very critical part of the workflow, and often only needs to be done once (or a small number of times) before being able to apply the processed data to a large number of different analyses.

-
-

What’s next?

-

Now that we know how to prepare data, it’s time to proceed to analysis, where we will explore a some simple machine learning approaches.

-
-
-
-

Resources and references

-
    -
  • Authored/adapted by Demetris Roumis circa Dec, 2022

  • -
  • The Landsat 8 banner image is from NASA

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- - \ No newline at end of file diff --git a/_preview/22/notebooks/3.0_Spectral_Clustering_PC.html b/_preview/22/notebooks/3.0_Spectral_Clustering_PC.html deleted file mode 100644 index d779c39..0000000 --- a/_preview/22/notebooks/3.0_Spectral_Clustering_PC.html +++ /dev/null @@ -1,7627 +0,0 @@ - - - - - - - - Spectral Clustering — Landsat ML Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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Spectral Clustering

-
-

Spectral Clustering

-
-
-

Overview

-

The current notebook will demonstrate a simplified machine learning approach to observe the change in a lake water’s extent across time. In order to identify the water, we can use spectral clustering to classify each grid cell into a category based on the similarity of the combined set of pixels across wavelength-bands in our image stacks.

-

Our example approach uses a version of spectral clustering from dask_ml that is a scalable equivalent of what is available in scikit-learn. We will begin this approach with a single image stack and then conduct a direct comparison on the results from different time points.

-

This workflow uses data from Microsoft Planetary Computer but it can be adapted to work with any data ingestion approach from this cookbook.

-
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-

Prerequisites

- - - - - - - - - - - - - - - - - - - - - -

Concepts

Importance

Notes

Data Ingestion - Planetary Computer

Necessary

scikit-learn

Helpful

Spectral clustering

dask_ml

Helpful

Spectral clustering at scale

-
    -
  • Time to learn: 20 minutes.

  • -
-
-
-

Imports

-
-
-
# Data
-import numpy as np
-import odc.stac
-import pandas as pd
-import planetary_computer
-import pystac_client
-import xarray as xr
-from dask.distributed import Client
-from pystac.extensions.eo import EOExtension as eo
-
-# Analysis
-from dask_ml.cluster import SpectralClustering
-
-# Viz
-import hvplot.xarray
-
-
-
-
-
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 12.0.1. Please consider upgrading.
-  warnings.warn(
-
-
-
-
-
-
-
-
-
-

Loading Data

-

Let’s start by loading some Landsat data. These steps are covered in the Data Ingestion - Planetary Computer prerequisite.

-
-

Search the catalog

-
-
-
catalog = pystac_client.Client.open(
-    "https://planetarycomputer.microsoft.com/api/stac/v1",
-    modifier=planetary_computer.sign_inplace,
-)
-
-bbox = [-118.89, 38.54, -118.57, 38.84]  # Region over a lake in Nevada, USA
-datetime = "2017-06-01/2017-09-30"  # Summer months of 2017
-collection = "landsat-c2-l2"
-platform = "landsat-8"
-cloudy_less_than = 1  # percent
-
-search = catalog.search(
-    collections=["landsat-c2-l2"],
-    bbox=bbox,
-    datetime=datetime,
-    query={"eo:cloud_cover": {"lt": cloudy_less_than}, "platform": {"in": [platform]}},
-)
-items = search.get_all_items()
-print(f"Returned {len(items)} Items:")
-[[i, item.id] for i, item in enumerate(items)]
-
-
-
-
-
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/pystac_client/item_search.py:849: FutureWarning: get_all_items() is deprecated, use item_collection() instead.
-  warnings.warn(
-
-
-
Returned 3 Items:
-
-
-
[[0, 'LC08_L2SP_042033_20170718_02_T1'],
- [1, 'LC08_L2SP_042033_20170702_02_T1'],
- [2, 'LC08_L2SP_042033_20170616_02_T1']]
-
-
-
-
-
-
-

Load a dataset

-
-
-
item = items[1]  # select one of the results
-
-
-
-
-
-
-
assets = []
-for _, asset in item.assets.items():
-    try:
-        assets.append(asset.extra_fields["eo:bands"][0])
-    except:
-        pass
-
-cols_ordered = [
-    "common_name",
-    "description",
-    "name",
-    "center_wavelength",
-    "full_width_half_max",
-]
-bands = pd.DataFrame.from_dict(assets)[cols_ordered]
-bands
-
-
-
-
-
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common_namedescriptionnamecenter_wavelengthfull_width_half_max
0redVisible redOLI_B40.650.04
1blueVisible blueOLI_B20.480.06
2greenVisible greenOLI_B30.560.06
3nir08Near infraredOLI_B50.870.03
4lwir11Long-wave infraredTIRS_B1010.900.59
5swir16Short-wave infraredOLI_B61.610.09
6swir22Short-wave infraredOLI_B72.200.19
7coastalCoastal/AerosolOLI_B10.440.02
-
-
-
-
-
ds_2017 = odc.stac.stac_load(
-    [item],
-    bands=bands.common_name.values,
-    bbox=bbox,
-    chunks={},  # <-- use Dask
-).isel(time=0)
-
-
-
-
-
-
-

Retain CRS Attribute

-
-
-
epsg = item.properties["proj:epsg"]
-ds_2017.attrs["crs"] = f"epsg:{epsg}"
-
-
-
-
-
-
-
da_2017 = ds_2017.to_array(dim="band")
-da_2017
-
-
-
-
-
- - - - - - - - - - - - - - -
<xarray.DataArray (band: 8, y: 1128, x: 950)>
-dask.array<stack, shape=(8, 1128, 950), dtype=uint16, chunksize=(1, 1128, 950), chunktype=numpy.ndarray>
-Coordinates:
-  * y            (y) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
-  * x            (x) float64 3.353e+05 3.353e+05 ... 3.637e+05 3.638e+05
-    spatial_ref  int32 32611
-    time         datetime64[ns] 2017-07-02T18:33:06.200763
-  * band         (band) object 'red' 'blue' 'green' ... 'swir22' 'coastal'
-Attributes:
-    crs:      epsg:32611
-
-
-
-
-

Reshaping Data

-

The shape of our data is currently n_bands, n_y, n_x. In order for dask-ml / scikit-learn to consume our data, we’ll need to reshape our image stacks into n_samples, n_features, where n_features is the number of wavelength-bands and n_samples is the total number of pixels in each wavelength-band image. Essentially, we’ll be creating a vector of pixels out of each image, where each pixel has multiple features (bands), but the ordering of the pixels is no longer relevant to the computation.

-

By using xarray methods to flatten the data, we can keep track of the coordinate labels ‘x’ and ‘y’ along the way. This means that we have the ability to reshape back to our original array at any time with no information loss!

-
-
-
flattened_xda = da_2017.stack(z=("x", "y"))  # flatten each band
-flattened_t_xda = flattened_xda.transpose("z", "band")
-flattened_t_xda
-
-
-
-
-
- - - - - - - - - - - - - - -
<xarray.DataArray (z: 1071600, band: 8)>
-dask.array<transpose, shape=(1071600, 8), dtype=uint16, chunksize=(1071600, 1), chunktype=numpy.ndarray>
-Coordinates:
-    spatial_ref  int32 32611
-    time         datetime64[ns] 2017-07-02T18:33:06.200763
-  * band         (band) object 'red' 'blue' 'green' ... 'swir22' 'coastal'
-  * z            (z) object MultiIndex
-  * x            (z) float64 3.353e+05 3.353e+05 ... 3.638e+05 3.638e+05
-  * y            (z) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
-Attributes:
-    crs:      epsg:32611
-
-
-
-

Standardize Data

-

Now that we have the data in the correct shape, let’s standardize (or rescale) the values of the data. We do this to get all the flattened image vectors onto a common scale while preserving the differences in the ranges of values. Again, we’ll demonstrate doing this first in NumPy and then xarray.

-
-
-
with xr.set_options(keep_attrs=True):
-    rescaled_xda = (flattened_t_xda - flattened_t_xda.mean()) / flattened_t_xda.std()
-rescaled_xda
-
-
-
-
-
- - - - - - - - - - - - - - -
<xarray.DataArray (z: 1071600, band: 8)>
-dask.array<truediv, shape=(1071600, 8), dtype=float64, chunksize=(1071600, 1), chunktype=numpy.ndarray>
-Coordinates:
-    spatial_ref  int32 32611
-    time         datetime64[ns] 2017-07-02T18:33:06.200763
-  * band         (band) object 'red' 'blue' 'green' ... 'swir22' 'coastal'
-  * z            (z) object MultiIndex
-  * x            (z) float64 3.353e+05 3.353e+05 ... 3.638e+05 3.638e+05
-  * y            (z) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
-Attributes:
-    crs:      epsg:32611
-
-
-

Info

-

Above, we are using a context manager “with xr.set_options(keep_attrs=True):” to retain the array’s attributes through the operations. That is, we want any metadata like ‘crs’ to stay with our result so we can use ‘geo=True’ in our plotting.

-
-

As rescaled_xda is still a Dask object, if we wanted to actually run the rescaling at this point (provided that all the data can fit into memory), we would use rescaled_xda.compute().

-
-
-

ML pipeline

-

Now that our data is in the proper shape and value range, we are ready to conduct spectral clustering. Here we will use a version of spectral clustering from dask_ml that is a scalable equivalent to operations from Scikit-learn that cluster pixels based on similarity (across all wavelength-bands, which makes it spectral clustering by spectra!)

-
-
-
client = Client(processes=False)
-client
-
-
-
-
-
-
-
-

Client

-

Client-63fdf8fc-ebc6-11ee-8ae0-6045bd012db1

- - - - - - - - - - - - - - - - -
Connection method: Cluster objectCluster type: distributed.LocalCluster
- Dashboard: http://10.1.0.116:8787/status -
- - - - -
-

Cluster Info

- -
- - -
-
-
-

Now we will compute and persist the rescaled data to feed into the ML pipeline. Notice that our X matrix below has the shape: n_samples, n_features as discussed earlier.

-
-
-
X = client.persist(rescaled_xda)
-X.shape
-
-
-
-
-
(1071600, 8)
-
-
-
-
-

First we will set up the model with the number of clusters, and other options.

-
-
-
clf = SpectralClustering(
-    n_clusters=4,
-    random_state=0,
-    gamma=None,
-    kmeans_params={"init_max_iter": 5},
-    persist_embedding=True,
-)
-
-
-
-
-

This next step is the slow part. We’ll fit the model to our matrix X. Depending on your setup, it could take seconds to minutes to run depending on the size of our data.

-
-
-
%time clf.fit(X)
-
-
-
-
-
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/distributed/client.py:3169: UserWarning: Sending large graph of size 81.80 MiB.
-This may cause some slowdown.
-Consider scattering data ahead of time and using futures.
-  warnings.warn(
-
-
-
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/dask/base.py:1462: UserWarning: Running on a single-machine scheduler when a distributed client is active might lead to unexpected results.
-  warnings.warn(
-
-
-
CPU times: user 22.4 s, sys: 12.7 s, total: 35.2 s
-Wall time: 29.1 s
-
-
-
SpectralClustering(gamma=None, kmeans_params={'init_max_iter': 5}, n_clusters=4,
-                   persist_embedding=True, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
-
-

Let’s check the shape of the result:

-
-
-
labels = clf.assign_labels_.labels_.compute()
-labels.shape
-
-
-
-
-
(1071600,)
-
-
-
-
-
-
-
labels
-
-
-
-
-
array([0, 2, 2, ..., 2, 2, 2], dtype=int32)
-
-
-
-
-

The result is a single vector of cluster labels.

-
-
-

Un-flattening

-

Once the computation is done, we can use the coordinates of our input array to restack our output array back into an image. Again, one of the main benefits of using xarray for this stacking and unstacking is that it keeps track of the coordinate information for us.

-

Since the original array is n_samples by n_features (90000, 6) and the cluster label output is (90000,), we just need the coordinates from one of the original features in the shape of n_samples. We can just copy the coordinates from the first input feature and populate is with our output data:

-
-
-
template = flattened_t_xda[:, 0]
-output_array = template.copy(data=labels)
-output_array
-
-
-
-
-
- - - - - - - - - - - - - - -
<xarray.DataArray (z: 1071600)>
-array([0, 2, 2, ..., 2, 2, 2], dtype=int32)
-Coordinates:
-    spatial_ref  int32 32611
-    time         datetime64[ns] 2017-07-02T18:33:06.200763
-    band         <U3 'red'
-  * z            (z) object MultiIndex
-  * x            (z) float64 3.353e+05 3.353e+05 ... 3.638e+05 3.638e+05
-  * y            (z) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
-Attributes:
-    crs:      epsg:32611
-
-

With this new output array with coordinates copied from the input array, we can unstack back to the original x and y image dimensions by just using .unstack().

-
-
-
unstacked_2017 = output_array.unstack()
-unstacked_2017
-
-
-
-
-
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/numpy/core/numeric.py:407: RuntimeWarning: invalid value encountered in cast
-  multiarray.copyto(res, fill_value, casting='unsafe')
-
-
-
- - - - - - - - - - - - - - -
<xarray.DataArray (x: 950, y: 1128)>
-array([[0, 2, 2, ..., 0, 0, 0],
-       [0, 0, 0, ..., 0, 0, 0],
-       [2, 0, 2, ..., 0, 0, 0],
-       ...,
-       [2, 2, 2, ..., 2, 2, 2],
-       [2, 2, 2, ..., 2, 2, 2],
-       [2, 2, 0, ..., 2, 2, 2]], dtype=int32)
-Coordinates:
-  * x            (x) float64 3.353e+05 3.353e+05 ... 3.637e+05 3.638e+05
-  * y            (y) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
-    spatial_ref  int32 32611
-    time         datetime64[ns] 2017-07-02T18:33:06.200763
-    band         <U3 'red'
-Attributes:
-    crs:      epsg:32611
-
-

Finally, we can visualize the results! By hovering over the resulting imge, we can see that the lake water has been clustered with a certain label or ‘value’.

-
-
-
raw_plot_2017 = da_2017.sel(band="red").hvplot.image(
-    x="x", y="y", geo=True, xlabel="lon", ylabel="lat", datashade=True, cmap="greys", title="Raw Image 2017",
-)
-
-result_plot_2017 = unstacked_2017.hvplot(
-    x="x", y="y", cmap="Set3", geo=True, xlabel="lon", ylabel="lat", colorbar=False,  title="Spectral Clustering 2017",
-)
-
-raw_plot_2017 + result_plot_2017
-
-
-
-
-
-
-
-
-
-
-
-

Spectral Clustering for 1988

-

We have conducted the spectral clustering for 2017 and now we want to compare this result to the lake in 1988. Let’s load data from 1988 and run the same analysis as above.

-

We will use the same catalog, but we will search it for a different point in time and different Landsat mission

-
-

Load the data

-
-
-
bbox = [-118.89, 38.54, -118.57, 38.84]  # Region over a lake in Nevada, USA
-datetime = "1988-06-01/1988-09-30"  # Summer months of 1988
-collection = "landsat-c2-l2"
-platform = "landsat-5"  # Searching through an earlier landsat mission
-cloudy_less_than = 1  # percent
-
-search = catalog.search(
-    collections=["landsat-c2-l2"],
-    bbox=bbox,
-    datetime=datetime,
-    query={"eo:cloud_cover": {"lt": cloudy_less_than}, "platform": {"in": [platform]}},
-)
-
-items = search.get_all_items()
-item = items[1]  # select one of the results
-
-
-
-
-
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/pystac_client/item_search.py:849: FutureWarning: get_all_items() is deprecated, use item_collection() instead.
-  warnings.warn(
-
-
-
-
-

Notice that Landsat 5 data from 1988 has slightly different spectra than Landsat 8 from 2017. Details like this are important to keep in mind when performing analyses that directly compare across missions.

-
-
-
assets = []
-for _, asset in item.assets.items():
-    try:
-        assets.append(asset.extra_fields["eo:bands"][0])
-    except:
-        pass
-
-cols_ordered = [
-    "common_name",
-    "description",
-    "name",
-    "center_wavelength",
-    "full_width_half_max",
-]
-bands = pd.DataFrame.from_dict(assets)[cols_ordered]
-bands
-
-
-
-
-
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
common_namedescriptionnamecenter_wavelengthfull_width_half_max
0redVisible redTM_B30.660.06
1blueVisible blueTM_B10.490.07
2lwirLong-wave infraredTM_B611.452.10
3greenVisible greenTM_B20.560.08
4nir08Near infraredTM_B40.830.14
5swir16Short-wave infraredTM_B51.650.20
6swir22Short-wave infraredTM_B72.220.27
-
-
-
-
-
ds_1988 = odc.stac.stac_load(
-    [item],
-    bands=bands.common_name.values,
-    bbox=bbox,
-    chunks={},  # <-- use Dask
-).isel(time=0)
-
-epsg = item.properties["proj:epsg"]
-ds_1988.attrs["crs"] = f"epsg:{epsg}"
-
-da_1988 = ds_1988.to_array(dim="band")
-da_1988
-
-
-
-
-
- - - - - - - - - - - - - - -
<xarray.DataArray (band: 7, y: 1128, x: 950)>
-dask.array<stack, shape=(7, 1128, 950), dtype=uint16, chunksize=(1, 1128, 950), chunktype=numpy.ndarray>
-Coordinates:
-  * y            (y) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
-  * x            (x) float64 3.353e+05 3.353e+05 ... 3.637e+05 3.638e+05
-    spatial_ref  int32 32611
-    time         datetime64[ns] 1988-07-02T18:03:59.010013
-  * band         (band) object 'red' 'blue' 'lwir' ... 'nir08' 'swir16' 'swir22'
-Attributes:
-    crs:      epsg:32611
-
-
-
-

Reshape and Standardize

-
-
-
flattened_xda = da_1988.stack(z=("x", "y"))
-flattened_t_xda = flattened_xda.transpose("z", "band")
-with xr.set_options(keep_attrs=True):
-    rescaled_xda = (flattened_t_xda - flattened_t_xda.mean()) / flattened_t_xda.std()
-rescaled_xda
-
-
-
-
-
- - - - - - - - - - - - - - -
<xarray.DataArray (z: 1071600, band: 7)>
-dask.array<truediv, shape=(1071600, 7), dtype=float64, chunksize=(1071600, 1), chunktype=numpy.ndarray>
-Coordinates:
-    spatial_ref  int32 32611
-    time         datetime64[ns] 1988-07-02T18:03:59.010013
-  * band         (band) object 'red' 'blue' 'lwir' ... 'nir08' 'swir16' 'swir22'
-  * z            (z) object MultiIndex
-  * x            (z) float64 3.353e+05 3.353e+05 ... 3.638e+05 3.638e+05
-  * y            (z) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
-Attributes:
-    crs:      epsg:32611
-
-
-
-

Spectral Clustering

-
-
-
X = client.persist(rescaled_xda)
-clf = SpectralClustering(
-    n_clusters=4,
-    random_state=0,
-    gamma=None,
-    kmeans_params={"init_max_iter": 5},
-    persist_embedding=True,
-)
-
-
-
-
-
-
-
%time clf.fit(X)
-
-
-
-
-
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/distributed/client.py:3169: UserWarning: Sending large graph of size 73.62 MiB.
-This may cause some slowdown.
-Consider scattering data ahead of time and using futures.
-  warnings.warn(
-
-
-
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/dask/base.py:1462: UserWarning: Running on a single-machine scheduler when a distributed client is active might lead to unexpected results.
-  warnings.warn(
-
-
-
CPU times: user 19.3 s, sys: 2.58 s, total: 21.8 s
-Wall time: 27.3 s
-
-
-
SpectralClustering(gamma=None, kmeans_params={'init_max_iter': 5}, n_clusters=4,
-                   persist_embedding=True, random_state=0)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
-
-
-
-
labels = clf.assign_labels_.labels_.compute()
-labels.shape
-
-
-
-
-
(1071600,)
-
-
-
-
-
-
-
labels
-
-
-
-
-
array([2, 0, 0, ..., 2, 2, 2], dtype=int32)
-
-
-
-
-
-
-

Unstack and Visualize

-
-
-
template = flattened_t_xda[:, 0]
-output_array = template.copy(data=labels)
-unstacked_1988 = output_array.unstack()
-
-
-
-
-
/home/runner/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/numpy/core/numeric.py:407: RuntimeWarning: invalid value encountered in cast
-  multiarray.copyto(res, fill_value, casting='unsafe')
-
-
-
-
-
-
-
unstacked_1988
-
-
-
-
-
- - - - - - - - - - - - - - -
<xarray.DataArray (x: 950, y: 1128)>
-array([[2, 0, 0, ..., 0, 0, 0],
-       [2, 2, 2, ..., 0, 0, 0],
-       [2, 2, 2, ..., 0, 0, 0],
-       ...,
-       [0, 0, 0, ..., 2, 2, 2],
-       [0, 0, 0, ..., 2, 2, 2],
-       [0, 0, 0, ..., 2, 2, 2]], dtype=int32)
-Coordinates:
-  * x            (x) float64 3.353e+05 3.353e+05 ... 3.637e+05 3.638e+05
-  * y            (y) float64 4.301e+06 4.301e+06 ... 4.267e+06 4.267e+06
-    spatial_ref  int32 32611
-    time         datetime64[ns] 1988-07-02T18:03:59.010013
-    band         <U3 'red'
-Attributes:
-    crs:      epsg:32611
-
-
-
-
raw_plot_1988 = da_1988.sel(band="red").hvplot.image(
-    x="x", y="y", geo=True, xlabel="lon", ylabel="lat", datashade=True, cmap="greys", title="Raw 1988"
-)
-
-result_plot_1988 = unstacked_1988.hvplot(
-    x="x", y="y", cmap="Set3", geo=True, xlabel="lon", ylabel="lat", colorbar=False, title="Spectral Clustering 1988",
-)
-
-raw_plot_1988 + result_plot_1988
-
-
-
-
-
-
-
-
-
-
-
-
-

Spectral Clustering Over Time

-

Our hypothesis is that the lake’s area is receding over time and so we want to visualize the potential change. Let’s first visually compare the plot of the clustering results from the different time points.

-
-
-
result_plot_1988 + result_plot_2017
-
-
-
-
-
-
-
-
-
-

By hovering over the lake in the center of each image, we can see that the water was labeled with a cluster label value in both images. Let’s programmatically grab the cluster label at the center for each image.

-
-
-
def get_center_value(arr):
-    center_y = arr.shape[0] // 2
-    center_x = arr.shape[1] // 2
-    center_value = arr[center_y, center_x]
-    return int(center_value.values)
-
-water_cluster_1988_label = get_center_value(result_plot_1988.data.value)
-water_cluster_2017_label = get_center_value(result_plot_2017.data.value)
-
-
-
-
-

Now, we want to align the cluster label for water. Let’s try to set anything that is water to 1 and otherwise 0.

-
-
-
water_1988 = xr.where(unstacked_1988 == water_cluster_1988_label, 1, 0)
-water_2017 = xr.where(unstacked_2017 == water_cluster_2017_label, 1, 0)
-
-
-
-
-
-
-
water_1988.hvplot(geo=True)
-
-
-
-
-
---------------------------------------------------------------------------
-IndexError                                Traceback (most recent call last)
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/IPython/core/formatters.py:977, in MimeBundleFormatter.__call__(self, obj, include, exclude)
-    974     method = get_real_method(obj, self.print_method)
-    976     if method is not None:
---> 977         return method(include=include, exclude=exclude)
-    978     return None
-    979 else:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/core/dimension.py:1286, in Dimensioned._repr_mimebundle_(self, include, exclude)
-   1279 def _repr_mimebundle_(self, include=None, exclude=None):
-   1280     """
-   1281     Resolves the class hierarchy for the class rendering the
-   1282     object using any display hooks registered on Store.display
-   1283     hooks.  The output of all registered display_hooks is then
-   1284     combined and returned.
-   1285     """
--> 1286     return Store.render(self)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/core/options.py:1428, in Store.render(cls, obj)
-   1426 data, metadata = {}, {}
-   1427 for hook in hooks:
--> 1428     ret = hook(obj)
-   1429     if ret is None:
-   1430         continue
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:287, in pprint_display(obj)
-    285 if not ip.display_formatter.formatters['text/plain'].pprint:
-    286     return None
---> 287 return display(obj, raw_output=True)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:255, in display(obj, raw_output, **kwargs)
-    253 elif isinstance(obj, (CompositeOverlay, ViewableElement)):
-    254     with option_state(obj):
---> 255         output = element_display(obj)
-    256 elif isinstance(obj, (Layout, NdLayout, AdjointLayout)):
-    257     with option_state(obj):
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:149, in display_hook.<locals>.wrapped(element)
-    147 try:
-    148     max_frames = OutputSettings.options['max_frames']
---> 149     mimebundle = fn(element, max_frames=max_frames)
-    150     if mimebundle is None:
-    151         return {}, {}
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:195, in element_display(element, max_frames)
-    192 if type(element) not in Store.registry[backend]:
-    193     return None
---> 195 return render(element)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:76, in render(obj, **kwargs)
-     73 if renderer.fig == 'pdf':
-     74     renderer = renderer.instance(fig='png')
----> 76 return renderer.components(obj, **kwargs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/renderer.py:396, in Renderer.components(self, obj, fmt, comm, **kwargs)
-    393 embed = (not (dynamic or streams or self.widget_mode == 'live') or config.embed)
-    395 if embed or config.comms == 'default':
---> 396     return self._render_panel(plot, embed, comm)
-    397 return self._render_ipywidget(plot)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/renderer.py:403, in Renderer._render_panel(self, plot, embed, comm)
-    401 doc = Document()
-    402 with config.set(embed=embed):
---> 403     model = plot.layout._render_model(doc, comm)
-    404 if embed:
-    405     return render_model(model, comm)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/viewable.py:748, in Viewable._render_model(self, doc, comm)
-    746 if comm is None:
-    747     comm = state._comm_manager.get_server_comm()
---> 748 model = self.get_root(doc, comm)
-    750 if self._design and self._design.theme.bokeh_theme:
-    751     doc.theme = self._design.theme.bokeh_theme
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/layout/base.py:311, in Panel.get_root(self, doc, comm, preprocess)
-    307 def get_root(
-    308     self, doc: Optional[Document] = None, comm: Optional[Comm] = None,
-    309     preprocess: bool = True
-    310 ) -> Model:
---> 311     root = super().get_root(doc, comm, preprocess)
-    312     # ALERT: Find a better way to handle this
-    313     if hasattr(root, 'styles') and 'overflow-x' in root.styles:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/viewable.py:666, in Renderable.get_root(self, doc, comm, preprocess)
-    664 wrapper = self._design._wrapper(self)
-    665 if wrapper is self:
---> 666     root = self._get_model(doc, comm=comm)
-    667     if preprocess:
-    668         self._preprocess(root)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/layout/base.py:177, in Panel._get_model(self, doc, root, parent, comm)
-    175 root = root or model
-    176 self._models[root.ref['id']] = (model, parent)
---> 177 objects, _ = self._get_objects(model, [], doc, root, comm)
-    178 props = self._get_properties(doc)
-    179 props[self._property_mapping['objects']] = objects
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/layout/base.py:159, in Panel._get_objects(self, model, old_objects, doc, root, comm)
-    157 else:
-    158     try:
---> 159         child = pane._get_model(doc, root, model, comm)
-    160     except RerenderError as e:
-    161         if e.layout is not None and e.layout is not self:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/pane/holoviews.py:423, in HoloViews._get_model(self, doc, root, parent, comm)
-    421     plot = self.object
-    422 else:
---> 423     plot = self._render(doc, comm, root)
-    425 plot.pane = self
-    426 backend = plot.renderer.backend
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/pane/holoviews.py:518, in HoloViews._render(self, doc, comm, root)
-    515     if comm:
-    516         kwargs['comm'] = comm
---> 518 return renderer.get_plot(self.object, **kwargs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/renderer.py:68, in BokehRenderer.get_plot(self_or_cls, obj, doc, renderer, **kwargs)
-     61 @bothmethod
-     62 def get_plot(self_or_cls, obj, doc=None, renderer=None, **kwargs):
-     63     """
-     64     Given a HoloViews Viewable return a corresponding plot instance.
-     65     Allows supplying a document attach the plot to, useful when
-     66     combining the bokeh model with another plot.
-     67     """
----> 68     plot = super().get_plot(obj, doc, renderer, **kwargs)
-     69     if plot.document is None:
-     70         plot.document = Document() if self_or_cls.notebook_context else curdoc()
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/renderer.py:240, in Renderer.get_plot(self_or_cls, obj, doc, renderer, comm, **kwargs)
-    237     defaults = [kd.default for kd in plot.dimensions]
-    238     init_key = tuple(v if d is None else d for v, d in
-    239                      zip(plot.keys[0], defaults))
---> 240     plot.update(init_key)
-    241 else:
-    242     plot = obj
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/plot.py:955, in DimensionedPlot.update(self, key)
-    953 def update(self, key):
-    954     if len(self) == 1 and key in (0, self.keys[0]) and not self.drawn:
---> 955         return self.initialize_plot()
-    956     item = self.__getitem__(key)
-    957     self.traverse(lambda x: setattr(x, '_updated', True))
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/plotting/bokeh/plot.py:108, in GeoPlot.initialize_plot(self, ranges, plot, plots, source)
-    106 def initialize_plot(self, ranges=None, plot=None, plots=None, source=None):
-    107     opts = {} if isinstance(self, HvOverlayPlot) else {'source': source}
---> 108     fig = super().initialize_plot(ranges, plot, plots, **opts)
-    109     if self.geographic and self.show_bounds and not self.overlaid:
-    110         from . import GeoShapePlot
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/element.py:1875, in ElementPlot.initialize_plot(self, ranges, plot, plots, source)
-   1873 # Initialize plot, source and glyph
-   1874 if plot is None:
--> 1875     plot = self._init_plot(key, style_element, ranges=ranges, plots=plots)
-   1876     self._populate_axis_handles(plot)
-   1877 else:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/element.py:644, in ElementPlot._init_plot(self, key, element, plots, ranges)
-    641 subplots = list(self.subplots.values()) if self.subplots else []
-    643 axis_specs = {'x': {}, 'y': {}}
---> 644 axis_specs['x']['x'] = self._axis_props(plots, subplots, element, ranges, pos=0) + (self.xaxis, {})
-    645 if self.multi_y:
-    646     if not bokeh32:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/element.py:477, in ElementPlot._axis_props(self, plots, subplots, element, ranges, pos, dim, range_tags_extras, extra_range_name)
-    475 else:
-    476     try:
---> 477         l, b, r, t = self.get_extents(range_el, ranges, dimension=dim)
-    478     except TypeError:
-    479         # Backward compatibility for e.g. GeoViews=<1.10.1 since dimension
-    480         # is a newly added keyword argument in HoloViews 1.17
-    481         l, b, r, t = self.get_extents(range_el, ranges)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/plotting/plot.py:73, in ProjectionPlot.get_extents(self, element, ranges, range_type, **kwargs)
-     71     extents = None
-     72 else:
----> 73     extents = project_extents(extents, element.crs, proj)
-     74 return (np.nan,)*4 if not extents else extents
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/util.py:100, in project_extents(extents, src_proj, dest_proj, tol)
-     98     geom_in_src_proj = geom_clipped_to_dest_proj
-     99 try:
---> 100     geom_in_crs = dest_proj.project_geometry(geom_in_src_proj, src_proj)
-    101 except ValueError as e:
-    102     src_name =type(src_proj).__name__
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/crs.py:817, in Projection.project_geometry(self, geometry, src_crs)
-    815 if not method_name:
-    816     raise ValueError(f'Unsupported geometry type {geom_type!r}')
---> 817 return getattr(self, method_name)(geometry, src_crs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/crs.py:953, in Projection._project_polygon(self, polygon, src_crs)
-    951     is_ccw = True
-    952 else:
---> 953     is_ccw = polygon.exterior.is_ccw
-    954 # Project the polygon exterior/interior rings.
-    955 # Each source ring will result in either a ring, or one or more
-    956 # lines.
-    957 rings = []
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/shapely/geometry/polygon.py:99, in LinearRing.is_ccw(self)
-     96 @property
-     97 def is_ccw(self):
-     98     """True is the ring is oriented counter clock-wise"""
----> 99     return bool(self.impl['is_ccw'](self))
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/shapely/algorithms/cga.py:14, in is_ccw_impl.<locals>.is_ccw_op(ring)
-     13 def is_ccw_op(ring):
----> 14     return signed_area(ring) >= 0.0
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/shapely/algorithms/cga.py:7, in signed_area(ring)
-      3 """Return the signed area enclosed by a ring in linear time using the
-      4 algorithm at: https://web.archive.org/web/20080209143651/http://cgafaq.info:80/wiki/Polygon_Area
-      5 """
-      6 xs, ys = ring.coords.xy
-----> 7 xs.append(xs[1])
-      8 ys.append(ys[1])
-      9 return sum(xs[i]*(ys[i+1]-ys[i-1]) for i in range(1, len(ring.coords)))/2.0
-
-IndexError: array index out of range
-
-
-
:Image   [y,x]   (value)
-
-
-
-
-
-
-
water_1988_plot = water_1988.hvplot(
-    x="x", y="y", cmap="greys", geo=True, colorbar=False, title="1988 Water"
-)
-
-water_2017_plot = water_2017.hvplot(
-    x="x", y="y", cmap="greys", geo=True, colorbar=False, title="2017 Water"
-)
-
-water_1988_plot + water_2017_plot
-
-
-
-
-
---------------------------------------------------------------------------
-IndexError                                Traceback (most recent call last)
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/IPython/core/formatters.py:977, in MimeBundleFormatter.__call__(self, obj, include, exclude)
-    974     method = get_real_method(obj, self.print_method)
-    976     if method is not None:
---> 977         return method(include=include, exclude=exclude)
-    978     return None
-    979 else:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/core/dimension.py:1286, in Dimensioned._repr_mimebundle_(self, include, exclude)
-   1279 def _repr_mimebundle_(self, include=None, exclude=None):
-   1280     """
-   1281     Resolves the class hierarchy for the class rendering the
-   1282     object using any display hooks registered on Store.display
-   1283     hooks.  The output of all registered display_hooks is then
-   1284     combined and returned.
-   1285     """
--> 1286     return Store.render(self)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/core/options.py:1428, in Store.render(cls, obj)
-   1426 data, metadata = {}, {}
-   1427 for hook in hooks:
--> 1428     ret = hook(obj)
-   1429     if ret is None:
-   1430         continue
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:287, in pprint_display(obj)
-    285 if not ip.display_formatter.formatters['text/plain'].pprint:
-    286     return None
---> 287 return display(obj, raw_output=True)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:258, in display(obj, raw_output, **kwargs)
-    256 elif isinstance(obj, (Layout, NdLayout, AdjointLayout)):
-    257     with option_state(obj):
---> 258         output = layout_display(obj)
-    259 elif isinstance(obj, (HoloMap, DynamicMap)):
-    260     with option_state(obj):
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:149, in display_hook.<locals>.wrapped(element)
-    147 try:
-    148     max_frames = OutputSettings.options['max_frames']
---> 149     mimebundle = fn(element, max_frames=max_frames)
-    150     if mimebundle is None:
-    151         return {}, {}
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:223, in layout_display(layout, max_frames)
-    220     max_frame_warning(max_frames)
-    221     return None
---> 223 return render(layout)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:76, in render(obj, **kwargs)
-     73 if renderer.fig == 'pdf':
-     74     renderer = renderer.instance(fig='png')
----> 76 return renderer.components(obj, **kwargs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/renderer.py:396, in Renderer.components(self, obj, fmt, comm, **kwargs)
-    393 embed = (not (dynamic or streams or self.widget_mode == 'live') or config.embed)
-    395 if embed or config.comms == 'default':
---> 396     return self._render_panel(plot, embed, comm)
-    397 return self._render_ipywidget(plot)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/renderer.py:403, in Renderer._render_panel(self, plot, embed, comm)
-    401 doc = Document()
-    402 with config.set(embed=embed):
---> 403     model = plot.layout._render_model(doc, comm)
-    404 if embed:
-    405     return render_model(model, comm)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/viewable.py:748, in Viewable._render_model(self, doc, comm)
-    746 if comm is None:
-    747     comm = state._comm_manager.get_server_comm()
---> 748 model = self.get_root(doc, comm)
-    750 if self._design and self._design.theme.bokeh_theme:
-    751     doc.theme = self._design.theme.bokeh_theme
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/layout/base.py:311, in Panel.get_root(self, doc, comm, preprocess)
-    307 def get_root(
-    308     self, doc: Optional[Document] = None, comm: Optional[Comm] = None,
-    309     preprocess: bool = True
-    310 ) -> Model:
---> 311     root = super().get_root(doc, comm, preprocess)
-    312     # ALERT: Find a better way to handle this
-    313     if hasattr(root, 'styles') and 'overflow-x' in root.styles:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/viewable.py:666, in Renderable.get_root(self, doc, comm, preprocess)
-    664 wrapper = self._design._wrapper(self)
-    665 if wrapper is self:
---> 666     root = self._get_model(doc, comm=comm)
-    667     if preprocess:
-    668         self._preprocess(root)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/layout/base.py:177, in Panel._get_model(self, doc, root, parent, comm)
-    175 root = root or model
-    176 self._models[root.ref['id']] = (model, parent)
---> 177 objects, _ = self._get_objects(model, [], doc, root, comm)
-    178 props = self._get_properties(doc)
-    179 props[self._property_mapping['objects']] = objects
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/layout/base.py:159, in Panel._get_objects(self, model, old_objects, doc, root, comm)
-    157 else:
-    158     try:
---> 159         child = pane._get_model(doc, root, model, comm)
-    160     except RerenderError as e:
-    161         if e.layout is not None and e.layout is not self:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/pane/holoviews.py:423, in HoloViews._get_model(self, doc, root, parent, comm)
-    421     plot = self.object
-    422 else:
---> 423     plot = self._render(doc, comm, root)
-    425 plot.pane = self
-    426 backend = plot.renderer.backend
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/pane/holoviews.py:518, in HoloViews._render(self, doc, comm, root)
-    515     if comm:
-    516         kwargs['comm'] = comm
---> 518 return renderer.get_plot(self.object, **kwargs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/renderer.py:68, in BokehRenderer.get_plot(self_or_cls, obj, doc, renderer, **kwargs)
-     61 @bothmethod
-     62 def get_plot(self_or_cls, obj, doc=None, renderer=None, **kwargs):
-     63     """
-     64     Given a HoloViews Viewable return a corresponding plot instance.
-     65     Allows supplying a document attach the plot to, useful when
-     66     combining the bokeh model with another plot.
-     67     """
----> 68     plot = super().get_plot(obj, doc, renderer, **kwargs)
-     69     if plot.document is None:
-     70         plot.document = Document() if self_or_cls.notebook_context else curdoc()
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/renderer.py:240, in Renderer.get_plot(self_or_cls, obj, doc, renderer, comm, **kwargs)
-    237     defaults = [kd.default for kd in plot.dimensions]
-    238     init_key = tuple(v if d is None else d for v, d in
-    239                      zip(plot.keys[0], defaults))
---> 240     plot.update(init_key)
-    241 else:
-    242     plot = obj
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/plot.py:955, in DimensionedPlot.update(self, key)
-    953 def update(self, key):
-    954     if len(self) == 1 and key in (0, self.keys[0]) and not self.drawn:
---> 955         return self.initialize_plot()
-    956     item = self.__getitem__(key)
-    957     self.traverse(lambda x: setattr(x, '_updated', True))
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/plot.py:945, in LayoutPlot.initialize_plot(self, plots, ranges)
-    942     continue
-    944 shared_plots = list(passed_plots) if self.shared_axes else None
---> 945 subplots = subplot.initialize_plot(ranges=ranges, plots=shared_plots)
-    946 nsubplots = len(subplots)
-    948 modes = {sp.sizing_mode for sp in subplots
-    949          if sp.sizing_mode not in (None, 'auto', 'fixed')}
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/plot.py:1094, in AdjointLayoutPlot.initialize_plot(self, ranges, plots)
-   1092     else:
-   1093         passed_plots = plots + adjoined_plots
--> 1094         adjoined_plots.append(subplot.initialize_plot(ranges=ranges, plots=passed_plots))
-   1095 self.drawn = True
-   1096 if not adjoined_plots: adjoined_plots = [None]
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/plotting/bokeh/plot.py:108, in GeoPlot.initialize_plot(self, ranges, plot, plots, source)
-    106 def initialize_plot(self, ranges=None, plot=None, plots=None, source=None):
-    107     opts = {} if isinstance(self, HvOverlayPlot) else {'source': source}
---> 108     fig = super().initialize_plot(ranges, plot, plots, **opts)
-    109     if self.geographic and self.show_bounds and not self.overlaid:
-    110         from . import GeoShapePlot
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/element.py:1875, in ElementPlot.initialize_plot(self, ranges, plot, plots, source)
-   1873 # Initialize plot, source and glyph
-   1874 if plot is None:
--> 1875     plot = self._init_plot(key, style_element, ranges=ranges, plots=plots)
-   1876     self._populate_axis_handles(plot)
-   1877 else:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/element.py:644, in ElementPlot._init_plot(self, key, element, plots, ranges)
-    641 subplots = list(self.subplots.values()) if self.subplots else []
-    643 axis_specs = {'x': {}, 'y': {}}
---> 644 axis_specs['x']['x'] = self._axis_props(plots, subplots, element, ranges, pos=0) + (self.xaxis, {})
-    645 if self.multi_y:
-    646     if not bokeh32:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/element.py:477, in ElementPlot._axis_props(self, plots, subplots, element, ranges, pos, dim, range_tags_extras, extra_range_name)
-    475 else:
-    476     try:
---> 477         l, b, r, t = self.get_extents(range_el, ranges, dimension=dim)
-    478     except TypeError:
-    479         # Backward compatibility for e.g. GeoViews=<1.10.1 since dimension
-    480         # is a newly added keyword argument in HoloViews 1.17
-    481         l, b, r, t = self.get_extents(range_el, ranges)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/plotting/plot.py:73, in ProjectionPlot.get_extents(self, element, ranges, range_type, **kwargs)
-     71     extents = None
-     72 else:
----> 73     extents = project_extents(extents, element.crs, proj)
-     74 return (np.nan,)*4 if not extents else extents
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/util.py:100, in project_extents(extents, src_proj, dest_proj, tol)
-     98     geom_in_src_proj = geom_clipped_to_dest_proj
-     99 try:
---> 100     geom_in_crs = dest_proj.project_geometry(geom_in_src_proj, src_proj)
-    101 except ValueError as e:
-    102     src_name =type(src_proj).__name__
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/crs.py:817, in Projection.project_geometry(self, geometry, src_crs)
-    815 if not method_name:
-    816     raise ValueError(f'Unsupported geometry type {geom_type!r}')
---> 817 return getattr(self, method_name)(geometry, src_crs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/crs.py:953, in Projection._project_polygon(self, polygon, src_crs)
-    951     is_ccw = True
-    952 else:
---> 953     is_ccw = polygon.exterior.is_ccw
-    954 # Project the polygon exterior/interior rings.
-    955 # Each source ring will result in either a ring, or one or more
-    956 # lines.
-    957 rings = []
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/shapely/geometry/polygon.py:99, in LinearRing.is_ccw(self)
-     96 @property
-     97 def is_ccw(self):
-     98     """True is the ring is oriented counter clock-wise"""
----> 99     return bool(self.impl['is_ccw'](self))
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/shapely/algorithms/cga.py:14, in is_ccw_impl.<locals>.is_ccw_op(ring)
-     13 def is_ccw_op(ring):
----> 14     return signed_area(ring) >= 0.0
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/shapely/algorithms/cga.py:7, in signed_area(ring)
-      3 """Return the signed area enclosed by a ring in linear time using the
-      4 algorithm at: https://web.archive.org/web/20080209143651/http://cgafaq.info:80/wiki/Polygon_Area
-      5 """
-      6 xs, ys = ring.coords.xy
-----> 7 xs.append(xs[1])
-      8 ys.append(ys[1])
-      9 return sum(xs[i]*(ys[i+1]-ys[i-1]) for i in range(1, len(ring.coords)))/2.0
-
-IndexError: array index out of range
-
-
-
:Layout
-   .Image.I  :Image   [x,y]   (value)
-   .Image.II :Image   [x,y]   (value)
-
-
-
-
-

Now we can take the difference of these water label arrays to see exactly where the water levels has changed.

-
-
-
with xr.set_options(keep_attrs=True):
-    water_diff = water_1988 - water_2017
-
-
-
-
-

Red pixels (array value ‘1’) of our image below are where water was lost from 1988 to 2017.

-
-
-
water_diff.hvplot(
-    x="x", y="y", cmap='coolwarm', geo=True, xlabel="long", ylabel="lat", colorbar=False, title="Water Change 1988-2017",
-)
-
-
-
-
-
---------------------------------------------------------------------------
-IndexError                                Traceback (most recent call last)
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/IPython/core/formatters.py:977, in MimeBundleFormatter.__call__(self, obj, include, exclude)
-    974     method = get_real_method(obj, self.print_method)
-    976     if method is not None:
---> 977         return method(include=include, exclude=exclude)
-    978     return None
-    979 else:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/core/dimension.py:1286, in Dimensioned._repr_mimebundle_(self, include, exclude)
-   1279 def _repr_mimebundle_(self, include=None, exclude=None):
-   1280     """
-   1281     Resolves the class hierarchy for the class rendering the
-   1282     object using any display hooks registered on Store.display
-   1283     hooks.  The output of all registered display_hooks is then
-   1284     combined and returned.
-   1285     """
--> 1286     return Store.render(self)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/core/options.py:1428, in Store.render(cls, obj)
-   1426 data, metadata = {}, {}
-   1427 for hook in hooks:
--> 1428     ret = hook(obj)
-   1429     if ret is None:
-   1430         continue
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:287, in pprint_display(obj)
-    285 if not ip.display_formatter.formatters['text/plain'].pprint:
-    286     return None
---> 287 return display(obj, raw_output=True)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:255, in display(obj, raw_output, **kwargs)
-    253 elif isinstance(obj, (CompositeOverlay, ViewableElement)):
-    254     with option_state(obj):
---> 255         output = element_display(obj)
-    256 elif isinstance(obj, (Layout, NdLayout, AdjointLayout)):
-    257     with option_state(obj):
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:149, in display_hook.<locals>.wrapped(element)
-    147 try:
-    148     max_frames = OutputSettings.options['max_frames']
---> 149     mimebundle = fn(element, max_frames=max_frames)
-    150     if mimebundle is None:
-    151         return {}, {}
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:195, in element_display(element, max_frames)
-    192 if type(element) not in Store.registry[backend]:
-    193     return None
---> 195 return render(element)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:76, in render(obj, **kwargs)
-     73 if renderer.fig == 'pdf':
-     74     renderer = renderer.instance(fig='png')
----> 76 return renderer.components(obj, **kwargs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/renderer.py:396, in Renderer.components(self, obj, fmt, comm, **kwargs)
-    393 embed = (not (dynamic or streams or self.widget_mode == 'live') or config.embed)
-    395 if embed or config.comms == 'default':
---> 396     return self._render_panel(plot, embed, comm)
-    397 return self._render_ipywidget(plot)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/renderer.py:403, in Renderer._render_panel(self, plot, embed, comm)
-    401 doc = Document()
-    402 with config.set(embed=embed):
---> 403     model = plot.layout._render_model(doc, comm)
-    404 if embed:
-    405     return render_model(model, comm)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/viewable.py:748, in Viewable._render_model(self, doc, comm)
-    746 if comm is None:
-    747     comm = state._comm_manager.get_server_comm()
---> 748 model = self.get_root(doc, comm)
-    750 if self._design and self._design.theme.bokeh_theme:
-    751     doc.theme = self._design.theme.bokeh_theme
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/layout/base.py:311, in Panel.get_root(self, doc, comm, preprocess)
-    307 def get_root(
-    308     self, doc: Optional[Document] = None, comm: Optional[Comm] = None,
-    309     preprocess: bool = True
-    310 ) -> Model:
---> 311     root = super().get_root(doc, comm, preprocess)
-    312     # ALERT: Find a better way to handle this
-    313     if hasattr(root, 'styles') and 'overflow-x' in root.styles:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/viewable.py:666, in Renderable.get_root(self, doc, comm, preprocess)
-    664 wrapper = self._design._wrapper(self)
-    665 if wrapper is self:
---> 666     root = self._get_model(doc, comm=comm)
-    667     if preprocess:
-    668         self._preprocess(root)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/layout/base.py:177, in Panel._get_model(self, doc, root, parent, comm)
-    175 root = root or model
-    176 self._models[root.ref['id']] = (model, parent)
---> 177 objects, _ = self._get_objects(model, [], doc, root, comm)
-    178 props = self._get_properties(doc)
-    179 props[self._property_mapping['objects']] = objects
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/layout/base.py:159, in Panel._get_objects(self, model, old_objects, doc, root, comm)
-    157 else:
-    158     try:
---> 159         child = pane._get_model(doc, root, model, comm)
-    160     except RerenderError as e:
-    161         if e.layout is not None and e.layout is not self:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/pane/holoviews.py:423, in HoloViews._get_model(self, doc, root, parent, comm)
-    421     plot = self.object
-    422 else:
---> 423     plot = self._render(doc, comm, root)
-    425 plot.pane = self
-    426 backend = plot.renderer.backend
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/pane/holoviews.py:518, in HoloViews._render(self, doc, comm, root)
-    515     if comm:
-    516         kwargs['comm'] = comm
---> 518 return renderer.get_plot(self.object, **kwargs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/renderer.py:68, in BokehRenderer.get_plot(self_or_cls, obj, doc, renderer, **kwargs)
-     61 @bothmethod
-     62 def get_plot(self_or_cls, obj, doc=None, renderer=None, **kwargs):
-     63     """
-     64     Given a HoloViews Viewable return a corresponding plot instance.
-     65     Allows supplying a document attach the plot to, useful when
-     66     combining the bokeh model with another plot.
-     67     """
----> 68     plot = super().get_plot(obj, doc, renderer, **kwargs)
-     69     if plot.document is None:
-     70         plot.document = Document() if self_or_cls.notebook_context else curdoc()
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/renderer.py:240, in Renderer.get_plot(self_or_cls, obj, doc, renderer, comm, **kwargs)
-    237     defaults = [kd.default for kd in plot.dimensions]
-    238     init_key = tuple(v if d is None else d for v, d in
-    239                      zip(plot.keys[0], defaults))
---> 240     plot.update(init_key)
-    241 else:
-    242     plot = obj
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/plot.py:955, in DimensionedPlot.update(self, key)
-    953 def update(self, key):
-    954     if len(self) == 1 and key in (0, self.keys[0]) and not self.drawn:
---> 955         return self.initialize_plot()
-    956     item = self.__getitem__(key)
-    957     self.traverse(lambda x: setattr(x, '_updated', True))
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/plotting/bokeh/plot.py:108, in GeoPlot.initialize_plot(self, ranges, plot, plots, source)
-    106 def initialize_plot(self, ranges=None, plot=None, plots=None, source=None):
-    107     opts = {} if isinstance(self, HvOverlayPlot) else {'source': source}
---> 108     fig = super().initialize_plot(ranges, plot, plots, **opts)
-    109     if self.geographic and self.show_bounds and not self.overlaid:
-    110         from . import GeoShapePlot
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/element.py:1875, in ElementPlot.initialize_plot(self, ranges, plot, plots, source)
-   1873 # Initialize plot, source and glyph
-   1874 if plot is None:
--> 1875     plot = self._init_plot(key, style_element, ranges=ranges, plots=plots)
-   1876     self._populate_axis_handles(plot)
-   1877 else:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/element.py:644, in ElementPlot._init_plot(self, key, element, plots, ranges)
-    641 subplots = list(self.subplots.values()) if self.subplots else []
-    643 axis_specs = {'x': {}, 'y': {}}
---> 644 axis_specs['x']['x'] = self._axis_props(plots, subplots, element, ranges, pos=0) + (self.xaxis, {})
-    645 if self.multi_y:
-    646     if not bokeh32:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/element.py:477, in ElementPlot._axis_props(self, plots, subplots, element, ranges, pos, dim, range_tags_extras, extra_range_name)
-    475 else:
-    476     try:
---> 477         l, b, r, t = self.get_extents(range_el, ranges, dimension=dim)
-    478     except TypeError:
-    479         # Backward compatibility for e.g. GeoViews=<1.10.1 since dimension
-    480         # is a newly added keyword argument in HoloViews 1.17
-    481         l, b, r, t = self.get_extents(range_el, ranges)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/plotting/plot.py:73, in ProjectionPlot.get_extents(self, element, ranges, range_type, **kwargs)
-     71     extents = None
-     72 else:
----> 73     extents = project_extents(extents, element.crs, proj)
-     74 return (np.nan,)*4 if not extents else extents
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/util.py:100, in project_extents(extents, src_proj, dest_proj, tol)
-     98     geom_in_src_proj = geom_clipped_to_dest_proj
-     99 try:
---> 100     geom_in_crs = dest_proj.project_geometry(geom_in_src_proj, src_proj)
-    101 except ValueError as e:
-    102     src_name =type(src_proj).__name__
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/crs.py:817, in Projection.project_geometry(self, geometry, src_crs)
-    815 if not method_name:
-    816     raise ValueError(f'Unsupported geometry type {geom_type!r}')
---> 817 return getattr(self, method_name)(geometry, src_crs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/crs.py:953, in Projection._project_polygon(self, polygon, src_crs)
-    951     is_ccw = True
-    952 else:
---> 953     is_ccw = polygon.exterior.is_ccw
-    954 # Project the polygon exterior/interior rings.
-    955 # Each source ring will result in either a ring, or one or more
-    956 # lines.
-    957 rings = []
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/shapely/geometry/polygon.py:99, in LinearRing.is_ccw(self)
-     96 @property
-     97 def is_ccw(self):
-     98     """True is the ring is oriented counter clock-wise"""
----> 99     return bool(self.impl['is_ccw'](self))
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/shapely/algorithms/cga.py:14, in is_ccw_impl.<locals>.is_ccw_op(ring)
-     13 def is_ccw_op(ring):
----> 14     return signed_area(ring) >= 0.0
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/shapely/algorithms/cga.py:7, in signed_area(ring)
-      3 """Return the signed area enclosed by a ring in linear time using the
-      4 algorithm at: https://web.archive.org/web/20080209143651/http://cgafaq.info:80/wiki/Polygon_Area
-      5 """
-      6 xs, ys = ring.coords.xy
-----> 7 xs.append(xs[1])
-      8 ys.append(ys[1])
-      9 return sum(xs[i]*(ys[i+1]-ys[i-1]) for i in range(1, len(ring.coords)))/2.0
-
-IndexError: array index out of range
-
-
-
:Image   [x,y]   (value)
-
-
-
-
-

We did it! We are observing the change in the lake shoreline over time using a simple spectral clustering approach.

-

Let’s finish things off by adding some geo tiles as a background. To only display the colored pixels overlaid on geo tiles, we could either set the array’s background value (‘0’) to ‘Not a Number’ (NaN), or we could just inform hvPlot that we want the background valued pixels to be transparent with .redim.nodata(value=0).

-
-
-
water_diff.hvplot(
-        x="x", y="y", width=400, height=400, cmap='coolwarm', geo=True, xlabel="lon", ylabel="lat", alpha=1, colorbar=False, title="Water Loss from 1988 to 2017", tiles="ESRI",
-).redim.nodata(value=0)
-
-
-
-
-
---------------------------------------------------------------------------
-IndexError                                Traceback (most recent call last)
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/IPython/core/formatters.py:977, in MimeBundleFormatter.__call__(self, obj, include, exclude)
-    974     method = get_real_method(obj, self.print_method)
-    976     if method is not None:
---> 977         return method(include=include, exclude=exclude)
-    978     return None
-    979 else:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/core/dimension.py:1286, in Dimensioned._repr_mimebundle_(self, include, exclude)
-   1279 def _repr_mimebundle_(self, include=None, exclude=None):
-   1280     """
-   1281     Resolves the class hierarchy for the class rendering the
-   1282     object using any display hooks registered on Store.display
-   1283     hooks.  The output of all registered display_hooks is then
-   1284     combined and returned.
-   1285     """
--> 1286     return Store.render(self)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/core/options.py:1428, in Store.render(cls, obj)
-   1426 data, metadata = {}, {}
-   1427 for hook in hooks:
--> 1428     ret = hook(obj)
-   1429     if ret is None:
-   1430         continue
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:287, in pprint_display(obj)
-    285 if not ip.display_formatter.formatters['text/plain'].pprint:
-    286     return None
---> 287 return display(obj, raw_output=True)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:255, in display(obj, raw_output, **kwargs)
-    253 elif isinstance(obj, (CompositeOverlay, ViewableElement)):
-    254     with option_state(obj):
---> 255         output = element_display(obj)
-    256 elif isinstance(obj, (Layout, NdLayout, AdjointLayout)):
-    257     with option_state(obj):
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:149, in display_hook.<locals>.wrapped(element)
-    147 try:
-    148     max_frames = OutputSettings.options['max_frames']
---> 149     mimebundle = fn(element, max_frames=max_frames)
-    150     if mimebundle is None:
-    151         return {}, {}
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:195, in element_display(element, max_frames)
-    192 if type(element) not in Store.registry[backend]:
-    193     return None
---> 195 return render(element)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/ipython/display_hooks.py:76, in render(obj, **kwargs)
-     73 if renderer.fig == 'pdf':
-     74     renderer = renderer.instance(fig='png')
----> 76 return renderer.components(obj, **kwargs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/renderer.py:396, in Renderer.components(self, obj, fmt, comm, **kwargs)
-    393 embed = (not (dynamic or streams or self.widget_mode == 'live') or config.embed)
-    395 if embed or config.comms == 'default':
---> 396     return self._render_panel(plot, embed, comm)
-    397 return self._render_ipywidget(plot)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/renderer.py:403, in Renderer._render_panel(self, plot, embed, comm)
-    401 doc = Document()
-    402 with config.set(embed=embed):
---> 403     model = plot.layout._render_model(doc, comm)
-    404 if embed:
-    405     return render_model(model, comm)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/viewable.py:748, in Viewable._render_model(self, doc, comm)
-    746 if comm is None:
-    747     comm = state._comm_manager.get_server_comm()
---> 748 model = self.get_root(doc, comm)
-    750 if self._design and self._design.theme.bokeh_theme:
-    751     doc.theme = self._design.theme.bokeh_theme
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/layout/base.py:311, in Panel.get_root(self, doc, comm, preprocess)
-    307 def get_root(
-    308     self, doc: Optional[Document] = None, comm: Optional[Comm] = None,
-    309     preprocess: bool = True
-    310 ) -> Model:
---> 311     root = super().get_root(doc, comm, preprocess)
-    312     # ALERT: Find a better way to handle this
-    313     if hasattr(root, 'styles') and 'overflow-x' in root.styles:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/viewable.py:666, in Renderable.get_root(self, doc, comm, preprocess)
-    664 wrapper = self._design._wrapper(self)
-    665 if wrapper is self:
---> 666     root = self._get_model(doc, comm=comm)
-    667     if preprocess:
-    668         self._preprocess(root)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/layout/base.py:177, in Panel._get_model(self, doc, root, parent, comm)
-    175 root = root or model
-    176 self._models[root.ref['id']] = (model, parent)
---> 177 objects, _ = self._get_objects(model, [], doc, root, comm)
-    178 props = self._get_properties(doc)
-    179 props[self._property_mapping['objects']] = objects
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/layout/base.py:159, in Panel._get_objects(self, model, old_objects, doc, root, comm)
-    157 else:
-    158     try:
---> 159         child = pane._get_model(doc, root, model, comm)
-    160     except RerenderError as e:
-    161         if e.layout is not None and e.layout is not self:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/pane/holoviews.py:423, in HoloViews._get_model(self, doc, root, parent, comm)
-    421     plot = self.object
-    422 else:
---> 423     plot = self._render(doc, comm, root)
-    425 plot.pane = self
-    426 backend = plot.renderer.backend
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/panel/pane/holoviews.py:518, in HoloViews._render(self, doc, comm, root)
-    515     if comm:
-    516         kwargs['comm'] = comm
---> 518 return renderer.get_plot(self.object, **kwargs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/renderer.py:68, in BokehRenderer.get_plot(self_or_cls, obj, doc, renderer, **kwargs)
-     61 @bothmethod
-     62 def get_plot(self_or_cls, obj, doc=None, renderer=None, **kwargs):
-     63     """
-     64     Given a HoloViews Viewable return a corresponding plot instance.
-     65     Allows supplying a document attach the plot to, useful when
-     66     combining the bokeh model with another plot.
-     67     """
----> 68     plot = super().get_plot(obj, doc, renderer, **kwargs)
-     69     if plot.document is None:
-     70         plot.document = Document() if self_or_cls.notebook_context else curdoc()
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/renderer.py:240, in Renderer.get_plot(self_or_cls, obj, doc, renderer, comm, **kwargs)
-    237     defaults = [kd.default for kd in plot.dimensions]
-    238     init_key = tuple(v if d is None else d for v, d in
-    239                      zip(plot.keys[0], defaults))
---> 240     plot.update(init_key)
-    241 else:
-    242     plot = obj
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/plot.py:955, in DimensionedPlot.update(self, key)
-    953 def update(self, key):
-    954     if len(self) == 1 and key in (0, self.keys[0]) and not self.drawn:
---> 955         return self.initialize_plot()
-    956     item = self.__getitem__(key)
-    957     self.traverse(lambda x: setattr(x, '_updated', True))
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/plotting/bokeh/plot.py:108, in GeoPlot.initialize_plot(self, ranges, plot, plots, source)
-    106 def initialize_plot(self, ranges=None, plot=None, plots=None, source=None):
-    107     opts = {} if isinstance(self, HvOverlayPlot) else {'source': source}
---> 108     fig = super().initialize_plot(ranges, plot, plots, **opts)
-    109     if self.geographic and self.show_bounds and not self.overlaid:
-    110         from . import GeoShapePlot
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/element.py:2886, in OverlayPlot.initialize_plot(self, ranges, plot, plots)
-   2884 self.tabs = self.tabs or any(isinstance(sp, TablePlot) for sp in self.subplots.values())
-   2885 if plot is None and not self.tabs and not self.batched:
--> 2886     plot = self._init_plot(key, element, ranges=ranges, plots=plots)
-   2887     self._populate_axis_handles(plot)
-   2888 self.handles['plot'] = plot
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/element.py:644, in ElementPlot._init_plot(self, key, element, plots, ranges)
-    641 subplots = list(self.subplots.values()) if self.subplots else []
-    643 axis_specs = {'x': {}, 'y': {}}
---> 644 axis_specs['x']['x'] = self._axis_props(plots, subplots, element, ranges, pos=0) + (self.xaxis, {})
-    645 if self.multi_y:
-    646     if not bokeh32:
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/bokeh/element.py:477, in ElementPlot._axis_props(self, plots, subplots, element, ranges, pos, dim, range_tags_extras, extra_range_name)
-    475 else:
-    476     try:
---> 477         l, b, r, t = self.get_extents(range_el, ranges, dimension=dim)
-    478     except TypeError:
-    479         # Backward compatibility for e.g. GeoViews=<1.10.1 since dimension
-    480         # is a newly added keyword argument in HoloViews 1.17
-    481         l, b, r, t = self.get_extents(range_el, ranges)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/plotting/plot.py:67, in ProjectionPlot.get_extents(self, element, ranges, range_type, **kwargs)
-     65     (x0, x1), (y0, y1) = proj.x_limits, proj.y_limits
-     66     return (x0, y0, x1, y1)
----> 67 extents = super().get_extents(element, ranges, range_type)
-     68 if not getattr(element, 'crs', None) or not self.geographic:
-     69     return extents
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/plot.py:1986, in GenericOverlayPlot.get_extents(self, overlay, ranges, range_type, dimension, **kwargs)
-   1985 def get_extents(self, overlay, ranges, range_type='combined', dimension=None, **kwargs):
--> 1986     subplot_extents = self._get_subplot_extents(overlay, ranges, range_type, dimension)
-   1987     zrange = isinstance(self.projection, str) and self.projection == '3d'
-   1988     extents = {k: util.max_extents(rs, zrange) for k, rs in subplot_extents.items()}
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/holoviews/plotting/plot.py:1981, in GenericOverlayPlot._get_subplot_extents(self, overlay, ranges, range_type, dimension)
-   1979         sp_ranges = util.match_spec(layer, ranges) if ranges else {}
-   1980     for rt in extents:
--> 1981         extent = subplot.get_extents(layer, sp_ranges, range_type=rt)
-   1982         extents[rt].append(extent)
-   1983 return extents
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/plotting/plot.py:73, in ProjectionPlot.get_extents(self, element, ranges, range_type, **kwargs)
-     71     extents = None
-     72 else:
----> 73     extents = project_extents(extents, element.crs, proj)
-     74 return (np.nan,)*4 if not extents else extents
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/geoviews/util.py:100, in project_extents(extents, src_proj, dest_proj, tol)
-     98     geom_in_src_proj = geom_clipped_to_dest_proj
-     99 try:
---> 100     geom_in_crs = dest_proj.project_geometry(geom_in_src_proj, src_proj)
-    101 except ValueError as e:
-    102     src_name =type(src_proj).__name__
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/crs.py:817, in Projection.project_geometry(self, geometry, src_crs)
-    815 if not method_name:
-    816     raise ValueError(f'Unsupported geometry type {geom_type!r}')
---> 817 return getattr(self, method_name)(geometry, src_crs)
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/cartopy/crs.py:953, in Projection._project_polygon(self, polygon, src_crs)
-    951     is_ccw = True
-    952 else:
---> 953     is_ccw = polygon.exterior.is_ccw
-    954 # Project the polygon exterior/interior rings.
-    955 # Each source ring will result in either a ring, or one or more
-    956 # lines.
-    957 rings = []
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/shapely/geometry/polygon.py:99, in LinearRing.is_ccw(self)
-     96 @property
-     97 def is_ccw(self):
-     98     """True is the ring is oriented counter clock-wise"""
----> 99     return bool(self.impl['is_ccw'](self))
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/shapely/algorithms/cga.py:14, in is_ccw_impl.<locals>.is_ccw_op(ring)
-     13 def is_ccw_op(ring):
----> 14     return signed_area(ring) >= 0.0
-
-File ~/miniconda3/envs/cookbook-dev/lib/python3.10/site-packages/shapely/algorithms/cga.py:7, in signed_area(ring)
-      3 """Return the signed area enclosed by a ring in linear time using the
-      4 algorithm at: https://web.archive.org/web/20080209143651/http://cgafaq.info:80/wiki/Polygon_Area
-      5 """
-      6 xs, ys = ring.coords.xy
-----> 7 xs.append(xs[1])
-      8 ys.append(ys[1])
-      9 return sum(xs[i]*(ys[i+1]-ys[i-1]) for i in range(1, len(ring.coords)))/2.0
-
-IndexError: array index out of range
-
-
-
:Overlay
-   .WMTS.I  :WMTS   [Longitude,Latitude]
-   .Image.I :Image   [x,y]   (value)
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-

Summary

-

Starting from raw Landsat data, we have used a simple spectral clustering approach to observe the change in a lake water’s extent across time.

-
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What’s next?

-

Adapt this notebook for your own use case or select another workflow example notebook.

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Resources and References

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    -
  • Authored by Demetris Roumis circa Jan, 2023

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# TODO: why doesn't `.interp` work when using the bounds of the diff as the ROI bbox?
-
-
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-
-

One last approach to selecting a region of interest - rather than starting from a center point and using a region around it, it may make sense to just use the bounds of the entire overlapping region between our images. Let’s try it:

-
-
-
xmin = NDVI_diff.x.values[0]
-xmax = NDVI_diff.x.values[-1]
-ymin = NDVI_diff.y.values[0]
-ymax = NDVI_diff.y.values[-1]
-bounding_box = [(xmin, ymin), (xmin, ymax), (xmax, ymax), (xmax, ymin)]
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[2], line 1
-----> 1 xmin = NDVI_diff.x.values[0]
-      2 xmax = NDVI_diff.x.values[-1]
-      3 ymin = NDVI_diff.y.values[0]
-
-NameError: name 'NDVI_diff' is not defined
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-
-
-
-
-
-
roi = gv.Polygons(bounding_box, crs=crs)
-
-
-
-
-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[3], line 1
-----> 1 roi = gv.Polygons(bounding_box, crs=crs)
-
-NameError: name 'gv' is not defined
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-
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-
-
gv.tile_sources.EsriImagery * roi.options(alpha=0.3)
-
-
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-
---------------------------------------------------------------------------
-NameError                                 Traceback (most recent call last)
-Cell In[4], line 1
-----> 1 gv.tile_sources.EsriImagery * roi.options(alpha=0.3)
-
-NameError: name 'gv' is not defined
-
-
-
-
-

That looks good! As you can see, there are many ways to select a region of interest. The next step is to put our data on a common grid within this region.

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How to Cite This Cookbook

-

The material in Project Pythia’s Landsat ML Cookbook is licensed for free and open consumption and reuse. All code is served under Apache 2.0, while all non-code content is licensed under Creative Commons BY 4.0 (CC BY 4.0). Effectively, this means you are free to share and adapt this material so long as you give appropriate credit to the Cookbook authors and the Project Pythia community.

-

The source code for the book is released on GitHub and archived on Zenodo. This DOI will always resolve to the latest release of the book source:

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DOI

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