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Advanced Visualization Cookbook

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nightly-build -Binder -DOI

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This Project Pythia Cookbook covers advanced visualization techniques building upon and combining various Python packages.

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Motivation

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The possibilities of data visualization in Python are almost endless. Already using matplotlib the workhorse behind many visualization packages, the user has a lot of customization options available to them. cartopy, metpy, seaborn, geocat-viz, and datashader are all also great packages that can offer unique additions to your Python visualization toolbox.

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This Cookbook will house various visualization workflow examples that use different visualization packages, highlight the differences in functionality between the packages, any noteable syntax distinctions, and demonstrate combining tools to achieve a specific outcome.

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Authors

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Julia Kent, John Clyne

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Contributors

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Structure

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This cookbook is broken up into a few sections - a “Basics of Geoscience Visualization” intro that compares different visualization packages and plot elements, and then example workflows of advanced visualization applications that are further subdivided.

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Basics of Geoscience Visualization

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Here we introduce the basics of geoscience visualization, the elements of a plot, different types of plots, and some unique considerations when dealing with model and measured data. Here we also share a comparison of different visualization packages available to the Scientific Python programmer.

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Specialty Plots

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There are some plot types that are unique to atmospheric science such as Taylor Diagrams or Skew-T plots. Here we will use metpy and geocat-viz to demonstrate these specialty plots.

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Visualization of Structured Grids

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In this section we will demonstrate how to visualize data that is on a structured grid. Here we will have workflows that utilize packages such as cartopy and geocat-viz.

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Visualization of Unstructured Grids

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There are lots of compelling reasons to use unstructured data. In this section we will go over these points and demonstrate how to visualizate unstructured grids using uxarray.

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Interactive Visualization

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Some plots allow users to iteract with them by toggling certain constants or changing the viewing angle. Here we use datashader to iteract with some plots.

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3D Visualization

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A lot of geoscience data is 3-dimensional. Here we discuss tools such as vapor that are designed for multidimensional data visualization.

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Animation

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Animated plots are great tools for science communication and outreach. We will demonstrate how to make your plots come to life. In this book, we use “animated plots” to refer to stable animations, such as the creation of gifs or videos.

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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 com

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  1. Clone the https://github.com/ProjectPythia/advanced-viz-cookbook repository:

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     git clone https://github.com/ProjectPythia/advanced-viz-cookbook.git
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  2. Move into the advanced-viz-cookbook directory

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  3. Create and activate your conda environment from the environment.yml file

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How to Cite This Cookbook

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Visualization Cookbook - -[![nightly-build](https://github.com/ProjectPythia/advanced-viz-cookbook-template/actions/workflows/nightly-build.yaml/badge.svg)](https://github.com/ProjectPythia/advanced-viz-cookbook/actions/workflows/nightly-build.yaml) -[![Binder](https://binder.projectpythia.org/badge_logo.svg)](https://binder.projectpythia.org/v2/gh/ProjectPythia/advanced-viz-cookbook/main?labpath=notebooks) -[![DOI](https://zenodo.org/badge/671205314.svg)](https://zenodo.org/badge/latestdoi/671205314) - -This Project Pythia Cookbook covers advanced visualization techniques building upon and combining various Python packages. - -## Motivation - -The possibilities of data visualization in Python are almost endless. Already using `matplotlib` the workhorse behind many visualization packages, the user has a lot of customization options available to them. `cartopy`, `metpy`, `seaborn`, `geocat-viz`, and `datashader` are all also great packages that can offer unique additions to your Python visualization toolbox. - -This Cookbook will house various visualization workflow examples that use different visualization packages, highlight the differences in functionality between the packages, any noteable syntax distinctions, and demonstrate combining tools to achieve a specific outcome. - -## Authors - -[Julia Kent](@jukent), [John Clyne](@clyne) - -### Contributors - - - - - -## Structure - -This cookbook is broken up into a few sections - a "Basics of Geoscience Visualization" intro that compares different visualization packages and plot elements, and then example workflows of advanced visualization applications that are further subdivided. - -### Basics of Geoscience Visualization - -Here we introduce the basics of geoscience visualization, the elements of a plot, different types of plots, and some unique considerations when dealing with model and measured data. Here we also share a comparison of different visualization packages available to the Scientific Python programmer. - -### Specialty Plots - -There are some plot types that are unique to atmospheric science such as Taylor Diagrams or Skew-T plots. Here we will use [`metpy`](https://unidata.github.io/MetPy/latest/index.html) and [`geocat-viz`](https://geocat-viz.readthedocs.io/en/latest/) to demonstrate these specialty plots. - -### Visualization of Structured Grids - -In this section we will demonstrate how to visualize data that is on a structured grid. Here we will have workflows that utilize packages such as [`cartopy`](https://scitools.org.uk/cartopy/docs/latest/) and [`geocat-viz`](https://geocat-viz.readthedocs.io/en/latest/). - -### Visualization of Unstructured Grids - -There are lots of compelling reasons to use unstructured data. In this section we will go over these points and demonstrate how to visualizate unstructured grids using [`uxarray`](https://uxarray.readthedocs.io/en/latest/). - -### Interactive Visualization - -Some plots allow users to iteract with them by toggling certain constants or changing the viewing angle. Here we use [`datashader`](ttps://datashader.org/) to iteract with some plots. - -### 3D Visualization - -A lot of geoscience data is 3-dimensional. Here we discuss tools such as [`vapor`](https://www.vapor.ucar.edu/) that are designed for multidimensional data visualization. - -### Animation - -Animated plots are great tools for science communication and outreach. We will demonstrate how to make your plots come to life. In this book, we use "animated plots" to refer to stable animations, such as the creation of gifs or videos. - -## 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 com - -1. Clone the `https://github.com/ProjectPythia/advanced-viz-cookbook` repository: - - ```bash - git clone https://github.com/ProjectPythia/advanced-viz-cookbook.git - ``` - -1. Move into the `advanced-viz-cookbook` directory - ```bash - cd advanced-viz-cookbook - ``` -1. Create and activate your conda environment from the `environment.yml` file - ```bash - conda env create -f environment.yml - conda activate advanced-viz-cookbook - ``` -1. Move into the `notebooks` directory and start up Jupyterlab - ```bash - cd notebooks/ - jupyter lab - ``` diff --git a/_preview/22/_sources/notebooks/animation.ipynb b/_preview/22/_sources/notebooks/animation.ipynb deleted file mode 100644 index 89274cb..0000000 --- a/_preview/22/_sources/notebooks/animation.ipynb +++ /dev/null @@ -1,144877 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Animation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "source": [ - "time stamp at 1:19" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "Summary text here\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n", - "| [Cartopy](https://foundations.projectpythia.org/core/cartopy.html) | Useful | Not necessary for animations in general, but useful for the examples in this notebook |\n", - "\n", - "- **Time to learn**: X minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import cartopy.crs as ccrs\n", - "import matplotlib.animation as animation\n", - "import numpy as np\n", - "import xarray as xr\n", - "from matplotlib import pyplot as plt\n", - "import os\n", - "from PIL import Image\n", - "\n", - "import geocat.datafiles as gdf\n", - "import geocat.viz as gv" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Animation fundamentals with matplotlib\n", - "\n", - "There are two different methods of creating animations with matplotlib:\n", - "1. Function animation iteratively modifies data on a pre-existing figure to produce an animation\n", - "2. Artist animations pulls from a list of pre-made [artists](https://matplotlib.org/stable/api/artist_api.html#matplotlib.artist.Artist) to draw in each frame to produce an animation\n", - "\n", - "Generally, function animation is easier to use, but artist animation can be more flexible for certain applications. We'll cover both methods in this notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Animating within jupyter notebooks\n", - "Before we get into either type of animation, we're going to alter [matplotlib's runtime configuration settings](https://matplotlib.org/stable/users/explain/customizing.html) (or `rcParams`) to allow us to use matplotlib's animation functions within jupyter notebooks. This next cell is not necessary if you want to create animations in a script or just want to save the animation to a file.\n", - "\n", - "Here, we're changing default animation html output setting to `\"jshtml\"`, which creates a JavaScript animation that can be displayed in a jupyter notebook. The default setting is `\"none\"`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams[\"animation.html\"] = \"jshtml\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Artist Animation\n", - "Artist animation uses pre-made artists to cycle through to produce an animation.\n", - "\n", - "In this example, we're going to plot images using matplotlib's [`imshow`](https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.imshow.html) function and save the resulting artist object to a list. Then, we'll use the [`ArtistAnimation`](https://matplotlib.org/stable/api/_as_gen/matplotlib.animation.ArtistAnimation.html) function to create an animation from that list of artists.\n", - "\n", - "### Data\n", - "Before we get into those steps, let's get some images to animate. First, we'll be looking at some [GeoColor](https://www.star.nesdis.noaa.gov/GOES/documents/QuickGuide_CIRA_Geocolor_20171019.pdf) satellite imagery from [GOES-16](https://eospso.nasa.gov/missions/geostationary-operational-environmental-satellite-16).\n", - "\n", - "In this repository, there is a script, `notebooks/scripts/goes_getter.py`, that will download hourly images from the last 24 hours from the [GOES-16 archive](https://cdn.star.nesdis.noaa.gov/GOES16/ABI/FD/GEOCOLOR/) if you'd like to play around with this yourself. We have already downloaded some images for this example in the `notebooks/data/goes16_hr/` directory and will be using those for this part of the notebook." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "### Get the images into a list\n", - "First, we need to ge the images from the directory into a list. We know the only files in this directory are the images we want to plot, so let's get get a list of all the files ending in `.jpg` from that path using `os.listdir()`.\n", - "\n", - "We'll also sort them using the built in `sorted` function to make sure that our list of images ends up in chronological order." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "im_dir = \"./data/goes16_hr/\"\n", - "im_paths = sorted([p for p in os.listdir(im_dir) if p.endswith(\".jpg\")])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating the figure\n", - "Now, we need to set up the figure we'll be plotting our animation on." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "

    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dpi = 100\n", - "figsize = tuple(t / dpi for t in Image.open(im_dir + im_paths[0]).size)\n", - "fig = plt.figure(figsize=figsize, dpi=dpi)\n", - "ax = fig.add_axes([0, 0, 1, 1]) # span the whole figure\n", - "ax.set_axis_off()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we set out dpi to be 100. This is technically not necessary at this point in creating the animation, but we're going to use it to set the size of the figure. Since we're plotting images, we want to make sure that the figure size is the same size as the images we're plotting.\n", - "\n", - "Next, we're going to figure out what our figsize should be based on the dpi and the size of the images we're about to plot. `tuple(t / dpi for t in Image.open(im_dir + im_paths[0]).size)` divides each dimension of the size of the image by the intended dpi and then returns the result as a tuple. We then use that tuple to create our figsize.\n", - "\n", - "Next, we create the figure using `plt.figure()` and the `figsize` and `dpi` we just calculated.\n", - "\n", - "Then, we create the axes object using `fig.add_axes([0, 0, 1, 1])`. This adds axes to our figure that span the entire range of the figure, allowing the images plot on these axes to take up the entire figure space.\n", - "\n", - "Finally, we turn off the axes using `ax.axis(\"off\")`. This is because we don't want to see axes on our final image plot.\n", - "\n", - "Running the above cell should have produced a blank figure in the jupyter notebook. This is expected since we've only created a blank figure to plot on, but haven't actually plotted anything yet.\n", - "\n", - ":::{tip}\n", - "Customizing the figure size, dpi, and axes is not necessary for creating an artist animation, but it will make our end result look nicer.\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating a list of artists\n", - "Now that we have a list of the filepaths for all the images we want to plot and axes to plot them on, we can use `imshow` to plot each image and save the resulting artist object to a list." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "ims = [[ax.imshow(Image.open(im_dir + im_path), animated=True)] for im_path in im_paths]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A couple of things to note here:\n", - "- You may notice that we're using list generation to create our final list, `ims`. But what might not be obvious is that we're actually making a list of lists. Each created `implot` object is put into its own one-item list, and then all of those lists are used to create `ims`. This is because `ArtistAnimation` expects a list of lists, where each inner list is a list of artists to be plotted in a single frame.\n", - "- We're using a kwarg you may not have seen before using `imshow`: `animated=True`. This allows the artist to only be drawn when called as part of an animation. This is a kwarg that is common to all artist objects." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating the artist animation\n", - "Now that we've set up our figure and list (of lists) of artists, we can create the animation using `ArtistAnimation`." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "ani = animation.ArtistAnimation(fig, ims, interval=150, repeat_delay=1000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "`ArtistAnimation` takes two required arguments: \n", - "- the figure to plot on (`fig` in our case) and \n", - "- the list of pre-created artist objects (`ims`) to plot. \n", - "\n", - "We've also provided a few optional arguments:\n", - "- `interval`: The time between frames in milliseconds. We've set this to 150 milliseconds, or 0.15 seconds.\n", - "- `repeat_delay`: The time in milliseconds to wait before repeating the animation. We've set this to 1000 milliseconds, or 1 second." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And that's it! We've created an animation in matplotlib using artist animation. Let's take a look at it." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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Set up all the artists that will be used in the animation and the initial frame of the animation\n", - "2. Create a function that updates the data in the plot to create each frame of the animation\n", - "3. Create a `FuncAnimation` object with the the previously created elements\n", - "4. Display and save the animation\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data\n", - "For this example, we're going to be looking at sample surface temperature data from our sample data repository, [geocat-datafiles](https://github.com/NCAR/geocat-datafiles)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "ds = xr.open_dataset(gdf.get(\"netcdf_files/meccatemp.cdf\"))\n", - "t = ds.t" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating the figure\n", - "The first step in creating a function animation is to create the figure that will be used to plot the animation.\n", - "\n", - "Since we're working with data represented by latitude and longitude coordinates, we're going to set up our figure and axes using [Cartopy](https://scitools.org.uk/cartopy/docs/latest/)." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Set up Axes with Cartopy Projection\n", - "fig = plt.figure()\n", - "ax = plt.axes(projection=ccrs.PlateCarree())\n", - "ax.coastlines()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Just like before, we used `plt.figure()` to create the figure. Then, we used `plt.axes` with the `projection` kwarg set to `ccrs.PlateCarree()` to create the axes object. This sets up the axes to use the [Plate Carree](https://scitools.org.uk/cartopy/docs/latest/reference/projections.html#cartopy.crs.PlateCarree) projection.\n", - "\n", - "Finally, we used `ax.coastlines()` to add coastlines to the plot." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating the initial frame\n", - "Now that we have our figure and axes set up, we can plot the initial frame of our animation." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vmin = t.min()\n", - "vmax = t.max()\n", - "levels = 30\n", - "\n", - "# create initial plot that we will update\n", - "t[0, :, :].plot.contourf(\n", - " ax=ax,\n", - " transform=ccrs.PlateCarree(),\n", - " vmin=vmin,\n", - " vmax=vmax,\n", - " levels=levels,\n", - " cmap=\"inferno\",\n", - " cbar_kwargs={\n", - " \"location\": \"bottom\",\n", - " },\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition to plotting the initial frame, we also set up the colorbar. In order for every frame of the animation to use the same colorbar, we need to create the colorbar before we create the animation.\n", - "\n", - "The `vmin` and `vmax` arguments of `plt.contourf` are set to the minimum and maximum values of the data, instead of each individual time slice. This ensures that the colorbar remains consistent across all frames of the animation. We also set a `levels` kwarg to similarly ensure that the contour levels are consistent, as well." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating the update function\n", - "Now that we have our initial frame, we need to create an update function that will update the data in the plot to create each frame of the animation. This function will be passed in as an argument to `FuncAnimation`, which expects a function that takes in a single argument, `i`, which is the frame number of the animation. The argument `i` is used to index into the data to get the data for the current frame to plot." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [], - "source": [ - "# create function to update plot\n", - "def animate(i):\n", - " # Plot the new frame\n", - " t[i, :, :].plot.contourf(\n", - " ax=ax,\n", - " transform=ccrs.PlateCarree(),\n", - " vmin=vmin,\n", - " vmax=vmax,\n", - " levels=levels,\n", - " cmap=\"inferno\",\n", - " add_colorbar=False,\n", - " )\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Note here that that the only differences from our initial frame set up are the indexing on the data to plot and the `add_colorbar = False` kwarg in `plt.contourf`. This is because we've already created a colorbar in the initial frame that we want to use for all frames of the animation.\n", - "\n", - ":::{warning}\n", - "You can accidentally create some funny looking animations if you forget the `add_colorbar = False` kwarg in the update function.\n", - "\n", - "\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Creating the animation\n", - "Now it's time to create the animation using `FuncAnimation`." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "ani = animation.FuncAnimation(fig, animate, frames=len(t), interval=150, repeat_delay=1000)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This function looks similar to `ArtistAnimation`, but with one main difference: instead of a list of artists to plot, `FuncAnimation` takes the update function we created as the second argument. Also, we've provided the `frames` kwarg, which is the number of times the update function will be called. We've set it to the number of time slices in the data to make sure the FuncAnimation object does not try to go outside the bounds of the data.\n", - "\n", - "And that's it! We've created a function animation in matplotlib. Let's take a look at it.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - "\n", - "\n", - "\n", - "
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    Note

    \n", - " The plotting libraries mentioned here are either ones used extensively by the authors of this Cookbook OR ones that we get asked about a lot when giving plotting tutorials. This does not cover every library that can be used for plotting in the Python scientific ecosystem, but should cover the more popular packages you might come across.\n", - "\n", - "Missing a plotting library that you use and want others to know more about? [Let us know!]()\n", - "
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n", - "| [Cartopy](https://foundations.projectpythia.org/core/cartopy.html) | Necessary | |\n", - "\n", - "- **Time to learn**: 50 minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Matplotlib\n", - "\n", - "\"Matplotlib\n", - "\n", - "Matplotlib is the workhorse of Python visualization needs. It is a comprehensive plotting library that has the capacity to make static, animated, or interactive visualizations. It is hard to imagine plotting in Python without first getting comfortable with Matplotlib. Be sure to check out the [Matplotlib documentation](https://matplotlib.org/) as well as the [Pythia foundations chapter on Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) for guidance.\n", - "\n", - "Matplotlib's syntax should feel familiar to anyone who has plotted data in Matlab.\n", - "\n", - "Here is a [simple plotting example from Matplotlib](https://matplotlib.org/stable/gallery/lines_bars_and_markers/simple_plot.html):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Data for plotting\n", - "t = np.arange(0.0, 2.0, 0.01)\n", - "s = 1 + np.sin(2 * np.pi * t)\n", - "\n", - "fig, ax = plt.subplots()\n", - "ax.plot(t, s)\n", - "\n", - "ax.set(xlabel='time (s)', ylabel='voltage (mV)',\n", - " title='About as simple as it gets, folks')\n", - "ax.grid()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Cartopy\n", - "\n", - "\"Cartopy\n", - "\n", - "Cartopy is a Python package for plotting data on the globe. It is the go-to package for plotting maps, dealing with different projections, and adding surface features to your plot. Cartopy is buit on top of [PROJ](https://proj.org/en/9.2/), NumPy and [Shapely](https://shapely.readthedocs.io/en/stable/manual.html), and Matplotlib. To learn more about what Cartopy can do, check out the [Cartopy documentation](https://scitools.org.uk/cartopy/docs/latest/) and the [Pythia foundations Cartopy chapter](https://foundations.projectpythia.org/core/cartopy.html).\n", - "\n", - "You may have heard about [Basemap](https://matplotlib.org/basemap/index.html), another geoscience plotting library, which was deprecated in favor of Cartopy.\n", - "\n", - "Here is a [simple plotting example from Cartopy](https://scitools.org.uk/cartopy/docs/v0.15/matplotlib/intro.html):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import cartopy.crs as ccrs\n", - "\n", - "ax = plt.axes(projection=ccrs.PlateCarree())\n", - "ax.coastlines()\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## GeoCAT-Viz\n", - "\n", - "\"GeoCAT\n", - "\n", - "The GeoCAT team at the National Center for Atmospheric Research (NCAR) aims to help scientists transitioning from [NCL](https://www.ncl.ucar.edu/) to Python. Out of this team come two different visualization aids: the [GeoCAT-examples Visualization Gallery](https://geocat-examples.readthedocs.io/en/latest/) which contains tons of different plotting examples that you can use as a starting place for your figures, and the [GeoCAT-Viz package (documentation)](https://geocat-viz.readthedocs.io/en/latest/) which contains many convenience functions that formerly existed in NCL or for making Python plots look publication-ready." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## MetPy\n", - "\n", - "\"Metpy\n", - "\n", - "Metpy is a collection of tools for data reading, analysis, and visualization with weather data. Matplotlib offers some useful functionality for unique plots such as Skew-T diagrams, as well as declaritive plotting functionality. Check out the [MetPy documentation](https://unidata.github.io/MetPy/latest/index.html).\n", - "\n", - "Here is a simple Skew-T plot from their [Getting Started documentation](https://unidata.github.io/MetPy/latest/userguide/startingguide.html):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import metpy.calc as mpcalc\n", - "from metpy.plots import SkewT\n", - "from metpy.units import units\n", - "\n", - "fig = plt.figure(figsize=(9, 9))\n", - "skew = SkewT(fig)\n", - "\n", - "# Create arrays of pressure, temperature, dewpoint, and wind components\n", - "p = [902, 897, 893, 889, 883, 874, 866, 857, 849, 841, 833, 824, 812, 796, 776, 751,\n", - " 727, 704, 680, 656, 629, 597, 565, 533, 501, 468, 435, 401, 366, 331, 295, 258,\n", - " 220, 182, 144, 106] * units.hPa\n", - "t = [-3, -3.7, -4.1, -4.5, -5.1, -5.8, -6.5, -7.2, -7.9, -8.6, -8.9, -7.6, -6, -5.1,\n", - " -5.2, -5.6, -5.4, -4.9, -5.2, -6.3, -8.4, -11.5, -14.9, -18.4, -21.9, -25.4,\n", - " -28, -32, -37, -43, -49, -54, -56, -57, -58, -60] * units.degC\n", - "td = [-22, -22.1, -22.2, -22.3, -22.4, -22.5, -22.6, -22.7, -22.8, -22.9, -22.4,\n", - " -21.6, -21.6, -21.9, -23.6, -27.1, -31, -38, -44, -46, -43, -37, -34, -36,\n", - " -42, -46, -49, -48, -47, -49, -55, -63, -72, -88, -93, -92] * units.degC\n", - "\n", - "# Calculate parcel profile\n", - "prof = mpcalc.parcel_profile(p, t[0], td[0]).to('degC')\n", - "u = np.linspace(-10, 10, len(p)) * units.knots\n", - "v = np.linspace(-20, 20, len(p)) * units.knots\n", - "\n", - "skew.plot(p, t, 'r')\n", - "skew.plot(p, td, 'g')\n", - "skew.plot(p, prof, 'k') # Plot parcel profile\n", - "skew.plot_barbs(p[::5], u[::5], v[::5])\n", - "\n", - "skew.ax.set_xlim(-50, 15)\n", - "skew.ax.set_ylim(1000, 100)\n", - "\n", - "# Add the relevant special lines\n", - "skew.plot_dry_adiabats()\n", - "skew.plot_moist_adiabats()\n", - "skew.plot_mixing_lines()\n", - "\n", - "plt.show();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## VAPOR\n", - "\n", - "\"VAPOR\n", - "\n", - "VAPOR stands for the Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers and is another project from NCAR. VAPOR provides an interactive 3D visualization environment. Learn more at the [VAPOR documentation](https://www.vapor.ucar.edu/) and the [VAPOR Pythia Cookbook](https://projectpythia.org/vapor-python-cookbook/README.html). VAPORrequires a GPU-enabled environment to run." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotly\n", - "\n", - "\"Plotly\n", - "\n", - "Plotly is another choice for interactive plotting. Plotly has functionality in several languags. Here is the [Plotly Python documentation](https://plotly.com/python/).\n", - "\n", - "Here is an example using their \"Express\" functionality:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import plotly.express as px\n", - "\n", - "fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])\n", - "fig.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Seaborn\n", - "\n", - "\"Seaborn\n", - "\n", - "Seaborn is a high level interactive interface for creating statistical visualizations built on matplotlib. Check out the [Seaborn documentation](https://seaborn.pydata.org/).\n", - "\n", - "Here is their [heatmap example](https://seaborn.pydata.org/examples/spreadsheet_heatmap.html):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import seaborn as sns\n", - "sns.set_theme()\n", - "\n", - "# Load the example flights dataset and convert to long-form\n", - "flights_long = sns.load_dataset(\"flights\")\n", - "flights = flights_long.pivot(index=\"month\", columns=\"year\", values=\"passengers\")\n", - "\n", - "# Draw a heatmap with the numeric values in each cell\n", - "f, ax = plt.subplots(figsize=(9, 6))\n", - "sns.heatmap(flights, annot=True, fmt=\"d\", linewidths=.5, ax=ax)\n", - "\n", - "plt.show();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Bokeh\n", - "\n", - "\"Bokeh\n", - "\n", - "Bokeh is a Javascript-powered tool for creating interactive visualizations in modern web browsers. Check out the [Bokeh documentation](https://bokeh.org/).\n", - "\n", - "Here is [scatter plot example](https://docs.bokeh.org/en/latest/docs/examples/basic/scatters/color_scatter.html):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from bokeh.plotting import figure, show\n", - "\n", - "N = 4000\n", - "x = np.random.random(size=N) * 100\n", - "y = np.random.random(size=N) * 100\n", - "radii = np.random.random(size=N) * 1.5\n", - "colors = np.array([(r, g, 150) for r, g in zip(50+2*x, 30+2*y)], dtype=\"uint8\")\n", - "\n", - "TOOLS=\"hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select,examine,help\"\n", - "\n", - "p = figure(tools=TOOLS)\n", - "\n", - "p.scatter(x, y, radius=radii,\n", - " fill_color=colors, fill_alpha=0.6,\n", - " line_color=None)\n", - "\n", - "show(p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## UXarray\n", - "\n", - "\"UXarray\n", - "\n", - "UXarray specializes in unstructured grids, built around [UGRID conventions](https://ugrid-conventions.github.io/ugrid-conventions/) and Xarray syntax. See the [UXarray documentation](https://uxarray.readthedocs.io/en/latest/)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## hvPlot\n", - "\n", - "\"Datashader\n", - "\n", - "hvPlot wraps both [Datashader](https://datashader.org/), a graphics pipeline, and [Holoviews](https://holoviews.org/), a tool for bundling data and metadata for intuitive interactive plotting, at a higher level. All 3 tools are by [Holoviz](https://holoviz.org/). Reference the [hvPlot documentation](https://hvplot.holoviz.org/).\n", - "\n", - "Here is a simple example from their [user guide](https://hvplot.holoviz.org/user_guide/Introduction.html):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import hvplot.pandas\n", - "\n", - "pd.options.plotting.backend = 'holoviews'\n", - "\n", - "index = pd.date_range('1/1/2000', periods=1000)\n", - "df = pd.DataFrame(np.random.randn(1000, 4), index=index, columns=list('ABCD')).cumsum()\n", - "\n", - "df.plot()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This useful diagram from [hvPlot's documentation](https://hvplot.holoviz.org/index.html) details how different high-level tools for data visualization interact.\n", - "\n", - "\"Datashader" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "Each Python plotting library offers a slightly different niche in the data visualization world. Some are better for creating publication figures (matplotlib, cartopy, metpy, geocat-viz, uxarray) while others offer interactive functionality that is great for websites, demonstrations, and other forms of engagement (holoviews, seaborn, plotly, bokeh, and vapor). Hopefully the mini examples on this page allow you to play around and see which user interfaces you like best for your visualization needs.\n", - "\n", - "\n", - "### What's next?\n", - "\n", - "Next up let's discuss elements of [good data visualization](good-viz)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resources and references\n", - "\n", - "- [Matplotlib documentation](https://matplotlib.org/)\n", - "- [Cartopy documentation](https://scitools.org.uk/cartopy/docs/latest/)\n", - "- [GeoCat-examples Visualization Gallery](https://geocat-examples.readthedocs.io/en/latest/)\n", - "- [GeoCAT-Viz documentation](https://geocat-viz.readthedocs.io/en/latest/)\n", - "- [MetPy documentation](https://unidata.github.io/MetPy/latest/index.html)\n", - "- [Vapor documentation](https://www.vapor.ucar.edu/)\n", - "- [Plotly Python documentation](https://plotly.com/python/)\n", - "- [Seaborn documentation](https://seaborn.pydata.org/)\n", - "- [Bokeh documentation](https://bokeh.org/)\n", - "- [UXarray documentation](https://uxarray.readthedocs.io/en/latest/)\n", - "- [hvPlot documentation](https://hvplot.holoviz.org/index.html)\n", - "- [Holoviews documentation](https://holoviews.org/)\n", - "- [Datashader documentation](https://datashader.org/)" - ] - } - ], - "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 - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_preview/22/_sources/notebooks/good-viz.ipynb b/_preview/22/_sources/notebooks/good-viz.ipynb deleted file mode 100644 index c7ce441..0000000 --- a/_preview/22/_sources/notebooks/good-viz.ipynb +++ /dev/null @@ -1,325 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# What Makes for Good Data Visualization?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "What Makes a Good Visualization? We want graphics to be eye catching and informative. In this chapter we'll discuss different aspects that can affect the quality of your figures and specific considerations relevant to the geosciences.\n", - "\n", - "1. The Importance of Data Visualization\n", - "1. Publication Ready Figures\n", - "1. The Problem with Rainbow Colormaps\n", - "1. Misleading Visualizations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n", - "| [Cartopy](https://foundations.projectpythia.org/core/cartopy.html) | Necessary | |\n", - "\n", - "- **Time to learn**: 3 hours\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import geocat.viz as gv" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Importance of Data Visualization\n", - "\n", - "It is important to use pictures to show data because we can visually detect patterns that could be lost in statistical analysis. All scientific disciplines use data visualizations to communicate concepts. \n", - "\n", - "Here we have a figure from [Autodesk](https://www.research.autodesk.com/publications/same-stats-different-graphs/) that shows a \"Datasaurus\" and 12 other datasets that share the same statistical information (mean, standard deviation, etc). We can see immediately that visually are telling very different stories: be it a dinosaur, a star, an oval, concentric ovals, or a series of lines (perhaps weather fronts).\n", - "\n", - "\"Same" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Publication Ready Figures\n", - "\n", - "For your figure to be publication rady, you probably want to change some of Matplotlib's default plotting settings: selecting fontsizes for your titles and labels, changing figure sizes, or subplot/colormap layout.\n", - "\n", - "To demonstrate this, let's look at an example:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fake data\n", - "x = [0, 1, 2, 3, 4, 5]\n", - "y = [0, 3, 6, 9, 12, 15]\n", - " \n", - "# plot\n", - "plt.plot(x, y)\n", - "\n", - "# annotate\n", - "plt.title('Title')\n", - "plt.xlabel('X Label')\n", - "plt.ylabel('Y Label')\n", - "\n", - "plt.show();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's show some customization options:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fake data\n", - "x = [0, 1, 2, 3, 4, 5]\n", - "y = [0, 3, 6, 9, 12, 15]\n", - " \n", - "# plot\n", - "plt.plot(x, y, '--', color='red')\n", - "\n", - "# annotate\n", - "plt.title('Title', fontsize=20)\n", - "plt.xlabel('X Label', fontsize=16)\n", - "plt.ylabel('Y Label', fontsize=16)\n", - "\n", - "plt.show();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Matplotlib Global Parameters\n", - "\n", - "Matplotlib has defaults for fontsizes and all sorts of attributes of a plot. Instead of setting your fontsize in every script, it is possible to set global parameters to change the default values of these attributes.\n", - "\n", - "You can veiw the globoal parameters options and their current settings with:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "mpl.rcParams.keys" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To change any given parameter you would use the following command (replacing your parameter and value, of course):" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "```\n", - "mpl.rcParams['font.family'] = 'Arial'\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Using GeoCAT-Viz\n", - "\n", - "The GeoCAT-Viz package also has many utility functions for making your plots looks publication ready in fewer lines of code. The defaults of GeoCAT-viz keword-arguments are set to resemble the style of NCL." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fake data\n", - "x = [0, 1, 2, 3, 4, 5]\n", - "y = [0, 3, 6, 9, 12, 15]\n", - " \n", - "# plot\n", - "plt.plot(x, y)\n", - "\n", - "# annotate\n", - "plt.title('Title')\n", - "plt.xlabel('X Label')\n", - "plt.ylabel('Y Label')\n", - "\n", - "gv.set_titles_and_labels(plt.gca())\n", - "\n", - "plt.show();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## The Problem with Rainbow Colormaps\n", - "\n", - "Rainbow colormaps are visually beautiful, but are falling out of favor because\n", - "1. They are not colorblind friendly and\n", - "2. They do not print out in grayscale in a meaningful way.\n", - "\n", - "Both of these issues can be addressed by bing careful about you colormaps lightness-values.\n", - "\n", - "Some colormaps options are perceptually uniform (the same lightness value), sequentially ordered (goes from lighter to darker), or diverging (lightest or darkest at a set point and uniformly changes lightness going out). A rainbow colormap however is lighter or darker in arbitrary places and it affects how we interpret data (especially if it was printed out in grayscale).\n", - "\n", - "For example, from [Matplotlib's Choosing a Colormap documentation](https://matplotlib.org/stable/tutorials/colors/colormaps.html) here are some \"good\" colormaps:\n", - "\n", - "\"Matplotlib\n", - "\n", - "And here are miscellaneous colormaps:\n", - "\n", - "\"Matplotlib\n", - "\n", - "Looking at the colors in grayscale helps to understand why we might prefer a sequentially ordered colormap. Some grayscale values are duplicated and the reader will not know if it is a high or low value.\n", - "\n", - "Another consideration that can help those who are visually impaired is to make sure your figure comments are substantial. Use words to paint the picture of what is displayed, not just the conclusions you want the reader to get." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Misleading Visualizations\n", - "\n", - "The scales or colors we choose to use for data visualization affect how people interpret figures. You should strive to make your visualizations as accurate and as informative as possible. Here are some examples that demonstrate just how different a figure can look based on these choices you make. Do not intentionally mislead your audience!\n", - "\n", - "Perhaps the most common data visualization manipulation is to change the Y-scale. If a plot does not begin at 0, small changes in magnitude can be exhaggerated. Similarly a logarithmic scale will amplify changes. This is not always disingenuous, sometimes these changes are what you want to highlight, the pattern you want to draw attention to. Just make sure it is appropriate for your use case and documented. Alternatively, extending the Y-axis too large has the opposite affect and smooths out the differences in data." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = [1, 2, 3, 4, 5]\n", - "y = [1101, 1011, 1111, 1070, 1050]\n", - "\n", - "\n", - "fig, (ax1, ax2, ax3, ax4) = plt.subplots(4)\n", - "fig.tight_layout()\n", - "\n", - "ax1.bar(x,y)\n", - "ax1.set_title(\"Default Y-Scale Starts at 0\")\n", - "\n", - "ax2.bar(x,y)\n", - "ax2.set_ylim(1000)\n", - "ax2.set_title(\"Y-Scale Starts at 1000\")\n", - "\n", - "ax3.bar(x,y)\n", - "ax3.set_yscale(\"log\")\n", - "ax3.set_title(\"Y-Scale is Logarithmic\");\n", - "\n", - "ax4.bar(x,y)\n", - "ax4.set_ylim(0, 2000)\n", - "ax4.set_title(\"Y-Scale is Extended\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Other examples of data visualization manipulation include improper scaling, cherry picking a small non-representative subset of the data to display, displaying pie charts at a slant (pie charts are hard to interpet accurately as is), and unusing unexpected colormaps." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "It is important to have accurate, engaging, and representative data visualization to accumpany your research, both for data exploration as part of the scientific process, for communication of results, and education/outreach efforts. Visually we pick up on patterns that statistics alone may not convey. However, an over reliance on data visualization can make science less accessible to those with vision disabilities. It is important to be cognicent of the patterns our minds pick up, be it based on color or y-axis scaling, so that we can avoid misleading our audience and more accurately convey the narrative inherent to the data.\n", - "\n", - "### What's next?\n", - "\n", - "[Plot Elements](plot-elements)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resources and references\n", - "\n", - "- [Autodesk](https://www.research.autodesk.com/publications/same-stats-different-graphs/)\n", - "- [Matplotlib's Choosing a Colormap documentation](https://matplotlib.org/stable/tutorials/colors/colormaps.html)\n", - "- [GeoCAT-examples Gallery](https://geocat-examples.readthedocs.io/en/latest/)\n", - "- [NWSC Script](https://docs.google.com/document/d/1PxJBbYJsI5nmR9pDQmcRM0ZghRPSI_xlzGVhw3AEUms/edit)\n", - "- [Beyond Visuals: Examining the Experiences of Geoscience\n", - "Professionals With Vision Disabilities in Accessing Data Visualizations](https://arxiv.org/pdf/2207.13220.pdf)\n", - "- [Same Stats Different Graphs: Generating Datasets with Varied Appearance and\n", - "Identical Statistics through Simulated Annealing](https://www.research.autodesk.com/app/uploads/2023/03/same-stats-different-graphs.pdf_rec2hRjLLGgM7Cn2T.pdf)\n", - "- Alberto Cairo's How Charts Lie: Getting Smarter about Visual Information\n", - "- [Misleading Data Visualization – What to Avoid](https://blog.coupler.io/misleading-data-visualization-examples/#:~:text=A%20very%20common%20misleading%20data,truncated%20graph%20might%20distort%20data.)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "advanced-viz-cookbook", - "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" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} 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 e2ca57e..0000000 --- a/_preview/22/_sources/notebooks/how-to-cite.md +++ /dev/null @@ -1,7 +0,0 @@ -# How to Cite This Cookbook - -The material in this Project Pythia 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/advanced-viz-cookbook) and archived on Zenodo. This DOI will always resolve to the latest release of the book source: - -[![DOI](https://zenodo.org/badge/671205314.svg)](https://zenodo.org/badge/latestdoi/671205314) diff --git a/_preview/22/_sources/notebooks/mpas-datashader.ipynb b/_preview/22/_sources/notebooks/mpas-datashader.ipynb deleted file mode 100644 index d0b4613..0000000 --- a/_preview/22/_sources/notebooks/mpas-datashader.ipynb +++ /dev/null @@ -1,452 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "# MPAS with Datashader and Geoviews" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "This example of interactively plotting unstructured grid MPAS data with Datashader and Geoviews demonstrates making use of the MPAS file's connectivity information to render data on the native grid, and also\n", - "avoid costly Delaunay triangulation that is required if the MPAS connectivity information is not used, rendering data that is sampled on both the 'primal' and 'dual' MPAS mesh, using geoviews/holoviews for interactive plotting in a Jupyter Notebook. The plotting is interactive in the sense that you can pan and zoom the data. Doing so will reveal greater and greater data fidelity, and sing Datashader to perform background rendering in place of Matplotlib. Unlike Matplotlib, Datashaderwas designed for performance with large data sets.\n", - "\n", - "1. Utility Functions\n", - "2. Loading Data\n", - "3. Using MPAS's cell connectivity array to plot primal mesh data\n", - "4. Synthesizing triangles from points using Delaunay triangulation\n", - "5. Using MPAS's cell connectivity array to plot dual mesh data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| []() | Necessary | |\n", - "\n", - "- **Time to learn**: X minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "import cartopy.crs as ccrs\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "import math as math\n", - "\n", - "import geocat.datafiles as gdf # Only for reading-in datasets\n", - "\n", - "from xarray import open_mfdataset\n", - "\n", - "from numba import jit\n", - "\n", - "import dask.dataframe as dd\n", - "\n", - "import holoviews as hv\n", - "from holoviews import opts\n", - "\n", - "from holoviews.operation.datashader import rasterize as hds_rasterize \n", - "#import geoviews.feature as gf # only needed for coastlines\n", - "\n", - "hv.extension(\"bokeh\",\"matplotlib\")\n", - "\n", - "opts.defaults(\n", - " opts.Image(frame_width=600, data_aspect=1),\n", - " opts.RGB(frame_width=600, data_aspect=1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Utility functions\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# This funtion splits a global mesh along longitude\n", - "#\n", - "# Examine the X coordinates of each triangle in 'tris'. Return an array of 'tris' where only those triangles\n", - "# with legs whose length is less than 't' are returned. \n", - "# \n", - "def unzipMesh(x,tris,t):\n", - " return tris[(np.abs((x[tris[:,0]])-(x[tris[:,1]])) < t) & (np.abs((x[tris[:,0]])-(x[tris[:,2]])) < t)]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Compute the signed area of a triangle\n", - "#\n", - "def triArea(x,y,tris):\n", - " return ((x[tris[:,1]]-x[tris[:,0]]) * (y[tris[:,2]]-y[tris[:,0]])) - ((x[tris[:,2]]-x[tris[:,0]]) * (y[tris[:,1]]-y[tris[:,0]]))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Reorder triangles as necessary so they all have counter clockwise winding order. CCW is what Datashader and MPL\n", - "# require.\n", - "#\n", - "def orderCCW(x,y,tris):\n", - " tris[triArea(x,y,tris)<0.0,:] = tris[triArea(x,y,tris)<0.0,::-1]\n", - " return(tris)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Create a Holoviews Triangle Mesh suitable for rendering with Datashader\n", - "#\n", - "# This function returns a Holoviews TriMesh that is created from a list of coordinates, 'x' and 'y',\n", - "# an array of triangle indices that addressess the coordinates in 'x' and 'y', and a data variable 'var'. The\n", - "# data variable's values will annotate the triangle vertices\n", - "#\n", - "\n", - "def createHVTriMesh(x,y,triangle_indices, var,n_workers=1):\n", - " # Declare verts array\n", - " verts = np.column_stack([x, y, var])\n", - "\n", - "\n", - " # Convert to pandas\n", - " verts_df = pd.DataFrame(verts, columns=['x', 'y', 'z'])\n", - " tris_df = pd.DataFrame(triangle_indices, columns=['v0', 'v1', 'v2'])\n", - "\n", - " # Convert to dask\n", - " verts_ddf = dd.from_pandas(verts_df, npartitions=n_workers)\n", - " tris_ddf = dd.from_pandas(tris_df, npartitions=n_workers)\n", - "\n", - " # Declare HoloViews element\n", - " tri_nodes = hv.Nodes(verts_ddf, ['x', 'y', 'index'], ['z'])\n", - " trimesh = hv.TriMesh((tris_ddf, tri_nodes))\n", - " return(trimesh)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "tags": [] - }, - "outputs": [], - "source": [ - "# Triangulate MPAS primary mesh:\n", - "#\n", - "# Triangulate each polygon in a heterogenous mesh of n-gons by connecting\n", - "# each internal polygon vertex to the first vertex. Uses the MPAS\n", - "# auxilliary variables verticesOnCell, and nEdgesOnCell.\n", - "#\n", - "# The function is decorated with Numba's just-in-time compiler so that it is translated into\n", - "# optimized machine code for better peformance\n", - "#\n", - "\n", - "@jit(nopython=True)\n", - "def triangulatePoly(verticesOnCell, nEdgesOnCell):\n", - "\n", - " # Calculate the number of triangles. nEdgesOnCell gives the number of vertices for each cell (polygon)\n", - " # The number of triangles per polygon is the number of vertices minus 2.\n", - " #\n", - " nTriangles = np.sum(nEdgesOnCell - 2)\n", - "\n", - " triangles = np.ones((nTriangles, 3), dtype=np.int64)\n", - " nCells = verticesOnCell.shape[0]\n", - " triIndex = 0\n", - " for j in range(nCells):\n", - " for i in range(nEdgesOnCell[j]-2):\n", - " triangles[triIndex][0] = verticesOnCell[j][0]\n", - " triangles[triIndex][1] = verticesOnCell[j][i+1]\n", - " triangles[triIndex][2] = verticesOnCell[j][i+2]\n", - " triIndex += 1\n", - "\n", - " return triangles" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data and coordinates\n", - "\n", - "The global data sets used in this example are from the same experiment, but run at several resolutions from\n", - "30km to 3.75km. Due to their size, the higher resolution data sets are only distributed with two variables\n", - "in them:\n", - "+ relhum_200hPa: Relative humidity vertically interpolated to 200 hPa\n", - "+ vorticity_200hPa: Relative vorticity vertically interpolated to 200 hPa\n", - "\n", - "The dyamond_1 data set is available in several resolutions, ranging from 30 km to 3.75 km.\n", - "\n", - "Currently, the 30-km resolution dataset used in this example is available at geocat-datafiles.\n", - "However, the other resolutions of these data are stored on Glade because of their size.\n", - "\n", - "The relhum_200hPa is computed on the MPAS 'primal' mesh, while the vorticity_200hPa is computed on the MPAS\n", - "'dual' mesh. Note that data may also be sampled on the edges of the primal mesh. This example does not\n", - "include/cover edge-centered data.\n", - "\n", - "These data are courtesy of NCAR's Falko Judt, and were produced as part of the DYAMOND initiative: \n", - " http://dx.doi.org/10.1186/s40645-019-0304-z.\n" - ] - }, - { - "cell_type": "raw", - "metadata": { - "tags": [] - }, - "source": [ - "# Load data\n", - "\n", - "datafiles = (gdf.get(\"netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/diag.2016-08-20_00.00.00_subset.nc\"),\n", - " gdf.get(\"netcdf_files/MPAS/FalkoJudt/dyamond_1/30km/x1.655362.grid_subset.nc\") )\n", - "\n", - "primalVarName = 'relhum_200hPa'\n", - "dualVarName = 'vorticity_200hPa'\n", - "central_longitude = 0.0\n", - "\n", - "ds = open_mfdataset(datafiles, decode_times=False)\n", - "primalVar = ds[primalVarName].isel(Time=0).values\n", - "dualVar = ds[dualVarName].isel(Time=0).values\n", - "\n", - "# Fetch lat and lon coordinates for the primal and dual mesh.\n", - "lonCell = ds['lonCell'].values * 180.0 / math.pi\n", - "latCell = ds['latCell'].values * 180.0 / math.pi\n", - "lonCell = ((lonCell - 180.0) % 360.0) - 180.0\n", - "\n", - "lonVertex = ds['lonVertex'].values * 180.0 / math.pi\n", - "latVertex = ds['latVertex'].values * 180.0 / math.pi\n", - "lonVertex = ((lonVertex - 180.0) % 360.0) - 180.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "tags": [] - }, - "source": [ - "## Using MPAS's cell connectivity array to plot primal mesh data\n", - "\n", - "In this example we use the MPAS `cellsOnVertex` auxilliary variable, which defines mesh connectivity for the MPAS grid.\n", - "Specifically, this variable tells us the cell IDs for each cell that contains each vertex.\n", - "\n", - "The benefits of this are twofold: 1. We're using the actual mesh description from the MPAS output file; and 2. \n", - "For large grid this is *much* faster than synthesizing the connectivity information as is done\n", - "in the next example\n" - ] - }, - { - "cell_type": "raw", - "metadata": { - "tags": [] - }, - "source": [ - "# Get triangle indices for each vertex in the MPAS file. Note, indexing in MPAS starts from 1, not zero :-(\n", - "#\n", - "tris = ds.cellsOnVertex.values - 1\n", - "\n", - "# The MPAS connectivity array unforunately does not seem to guarantee consistent clockwise winding order, which\n", - "# is required by Datashader (and Matplotlib)\n", - "#\n", - "tris = orderCCW(lonCell,latCell,tris)\n", - "\n", - "# Lastly, we need to \"unzip\" the mesh along a constant line of longitude so that when we project to PCS coordinates\n", - "# cells don't wrap around from east to west. The function below does the job, but it assumes that the \n", - "# central_longitude from the map projection is 0.0. I.e. it will cut the mesh where longitude \n", - "# wraps around from -180.0 to 180.0. We'll need to generalize this\n", - "#\n", - "tris = unzipMesh(lonCell,tris,90.0)\n", - "\n", - "\n", - "# Project verts from geographic to PCS coordinates\n", - "#\n", - "projection = ccrs.Robinson(central_longitude=central_longitude)\n", - "xPCS, yPCS, _ = projection.transform_points(ccrs.PlateCarree(), lonCell, latCell).T\n", - "\n", - "\n", - "trimesh = createHVTriMesh(xPCS,yPCS,tris, primalVar,n_workers=n_workers)" - ] - }, - { - "cell_type": "raw", - "metadata": { - "tags": [] - }, - "source": [ - "# Use precompute so it caches the data internally\n", - "rasterized = hds_rasterize(trimesh, aggregator='mean', precompute=True)\n", - "rasterized.opts(tools=['hover'], colorbar=True, cmap='coolwarm') * gf.coastline(projection=projection)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Synthesizing triangles from points using Delaunay triangulation\n", - "\n", - "In this second example we do not use the triangle connectivity information stored in the MPAS file. Instead we\n", - "use Delaunay triangulation to artifically create a triangle mesh. The benefit of this approach is that we do not\n", - "need the MPAS cellsOnVertex variable if it is not available. Also, since the triangulation algorithm is run on the \n", - "coordinates after they are projected to meters we do not have to worry about wraparound. The downside is that for\n", - "high-resolution data Delaunay triangulation is prohibitively expensive. The highest resolution data set included\n", - "in this notebook (3.75km) will not triangulate in a reasonable amount of time, if at all \n" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# Use Delaunay triangulation to generate the triangle connectivity. Note, it's important that the coordinate \n", - "# arrays already be in PCS coordinates (not lat-lon) for the triangulation to perform optimally\n", - "#\n", - "\n", - "from matplotlib.tri import Triangulation\n", - "\n", - "tris = Triangulation(xPCS,yPCS).triangles\n", - "\n", - "trimesh = createHVTriMesh(xPCS,yPCS,tris, primalVar,n_workers=n_workers)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "# Use precompute so it caches the data internally\n", - "rasterized = hds_rasterize(trimesh, aggregator='mean', precompute=True)\n", - "rasterized.opts(tools=['hover'], colorbar=True, cmap='coolwarm') * gf.coastline(projection=projection)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Using MPAS's cell connectivity array to plot dual mesh data\n", - "\n", - "In this example we use the MPAS `verticesOnCell` and `nEdgesOnCell` auxilliary variables, which defines mesh connectivity for the\n", - "MPAS dual grid.\n", - "\n", - "As with the first example using the MPAS primal grid, data on the dual grid could be plotted by first\n", - "triangulating them with, for example, Delaunay triangulation. But using grid's native connectivity information \n", - "is faster.\n" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "verticesOnCell = ds.verticesOnCell.values - 1\n", - "nEdgesOnCell = ds.nEdgesOnCell.values\n", - "\n", - "# For the dual mesh the data are located on triangle centers, which correspond to cell (polygon) vertices. Here\n", - "# we decompose each cell into triangles\n", - "#\n", - "tris = triangulatePoly(verticesOnCell, nEdgesOnCell)\n", - "\n", - "tris = unzipMesh(lonVertex,tris,90.0)\n", - "\n", - "# Project verts from geographic to PCS coordinates\n", - "#\n", - "projection = ccrs.Robinson(central_longitude=central_longitude)\n", - "xPCS, yPCS, _ = projection.transform_points(ccrs.PlateCarree(), lonVertex, latVertex).T\n", - "\n", - "trimesh = createHVTriMesh(xPCS,yPCS,tris, dualVar,n_workers=n_workers)" - ] - }, - { - "cell_type": "raw", - "metadata": {}, - "source": [ - "rasterized = hds_rasterize(trimesh, aggregator='mean', precompute=True)\n", - "rasterized.opts(tools=['hover'], colorbar=True, cmap='coolwarm') * gf.coastline(projection=projection)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "### What's next?\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resources and references\n", - "\n", - "- []()" - ] - } - ], - "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" - }, - "widgets": { - "application/vnd.jupyter.widget-state+json": { - "state": {}, - "version_major": 2, - "version_minor": 0 - } - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_preview/22/_sources/notebooks/notebook-template.ipynb b/_preview/22/_sources/notebooks/notebook-template.ipynb deleted file mode 100644 index dad9f26..0000000 --- a/_preview/22/_sources/notebooks/notebook-template.ipynb +++ /dev/null @@ -1,358 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's start here! If you can directly link to an image relevant to your notebook, such as [canonical logos](https://github.com/numpy/numpy/blob/main/doc/source/_static/numpylogo.svg), do so here at the top of your notebook. You can do this with Markdown syntax,\n", - "\n", - "> `![](http://link.com/to/image.png \"image alt text\")`\n", - "\n", - "or edit this cell to see raw HTML `img` demonstration. This is preferred if you need to shrink your embedded image. **Either way be sure to include `alt` text for any embedded images to make your content more accessible.**\n", - "\n", - "\"Project" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Project Pythia Notebook Template\n", - "\n", - "Next, title your notebook appropriately with a top-level Markdown header, `#`. Do not use this level header anywhere else in the notebook. Our book build process will use this title in the navbar, table of contents, etc. Keep it short, keep it descriptive. Follow this with a `---` cell to visually distinguish the transition to the prerequisites section." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "If you have an introductory paragraph, lead with it here! Keep it short and tied to your material, then be sure to continue into the required list of topics below,\n", - "\n", - "1. This is a numbered list of the specific topics\n", - "1. These should map approximately to your main sections of content\n", - "1. Or each second-level, `##`, header in your notebook\n", - "1. Keep the size and scope of your notebook in check\n", - "1. And be sure to let the reader know up front the important concepts they'll be leaving with" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "This section was inspired by [this template](https://github.com/alan-turing-institute/the-turing-way/blob/master/book/templates/chapter-template/chapter-landing-page.md) of the wonderful [The Turing Way](https://the-turing-way.netlify.app) Jupyter Book.\n", - "\n", - "Following your overview, tell your reader what concepts, packages, or other background information they'll **need** before learning your material. Tie this explicitly with links to other pages here in Foundations or to relevant external resources. Remove this body text, then populate the Markdown table, denoted in this cell with `|` vertical brackets, below, and fill out the information following. In this table, lay out prerequisite concepts by explicitly linking to other Foundations material or external resources, or describe generally helpful concepts.\n", - "\n", - "Label the importance of each concept explicitly as **helpful/necessary**.\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Necessary | |\n", - "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n", - "| Project management | Helpful | |\n", - "\n", - "- **Time to learn**: estimate in minutes. For a rough idea, use 5 mins per subsection, 10 if longer; add these up for a total. Safer to round up and overestimate.\n", - "- **System requirements**:\n", - " - Populate with any system, version, or non-Python software requirements if necessary\n", - " - Otherwise use the concepts table above and the Imports section below to describe required packages as necessary\n", - " - If no extra requirements, remove the **System requirements** point altogether" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Imports\n", - "Begin your body of content with another `---` divider before continuing into this section, then remove this body text and populate the following code cell with all necessary Python imports **up-front**:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Your first content section" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This is where you begin your first section of material, loosely tied to your objectives stated up front. Tie together your notebook as a narrative, with interspersed Markdown text, images, and more as necessary," - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# as well as any and all of your code cells\n", - "print(\"Hello world!\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### A content subsection\n", - "Divide and conquer your objectives with Markdown subsections, which will populate the helpful navbar in Jupyter Lab and here on the Jupyter Book!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# some subsection code\n", - "new = \"helpful information\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Another content subsection\n", - "Keep up the good work! A note, *try to avoid using code comments as narrative*, and instead let them only exist as brief clarifications where necessary." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Your second content section\n", - "Here we can move on to our second objective, and we can demonstrate" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Subsection to the second section\n", - "\n", - "#### a quick demonstration\n", - "\n", - "##### of further and further\n", - "\n", - "###### header levels" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "as well $m = a * t / h$ text! Similarly, you have access to other $\\LaTeX$ equation [**functionality**](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Typesetting%20Equations.html) via MathJax (demo below from link),\n", - "\n", - "\\begin{align}\n", - "\\dot{x} & = \\sigma(y-x) \\\\\n", - "\\dot{y} & = \\rho x - y - xz \\\\\n", - "\\dot{z} & = -\\beta z + xy\n", - "\\end{align}" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Check out [**any number of helpful Markdown resources**](https://www.markdownguide.org/basic-syntax/) for further customizing your notebooks and the [**Jupyter docs**](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Working%20With%20Markdown%20Cells.html) for Jupyter-specific formatting information. Don't hesitate to ask questions if you have problems getting it to look *just right*." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Last Section\n", - "\n", - "If you're comfortable, and as we briefly used for our embedded logo up top, you can embed raw html into Jupyter Markdown cells (edit to see):" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    \n", - "

    Info

    \n", - " Your relevant information here!\n", - "
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Feel free to copy this around and edit or play around with yourself. Some other `admonitions` you can put in:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    \n", - "

    Success

    \n", - " We got this done after all!\n", - "
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    \n", - "

    Warning

    \n", - " Be careful!\n", - "
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "
    \n", - "

    Danger

    \n", - " Scary stuff be here.\n", - "
    " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We also suggest checking out Jupyter Book's [brief demonstration](https://jupyterbook.org/content/metadata.html#jupyter-cell-tags) on adding cell tags to your cells in Jupyter Notebook, Lab, or manually. Using these cell tags can allow you to [customize](https://jupyterbook.org/interactive/hiding.html) how your code content is displayed and even [demonstrate errors](https://jupyterbook.org/content/execute.html#dealing-with-code-that-raises-errors) without altogether crashing our loyal army of machines!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "Add one final `---` marking the end of your body of content, and then conclude with a brief single paragraph summarizing at a high level the key pieces that were learned and how they tied to your objectives. Look to reiterate what the most important takeaways were.\n", - "\n", - "### What's next?\n", - "Let Jupyter book tie this to the next (sequential) piece of content that people could move on to down below and in the sidebar. However, if this page uniquely enables your reader to tackle other nonsequential concepts throughout this book, or even external content, link to it here!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resources and references\n", - "Finally, be rigorous in your citations and references as necessary. Give credit where credit is due. Also, feel free to link to relevant external material, further reading, documentation, etc. Then you're done! Give yourself a quick review, a high five, and send us a pull request. A few final notes:\n", - " - `Kernel > Restart Kernel and Run All Cells...` to confirm that your notebook will cleanly run from start to finish\n", - " - `Kernel > Restart Kernel and Clear All Outputs...` before committing your notebook, our machines will do the heavy lifting\n", - " - Take credit! Provide author contact information if you'd like; if so, consider adding information here at the bottom of your notebook\n", - " - Give credit! Attribute appropriate authorship for referenced code, information, images, etc.\n", - " - Only include what you're legally allowed: **no copyright infringement or plagiarism**\n", - " \n", - "Thank you for your contribution!" - ] - } - ], - "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" - }, - "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 - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_preview/22/_sources/notebooks/plot-elements.ipynb b/_preview/22/_sources/notebooks/plot-elements.ipynb deleted file mode 100644 index 2febf5e..0000000 --- a/_preview/22/_sources/notebooks/plot-elements.ipynb +++ /dev/null @@ -1,308 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Plot Elements" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n", - "| [Cartopy](https://foundations.projectpythia.org/core/cartopy.html) | Necessary | |\n", - "\n", - "- **Time to learn**: 40 minutes\n", - "---" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import xarray as xr\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import geocat.viz as gv" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data\n", - "\n", - "The first piece of data visualization is the data!\n", - "\n", - "Let's generate some dummy data to work with:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "x = np.linspace(0, 20, 1000)\n", - "y1 = np.sin(x)\n", - "y2 = np.cos(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Figure\n", - "\n", - "The figure is the object that contains your entire visualization. Creating a figure tends to be the first step in plotting, even if it doesn't currently show anything:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(9.5, 8))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Axis\n", - "\n", - "We then add axes to our plot. You can add multiple axes to one plot in order to produce subplots, or just one. Axes will automatically inherit their limits from the data plotted, or can be manually set." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(9.5, 8))\n", - "ax = plt.axes()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot\n", - "\n", - "Adding the data to the figure can be done through several different plot types: line, contour, bar, histogram. Here we use two line plots:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(9.5, 8))\n", - "ax = plt.axes()\n", - "\n", - "ax.plot(x,y1)\n", - "ax.plot(x,y2);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Titles and Labels\n", - "\n", - "Titles and labels are important for indicating what the figure is plotting. It is a good idea to include relevant units in your axis labels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(9.5, 8))\n", - "ax = plt.axes()\n", - "\n", - "ax.plot(x,y1)\n", - "ax.plot(x,y2)\n", - "\n", - "ax.set_title(\"Dummy Data\")\n", - "ax.set_xlabel(\"X (radians)\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Legends\n", - "\n", - "If you're plotting multiple lines of data, it's a good idea to include a legend. Here is how you call or point to the legend:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(9.5, 8))\n", - "ax = plt.axes()\n", - "\n", - "ax.plot(x,y1,label='sine')\n", - "ax.plot(x,y2,label='cosine')\n", - "\n", - "ax.set_title(\"Dummy Data\")\n", - "ax.set_xlabel(\"X (radians)\")\n", - "\n", - "plt.legend(loc=\"upper left\");" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Colorbars\n", - "\n", - "While legends are more appropriate for line or bar plots, colorbars are most commonly used for contour plots and sometimes to apply a third level of dimension to a scatter plot.\n", - "\n", - "Let's shift our example to better demonstrate a colorbar by workign with a filled contour plot:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate dummy data\n", - "data = [[1, 4, 5, 6, 8.2],\n", - " [9, 8.4, 10, 10.6, 9.7],\n", - " [4.4, 5, 0, 6.6, 1.4],\n", - " [4.6, 5.2, 1.5, 7.6, 2.4]]\n", - "\n", - "# Convert data into type xarray.DataArray\n", - "data = xr.DataArray(data,\n", - " dims=[\"lat\", \"lon\"],\n", - " coords=dict(lat=np.arange(4), lon=np.arange(5)))\n", - "\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(9.5, 8))\n", - "ax = plt.axes()\n", - "\n", - "pcm = ax.contourf(data,cmap='viridis')\n", - "\n", - "ax.set_title(\"Dummy Data\")\n", - "ax.set_xlabel(\"Longitude (\\N{DEGREE SIGN})\")\n", - "ax.set_ylabel(\"Latitude (\\N{DEGREE SIGN})\")\n", - "\n", - "fig.colorbar(pcm,ax=ax);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Annotations\n", - "\n", - "Additional annotations allow you to specify some text and a location to indicate almost anything. \n", - "\n", - "Here we use GeoCAT-viz to add annotations to the maxima in a contour plot:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig = plt.figure(figsize=(9.5, 8))\n", - "ax = plt.axes()\n", - "\n", - "pcm = ax.contourf(data,cmap='viridis')\n", - "\n", - "ax.set_title(\"Dummy Data\")\n", - "ax.set_xlabel(\"Longitude (\\N{DEGREE SIGN})\")\n", - "ax.set_ylabel(\"Latitude (\\N{DEGREE SIGN})\")\n", - "\n", - "fig.colorbar(pcm,ax=ax)\n", - "\n", - "# Find local maximum with GeoCAT-Viz find_local_extrema\n", - "lmax = gv.find_local_extrema(data, eType='High')[0]\n", - "\n", - "# Plot labels for local mins\n", - "max_value = data.data[lmax[1]][lmax[0]]\n", - "ax.text(lmax[0], lmax[1],'Maxima = '+str(max_value))\n", - "\n", - "# Show plot\n", - "plt.show();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "There are several key elements to a Python plot and knowing what they are called is instrumental to begin your journey for further customization.\n", - "\n", - "### What's next?\n", - "\n", - "[Specialty Plots: Taylor Diagrams](taylor-diagrams)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resources and references" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "advanced-viz-cookbook", - "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" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_preview/22/_sources/notebooks/skewt.ipynb b/_preview/22/_sources/notebooks/skewt.ipynb deleted file mode 100644 index 7976391..0000000 --- a/_preview/22/_sources/notebooks/skewt.ipynb +++ /dev/null @@ -1,599 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Skew T Diagrams" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "Summary text here\n", - "\n", - "1. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| []() | Necessary | |\n", - "\n", - "- **Time to learn**: X minutes\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## What is a Skew-T plot?" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd\n", - "\n", - "from metpy.plots import SkewT\n", - "import metpy.calc as mpcalc\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If you want to get your own sounding data, run the following code in a new cell using the date and station of your choice:\n", - "\n", - "```python\n", - "from datetime import datetime\n", - "from siphon.simplewebservice.wyoming import WyomingUpperAir\n", - "\n", - "date = datetime(2023, 11, 20, 12)\n", - "station = 'GJT'\n", - "df = WyomingUpperAir.request_data(date, station)|\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We've already done this for you and saved the data in a file, `notebooks/data/gjt_sounding.csv` for you to use. We'll use that file's data for the rest of the notebook" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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    152 rows × 15 columns

    \n", - "
    " - ], - "text/plain": [ - " pressure height temperature dewpoint direction speed u_wind \\\n", - "0 853.0 1475 5.2 2.0 305.0 3.0 2.457456 \n", - "1 850.0 1508 7.2 1.2 280.0 13.0 12.802501 \n", - "2 848.0 1527 7.4 1.4 287.0 13.0 12.431962 \n", - "3 831.0 1693 6.4 -2.6 350.0 15.0 2.604723 \n", - "4 820.0 1802 5.4 -2.8 10.0 13.0 -2.257426 \n", - ".. ... ... ... ... ... ... ... \n", - "147 13.4 28951 -55.7 -85.7 53.0 17.0 -13.576804 \n", - "148 13.0 29144 -55.5 -85.5 75.0 20.0 -19.318517 \n", - "149 12.7 29293 -55.3 -85.3 NaN NaN NaN \n", - "150 12.1 29601 -55.5 -85.5 NaN NaN NaN \n", - "151 12.0 29654 -55.7 -85.7 NaN NaN NaN \n", - "\n", - " v_wind station station_number time latitude \\\n", - "0 -1.720729 GJT 72476 2023-11-20 12:00:00 39.11 \n", - "1 -2.257426 GJT 72476 2023-11-20 12:00:00 39.11 \n", - "2 -3.800832 GJT 72476 2023-11-20 12:00:00 39.11 \n", - "3 -14.772116 GJT 72476 2023-11-20 12:00:00 39.11 \n", - "4 -12.802501 GJT 72476 2023-11-20 12:00:00 39.11 \n", - ".. ... ... ... ... ... \n", - "147 -10.230855 GJT 72476 2023-11-20 12:00:00 39.11 \n", - "148 -5.176381 GJT 72476 2023-11-20 12:00:00 39.11 \n", - "149 NaN GJT 72476 2023-11-20 12:00:00 39.11 \n", - "150 NaN GJT 72476 2023-11-20 12:00:00 39.11 \n", - "151 NaN GJT 72476 2023-11-20 12:00:00 39.11 \n", - "\n", - " longitude elevation pw \n", - "0 -108.53 1475.0 9.12 \n", - "1 -108.53 1475.0 9.12 \n", - "2 -108.53 1475.0 9.12 \n", - "3 -108.53 1475.0 9.12 \n", - "4 -108.53 1475.0 9.12 \n", - ".. ... ... ... \n", - "147 -108.53 1475.0 9.12 \n", - "148 -108.53 1475.0 9.12 \n", - "149 -108.53 1475.0 9.12 \n", - "150 -108.53 1475.0 9.12 \n", - "151 -108.53 1475.0 9.12 \n", - "\n", - "[152 rows x 15 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = pd.read_csv('data/gjt_sounding.csv')\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "p = df['pressure'].values\n", - "T = df['temperature'].values\n", - "Td = df['dewpoint'].values\n", - "u = df['u_wind'].values\n", - "v = df['v_wind'].values" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Elements of a Skew-T Plot\n", - "Let's start out by talking about the structural elements of a Skew-T plot.\n", - "\n", - "1. **Temperature Lines**: These lines are drawn at an angle up from the x-axis and are where the name \"Skew-T\" comes from.\n", - "2. **Pressure Lines**: These lines are horizontal from the y-axis, where pressure is plotted at a logarithmic scale.\n", - "3. **Dry Adiabats**:\n", - "4. **Moist Adiabats**:\n", - "5. **Mixing Ratio Lines**:\n", - "\n", - "On all those structural elements, Skew-T plots have two lines plotted on them, **air temperature** and **dew point**. In this notebook, we'll be plotting the air temperature in red and the dew point in blue.\n", - "\n", - "Additionally, Skew-T plots have **wind barbs**. These describe the wind speed and direction at different pressure levels and are plotted on the right side of the diagram.\n", - "\n", - ":::{tip}\n", - "For a more detailed description and a cool interactive diagram, visit [NOAA's Skew-T page](https://www.noaa.gov/jetstream/upperair/skew-t-log-p-diagrams).\n", - ":::" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Making a Skew-T plot in Python (with MetPy!)\n", - "So, all of that might seem a little abstract without a visual. We're going to use MetPy's SkewT module to make an actual Skew-T plot with the sounding data we downloaded earlier.\n", - "\n", - "From the [MetPy documentation](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html):\n", - "> \"This class simplifies the process of creating Skew-T log-P plots in using matplotlib. It handles requesting the appropriate skewed projection, and provides simplified wrappers to make it easy to plot data, add wind barbs, and add other lines to the plots (e.g. dry adiabats)\"\n", - "\n", - "### Just the basics\n", - "To start with, let's create a very minimal Skew-T plot with just the pressure and temperature lines under the sounding data." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# make figure and `SkewT` object\n", - "fig = plt.figure(figsize=(9, 9))\n", - "skewt = SkewT(fig=fig)\n", - "\n", - "# plot sounding data\n", - "skewt.plot(p, T, 'r') # air temperature\n", - "skewt.plot(p, Td, 'b') # dew point\n", - "skewt.plot_barbs(p, u, v) # wind barbs" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "Then, we're going to plot the air temperature and dew point and add the wind barbs.\n", - "\n", - "Let's talk break that down a bit.\n", - " \n", - "```python showLineNumbers highlightLine=2-3\n", - "# make figure and `SkewT` object\n", - "fig = plt.figure(figsize=(9, 9))\n", - "skewt = SkewT(fig=fig)\n", - "\n", - "# plot sounding data\n", - "skewt.plot(p, T, 'r') # air temperature\n", - "skewt.plot(p, Td, 'b') # dew point\n", - "skewt.plot_barbs(p, u, v) # wind barbs\n", - "```\n", - "First, we'll make a new figure and use that figure to make a new skew-T plot. If you don't provide a figure to `SkewT`, one will be created for you, but it's useful to make the default figure size a bit larger for this tutorial.\n", - "\n", - "First, we're " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['__class__',\n", - " '__delattr__',\n", - " '__dict__',\n", - " '__dir__',\n", - " '__doc__',\n", - " '__eq__',\n", - " '__format__',\n", - " '__ge__',\n", - " '__getattribute__',\n", - " '__getstate__',\n", - " '__gt__',\n", - " '__hash__',\n", - " '__init__',\n", - " '__init_subclass__',\n", - " '__le__',\n", - " '__lt__',\n", - " '__module__',\n", - " '__ne__',\n", - " '__new__',\n", - " '__reduce__',\n", - " '__reduce_ex__',\n", - " '__repr__',\n", - " '__setattr__',\n", - " '__sizeof__',\n", - " '__str__',\n", - " '__subclasshook__',\n", - " '__weakref__',\n", - " '_fig',\n", - " 'ax',\n", - " 'dry_adiabats',\n", - " 'mixing_lines',\n", - " 'moist_adiabats',\n", - " 'plot',\n", - " 'plot_barbs',\n", - " 'plot_dry_adiabats',\n", - " 'plot_mixing_lines',\n", - " 'plot_moist_adiabats',\n", - " 'shade_area',\n", - " 'shade_cape',\n", - " 'shade_cin']" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dir(skewt)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "### What's next?\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resources and references\n", - "\n", - "- [Wyoming Upper Air](https://weather.uwyo.edu/upperair/)\n", - "- [Siphon](https://unidata.github.io/siphon/latest/examples/upperair/Wyoming_Request.html)\n", - "- [MetPy's SkewT documentation](https://unidata.github.io/MetPy/latest/api/generated/metpy.plots.SkewT.html)\n", - "- [NOAA's JetStream](https://www.noaa.gov/jetstream) [Skew-T Plot](https://www.noaa.gov/jetstream/upperair/skew-t-plots) Section" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "advanced-viz-cookbook", - "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": "3.11.6" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/_preview/22/_sources/notebooks/spagetti.ipynb b/_preview/22/_sources/notebooks/spagetti.ipynb deleted file mode 100644 index 57909ae..0000000 --- a/_preview/22/_sources/notebooks/spagetti.ipynb +++ /dev/null @@ -1,857 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Spagetti Plots" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "Spagetti Plots are a tool typically used to visualize movement. Essentially they are many line plots displayed on the same axes. By drawing the same path at different times or from different forecasts, we can see the patterns and chaos associated with the plotted variable.\n", - "\n", - "1. Spagetti Hurricane Plot\n", - "1. Spagetti Contour Plot\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n", - "| [Cartopy](https://foundations.projectpythia.org/core/cartopy.html) | Necessary | |\n", - "\n", - "- **Time to learn**: 50 minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import xarray as xr\n", - "import datetime\n", - "\n", - "import matplotlib as mpl\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.ticker as mticker\n", - "import matplotlib.pylab as pl\n", - "\n", - "import cartopy.crs as ccrs\n", - "import cartopy.feature as cfeature\n", - "\n", - "import geocat.viz as gv\n", - "import geocat.datafiles as gdf\n", - "\n", - "import tropycal.tracks as tracks\n", - "\n", - "import warnings\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Spagetti Hurricane Plot\n", - "\n", - "Visualizing the predicted path of an incoming hurricane is both complicated and important. There are many plots that meteorologists are trained to read, but when shared with the public can be confusing or alarming. There are strengths and weaknesses to each hurricane visualization approach. The cone of uncertainty, for example, is often misinterpreted to suggest the hurricane growth in time rather than widening of path possibilities. Spagetti plots on the other hand, clearly show hurricane paths but show them as equal to each other.\n", - "\n", - "In this example we will plot some forecasted paths from the 2012 North-Atlantic storm Hurricane Sandy. Each forecast is from the Global Ensemble Forecast System (GEFS) provided by the National Centers for Environmental Prediction at NOAA.\n", - "\n", - "We'll use the package [`tropycal`](tropycal.github.io/tropycal/) to easily access HURDAT2 and IBTrACS reanalysis data and operational National Hurricane Center (NHC) Best Track data. `tropycal` has a lot of great features for real time hurricane visualization, but since this Cookbook is comparatively static we're using a past hurricane and only using this package to access the data. Our plotting will be done with `matplotlib` and `cartopy`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Read in Data\n", - "\n", - "First, to grab our hurricane data from tropycal we need to specify a basin:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "basin = tracks.TrackDataset(basin='north_atlantic')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Find your storm by name and year:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "storm = basin.get_storm(('sandy',2012))\n", - "\n", - "sandy_ds = storm.to_xarray()\n", - "sandy_ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And we can grab any of a number of forecasts:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "forecasts = storm.get_operational_forecasts()\n", - "print(forecasts.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Each key represents a forecast model, we'll select the GFS AP01 forecast which has many initializations. These initializations are named by time in YYYYMMDDHH format:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "forecasts_AP01 = forecasts['AP01']\n", - "print(forecasts_AP01.keys())" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spagetti Plot of One Esemble Member\n", - "\n", - "Looking at GFS Ensemble Member Forecast AP01, we can make a spagetti plot of each of these initializations. The crux of the plot is that we need a for loop through each initialization:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set up Cartopy Projection with land features\n", - "ax = plt.axes(projection=ccrs.PlateCarree())\n", - "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n", - "\n", - "# Add Gridlines to right and bottom\n", - "gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n", - " linewidth=.25, color='gray', alpha=0.5, linestyle='--')\n", - "gl.xlabels_top = False\n", - "gl.ylabels_left = False\n", - "gl.xlabel_style = {'size': 8,}\n", - "gl.ylabel_style = {'size': 8,}\n", - "\n", - "# Spagetti Plot of AP01 forecasts\n", - "forecasts_AP01 = forecasts['AP01']\n", - "for i in forecasts_AP01:\n", - " # We're naming this line even though it is over-written each loop,\n", - " # so that we can reference the last line in the legend\n", - " # (as they all share the same formatting)\n", - " forecast_path = plt.plot(forecasts_AP01[i]['lon'],\n", - " forecasts_AP01[i]['lat'],\n", - " color='cornflowerblue',\n", - " linewidth=0.5)\n", - "\n", - "# Plot the real storm path in a thicker black line\n", - "true_path = plt.plot(sandy_ds.lon,\n", - " sandy_ds.lat,\n", - " color='k',\n", - " linewidth=1) # Make it thicker than the ensemble paths\n", - "\n", - "# Add a legend with only one forecast_path and the true_path\n", - "plt.legend([true_path[0], forecast_path[0]], ['True Path', 'GFS AP01 Forecasts'])\n", - "\n", - "plt.title('Hurricane Sandy (2012)');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This plot is a great example of a spagetti plot, but is it super useful? Is it confusing? Each line looks like it carries the same weight, when some of these possible paths are from hours before Sandy hit the NorthEast and others are from days before.\n", - "\n", - "Maybe it is better to show the user some indication of how the forecast for this ensemble converged on the true path with later and later initialization times." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spagetti Plot of One Esemble Member with Temporal Colormapping" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set up Cartopy Projection with land features\n", - "ax = plt.axes(projection=ccrs.PlateCarree())\n", - "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n", - "\n", - "# Add Gridlines to right and bottom\n", - "gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n", - " linewidth=.25, color='gray', alpha=0.5, linestyle='--')\n", - "gl.xlabels_top = False\n", - "gl.ylabels_left = False\n", - "gl.xlabel_style = {'size': 8,}\n", - "gl.ylabel_style = {'size': 8,}\n", - "\n", - "# Spagetti Plot of AP01 forecasts\n", - "forecasts_AP01 = forecasts['AP01']\n", - "\n", - "# Get time information from initialization name\n", - "format = '%Y%m%d%H'\n", - "times = [datetime.datetime.strptime(i, format) for i in list(forecasts_AP01.keys())]\n", - "normalized_times = [(i - times[0]) / (times[-1] - times[0]) for i in times]\n", - "\n", - "# Create a color list for forecast iteration\n", - "cmap = mpl.colors.ListedColormap(plt.cm.autumn_r(normalized_times))\n", - "\n", - "c = 0\n", - "for i in forecasts_AP01:\n", - " plt.plot(forecasts_AP01[i]['lon'],\n", - " forecasts_AP01[i]['lat'],\n", - " color=cmap(c),\n", - " linewidth=0.5)\n", - " c += 1\n", - "\n", - "# Plot the real storm path\n", - "true_path = plt.plot(sandy_ds.lon,\n", - " sandy_ds.lat,\n", - " color='red', # Selecting a color matching one of the cmap extremes\n", - " linewidth=1,\n", - " label='True Path') # The easiest way to add a plot to the legend is with the label kwarg\n", - "\n", - "# Add a legend with only one the true_path\n", - "# Forecasted paths will be shown in a colorbar\n", - "plt.legend()\n", - "\n", - "plt.title('Hurricane Sandy')\n", - "\n", - "# Add colorbar\n", - "cbar = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap), ax=ax, orientation='horizontal', shrink=0.8, pad=0.075)\n", - "cbar.set_label('GFS AP01 Forecasts', labelpad=6)\n", - "\n", - "# Set tick locations and labels for every 4th tick\n", - "# i.e. once a day (a new initialiation every 6 hours)\n", - "tick_indices = range(0, len(times), 4)\n", - "cbar.set_ticks([normalized_times[i] for i in tick_indices])\n", - "cbar.set_ticklabels([times[i].strftime('%d') for i in tick_indices], fontsize=8)\n", - "cbar.ax.text(1.02, 0.5, 'OCT-2012', va='top', ha='left', transform=cbar.ax.transAxes);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can see that as the storm progressed, the AP01 GFS Forecast Ensemble Member converges on Sandy's true path as the storm progresses through October, 2012." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, we may want to plot the possible hurricane paths from multiple GFS Forecast Ensemble members from the same iteration timestamp as a spagetti plot." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Spagetti Plot of All Esemble Members at One Point in Time\n", - "\n", - "First, we need to grab all of the relevant forecast keys to GFS models (the ones that are titled `AP##` from 0 to 20):" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# List of valid AP## keys from 0 to 20\n", - "GFS_keys = ['AP' + str(i).zfill(2) for i in range(1, 21)]\n", - "\n", - "# Arbitrarily selected midnight on October 27, 2012 to plot all forecasts at\n", - "time = '2012102700'\n", - "\n", - "# Set up Cartopy Projection with land features\n", - "ax = plt.axes(projection=ccrs.PlateCarree())\n", - "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n", - "\n", - "# Add Gridlines to right and bottom\n", - "gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,\n", - " linewidth=.25, color='gray', alpha=0.5, linestyle='--')\n", - "gl.xlabels_top = False\n", - "gl.ylabels_left = False\n", - "gl.xlabel_style = {'size': 8,}\n", - "gl.ylabel_style = {'size': 8,}\n", - "\n", - "# Spagetti Plot of forecasts\n", - "for i in range(20):\n", - " ap = forecasts[GFS_keys[i]]\n", - " forecast_path = plt.plot(ap[time]['lon'],\n", - " ap[time]['lat'],\n", - " color='cornflowerblue',\n", - " linewidth=0.5)\n", - "\n", - "# Plot the real storm path in a thicker black line\n", - "true_path = plt.plot(sandy_ds.lon, sandy_ds.lat, color='k', linewidth=1)\n", - "\n", - "# Add a legend with only one forecast_path and the true_path\n", - "plt.legend([true_path[0], forecast_path[0]],\n", - " ['True Path', 'AP01 - AP20'],\n", - " loc='lower right')\n", - "\n", - "plt.title('Hurricane Sandy - GFS Forecasts from Oct-27-2012');" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Hurricane Sandy hit the NorthEast on October 29, 2012. From this spagetti plot we can see that by the 27th most ensemble members of the GFS forecast predicted a similar behavior for the storm.\n", - "\n", - "There is more analysis that could be done on hurriane trajectories. We have covered some plotting customization that might be useful for your analysis and data visualization." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Spagetti Contour Plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this example we will read in the geopotential height datafile `HGT500_MON_1958-1997.nc` from using geocat-datafiles. Then we will look at different timesteps of the `HGT` geopotential height variable at the 5500 gpm level, plotting this contour's locations through time. This example is adapted from [GeoCAT](https://geocat.ucar.edu/)'s [NCL_conOncon_5](https://geocat-examples.readthedocs.io/en/latest/gallery/Contours/NCL_conOncon_5.html) script." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Read in data:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "ds = xr.open_dataset(gdf.get(\"netcdf_files/HGT500_MON_1958-1997.nc\"),\n", - " decode_times=False)\n", - "\n", - "ds" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Initial Spagetti Plot on North Polar Stereographic Projection" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set up Cartopy Map Projection\n", - "fig = plt.figure(figsize=(8, 8))\n", - "ax = plt.axes(projection=ccrs.NorthPolarStereo())\n", - "\n", - "gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)\n", - "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n", - "\n", - "# Set draw_labels to False so that we can manually manipulate it\n", - "gl = ax.gridlines(ccrs.PlateCarree(),\n", - " draw_labels=False,\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - " color='darkgray',\n", - " zorder=2)\n", - "\n", - "# Iterate through the 19 timesteps, plotting the data\n", - "n = 19\n", - "for x in range(n):\n", - "\n", - " # Get a slice of data at the 12*x timestep\n", - " p = ds.HGT.isel(time=12*x)\n", - "\n", - " # Use geocat-viz utility function to handle the no-shown-data artifact\n", - " # of 0 and 360-degree longitudes\n", - " slon = gv.xr_add_cyclic_longitudes(p, \"lon\")\n", - "\n", - " # Plot contour data at pressure level 5500 for the 12*x timestep\n", - " p = slon.plot.contour(ax=ax,\n", - " transform=ccrs.PlateCarree(),\n", - " linewidths=0.5,\n", - " levels=[5500],\n", - " colors='blue',\n", - " add_labels=False)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Adding Directional Labels to Polar Stereographic Projection\n", - "\n", - "Adding labels to a map projection that aren't lat/lon coordinates is less than intuitive. In this example we manually add labels and select their locations so that you can see NESW labels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate a figure\n", - "fig = plt.figure(figsize=(8, 8))\n", - "\n", - "# Create an axis with a polar stereographic projection\n", - "ax = plt.axes(projection=ccrs.NorthPolarStereo())\n", - "\n", - "# Add land feature to map\n", - "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n", - "\n", - "# Set map boundary to include latitudes between 0 and 40 and longitudes\n", - "# between -180 and 180 only\n", - "gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)\n", - "\n", - "# Set draw_labels to False so that you can manually manipulate it later\n", - "gl = ax.gridlines(ccrs.PlateCarree(),\n", - " draw_labels=False,\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - " color='darkgray',\n", - " zorder=2)\n", - "\n", - "# Manipulate latitude and longitude gridline numbers and spacing\n", - "gl.ylocator = mticker.FixedLocator(np.arange(0, 90, 15))\n", - "gl.xlocator = mticker.FixedLocator(np.arange(-180, 180, 30))\n", - "\n", - "# Manipulate longitude labels (0, 30 E, 60 E, ..., 30 W, etc.)\n", - "ticks = np.arange(0, 210, 30)\n", - "etick = ['0'] + [\n", - " r'%dE' % tick for tick in ticks if (tick != 0) & (tick != 180)\n", - "] + ['180']\n", - "wtick = [r'%dW' % tick for tick in ticks if (tick != 0) & (tick != 180)]\n", - "labels = etick + wtick\n", - "xticks = np.arange(0, 360, 30)\n", - "yticks = np.full_like(xticks, -5) # Latitude where the labels will be drawn\n", - "for xtick, ytick, label in zip(xticks, yticks, labels):\n", - " if label == '180':\n", - " ax.text(xtick,\n", - " ytick,\n", - " label,\n", - " fontsize=12,\n", - " horizontalalignment='center',\n", - " verticalalignment='top',\n", - " transform=ccrs.Geodetic())\n", - " elif label == '0':\n", - " ax.text(xtick,\n", - " ytick,\n", - " label,\n", - " fontsize=12,\n", - " horizontalalignment='center',\n", - " verticalalignment='bottom',\n", - " transform=ccrs.Geodetic())\n", - " else:\n", - " ax.text(xtick,\n", - " ytick,\n", - " label,\n", - " fontsize=12,\n", - " horizontalalignment='center',\n", - " verticalalignment='center',\n", - " transform=ccrs.Geodetic())\n", - "\n", - "# Iterate through 18 different timesteps\n", - "for x in range(19):\n", - "\n", - " # Get a slice of data at the 12*x+1 timestep\n", - " p = ds.HGT.isel(time=12 * x + 1)\n", - "\n", - " # Use geocat-viz utility function to handle the no-shown-data artifact\n", - " # of 0 and 360-degree longitudes\n", - " slon = gv.xr_add_cyclic_longitudes(p, \"lon\")\n", - "\n", - " # Plot contour data at pressure level 5500 for the 12*x+1 timestep\n", - " p = slon.plot.contour(ax=ax,\n", - " transform=ccrs.PlateCarree(),\n", - " linewidths=0.5,\n", - " levels=[5500],\n", - " colors='blue',\n", - " add_labels=False)\n", - "\n", - "# Use geocat.viz.util convenience function to add titles\n", - "gv.set_titles_and_labels(ax,\n", - " maintitle=r\"$\\bf{Spaghetti}$\" + \" \" + r\"$\\bf{Plot}$\",\n", - " lefttitle=slon.long_name,\n", - " righttitle='5500 '+slon.units)\n", - "\n", - "# Make tight layout\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now in this example, there isn't necessarily a temporal progression of geopotential height, but to be sure let's add a colormap component to each of our loops. \n", - "\n", - "This is also useful because for your data visualization application there might be, and the commands are slightly different for a contour plot as for a line plot in the above example." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Contour Spagetti Plot Temporal Colorbar Manipulation\n", - "\n", - "Let's update add a discrete colorbar that has yearly ticklabels. One challenge addressed in this example is setting the ticklabels to be in the center of each discrete color box." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Set up Cartopy Map Projection\n", - "fig = plt.figure(figsize=(8, 8))\n", - "ax = plt.axes(projection=ccrs.NorthPolarStereo())\n", - "\n", - "gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)\n", - "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n", - "\n", - "# Set draw_labels to False so that we can manually manipulate it\n", - "gl = ax.gridlines(ccrs.PlateCarree(),\n", - " draw_labels=False,\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - " color='darkgray',\n", - " zorder=2)\n", - "\n", - "# Manipulate latitude and longitude gridline numbers and spacing\n", - "gl.ylocator = mticker.FixedLocator(np.arange(0, 90, 15))\n", - "gl.xlocator = mticker.FixedLocator(np.arange(-180, 180, 30))\n", - "\n", - "# Manipulate longitude labels (0, 30 E, 60 E, ..., 30 W, etc.)\n", - "ticks = np.arange(0, 210, 30)\n", - "etick = ['0'] + [\n", - " r'%dE' % tick for tick in ticks if (tick != 0) & (tick != 180)\n", - "] + ['180']\n", - "wtick = [r'%dW' % tick for tick in ticks if (tick != 0) & (tick != 180)]\n", - "labels = etick + wtick\n", - "xticks = np.arange(0, 360, 30)\n", - "yticks = np.full_like(xticks, -5) # Latitude where the labels will be drawn\n", - "for xtick, ytick, label in zip(xticks, yticks, labels):\n", - " if label == '180':\n", - " ax.text(xtick,\n", - " ytick,\n", - " label,\n", - " fontsize=12,\n", - " horizontalalignment='center',\n", - " verticalalignment='top',\n", - " transform=ccrs.Geodetic())\n", - " elif label == '0':\n", - " ax.text(xtick,\n", - " ytick,\n", - " label,\n", - " fontsize=12,\n", - " horizontalalignment='center',\n", - " verticalalignment='bottom',\n", - " transform=ccrs.Geodetic())\n", - " else:\n", - " ax.text(xtick,\n", - " ytick,\n", - " label,\n", - " fontsize=12,\n", - " horizontalalignment='center',\n", - " verticalalignment='center',\n", - " transform=ccrs.Geodetic())\n", - "\n", - "# Create a color list for each of the 19 contours\n", - "n = 19\n", - "cmap = plt.get_cmap('winter_r', n) # the `, n` makes the colormap display discretized\n", - "bounds = np.linspace(0, 1, n)\n", - "\n", - "# Iterate through the timesteps\n", - "for x in range(n):\n", - "\n", - " # Get a slice of data at the 12*x timestep\n", - " p = ds.HGT.isel(time=12*x)\n", - "\n", - " # Handle wrapping artifacts\n", - " slon = gv.xr_add_cyclic_longitudes(p, \"lon\")\n", - "\n", - " # Plot contour data at pressure level 5500 for the 12*x timestep\n", - " p = slon.plot.contour(ax=ax,\n", - " transform=ccrs.PlateCarree(),\n", - " linewidths=0.5,\n", - " levels=[5500],\n", - " colors=[cmap(bounds)[x]], # set colors to use our new cmap\n", - " add_labels=False)\n", - "\n", - "# Add a colorbar\n", - "# The default time unit is in months since 1958, years is more intuitive\n", - "year_0 = 1958\n", - "year_n = (ds.time.isel(time=12*n) / 12).astype(int) + year_0\n", - "\n", - "norm = plt.Normalize(vmin=year_0, vmax=year_n)\n", - "cbar = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap, norm=norm),\n", - " ax=ax,\n", - " orientation='vertical',\n", - " shrink=0.8, # Shrink to the approximate size of the map\n", - " pad = 0.1) # Pad so colorbar doesn't overlap with directional label\n", - "\n", - "cbar.set_ticks(np.arange(year_0+0.5, year_n)) # Set tick locations to be at color midpoints\n", - "cbar.set_ticklabels(np.arange(year_0, year_n)) # Set tick labels to be years (despite their location value being year + 0.5)\n", - "cbar.set_label('Time (years)')\n", - "\n", - "# Use geocat.viz.util convenience function to add titles\n", - "gv.set_titles_and_labels(ax,\n", - " maintitle=r\"$\\bf{Spaghetti}$\" + \" \" + r\"$\\bf{Plot}$\",\n", - " lefttitle=slon.long_name,\n", - " righttitle='5500 '+slon.units)\n", - "\n", - "# Make tight layout\n", - "plt.tight_layout();" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Contour Spagetti Plot with Hand-Picked Colors\n", - "\n", - "If you want your plot to be visually appealing it might be worth selecting different colors for each contour plot in the for-loop, however these do not have to be sequentially ordered or time-aware. It is actually simplest to hand-pick colors for each loop. In this iteration of the plot we hand pick colors and plot the first time step on its own to demonstrate plotting one loop unlike the others." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Generate a figure\n", - "fig = plt.figure(figsize=(8, 8))\n", - "\n", - "# Create an axis with a polar stereographic projection\n", - "ax = plt.axes(projection=ccrs.NorthPolarStereo())\n", - "\n", - "# Add land feature to map\n", - "ax.add_feature(cfeature.LAND, facecolor='lightgray')\n", - "\n", - "# Set map boundary to include latitudes between 0 and 40 and longitudes\n", - "# between -180 and 180 only\n", - "gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)\n", - "\n", - "# Set draw_labels to False so that you can manually manipulate it later\n", - "gl = ax.gridlines(ccrs.PlateCarree(),\n", - " draw_labels=False,\n", - " linestyle=\"--\",\n", - " linewidth=1,\n", - " color='darkgray',\n", - " zorder=2)\n", - "\n", - "# Manipulate latitude and longitude gridline numbers and spacing\n", - "gl.ylocator = mticker.FixedLocator(np.arange(0, 90, 15))\n", - "gl.xlocator = mticker.FixedLocator(np.arange(-180, 180, 30))\n", - "\n", - "# Manipulate longitude labels (0, 30 E, 60 E, ..., 30 W, etc.)\n", - "ticks = np.arange(0, 210, 30)\n", - "etick = ['0'] + [\n", - " r'%dE' % tick for tick in ticks if (tick != 0) & (tick != 180)\n", - "] + ['180']\n", - "wtick = [r'%dW' % tick for tick in ticks if (tick != 0) & (tick != 180)]\n", - "labels = etick + wtick\n", - "xticks = np.arange(0, 360, 30)\n", - "yticks = np.full_like(xticks, -5) # Latitude where the labels will be drawn\n", - "for xtick, ytick, label in zip(xticks, yticks, labels):\n", - " if label == '180':\n", - " ax.text(xtick,\n", - " ytick,\n", - " label,\n", - " fontsize=12,\n", - " horizontalalignment='center',\n", - " verticalalignment='top',\n", - " transform=ccrs.Geodetic())\n", - " elif label == '0':\n", - " ax.text(xtick,\n", - " ytick,\n", - " label,\n", - " fontsize=12,\n", - " horizontalalignment='center',\n", - " verticalalignment='bottom',\n", - " transform=ccrs.Geodetic())\n", - " else:\n", - " ax.text(xtick,\n", - " ytick,\n", - " label,\n", - " fontsize=12,\n", - " horizontalalignment='center',\n", - " verticalalignment='center',\n", - " transform=ccrs.Geodetic())\n", - "\n", - "# Get slice of data at the 0th timestep - plot this contour line separately\n", - "# because it will be thicker than the other contour lines\n", - "p = ds.HGT.isel(time=0)\n", - "\n", - "# Use geocat-viz utility function to handle the no-shown-data\n", - "# artifact of 0 and 360-degree longitudes\n", - "slon = gv.xr_add_cyclic_longitudes(p, \"lon\")\n", - "\n", - "# Plot contour data at pressure level 5500 at the first timestep\n", - "p = slon.plot.contour(ax=ax,\n", - " transform=ccrs.PlateCarree(),\n", - " linewidths=1.5,\n", - " levels=[5500],\n", - " colors='black',\n", - " add_labels=False)\n", - "\n", - "# Create a color list for each of the next 18 contours\n", - "colorlist = [\n", - " \"crimson\", \"green\", \"blue\", \"yellow\", \"cyan\", \"hotpink\", \"crimson\",\n", - " \"skyblue\", \"navy\", \"lightyellow\", \"mediumorchid\", \"orange\", \"slateblue\",\n", - " \"palegreen\", \"magenta\", \"springgreen\", \"pink\", \"forestgreen\", \"violet\"\n", - "]\n", - "\n", - "# Iterate through 18 different timesteps\n", - "for x in range(18):\n", - "\n", - " # Get a slice of data at the 12*x+1 timestep\n", - " p = ds.HGT.isel(time=12 * x + 1)\n", - "\n", - " # Use geocat-viz utility function to handle the no-shown-data artifact\n", - " # of 0 and 360-degree longitudes\n", - " slon = gv.xr_add_cyclic_longitudes(p, \"lon\")\n", - "\n", - " # Plot contour data at pressure level 5500 for the 12*x+1 timestep\n", - " p = slon.plot.contour(ax=ax,\n", - " transform=ccrs.PlateCarree(),\n", - " linewidths=0.5,\n", - " levels=[5500],\n", - " colors=colorlist[x],\n", - " add_labels=False)\n", - "\n", - "# Use geocat.viz.util convenience function to add titles\n", - "gv.set_titles_and_labels(ax,\n", - " maintitle=r\"$\\bf{Spaghetti}$\" + \" \" + r\"$\\bf{Plot}$\",\n", - " lefttitle=slon.long_name,\n", - " righttitle='5500 '+slon.units)\n", - "\n", - "# Make tight layout\n", - "plt.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "Spagetti Plots are many lines drawn on the same figure. They have pros and cons. They are visually stunning but can be confusing, so it is important to make sure your data visualization conveys accurate information either by manipulating color or linewidth. Since the manipulation of spagetti plots have their own considerations, this chapter shows several design choices that you can use to jumpstart your visualization needs.\n", - "\n", - "\n", - "### What's next?\n", - "\n", - "Next up let's discuss elements of [Visualization of Unstructured Grids](uxarray)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resources and references\n", - "\n", - "- [Tropycal documentation](https://tropycal.github.io/tropycal/)\n", - "- [GeoCat-examples Visualization Gallery](https://geocat-examples.readthedocs.io/en/latest/)\n", - "- [GeoCAT-Viz documentation](https://geocat-viz.readthedocs.io/en/latest/)\n" - ] - } - ], - "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" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_preview/22/_sources/notebooks/taylor-diagrams.ipynb b/_preview/22/_sources/notebooks/taylor-diagrams.ipynb deleted file mode 100644 index f73c473..0000000 --- a/_preview/22/_sources/notebooks/taylor-diagrams.ipynb +++ /dev/null @@ -1,320 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Taylor Diagrams" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Overview\n", - "\n", - "Taylor diagrams are a \"concise statistical summary of how well patterns match each other in terms of their correlation, their root-mean-square difference and the ratio of their variances\". Taylor diagrams plot the weighted centered pattern correlations, the ratios of the normalized root-mean-squared differences between the test and reference data sets, and optionally a bias statistic. This notebook explores how to create and customize Taylor diagrams using `geocat-viz`.\n", - "\n", - "1. Creating a Simple Taylor Diagram\n", - "1. Displaying Distinct Datasets\n", - "1. Finishing Touches\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Prerequisites\n", - "\n", - "| Concepts | Importance | Notes |\n", - "| --- | --- | --- |\n", - "| [Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n", - "\n", - "- **Time to learn**: 50 minutes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false, - "jupyter": { - "outputs_hidden": false - } - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "import geocat.viz as gv" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\"Generally, the plotted values are derived from climatological monthly, seasonal or annual means. Because the different variables (eg: precipitation, temperature) may have widely varying numerical values, the results are normalized by the reference variables. The ratio of the normalized variances indicates the relative amplitude of the model and observed variations.\" - from NCL" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Creating a Simple Taylor Diagram" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create figure and Taylor Diagram instance\n", - "fig = plt.figure(figsize=(6, 6))\n", - "taylor = gv.TaylorDiagram(fig=fig, label='REF')\n", - "\n", - "# Draw diagonal dashed lines from origin to correlation values\n", - "# Also enforces proper X-Y ratio\n", - "taylor.add_xgrid(np.array([0.6, 0.9]))\n", - "\n", - "# Add a model dataset of one point\n", - "taylor.add_model_set(stddev=[.6], corrcoef=[.24]);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Displaying Distinct Datasets\n", - "\n", - "When working with data, you'll want to make two datasets distinct by providing different kwargs for how to draw them.\n", - "\n", - "First let's creat two sets of dummy data:" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [], - "source": [ - "# Case A\n", - "a_std = [1.230, 0.988, 1.092, 1.172, 1.064, 0.966, 1.079, 0.781] # standard deviation\n", - "a_cc = [0.958, 0.973, 0.740, 0.743, 0.922, 0.982, 0.952, 0.433] # correlation coefficient\n", - "\n", - "# Case B\n", - "b_std = [1.129, 0.996, 1.016, 1.134, 1.023, 0.962, 1.048, 0.852] # standard deviation\n", - "b_cc = [0.963, 0.975, 0.801, 0.814, 0.946, 0.984, 0.968, 0.647] # correlation coefficient" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And let's plot it:" - ] - }, - { - "cell_type": "code", - "execution_count": 51, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create figure and Taylor Diagram instance\n", - "fig = plt.figure(figsize=(12, 12))\n", - "taylor = gv.TaylorDiagram(fig=fig, label='REF')\n", - "ax = plt.gca()\n", - "\n", - "# Draw diagonal dashed lines from origin to correlation values\n", - "# Also enforces proper X-Y ratio\n", - "taylor.add_xgrid(np.array([0.6, 0.9]))\n", - "\n", - "# Add model sets for p and t datasets\n", - "taylor.add_model_set(\n", - " a_std,\n", - " a_cc,\n", - " fontsize=20,\n", - " xytext=(-5, 10), # marker label location, in pixels\n", - " color='red',\n", - " marker='o',\n", - " facecolors='none',\n", - " s=100) # marker size\n", - "taylor.add_model_set(\n", - " b_std,\n", - " b_cc,\n", - " fontsize=20,\n", - " xytext=(-5, 10), # marker label location, in pixels\n", - " color='blue',\n", - " marker='D',\n", - " facecolors='none',\n", - " s=100)\n", - "\n", - "# Add figure title\n", - "plt.title(\"Example\", size=26, pad=45);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Finishing touches" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
    " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Create figure and Taylor Diagram instance\n", - "fig = plt.figure(figsize=(12, 12))\n", - "taylor = gv.TaylorDiagram(fig=fig, label='REF')\n", - "ax = plt.gca()\n", - "\n", - "# Draw diagonal dashed lines from origin to correlation values\n", - "# Also enforces proper X-Y ratio\n", - "taylor.add_xgrid(np.array([0.6, 0.9]))\n", - "\n", - "# Add model sets for p and t datasets\n", - "taylor.add_model_set(\n", - " a_std,\n", - " a_cc,\n", - " fontsize=20,\n", - " xytext=(-5, 10), # marker label location, in pixels\n", - " color='red',\n", - " marker='o',\n", - " facecolors='none',\n", - " label='Case A',\n", - " s=100) # marker size\n", - "taylor.add_model_set(\n", - " b_std,\n", - " b_cc,\n", - " fontsize=20,\n", - " xytext=(-5, 10), # marker label location, in pixels\n", - " color='blue',\n", - " marker='D',\n", - " facecolors='none',\n", - " label='Case B',\n", - " s=100)\n", - "\n", - "# Add Add constant centered RMS difference contours.\n", - "taylor.add_contours(levels=np.arange(0, 1.1, 0.25),\n", - " colors='lightgrey',\n", - " linewidths=0.5)\n", - "\n", - "# Add more standard deviation grid lines\n", - "taylor.add_ygrid(np.array([0.5, 1.5]), color='grey')\n", - "\n", - "# Add figure title\n", - "plt.title(\"Example\", size=26, pad=45);\n", - "\n", - "# Add model name\n", - "namearr = ['SLP', 'Tsfc', 'Prc', 'Prc 30S-30N', 'LW', 'SW', 'U300', 'Guess']\n", - "taylor.add_model_name(namearr, fontsize=16)\n", - "\n", - "# Add figure legend\n", - "taylor.add_legend(fontsize=16);" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "---" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Summary\n", - "\n", - "### What's next?\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Resources and references\n", - "\n", - "- [Karl E. Taylor - \"Summarizing multiple aspects of model performance in a single diagram\", AGU 2001](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2000JD900719)\n", - "- [Plotting with GeoCAT Tutorial](https://github.com/anissa111/plotting-with-geocat-tutorial/blob/main/notebooks/02-geocat-viz.ipyn)\n", - "- [NCL Graphics: Taylor Diagrams](https://www.ncl.ucar.edu/Applications/taylor.shtml)" - ] - } - ], - "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" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/_preview/22/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css 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manual; -} - -/* -- hlist styles ---------------------------------------------------------- */ - -table.hlist { - margin: 1em 0; -} - -table.hlist td { - vertical-align: top; -} - -/* -- object description styles --------------------------------------------- */ - -.sig { - font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace; -} - -.sig-name, code.descname { - background-color: transparent; - font-weight: bold; -} - -.sig-name { - font-size: 1.1em; -} - -code.descname { - font-size: 1.2em; -} - -.sig-prename, code.descclassname { - background-color: transparent; -} - -.optional { - font-size: 1.3em; -} - -.sig-paren { - font-size: larger; -} - -.sig-param.n { - font-style: italic; -} - -/* C++ specific styling */ - -.sig-inline.c-texpr, -.sig-inline.cpp-texpr { - font-family: unset; -} - -.sig.c .k, .sig.c .kt, -.sig.cpp .k, .sig.cpp .kt { - color: #0033B3; -} - -.sig.c .m, -.sig.cpp .m { - color: #1750EB; -} - -.sig.c .s, .sig.c .sc, -.sig.cpp .s, .sig.cpp .sc { - color: #067D17; -} - - -/* -- other body styles ----------------------------------------------------- */ - -ol.arabic { - list-style: decimal; -} - -ol.loweralpha { - list-style: lower-alpha; -} - -ol.upperalpha { - list-style: upper-alpha; -} - -ol.lowerroman { - list-style: lower-roman; -} - -ol.upperroman { - list-style: upper-roman; -} - -:not(li) > ol > li:first-child > :first-child, -:not(li) > ul > li:first-child > :first-child { - margin-top: 0px; -} - -:not(li) > ol > li:last-child > :last-child, -:not(li) > ul > li:last-child > :last-child { - margin-bottom: 0px; -} - -ol.simple ol p, -ol.simple ul p, -ul.simple ol p, -ul.simple ul p { - margin-top: 0; -} - -ol.simple > li:not(:first-child) > p, -ul.simple > li:not(:first-child) > p { - margin-top: 0; -} - -ol.simple p, -ul.simple p { - margin-bottom: 0; -} - -dl.footnote > dt, -dl.citation > dt { - float: left; - margin-right: 0.5em; -} - -dl.footnote > dd, -dl.citation > dd { - margin-bottom: 0em; -} - -dl.footnote > dd:after, -dl.citation > dd:after { - content: ""; - clear: both; -} - -dl.field-list { - display: grid; - grid-template-columns: fit-content(30%) auto; -} - -dl.field-list > dt { - font-weight: bold; - word-break: break-word; - padding-left: 0.5em; - padding-right: 5px; -} - -dl.field-list > dt:after { - content: ":"; -} - -dl.field-list > dd { - padding-left: 0.5em; - margin-top: 0em; - margin-left: 0em; - margin-bottom: 0em; -} - -dl { - margin-bottom: 15px; -} - -dd > :first-child { - margin-top: 0px; -} - -dd ul, dd table { - margin-bottom: 10px; -} - -dd { - margin-top: 3px; - margin-bottom: 10px; - margin-left: 30px; -} - -dl > dd:last-child, -dl > dd:last-child > :last-child { - margin-bottom: 0; -} - -dt:target, span.highlighted { - background-color: #fbe54e; -} - -rect.highlighted { - fill: #fbe54e; -} - -dl.glossary dt { - font-weight: bold; - font-size: 1.1em; -} - -.versionmodified { - font-style: italic; -} - -.system-message { - background-color: #fda; - padding: 5px; - border: 3px solid red; -} - -.footnote:target { - background-color: #ffa; -} - -.line-block { - display: block; - margin-top: 1em; - margin-bottom: 1em; -} - -.line-block .line-block { - margin-top: 0; - margin-bottom: 0; - margin-left: 1.5em; -} - -.guilabel, .menuselection { - font-family: sans-serif; -} - -.accelerator { - text-decoration: underline; -} - -.classifier { - font-style: oblique; -} - -.classifier:before { - font-style: normal; - margin: 0 0.5em; - content: ":"; - display: inline-block; -} - -abbr, acronym { - border-bottom: dotted 1px; - cursor: help; -} - -/* -- code displays --------------------------------------------------------- */ - -pre { - overflow: auto; - overflow-y: hidden; /* fixes display issues on Chrome browsers */ -} - -pre, div[class*="highlight-"] { - clear: both; -} - -span.pre { - -moz-hyphens: none; - -ms-hyphens: none; - -webkit-hyphens: none; - hyphens: none; - white-space: nowrap; -} - -div[class*="highlight-"] { - margin: 1em 0; -} - -td.linenos pre { - border: 0; - background-color: transparent; - color: #aaa; -} - -table.highlighttable { - display: block; -} - -table.highlighttable tbody { - display: block; -} - -table.highlighttable tr { - display: flex; -} - -table.highlighttable td { - margin: 0; - padding: 0; -} - -table.highlighttable td.linenos { - padding-right: 0.5em; -} - -table.highlighttable td.code { - flex: 1; - overflow: hidden; -} - -.highlight .hll { - display: block; -} - -div.highlight pre, -table.highlighttable pre { - margin: 0; -} - -div.code-block-caption + div { - margin-top: 0; -} - -div.code-block-caption { - margin-top: 1em; - padding: 2px 5px; - font-size: small; -} - -div.code-block-caption code { - background-color: transparent; -} - -table.highlighttable td.linenos, -span.linenos, -div.highlight span.gp { /* gp: Generic.Prompt */ - user-select: none; - -webkit-user-select: text; /* Safari fallback only */ - -webkit-user-select: none; /* Chrome/Safari */ - -moz-user-select: none; /* Firefox */ - -ms-user-select: none; /* IE10+ */ -} - -div.code-block-caption span.caption-number { - padding: 0.1em 0.3em; - font-style: italic; -} - -div.code-block-caption span.caption-text { -} - -div.literal-block-wrapper { - margin: 1em 0; -} - -code.xref, a code { - background-color: transparent; - font-weight: bold; -} - -h1 code, h2 code, h3 code, h4 code, h5 code, h6 code { - background-color: transparent; -} - -.viewcode-link { - float: right; -} - -.viewcode-back { - float: right; - font-family: sans-serif; -} - -div.viewcode-block:target { - margin: -1px -10px; - padding: 0 10px; -} - -/* -- math display ---------------------------------------------------------- */ - -img.math { - vertical-align: middle; -} - -div.body div.math p { - text-align: center; -} - -span.eqno { - float: right; -} - -span.eqno a.headerlink { - position: absolute; - z-index: 1; -} - -div.math:hover a.headerlink { - visibility: visible; -} - -/* -- printout stylesheet --------------------------------------------------- */ - -@media print { - div.document, - div.documentwrapper, - div.bodywrapper { - margin: 0 !important; - width: 100%; - } - - div.sphinxsidebar, - div.related, - div.footer, - #top-link { - display: none; - } -} \ No newline at end of file diff --git a/_preview/22/_static/check-solid.svg b/_preview/22/_static/check-solid.svg deleted file mode 100644 index 92fad4b..0000000 --- a/_preview/22/_static/check-solid.svg +++ /dev/null @@ -1,4 +0,0 @@ - - - - diff --git a/_preview/22/_static/clipboard.min.js b/_preview/22/_static/clipboard.min.js deleted file mode 100644 index 54b3c46..0000000 --- a/_preview/22/_static/clipboard.min.js +++ /dev/null @@ -1,7 +0,0 @@ -/*! - * clipboard.js v2.0.8 - * https://clipboardjs.com/ - * - * Licensed MIT © Zeno Rocha - */ -!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1 - - - - diff --git a/_preview/22/_static/copybutton.css b/_preview/22/_static/copybutton.css deleted file mode 100644 index f1916ec..0000000 --- a/_preview/22/_static/copybutton.css +++ /dev/null @@ -1,94 +0,0 @@ -/* Copy buttons */ -button.copybtn { - position: absolute; - display: flex; - top: .3em; - right: .3em; - width: 1.7em; - height: 1.7em; - opacity: 0; - transition: opacity 0.3s, border .3s, background-color .3s; - 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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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. 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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) ? 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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" ? 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    Animation

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    time stamp at 1:19

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    Overview

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    Summary text here

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    Prerequisites

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    Concepts

    Importance

    Notes

    Matplotlib

    Necessary

    Cartopy

    Useful

    Not necessary for animations in general, but useful for the examples in this notebook

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    • Time to learn: X minutes

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    import cartopy.crs as ccrs
-import matplotlib.animation as animation
-import numpy as np
-import xarray as xr
-from matplotlib import pyplot as plt
-import os
-from PIL import Image
-
-import geocat.datafiles as gdf
-import geocat.viz as gv
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    -
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    Downloading file 'registry.txt' from 'https://github.com/NCAR/GeoCAT-datafiles/raw/main/registry.txt' to '/home/runner/.cache/geocat'.
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    Animation fundamentals with matplotlib

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    There are two different methods of creating animations with matplotlib:

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    1. Function animation iteratively modifies data on a pre-existing figure to produce an animation

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    2. Artist animations pulls from a list of pre-made artists to draw in each frame to produce an animation

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    Generally, function animation is easier to use, but artist animation can be more flexible for certain applications. We’ll cover both methods in this notebook.

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    Animating within jupyter notebooks

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    Before we get into either type of animation, we’re going to alter matplotlib’s runtime configuration settings (or rcParams) to allow us to use matplotlib’s animation functions within jupyter notebooks. This next cell is not necessary if you want to create animations in a script or just want to save the animation to a file.

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    Here, we’re changing default animation html output setting to "jshtml", which creates a JavaScript animation that can be displayed in a jupyter notebook. The default setting is "none".

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    plt.rcParams["animation.html"] = "jshtml"
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    Artist Animation

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    Artist animation uses pre-made artists to cycle through to produce an animation.

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    In this example, we’re going to plot images using matplotlib’s imshow function and save the resulting artist object to a list. Then, we’ll use the ArtistAnimation function to create an animation from that list of artists.

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    Data

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    Before we get into those steps, let’s get some images to animate. First, we’ll be looking at some GeoColor satellite imagery from GOES-16.

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    In this repository, there is a script, notebooks/scripts/goes_getter.py, that will download hourly images from the last 24 hours from the GOES-16 archive if you’d like to play around with this yourself. We have already downloaded some images for this example in the notebooks/data/goes16_hr/ directory and will be using those for this part of the notebook.

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    Get the images into a list

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    First, we need to ge the images from the directory into a list. We know the only files in this directory are the images we want to plot, so let’s get get a list of all the files ending in .jpg from that path using os.listdir().

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    We’ll also sort them using the built in sorted function to make sure that our list of images ends up in chronological order.

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    im_dir = "./data/goes16_hr/"
-im_paths = sorted([p for p in os.listdir(im_dir) if p.endswith(".jpg")])
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    Creating the figure

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    Now, we need to set up the figure we’ll be plotting our animation on.

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    dpi = 100
-figsize = tuple(t / dpi for t in Image.open(im_dir + im_paths[0]).size)
-fig = plt.figure(figsize=figsize, dpi=dpi)
-ax = fig.add_axes([0, 0, 1, 1])  # span the whole figure
-ax.set_axis_off()
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    -../_images/6c099ce251ddad2e78a37d77977212e5ecc008069bc0894f5575d9f12d5c13a1.png -
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    First, we set out dpi to be 100. This is technically not necessary at this point in creating the animation, but we’re going to use it to set the size of the figure. Since we’re plotting images, we want to make sure that the figure size is the same size as the images we’re plotting.

    -

    Next, we’re going to figure out what our figsize should be based on the dpi and the size of the images we’re about to plot. tuple(t / dpi for t in Image.open(im_dir + im_paths[0]).size) divides each dimension of the size of the image by the intended dpi and then returns the result as a tuple. We then use that tuple to create our figsize.

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    Next, we create the figure using plt.figure() and the figsize and dpi we just calculated.

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    Then, we create the axes object using fig.add_axes([0, 0, 1, 1]). This adds axes to our figure that span the entire range of the figure, allowing the images plot on these axes to take up the entire figure space.

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    Finally, we turn off the axes using ax.axis("off"). This is because we don’t want to see axes on our final image plot.

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    Running the above cell should have produced a blank figure in the jupyter notebook. This is expected since we’ve only created a blank figure to plot on, but haven’t actually plotted anything yet.

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    Tip

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    Customizing the figure size, dpi, and axes is not necessary for creating an artist animation, but it will make our end result look nicer.

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    Creating a list of artists

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    Now that we have a list of the filepaths for all the images we want to plot and axes to plot them on, we can use imshow to plot each image and save the resulting artist object to a list.

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    ims = [[ax.imshow(Image.open(im_dir + im_path), animated=True)] for im_path in im_paths]
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    A couple of things to note here:

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    • You may notice that we’re using list generation to create our final list, ims. But what might not be obvious is that we’re actually making a list of lists. Each created implot object is put into its own one-item list, and then all of those lists are used to create ims. This is because ArtistAnimation expects a list of lists, where each inner list is a list of artists to be plotted in a single frame.

      • -

    • We’re using a kwarg you may not have seen before using imshow: animated=True. This allows the artist to only be drawn when called as part of an animation. This is a kwarg that is common to all artist objects.

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    Creating the artist animation

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    Now that we’ve set up our figure and list (of lists) of artists, we can create the animation using ArtistAnimation.

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    ani = animation.ArtistAnimation(fig, ims, interval=150, repeat_delay=1000)
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    ArtistAnimation takes two required arguments:

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    • the figure to plot on (fig in our case) and

      • -

    • the list of pre-created artist objects (ims) to plot.

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    We’ve also provided a few optional arguments:

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    • interval: The time between frames in milliseconds. We’ve set this to 150 milliseconds, or 0.15 seconds.

      • -

    • repeat_delay: The time in milliseconds to wait before repeating the animation. We’ve set this to 1000 milliseconds, or 1 second.

      • -
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    And that’s it! We’ve created an animation in matplotlib using artist animation. Let’s take a look at it.

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    Saving the animation

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    If displaying your animation in a jupyter notebook was your end goal, then you’re done! But if you want to save your animation to a file for later use, you can use the save method of the ArtistAnimation object.

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    ani.save("goes16_hr.gif")
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    MovieWriter ffmpeg unavailable; using Pillow instead.
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    Function animation

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    The steps for function animation in matplotlib are:

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    1. Set up all the artists that will be used in the animation and the initial frame of the animation

      2. -

    2. Create a function that updates the data in the plot to create each frame of the animation

      3. -

    3. Create a FuncAnimation object with the the previously created elements

      4. -

    4. Display and save the animation

      5. -
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    Data

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    For this example, we’re going to be looking at sample surface temperature data from our sample data repository, geocat-datafiles.

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    ds = xr.open_dataset(gdf.get("netcdf_files/meccatemp.cdf"))
-t = ds.t
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    Downloading file 'netcdf_files/meccatemp.cdf' from 'https://github.com/NCAR/GeoCAT-datafiles/raw/main/netcdf_files/meccatemp.cdf' to '/home/runner/.cache/geocat'.
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    Creating the figure

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    The first step in creating a function animation is to create the figure that will be used to plot the animation.

    -

    Since we’re working with data represented by latitude and longitude coordinates, we’re going to set up our figure and axes using Cartopy.

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    # Set up Axes with Cartopy Projection
-fig = plt.figure()
-ax = plt.axes(projection=ccrs.PlateCarree())
-ax.coastlines()
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    <cartopy.mpl.feature_artist.FeatureArtist at 0x7fe3126bf880>
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    /usr/share/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)
-
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    -../_images/2891b192d7943dacdc88fa21a2d346977d47742b1b4d76d7ce5ef7f565ebcfeb.png -
    -
    -

    Just like before, we used plt.figure() to create the figure. Then, we used plt.axes with the projection kwarg set to ccrs.PlateCarree() to create the axes object. This sets up the axes to use the Plate Carree projection.

    -

    Finally, we used ax.coastlines() to add coastlines to the plot.

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    Creating the initial frame

    -

    Now that we have our figure and axes set up, we can plot the initial frame of our animation.

    -
    -
    -
    vmin = t.min()
-vmax = t.max()
-levels = 30
-
-# create initial plot that we will update
-t[0, :, :].plot.contourf(
-    ax=ax,
-    transform=ccrs.PlateCarree(),
-    vmin=vmin,
-    vmax=vmax,
-    levels=levels,
-    cmap="inferno",
-    cbar_kwargs={
-        "location": "bottom",
-    },
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    <cartopy.mpl.contour.GeoContourSet at 0x7fe30f5d6260>
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    In addition to plotting the initial frame, we also set up the colorbar. In order for every frame of the animation to use the same colorbar, we need to create the colorbar before we create the animation.

    -

    The vmin and vmax arguments of plt.contourf are set to the minimum and maximum values of the data, instead of each individual time slice. This ensures that the colorbar remains consistent across all frames of the animation. We also set a levels kwarg to similarly ensure that the contour levels are consistent, as well.

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    Creating the update function

    -

    Now that we have our initial frame, we need to create an update function that will update the data in the plot to create each frame of the animation. This function will be passed in as an argument to FuncAnimation, which expects a function that takes in a single argument, i, which is the frame number of the animation. The argument i is used to index into the data to get the data for the current frame to plot.

    -
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    -
    # create function to update plot
-def animate(i):
-    # Plot the new frame
-    t[i, :, :].plot.contourf(
-        ax=ax,
-        transform=ccrs.PlateCarree(),
-        vmin=vmin,
-        vmax=vmax,
-        levels=levels,
-        cmap="inferno",
-        add_colorbar=False,
-    )
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    -
    -

    Note here that that the only differences from our initial frame set up are the indexing on the data to plot and the add_colorbar = False kwarg in plt.contourf. This is because we’ve already created a colorbar in the initial frame that we want to use for all frames of the animation.

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    Warning

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    You can accidentally create some funny looking animations if you forget the add_colorbar = False kwarg in the update function.

    -notebooks/images/animaion_colorbar_oops.gif -
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    Creating the animation

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    Now it’s time to create the animation using FuncAnimation.

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    ani = animation.FuncAnimation(fig, animate, frames=len(t), interval=150, repeat_delay=1000)
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    -
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    -
    -

    This function looks similar to ArtistAnimation, but with one main difference: instead of a list of artists to plot, FuncAnimation takes the update function we created as the second argument. Also, we’ve provided the frames kwarg, which is the number of times the update function will be called. We’ve set it to the number of time slices in the data to make sure the FuncAnimation object does not try to go outside the bounds of the data.

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    And that’s it! We’ve created a function animation in matplotlib. Let’s take a look at it.

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    Just like with ArtistAnimation, we can save the animation using the save method of the FuncAnimation object.

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    ani.save("temp.gif")
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    MovieWriter ffmpeg unavailable; using Pillow instead.
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    Summary

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    We learned the two ways to make animations in matplotlib: artist animation and function animation, how to display them in jupyter notebooks, and how to save them to a file.

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    Comparison of Visualization Packages

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    Overview

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    There are nearly endless possibilities when it comes to data visualization in Python. Some of these choices can be overwhelming. This chapter aims to lay out and distinguish different Python visualization libraries so that you are more equipped to make the right choice for your data visualization needs. This Cookbook is not a comprehensive tutorial on these packages, but we can offere enough information and links to documentation or relevant tutorials to help get you started.

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    1. Matplotlib

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    2. Cartopy

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    3. GeoCAT-viz

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    4. MetPy

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    5. Vapor

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    6. Plotly

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    7. Seaborn

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    8. Bokeh

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    9. UXarray

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    10. hvPlot

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    Note

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    The plotting libraries mentioned here are either ones used extensively by the authors of this Cookbook OR ones that we get asked about a lot when giving plotting tutorials. This does not cover every library that can be used for plotting in the Python scientific ecosystem, but should cover the more popular packages you might come across.

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    Missing a plotting library that you use and want others to know more about? Let us know!

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    Prerequisites

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    Concepts

    Importance

    Notes

    Matplotlib

    Necessary

    Cartopy

    Necessary

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    • Time to learn: 50 minutes

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    Matplotlib

    -

    Matplotlib Logo

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    Matplotlib is the workhorse of Python visualization needs. It is a comprehensive plotting library that has the capacity to make static, animated, or interactive visualizations. It is hard to imagine plotting in Python without first getting comfortable with Matplotlib. Be sure to check out the Matplotlib documentation as well as the Pythia foundations chapter on Matplotlib for guidance.

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    Matplotlib’s syntax should feel familiar to anyone who has plotted data in Matlab.

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    Here is a simple plotting example from Matplotlib:

    -
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    -
    import matplotlib.pyplot as plt
-import numpy as np
-
-# Data for plotting
-t = np.arange(0.0, 2.0, 0.01)
-s = 1 + np.sin(2 * np.pi * t)
-
-fig, ax = plt.subplots()
-ax.plot(t, s)
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-ax.set(xlabel='time (s)', ylabel='voltage (mV)',
-       title='About as simple as it gets, folks')
-ax.grid()
-
-plt.show()
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    -../_images/6f2bb0037d03edaa75059ac8c2c36dab9870d53dd42cdd540891d468ea1b0e37.png -
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    Cartopy

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    Cartopy Logo

    -

    Cartopy is a Python package for plotting data on the globe. It is the go-to package for plotting maps, dealing with different projections, and adding surface features to your plot. Cartopy is buit on top of PROJ, NumPy and Shapely, and Matplotlib. To learn more about what Cartopy can do, check out the Cartopy documentation and the Pythia foundations Cartopy chapter.

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    You may have heard about Basemap, another geoscience plotting library, which was deprecated in favor of Cartopy.

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    Here is a simple plotting example from Cartopy:

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    import cartopy.crs as ccrs
-
-ax = plt.axes(projection=ccrs.PlateCarree())
-ax.coastlines()
-
-plt.show()
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    GeoCAT-Viz

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    GeoCAT Logo

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    The GeoCAT team at the National Center for Atmospheric Research (NCAR) aims to help scientists transitioning from NCL to Python. Out of this team come two different visualization aids: the GeoCAT-examples Visualization Gallery which contains tons of different plotting examples that you can use as a starting place for your figures, and the GeoCAT-Viz package (documentation) which contains many convenience functions that formerly existed in NCL or for making Python plots look publication-ready.

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    MetPy

    -

    Metpy Logo

    -

    Metpy is a collection of tools for data reading, analysis, and visualization with weather data. Matplotlib offers some useful functionality for unique plots such as Skew-T diagrams, as well as declaritive plotting functionality. Check out the MetPy documentation.

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    Here is a simple Skew-T plot from their Getting Started documentation:

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    import metpy.calc as mpcalc
-from metpy.plots import SkewT
-from metpy.units import units
-
-fig = plt.figure(figsize=(9, 9))
-skew = SkewT(fig)
-
-# Create arrays of pressure, temperature, dewpoint, and wind components
-p = [902, 897, 893, 889, 883, 874, 866, 857, 849, 841, 833, 824, 812, 796, 776, 751,
-     727, 704, 680, 656, 629, 597, 565, 533, 501, 468, 435, 401, 366, 331, 295, 258,
-     220, 182, 144, 106] * units.hPa
-t = [-3, -3.7, -4.1, -4.5, -5.1, -5.8, -6.5, -7.2, -7.9, -8.6, -8.9, -7.6, -6, -5.1,
-     -5.2, -5.6, -5.4, -4.9, -5.2, -6.3, -8.4, -11.5, -14.9, -18.4, -21.9, -25.4,
-     -28, -32, -37, -43, -49, -54, -56, -57, -58, -60] * units.degC
-td = [-22, -22.1, -22.2, -22.3, -22.4, -22.5, -22.6, -22.7, -22.8, -22.9, -22.4,
-      -21.6, -21.6, -21.9, -23.6, -27.1, -31, -38, -44, -46, -43, -37, -34, -36,
-      -42, -46, -49, -48, -47, -49, -55, -63, -72, -88, -93, -92] * units.degC
-
-# Calculate parcel profile
-prof = mpcalc.parcel_profile(p, t[0], td[0]).to('degC')
-u = np.linspace(-10, 10, len(p)) * units.knots
-v = np.linspace(-20, 20, len(p)) * units.knots
-
-skew.plot(p, t, 'r')
-skew.plot(p, td, 'g')
-skew.plot(p, prof, 'k')  # Plot parcel profile
-skew.plot_barbs(p[::5], u[::5], v[::5])
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-skew.ax.set_xlim(-50, 15)
-skew.ax.set_ylim(1000, 100)
-
-# Add the relevant special lines
-skew.plot_dry_adiabats()
-skew.plot_moist_adiabats()
-skew.plot_mixing_lines()
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-plt.show();
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    VAPOR

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    VAPOR Logo

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    VAPOR stands for the Visualization and Analysis Platform for Ocean, Atmosphere, and Solar Researchers and is another project from NCAR. VAPOR provides an interactive 3D visualization environment. Learn more at the VAPOR documentation and the VAPOR Pythia Cookbook. VAPORrequires a GPU-enabled environment to run.

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    Plotly

    -

    Plotly Logo

    -

    Plotly is another choice for interactive plotting. Plotly has functionality in several languags. Here is the Plotly Python documentation.

    -

    Here is an example using their “Express” functionality:

    -
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    import plotly.express as px
-
-fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])
-fig.show()
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    Seaborn

    -

    Seaborn Logo

    -

    Seaborn is a high level interactive interface for creating statistical visualizations built on matplotlib. Check out the Seaborn documentation.

    -

    Here is their heatmap example:

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    import seaborn as sns
-sns.set_theme()
-
-# Load the example flights dataset and convert to long-form
-flights_long = sns.load_dataset("flights")
-flights = flights_long.pivot(index="month", columns="year", values="passengers")
-
-# Draw a heatmap with the numeric values in each cell
-f, ax = plt.subplots(figsize=(9, 6))
-sns.heatmap(flights, annot=True, fmt="d", linewidths=.5, ax=ax)
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-plt.show();
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    Bokeh

    -

    Bokeh Logo

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    Bokeh is a Javascript-powered tool for creating interactive visualizations in modern web browsers. Check out the Bokeh documentation.

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    Here is scatter plot example:

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    from bokeh.plotting import figure, show
-
-N = 4000
-x = np.random.random(size=N) * 100
-y = np.random.random(size=N) * 100
-radii = np.random.random(size=N) * 1.5
-colors = np.array([(r, g, 150) for r, g in zip(50+2*x, 30+2*y)], dtype="uint8")
-
-TOOLS="hover,crosshair,pan,wheel_zoom,zoom_in,zoom_out,box_zoom,undo,redo,reset,tap,save,box_select,poly_select,lasso_select,examine,help"
-
-p = figure(tools=TOOLS)
-
-p.scatter(x, y, radius=radii,
-          fill_color=colors, fill_alpha=0.6,
-          line_color=None)
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-show(p)
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    UXarray

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    UXarray Logo

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    UXarray specializes in unstructured grids, built around UGRID conventions and Xarray syntax. See the UXarray documentation.

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    hvPlot

    -

    Datashader Logo

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    hvPlot wraps both Datashader, a graphics pipeline, and Holoviews, a tool for bundling data and metadata for intuitive interactive plotting, at a higher level. All 3 tools are by Holoviz. Reference the hvPlot documentation.

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    Here is a simple example from their user guide:

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    import pandas as pd
-import hvplot.pandas
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-pd.options.plotting.backend = 'holoviews'
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-index = pd.date_range('1/1/2000', periods=1000)
-df = pd.DataFrame(np.random.randn(1000, 4), index=index, columns=list('ABCD')).cumsum()
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    This useful diagram from hvPlot’s documentation details how different high-level tools for data visualization interact.

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    Datashader Logo

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    Summary

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    Each Python plotting library offers a slightly different niche in the data visualization world. Some are better for creating publication figures (matplotlib, cartopy, metpy, geocat-viz, uxarray) while others offer interactive functionality that is great for websites, demonstrations, and other forms of engagement (holoviews, seaborn, plotly, bokeh, and vapor). Hopefully the mini examples on this page allow you to play around and see which user interfaces you like best for your visualization needs.

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    What’s next?

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    Overview

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    What Makes a Good Visualization? We want graphics to be eye catching and informative. In this chapter we’ll discuss different aspects that can affect the quality of your figures and specific considerations relevant to the geosciences.

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    1. The Importance of Data Visualization

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    • Time to learn: 3 hours

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    import matplotlib as mpl
-import matplotlib.pyplot as plt
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    The Importance of Data Visualization

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    It is important to use pictures to show data because we can visually detect patterns that could be lost in statistical analysis. All scientific disciplines use data visualizations to communicate concepts.

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    Here we have a figure from Autodesk that shows a “Datasaurus” and 12 other datasets that share the same statistical information (mean, standard deviation, etc). We can see immediately that visually are telling very different stories: be it a dinosaur, a star, an oval, concentric ovals, or a series of lines (perhaps weather fronts).

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    Same Stats

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    Publication Ready Figures

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    For your figure to be publication rady, you probably want to change some of Matplotlib’s default plotting settings: selecting fontsizes for your titles and labels, changing figure sizes, or subplot/colormap layout.

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    -
    # fake data
-x = [0, 1, 2, 3, 4, 5]
-y = [0, 3, 6, 9, 12, 15]
- 
-# plot
-plt.plot(x, y)
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-# annotate
-plt.title('Title')
-plt.xlabel('X Label')
-plt.ylabel('Y Label')
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-plt.show();
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-x = [0, 1, 2, 3, 4, 5]
-y = [0, 3, 6, 9, 12, 15]
- 
-# plot
-plt.plot(x, y, '--', color='red')
-
-# annotate
-plt.title('Title', fontsize=20)
-plt.xlabel('X Label', fontsize=16)
-plt.ylabel('Y Label', fontsize=16)
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    <bound method Mapping.keys of RcParams({'_internal.classic_mode': False,
-          'agg.path.chunksize': 0,
-          'animation.bitrate': -1,
-          'animation.codec': 'h264',
-          'animation.convert_args': ['-layers', 'OptimizePlus'],
-          'animation.convert_path': 'convert',
-          'animation.embed_limit': 20.0,
-          'animation.ffmpeg_args': [],
-          'animation.ffmpeg_path': 'ffmpeg',
-          'animation.frame_format': 'png',
-          'animation.html': 'none',
-          'animation.writer': 'ffmpeg',
-          'axes.autolimit_mode': 'data',
-          'axes.axisbelow': 'line',
-          'axes.edgecolor': 'black',
-          'axes.facecolor': 'white',
-          'axes.formatter.limits': [-5, 6],
-          'axes.formatter.min_exponent': 0,
-          'axes.formatter.offset_threshold': 4,
-          'axes.formatter.use_locale': False,
-          'axes.formatter.use_mathtext': False,
-          'axes.formatter.useoffset': True,
-          'axes.grid': False,
-          'axes.grid.axis': 'both',
-          'axes.grid.which': 'major',
-          'axes.labelcolor': 'black',
-          'axes.labelpad': 4.0,
-          'axes.labelsize': 'medium',
-          'axes.labelweight': 'normal',
-          'axes.linewidth': 0.8,
-          'axes.prop_cycle': cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']),
-          'axes.spines.bottom': True,
-          'axes.spines.left': True,
-          'axes.spines.right': True,
-          'axes.spines.top': True,
-          'axes.titlecolor': 'auto',
-          'axes.titlelocation': 'center',
-          'axes.titlepad': 6.0,
-          'axes.titlesize': 'large',
-          'axes.titleweight': 'normal',
-          'axes.titley': None,
-          'axes.unicode_minus': True,
-          'axes.xmargin': 0.05,
-          'axes.ymargin': 0.05,
-          'axes.zmargin': 0.05,
-          'axes3d.grid': True,
-          'axes3d.xaxis.panecolor': (0.95, 0.95, 0.95, 0.5),
-          'axes3d.yaxis.panecolor': (0.9, 0.9, 0.9, 0.5),
-          'axes3d.zaxis.panecolor': (0.925, 0.925, 0.925, 0.5),
-          'backend': 'module://matplotlib_inline.backend_inline',
-          'backend_fallback': True,
-          'boxplot.bootstrap': None,
-          'boxplot.boxprops.color': 'black',
-          'boxplot.boxprops.linestyle': '-',
-          'boxplot.boxprops.linewidth': 1.0,
-          'boxplot.capprops.color': 'black',
-          'boxplot.capprops.linestyle': '-',
-          'boxplot.capprops.linewidth': 1.0,
-          'boxplot.flierprops.color': 'black',
-          'boxplot.flierprops.linestyle': 'none',
-          'boxplot.flierprops.linewidth': 1.0,
-          'boxplot.flierprops.marker': 'o',
-          'boxplot.flierprops.markeredgecolor': 'black',
-          'boxplot.flierprops.markeredgewidth': 1.0,
-          'boxplot.flierprops.markerfacecolor': 'none',
-          'boxplot.flierprops.markersize': 6.0,
-          'boxplot.meanline': False,
-          'boxplot.meanprops.color': 'C2',
-          'boxplot.meanprops.linestyle': '--',
-          'boxplot.meanprops.linewidth': 1.0,
-          'boxplot.meanprops.marker': '^',
-          'boxplot.meanprops.markeredgecolor': 'C2',
-          'boxplot.meanprops.markerfacecolor': 'C2',
-          'boxplot.meanprops.markersize': 6.0,
-          'boxplot.medianprops.color': 'C1',
-          'boxplot.medianprops.linestyle': '-',
-          'boxplot.medianprops.linewidth': 1.0,
-          'boxplot.notch': False,
-          'boxplot.patchartist': False,
-          'boxplot.showbox': True,
-          'boxplot.showcaps': True,
-          'boxplot.showfliers': True,
-          'boxplot.showmeans': False,
-          'boxplot.vertical': True,
-          'boxplot.whiskerprops.color': 'black',
-          'boxplot.whiskerprops.linestyle': '-',
-          'boxplot.whiskerprops.linewidth': 1.0,
-          'boxplot.whiskers': 1.5,
-          'contour.algorithm': 'mpl2014',
-          'contour.corner_mask': True,
-          'contour.linewidth': None,
-          'contour.negative_linestyle': 'dashed',
-          'date.autoformatter.day': '%Y-%m-%d',
-          'date.autoformatter.hour': '%m-%d %H',
-          'date.autoformatter.microsecond': '%M:%S.%f',
-          'date.autoformatter.minute': '%d %H:%M',
-          'date.autoformatter.month': '%Y-%m',
-          'date.autoformatter.second': '%H:%M:%S',
-          'date.autoformatter.year': '%Y',
-          'date.converter': 'auto',
-          'date.epoch': '1970-01-01T00:00:00',
-          'date.interval_multiples': True,
-          'docstring.hardcopy': False,
-          'errorbar.capsize': 0.0,
-          'figure.autolayout': False,
-          'figure.constrained_layout.h_pad': 0.04167,
-          'figure.constrained_layout.hspace': 0.02,
-          'figure.constrained_layout.use': False,
-          'figure.constrained_layout.w_pad': 0.04167,
-          'figure.constrained_layout.wspace': 0.02,
-          'figure.dpi': 100.0,
-          'figure.edgecolor': 'white',
-          'figure.facecolor': 'white',
-          'figure.figsize': [6.4, 4.8],
-          'figure.frameon': True,
-          'figure.hooks': [],
-          'figure.labelsize': 'large',
-          'figure.labelweight': 'normal',
-          'figure.max_open_warning': 20,
-          'figure.raise_window': True,
-          'figure.subplot.bottom': 0.11,
-          'figure.subplot.hspace': 0.2,
-          'figure.subplot.left': 0.125,
-          'figure.subplot.right': 0.9,
-          'figure.subplot.top': 0.88,
-          'figure.subplot.wspace': 0.2,
-          'figure.titlesize': 'large',
-          'figure.titleweight': 'normal',
-          'font.cursive': ['Apple Chancery',
-                           'Textile',
-                           'Zapf Chancery',
-                           'Sand',
-                           'Script MT',
-                           'Felipa',
-                           'Comic Neue',
-                           'Comic Sans MS',
-                           'cursive'],
-          'font.family': ['sans-serif'],
-          'font.fantasy': ['Chicago',
-                           'Charcoal',
-                           'Impact',
-                           'Western',
-                           'xkcd script',
-                           'fantasy'],
-          'font.monospace': ['DejaVu Sans Mono',
-                             'Bitstream Vera Sans Mono',
-                             'Computer Modern Typewriter',
-                             'Andale Mono',
-                             'Nimbus Mono L',
-                             'Courier New',
-                             'Courier',
-                             'Fixed',
-                             'Terminal',
-                             'monospace'],
-          'font.sans-serif': ['DejaVu Sans',
-                              'Bitstream Vera Sans',
-                              'Computer Modern Sans Serif',
-                              'Lucida Grande',
-                              'Verdana',
-                              'Geneva',
-                              'Lucid',
-                              'Arial',
-                              'Helvetica',
-                              'Avant Garde',
-                              'sans-serif'],
-          'font.serif': ['DejaVu Serif',
-                         'Bitstream Vera Serif',
-                         'Computer Modern Roman',
-                         'New Century Schoolbook',
-                         'Century Schoolbook L',
-                         'Utopia',
-                         'ITC Bookman',
-                         'Bookman',
-                         'Nimbus Roman No9 L',
-                         'Times New Roman',
-                         'Times',
-                         'Palatino',
-                         'Charter',
-                         'serif'],
-          'font.size': 10.0,
-          'font.stretch': 'normal',
-          'font.style': 'normal',
-          'font.variant': 'normal',
-          'font.weight': 'normal',
-          'grid.alpha': 1.0,
-          'grid.color': '#b0b0b0',
-          'grid.linestyle': '-',
-          'grid.linewidth': 0.8,
-          'hatch.color': 'black',
-          'hatch.linewidth': 1.0,
-          'hist.bins': 10,
-          'image.aspect': 'equal',
-          'image.cmap': 'viridis',
-          'image.composite_image': True,
-          'image.interpolation': 'antialiased',
-          'image.lut': 256,
-          'image.origin': 'upper',
-          'image.resample': True,
-          'interactive': True,
-          'keymap.back': ['left', 'c', 'backspace', 'MouseButton.BACK'],
-          'keymap.copy': ['ctrl+c', 'cmd+c'],
-          'keymap.forward': ['right', 'v', 'MouseButton.FORWARD'],
-          'keymap.fullscreen': ['f', 'ctrl+f'],
-          'keymap.grid': ['g'],
-          'keymap.grid_minor': ['G'],
-          'keymap.help': ['f1'],
-          'keymap.home': ['h', 'r', 'home'],
-          'keymap.pan': ['p'],
-          'keymap.quit': ['ctrl+w', 'cmd+w', 'q'],
-          'keymap.quit_all': [],
-          'keymap.save': ['s', 'ctrl+s'],
-          'keymap.xscale': ['k', 'L'],
-          'keymap.yscale': ['l'],
-          'keymap.zoom': ['o'],
-          'legend.borderaxespad': 0.5,
-          'legend.borderpad': 0.4,
-          'legend.columnspacing': 2.0,
-          'legend.edgecolor': '0.8',
-          'legend.facecolor': 'inherit',
-          'legend.fancybox': True,
-          'legend.fontsize': 'medium',
-          'legend.framealpha': 0.8,
-          'legend.frameon': True,
-          'legend.handleheight': 0.7,
-          'legend.handlelength': 2.0,
-          'legend.handletextpad': 0.8,
-          'legend.labelcolor': 'None',
-          'legend.labelspacing': 0.5,
-          'legend.loc': 'best',
-          'legend.markerscale': 1.0,
-          'legend.numpoints': 1,
-          'legend.scatterpoints': 1,
-          'legend.shadow': False,
-          'legend.title_fontsize': None,
-          'lines.antialiased': True,
-          'lines.color': 'C0',
-          'lines.dash_capstyle': <CapStyle.butt: 'butt'>,
-          'lines.dash_joinstyle': <JoinStyle.round: 'round'>,
-          'lines.dashdot_pattern': [6.4, 1.6, 1.0, 1.6],
-          'lines.dashed_pattern': [3.7, 1.6],
-          'lines.dotted_pattern': [1.0, 1.65],
-          'lines.linestyle': '-',
-          'lines.linewidth': 1.5,
-          'lines.marker': 'None',
-          'lines.markeredgecolor': 'auto',
-          'lines.markeredgewidth': 1.0,
-          'lines.markerfacecolor': 'auto',
-          'lines.markersize': 6.0,
-          'lines.scale_dashes': True,
-          'lines.solid_capstyle': <CapStyle.projecting: 'projecting'>,
-          'lines.solid_joinstyle': <JoinStyle.round: 'round'>,
-          'macosx.window_mode': 'system',
-          'markers.fillstyle': 'full',
-          'mathtext.bf': 'sans:bold',
-          'mathtext.bfit': 'sans:italic:bold',
-          'mathtext.cal': 'cursive',
-          'mathtext.default': 'it',
-          'mathtext.fallback': 'cm',
-          'mathtext.fontset': 'dejavusans',
-          'mathtext.it': 'sans:italic',
-          'mathtext.rm': 'sans',
-          'mathtext.sf': 'sans',
-          'mathtext.tt': 'monospace',
-          'patch.antialiased': True,
-          'patch.edgecolor': 'black',
-          'patch.facecolor': 'C0',
-          'patch.force_edgecolor': False,
-          'patch.linewidth': 1.0,
-          'path.effects': [],
-          'path.simplify': True,
-          'path.simplify_threshold': 0.111111111111,
-          'path.sketch': None,
-          'path.snap': True,
-          'pcolor.shading': 'auto',
-          'pcolormesh.snap': True,
-          'pdf.compression': 6,
-          'pdf.fonttype': 3,
-          'pdf.inheritcolor': False,
-          'pdf.use14corefonts': False,
-          'pgf.preamble': '',
-          'pgf.rcfonts': True,
-          'pgf.texsystem': 'xelatex',
-          'polaraxes.grid': True,
-          'ps.distiller.res': 6000,
-          'ps.fonttype': 3,
-          'ps.papersize': 'letter',
-          'ps.useafm': False,
-          'ps.usedistiller': None,
-          'savefig.bbox': None,
-          'savefig.directory': '~',
-          'savefig.dpi': 'figure',
-          'savefig.edgecolor': 'auto',
-          'savefig.facecolor': 'auto',
-          'savefig.format': 'png',
-          'savefig.orientation': 'portrait',
-          'savefig.pad_inches': 0.1,
-          'savefig.transparent': False,
-          'scatter.edgecolors': 'face',
-          'scatter.marker': 'o',
-          'svg.fonttype': 'path',
-          'svg.hashsalt': None,
-          'svg.image_inline': True,
-          'text.antialiased': True,
-          'text.color': 'black',
-          'text.hinting': 'force_autohint',
-          'text.hinting_factor': 8,
-          'text.kerning_factor': 0,
-          'text.latex.preamble': '',
-          'text.parse_math': True,
-          'text.usetex': False,
-          'timezone': 'UTC',
-          'tk.window_focus': False,
-          'toolbar': 'toolbar2',
-          'webagg.address': '127.0.0.1',
-          'webagg.open_in_browser': True,
-          'webagg.port': 8988,
-          'webagg.port_retries': 50,
-          'xaxis.labellocation': 'center',
-          'xtick.alignment': 'center',
-          'xtick.bottom': True,
-          'xtick.color': 'black',
-          'xtick.direction': 'out',
-          'xtick.labelbottom': True,
-          'xtick.labelcolor': 'inherit',
-          'xtick.labelsize': 'medium',
-          'xtick.labeltop': False,
-          'xtick.major.bottom': True,
-          'xtick.major.pad': 3.5,
-          'xtick.major.size': 3.5,
-          'xtick.major.top': True,
-          'xtick.major.width': 0.8,
-          'xtick.minor.bottom': True,
-          'xtick.minor.ndivs': 'auto',
-          'xtick.minor.pad': 3.4,
-          'xtick.minor.size': 2.0,
-          'xtick.minor.top': True,
-          'xtick.minor.visible': False,
-          'xtick.minor.width': 0.6,
-          'xtick.top': False,
-          'yaxis.labellocation': 'center',
-          'ytick.alignment': 'center_baseline',
-          'ytick.color': 'black',
-          'ytick.direction': 'out',
-          'ytick.labelcolor': 'inherit',
-          'ytick.labelleft': True,
-          'ytick.labelright': False,
-          'ytick.labelsize': 'medium',
-          'ytick.left': True,
-          'ytick.major.left': True,
-          'ytick.major.pad': 3.5,
-          'ytick.major.right': True,
-          'ytick.major.size': 3.5,
-          'ytick.major.width': 0.8,
-          'ytick.minor.left': True,
-          'ytick.minor.ndivs': 'auto',
-          'ytick.minor.pad': 3.4,
-          'ytick.minor.right': True,
-          'ytick.minor.size': 2.0,
-          'ytick.minor.visible': False,
-          'ytick.minor.width': 0.6,
-          'ytick.right': False})>
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    To change any given parameter you would use the following command (replacing your parameter and value, of course):

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    mpl.rcParams['font.family'] = 'Arial'
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    Using GeoCAT-Viz

    -

    The GeoCAT-Viz package also has many utility functions for making your plots looks publication ready in fewer lines of code. The defaults of GeoCAT-viz keword-arguments are set to resemble the style of NCL.

    -
    -
    -
    # fake data
-x = [0, 1, 2, 3, 4, 5]
-y = [0, 3, 6, 9, 12, 15]
- 
-# plot
-plt.plot(x, y)
-
-# annotate
-plt.title('Title')
-plt.xlabel('X Label')
-plt.ylabel('Y Label')
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-gv.set_titles_and_labels(plt.gca())
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-plt.show();
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    The Problem with Rainbow Colormaps

    -

    Rainbow colormaps are visually beautiful, but are falling out of favor because

    -
      -

    1. They are not colorblind friendly and

      2. -

    2. They do not print out in grayscale in a meaningful way.

      3. -
    -

    Both of these issues can be addressed by bing careful about you colormaps lightness-values.

    -

    Some colormaps options are perceptually uniform (the same lightness value), sequentially ordered (goes from lighter to darker), or diverging (lightest or darkest at a set point and uniformly changes lightness going out). A rainbow colormap however is lighter or darker in arbitrary places and it affects how we interpret data (especially if it was printed out in grayscale).

    -

    For example, from Matplotlib’s Choosing a Colormap documentation here are some “good” colormaps:

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    Matplotlib Logo

    -

    And here are miscellaneous colormaps:

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    Matplotlib Logo

    -

    Looking at the colors in grayscale helps to understand why we might prefer a sequentially ordered colormap. Some grayscale values are duplicated and the reader will not know if it is a high or low value.

    -

    Another consideration that can help those who are visually impaired is to make sure your figure comments are substantial. Use words to paint the picture of what is displayed, not just the conclusions you want the reader to get.

    -
    -
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    Misleading Visualizations

    -

    The scales or colors we choose to use for data visualization affect how people interpret figures. You should strive to make your visualizations as accurate and as informative as possible. Here are some examples that demonstrate just how different a figure can look based on these choices you make. Do not intentionally mislead your audience!

    -

    Perhaps the most common data visualization manipulation is to change the Y-scale. If a plot does not begin at 0, small changes in magnitude can be exhaggerated. Similarly a logarithmic scale will amplify changes. This is not always disingenuous, sometimes these changes are what you want to highlight, the pattern you want to draw attention to. Just make sure it is appropriate for your use case and documented. Alternatively, extending the Y-axis too large has the opposite affect and smooths out the differences in data.

    -
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    -
    x = [1, 2, 3, 4, 5]
-y = [1101, 1011, 1111, 1070, 1050]
-
-
-fig, (ax1, ax2, ax3, ax4) = plt.subplots(4)
-fig.tight_layout()
-
-ax1.bar(x,y)
-ax1.set_title("Default Y-Scale Starts at 0")
-
-ax2.bar(x,y)
-ax2.set_ylim(1000)
-ax2.set_title("Y-Scale Starts at 1000")
-
-ax3.bar(x,y)
-ax3.set_yscale("log")
-ax3.set_title("Y-Scale is Logarithmic");
-
-ax4.bar(x,y)
-ax4.set_ylim(0, 2000)
-ax4.set_title("Y-Scale is Extended");
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    -../_images/4599f415b04d1af5f7b2d190421e92233f4bb462bd5fd0e6457cb98106afa2cb.png -
    -
    -

    Other examples of data visualization manipulation include improper scaling, cherry picking a small non-representative subset of the data to display, displaying pie charts at a slant (pie charts are hard to interpet accurately as is), and unusing unexpected colormaps.

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    Summary

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    It is important to have accurate, engaging, and representative data visualization to accumpany your research, both for data exploration as part of the scientific process, for communication of results, and education/outreach efforts. Visually we pick up on patterns that statistics alone may not convey. However, an over reliance on data visualization can make science less accessible to those with vision disabilities. It is important to be cognicent of the patterns our minds pick up, be it based on color or y-axis scaling, so that we can avoid misleading our audience and more accurately convey the narrative inherent to the data.

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    What’s next?

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    Plot Elements

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    - © 2023. - - By the Project Pythia Community. - - Last updated on 28 November 2023. -

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    - - \ No newline at end of file diff --git a/_preview/22/notebooks/how-to-cite.html b/_preview/22/notebooks/how-to-cite.html deleted file mode 100644 index 4d0c9b4..0000000 --- a/_preview/22/notebooks/how-to-cite.html +++ /dev/null @@ -1,455 +0,0 @@ - - - - - - - - How to Cite This Cookbook — Advanced Visualization Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    How to Cite This Cookbook

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    The material in this Project Pythia 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.

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    - - \ No newline at end of file diff --git a/_preview/22/notebooks/mpas-datashader.html b/_preview/22/notebooks/mpas-datashader.html deleted file mode 100644 index 6757a78..0000000 --- a/_preview/22/notebooks/mpas-datashader.html +++ /dev/null @@ -1,1467 +0,0 @@ - - - - - - - - MPAS with Datashader and Geoviews — Advanced Visualization Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    MPAS with Datashader and Geoviews

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    Overview

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    This example of interactively plotting unstructured grid MPAS data with Datashader and Geoviews demonstrates making use of the MPAS file’s connectivity information to render data on the native grid, and also -avoid costly Delaunay triangulation that is required if the MPAS connectivity information is not used, rendering data that is sampled on both the ‘primal’ and ‘dual’ MPAS mesh, using geoviews/holoviews for interactive plotting in a Jupyter Notebook. The plotting is interactive in the sense that you can pan and zoom the data. Doing so will reveal greater and greater data fidelity, and sing Datashader to perform background rendering in place of Matplotlib. Unlike Matplotlib, Datashaderwas designed for performance with large data sets.

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    1. Utility Functions

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    2. Loading Data

      3. -

    3. Using MPAS’s cell connectivity array to plot primal mesh data

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    4. Synthesizing triangles from points using Delaunay triangulation

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    5. Using MPAS’s cell connectivity array to plot dual mesh data

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    Prerequisites

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    Concepts

    Importance

    Notes

    Necessary

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    • Time to learn: X minutes

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    import cartopy.crs as ccrs
-import numpy as np
-import pandas as pd
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-import math as math
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-import geocat.datafiles as gdf  # Only for reading-in datasets
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-from xarray import open_mfdataset
-
-from numba import jit
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-import dask.dataframe as dd
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-import holoviews as hv
-from holoviews import opts
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-from holoviews.operation.datashader import rasterize as hds_rasterize 
-#import geoviews.feature as gf # only needed for coastlines
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-hv.extension("bokeh","matplotlib")
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-opts.defaults(
-    opts.Image(frame_width=600, data_aspect=1),
-    opts.RGB(frame_width=600, data_aspect=1))
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    # This funtion splits a global mesh along longitude
-#
-# Examine the X coordinates of each triangle in 'tris'. Return an array of 'tris' where only those triangles
-# with legs whose length is less than 't' are returned. 
-# 
-def unzipMesh(x,tris,t):
-    return tris[(np.abs((x[tris[:,0]])-(x[tris[:,1]])) < t) & (np.abs((x[tris[:,0]])-(x[tris[:,2]])) < t)]
-
    -
    -
    -
    -
    -
    -
    # Compute the signed area of a triangle
-#
-def triArea(x,y,tris):
-    return ((x[tris[:,1]]-x[tris[:,0]]) * (y[tris[:,2]]-y[tris[:,0]])) - ((x[tris[:,2]]-x[tris[:,0]]) * (y[tris[:,1]]-y[tris[:,0]]))
-
    -
    -
    -
    -
    -
    -
    # Reorder triangles as necessary so they all have counter clockwise winding order. CCW is what Datashader and MPL
-# require.
-#
-def orderCCW(x,y,tris):
-    tris[triArea(x,y,tris)<0.0,:] = tris[triArea(x,y,tris)<0.0,::-1]
-    return(tris)
-
    -
    -
    -
    -
    -
    -
    # Create a Holoviews Triangle Mesh suitable for rendering with Datashader
-#
-# This function returns a Holoviews TriMesh that is created from a list of coordinates, 'x' and 'y',
-# an array of triangle indices that addressess the coordinates in 'x' and 'y', and a data variable 'var'. The
-# data variable's values will annotate the triangle vertices
-#
-
-def createHVTriMesh(x,y,triangle_indices, var,n_workers=1):
-    # Declare verts array
-    verts = np.column_stack([x, y, var])
-
-
-    # Convert to pandas
-    verts_df  = pd.DataFrame(verts,  columns=['x', 'y', 'z'])
-    tris_df   = pd.DataFrame(triangle_indices, columns=['v0', 'v1', 'v2'])
-
-    # Convert to dask
-    verts_ddf = dd.from_pandas(verts_df, npartitions=n_workers)
-    tris_ddf = dd.from_pandas(tris_df, npartitions=n_workers)
-
-    # Declare HoloViews element
-    tri_nodes = hv.Nodes(verts_ddf, ['x', 'y', 'index'], ['z'])
-    trimesh = hv.TriMesh((tris_ddf, tri_nodes))
-    return(trimesh)
-
    -
    -
    -
    -
    -
    -
    # Triangulate MPAS primary mesh:
-#
-# Triangulate each polygon in a heterogenous mesh of n-gons by connecting
-# each internal polygon vertex to the first vertex. Uses the MPAS
-# auxilliary variables verticesOnCell, and nEdgesOnCell.
-#
-# The function is decorated with Numba's just-in-time compiler so that it is translated into
-# optimized machine code for better peformance
-#
-
-@jit(nopython=True)
-def triangulatePoly(verticesOnCell, nEdgesOnCell):
-
-    # Calculate the number of triangles. nEdgesOnCell gives the number of vertices for each cell (polygon)
-    # The number of triangles per polygon is the number of vertices minus 2.
-    #
-    nTriangles = np.sum(nEdgesOnCell - 2)
-
-    triangles = np.ones((nTriangles, 3), dtype=np.int64)
-    nCells = verticesOnCell.shape[0]
-    triIndex = 0
-    for j in range(nCells):
-        for i in range(nEdgesOnCell[j]-2):
-            triangles[triIndex][0] = verticesOnCell[j][0]
-            triangles[triIndex][1] = verticesOnCell[j][i+1]
-            triangles[triIndex][2] = verticesOnCell[j][i+2]
-            triIndex += 1
-
-    return triangles
-
    -
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    -

    Load data and coordinates

    -

    The global data sets used in this example are from the same experiment, but run at several resolutions from -30km to 3.75km. Due to their size, the higher resolution data sets are only distributed with two variables -in them:

    -
      -

    • relhum_200hPa: Relative humidity vertically interpolated to 200 hPa

      • -

    • vorticity_200hPa: Relative vorticity vertically interpolated to 200 hPa

      • -
    -

    The dyamond_1 data set is available in several resolutions, ranging from 30 km to 3.75 km.

    -

    Currently, the 30-km resolution dataset used in this example is available at geocat-datafiles. -However, the other resolutions of these data are stored on Glade because of their size.

    -

    The relhum_200hPa is computed on the MPAS ‘primal’ mesh, while the vorticity_200hPa is computed on the MPAS -‘dual’ mesh. Note that data may also be sampled on the edges of the primal mesh. This example does not -include/cover edge-centered data.

    -

    These data are courtesy of NCAR’s Falko Judt, and were produced as part of the DYAMOND initiative: -http://dx.doi.org/10.1186/s40645-019-0304-z.

    -
    -
    -

    Using MPAS’s cell connectivity array to plot primal mesh data

    -

    In this example we use the MPAS cellsOnVertex auxilliary variable, which defines mesh connectivity for the MPAS grid. -Specifically, this variable tells us the cell IDs for each cell that contains each vertex.

    -

    The benefits of this are twofold: 1. We’re using the actual mesh description from the MPAS output file; and 2. -For large grid this is much faster than synthesizing the connectivity information as is done -in the next example

    -
    -
    -

    Synthesizing triangles from points using Delaunay triangulation

    -

    In this second example we do not use the triangle connectivity information stored in the MPAS file. Instead we -use Delaunay triangulation to artifically create a triangle mesh. The benefit of this approach is that we do not -need the MPAS cellsOnVertex variable if it is not available. Also, since the triangulation algorithm is run on the -coordinates after they are projected to meters we do not have to worry about wraparound. The downside is that for -high-resolution data Delaunay triangulation is prohibitively expensive. The highest resolution data set included -in this notebook (3.75km) will not triangulate in a reasonable amount of time, if at all

    -
    -
    -

    Using MPAS’s cell connectivity array to plot dual mesh data

    -

    In this example we use the MPAS verticesOnCell and nEdgesOnCell auxilliary variables, which defines mesh connectivity for the -MPAS dual grid.

    -

    As with the first example using the MPAS primal grid, data on the dual grid could be plotted by first -triangulating them with, for example, Delaunay triangulation. But using grid’s native connectivity information -is faster.

    -
    -
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    Summary

    -
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    What’s next?

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    Resources and references

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    - © 2023. - - By the Project Pythia Community. - - Last updated on 28 November 2023. -

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    - - \ No newline at end of file diff --git a/_preview/22/notebooks/notebook-template.html b/_preview/22/notebooks/notebook-template.html deleted file mode 100644 index ab3c306..0000000 --- a/_preview/22/notebooks/notebook-template.html +++ /dev/null @@ -1,719 +0,0 @@ - - - - - - - - Project Pythia Notebook Template — Advanced Visualization Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    - On this page -
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    Let’s start here! If you can directly link to an image relevant to your notebook, such as canonical logos, do so here at the top of your notebook. You can do this with Markdown syntax,

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    ![<image title>](http://link.com/to/image.png "image alt text")

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    or edit this cell to see raw HTML img demonstration. This is preferred if you need to shrink your embedded image. Either way be sure to include alt text for any embedded images to make your content more accessible.

    -

    Project Pythia Logo

    -
    -

    Project Pythia Notebook Template

    -

    Next, title your notebook appropriately with a top-level Markdown header, #. Do not use this level header anywhere else in the notebook. Our book build process will use this title in the navbar, table of contents, etc. Keep it short, keep it descriptive. Follow this with a --- cell to visually distinguish the transition to the prerequisites section.

    -
    -
    -

    Overview

    -

    If you have an introductory paragraph, lead with it here! Keep it short and tied to your material, then be sure to continue into the required list of topics below,

    -
      -

    1. This is a numbered list of the specific topics

      2. -

    2. These should map approximately to your main sections of content

      3. -

    3. Or each second-level, ##, header in your notebook

      4. -

    4. Keep the size and scope of your notebook in check

      5. -

    5. And be sure to let the reader know up front the important concepts they’ll be leaving with

      6. -
    -
    -
    -

    Prerequisites

    -

    This section was inspired by this template of the wonderful The Turing Way Jupyter Book.

    -

    Following your overview, tell your reader what concepts, packages, or other background information they’ll need before learning your material. Tie this explicitly with links to other pages here in Foundations or to relevant external resources. Remove this body text, then populate the Markdown table, denoted in this cell with | vertical brackets, below, and fill out the information following. In this table, lay out prerequisite concepts by explicitly linking to other Foundations material or external resources, or describe generally helpful concepts.

    -

    Label the importance of each concept explicitly as helpful/necessary.

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

    Concepts

    Importance

    Notes

    Intro to Cartopy

    Necessary

    Understanding of NetCDF

    Helpful

    Familiarity with metadata structure

    Project management

    Helpful

    -
      -

    • Time to learn: estimate in minutes. For a rough idea, use 5 mins per subsection, 10 if longer; add these up for a total. Safer to round up and overestimate.

      • -

    • System requirements:

      -
        -

      • Populate with any system, version, or non-Python software requirements if necessary

        • -

      • Otherwise use the concepts table above and the Imports section below to describe required packages as necessary

        • -

      • If no extra requirements, remove the System requirements point altogether

        • -
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    -
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    Imports

    -

    Begin your body of content with another --- divider before continuing into this section, then remove this body text and populate the following code cell with all necessary Python imports up-front:

    -
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    -
    import sys
-
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    -
    -
    -
    -

    Your first content section

    -

    This is where you begin your first section of material, loosely tied to your objectives stated up front. Tie together your notebook as a narrative, with interspersed Markdown text, images, and more as necessary,

    -
    -
    -
    # as well as any and all of your code cells
-print("Hello world!")
-
    -
    -
    -
    -
    Hello world!
-
    -
    -
    -
    -
    -

    A content subsection

    -

    Divide and conquer your objectives with Markdown subsections, which will populate the helpful navbar in Jupyter Lab and here on the Jupyter Book!

    -
    -
    -
    # some subsection code
-new = "helpful information"
-
    -
    -
    -
    -
    -
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    Another content subsection

    -

    Keep up the good work! A note, try to avoid using code comments as narrative, and instead let them only exist as brief clarifications where necessary.

    -
    -
    -
    -

    Your second content section

    -

    Here we can move on to our second objective, and we can demonstrate

    -
    -

    Subsection to the second section

    -
    -

    a quick demonstration

    -
    -
    of further and further
    -
    -
    header levels
    -

    as well \(m = a * t / h\) text! Similarly, you have access to other \(\LaTeX\) equation functionality via MathJax (demo below from link),

    -
    -()\[\begin{align} -\dot{x} & = \sigma(y-x) \\ -\dot{y} & = \rho x - y - xz \\ -\dot{z} & = -\beta z + xy -\end{align}\]
    -

    Check out any number of helpful Markdown resources for further customizing your notebooks and the Jupyter docs for Jupyter-specific formatting information. Don’t hesitate to ask questions if you have problems getting it to look just right.

    -
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    -
    -

    Last Section

    -

    If you’re comfortable, and as we briefly used for our embedded logo up top, you can embed raw html into Jupyter Markdown cells (edit to see):

    -
    -

    Info

    -

    Your relevant information here!

    -
    -

    Feel free to copy this around and edit or play around with yourself. Some other admonitions you can put in:

    -
    -

    Success

    -

    We got this done after all!

    -
    -
    -

    Warning

    -

    Be careful!

    -
    -
    -

    Danger

    -

    Scary stuff be here.

    -
    -

    We also suggest checking out Jupyter Book’s brief demonstration on adding cell tags to your cells in Jupyter Notebook, Lab, or manually. Using these cell tags can allow you to customize how your code content is displayed and even demonstrate errors without altogether crashing our loyal army of machines!

    -
    -
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    -

    Summary

    -

    Add one final --- marking the end of your body of content, and then conclude with a brief single paragraph summarizing at a high level the key pieces that were learned and how they tied to your objectives. Look to reiterate what the most important takeaways were.

    -
    -

    What’s next?

    -

    Let Jupyter book tie this to the next (sequential) piece of content that people could move on to down below and in the sidebar. However, if this page uniquely enables your reader to tackle other nonsequential concepts throughout this book, or even external content, link to it here!

    -
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    Resources and references

    -

    Finally, be rigorous in your citations and references as necessary. Give credit where credit is due. Also, feel free to link to relevant external material, further reading, documentation, etc. Then you’re done! Give yourself a quick review, a high five, and send us a pull request. A few final notes:

    -
      -

    • Kernel > Restart Kernel and Run All Cells... to confirm that your notebook will cleanly run from start to finish

      • -

    • Kernel > Restart Kernel and Clear All Outputs... before committing your notebook, our machines will do the heavy lifting

      • -

    • Take credit! Provide author contact information if you’d like; if so, consider adding information here at the bottom of your notebook

      • -

    • Give credit! Attribute appropriate authorship for referenced code, information, images, etc.

      • -

    • Only include what you’re legally allowed: no copyright infringement or plagiarism

      • -
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    Thank you for your contribution!

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    - - \ No newline at end of file diff --git a/_preview/22/notebooks/plot-elements.html b/_preview/22/notebooks/plot-elements.html deleted file mode 100644 index be5b7ca..0000000 --- a/_preview/22/notebooks/plot-elements.html +++ /dev/null @@ -1,1144 +0,0 @@ - - - - - - - - Plot Elements — Advanced Visualization Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Plot Elements

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    Prerequisites

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    Concepts

    Importance

    Notes

    Matplotlib

    Necessary

    Cartopy

    Necessary

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    • Time to learn: 40 minutes

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    import xarray as xr
-import numpy as np
-import matplotlib.pyplot as plt
-import geocat.viz as gv
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    Data

    -

    The first piece of data visualization is the data!

    -

    Let’s generate some dummy data to work with:

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    x = np.linspace(0, 20, 1000)
-y1 = np.sin(x)
-y2 = np.cos(x)
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    Figure

    -

    The figure is the object that contains your entire visualization. Creating a figure tends to be the first step in plotting, even if it doesn’t currently show anything:

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    fig = plt.figure(figsize=(9.5, 8))
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    <Figure size 950x800 with 0 Axes>
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    Axis

    -

    We then add axes to our plot. You can add multiple axes to one plot in order to produce subplots, or just one. Axes will automatically inherit their limits from the data plotted, or can be manually set.

    -
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    fig = plt.figure(figsize=(9.5, 8))
-ax = plt.axes()
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    -../_images/735794c51ad88bfd2928f35f08230fef70ac649d161831ab6fcd351d6606e184.png -
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    Plot

    -

    Adding the data to the figure can be done through several different plot types: line, contour, bar, histogram. Here we use two line plots:

    -
    -
    -
    fig = plt.figure(figsize=(9.5, 8))
-ax = plt.axes()
-
-ax.plot(x,y1)
-ax.plot(x,y2);
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    Titles and Labels

    -

    Titles and labels are important for indicating what the figure is plotting. It is a good idea to include relevant units in your axis labels.

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    -
    fig = plt.figure(figsize=(9.5, 8))
-ax = plt.axes()
-
-ax.plot(x,y1)
-ax.plot(x,y2)
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-ax.set_title("Dummy Data")
-ax.set_xlabel("X (radians)");
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    Legends

    -

    If you’re plotting multiple lines of data, it’s a good idea to include a legend. Here is how you call or point to the legend:

    -
    -
    -
    fig = plt.figure(figsize=(9.5, 8))
-ax = plt.axes()
-
-ax.plot(x,y1,label='sine')
-ax.plot(x,y2,label='cosine')
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-ax.set_title("Dummy Data")
-ax.set_xlabel("X (radians)")
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-plt.legend(loc="upper left");
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    Colorbars

    -

    While legends are more appropriate for line or bar plots, colorbars are most commonly used for contour plots and sometimes to apply a third level of dimension to a scatter plot.

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    Let’s shift our example to better demonstrate a colorbar by workign with a filled contour plot:

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    -
    # Generate dummy data
-data = [[1, 4, 5, 6, 8.2],
-        [9, 8.4, 10, 10.6, 9.7],
-        [4.4, 5, 0, 6.6, 1.4],
-        [4.6, 5.2, 1.5, 7.6, 2.4]]
-
-# Convert data into type xarray.DataArray
-data = xr.DataArray(data,
-                    dims=["lat", "lon"],
-                    coords=dict(lat=np.arange(4), lon=np.arange(5)))
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-data
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    <xarray.DataArray (lat: 4, lon: 5)>
-array([[ 1. ,  4. ,  5. ,  6. ,  8.2],
-       [ 9. ,  8.4, 10. , 10.6,  9.7],
-       [ 4.4,  5. ,  0. ,  6.6,  1.4],
-       [ 4.6,  5.2,  1.5,  7.6,  2.4]])
-Coordinates:
-  * lat      (lat) int64 0 1 2 3
-  * lon      (lon) int64 0 1 2 3 4
    -
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    -
    fig = plt.figure(figsize=(9.5, 8))
-ax = plt.axes()
-
-pcm = ax.contourf(data,cmap='viridis')
-
-ax.set_title("Dummy Data")
-ax.set_xlabel("Longitude (\N{DEGREE SIGN})")
-ax.set_ylabel("Latitude (\N{DEGREE SIGN})")
-
-fig.colorbar(pcm,ax=ax);
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    Annotations

    -

    Additional annotations allow you to specify some text and a location to indicate almost anything.

    -

    Here we use GeoCAT-viz to add annotations to the maxima in a contour plot:

    -
    -
    -
    fig = plt.figure(figsize=(9.5, 8))
-ax = plt.axes()
-
-pcm = ax.contourf(data,cmap='viridis')
-
-ax.set_title("Dummy Data")
-ax.set_xlabel("Longitude (\N{DEGREE SIGN})")
-ax.set_ylabel("Latitude (\N{DEGREE SIGN})")
-
-fig.colorbar(pcm,ax=ax)
-
-# Find local maximum with GeoCAT-Viz find_local_extrema
-lmax = gv.find_local_extrema(data, eType='High')[0]
-
-# Plot labels for local mins
-max_value = data.data[lmax[1]][lmax[0]]
-ax.text(lmax[0], lmax[1],'Maxima = '+str(max_value))
-
-# Show plot
-plt.show();
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    Summary

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    There are several key elements to a Python plot and knowing what they are called is instrumental to begin your journey for further customization.

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    Resources and references

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    previous

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    What Makes for Good Data Visualization?

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    Taylor Diagrams

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    Skew T Diagrams

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    Overview

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    Summary text here

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    Prerequisites

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    Concepts

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    Necessary

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    • Time to learn: X minutes

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    What is a Skew-T plot?

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    import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
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-from metpy.plots import SkewT
-import metpy.calc as mpcalc
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    If you want to get your own sounding data, run the following code in a new cell using the date and station of your choice:

    -
    from datetime import datetime
-from siphon.simplewebservice.wyoming import WyomingUpperAir
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-date = datetime(2023, 11, 20, 12)
-station = 'GJT'
-df = WyomingUpperAir.request_data(date, station)|
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    We’ve already done this for you and saved the data in a file, notebooks/data/gjt_sounding.csv for you to use. We’ll use that file’s data for the rest of the notebook

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    df = pd.read_csv('data/gjt_sounding.csv')
-df
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    pressureheighttemperaturedewpointdirectionspeedu_windv_windstationstation_numbertimelatitudelongitudeelevationpw
    0853.014755.22.0305.03.02.457456-1.720729GJT724762023-11-20 12:00:0039.11-108.531475.09.12
    1850.015087.21.2280.013.012.802501-2.257426GJT724762023-11-20 12:00:0039.11-108.531475.09.12
    2848.015277.41.4287.013.012.431962-3.800832GJT724762023-11-20 12:00:0039.11-108.531475.09.12
    3831.016936.4-2.6350.015.02.604723-14.772116GJT724762023-11-20 12:00:0039.11-108.531475.09.12
    4820.018025.4-2.810.013.0-2.257426-12.802501GJT724762023-11-20 12:00:0039.11-108.531475.09.12
    ................................................
    14713.428951-55.7-85.753.017.0-13.576804-10.230855GJT724762023-11-20 12:00:0039.11-108.531475.09.12
    14813.029144-55.5-85.575.020.0-19.318517-5.176381GJT724762023-11-20 12:00:0039.11-108.531475.09.12
    14912.729293-55.3-85.3NaNNaNNaNNaNGJT724762023-11-20 12:00:0039.11-108.531475.09.12
    15012.129601-55.5-85.5NaNNaNNaNNaNGJT724762023-11-20 12:00:0039.11-108.531475.09.12
    15112.029654-55.7-85.7NaNNaNNaNNaNGJT724762023-11-20 12:00:0039.11-108.531475.09.12
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    152 rows × 15 columns

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    p = df['pressure'].values
-T = df['temperature'].values
-Td = df['dewpoint'].values
-u = df['u_wind'].values
-v = df['v_wind'].values
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    Elements of a Skew-T Plot

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    Let’s start out by talking about the structural elements of a Skew-T plot.

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    1. Temperature Lines: These lines are drawn at an angle up from the x-axis and are where the name “Skew-T” comes from.

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    2. Pressure Lines: These lines are horizontal from the y-axis, where pressure is plotted at a logarithmic scale.

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    3. Dry Adiabats:

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    4. Moist Adiabats:

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    5. Mixing Ratio Lines:

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    On all those structural elements, Skew-T plots have two lines plotted on them, air temperature and dew point. In this notebook, we’ll be plotting the air temperature in red and the dew point in blue.

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    Additionally, Skew-T plots have wind barbs. These describe the wind speed and direction at different pressure levels and are plotted on the right side of the diagram.

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    Tip

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    For a more detailed description and a cool interactive diagram, visit NOAA’s Skew-T page.

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    Making a Skew-T plot in Python (with MetPy!)

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    So, all of that might seem a little abstract without a visual. We’re going to use MetPy’s SkewT module to make an actual Skew-T plot with the sounding data we downloaded earlier.

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    From the MetPy documentation:

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    “This class simplifies the process of creating Skew-T log-P plots in using matplotlib. It handles requesting the appropriate skewed projection, and provides simplified wrappers to make it easy to plot data, add wind barbs, and add other lines to the plots (e.g. dry adiabats)”

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    Just the basics

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    To start with, let’s create a very minimal Skew-T plot with just the pressure and temperature lines under the sounding data.

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    # make figure and `SkewT` object
-fig = plt.figure(figsize=(9, 9))
-skewt = SkewT(fig=fig)
-
-# plot sounding data
-skewt.plot(p, T, 'r') # air temperature
-skewt.plot(p, Td, 'b') # dew point
-skewt.plot_barbs(p, u, v) # wind barbs
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    <matplotlib.quiver.Barbs at 0x7f8a710523e0>
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    -../_images/0ccb04ef518fc4756b7d46ffaa1a95ec3b849e9a686f4a56079d46e719a803f2.png -
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    Then, we’re going to plot the air temperature and dew point and add the wind barbs.

    -

    Let’s talk break that down a bit.

    -
    # make figure and `SkewT` object
-fig = plt.figure(figsize=(9, 9))
-skewt = SkewT(fig=fig)
-
-# plot sounding data
-skewt.plot(p, T, 'r') # air temperature
-skewt.plot(p, Td, 'b') # dew point
-skewt.plot_barbs(p, u, v) # wind barbs
-
    -
    -

    First, we’ll make a new figure and use that figure to make a new skew-T plot. If you don’t provide a figure to SkewT, one will be created for you, but it’s useful to make the default figure size a bit larger for this tutorial.

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    First, we’re

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    dir(skewt)
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    Summary

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    What’s next?

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    previous

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    Taylor Diagrams

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    Spagetti Plots

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    - © 2023. - - By the Project Pythia Community. - - Last updated on 28 November 2023. -

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    - - \ No newline at end of file diff --git a/_preview/22/notebooks/spagetti.html b/_preview/22/notebooks/spagetti.html deleted file mode 100644 index f50462b..0000000 --- a/_preview/22/notebooks/spagetti.html +++ /dev/null @@ -1,2155 +0,0 @@ - - - - - - - - Spagetti Plots — Advanced Visualization Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Spagetti Plots

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    Overview

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    Spagetti Plots are a tool typically used to visualize movement. Essentially they are many line plots displayed on the same axes. By drawing the same path at different times or from different forecasts, we can see the patterns and chaos associated with the plotted variable.

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    1. Spagetti Hurricane Plot

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    2. Spagetti Contour Plot

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    import numpy as np
-import xarray as xr
-import datetime
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-import matplotlib as mpl
-import matplotlib.pyplot as plt
-import matplotlib.ticker as mticker
-import matplotlib.pylab as pl
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-import cartopy.crs as ccrs
-import cartopy.feature as cfeature
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-import geocat.viz as gv
-import geocat.datafiles as gdf
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-import tropycal.tracks as tracks
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-import warnings
-warnings.filterwarnings('ignore')
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    Spagetti Hurricane Plot

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    Visualizing the predicted path of an incoming hurricane is both complicated and important. There are many plots that meteorologists are trained to read, but when shared with the public can be confusing or alarming. There are strengths and weaknesses to each hurricane visualization approach. The cone of uncertainty, for example, is often misinterpreted to suggest the hurricane growth in time rather than widening of path possibilities. Spagetti plots on the other hand, clearly show hurricane paths but show them as equal to each other.

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    In this example we will plot some forecasted paths from the 2012 North-Atlantic storm Hurricane Sandy. Each forecast is from the Global Ensemble Forecast System (GEFS) provided by the National Centers for Environmental Prediction at NOAA.

    -

    We’ll use the package tropycal to easily access HURDAT2 and IBTrACS reanalysis data and operational National Hurricane Center (NHC) Best Track data. tropycal has a lot of great features for real time hurricane visualization, but since this Cookbook is comparatively static we’re using a past hurricane and only using this package to access the data. Our plotting will be done with matplotlib and cartopy.

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    Read in Data

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    First, to grab our hurricane data from tropycal we need to specify a basin:

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    basin = tracks.TrackDataset(basin='north_atlantic')
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    --> Starting to read in HURDAT2 data
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    --> Completed reading in HURDAT2 data (1.57 seconds)
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    Find your storm by name and year:

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    storm = basin.get_storm(('sandy',2012))
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-sandy_ds = storm.to_xarray()
-sandy_ds
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    <xarray.Dataset>
-Dimensions:    (time: 45)
-Coordinates:
-  * time       (time) datetime64[ns] 2012-10-21T18:00:00 ... 2012-10-31T12:00:00
-Data variables:
-    extra_obs  (time) int64 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 1 1 0 0 0 0 0 0 0
-    special    (time) <U1 '' '' '' '' '' '' '' '' ... 'L' '' '' '' '' '' '' ''
-    type       (time) <U2 'LO' 'LO' 'LO' 'TD' 'TS' ... 'EX' 'EX' 'EX' 'EX' 'EX'
-    lat        (time) float64 14.3 13.9 13.5 13.1 12.7 ... 40.4 40.7 41.1 41.5
-    lon        (time) float64 -77.4 -77.8 -78.2 -78.6 ... -79.8 -80.3 -80.7
-    vmax       (time) int64 25 25 25 30 35 40 40 40 ... 70 70 55 50 40 35 35 30
-    mslp       (time) int64 1006 1005 1003 1002 1000 998 ... 978 986 992 993 995
-    wmo_basin  (time) <U14 'north_atlantic' ... 'north_atlantic'
-Attributes:
-    id:              AL182012
-    operational_id:  AL182012
-    name:            SANDY
-    year:            2012
-    season:          2012
-    basin:           north_atlantic
-    source_info:     NHC Hurricane Database
-    source:          hurdat
-    ace:             13.6675
    -
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    And we can grab any of a number of forecasts:

    -
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    forecasts = storm.get_operational_forecasts()
-print(forecasts.keys())
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    dict_keys(['CARQ', 'CMC', 'NAM', 'NGX', 'UKX', 'AC00', 'AEM2', 'AEMN', 'AP01', 'AP02', 'AP03', 'AP04', 'AP05', 'AP06', 'AP07', 'AP08', 'AP09', 'AP10', 'AP12', 'AP13', 'AP14', 'AP15', 'AP16', 'AP17', 'AP18', 'AP20', 'AVN2', 'AVNO', 'BAMD', 'BAMM', 'BAMS', 'CEMN', 'CLIP', 'CLP5', 'CMC2', 'COT2', 'COTC', 'DSHP', 'FIM9', 'FM92', 'G012', 'GFD2', 'GFDE', 'GFDL', 'GFDT', 'GFT2', 'GHM2', 'GP01', 'GPM2', 'GPMN', 'HWE2', 'HWF2', 'HWFE', 'HWRF', 'ICON', 'IV15', 'IVCN', 'IVCR', 'LBAR', 'LGEM', 'NAM2', 'NGX2', 'OFCP', 'OFP2', 'SHF5', 'SHFR', 'SHIP', 'TCLP', 'TV15', 'TVCA', 'TVCC', 'TVCE', 'TVCN', 'UWN2', 'UWN8', 'XTRP', 'ZGFS', 'AEMI', 'AP11', 'AP19', 'AVNI', 'CMCI', 'COTI', 'FM9I', 'GFDI', 'GFTI', 'GHMI', 'GPMI', 'HWFI', 'NAMI', 'NGXI', 'OFPI', 'RI25', 'SPC3', 'UKXI', 'DRCL', 'GFE2', 'MRCL', 'MRFO', 'UKX2', 'UKM', 'AHW4', 'G01I', 'OFCL', 'OCD5', 'BCD5', 'OCS5', 'BCS5', 'OFCI', 'UKMI', 'AHW2', 'EGRR', 'FSSE', 'RI30', 'RI35', 'RYOC', 'UKM2', 'AHWI', 'EGRI', 'TCOA', 'EGR2', 'UWNI', 'HWEI', 'APSU', 'APSI', 'APS2', 'OFC2'])
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    Each key represents a forecast model, we’ll select the GFS AP01 forecast which has many initializations. These initializations are named by time in YYYYMMDDHH format:

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    forecasts_AP01 = forecasts['AP01']
-print(forecasts_AP01.keys())
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    dict_keys(['2012102112', '2012102118', '2012102200', '2012102206', '2012102212', '2012102218', '2012102300', '2012102306', '2012102312', '2012102318', '2012102400', '2012102406', '2012102412', '2012102418', '2012102500', '2012102506', '2012102512', '2012102518', '2012102600', '2012102606', '2012102612', '2012102618', '2012102700', '2012102706', '2012102712', '2012102718', '2012102800', '2012102806', '2012102812', '2012102818', '2012102900', '2012102906', '2012102912', '2012102918', '2012103000', '2012103006', '2012103012', '2012103018', '2012103100'])
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    Spagetti Plot of One Esemble Member

    -

    Looking at GFS Ensemble Member Forecast AP01, we can make a spagetti plot of each of these initializations. The crux of the plot is that we need a for loop through each initialization:

    -
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    -
    # Set up Cartopy Projection with land features
-ax = plt.axes(projection=ccrs.PlateCarree())
-ax.add_feature(cfeature.LAND, facecolor='lightgray')
-
-# Add Gridlines to right and bottom
-gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
-                  linewidth=.25, color='gray', alpha=0.5, linestyle='--')
-gl.xlabels_top = False
-gl.ylabels_left = False
-gl.xlabel_style = {'size': 8,}
-gl.ylabel_style = {'size': 8,}
-
-# Spagetti Plot of AP01 forecasts
-forecasts_AP01 = forecasts['AP01']
-for i in forecasts_AP01:
-    # We're naming this line even though it is over-written each loop,
-    # so that we can reference the last line in the legend
-    # (as they all share the same formatting)
-    forecast_path = plt.plot(forecasts_AP01[i]['lon'],
-        forecasts_AP01[i]['lat'],
-        color='cornflowerblue',
-        linewidth=0.5)
-
-# Plot the real storm path in a thicker black line
-true_path = plt.plot(sandy_ds.lon,
-    sandy_ds.lat,
-    color='k',
-    linewidth=1) # Make it thicker than the ensemble paths
-
-# Add a legend with only one forecast_path and the true_path
-plt.legend([true_path[0], forecast_path[0]], ['True Path', 'GFS AP01 Forecasts'])
-
-plt.title('Hurricane Sandy (2012)');
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    -../_images/8f75a2cfe44648efbd4ab8de68816be34f387e77f339b13a7f13517cbf1d9f51.png -
    -
    -

    This plot is a great example of a spagetti plot, but is it super useful? Is it confusing? Each line looks like it carries the same weight, when some of these possible paths are from hours before Sandy hit the NorthEast and others are from days before.

    -

    Maybe it is better to show the user some indication of how the forecast for this ensemble converged on the true path with later and later initialization times.

    -
    -
    -

    Spagetti Plot of One Esemble Member with Temporal Colormapping

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    -
    # Set up Cartopy Projection with land features
-ax = plt.axes(projection=ccrs.PlateCarree())
-ax.add_feature(cfeature.LAND, facecolor='lightgray')
-
-# Add Gridlines to right and bottom
-gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
-                  linewidth=.25, color='gray', alpha=0.5, linestyle='--')
-gl.xlabels_top = False
-gl.ylabels_left = False
-gl.xlabel_style = {'size': 8,}
-gl.ylabel_style = {'size': 8,}
-
-# Spagetti Plot of AP01 forecasts
-forecasts_AP01 = forecasts['AP01']
-
-# Get time information from initialization name
-format = '%Y%m%d%H'
-times = [datetime.datetime.strptime(i, format) for i in list(forecasts_AP01.keys())]
-normalized_times = [(i - times[0]) / (times[-1] - times[0]) for i in times]
-
-# Create a color list for forecast iteration
-cmap = mpl.colors.ListedColormap(plt.cm.autumn_r(normalized_times))
-
-c = 0
-for i in forecasts_AP01:
-    plt.plot(forecasts_AP01[i]['lon'],
-        forecasts_AP01[i]['lat'],
-        color=cmap(c),
-        linewidth=0.5)
-    c += 1
-
-# Plot the real storm path
-true_path = plt.plot(sandy_ds.lon,
-    sandy_ds.lat,
-    color='red', # Selecting a color matching one of the cmap extremes
-    linewidth=1,
-    label='True Path') # The easiest way to add a plot to the legend is with the label kwarg
-
-# Add a legend with only one the true_path
-# Forecasted paths will be shown in a colorbar
-plt.legend()
-
-plt.title('Hurricane Sandy')
-
-# Add colorbar
-cbar = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap), ax=ax, orientation='horizontal', shrink=0.8, pad=0.075)
-cbar.set_label('GFS AP01 Forecasts', labelpad=6)
-
-# Set tick locations and labels for every 4th tick
-# i.e. once a day (a new initialiation every 6 hours)
-tick_indices = range(0, len(times), 4)
-cbar.set_ticks([normalized_times[i] for i in tick_indices])
-cbar.set_ticklabels([times[i].strftime('%d') for i in tick_indices], fontsize=8)
-cbar.ax.text(1.02, 0.5, 'OCT-2012', va='top', ha='left', transform=cbar.ax.transAxes);
-
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    -../_images/aff8248aaf9a3c9d9f7141986022d58ba3c1e3a414d0fe3dee9cc3bceee2cd49.png -
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    Now we can see that as the storm progressed, the AP01 GFS Forecast Ensemble Member converges on Sandy’s true path as the storm progresses through October, 2012.

    -

    Alternatively, we may want to plot the possible hurricane paths from multiple GFS Forecast Ensemble members from the same iteration timestamp as a spagetti plot.

    -
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    Spagetti Plot of All Esemble Members at One Point in Time

    -

    First, we need to grab all of the relevant forecast keys to GFS models (the ones that are titled AP## from 0 to 20):

    -
    -
    -
    # List of valid AP## keys from 0 to 20
-GFS_keys = ['AP' + str(i).zfill(2) for i in range(1, 21)]
-
-# Arbitrarily selected midnight on October 27, 2012 to plot all forecasts at
-time = '2012102700'
-
-# Set up Cartopy Projection with land features
-ax = plt.axes(projection=ccrs.PlateCarree())
-ax.add_feature(cfeature.LAND, facecolor='lightgray')
-
-# Add Gridlines to right and bottom
-gl = ax.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
-                  linewidth=.25, color='gray', alpha=0.5, linestyle='--')
-gl.xlabels_top = False
-gl.ylabels_left = False
-gl.xlabel_style = {'size': 8,}
-gl.ylabel_style = {'size': 8,}
-
-# Spagetti Plot of forecasts
-for i in range(20):
-    ap = forecasts[GFS_keys[i]]
-    forecast_path = plt.plot(ap[time]['lon'],
-        ap[time]['lat'],
-        color='cornflowerblue',
-        linewidth=0.5)
-
-# Plot the real storm path in a thicker black line
-true_path = plt.plot(sandy_ds.lon, sandy_ds.lat, color='k', linewidth=1)
-
-# Add a legend with only one forecast_path and the true_path
-plt.legend([true_path[0], forecast_path[0]],
-    ['True Path', 'AP01 - AP20'],
-    loc='lower right')
-
-plt.title('Hurricane Sandy - GFS Forecasts from Oct-27-2012');
-
    -
    -
    -
    -../_images/0b0a78930c416817bd0f84619dd4d0d624f65427fb8550d280e3644eddb118fd.png -
    -
    -

    Hurricane Sandy hit the NorthEast on October 29, 2012. From this spagetti plot we can see that by the 27th most ensemble members of the GFS forecast predicted a similar behavior for the storm.

    -

    There is more analysis that could be done on hurriane trajectories. We have covered some plotting customization that might be useful for your analysis and data visualization.

    -
    -
    -
    -

    Spagetti Contour Plot

    -

    In this example we will read in the geopotential height datafile HGT500_MON_1958-1997.nc from using geocat-datafiles. Then we will look at different timesteps of the HGT geopotential height variable at the 5500 gpm level, plotting this contour’s locations through time. This example is adapted from GeoCAT’s NCL_conOncon_5 script.

    -
    -

    Read in data:

    -
    -
    -
    ds = xr.open_dataset(gdf.get("netcdf_files/HGT500_MON_1958-1997.nc"),
-                     decode_times=False)
-
-ds
-
    -
    -
    -
    -
    Downloading file 'netcdf_files/HGT500_MON_1958-1997.nc' from 'https://github.com/NCAR/GeoCAT-datafiles/raw/main/netcdf_files/HGT500_MON_1958-1997.nc' to '/home/runner/.cache/geocat'.
-
    -
    -
    - - - - - - - - - - - - - - -
    <xarray.Dataset>
-Dimensions:  (time: 480, lat: 73, lon: 144)
-Coordinates:
-  * time     (time) int64 0 1 2 3 4 5 6 7 8 ... 472 473 474 475 476 477 478 479
-  * lat      (lat) float32 -90.0 -87.5 -85.0 -82.5 -80.0 ... 82.5 85.0 87.5 90.0
-  * lon      (lon) float32 0.0 2.5 5.0 7.5 10.0 ... 350.0 352.5 355.0 357.5
-Data variables:
-    yrmon    (time) float64 ...
-    HGT      (time, lat, lon) float32 ...
-Attributes:
-    conventions:    None
-    history:        NCEP/NCAR REANALYSIS MONTHLY MEAN SUBSETS\nftp://ncardata...
-    source:         NCEP Reanalysis; ds090.2
-    title:          500mb Geopotential Height: 1958-1997
-    source_mss:     /SHEA/HVL/HGT_1958-1997.nc:  500 mb extracted
-    creation_date:  creation date: Tue Aug  7 16:31:48 MDT 2001
    -
    -
    -
    -

    Initial Spagetti Plot on North Polar Stereographic Projection

    -
    -
    -
    # Set up Cartopy Map Projection
-fig = plt.figure(figsize=(8, 8))
-ax = plt.axes(projection=ccrs.NorthPolarStereo())
-
-gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)
-ax.add_feature(cfeature.LAND, facecolor='lightgray')
-
-# Set draw_labels to False so that we can manually manipulate it
-gl = ax.gridlines(ccrs.PlateCarree(),
-                  draw_labels=False,
-                  linestyle="--",
-                  linewidth=1,
-                  color='darkgray',
-                  zorder=2)
-
-# Iterate through the 19 timesteps, plotting the data
-n = 19
-for x in range(n):
-
-    # Get a slice of data at the 12*x timestep
-    p = ds.HGT.isel(time=12*x)
-
-    # Use geocat-viz utility function to handle the no-shown-data artifact
-    # of 0 and 360-degree longitudes
-    slon = gv.xr_add_cyclic_longitudes(p, "lon")
-
-    # Plot contour data at pressure level 5500 for the 12*x timestep
-    p = slon.plot.contour(ax=ax,
-                          transform=ccrs.PlateCarree(),
-                          linewidths=0.5,
-                          levels=[5500],
-                          colors='blue',
-                          add_labels=False)
-
    -
    -
    -
    -../_images/72e49b6ea417f0b974401d0908dd153d5c181c2a65fb5acc523fee90352e3c2a.png -
    -
    -
    -
    -

    Adding Directional Labels to Polar Stereographic Projection

    -

    Adding labels to a map projection that aren’t lat/lon coordinates is less than intuitive. In this example we manually add labels and select their locations so that you can see NESW labels.

    -
    -
    -
    # Generate a figure
-fig = plt.figure(figsize=(8, 8))
-
-# Create an axis with a polar stereographic projection
-ax = plt.axes(projection=ccrs.NorthPolarStereo())
-
-# Add land feature to map
-ax.add_feature(cfeature.LAND, facecolor='lightgray')
-
-# Set map boundary to include latitudes between 0 and 40 and longitudes
-# between -180 and 180 only
-gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)
-
-# Set draw_labels to False so that you can manually manipulate it later
-gl = ax.gridlines(ccrs.PlateCarree(),
-                  draw_labels=False,
-                  linestyle="--",
-                  linewidth=1,
-                  color='darkgray',
-                  zorder=2)
-
-# Manipulate latitude and longitude gridline numbers and spacing
-gl.ylocator = mticker.FixedLocator(np.arange(0, 90, 15))
-gl.xlocator = mticker.FixedLocator(np.arange(-180, 180, 30))
-
-# Manipulate longitude labels (0, 30 E, 60 E, ..., 30 W, etc.)
-ticks = np.arange(0, 210, 30)
-etick = ['0'] + [
-    r'%dE' % tick for tick in ticks if (tick != 0) & (tick != 180)
-] + ['180']
-wtick = [r'%dW' % tick for tick in ticks if (tick != 0) & (tick != 180)]
-labels = etick + wtick
-xticks = np.arange(0, 360, 30)
-yticks = np.full_like(xticks, -5)  # Latitude where the labels will be drawn
-for xtick, ytick, label in zip(xticks, yticks, labels):
-    if label == '180':
-        ax.text(xtick,
-                ytick,
-                label,
-                fontsize=12,
-                horizontalalignment='center',
-                verticalalignment='top',
-                transform=ccrs.Geodetic())
-    elif label == '0':
-        ax.text(xtick,
-                ytick,
-                label,
-                fontsize=12,
-                horizontalalignment='center',
-                verticalalignment='bottom',
-                transform=ccrs.Geodetic())
-    else:
-        ax.text(xtick,
-                ytick,
-                label,
-                fontsize=12,
-                horizontalalignment='center',
-                verticalalignment='center',
-                transform=ccrs.Geodetic())
-
-# Iterate through 18 different timesteps
-for x in range(19):
-
-    # Get a slice of data at the 12*x+1 timestep
-    p = ds.HGT.isel(time=12 * x + 1)
-
-    # Use geocat-viz utility function to handle the no-shown-data artifact
-    # of 0 and 360-degree longitudes
-    slon = gv.xr_add_cyclic_longitudes(p, "lon")
-
-    # Plot contour data at pressure level 5500 for the 12*x+1 timestep
-    p = slon.plot.contour(ax=ax,
-                          transform=ccrs.PlateCarree(),
-                          linewidths=0.5,
-                          levels=[5500],
-                          colors='blue',
-                          add_labels=False)
-
-# Use geocat.viz.util convenience function to add titles
-gv.set_titles_and_labels(ax,
-                         maintitle=r"$\bf{Spaghetti}$" + " " + r"$\bf{Plot}$",
-                         lefttitle=slon.long_name,
-                         righttitle='5500 '+slon.units)
-
-# Make tight layout
-plt.tight_layout()
-
    -
    -
    -
    -../_images/2bb3df9d8f98e1c6df0f7cd73491e1251ffe9e4eadbfd4669bce0b5c59594324.png -
    -
    -

    Now in this example, there isn’t necessarily a temporal progression of geopotential height, but to be sure let’s add a colormap component to each of our loops.

    -

    This is also useful because for your data visualization application there might be, and the commands are slightly different for a contour plot as for a line plot in the above example.

    -
    -
    -

    Contour Spagetti Plot Temporal Colorbar Manipulation

    -

    Let’s update add a discrete colorbar that has yearly ticklabels. One challenge addressed in this example is setting the ticklabels to be in the center of each discrete color box.

    -
    -
    -
    # Set up Cartopy Map Projection
-fig = plt.figure(figsize=(8, 8))
-ax = plt.axes(projection=ccrs.NorthPolarStereo())
-
-gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)
-ax.add_feature(cfeature.LAND, facecolor='lightgray')
-
-# Set draw_labels to False so that we can manually manipulate it
-gl = ax.gridlines(ccrs.PlateCarree(),
-                  draw_labels=False,
-                  linestyle="--",
-                  linewidth=1,
-                  color='darkgray',
-                  zorder=2)
-
-# Manipulate latitude and longitude gridline numbers and spacing
-gl.ylocator = mticker.FixedLocator(np.arange(0, 90, 15))
-gl.xlocator = mticker.FixedLocator(np.arange(-180, 180, 30))
-
-# Manipulate longitude labels (0, 30 E, 60 E, ..., 30 W, etc.)
-ticks = np.arange(0, 210, 30)
-etick = ['0'] + [
-    r'%dE' % tick for tick in ticks if (tick != 0) & (tick != 180)
-] + ['180']
-wtick = [r'%dW' % tick for tick in ticks if (tick != 0) & (tick != 180)]
-labels = etick + wtick
-xticks = np.arange(0, 360, 30)
-yticks = np.full_like(xticks, -5)  # Latitude where the labels will be drawn
-for xtick, ytick, label in zip(xticks, yticks, labels):
-    if label == '180':
-        ax.text(xtick,
-                ytick,
-                label,
-                fontsize=12,
-                horizontalalignment='center',
-                verticalalignment='top',
-                transform=ccrs.Geodetic())
-    elif label == '0':
-        ax.text(xtick,
-                ytick,
-                label,
-                fontsize=12,
-                horizontalalignment='center',
-                verticalalignment='bottom',
-                transform=ccrs.Geodetic())
-    else:
-        ax.text(xtick,
-                ytick,
-                label,
-                fontsize=12,
-                horizontalalignment='center',
-                verticalalignment='center',
-                transform=ccrs.Geodetic())
-
-# Create a color list for each of the 19 contours
-n = 19
-cmap = plt.get_cmap('winter_r', n) # the `, n` makes the colormap display discretized
-bounds = np.linspace(0, 1, n)
-
-# Iterate through the timesteps
-for x in range(n):
-
-    # Get a slice of data at the 12*x timestep
-    p = ds.HGT.isel(time=12*x)
-
-    # Handle wrapping artifacts
-    slon = gv.xr_add_cyclic_longitudes(p, "lon")
-
-    # Plot contour data at pressure level 5500 for the 12*x timestep
-    p = slon.plot.contour(ax=ax,
-                          transform=ccrs.PlateCarree(),
-                          linewidths=0.5,
-                          levels=[5500],
-                          colors=[cmap(bounds)[x]], # set colors to use our new cmap
-                          add_labels=False)
-
-# Add a colorbar
-# The default time unit is in months since 1958, years is more intuitive
-year_0 = 1958
-year_n = (ds.time.isel(time=12*n) / 12).astype(int) + year_0
-
-norm = plt.Normalize(vmin=year_0, vmax=year_n)
-cbar = plt.colorbar(plt.cm.ScalarMappable(cmap=cmap, norm=norm),
-    ax=ax,
-    orientation='vertical',
-    shrink=0.8, # Shrink to the approximate size of the map
-    pad = 0.1) # Pad so colorbar doesn't overlap with directional label
-
-cbar.set_ticks(np.arange(year_0+0.5, year_n)) # Set tick locations to be at color midpoints
-cbar.set_ticklabels(np.arange(year_0, year_n)) # Set tick labels to be years (despite their location value being year + 0.5)
-cbar.set_label('Time (years)')
-
-# Use geocat.viz.util convenience function to add titles
-gv.set_titles_and_labels(ax,
-                         maintitle=r"$\bf{Spaghetti}$" + " " + r"$\bf{Plot}$",
-                         lefttitle=slon.long_name,
-                         righttitle='5500 '+slon.units)
-
-# Make tight layout
-plt.tight_layout();
-
    -
    -
    -
    -../_images/06ea4b431cbb8620d94d462fdb281865df97af499506d091ded3ab839bc978bc.png -
    -
    -
    -
    -

    Contour Spagetti Plot with Hand-Picked Colors

    -

    If you want your plot to be visually appealing it might be worth selecting different colors for each contour plot in the for-loop, however these do not have to be sequentially ordered or time-aware. It is actually simplest to hand-pick colors for each loop. In this iteration of the plot we hand pick colors and plot the first time step on its own to demonstrate plotting one loop unlike the others.

    -
    -
    -
    # Generate a figure
-fig = plt.figure(figsize=(8, 8))
-
-# Create an axis with a polar stereographic projection
-ax = plt.axes(projection=ccrs.NorthPolarStereo())
-
-# Add land feature to map
-ax.add_feature(cfeature.LAND, facecolor='lightgray')
-
-# Set map boundary to include latitudes between 0 and 40 and longitudes
-# between -180 and 180 only
-gv.set_map_boundary(ax, [-180, 180], [0, 40], south_pad=1)
-
-# Set draw_labels to False so that you can manually manipulate it later
-gl = ax.gridlines(ccrs.PlateCarree(),
-                  draw_labels=False,
-                  linestyle="--",
-                  linewidth=1,
-                  color='darkgray',
-                  zorder=2)
-
-# Manipulate latitude and longitude gridline numbers and spacing
-gl.ylocator = mticker.FixedLocator(np.arange(0, 90, 15))
-gl.xlocator = mticker.FixedLocator(np.arange(-180, 180, 30))
-
-# Manipulate longitude labels (0, 30 E, 60 E, ..., 30 W, etc.)
-ticks = np.arange(0, 210, 30)
-etick = ['0'] + [
-    r'%dE' % tick for tick in ticks if (tick != 0) & (tick != 180)
-] + ['180']
-wtick = [r'%dW' % tick for tick in ticks if (tick != 0) & (tick != 180)]
-labels = etick + wtick
-xticks = np.arange(0, 360, 30)
-yticks = np.full_like(xticks, -5)  # Latitude where the labels will be drawn
-for xtick, ytick, label in zip(xticks, yticks, labels):
-    if label == '180':
-        ax.text(xtick,
-                ytick,
-                label,
-                fontsize=12,
-                horizontalalignment='center',
-                verticalalignment='top',
-                transform=ccrs.Geodetic())
-    elif label == '0':
-        ax.text(xtick,
-                ytick,
-                label,
-                fontsize=12,
-                horizontalalignment='center',
-                verticalalignment='bottom',
-                transform=ccrs.Geodetic())
-    else:
-        ax.text(xtick,
-                ytick,
-                label,
-                fontsize=12,
-                horizontalalignment='center',
-                verticalalignment='center',
-                transform=ccrs.Geodetic())
-
-# Get slice of data at the 0th timestep - plot this contour line separately
-# because it will be thicker than the other contour lines
-p = ds.HGT.isel(time=0)
-
-# Use geocat-viz utility function to handle the no-shown-data
-# artifact of 0 and 360-degree longitudes
-slon = gv.xr_add_cyclic_longitudes(p, "lon")
-
-# Plot contour data at pressure level 5500 at the first timestep
-p = slon.plot.contour(ax=ax,
-                      transform=ccrs.PlateCarree(),
-                      linewidths=1.5,
-                      levels=[5500],
-                      colors='black',
-                      add_labels=False)
-
-# Create a color list for each of the next 18 contours
-colorlist = [
-    "crimson", "green", "blue", "yellow", "cyan", "hotpink", "crimson",
-    "skyblue", "navy", "lightyellow", "mediumorchid", "orange", "slateblue",
-    "palegreen", "magenta", "springgreen", "pink", "forestgreen", "violet"
-]
-
-# Iterate through 18 different timesteps
-for x in range(18):
-
-    # Get a slice of data at the 12*x+1 timestep
-    p = ds.HGT.isel(time=12 * x + 1)
-
-    # Use geocat-viz utility function to handle the no-shown-data artifact
-    # of 0 and 360-degree longitudes
-    slon = gv.xr_add_cyclic_longitudes(p, "lon")
-
-    # Plot contour data at pressure level 5500 for the 12*x+1 timestep
-    p = slon.plot.contour(ax=ax,
-                          transform=ccrs.PlateCarree(),
-                          linewidths=0.5,
-                          levels=[5500],
-                          colors=colorlist[x],
-                          add_labels=False)
-
-# Use geocat.viz.util convenience function to add titles
-gv.set_titles_and_labels(ax,
-                         maintitle=r"$\bf{Spaghetti}$" + " " + r"$\bf{Plot}$",
-                         lefttitle=slon.long_name,
-                         righttitle='5500 '+slon.units)
-
-# Make tight layout
-plt.tight_layout()
-
    -
    -
    -
    -../_images/e5a381d3131348ac48520f82ca1a50faa1f06c9acdf6678993eeb4231148a6dc.png -
    -
    -
    -
    -
    -
    -

    Summary

    -

    Spagetti Plots are many lines drawn on the same figure. They have pros and cons. They are visually stunning but can be confusing, so it is important to make sure your data visualization conveys accurate information either by manipulating color or linewidth. Since the manipulation of spagetti plots have their own considerations, this chapter shows several design choices that you can use to jumpstart your visualization needs.

    -
    -

    What’s next?

    -

    Next up let’s discuss elements of Visualization of Unstructured Grids.

    -
    -
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    - - - - -
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    - © 2023. - - By the Project Pythia Community. - - Last updated on 28 November 2023. -

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    - - \ No newline at end of file diff --git a/_preview/22/notebooks/taylor-diagrams.html b/_preview/22/notebooks/taylor-diagrams.html deleted file mode 100644 index bb06cdd..0000000 --- a/_preview/22/notebooks/taylor-diagrams.html +++ /dev/null @@ -1,716 +0,0 @@ - - - - - - - - Taylor Diagrams — Advanced Visualization Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    Taylor Diagrams

    -
    -

    Overview

    -

    Taylor diagrams are a “concise statistical summary of how well patterns match each other in terms of their correlation, their root-mean-square difference and the ratio of their variances”. Taylor diagrams plot the weighted centered pattern correlations, the ratios of the normalized root-mean-squared differences between the test and reference data sets, and optionally a bias statistic. This notebook explores how to create and customize Taylor diagrams using geocat-viz.

    -
      -

    1. Creating a Simple Taylor Diagram

      2. -

    2. Displaying Distinct Datasets

      3. -

    3. Finishing Touches

      4. -
    -
    -
    -

    Prerequisites

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

    Concepts

    Importance

    Notes

    Matplotlib

    Necessary

    -
      -

    • Time to learn: 50 minutes

      • -
    -
    -
    -
    -
    import matplotlib.pyplot as plt
-import numpy as np
-
-import geocat.viz as gv
-
    -
    -
    -
    -

    “Generally, the plotted values are derived from climatological monthly, seasonal or annual means. Because the different variables (eg: precipitation, temperature) may have widely varying numerical values, the results are normalized by the reference variables. The ratio of the normalized variances indicates the relative amplitude of the model and observed variations.” - from NCL

    -
    -
    -

    Creating a Simple Taylor Diagram

    -
    -
    -
    # Create figure and Taylor Diagram instance
-fig = plt.figure(figsize=(6, 6))
-taylor = gv.TaylorDiagram(fig=fig, label='REF')
-
-# Draw diagonal dashed lines from origin to correlation values
-# Also enforces proper X-Y ratio
-taylor.add_xgrid(np.array([0.6, 0.9]))
-
-# Add a model dataset of one point
-taylor.add_model_set(stddev=[.6], corrcoef=[.24]);
-
    -
    -
    -
    -../_images/46c23052e74dfa8b2dfd2728d82aa1f8f9565b32dc6d39fd6fc30fd46b6d5fb7.png -
    -
    -
    -
    -

    Displaying Distinct Datasets

    -

    When working with data, you’ll want to make two datasets distinct by providing different kwargs for how to draw them.

    -

    First let’s creat two sets of dummy data:

    -
    -
    -
    # Case A
-a_std = [1.230, 0.988, 1.092, 1.172, 1.064, 0.966, 1.079, 0.781]  # standard deviation
-a_cc = [0.958, 0.973, 0.740, 0.743, 0.922, 0.982, 0.952, 0.433]  # correlation coefficient
-
-# Case B
-b_std = [1.129, 0.996, 1.016, 1.134, 1.023, 0.962, 1.048, 0.852]  # standard deviation
-b_cc = [0.963, 0.975, 0.801, 0.814, 0.946, 0.984, 0.968, 0.647]  # correlation coefficient
-
    -
    -
    -
    -

    And let’s plot it:

    -
    -
    -
    # Create figure and Taylor Diagram instance
-fig = plt.figure(figsize=(12, 12))
-taylor = gv.TaylorDiagram(fig=fig, label='REF')
-ax = plt.gca()
-
-# Draw diagonal dashed lines from origin to correlation values
-# Also enforces proper X-Y ratio
-taylor.add_xgrid(np.array([0.6, 0.9]))
-
-# Add model sets for p and t datasets
-taylor.add_model_set(
-    a_std,
-    a_cc,
-    fontsize=20,
-    xytext=(-5, 10),  # marker label location, in pixels
-    color='red',
-    marker='o',
-    facecolors='none',
-    s=100)  # marker size
-taylor.add_model_set(
-    b_std,
-    b_cc,
-    fontsize=20,
-    xytext=(-5, 10),  # marker label location, in pixels
-    color='blue',
-    marker='D',
-    facecolors='none',
-    s=100)
-
-# Add figure title
-plt.title("Example", size=26, pad=45);
-
    -
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    -../_images/1a2a12da79065dd531ea089264276d2fe4dd4a0fc20e9f1a46d0dc431ac911c8.png -
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    Finishing touches

    -
    -
    -
    # Create figure and Taylor Diagram instance
-fig = plt.figure(figsize=(12, 12))
-taylor = gv.TaylorDiagram(fig=fig, label='REF')
-ax = plt.gca()
-
-# Draw diagonal dashed lines from origin to correlation values
-# Also enforces proper X-Y ratio
-taylor.add_xgrid(np.array([0.6, 0.9]))
-
-# Add model sets for p and t datasets
-taylor.add_model_set(
-    a_std,
-    a_cc,
-    fontsize=20,
-    xytext=(-5, 10),  # marker label location, in pixels
-    color='red',
-    marker='o',
-    facecolors='none',
-    label='Case A',
-    s=100)  # marker size
-taylor.add_model_set(
-    b_std,
-    b_cc,
-    fontsize=20,
-    xytext=(-5, 10),  # marker label location, in pixels
-    color='blue',
-    marker='D',
-    facecolors='none',
-    label='Case B',
-    s=100)
-
-# Add Add constant centered RMS difference contours.
-taylor.add_contours(levels=np.arange(0, 1.1, 0.25),
-                 colors='lightgrey',
-                 linewidths=0.5)
-
-# Add more standard deviation grid lines
-taylor.add_ygrid(np.array([0.5, 1.5]), color='grey')
-
-# Add figure title
-plt.title("Example", size=26, pad=45);
-
-# Add model name
-namearr = ['SLP', 'Tsfc', 'Prc', 'Prc 30S-30N', 'LW', 'SW', 'U300', 'Guess']
-taylor.add_model_name(namearr, fontsize=16)
-
-# Add figure legend
-taylor.add_legend(fontsize=16);
-
    -
    -
    -
    -../_images/8584cd1e4096ee795add995f6fc4f740ce1f2e2a2739ed8a4273c934afc4a490.png -
    -
    -
    -
    -
    -

    Summary

    -
    -

    What’s next?

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

    previous

    -

    Plot Elements

    -
    -     - -
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    next

    -

    Skew T Diagrams

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    - -
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    - NCAR -
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    - Unidata -
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    - UAlbany -
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    - -
    - -
    - -
    -
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    -

    - © 2023. - - By the Project Pythia Community. - - Last updated on 28 November 2023. -

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    - -
    - -
    -
    - - \ No newline at end of file diff --git a/_preview/22/objects.inv b/_preview/22/objects.inv deleted file mode 100644 index 6c8a8df..0000000 --- a/_preview/22/objects.inv +++ /dev/null @@ -1,7 +0,0 @@ -# Sphinx inventory version 2 -# Project: Python -# Version: -# The remainder of this file is compressed using zlib. -xڅN0 { -@@\iPMcְ2IttP)?>;7vN - - - - - - Search — Advanced Visualization Cookbook - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - - -
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    - - - - - - - -
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    Search

    - - - - -

    - Searching for multiple words only shows matches that contain - all words. -

    - - -
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    - - - - - - \ No newline at end of file diff --git a/_preview/22/searchindex.js b/_preview/22/searchindex.js deleted file mode 100644 index 08f4332..0000000 --- a/_preview/22/searchindex.js +++ /dev/null @@ -1 +0,0 @@ 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