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When it is not found, a full rebuild will be done. +config: 9c41254e7f24206330a1a64f1f60c3dd +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/_preview/16/README.html b/_preview/16/README.html new file mode 100644 index 0000000..7a1781d --- /dev/null +++ b/_preview/16/README.html @@ -0,0 +1,584 @@ + + + + + + + + Dask Cookbook — Dask Cookbook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ On this page +
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+

Dask Cookbook

+

nightly-build +Binder +DOI

+

This Project Pythia Cookbook provides a comprehensive guide to understanding the basic concepts and collections of Dask as well as its integration with Xarray. +Dask is a parallel computing library that allows you to scale your computations to multiple cores or even clusters, while Xarray is a library that enables working with labelled multi-dimensional arrays, with a focus on working with netCDF datasets.

+
+

Motivation

+

The motivation behind this repository is to provide a clear and concise resource for anyone looking to learn about the basic concepts of Dask and its integration with Xarray. By providing step-by-step tutorials, we hope to make it easy for users to understand the fundamental concepts of parallel computing and distributed data processing, as well as how to apply them in practice using Dask and Dask+Xarray.

+
+
+

Authors

+

Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk

+
+

Contributors

+ + + + + + + + + +
+
+
+

Note on Content Origin

+

This cookbook is derived from the extensive material used in the NCAR tutorial, “Using Dask on HPC systems”, which was held in February 2023. The NCAR tutorial series also includes an in-depth exploration and practical use cases of Dask on HPC systems and best practices for Dask on HPC. For the complete set of NCAR tutorial materials, including these additional insights +on Dask on HPC, please refer to the main NCAR tutorial content available here.

+
+
+

Structure

+

In the first chapter of this cookbook, we provide step-by-step tutorials on the basic concepts of Dask, including Dask arrays and Dask dataframes, which are powerful tools for parallel computing and distributed data processing. We explain the key differences between these Dask data structures and their counterparts in NumPy and Pandas.

+

In the second chapter of the repository, we move on to more advanced topics, such as distributed computing and Dask+Xarray integration. We provide examples of how to use Dask+Xarray to efficiently work with large, labelled multi-dimensional datasets. +Finally, we will discuss some best practices regarding Dask+Xarray.

+
+
+

Running the Notebooks

+

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

+
+

Running on Binder

+

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.

+
+
+

Running on Your Own Machine

+

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

+
    +

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

    +
     git clone https://github.com/ProjectPythia/dask-cookbook.git
+
    +
    +
    2. +

  2. Move into the dask-cookbook directory

    +
    cd dask-cookbook
+
    +
    +
    3. +

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

    +
    conda env create -f environment.yml
+conda activate dask-cookbook
+
    +
    +
    4. +

  4. Move into the notebooks directory and start up Jupyterlab

    +
    cd notebooks/
+jupyter lab
+
    +
    +
    5. +
+
+
+

Acknowledgments

+
    +

  • NCAR CISL/CSG Team

    • +

  • ESDS Initiative

    • +
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next

+

How to Cite This Cookbook

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The NCAR tutorial series also includes an in-depth exploration and practical use cases of Dask on HPC systems and best practices for Dask on HPC. For the complete set of NCAR tutorial materials, including these additional insights +on Dask on HPC, please refer to the main NCAR tutorial content available [here](https://ncar.github.io/dask-tutorial/README.html). + +## Structure + +In the first chapter of this cookbook, we provide step-by-step tutorials on the basic concepts of Dask, including Dask arrays and Dask dataframes, which are powerful tools for parallel computing and distributed data processing. We explain the key differences between these Dask data structures and their counterparts in NumPy and Pandas. + +In the second chapter of the repository, we move on to more advanced topics, such as distributed computing and Dask+Xarray integration. We provide examples of how to use Dask+Xarray to efficiently work with large, labelled multi-dimensional datasets. +Finally, we will discuss some best practices regarding Dask+Xarray. + +## Running the Notebooks + +You can either run the notebook using [Binder](https://mybinder.org/) or on your local machine. + +### Running on Binder + +The simplest way to interact with a Jupyter Notebook is through +[Binder](https://mybinder.org/), which enables the execution of a +[Jupyter Book](https://jupyterbook.org) in the cloud. The details of how this works are not +important for now. All you need to know is how to launch a Pythia +Cookbooks chapter via Binder. Simply navigate your mouse to +the top right corner of the book chapter you are viewing and click +on the rocket ship icon, (see figure below), and be sure to select +“launch Binder”. After a moment you should be presented with a +notebook that you can interact with. I.e. you’ll be able to execute +and even change the example programs. You’ll see that the code cells +have no output at first, until you execute them by pressing +{kbd}`Shift`\+{kbd}`Enter`. Complete details on how to interact with +a live Jupyter notebook are described in [Getting Started with +Jupyter](https://foundations.projectpythia.org/foundations/getting-started-jupyter.html). + +### Running on Your Own Machine + +If you are interested in running this material locally on your computer, you will need to follow this workflow: + +1. Clone the `https://github.com/ProjectPythia/dask-cookbook` repository: + + ```bash + git clone https://github.com/ProjectPythia/dask-cookbook.git + ``` + +1. Move into the `dask-cookbook` directory + + ```bash + cd dask-cookbook + ``` + +1. Create and activate your conda environment from the `environment.yml` file + + ```bash + conda env create -f environment.yml + conda activate dask-cookbook + ``` + +1. Move into the `notebooks` directory and start up Jupyterlab + + ```bash + cd notebooks/ + jupyter lab + ``` + +### Acknowledgments + +- NCAR CISL/CSG Team +- ESDS Initiative diff --git a/_preview/16/_sources/notebooks/00-dask-overview.ipynb b/_preview/16/_sources/notebooks/00-dask-overview.ipynb new file mode 100644 index 0000000..85ea55e --- /dev/null +++ b/_preview/16/_sources/notebooks/00-dask-overview.ipynb @@ -0,0 +1,226 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a1ccac1c-5d4e-47dc-9d5e-4f417126df94", + "metadata": {}, + "source": [ + "\"Dask\n", + "\n", + "# Dask Overview\n", + "\n", + "### In this tutorial, you learn:\n", + "\n", + "* What is Dask?\n", + "* Why Dask in Geosciences?\n", + "* Dask Data Structures and Schedulers\n", + "* When to use Dask?\n" + ] + }, + { + "cell_type": "markdown", + "id": "a26e012c-213f-43ad-997c-1f895241826e", + "metadata": { + "tags": [] + }, + "source": [ + "## Introduction\n", + "\n", + "Complex data structures enable data science in Python. For example:\n", + "* [NumPy arrays](https://numpy.org/doc/stable/)\n", + "* [Pandas series and dataframes](https://pandas.pydata.org/)\n", + "* [XArray datasets](https://docs.xarray.dev/)\n", + "\n", + "*But datasets are getting larger all of the time! What if my dataset is too big to fit into memory, or it takes too long to complete an analysis?*\n", + "\n", + "## What is Dask?\n", + "\n", + "* Dask is an open-source Python library for parallel and distributed computing that scales the existing Python ecosystem.\n", + "\n", + "* Dask was developed to scale Python packages such as Numpy, Pandas, and Xarray to multi-core machines and distributed clusters when datasets exceed memory.\n", + "\n", + "\n", + "## Why Dask?\n", + "\n", + "### Familiar Interface \n", + "\n", + "Dask provides interfaces which mimics significant portions of the NumPy and Pandas APIs. \n", + "\n", + "This means Dask provides ways to parallelize Pandas, Xarray, and Numpy workflows with minimal code rewriting (no massive code-restructure or writing a script in another language).\n", + "\n", + "### Scalability\n", + "Dask is designed to scale well from single machine (laptop) to thousand-node HPC clusters, and on the cloud.\n", + "\n", + "This allows users to use their existing hardware, or add more machines as needed, to handle increasingly large and complex datasets.\n", + "\n", + "### Flexibility\n", + "Dask provides several tools that help with data analysis on large datasets. For example, you can easily wrap your function in `dask.delayed` decorator to make it run in parallel. \n", + "\n", + "Dask provides seamless integration with well-known HPC resource managers and job scheduling systems, including PBS, SLURM, and SGE.\n", + "\n", + "### Built-in Diagnostic Tools\n", + "Dask provides responsive feedback via the client as well as a real-time interactive diagnostic dashboard to keep users informed on how the computation is progressing. \n", + "\n", + "This helps users identify and resolve potential issues without waiting for the work to be completed. \n", + "\n", + "\n", + "## First Rule of Dask\n", + "\n", + "While Dask is a powerful tool for parallel and distributed computing, it is not always the best solution for every problem. \n", + "In some cases, using Dask may introduce additional complexity and overhead, without providing any substantial benefits in terms of performance or scalability.\n", + "\n", + "* Keep in mind the time spent parallelizing and optimizing your workflow when using Dask vs. the time saved because of that parallelization.\n", + "\n", + "* Consider how many times you plan to run your code - if only once, is it worth it?\n", + "\n", + "

\n", + "\n", + "NOTE: **Dask should only be used when necessary.** \n", + "\n", + "Avoid Dask if you can easily:\n", + "
    \n", + "
  • Speed up your code with use of compiled routines in libraries like NumPy
    • \n", + "
  • Profile and optimize your serial code to minimize bottlenecks
    • \n", + "
  • Read in a subset of data to gain the insight you need
    • \n", + "\n", + "
\n", + "And keep in mind - all of the above steps improve your code whether you end up using Dask or not!

\n", + "\n", + "\n", + "
\n", + "\n", + "\n", + "## When to use Dask?\n", + "Here are some general guidelines for when to use Dask and when to avoid it:\n", + "\n", + "#### Use Dask:\n", + "* When you have large datasets that don't fit into memory on a single machine.\n", + "* When you need to perform parallel computations, such as big data analysis. \n", + "\n", + "#### Avoid Dask:\n", + "\n", + "* When you have small datasets that can be processed efficiently on a single machine.\n", + "* When you don't need parallel processing, as the overhead of managing a distributed computing environment may not be worth the benefits.\n", + "* When you need to debug or troubleshoot problems, as distributed computing environments can be challenging for debugging. If the problem is complex, using Dask may make debugging more difficult." + ] + }, + { + "cell_type": "markdown", + "id": "77b446ce-24a6-48f6-a197-a5d1b97fb0db", + "metadata": { + "tags": [] + }, + "source": [ + "## Dask Components\n", + "\n", + "Dask is composed of two main parts:\n", + "\n", + "### 1. Dask Collections\n", + "\n", + "Dask *Collections* are the user interfaces we use for parallel and distributed computing with Dask.\n", + "\n", + "Dask features different levels of collection types:\n", + "\n", + "#### High-level collections \n", + "Dask provides high-level collections Dask Arrays, Dask DataFrames, and Dask Bags that mimic NumPy, pandas, and lists but can operate in parallel on datasets that don’t fit into memory.\n", + "\n", + "Most of the time, you will probably use one of the following *high-level* (big) data structures (or an even higher-level derivative type like Xarrays):\n", + "\n", + "| Collection | Serial | Dask |\n", + "|-|-|-|\n", + "| Arrays | numpy.array | dask.array.from_array |\n", + "| Dataframes | pandas.read_csv | dask.dataframe.read_csv |\n", + "| Unstructured | [1,2,3] | dask.bag.from_sequence([1,2,3]) |\n", + "\n", + "\n", + "#### Low-level collections\n", + "Dask also features two *low-level* collection types - `delayed` and `futures`. These collections give users finer control to build custom parallel and distributed computations.\n", + "\n", + "* **delayed** - run any arbitrary Python function using Dask task parallelism (think looped function calls)\n", + "* **futures** - similar to delayed but allows for concurrent commands in the client script (think backgrounded processes)\n", + "\n", + "These are very powerfull tools, but it is easy to write something using a delayed function that could be executed faster and more simply using a high-level collection \n", + "\n", + "\"Dask\n", + " \n", + "*Image credit: Dask Contributors*\n", + "\n", + "\n", + "### 2. Dynamic Task Scheduling\n", + "**We can basically think of the Dask scheduler as our task orchestrator.**\n", + "\n", + "When a computation is submitted, work is segmented into discrete tasks which are assigned to workers by the Dask scheduler. \n", + "\n", + "To perform work, a scheduler must be assigned resources in the form of a Dask cluster. The cluster consists of the following components: \n", + "\n", + "* **scheduler** : A scheduler creates and manages task graphs and distributes tasks to workers.\n", + "\n", + "* **workers** : A worker is typically a separate Python process on either the local host or a remote machine. A Dask cluster usually consists of many workers. Basically, a worker is a Python interpretor which will perform work on a subset of our dataset.\n", + "\n", + "* **client** - A high-level interface that points to the scheduler (often local but not always). A client serves as the entry point for interacting with a Dask scheduler.\n", + "\n", + "\"Dask\n", + " \n", + "*Image credit: Dask Contributors*\n", + "\n", + "\n", + "We will learn more about Dask Collections and Dynamic Task Scheduling in the next tutorials." + ] + }, + { + "cell_type": "markdown", + "id": "9cbea0a3-6f90-41a1-9827-8f60771e67af", + "metadata": {}, + "source": [ + "## Useful Resources\n", + "\n", + "* Reference\n", + " * [Docs](https://dask.org/)\n", + " * [Examples](https://examples.dask.org/)\n", + " * [Code](https://github.com/dask/dask/)\n", + " * [Blog](https://blog.dask.org/)\n", + "* Ask for help\n", + " * [`dask`](http://stackoverflow.com/questions/tagged/dask) tag on Stack Overflow, for usage questions\n", + " * [github issues](https://github.com/dask/dask/issues/new) for bug reports and feature requests\n", + " * [discourse forum](https://dask.discourse.group/) for general, non-bug, questions and discussion" + ] + } + ], + "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": "3.9.18" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_preview/16/_sources/notebooks/01-dask-array.ipynb b/_preview/16/_sources/notebooks/01-dask-array.ipynb new file mode 100644 index 0000000..fc7d2bf --- /dev/null +++ b/_preview/16/_sources/notebooks/01-dask-array.ipynb @@ -0,0 +1,1121 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "990cfa4c-2117-4435-9806-ff9048890398", + "metadata": { + "tags": [] + }, + "source": [ + "\"Dask\n", + "\n", + "# Dask Array\n", + "\n", + "### In this tutorial, you learn:\n", + "\n", + "* What is a Dask Array?\n", + "* Basic concepts and features of Dask Arrays\n", + "* Working with Dask arrays\n", + "\n", + "**Related Dask Array Documentation**\n", + "\n", + "* [Dask Array documentation](https://docs.dask.org/en/stable/array.html)\n", + "* [Dask Array API](https://docs.dask.org/en/stable/array-api.html)\n", + "* [Dask Array examples](https://examples.dask.org/array.html)\n", + "\n", + "\n", + "## Dask Arrays\n", + "Dask Arrays are basically parallelized version of NumPy arrays for processing *larger-than-memory data sets*. \n", + "\n", + "\n", + "\n", + "*Image credit: Dask Contributors*\n", + "\n", + "Dask Array can be used as a drop-in replacement for NumPy arrays, with a similar API and support for a subset of NumPy functions. \n", + "\n", + "Dask effectively reduces the memory footprint of large array computations by dividing the arrays into smaller pieces (called **chunks**) that can fit into memory and stream the data from disk." + ] + }, + { + "cell_type": "markdown", + "id": "106238d9-c519-47bc-b5c6-5c703ad47fe6", + "metadata": {}, + "source": [ + "**Dask Arrays are lazy:** Unlike Numpy, operations on Dask arrays are not computed until you explicitly request them. \n", + "\n", + "
\n", + "\n", + "Lazy Evaluation: objects are evaluated just in time when the results are needed! \n", + "\n", + "Lazy evaluation help us avoid having large pieces of memory resident on the workers and optimize the resource requirements.\n", + "\n", + "
\n", + "\n", + "Dask Arrays don't directly hold any data. Instead, they provide **a symbolic representation of the necessary computations** to generate the data. We will explain this more below. \n", + "\n", + "\n", + "\n", + "Let's start exploring Dask Arrays:" + ] + }, + { + "cell_type": "markdown", + "id": "72b6cdf8-db59-4a86-8f8d-c30ab67a46b2", + "metadata": {}, + "source": [ + "## Setup: Start a Dask Client\n", + "We will talk in-depth about Dask Cluster and Dask Clients later in this tutorial. Here we just created a local cluster and attached a client to it. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "7ebce44c-e6f6-4cd5-a23d-aa6963d1d8d0", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2023-03-24 14:09:07,939 - distributed.diskutils - INFO - Found stale lock file and directory '/var/folders/99/q6kpb9290c3f6grv0f9mqxzm0000gp/T/dask-worker-space/worker-3nchxpcu', purging\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from dask.distributed import Client\n", + "\n", + "client = Client()\n", + "client" + ] + }, + { + "cell_type": "markdown", + "id": "ed8c0a5c-a3fa-4f7e-8c84-7bc96fd0a817", + "metadata": {}, + "source": [ + "## Blocked Algorithms\n", + "\n", + "Dask Arrays use blocked algorithms to split large computations into smaller computations which operate on subsets of the data (called **chunks**).\n", + "\n", + "Let's see what this means in an example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3063d2a-b923-4042-b37c-3edb1cf29c2f", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import dask.array as da\n", + "\n", + "# A 4x4 numpy array that goes from 1 to 16 \n", + "\n", + "narr = np.array([\n", + " [ 1, 2, 3, 4],\n", + " [ 5, 6, 7, 8],\n", + " [ 9, 10, 11, 12],\n", + " [13, 14, 15, 16]\n", + " ])\n", + "\n", + "# -- convert numpy array to dask array with 4 chunks\n", + "darr = da.from_array( narr,chunks=(2, 2))" + ] + }, + { + "cell_type": "markdown", + "id": "0f502592-b50f-458d-bf23-3550f06b93ad", + "metadata": {}, + "source": [ + "Now we can calculate the sum of this array using `darr.sum()` similar to numpy. **But how is it different from numpy?**\n", + "\n", + "\n", + "When you take the sum of the Dask array, Dask first takes the sum of each chunk and only after each of those is completed, takes the sum of the results from each chunk.\n", + "\n", + "\n", + "\n", + "\n", + "*Image adapted from saturncloud.io*\n" + ] + }, + { + "cell_type": "markdown", + "id": "af956be7-75fe-4d95-8212-76b8fabf1a1a", + "metadata": {}, + "source": [ + "## Task Graph\n", + "\n", + "The Dask Task Graph serves as a **blueprint** for executing the computations. \n", + "\n", + "The Task Graph defines the (1) relationships between tasks, and (2) the order in which they should be executed.\n", + "\n", + "In a task graph each node in the graph represents a task and lines represent the dependencies/relationships between tasks." + ] + }, + { + "cell_type": "markdown", + "id": "0fd27324-4077-46a4-97d9-6a01a61ab61d", + "metadata": {}, + "source": [ + "We can visualize the low-level task graph using `.visualize()` method." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "32303572-b72a-49e2-9a59-165d11e2da1c", + "metadata": {}, + "outputs": [], + "source": [ + "darr.sum().visualize(rankdir=\"LR\")" + ] + }, + { + "cell_type": "markdown", + "id": "9cf9a3cb-9187-405d-a176-9530ab64cdd7", + "metadata": {}, + "source": [ + "It is generally good practice to look at the task graph before executing the computation. By looking at the task graph, you can learn about potential bottlenecks where parallelism is not possible. \n", + "\n", + "
\n", + "\n", + "TIP: For big computations, low-level task graphs gets very confusing. An alternative that provides a more concise graph is using `.dask.visualize()`.\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c75b77b1-65e1-48d9-ba92-0ffb9fd681cc", + "metadata": {}, + "outputs": [], + "source": [ + "#darr.sum().dask.visualize()" + ] + }, + { + "cell_type": "markdown", + "id": "ea4672b8-fa4b-4709-98a0-7d3a36b592f1", + "metadata": {}, + "source": [ + "\n", + "----\n", + "\n", + "Now, let's start with another example. Here we create a 2D array of ones using NumPy." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20392c9f-8902-43f6-bbf1-38d88532c361", + "metadata": {}, + "outputs": [], + "source": [ + "shape = (10000,12000)\n", + "\n", + "ones_np = np.ones(shape)\n", + "ones_np" + ] + }, + { + "cell_type": "markdown", + "id": "8940853a-9022-481e-934e-57b1742bf9cf", + "metadata": {}, + "source": [ + "Now, let's create the same array using Dask:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5466813e-e71b-446a-bc14-f4d3b98a27df", + "metadata": {}, + "outputs": [], + "source": [ + "ones_da = da.ones(shape)\n", + "ones_da" + ] + }, + { + "cell_type": "markdown", + "id": "67c8f051-3889-41f6-99ca-87074f336bd0", + "metadata": {}, + "source": [ + "We see a Dask Array representation of the data.\n", + "This is a symbolic representation; no data has actually been generated yet. \n", + "\n", + "As we discussed previously, this mode of operation is called \"lazy\". \n", + "\n", + "This allows the user to build up a series of computations or tasks before being passed to the scheduler for execution." + ] + }, + { + "cell_type": "markdown", + "id": "9c581c28-1e99-4571-a5fe-88e45727b1a7", + "metadata": {}, + "source": [ + "## Chunks\n", + "\n", + "When checking the Dask array, the symbolic representation illustrates the concept of chunks. Dask arrays split the data into **sub-arrays** (or **chunks**) to optimize computation with large arrays. " + ] + }, + { + "cell_type": "markdown", + "id": "3911489b-792c-4dd6-9e1e-4ca87de888a1", + "metadata": {}, + "source": [ + "### Chunking an array\n", + "\n", + "**The way that arrays are chunked can significantly affect total performance.**\n", + "\n", + "For specifying the chunking of an array, we use the `chunks` argument when creating our `dask.array`.\n", + "\n", + "
\n", + "\n", + "⚠️ WARNING: Please note that `chunks` argument stands for **chunk shape** rather than “number of chunks”. \n", + "For example, `chunks=1` means that you will have several chunks with one element. \n", + "
\n", + "\n", + "There are several ways to define `chunks`. For example:\n", + "\n", + "1. A uniform dimension size like 1000, meaning chunks of size 1000 in each dimension. \n", + "\n", + "2. A uniform chunk shape like `(1000, 2000, 3000)`, meaning chunks of size 1000 in the first axis, 2000 in the second axis, and 3000 in the third. \n", + "\n", + "3. Fully explicit sizes of all blocks for all dimensions, like `((1000, 1000, 500), (400, 400), (5, 5, 5, 5, 5))`\n", + "\n", + "4. A dictionary specifying chunk size per dimension like `{0: 1000, 1: 2000, 2: 3000}`.\n", + "\n", + "\n", + "Let's recreate the above Dask array, but this time we will specify chunk sizes (a.k.a. shapes) using the argument `chunks`. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9691bac8-8368-4311-a28e-5e2ab2a253f5", + "metadata": {}, + "outputs": [], + "source": [ + "# -- remember what the shape of our data array was\n", + "shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c5bf84b-761e-445e-8c0e-f897ad5ab19a", + "metadata": {}, + "outputs": [], + "source": [ + "# create a dask array with 6 chunks\n", + "chunk_shape = (5000,4000)\n", + "ones_da = da.ones(shape,chunks=chunk_shape)\n", + "ones_da" + ] + }, + { + "cell_type": "markdown", + "id": "a322cb9f-e4c1-45ac-9727-0831b3d55054", + "metadata": {}, + "source": [ + "You can see in the above dask array representation that we now have 6 chunks, each of shape (5000,4000) and size of ~ 160.0 MiB." + ] + }, + { + "cell_type": "markdown", + "id": "2a12d097-9c1d-4a78-bbcf-4d7f52e47388", + "metadata": {}, + "source": [ + "## Performance Comparison" + ] + }, + { + "cell_type": "markdown", + "id": "bf6b8586-e191-4e55-b476-8099c612971b", + "metadata": {}, + "source": [ + "To compare the performance between a NumPy array and an equivalent Dask array, let's calculate the mean. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9856fcec-e006-446b-b426-bff50a92cbd9", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# The %%time cell magic measures the execution time of the whole cell\n", + "ones_np.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86cc3673-c8a4-421b-8975-3cd2c30c7b1b", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# Remember, we are not doing any computation here, just constructing our task graph\n", + "mean_of_ones_da = ones_da.mean()" + ] + }, + { + "cell_type": "markdown", + "id": "fcfee3c0-291b-4e53-a64d-2871b621ce2f", + "metadata": {}, + "source": [ + "Remember :\n", + "> *Dask doesn't do anything until you tell it... It is lazy!*\n", + "\n", + "So far we have just constructed our task graph but no computations yet!\n", + "\n", + "
\n", + "\n", + "NOTE: In order to generate the data, we need to call the `.compute()` method on the Dask Array to trigger our computation. \n", + "\n", + "
\n", + "\n", + "Let's run the `.compute()` method to see how this works:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1179dc52-9da9-419f-961b-ba806d9de498", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "mean_of_ones_da.compute()" + ] + }, + { + "cell_type": "markdown", + "id": "86931ead-dc2e-475c-80ab-c2e020b9deea", + "metadata": {}, + "source": [ + "`.compute()` method convertes Dask Arrays to Numpy Arrays. Let's check to see if this is true:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "44c17fc5-d425-4a1c-9232-5fd6184fc3ec", + "metadata": {}, + "outputs": [], + "source": [ + "type (ones_da.compute())" + ] + }, + { + "cell_type": "markdown", + "id": "2ba26970-bb89-4ea1-a40b-d885368651bb", + "metadata": {}, + "source": [ + "\n", + "\n", + "
\n", + "\n", + "WARNING: Typically, when working with Dask arrays, we do not want to generate the data right away by calling `.compute()` on a large array. \n", + "\n", + "
\n", + "\n", + "\n", + "We usually want to perform some computations that reduce the data size. For example, we might compute statistics like the mean or standard deviation.\n", + "\n", + "Let's look at an example of taking the mean and visualize the task graph. Remember, that no actual computation is taking place until we call `.compute()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9350bd2-0e92-4c92-8dae-cee7c2027c87", + "metadata": {}, + "outputs": [], + "source": [ + "mean_of_ones_da = ones_da.mean()\n", + "mean_of_ones_da.visualize(rankdir=\"LR\")" + ] + }, + { + "cell_type": "markdown", + "id": "f7e2a1d5-f606-4403-b3e3-d0d64b946035", + "metadata": {}, + "source": [ + "-------\n", + "\n", + "\n", + "What are the sizes of these arrays in memory? \n", + "\n", + "First, let's define a function that returns array size in MiB. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c878ec8-0c21-45f7-83be-aaae9ba09897", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "# Define function to display variable size in MiB\n", + "def var_size(in_var):\n", + " result = sys.getsizeof(in_var) / 1024/1024\n", + " return (result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "063d3891-65b8-48ad-a50f-10e4f88e32f1", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Shape of the numpy array : \", ones_np.shape) \n", + "print(\"Shape of the dask array : \", ones_da.shape) \n", + "\n", + "# memory size of numpy array in MiB\n", + "print(f\"Memory size of numpy array in MB : {var_size(ones_np):.2f} MiB\")\n", + "# memory size of dask array in MiB\n", + "print(f\"Memory size of dask array in MB : {var_size(ones_da):.2f} MiB\")" + ] + }, + { + "cell_type": "markdown", + "id": "249d762a-91b6-403f-ad86-cddf0e4d71db", + "metadata": {}, + "source": [ + "**Why memory size for the above Dask array is zero?**\n", + "\n", + "Remember, this variable is only a graph representation of the full array which will be split across workers.\n", + "\n", + "However, Dask does give us ways to see the full size of the data (often much larger than your client machine can handle)!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffaf52e9-e7bd-4023-aa2f-9576ddafa9e7", + "metadata": {}, + "outputs": [], + "source": [ + "print(\"Size of Dask dataset: {:.2f} MiB\".format(ones_da.nbytes / 1024/1024))" + ] + }, + { + "cell_type": "markdown", + "id": "878a7cdc-ef9c-4e83-9ae8-d10c2c140413", + "metadata": {}, + "source": [ + "## Larger Data\n", + "The previous example illustrated how Dask works, but using Dask is not really necessary (nor advisable) for an array of size 915.53 MiB. \n", + "Let's try an example using bigger data and bigger calculations:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da9d506b-ffcf-4faf-8544-08ad76014819", + "metadata": {}, + "outputs": [], + "source": [ + "big_shape = (2000, 200, 2000)\n", + "\n", + "# -- this will make a big numpy array that might not fit on your machine\n", + "#big_np = np.ones(big_shape)" + ] + }, + { + "cell_type": "markdown", + "id": "80f66611-b525-40c8-9998-9dc09a45a8f1", + "metadata": {}, + "source": [ + "Make a similar Dask Array with similar shape but specifying the `chunks` size:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "570b4e69-2663-47fc-8e3f-0aaa50eb6b29", + "metadata": {}, + "outputs": [], + "source": [ + "big_da = da.ones(big_shape)\n", + "big_da" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "264d7f95-3739-4e6f-b9bf-3bc722b30767", + "metadata": {}, + "outputs": [], + "source": [ + "# size of data\n", + "#print(\"Memory size of NumPy dataset : {:.2f} GiB\".format(big_np.nbytes / 1024/1024/1024))\n", + "print(\"Memory size of Dask dataset : {:.2f} GiB\".format(big_da.nbytes / 1024/1024/1024))" + ] + }, + { + "cell_type": "markdown", + "id": "6269ea15-fdb2-4cec-962b-72c0e123c638", + "metadata": {}, + "source": [ + "This may be close to the available memory/RAM that you have in your computer.\n", + "\n", + "Let's try bigger calculations on this array:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cde2f9a0-974c-43ed-a922-a587a34c79ec", + "metadata": {}, + "outputs": [], + "source": [ + "#%%time \n", + "#z_np = (big_np + big_np.T)[::2,:].mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5ede8e0b-e62d-4f3f-b815-dcb123c092d4", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "z_da = (big_da + big_da.T)[::2,:].mean(axis=2)\n", + "\n", + "result = z_da.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "acc1695d-ed28-4bcd-89aa-41c79de8cd3f", + "metadata": {}, + "outputs": [], + "source": [ + "#-- warning : do not try low level visualization with big arrays\n", + "\n", + "#z_da.visualize()" + ] + }, + { + "cell_type": "markdown", + "id": "94f702a5-2af8-4b85-a466-b5bcd09635bd", + "metadata": {}, + "source": [ + "All the usual NumPy functions work on dask arrays, though the computations will remain lazy until you either call `.compute()`, `.load()` or your want to plot the data." + ] + }, + { + "cell_type": "markdown", + "id": "39adc87d-a41c-4e7e-bb31-0a51ba6aaed5", + "metadata": {}, + "source": [ + "As we discussed above, the way that Dask arrays are chunked can significantly affect the performance. In the remainder of this notebook, let's do a similar calculation using a different `chunks` size. \n", + "\n", + "We will learn more about best practices regarding `chunk` size later during the tutorial. \n", + "\n", + "\n", + "## Supplementary Material: Rechunking Arrays\n", + "\n", + "We can change the chunking of a Dask array, using the `rechunk` method. Please note that rechunking Dask arrays can be very expensive, so choosing an appropriate chunk size initially is ideal." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68dca5a5-b67b-4205-9e83-48faefe44594", + "metadata": {}, + "outputs": [], + "source": [ + "new_chunk_shape = (50,50,50)\n", + "rechunked_big_da = big_da.rechunk(new_chunk_shape)\n", + "rechunked_big_da" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ff5e0a0b-3d2f-46d4-a839-079dd3664572", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# perform big computation on chunked array\n", + "\n", + "z_da_rechunked = (rechunked_big_da + rechunked_big_da.T)[::2,:].mean(axis=2)\n", + "\n", + "result = z_da_rechunked.compute()" + ] + }, + { + "cell_type": "markdown", + "id": "797cbbc7-e2c9-46b2-b58b-0f9be29f7bb9", + "metadata": {}, + "source": [ + "We can see how the choice of smaller chunks (more total chunks) **significantly** reduce the total performance of our computation. \n", + "\n", + "
\n", + "\n", + "TIP: As a rule of thumb, a chunk should be big enough so that the computation on that chunk take significantly longer than the overhead from Dask scheduler. The Dask scheduler takes roughly 1ms per task for scheduling. \n", + "\n", + "
\n", + "\n", + "Let's try a bigger chunk size:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfd36aa9-d676-4c14-b2ad-3f8414b107c5", + "metadata": {}, + "outputs": [], + "source": [ + "new_chunk_shape = (500, 100, 500)\n", + "#big_chunk = \n", + "\n", + "rechunked_big_da = big_da.rechunk(new_chunk_shape)\n", + "rechunked_big_da" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6e90dceb-56d1-4ef5-a6f3-ab0b25606288", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# perform big computation on chunked array\n", + "\n", + "z_da_rechunked = (rechunked_big_da + rechunked_big_da.T)[::2,:].mean(axis=2)\n", + "\n", + "result = z_da_rechunked.compute()" + ] + }, + { + "cell_type": "markdown", + "id": "fcc07937-e204-428e-af39-57bd7df1372b", + "metadata": {}, + "source": [ + "\n", + "
\n", + "\n", + "TIP: As a rule of thumb, a chunk should be small enough to fit comfortably in the memory. Chunk sizes between 10MB-1GB are common, depending on your machine,\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8dd1bc53-ead4-409c-a349-2e2abd78f59f", + "metadata": {}, + "outputs": [], + "source": [ + "client.close()" + ] + }, + { + "cell_type": "markdown", + "id": "7b9f93a6-d792-47b6-a766-4cb6c0972d2a", + "metadata": {}, + "source": [ + "## Summary:\n", + "\n", + "* Dask Array provides parallel computing capabilities by dividing arrays into smaller pieces called chunks.\n", + "* Blocked algorithms split large computations into smaller computations which operate on subsets of the array.\n", + "* Dask Array supports efficient computation on large arrays through a combination of lazy evaluation and task parallelism.\n", + "* Dask Array can be used as a drop-in replacement for NumPy ndarray, with a similar API and support for a subset of NumPy functions.\n", + "* The way that arrays are chunked can significantly affect total performance. Poor chunking can singifincantly worsen performance of Dask compared to NumPy. " + ] + }, + { + "cell_type": "markdown", + "id": "984ea6ba-f37c-47ab-86ca-b5396ba6c071", + "metadata": {}, + "source": [ + "## Resources and references\n", + "\n", + "* Reference\n", + " * [Dask Docs](https://dask.org/)\n", + " * [Dask Examples](https://examples.dask.org/)\n", + " * [Dask Code](https://github.com/dask/dask/)\n", + " * [Dask Blog](https://blog.dask.org/)\n", + " \n", + " \n", + " \n", + "* Ask for help\n", + " * [`dask`](http://stackoverflow.com/questions/tagged/dask) tag on Stack Overflow, for usage questions\n", + " * [github discussions: dask](https://github.com/dask/dask/discussions) for general, non-bug, discussion, and usage questions\n", + " * [github issues: dask](https://github.com/dask/dask/issues/new) for bug reports and feature requests\n", + " \n", + "\n", + "* Pieces of this notebook are adapted from the following sources\n", + " * [Dask Performace Comparison](https://tutorial.dask.org/02_array.html#Performance-comparison)\n", + " * [Dask Arrays by EEDS](https://earth-env-data-science.github.io/lectures/dask/dask_arrays.html)" + ] + } + ], + "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": "3.9.18" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_preview/16/_sources/notebooks/02-dask-dataframe.ipynb b/_preview/16/_sources/notebooks/02-dask-dataframe.ipynb new file mode 100644 index 0000000..06b18f7 --- /dev/null +++ b/_preview/16/_sources/notebooks/02-dask-dataframe.ipynb @@ -0,0 +1,972 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "990cfa4c-2117-4435-9806-ff9048890398", + "metadata": {}, + "source": [ + "\"Dask\n", + "\n", + "# Dask DataFrame\n", + "\n", + "### In this tutorial, you learn:\n", + "\n", + "* Basic concepts and features of Dask DataFrames\n", + "* Applications of Dask DataFrames\n", + "* Interacting with Dask DataFrames\n", + "* Built-in operations with Dask DataFrames\n", + "* Dask DataFrames Best Practices\n", + "\n", + "### Related Documentation\n", + "\n", + "* [Dask DataFrame documentation](https://docs.dask.org/en/stable/dataframe.html)\n", + "* [Dask DataFrame API](https://docs.dask.org/en/stable/dataframe-api.html)\n", + "* [Dask DataFrame examples](https://examples.dask.org/dataframe.html)\n", + "* [pandas documentation](https://pandas.pydata.org/pandas-docs/stable/)\n", + "\n", + "### Prerequisites\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Familiarity with Pandas DataFrame](https://pandas.pydata.org/docs/user_guide/10min.html) | Necessary | |\n", + "| [Dask Overview](https://pandas.pydata.org/docs/user_guide/10min.html) | Necessary | |\n", + "\n", + "- **Time to learn**: 40 minutes\n", + "---------\n", + "\n", + "## Introduction\n", + "\n", + "\"Dask\n", + "\n", + "*Image credit: Dask Contributors*\n", + "\n", + "pandas is a very popular tool for working with **tabular datasets**, but the dataset needs to **fit into the memory**. \n", + "\n", + "pandas operates best with smaller datasets, and if you have a large dataset, you’ll receive an out of memory error using pandas. A general rule of thumb for pandas is:\n", + "\n", + "> \"Have 5 to 10 times as much RAM as the size of your dataset\"\n", + ">\n", + "> ~ Wes McKinney (2017) in [10 things I hate about pandas](https://wesmckinney.com/blog/apache-arrow-pandas-internals/)\n", + "\n", + "But Dask DataFrame can be used to solve pandas performance issues with larger-than-memory datasets.\n", + "\n", + "\n", + "### What is Dask DataFrame?\n", + "\n", + "\n", + "* **A Dask DataFrame is a parallel DataFrame composed of smaller pandas DataFrames (also known as *partitions*).**\n", + "\n", + "* Dask Dataframes look and feel like the pandas DataFrames on the surface. \n", + "\n", + "* Dask DataFrames partition the data into manageable **partitions** that can be processed in parallel and across multiple cores or computers. \n", + "\n", + "* Similar to Dask Arrays, Dask DataFrames are lazy!\n", + "\n", + " Unlike pandas, operations on Dask DataFrames are not computed until you explicitly request them (e.g. by calling `.compute`).\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "bae50738-0e67-4a64-8190-12ab8c06e06c", + "metadata": {}, + "source": [ + "## When to use Dask DataFrame and when to avoid it?\n", + "\n", + "Dask DataFrames are used in situations where pandas *fails* or has *poor performance due to data size*. \n", + "\n", + "Dask DataFrame is a good choice when doing **parallalizeable computations**. \n", + "Some examples are:\n", + "* Element-wise operations such as `df.x + df.y`\n", + "* Row-wise filtering such as `df[df.x>0]`\n", + "* Common aggregations such as `df.x.max()`\n", + "* Dropping duplicates such as `df.x.drop_duplicate()`\n", + "\n", + "However, Dask is not great for operations that requires shuffling or re-indexing. \n", + "Some examples are:\n", + "* Set index: `df.set_index(df.x)`\n", + "\n", + "\n", + " \n", + "
\n", + "WARNING: Although, Dask DataFrame has a very similar interface to the pandas DataFrame (as we will see in this tutorial), it does NOT include some of the pandas interface yet.\n", + "\n", + "See the [Dask DataFrame API documentation](https://docs.dask.org/en/stable/dataframe-api.html) for a compehnsive list of available functions. \n", + "
\n", + "\n", + "\n", + "--------------------" + ] + }, + { + "cell_type": "markdown", + "id": "17323017-5697-458a-a2e0-f13af6262872", + "metadata": { + "tags": [] + }, + "source": [ + "## Tutorial Dataset\n", + "In this tutorial, we are going to use the NOAA Global Historical Climatology Network Daily (GHCN-D) dataset. \n", + "GHCN-D is a public available dataset that includes daily climate records from +100,000 surface observations around the world. \n", + "This is an example of a real dataset that is used by NCAR scientists for their research. GHCN-D raw dataset for all stations is available through [NOAA Climate Data Online](https://www.ncdc.noaa.gov/cdo-web/search?datasetid=GHCND). \n", + "\n", + "**To learn more about GHCNd dataset, please visit:**\n", + "* [GHCNd Journal Paper](https://journals.ametsoc.org/view/journals/atot/29/7/jtech-d-11-00103_1.xml)\n", + "* [GHCNd Official Website](https://www.ncei.noaa.gov/products/land-based-station/global-historical-climatology-network-daily)\n", + "\n", + "### Download the data\n", + "For this example, we are going to look through a subset of data from the GHCN-D dataset.\n", + "\n", + "First, we look at the daily observations from Denver International Airport, next we are going to look through selected stations in the US. \n", + "\n", + "The access the preprocessed dataset for this tutorial, please run the following script:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36376778-77b8-4fe9-b370-6a76a335fb66", + "metadata": {}, + "outputs": [], + "source": [ + "!./get_data.sh" + ] + }, + { + "cell_type": "markdown", + "id": "c0cfddcf-0f62-4f8b-bbfa-50250ac5a6c4", + "metadata": {}, + "source": [ + "This script should save the preprocessed GHCN-D data in `../data` path.\n" + ] + }, + { + "cell_type": "markdown", + "id": "f4cc4275-c0ce-42fa-a6b4-033c597b5c9c", + "metadata": {}, + "source": [ + "-----------\n", + "## Pandas DataFrame Basics\n", + "Let's start with an example using pandas DataFrame.\n", + "\n", + "First, let's read in the comma-seperated GHCN-D dataset for one station at **Denver International Airport (DIA), CO** (site ID : `USW00003017`).\n", + "\n", + "To see the list of all available GHCN-D sites and their coordinates and IDs, please see [this link](https://www.ncei.noaa.gov/pub/data/ghcn/daily/ghcnd-stations.txt)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1b5337d-28c3-4f88-b9c3-ce3d92445c37", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "# DIA ghcnd id\n", + "site = 'USW00003017'\n", + "data_dir = '../data/'\n", + "\n", + "\n", + "df = pd.read_csv(os.path.join(data_dir, site+'.csv'), parse_dates=['DATE'], index_col=0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be6fa1c8-a550-4fd1-8418-cae2844fe657", + "metadata": {}, + "outputs": [], + "source": [ + "# Display the top five rows of the dataframe\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "id": "10cc065d-06e1-4cd5-85b4-9d30eb742f6d", + "metadata": {}, + "source": [ + "**Question:** What variables are available?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d7b80f27-b70f-43bd-af0d-85def9d2cb73", + "metadata": {}, + "outputs": [], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "markdown", + "id": "1c5b5e52-31f8-422b-8f02-05535c064f8f", + "metadata": {}, + "source": [ + "The description and units of the dataset is available [here](https://www.ncei.noaa.gov/pub/data/ghcn/daily/readme.txt). " + ] + }, + { + "cell_type": "markdown", + "id": "1f0c6ad4-51fe-4e65-8b07-542eccf0c6b8", + "metadata": {}, + "source": [ + "### Operations on pandas DataFrame\n", + "\n", + "pandas DataFrames has several features that give us flexibility to do different calculations and analysis on our dataset. Let's check some out:\n", + "#### Simple Analysis\n" + ] + }, + { + "cell_type": "markdown", + "id": "7a27a505-3ceb-469a-b6aa-9ded4f74e16e", + "metadata": {}, + "source": [ + "For example: \n", + "* When was the coldest day at this station during December of last year?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99fd3450-904c-44a0-820a-266cc1327871", + "metadata": {}, + "outputs": [], + "source": [ + "# use python slicing notation inside .loc \n", + "# use idxmin() to find the index of minimum valus\n", + "df.loc['2022-12-01':'2022-12-31'].TMIN.idxmin()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b60c1ae0-49bf-4d47-b797-a65c31f9c4cf", + "metadata": {}, + "outputs": [], + "source": [ + "# Here we easily plot the prior data using matplotlib from pandas\n", + "# -- .loc for value based indexing\n", + "df.loc['2022-12-01':'2022-12-31'].SNWD.plot(ylabel= 'Daily Average Snow Depth [mm]')" + ] + }, + { + "cell_type": "markdown", + "id": "64fc3daf-896d-482e-bbfe-1d5dfe45c049", + "metadata": {}, + "source": [ + "* How many snow days do we have each year at this station?\n", + "\n", + "Pandas groupby is used for grouping the data according to the categories." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3b10c3ff-4dff-4e5a-aa1e-059583272506", + "metadata": {}, + "outputs": [], + "source": [ + "# 1- First select days with snow > 0\n", + "# 2- Create a \"groupby object\" based on the selected columns\n", + "# 3- use .size() to compute the size of each group\n", + "# 4- sort the values descending \n", + "\n", + "# we count days where SNOW>0, and sort them and show top 5 years:\n", + "df[df['SNOW']>0].groupby('YEAR').size().sort_values(ascending=False).head()\n" + ] + }, + { + "cell_type": "markdown", + "id": "e539216a-6cf8-4b6f-bc48-d9f670ef6176", + "metadata": {}, + "source": [ + "Or for a more complex analysis:\n", + "\n", + "For example, we have heard that this could be Denver's first January in 13 years with no 60-degree days. \n", + "\n", + "\"News\n", + "\n", + "Below, we show all days with high temperature above 60°F (155.5°C/10) since 2010:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29c25614-b47c-4baa-b429-8e3f17e3a958", + "metadata": {}, + "outputs": [], + "source": [ + "df[(df['MONTH']==1) & (df['YEAR']>=2010) & (df['TMAX']>155.5)].groupby(['YEAR']).size()" + ] + }, + { + "cell_type": "markdown", + "id": "86eb54cb-9358-4ef9-b913-a8f653993ae5", + "metadata": {}, + "source": [ + "This is great! But how big is this dataset for one station?\n", + "\n", + "First, let's check the file size:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eb041314-2253-4933-ab2c-c81f742f44fc", + "metadata": {}, + "outputs": [], + "source": [ + "!ls -lh ../data/USW00003017.csv" + ] + }, + { + "cell_type": "markdown", + "id": "5c84f8f2-94f1-45d0-b789-82c779aa3ce0", + "metadata": {}, + "source": [ + "Similar to the previous tutorial, we can use the following function to find the size of a variable on memory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de8d54fc-ec05-41c1-a624-274e40772d13", + "metadata": {}, + "outputs": [], + "source": [ + "# Define function to display variable size in MB\n", + "import sys\n", + "def var_size(in_var):\n", + " result = sys.getsizeof(in_var) / 1e6\n", + " print(f\"Size of variable: {result:.2f} MB\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16c0b151-1454-4e38-96c6-41c38434b9cb", + "metadata": {}, + "outputs": [], + "source": [ + "var_size(df)" + ] + }, + { + "cell_type": "markdown", + "id": "2cae6ce6-0dca-47f1-bf84-ca3b46bfbb91", + "metadata": {}, + "source": [ + "Remember, the above rule?\n", + "\n", + "> \"Have 5 to 10 times as much RAM as the size of your dataset\"\n", + ">\n", + "> ~ Wes McKinney (2017) in [10 things I hate about pandas](https://wesmckinney.com/blog/apache-arrow-pandas-internals/)\n" + ] + }, + { + "cell_type": "markdown", + "id": "ccf0ba8b-8c65-40ce-81cb-0c313177fc8b", + "metadata": {}, + "source": [ + "**So far, we read in and analyzed data for one station. We have a total of +118,000 stations over the world and +4500 stations in Colorado alone!**\n", + "\n", + "**What if we want to look at the larger dataset?**\n", + "\n", + "## Scaling up to a larger dataset\n", + "\n", + "Let's start by reading data from selected stations. The downloaded data for this example includes the climatology observations from 66 selected sites in Colorado.\n", + "\n", + "Pandas can concatenate data to load data spread across multiple files:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25e0edd0-a58a-4ad4-8ab7-551122c92d14", + "metadata": {}, + "outputs": [], + "source": [ + "!du -csh ../data/*.csv |tail -n1" + ] + }, + { + "cell_type": "markdown", + "id": "d1258cc3-d24a-4268-ad19-62e02905b635", + "metadata": {}, + "source": [ + "Using a for loop with `pandas.concat`, we can read multiple files at the same time:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ddfc9c27-00da-426d-bb45-83f959f411ec", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "import glob\n", + "co_sites = glob.glob(os.path.join(data_dir, '*.csv'))\n", + "df = pd.concat(pd.read_csv(f, index_col=0, parse_dates=['DATE']) for f in co_sites)" + ] + }, + { + "cell_type": "markdown", + "id": "3d16de06-b7f5-4d4a-ac71-011d7ce5e1b1", + "metadata": {}, + "source": [ + "* How many stations have we read in?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4441c14e-a51b-4f59-b7e9-2fa0bdb1f6eb", + "metadata": {}, + "outputs": [], + "source": [ + "print (\"Concatenated data for\", len(df.ID.unique()), \"unique sites.\")" + ] + }, + { + "cell_type": "markdown", + "id": "9ab38e3b-1c37-48f8-86ca-903192cfcc1f", + "metadata": {}, + "source": [ + "Now that we concatenated the data for all sites in one DataFrame, we can do similar analysis on it:" + ] + }, + { + "cell_type": "markdown", + "id": "e8fee981-ef7c-4bbb-b7a2-db4705b3902e", + "metadata": {}, + "source": [ + "* Which site has recorded the most snow days in a year?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4509a4e3-ab07-4bd5-bbb8-fe121e481891", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "# ~90s on 4GB RAM\n", + "snowy_days = df[df['SNOW']>0].groupby(['ID','YEAR']).size()\n", + "\n", + "print ('This site has the highest number of snow days in a year : ')\n", + "snowy_days.agg(['idxmax','max'])" + ] + }, + { + "cell_type": "markdown", + "id": "160a297c-96cb-4014-a196-72e1482901a9", + "metadata": {}, + "source": [ + "**Excersise:** Which Colorado site has recorded the most snow days in 2023?" + ] + }, + { + "cell_type": "markdown", + "id": "1bbd5656-49a3-448c-b339-c6844304f61f", + "metadata": {}, + "source": [ + "**Dask allows us to conceptualize all of these files as a single dataframe!**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "63aff343-4431-4224-9c82-9ed725c9ea0c", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's do a little cleanup\n", + "del df, snowy_days" + ] + }, + { + "cell_type": "markdown", + "id": "3746273b-4057-466d-b7c9-a78932cd5808", + "metadata": {}, + "source": [ + "## Computations on Dask DataFrame" + ] + }, + { + "cell_type": "markdown", + "id": "6df1339a-7ee4-4125-b281-c467358d79a5", + "metadata": {}, + "source": [ + "### Create a \"LocalCluster\" Client with Dask" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb859ca6-a98d-40cd-a389-289c5246f670", + "metadata": {}, + "outputs": [], + "source": [ + "from dask.distributed import Client, LocalCluster\n", + "\n", + "cluster = LocalCluster()\n", + "client = Client(cluster)\n", + "client" + ] + }, + { + "cell_type": "markdown", + "id": "db5ae984-5763-4360-ab1b-2ecf92500952", + "metadata": {}, + "source": [ + "☝️ Click the Dashboard link above.\n", + "\n", + "👈 Or click the “Search” 🔍 button in the dask-labextension dashboard.\n", + "\n", + "### Dask DataFrame `read_csv` to read multiple files\n", + "\n", + "`dask.dataframe.read_csv` function can be used in conjunction with `glob` to read multiple csv files at the same time. \n", + "\n", + "Remember we can read one file with `pandas.read_csv`. For reading multiple files with pandas, we have to concatenate them with `pd.concatenate`. However, we can read many files at once just using `dask.dataframe.read_csv`.\n", + "\n", + "Overall, Dask is designed to perform I/O in parallel and is more performant than pandas for operations with multiple files or large files." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6d63add1-7e7f-4631-ae82-19800f4122a5", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "import dask\n", + "import dask.dataframe as dd\n", + "\n", + "ddf = dd.read_csv(co_sites, parse_dates=['DATE'])\n", + "ddf" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c8147b02-8f8d-4591-92c7-be3441bea8ae", + "metadata": {}, + "outputs": [], + "source": [ + "ddf.TMAX.mean()" + ] + }, + { + "cell_type": "markdown", + "id": "515a8028-c8f0-4bcf-8841-d4506fafb9d7", + "metadata": {}, + "source": [ + "**Notice that the representation of the DataFrame object contains no data just headers and datatypes. Why?**\n", + "\n", + "#### Lazy Evaluation\n", + "\n", + "Similar to Dask Arrays, Dask DataFrames are lazy. Here the data has not yet been read into the dataframe yet (a.k.a. lazy evaluation). \n", + "Dask just construct the task graph of the computation but it will \"evaluate\" them only when necessary.\n", + "\n", + "**So how does Dask know the name and dtype of each column?**\n", + "\n", + "Dask has just read the start of the first file and infers the column names and dtypes. \n", + "\n", + "Unlike `pandas.read_csv` that reads in all files before inferring data types, `dask.dataframe.read_csv` only reads in a sample from the beginning of the file (or first file if using a glob). The column names and dtypes are then enforced when reading the specific partitions (*Dask can make mistakes on these inferences if there is missing or misleading data in the early rows*)." + ] + }, + { + "cell_type": "markdown", + "id": "f3f062ee-ca69-4262-8316-807705a8fa07", + "metadata": {}, + "source": [ + "Let's take a look at the start of our dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f7b4823d-a785-495d-b2f5-e09a539f48d1", + "metadata": {}, + "outputs": [], + "source": [ + "ddf.head()" + ] + }, + { + "cell_type": "markdown", + "id": "b4bb8ab3-444e-41e3-94a0-8ec1b6354751", + "metadata": {}, + "source": [ + "
\n", + "\n", + "NOTE: Whenever we operate on our dataframe we read through all of our CSV data so that we don’t fill up RAM. Dask will delete intermediate results (like the full pandas DataFrame for each file) as soon as possible. This enables you to handle larger than memory datasets but, repeated computations will have to load all of the data in each time.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "2e1c0a3a-f70a-44ee-912f-da92344c0fb2", + "metadata": {}, + "source": [ + "Similar data manipulations as `pandas.dataframe` can be done for `dask.dataframes`. \n", + "For example, let's find the highest number of snow days in Colorado:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "869c85cb-cb7b-41ad-b3aa-c5134c46e240", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "print ('This site has the highest number of snow days in a year : ')\n", + "snowy_days = ddf[ddf['SNOW']>0].groupby(['ID','YEAR']).size()\n", + "snowy_days.compute().agg(['idxmax','max'])" + ] + }, + { + "cell_type": "markdown", + "id": "61d6f60b-14a5-4816-8f98-c0f0619e3a32", + "metadata": {}, + "source": [ + "#### Nice, but what did Dask do?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "50c119c3-68df-457a-af03-fcee413e67ac", + "metadata": {}, + "outputs": [], + "source": [ + "# Requires ipywidgets\n", + "\n", + "snowy_days.dask" + ] + }, + { + "cell_type": "markdown", + "id": "e02b624a-3418-44bb-95af-766a649ca328", + "metadata": {}, + "source": [ + "You can also view the underlying task graph using `.visualize()`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7f061feb-4e52-4b3e-a62f-cbadca2c06d1", + "metadata": {}, + "outputs": [], + "source": [ + "#graph is too large\n", + "snowy_days.visualize()" + ] + }, + { + "cell_type": "markdown", + "id": "cb29864d-693b-4afe-abbf-2c6d350fc777", + "metadata": {}, + "source": [ + "### Use `.compute` wisely!\n", + "#### Share intermediate results" + ] + }, + { + "cell_type": "markdown", + "id": "7cf9e11d-2053-4758-a16f-901a540e8659", + "metadata": {}, + "source": [ + "For most operations, `dask.dataframe` hashes the arguments, allowing duplicate computations to be shared, and only computed once.\n", + "\n", + "For example, let’s compute the mean and standard deviation for Maximum daily temperature of all snow days." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "95d16724-c92c-459f-811a-7194dfb9bfc2", + "metadata": {}, + "outputs": [], + "source": [ + "snowy_days = ddf[ddf['SNOW']>0]\n", + "mean_tmax = snowy_days.TMAX.mean()\n", + "std_tmax = snowy_days.TMAX.std()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1d1b711-c71c-4bec-a61e-3db9745db201", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "mean_tmax_result = mean_tmax.compute()\n", + "std_tmax_result = std_tmax.compute()" + ] + }, + { + "cell_type": "markdown", + "id": "439b2065-f354-448f-a347-e52892c77dc6", + "metadata": {}, + "source": [ + "But if we pass both arguments in a single `.compute`, we can share the intermediate results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9a657e65-e968-4c2f-b532-437798a10202", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "mean_tmax_result, std_tmax_result = dask.compute(mean_tmax, std_tmax)" + ] + }, + { + "cell_type": "markdown", + "id": "31c6c726-ab95-42c4-94a7-63ccaecdc34b", + "metadata": {}, + "source": [ + "Here using `dask.compute` only one allowed sharing intermediate results between TMAX mean and median calculations and improved total performance. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1c3a1fc0-7ef5-462d-befd-811291d60441", + "metadata": {}, + "outputs": [], + "source": [ + "mean_tmax.dask" + ] + }, + { + "cell_type": "markdown", + "id": "59987c79-7adb-4903-9d5d-5517abbab38c", + "metadata": {}, + "source": [ + "Here some operations such as the calls to read the csv files, the filtering, and the grouping is exactly similar between both operations, so they can share intermediate results. Remember, Dask will delete intermediate results (like the full pandas DataFrame for each file) as soon as possible. " + ] + }, + { + "cell_type": "markdown", + "id": "11f87712-b502-48bc-be7f-8c5623003c1f", + "metadata": {}, + "source": [ + "### `.persist` or caching\n", + "Sometimes you might want your computers to keep intermediate results in memory, if it fits in the memory. \n", + "\n", + "The `.persist()` method can be used to “cache” data and tell Dask what results to keep around. You should only use `.persist()` with any data or computation that fits in memory." + ] + }, + { + "cell_type": "markdown", + "id": "b9df12cb-bca1-4729-a2b4-3b3f51d4fcbb", + "metadata": {}, + "source": [ + "For example, if we want to only do analysis on a subset of data (for example snow days at Boulder site):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2bf95ec2-6c65-4528-8f40-2ffac9f964f4", + "metadata": {}, + "outputs": [], + "source": [ + "boulder_snow = ddf[(ddf['SNOW']>0)&(ddf['ID']=='USC00050848')]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7caf6554-c94a-436a-b603-3701bc30b433", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "tmax = boulder_snow.TMAX.mean().compute()\n", + "tmin = boulder_snow.TMIN.mean().compute()\n", + "\n", + "print (tmin, tmax)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4c09219c-44b7-40fe-bfa2-0e5ab5c963cc", + "metadata": {}, + "outputs": [], + "source": [ + "boulder_snow = ddf[(ddf['SNOW']>0)&(ddf['ID']=='USC00050848')].persist()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c6d9a9d-2096-4a33-8c95-b2e830ac7cc8", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "tmax = boulder_snow.TMAX.mean().compute()\n", + "tmin = boulder_snow.TMIN.mean().compute()\n", + "print (tmin, tmax)" + ] + }, + { + "cell_type": "markdown", + "id": "5e02a943-72ec-4192-8ac5-1946b558fcc9", + "metadata": {}, + "source": [ + "As you can see the analysis on this persisted data is much faster because we are not repeating the loading and selecting." + ] + }, + { + "cell_type": "markdown", + "id": "0453ad26-ce5c-42cc-8c08-2b40e844a22d", + "metadata": {}, + "source": [ + "## Dask DataFrames Best Practices\n", + "\n", + "### Use pandas (when you can)\n", + "For data that fits into RAM, pandas can often be easier and more efficient to use than Dask DataFrame. However, Dask DataFrame is a powerful tool for larger-than-memory datasets. \n", + "\n", + "When the data is still larger than memory, Dask DataFrame can be used to **reduce** the larger datasets to a manageable level that pandas can handle. Next, use pandas at that point.\n", + "\n", + "### Avoid Full-Data Shuffling\n", + "Some operations are more expensive to compute in a parallel setting than if they are in-memory on a single machine (for example, `set_index` or `merge`). In particular, **shuffling** operations that rearrange data can become very communication intensive. \n", + "\n", + "\n", + "### pandas performance tips\n", + "\n", + "pandas performance tips such as using vectorized operations also apply to Dask DataFrames. See [Modern Pandas notebook](https://github.com/TomAugspurger/effective-pandas/blob/master/modern_1_intro.ipynb) for more tips on better performance with pandas. \n", + "\n", + "\n", + "### Check Partition Size\n", + "\n", + "Similar to chunks, partitions should be small enough that they fit in the memory, but large enough to avoid that the communication overhead. \n", + "\n", + "\n", + "#### `blocksize`\n", + "* The number of partitions can be set using the `blocksize` argument. \n", + "If none is given, the number of partitions/blocksize is calculated depending on the available memory and the number of cores on a machine up to a max of 64 MB. As we increase the blocksize, the number of partitions (calculated by Dask) will decrease. This is especially important when reading one large csv file. \n", + "\n", + "\n", + "**As a good rule of thumb, you should aim for partitions that have around 100MB of data each.**\n", + "\n", + "\n", + "### Smart use of `.compute()`\n", + "Try avoiding running `.compute()` operation as long as possible. Dask works best when users avoid computation until results are needed. The `.compute()` command informs Dask to trigger computations on the Dask DataFrame. \n", + "As shown in the above example, the intermediate results can also be shared by calling `.compute()` only once. \n" + ] + }, + { + "cell_type": "markdown", + "id": "19a91d09-116f-4ea3-b1e4-e7755d406b4a", + "metadata": {}, + "source": [ + "### Close your local Dask Cluster\n", + "It is always a good practice to close the Dask cluster you created." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9bd19bbe-d887-4fbb-b4eb-5dc0881e9bf6", + "metadata": {}, + "outputs": [], + "source": [ + "client.shutdown()" + ] + }, + { + "cell_type": "markdown", + "id": "7874d8bc-faf5-480d-848f-b2f93d725dac", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "e0d12769-eaba-47fc-97e0-6d5a4298fbc4", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, we have learned about:\n", + "\n", + "* Dask DataFrame concept and component.\n", + "* When to use and when to avoid Dask DataFrames?\n", + "* How to use Dask DataFrame?\n", + "* Some best practices around Dask DataFrames.\n", + "\n", + "\n", + "## Resources and references\n", + "\n", + "* Reference\n", + " * [Dask Docs](https://dask.org/)\n", + " * [Dask Examples](https://examples.dask.org/)\n", + " * [Dask Code](https://github.com/dask/dask/)\n", + " * [Dask Blog](https://blog.dask.org/)\n", + " * [Pandas Docs](https://pandas.pydata.org/docs/)\n", + " \n", + "* Ask for help\n", + " * [`dask`](http://stackoverflow.com/questions/tagged/dask) tag on Stack Overflow, for usage questions\n", + " * [github discussions: dask](https://github.com/dask/dask/discussions) for general, non-bug, discussion, and usage questions\n", + " * [github issues: dask](https://github.com/dask/dask/issues/new) for bug reports and feature requests\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": "3.9.18" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_preview/16/_sources/notebooks/03-dask-xarray.ipynb b/_preview/16/_sources/notebooks/03-dask-xarray.ipynb new file mode 100644 index 0000000..4ff3232 --- /dev/null +++ b/_preview/16/_sources/notebooks/03-dask-xarray.ipynb @@ -0,0 +1,1004 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "990cfa4c-2117-4435-9806-ff9048890398", + "metadata": { + "tags": [] + }, + "source": [ + "\"Dask\n", + "\"Xarray\"\n",\n", + "\n", + "# Parallelizing Xarray with Dask\n", + "\n", + "### In this tutorial, you learn:\n", + "\n", + "* Using Dask with Xarray\n", + "* Read/write netCDF files with Dask\n", + "* Dask backed Xarray objects and operations\n", + "* Extract Dask arrays from Xarray objects and use Dask array directly.\n", + "* Xarray built-in operations can transparently use dask\n", + "\n", + "### Prerequisites\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n", + "| Dask Arrays | Necessary | |\n", + "| Dask DataFrames | Necessary | |\n", + "\n", + "- **Time to learn**: 40 minutes\n", + "---------\n", + "\n", + "## Introduction\n", + "\n", + "### Xarray Quick Overview\n", + "\n", + "\n", + " \n", + "Xarray is an open-source Python library designed for working with *labelled multi-dimensional* data. By *multi-dimensional* data (also often called *N-dimensional*), we mean data that has many independent dimensions or axes (e.g. latitude, longitude, time). By labelled we mean that these axes or dimensions are associated with coordinate names (like \"latitude\") and coordinate labels like \"30 degrees North\".\n", + "\n", + "Xarray provides pandas-level convenience for working with this type of data.\n", + "\n", + "\n", + "\"Xarray\"\n",\n", + "\n", + "*Image credit: Xarray Contributors*\n", + "\n", + "The dataset illustrated has two variables (`temperature` and `precipitation`) that have three dimensions. Coordinate vectors (e.g., latitude, longitude, time) that describe the data are also included.\n", + "\n", + " \n", + "#### Xarray Data Structures\n", + "\n", + "Xarray has two fundamental data structures:\n", + "\n", + "* `DataArray` : holds a single multi-dimensional variable and its coordinates\n", + "* `Dataset` : holds multiple DataArrays that potentially share the same coordinates\n", + "\n", + "\n", + "**Xarray DataArray**\n", + "\n", + "A `DataArray` has four essential attributes:\n", + "* `data`: a `numpy.ndarray` holding the values.\n", + "* `dims`: dimension names for each axis (e.g., latitude, longitude, time).\n", + "* `coords`: a dict-like container of arrays (coordinates) that label each point (e.g., 1-dimensional arrays of numbers, datetime objects or strings).\n", + "* `attrs`: a dictionary to hold arbitrary metadata (attributes).\n", + "\n", + "**Xarray DataSet**\n", + "\n", + "A dataset is simply an object containing multiple Xarray DataArrays indexed by variable name." + ] + }, + { + "cell_type": "markdown", + "id": "598055e0-3bac-491b-8b7f-d7313a306bc8", + "metadata": {}, + "source": [ + "### Xarray can wrap many array types like Numpy and Dask.\n", + "\n", + "Let's start with a random 2D NumPy array, for example this can be SST (sea-surface temperature) values of a domain with dimension of 300x450 grid:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b6d9a1d-6520-4374-a178-ad91af454628", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np \n", + "import dask.array as da\n", + "import xarray as xr\n", + "\n", + "xr.set_options(display_expand_data=False);" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c5185246-289d-4bc3-a355-4c5101bd6ddd", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# -- numpy array \n", + "sst_np = np.random.rand(300,450)\n", + "type(sst_np)" + ] + }, + { + "cell_type": "markdown", + "id": "c17adcd3-672d-4a46-8b16-1f579aa29e8b", + "metadata": {}, + "source": [ + "As we saw in the previous tutorial, we can convert them to a Dask Array:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "744e09ca-7a23-428b-9032-1808610c19b0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "sst_da = da.from_array( sst_np)\n", + "sst_da" + ] + }, + { + "cell_type": "markdown", + "id": "62d098b3-a7fc-4562-bc7f-41c19b3c9280", + "metadata": {}, + "source": [ + "This is great and fast! BUT\n", + "* What if we want to attach coordinate values to this array?\n", + "* What if we want to add metadata (e.g. units) to this array?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2006822d-c6db-4e67-995e-732d92ff10b6", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# similarly we can convert them to xarray datarray\n", + "sst_xr = xr.DataArray(sst_da)\n", + "sst_xr" + ] + }, + { + "cell_type": "markdown", + "id": "d1eb3ef7-8413-4578-bd1f-93f488d6b344", + "metadata": {}, + "source": [ + "A simple DataArray without dimensions or coordinates isn't much use." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81aad904-f133-4a28-b1fc-aecc3fab7a11", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# we can add dimension names to this:\n", + "sst_xr = xr.DataArray(sst_da,dims=['lat','lon'])\n", + "\n", + "sst_xr.dims" + ] + }, + { + "cell_type": "markdown", + "id": "5dd6bea8-35ff-4f7f-91aa-cb808d30621c", + "metadata": {}, + "source": [ + "We can add our coordinates with values to it :\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d9525a4b-e99b-46e5-9154-f0badf205ff9", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# -- create some dummy values for lat and lon dimensions\n", + "lat = np.random.uniform(low=-90, high=90, size=300)\n", + "lon = np.random.uniform(low=-180, high=180, size=450)\n", + "\n", + "sst_xr = xr.DataArray(sst_da,\n", + " dims=['lat','lon'],\n", + " coords={'lat': lat, 'lon':lon},\n", + " attrs=dict(\n", + " description=\"Sea Surface Temperature.\",\n", + " units=\"degC\")\n", + " )\n", + "sst_xr" + ] + }, + { + "cell_type": "markdown", + "id": "c58938ac-372a-42dc-8168-a59e4b45294b", + "metadata": {}, + "source": [ + "Xarray data structures are a very powerful tool that allows us to use metadata to express different analysis patterns (slicing, selecting, groupby, averaging, and many other things). " + ] + }, + { + "cell_type": "markdown", + "id": "a9be8aad-9135-45f5-a0e7-3d1d26e77869", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Take Away \n", + "\n", + "Xarray DataArray provides a wrapper around arrays, and uses labeled dimensions and coordinates to support metadata-aware operations (e.g. `da.sum(dim=\"time\")` instead of `array.sum(axis=-1)`)\n", + "\n", + "**Xarray can wrap dask arrays instead of numpy arrays.** \n", + "\n", + "This capability turns Xarray into an extremely useful tool for Big Data earth science.\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "3e848a64-407d-4492-a680-56a292e9eec3", + "metadata": {}, + "source": [ + "With this introduction, let's start our tutorial on features of Xarray and Dask:" + ] + }, + { + "cell_type": "markdown", + "id": "82b9060b-7aab-48a0-ab22-7f07300b3fa9", + "metadata": {}, + "source": [ + "### Setup: Spinning up a cluster" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "778c8357-fcca-4b06-bce6-9c7531c0181b", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "from dask.distributed import LocalCluster, Client\n", + "cluster = LocalCluster()\n", + "client = Client(cluster)\n", + "client\n" + ] + }, + { + "cell_type": "markdown", + "id": "f4cc4275-c0ce-42fa-a6b4-033c597b5c9c", + "metadata": {}, + "source": [ + "-----------\n", + "## Reading data with Dask and Xarray\n", + "\n", + "### Reading multiple netCDF files with `open_mfdataset`\n", + "\n", + "Xarray provides a function called `open_dataset` function that allows us to load a netCDF dataset into a Python data structure. To read more about this function, please see [xarray `open_dataset` API documentation](https://docs.xarray.dev/en/stable/generated/xarray.open_dataset.html). \n", + "\n", + "Xarray also provides `open_mfdataset`, which open multiple files as a single xarray dataset. Passing the argument `parallel=True` will speed up reading multiple datasets by executing these tasks in parallel using Dask Delayed under the hood. \n", + "\n", + "In this example, we are going to examine a subset of CESM2 Large Ensemble Data Sets (LENS). We will use 2m temperature (TREFHT) for this analysis. \n", + "\n", + "To learn more about LENS dataset, please visit:\n", + "* [LENS official website](https://www.cesm.ucar.edu/community-projects/lens/data-sets)\n", + "* [LENS paper](https://www.cesm.ucar.edu/community-projects/lens/data-sets)\n", + "\n", + "For this tutorial, we only look at a small subset of data. If you don't have the data, running the following code enables you to download, prepare, and stage the required datasets (`../data/` folder) for this cookbook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a53169fd-46dd-41bd-94ca-aed13e0f6357", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "!./get_data.sh notebook3" + ] + }, + { + "cell_type": "markdown", + "id": "172706d1-73e3-41b8-b0af-3ec6168d6242", + "metadata": {}, + "source": [ + "We can open up multiple files using `open_mfdataset` function. " + ] + }, + { + "cell_type": "markdown", + "id": "8c23d960-93f4-44c0-9f3d-b92ff8083848", + "metadata": {}, + "source": [ + "### Constructing Xarray Datasets from files" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d1b5337d-28c3-4f88-b9c3-ce3d92445c37", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "import glob\n", + "\n", + "var = 'TREFHT'\n", + "\n", + "# find all LENS files for 1 ensemble\n", + "data_dir = '../data/data_for_cesm'\n", + "files = glob.glob(os.path.join(data_dir, 'b.e21.BSSP370smbb.f09_g17.LE2-1301.013*.nc'))\n", + "\n", + "print(\"All files: [\", len(files), \"files]\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "920f5f93-db35-4ae8-b9bd-d9374efabe40", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "%%time\n", + "ds = xr.open_mfdataset(\n", + " sorted(files),\n", + " # concatenate along this dimension\n", + " concat_dim=\"time\",\n", + " # concatenate files in the order provided\n", + " combine=\"nested\",\n", + " # parallelize the reading of individual files using dask\n", + " # This means the returned arrays will be dask arrays\n", + " parallel=True,\n", + " # these are netCDF4 files, use the h5netcdf package to read them\n", + " engine=\"h5netcdf\",\n", + " # hold off on decoding time\n", + " decode_cf=False,\n", + " # specify that data should be automatically chunked\n", + " chunks=\"auto\",\n", + ")\n", + "ds = xr.decode_cf(ds)\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "2d757c05-8258-44ff-a232-1a278edc2e28", + "metadata": {}, + "source": [ + "For complex scenarios, you can access each file individually by utilizing the `open_dataset` function with the specified `chunk`s and then combine the outputs into a single dataset later." + ] + }, + { + "cell_type": "markdown", + "id": "56497135-6ad3-4323-bb8a-4f073eaf8939", + "metadata": {}, + "source": [ + "Note that the \"real\" values are not displayed, since that would trigger actual computation. \n", + "
\n", + "\n", + "Xarray automatically wraps Dask Arrays and Dask is lazy, meaning that operations are not computed until we explicitly request them, for example by calling `.compute()`.\n", + "\n", + "
\n", + "\n", + "\n", + "Please see previous notebooks for more information on \"lazy evaluation\". \n", + "\n", + "The represntation of `TREFHT` DataArray shows details of chunks and chunk-sizes of Xarray DataArray:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac192d70-f708-4db9-822c-4f2848cb0e02", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tref = ds.TREFHT\n", + "tref" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d511a271-fe12-4552-b59c-672605543cb2", + "metadata": {}, + "outputs": [], + "source": [ + "tref.chunks" + ] + }, + { + "cell_type": "markdown", + "id": "0fbf4db0-f4b8-4b48-8337-12ace3882861", + "metadata": {}, + "source": [ + "* How many chunks do we have? \n", + "* What is the size of each chunk size?\n", + "\n", + "Here we can see that we have a total of 9 chunks - equal to the number of our netCDF files. In general `open_mfdataset` will return one chunk per netCDF file.\n", + "\n", + "
\n", + "\n", + "**WARNING:** The chunk structure within the file is important. When re-chunking the dataset after creation with `ds.chunk()` it is recommended to only use multiples of the on-file chunk shape.\n", + "\n", + "
\n", + "\n", + "We can check what that shape is by looking at the encoding:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c54779d9-92d5-4272-b300-94391b34bb52", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "tref.encoding" + ] + }, + { + "cell_type": "markdown", + "id": "d9592706-78ac-4ae1-b0b7-c626ab95ec16", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**TIP:** The `chunks` parameter can significantly affect total performance when using Dask Arrays. `chunks` should be small enough that each chunk fit in the memory, but large enough to avoid that the communication overhead. \n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "c094dcba-fdc8-4457-bf49-5c6ddf9b28a3", + "metadata": {}, + "source": [ + "A good rule of thumb is to create arrays with a minimum chunksize of at least one million elements. Here we have 120x192x288 elements in each chunk (except for the last chunk). \n", + "With large arrays (10+ GB), the cost of queuing up Dask operations can be noticeable, and you may need even larger chunksizes. \n", + "\n", + "**Additional Reading**\n", + "\n", + " - [dask.array best practices](https://docs.dask.org/en/stable/array-best-practices.html#select-a-good-chunk-size)\n", + " - [NCAR chunking tutorial](https://ncar.github.io/dask-tutorial/notebooks/06-dask-chunking.html)\n", + " - [Dask blog post on chunking](https://blog.dask.org/2021/11/02/choosing-dask-chunk-sizes)" + ] + }, + { + "cell_type": "markdown", + "id": "ad1c1948-992e-463d-9631-35779ea204df", + "metadata": {}, + "source": [ + "### Xarray data structures are Dask collections.\n", + "\n", + "This means you can call the following Dask-related functions on Xarray Data Arrays and Datasets:\n", + "\n", + "* `.visualize()`\n", + "* `.compute()`\n", + "* `.persist()`\n", + "\n", + "For more information about Dask Arrays, please see [Dask Array chapter](https://projectpythia.org/dask-cookbook/notebooks/01-dask-array.html)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d5468f03-be6e-44b9-92ef-2de287f3d990", + "metadata": {}, + "outputs": [], + "source": [ + "tref_mean = tref.mean('time')\n", + "tref_mean.data.dask" + ] + }, + { + "cell_type": "markdown", + "id": "f1628d55-7bda-42f4-b615-b86274571ec9", + "metadata": {}, + "source": [ + "If we check Dask Task Graph for `tref_mean`, we can see all the steps required for calculating it (from opening the netcdf file to calculating mean and aggreagting it). " + ] + }, + { + "cell_type": "markdown", + "id": "889e9971-43eb-4837-8d13-e560de21f908", + "metadata": {}, + "source": [ + "### Getting concrete values\n", + "At some point, you will want to actually do the calculations and receive concrete values from Dask.\n", + "\n", + "There are two ways to compute values on dask arrays.\n", + "\n", + " 1. `compute()` returns a new xarray object with the data now represented as a numpy array.\n", + " 2. `load()` replaces the dask array in the xarray object with a numpy array. Equivalent to `ds = ds.compute()`.\n", + "\n", + "`.load()` operates *inplace* and `.compute()` returns a new xarray object.\n", + "\n", + "#### Distributed non-blocking concrete values\n", + "\n", + "There is another option available third option : “persisting”. `.persist()` loads the values into distributed RAM. The values are computed but remain distributed across workers. So essentially `persist` turns a lazy Dask collection into a Dask collection where the results are either fully computed or actively computing in the background.\n", + "\n", + "So `ds.air.persist()` is still backed by a Dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory. \n", + "\n", + "Read more: [Dask user guide](https://docs.dask.org/en/stable/generated/dask.dataframe.Series.persist.html)\n" + ] + }, + { + "cell_type": "markdown", + "id": "8ce5b8a3-cea9-4d04-bac0-dd574da5877d", + "metadata": { + "tags": [] + }, + "source": [ + "## How to access underlying data in an Xarray object?" + ] + }, + { + "cell_type": "markdown", + "id": "cc67499d-dbba-4844-9fed-cb125cc5048a", + "metadata": {}, + "source": [ + "There are two basic ways to extract values from an Xarray object:\n", + "\n", + "1. Using `.data` will return a Dask array. For example:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de86a448-d04f-4a39-b1c6-85366734d590", + "metadata": {}, + "outputs": [], + "source": [ + "tref.data" + ] + }, + { + "cell_type": "markdown", + "id": "3c3f5541-c879-40be-a0dc-85b54d498969", + "metadata": {}, + "source": [ + "**This means that for Dask-backed Xarray object, we can access the values using `.compute`**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68961208-6a78-49a4-8787-b7937d66a5b6", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "tref.data.compute()" + ] + }, + { + "cell_type": "markdown", + "id": "56b9429c-7a4f-4c86-982f-90656cc65662", + "metadata": {}, + "source": [ + "2. We can also use `.values` to see the \"real\" values of Xarray object. Another option is using `.to_numpy`. Both of these option return the values of underlying Dask object in a numpy array. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e56f49b0-7865-4be4-8e36-011377cd20b4", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "tref.to_numpy()" + ] + }, + { + "cell_type": "markdown", + "id": "7f2ce398-ff13-4251-945b-7b871bdfcb81", + "metadata": {}, + "source": [ + "## Computation" + ] + }, + { + "cell_type": "markdown", + "id": "b692aefa-001a-4dcc-9247-039f1790863f", + "metadata": {}, + "source": [ + "All built-in Xarray methods (`.mean`, `.max`, `.rolling`, `.groupby` etc.) support dask arrays.\n", + "\n", + "Now, let's do some computations on this Xarray dataset. " + ] + }, + { + "cell_type": "markdown", + "id": "0f4d163a-bc44-41f9-b190-ea5cdcd91cd1", + "metadata": {}, + "source": [ + "### Single Point Calculations\n", + "\n", + "To start out, let's do the calculations on a single point first. First, we extract the time series data at a grid point and save it to a variable. Here we select the closest point using `.sel` and load the data. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c28fb0e2-6de1-45ca-ba79-7d50b3aa1842", + "metadata": {}, + "outputs": [], + "source": [ + "tref_boulder = tref.sel(lat=40.0150, lon=-105.2705, method='nearest').load()" + ] + }, + { + "cell_type": "markdown", + "id": "dea9cfcd-ba5e-4c45-bfd1-c079c7a34e73", + "metadata": {}, + "source": [ + "
\n", + "\n", + "**WARNING:** Remember as soon as you call `.load()` you are telling Dask to trigger computation.\n", + "\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "afa4f016-4a0a-4220-ad1f-bc4b7d1ecc32", + "metadata": {}, + "outputs": [], + "source": [ + "# -- take annual average\n", + "tb = tref_boulder.resample(time='AS').mean()\n", + "tb" + ] + }, + { + "cell_type": "markdown", + "id": "06872e46-54ec-4df1-a0cb-4e25653f5f7d", + "metadata": {}, + "source": [ + "We can either see the values of our DataArray in the text representation above or by plotting it:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2e829f32-11ff-4e7a-bf7c-ceb3d606793f", + "metadata": {}, + "outputs": [], + "source": [ + "tb.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "9c868e1f-6d5a-42a9-94c0-3c8bf70e8020", + "metadata": {}, + "source": [ + "### Calculations over all grids" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4f8ecc4c-f99a-4a6f-b46c-608e7ec2e60e", + "metadata": {}, + "outputs": [], + "source": [ + "# change the unit from Kelvin to degree Celsius \n", + "tref_c = tref - 273.15\n", + "tref_c" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "225daee2-b0f5-4cca-8105-c053b6f14106", + "metadata": {}, + "outputs": [], + "source": [ + "%%time \n", + "\n", + "tref_c = tref_c.load()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be6fa1c8-a550-4fd1-8418-cae2844fe657", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "\n", + "# Compute monthly anomaly\n", + "\n", + "# -- 1. calculate monthly average\n", + "tref_grouped = tref.groupby('time.month')\n", + "tmean = tref_grouped.mean(dim='time')\n", + "\n", + "#-- 2. calculate monthly anomaly\n", + "tos_anom = tref_grouped - tmean\n", + "tos_anom" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e9df3af5-4089-4014-aecc-cb2fe62a0f0d", + "metadata": {}, + "outputs": [], + "source": [ + "%%time\n", + "computed_anom = tos_anom.load()\n", + "type(computed_anom)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a161d2b-9321-459f-9b36-c4ae73115eab", + "metadata": {}, + "outputs": [], + "source": [ + "tos_anom.sel(lon=310, lat=50, method='nearest').plot( size=4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40d5ee76-a2ae-41d7-9c45-46cf9c19bc7a", + "metadata": {}, + "outputs": [], + "source": [ + "tos_anom.sel(time='2030-01-01').plot()" + ] + }, + { + "cell_type": "markdown", + "id": "b6d15e18-788b-4763-b4ae-f4ee0f644cf3", + "metadata": {}, + "source": [ + "
\n", + "\n", + "TIP: Using Xarray plotting functionality automatically triggers computations on the Dask Array, similar to `.compute()`.\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "3da23b0c-d3f7-4bfb-aa80-19eeb640823e", + "metadata": {}, + "source": [ + "We can do more complex calculations too:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb13b6b0-b881-44b4-82af-60514515a8eb", + "metadata": {}, + "outputs": [], + "source": [ + "rolling_mean = tref.rolling(time=5).mean()\n", + "rolling_mean # contains dask array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "edacaaa8-61b7-4f68-a751-a9f71d5c4dde", + "metadata": {}, + "outputs": [], + "source": [ + "timeseries = rolling_mean.isel(lon=1, lat=20) # no activity on dashboard\n", + "timeseries # contains dask array" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20a6caff-aa35-4968-8c71-bd083c3cd136", + "metadata": {}, + "outputs": [], + "source": [ + "computed = rolling_mean.compute() # activity on dashboard\n", + "computed # has real numpy values" + ] + }, + { + "cell_type": "markdown", + "id": "dfe03506-897e-4ae8-bf5c-b990f43882d8", + "metadata": {}, + "source": [ + "### Supplementary Material: Advanced workflows and automatic parallelization using `apply_ufunc`\n", + "Most of xarray’s built-in operations work on Dask arrays. If you want to use a function that isn’t wrapped by Xarray to work with Dask, one option is to extract Dask arrays from xarray objects (.data) and use Dask directly.\n", + "\n", + "Another option is to use xarray’s `apply_ufunc()` function. `xr.apply_ufunc()` can automate embarrassingly parallel “map” type operations where a function written for processing NumPy arrays, but we want to apply it on our Xarray DataArray. \n", + "\n", + "`xr.apply_ufunc()` give users capability to run custom-written functions such as parameter calculations in a parallel way. See the [Xarray tutorial material on apply_ufunc](https://tutorial.xarray.dev/advanced/apply_ufunc/apply_ufunc.html) for more.\n", + "\n", + "In the example below, we calculate the saturation vapor pressure by using `apply_ufunc()` to apply this function to our Dask Array chunk by chunk. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c67a70aa-93f2-40d8-b8f3-896602603648", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def sat_p(t):\n", + " \"\"\"Calculate saturation vapor pressure using Clausius-Clapeyron equation\"\"\"\n", + " return 0.611 * np.exp(17.67 * (t-273.15)*((t-29.65)**(-1)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e3aa9ad-67e6-4b95-b1ed-dd238dc12d32", + "metadata": {}, + "outputs": [], + "source": [ + "es = xr.apply_ufunc(sat_p, tref, dask=\"parallelized\", output_dtypes=[float])\n", + "es" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2dea08bd-fb26-49ab-ab13-f7c0ebd5c25c", + "metadata": {}, + "outputs": [], + "source": [ + "es.compute()" + ] + }, + { + "cell_type": "markdown", + "id": "43e90ad3-3af4-43bc-8f36-d4aff2960263", + "metadata": {}, + "source": [ + "The data used for this tutorial is from one ensemble member. What if we want to use multiple ensemble members? So far, we only run on one machine, what if we run an HPC cluster? We will go over this in the next tutorial." + ] + }, + { + "cell_type": "markdown", + "id": "5169db51-92ba-4257-a4ab-d2bd6b524909", + "metadata": {}, + "source": [ + "### Dask + Xarray Good Practices\n", + "\n", + "
\n", + "\n", + "Summary of Dask + Xarray Good Practices \n", + "\n", + "\n", + "The good practices regarding Dask + Xarray is the same as the good practices for Dask only.\n", + "\n", + "* Similar to Dask DataFrames, it is more efficient to first do spatial and temporal indexing (e.g. .sel() or .isel()) and filter the dataset early in the pipeline, especially before calling resample() or groupby(). \n", + "\n", + "* Chunk sizes should be small enough to fit into the memory at once but large enough to avoid the additional communication overhead. Good chunk size ~100 MB. \n", + "\n", + "* It is always better to chunk along the `time` dimension. \n", + "\n", + "* Avoid too many tasks since each task will introduce 1ms of overhead. \n", + "\n", + "* When possible, use `xr.apply_ufunc` to apply an unvectorized function to the Xarray object. \n", + "\n", + "
\n", + "\n", + "\n", + "\n", + "### Close you local Dask Cluster\n", + "It is always a good practice to close the Dask cluster you created." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9bd19bbe-d887-4fbb-b4eb-5dc0881e9bf6", + "metadata": {}, + "outputs": [], + "source": [ + "client.shutdown()" + ] + }, + { + "cell_type": "markdown", + "id": "e0d12769-eaba-47fc-97e0-6d5a4298fbc4", + "metadata": {}, + "source": [ + "## Summary\n", + "\n", + "In this notebook, we have learned about:\n", + "\n", + "* Using Dask with Xarray\n", + "* Read/write netCDF files with Dask\n", + "* Dask backed Xarray objects and operations\n", + "* Extract Dask arrays from Xarray objects and use Dask array directly..\n", + "* Customized workflows using `apply_ufunc`\n", + "\n", + "## Resources and references\n", + "\n", + "### Reference\n", + "* [Dask Array Docs](https://docs.dask.org/en/stable/array.html)\n", + "* [Dask Examples](https://examples.dask.org/)\n", + "* [Dask Code](https://github.com/dask/dask/)\n", + "* [Dask Blog](https://blog.dask.org/)\n", + "* [Xarray Docs](https://xarray.pydata.org/)\n", + "* [Xarray + Dask docs](https://docs.xarray.dev/en/stable/user-guide/dask.html), particularly the [optimization tips](https://docs.xarray.dev/en/stable/user-guide/dask.html#optimization-tips)\n", + "* [Xarray Tutorial material](https://tutorial.xarray.dev/intro.html)\n", + " \n", + "\n", + "### Ask for help\n", + "* [`dask`](http://stackoverflow.com/questions/tagged/dask) tag on Stack Overflow, for usage questions\n", + "* [github discussions: dask](https://github.com/dask/dask/discussions) for general, non-bug, discussion, and usage questions\n", + "* [github issues: dask](https://github.com/dask/dask/issues/new) for bug reports and feature requests\n", + "* [github discussions: xarray](https://github.com/pydata/xarray/discussions) for general, non-bug, discussion, and usage questions\n", + "* [github issues: xarray](https://github.com/pydata/xarray/issues/new) for bug reports and feature requests" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "57f2ad23-bd82-4fd2-89ef-8ca07feba117", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_preview/16/_sources/notebooks/04-dask-cluster.ipynb b/_preview/16/_sources/notebooks/04-dask-cluster.ipynb new file mode 100644 index 0000000..b0aa5e9 --- /dev/null +++ b/_preview/16/_sources/notebooks/04-dask-cluster.ipynb @@ -0,0 +1,1320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a085b1ec-c6a0-4a39-afab-723e268a255b", + "metadata": {}, + "source": [ + "\"Dask\n", + "\n", + "# Dask Schedulers\n", + "\n", + "### In this tutorial, you learn:\n", + "\n", + "* Components of Dask Schedulers\n", + "* Types of Dask Schedulers\n", + "* Single Machine Schedulers\n", + "\n", + "\n", + "**Related Documentation**\n", + "\n", + "* [Dask Scheduling](https://docs.dask.org/en/stable/scheduling.html) \n", + "* [Dask Local Cluster](https://docs.dask.org/en/stable/deploying-python.html) \n", + "* [Deploying Dask Cluster Manager](https://docs.dask.org/en/stable/deploying.html) " + ] + }, + { + "cell_type": "markdown", + "id": "214569a5-8969-4e1f-8b24-d8d3183219a9", + "metadata": { + "tags": [] + }, + "source": [ + "## Introduction\n", + "As we mentioned in our Dask overview, Dask is composed of two main parts:\n", + "\n", + "1. Dask Collections (APIs)\n", + "2. Dynamic Task Scheduling\n", + "\n", + "So far, we have talked about different Dask collections, but in this tutorial we are going to talk more about the second part. \n", + "\n", + "\n", + "## The Dask scheduler - our task orchestrator\n", + "\n", + "The `Dask.distributed` task *scheduler* is a centralized, dynamic system that coordinates the efforts of various dask *worker* processes spread accross different machines.\n", + "\n", + "When a computational task is submitted, the Dask distributed *scheduler* sends it off to a Dask *cluster* - simply a collection of Dask *workers*. A worker is typically a separate Python process on either the local host or a remote machine. \n", + "\n", + "To perform work, a scheduler must be assigned resources in the form of a Dask cluster. The cluster consists of the following components: \n", + "\n", + "* **scheduler** : A scheduler creates and manages task graphs and distributes tasks to workers.\n", + "\n", + "* **workers** : A worker is typically a separate Python process on either the local host or a remote machine. A Dask **cluster** usually consists of many workers. Basically, a worker is a Python interpretor which will perform work on a subset of our dataset.\n", + "\n", + "* **client** - A high-level interface that points to the scheduler (often local but not always). A client serves as the entry point for interacting with a Dask scheduler.\n", + "\n", + "\"Dask\n", + " \n", + "*Image credit: Dask Contributors*\n" + ] + }, + { + "cell_type": "markdown", + "id": "263fd4a3-0475-41a4-abae-417e8731100a", + "metadata": {}, + "source": [ + "## Schedulers\n", + "Dask essentially offers two types of schedulers:\n", + "\n", + "\n", + "\"High\n", + "\n", + "*Image credit: Dask Contributors*\n", + "\n", + "### 1. Single machine scheduler \n", + "* The Single-machine Scheduler schedules tasks and manages the execution of those tasks on the same machine where the scheduler is running. \n", + "* It is designed to be used in situations where the amount of data or the computational requirements are too large for a single process to handle, but not large enough to warrant the use of a cluster of machines.\n", + "* It is relatively simple and cheap to use but it does not scale as it only runs on a single machine. \n", + "\n", + "**Single machine scheduler is the default choice used by Dask.**\n", + "\n", + "In Dask, there are several types of single machine schedulers that can be used to schedule computations on a single machine:\n", + "#### 1.1. Single-threaded scheduler\n", + "This scheduler runs all tasks **serially** on a single thread. \n", + "This is only useful for debugging and profiling, but does not have any parallelization. \n", + "\n", + "#### 1.2. Threaded scheduler\n", + "The threaded scheduler uses a pool of **local** threads to execute tasks concurrently. \n", + "This is the default scheduler for Dask, and is suitable for most use cases on a single machine. Multithreading works well for Dask Array and Dask DataFrame. \n", + "\n", + "To select one of the above scheduler for your computation, you can specify it when doing `.compute()`:\n", + "\n", + "For example: \n", + "```python\n", + "this.compute(scheduler=\"single-threaded\") # for debugging and profiling only\n", + "```\n", + "\n", + "\n", + "As mentioned above the *threaded scheduler* is the default scheduler in Dask. But you can set the default scheduler to Single-threaded or multi-processing by: \n", + "\n", + "```python\n", + "import dask\n", + "dask.config.set(scheduler='synchronous') # overwrite default with single-threaded scheduler\n", + "```\n", + "\n", + "Multi-processing works well for pure Python code - *delayed* functions and operations on Dask Bags.\n", + "\n", + "Let's compare the performance of each of these single-machine schedulers:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5ed871a9-45d4-4b0e-9f39-4dbb0b871c64", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import dask.array as da" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f61fb1fe-bbd7-4207-8ac6-8eb784d6710f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 14.9 s, sys: 1.32 s, total: 16.2 s\n", + "Wall time: 16.1 s\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ 9.99987393, 9.99942047, 10.00069322, ..., 9.99997333,\n", + " 9.99945909, 10.00094973])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "## - numpy performance\n", + "xn = np.random.normal(10, 0.1, size=(20_000, 20_000))\n", + "yn = xn.mean(axis=0)\n", + "yn" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "af609dc9-9482-41e0-8a93-5cbcf096e376", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 14.8 s, sys: 112 ms, total: 14.9 s\n", + "Wall time: 3.83 s\n" + ] + }, + { + "data": { + "text/plain": [ + "array([ 9.99928454, 9.99968075, 10.00027327, ..., 10.00030439,\n", + " 9.9999113 , 9.99947802])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "# -- dask array using the default\n", + "xd = da.random.normal(10, 0.1, size=(20_000, 20_000), chunks=(2000, 2000))\n", + "yd = xd.mean(axis=0)\n", + "yd.compute()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "141053bf-942c-46be-b169-2709ca569451", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " threading : 3.7886 s\n", + " processes : 5.2656 s\n", + " sync : 14.7481 s\n" + ] + } + ], + "source": [ + "import time\n", + "# -- dask testing different schedulers:\n", + "for sch in ['threading', 'processes', 'sync']:\n", + " t0 = time.time()\n", + " r = yd.compute(scheduler=sch)\n", + " t1 = time.time()\n", + " print(f\"{sch:>10} : {t1 - t0:0.4f} s\")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "4c33d184-7c25-4052-aece-50dd9382e32e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "dask.array" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yd" + ] + }, + { + "cell_type": "markdown", + "id": "fe2398e2-9ac6-4e85-89fe-8c794571dfeb", + "metadata": {}, + "source": [ + "* Notice how `sync` scheduler takes almost the same time as pure NumPy code. \n", + "* Why is the multiprocessing scheduler so much slower?\n", + "\n", + "If you use the multiprocessing backend, all communication between processes still needs to pass through the main process because processes are isolated from other processes. This introduces a large overhead. \n", + "\n", + "**The Dask developers recommend using the Dask Distributed Scheduler which we will cover now.**\n" + ] + }, + { + "cell_type": "markdown", + "id": "8292ca19-7d31-4702-ae86-ecb75c16a47d", + "metadata": {}, + "source": [ + "\n", + "### 2. Distributed scheduler\n", + "* The Distributed scheduler or `dask.distributed` schedules tasks and manages the execution of those tasks on workers from a single or multiple machines. \n", + "* This scheduler is more sophisticated and offers more features including a live diagnostic dashboard which provides live insight on performance and progress of the calculations.\n", + "\n", + "\n", + "In most cases, `dask.distributed` is preferred since it is very scalable, and provides and informative interactive dashboard and access to more complex Dask collections such as `futures`. \n" + ] + }, + { + "cell_type": "markdown", + "id": "e2d506bb-1c20-46f7-a0ea-2d9b10ffce6f", + "metadata": {}, + "source": [ + "#### 2.1. Local Cluster\n", + "\n", + "A Dask Local Cluster refers to a group of worker processes that run on a single machine and are managed by a single Dask scheduler. \n", + "\n", + "This is useful for situations where the computational requirements are not large enough to warrant the use of a full cluster of separate machines. It provides an easy way to run parallel computations on a single machine, without the need for complex cluster management or other infrastructure." + ] + }, + { + "cell_type": "markdown", + "id": "bc1a0d22-e14f-46cc-9e54-157d098f5380", + "metadata": {}, + "source": [ + "##### Let's start by creating a Local Cluster\n", + "\n", + "For this we need to set up a `LocalCluster` using `dask.distributed` and connect a `client` to it. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "bcf3e27b-7ac6-4250-8e01-3836fd7e0c18", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from dask.distributed import LocalCluster, Client\n", + "\n", + "cluster = LocalCluster()\n", + "client = Client(cluster)\n", + "client" + ] + }, + { + "cell_type": "markdown", + "id": "2e3384ef-9a9a-4425-bd77-6ca811ff0577", + "metadata": {}, + "source": [ + "☝️ Click the Dashboard link above.\n", + "\n", + "👈 Or click the “Search” 🔍 button in the dask-labextension dashboard.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "d556c2ff-333f-48bf-9183-689ff589f011", + "metadata": {}, + "source": [ + "If no arguments are specified in `LocalCluster` it will automatically detect the number of CPU cores your system has and the amount of memory and create workers to appropriately fill that.\n", + "\n", + "A `LocalCluster` will use the full resources of the current JupyterLab session. For example, if you used BinderHub, it will use the number of CPUs selected. \n", + "\n", + "Note that `LocalCluster()` takes a lot of optional arguments, allowing you to configure the number of processes/threads, memory limits and other settings.\n" + ] + }, + { + "cell_type": "markdown", + "id": "0b0f0121-2307-4ea6-b3f7-9e7925c53ca8", + "metadata": {}, + "source": [ + "You can also find your cluster dashboard link using : " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c7a9a540-2592-44fb-b77e-4474d8521125", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'https://jupyterhub.hpc.ucar.edu/stable/user/negins/casper_16_4/proxy/8787/status'" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cluster.dashboard_link" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a1f41158-4165-4da8-a2d1-5afb68edf513", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 499 ms, sys: 142 ms, total: 641 ms\n", + "Wall time: 10.1 s\n" + ] + }, + { + "data": { + "text/plain": [ + "array([10.00024901, 10.00025024, 10.00001342, ..., 10.00006029,\n", + " 9.99957823, 10.00021491])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%%time\n", + "# -- dask array using the default\n", + "xd = da.random.normal(10, 0.1, size=(30_000, 30_000), chunks=(3000, 3000))\n", + "yd = xd.mean(axis=0)\n", + "yd.compute()" + ] + }, + { + "cell_type": "markdown", + "id": "1bf2d2b0-7cd9-4e25-98f4-46a801d29964", + "metadata": {}, + "source": [ + "Always remember to close your local Dask cluster:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0a292434-3f33-480e-b332-be809f8c8571", + "metadata": {}, + "outputs": [], + "source": [ + "client.shutdown()" + ] + }, + { + "cell_type": "markdown", + "id": "8c031818-16fd-44ef-b94c-f0f1843567f7", + "metadata": {}, + "source": [ + "### Dask Distributed (Cluster Managers)\n", + "\n", + "So far we have talked about running a job on a local machine.\n", + "\n", + "Dask can be deployed on distributed infrastructure, such as a an HPC system or a cloud computing system.\n", + "\n", + "\"High\n", + "\n", + "*Image credit: Dask Contributors*\n", + "\n", + "Dask Cluster Managers have different names corresponding to different computing environments. Some examples are `dask-jobqueue` for your HPC systems (including `PBSCluster`) or Kubernetes Cluster for machines on the Cloud. \n", + "\n", + "The NCAR tutorial series includes an in-depth exploration and practical use cases of Dask on HPC systems and best practices for Dask on HPC. For the complete set of NCAR tutorial materials, please refer to the main NCAR tutorial content available [here](https://ncar.github.io/dask-tutorial/README.html).\n", + "\n", + "For more information visit the [Dask Docs](https://docs.dask.org/en/stable/deploying.html).\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": "3.9.18" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/_preview/16/_sources/notebooks/how-to-cite.md b/_preview/16/_sources/notebooks/how-to-cite.md new file mode 100644 index 0000000..7fce57a --- /dev/null +++ b/_preview/16/_sources/notebooks/how-to-cite.md @@ -0,0 +1,7 @@ +# How to Cite This Cookbook + +The material in Project Pythia's Dask 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/dask-cookbook) and archived on Zenodo. This DOI will always resolve to the latest release of the book source: + +[![DOI](https://zenodo.org/badge/610934658.svg)](https://zenodo.org/badge/latestdoi/610934658) diff --git a/_preview/16/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_preview/16/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css new file mode 100644 index 0000000..3225661 --- /dev/null +++ b/_preview/16/_sphinx_design_static/design-style.4045f2051d55cab465a707391d5b2007.min.css @@ -0,0 +1 @@ +.sd-bg-primary{background-color:var(--sd-color-primary) !important}.sd-bg-text-primary{color:var(--sd-color-primary-text) !important}button.sd-bg-primary:focus,button.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}a.sd-bg-primary:focus,a.sd-bg-primary:hover{background-color:var(--sd-color-primary-highlight) !important}.sd-bg-secondary{background-color:var(--sd-color-secondary) 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+ 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/16/_static/check-solid.svg b/_preview/16/_static/check-solid.svg new file mode 100644 index 0000000..92fad4b --- /dev/null +++ b/_preview/16/_static/check-solid.svg @@ -0,0 +1,4 @@ + + + + diff --git a/_preview/16/_static/clipboard.min.js b/_preview/16/_static/clipboard.min.js new file mode 100644 index 0000000..54b3c46 --- /dev/null +++ b/_preview/16/_static/clipboard.min.js @@ -0,0 +1,7 @@ +/*! + * 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/16/_static/copybutton.css b/_preview/16/_static/copybutton.css new file mode 100644 index 0000000..f1916ec --- /dev/null +++ b/_preview/16/_static/copybutton.css @@ -0,0 +1,94 @@ +/* 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/16/_static/copybutton.js b/_preview/16/_static/copybutton.js new file mode 100644 index 0000000..2ea7ff3 --- /dev/null +++ b/_preview/16/_static/copybutton.js @@ -0,0 +1,248 @@ +// 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/16/_static/copybutton_funcs.js b/_preview/16/_static/copybutton_funcs.js new file mode 100644 index 0000000..dbe1aaa --- /dev/null +++ b/_preview/16/_static/copybutton_funcs.js @@ -0,0 +1,73 @@ +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/16/_static/css/blank.css b/_preview/16/_static/css/blank.css new file mode 100644 index 0000000..8a686ec --- /dev/null +++ b/_preview/16/_static/css/blank.css @@ -0,0 +1,2 @@ +/* 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/16/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css b/_preview/16/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css new file mode 100644 index 0000000..9b1c5d7 --- /dev/null +++ b/_preview/16/_static/css/index.ff1ffe594081f20da1ef19478df9384b.css @@ -0,0 +1,6 @@ +/*! + * 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) + */:root{--blue:#007bff;--indigo:#6610f2;--purple:#6f42c1;--pink:#e83e8c;--red:#dc3545;--orange:#fd7e14;--yellow:#ffc107;--green:#28a745;--teal:#20c997;--cyan:#17a2b8;--white:#fff;--gray:#6c757d;--gray-dark:#343a40;--primary:#007bff;--secondary:#6c757d;--success:#28a745;--info:#17a2b8;--warning:#ffc107;--danger:#dc3545;--light:#f8f9fa;--dark:#343a40;--breakpoint-xs:0;--breakpoint-sm:540px;--breakpoint-md:720px;--breakpoint-lg:960px;--breakpoint-xl:1200px;--font-family-sans-serif:-apple-system,BlinkMacSystemFont,"Segoe UI",Roboto,"Helvetica Neue",Arial,"Noto Sans",sans-serif,"Apple Color Emoji","Segoe UI Emoji","Segoe UI Symbol","Noto Color Emoji";--font-family-monospace:SFMono-Regular,Menlo,Monaco,Consolas,"Liberation Mono","Courier 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(position:sticky){.bd-links{max-height:calc(100vh - 11rem);overflow-y:auto}}}.bd-sidenav{display:none}.bd-content{padding-top:20px}.bd-content .section{max-width:100%}.bd-content .section table{display:block;overflow:auto}.bd-toc-link{display:block;padding:.25rem 1.5rem;font-weight:600;color:rgba(0,0,0,.65)}.bd-toc-link:hover{color:rgba(0,0,0,.85);text-decoration:none}.bd-toc-item.active{margin-bottom:1rem}.bd-toc-item.active:not(:first-child){margin-top:1rem}.bd-toc-item.active>.bd-toc-link{color:rgba(0,0,0,.85)}.bd-toc-item.active>.bd-toc-link:hover{background-color:transparent}.bd-toc-item.active>.bd-sidenav{display:block}nav.bd-links p.caption{font-size:var(--pst-sidebar-caption-font-size);text-transform:uppercase;font-weight:700;position:relative;margin-top:1.25em;margin-bottom:.5em;padding:0 1.5rem;color:rgba(var(--pst-color-sidebar-caption),1)}nav.bd-links p.caption:first-child{margin-top:0}.bd-sidebar .nav{font-size:var(--pst-sidebar-font-size)}.bd-sidebar .nav 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/16/_static/css/theme.css b/_preview/16/_static/css/theme.css new file mode 100644 index 0000000..2e03fe3 --- /dev/null +++ b/_preview/16/_static/css/theme.css @@ -0,0 +1,120 @@ +/* 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/16/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css b/_preview/16/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css new file mode 100644 index 0000000..3225661 --- /dev/null +++ b/_preview/16/_static/design-style.4045f2051d55cab465a707391d5b2007.min.css @@ -0,0 +1 @@ +.sd-bg-primary{background-color:var(--sd-color-primary) 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= true; + } + window.localStorage.setItem("sphinx-design-last-tab", syncId); +} + +document.addEventListener("DOMContentLoaded", ready, false); diff --git a/_preview/16/_static/doctools.js b/_preview/16/_static/doctools.js new file mode 100644 index 0000000..e1bfd70 --- /dev/null +++ b/_preview/16/_static/doctools.js @@ -0,0 +1,358 @@ +/* + * doctools.js + * ~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for all documentation. + * + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + +/** + * make the code below compatible with browsers without + * an installed firebug like debugger +if (!window.console || !console.firebug) { + var names = ["log", "debug", "info", "warn", "error", "assert", "dir", + "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace", + "profile", "profileEnd"]; + window.console = {}; + for (var i = 0; i < names.length; ++i) + window.console[names[i]] = function() {}; +} + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. Multiple values per key are supported, + * it will always return arrays of strings for the value parts. + */ +jQuery.getQueryParameters = function(s) { + if (typeof s === 'undefined') + s = document.location.search; + var parts = s.substr(s.indexOf('?') + 1).split('&'); + var result = {}; + for (var i = 0; i < parts.length; i++) { + var tmp = parts[i].split('=', 2); + var key = jQuery.urldecode(tmp[0]); + var value = jQuery.urldecode(tmp[1]); + if (key in result) + result[key].push(value); + else + result[key] = [value]; + } + return result; +}; + +/** + * highlight a given string on a jquery object by wrapping it in + * span elements with the given class name. + */ +jQuery.fn.highlightText = function(text, className) { + function highlight(node, addItems) { + if (node.nodeType === 3) { + var val = node.nodeValue; + var pos = val.toLowerCase().indexOf(text); + if (pos >= 0 && + !jQuery(node.parentNode).hasClass(className) && + !jQuery(node.parentNode).hasClass("nohighlight")) { + var span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} + +/** + * Small JavaScript module for the documentation. + */ +var Documentation = { + + init : function() { + this.fixFirefoxAnchorBug(); + this.highlightSearchWords(); + this.initIndexTable(); + this.initOnKeyListeners(); + }, + + /** + * i18n support + */ + TRANSLATIONS : {}, + PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; }, + LOCALE : 'unknown', + + // gettext and ngettext don't access this so that the functions + // can safely bound to a different name (_ = Documentation.gettext) + gettext : function(string) { + var translated = Documentation.TRANSLATIONS[string]; + if (typeof translated === 'undefined') + return string; + return (typeof translated === 'string') ? translated : translated[0]; + }, + + ngettext : function(singular, plural, n) { + var translated = Documentation.TRANSLATIONS[singular]; + if (typeof translated === 'undefined') + return (n == 1) ? singular : plural; + return translated[Documentation.PLURALEXPR(n)]; + }, + + addTranslations : function(catalog) { + for (var key in catalog.messages) + this.TRANSLATIONS[key] = catalog.messages[key]; + this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')'); + this.LOCALE = catalog.locale; + }, + + /** + * add context elements like header anchor links + */ + addContextElements : function() { + $('div[id] > :header:first').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this headline')). + appendTo(this); + }); + $('dt[id]').each(function() { + $('\u00B6'). + attr('href', '#' + this.id). + attr('title', _('Permalink to this definition')). + appendTo(this); + }); + }, + + /** + * workaround a firefox stupidity + * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075 + */ + fixFirefoxAnchorBug : function() { + if (document.location.hash && $.browser.mozilla) + window.setTimeout(function() { + document.location.href += ''; + }, 10); + }, + + /** + * highlight the search words provided in the url in the text + */ + highlightSearchWords : function() { + var params = $.getQueryParameters(); + var terms = (params.highlight) ? 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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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Index

+ +
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+
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+ + + +
+
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+

+ © 2023. + + By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk. + + Last updated on 19 October 2023. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/16/index.html b/_preview/16/index.html new file mode 100644 index 0000000..960bb69 --- /dev/null +++ b/_preview/16/index.html @@ -0,0 +1 @@ + diff --git a/_preview/16/notebooks/00-dask-overview.html b/_preview/16/notebooks/00-dask-overview.html new file mode 100644 index 0000000..971c99b --- /dev/null +++ b/_preview/16/notebooks/00-dask-overview.html @@ -0,0 +1,729 @@ + + + + + + + + Dask Overview — Dask Cookbook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +

Dask logo

+
+

Dask Overview

+
+

In this tutorial, you learn:

+
    +

  • What is Dask?

    • +

  • Why Dask in Geosciences?

    • +

  • Dask Data Structures and Schedulers

    • +

  • When to use Dask?

    • +
+
+
+

Introduction

+

Complex data structures enable data science in Python. For example:

+ +

But datasets are getting larger all of the time! What if my dataset is too big to fit into memory, or it takes too long to complete an analysis?

+
+
+

What is Dask?

+
    +

  • Dask is an open-source Python library for parallel and distributed computing that scales the existing Python ecosystem.

    • +

  • Dask was developed to scale Python packages such as Numpy, Pandas, and Xarray to multi-core machines and distributed clusters when datasets exceed memory.

    • +
+
+
+

Why Dask?

+
+

Familiar Interface

+

Dask provides interfaces which mimics significant portions of the NumPy and Pandas APIs.

+

This means Dask provides ways to parallelize Pandas, Xarray, and Numpy workflows with minimal code rewriting (no massive code-restructure or writing a script in another language).

+
+
+

Scalability

+

Dask is designed to scale well from single machine (laptop) to thousand-node HPC clusters, and on the cloud.

+

This allows users to use their existing hardware, or add more machines as needed, to handle increasingly large and complex datasets.

+
+
+

Flexibility

+

Dask provides several tools that help with data analysis on large datasets. For example, you can easily wrap your function in dask.delayed decorator to make it run in parallel.

+

Dask provides seamless integration with well-known HPC resource managers and job scheduling systems, including PBS, SLURM, and SGE.

+
+
+

Built-in Diagnostic Tools

+

Dask provides responsive feedback via the client as well as a real-time interactive diagnostic dashboard to keep users informed on how the computation is progressing.

+

This helps users identify and resolve potential issues without waiting for the work to be completed.

+
+
+
+

First Rule of Dask

+

While Dask is a powerful tool for parallel and distributed computing, it is not always the best solution for every problem. +In some cases, using Dask may introduce additional complexity and overhead, without providing any substantial benefits in terms of performance or scalability.

+
    +

  • Keep in mind the time spent parallelizing and optimizing your workflow when using Dask vs. the time saved because of that parallelization.

    • +

  • Consider how many times you plan to run your code - if only once, is it worth it?

    • +
+
+

NOTE: Dask should only be used when necessary.

+

Avoid Dask if you can easily:

+
    +
  • Speed up your code with use of compiled routines in libraries like NumPy
    • +
  • Profile and optimize your serial code to minimize bottlenecks
    • +
  • Read in a subset of data to gain the insight you need
    • +
+And keep in mind - all of the above steps improve your code whether you end up using Dask or not!

+ +
+
+
+

When to use Dask?

+

Here are some general guidelines for when to use Dask and when to avoid it:

+
+

Use Dask:

+
    +

  • When you have large datasets that don’t fit into memory on a single machine.

    • +

  • When you need to perform parallel computations, such as big data analysis.

    • +
+
+
+

Avoid Dask:

+
    +

  • When you have small datasets that can be processed efficiently on a single machine.

    • +

  • When you don’t need parallel processing, as the overhead of managing a distributed computing environment may not be worth the benefits.

    • +

  • When you need to debug or troubleshoot problems, as distributed computing environments can be challenging for debugging. If the problem is complex, using Dask may make debugging more difficult.

    • +
+
+
+
+

Dask Components

+

Dask is composed of two main parts:

+
+

1. Dask Collections

+

Dask Collections are the user interfaces we use for parallel and distributed computing with Dask.

+

Dask features different levels of collection types:

+
+

High-level collections

+

Dask provides high-level collections Dask Arrays, Dask DataFrames, and Dask Bags that mimic NumPy, pandas, and lists but can operate in parallel on datasets that don’t fit into memory.

+

Most of the time, you will probably use one of the following high-level (big) data structures (or an even higher-level derivative type like Xarrays):

+ + + + + + + + + + + + + + + + + + + + + +

Collection

Serial

Dask

Arrays

numpy.array

dask.array.from_array

Dataframes

pandas.read_csv

dask.dataframe.read_csv

Unstructured

[1,2,3]

dask.bag.from_sequence([1,2,3])

+
+
+

Low-level collections

+

Dask also features two low-level collection types - delayed and futures. These collections give users finer control to build custom parallel and distributed computations.

+
    +

  • delayed - run any arbitrary Python function using Dask task parallelism (think looped function calls)

    • +

  • futures - similar to delayed but allows for concurrent commands in the client script (think backgrounded processes)

    • +
+

These are very powerfull tools, but it is easy to write something using a delayed function that could be executed faster and more simply using a high-level collection

+

Dask Collections

+

Image credit: Dask Contributors

+
+
+
+

2. Dynamic Task Scheduling

+

We can basically think of the Dask scheduler as our task orchestrator.

+

When a computation is submitted, work is segmented into discrete tasks which are assigned to workers by the Dask scheduler.

+

To perform work, a scheduler must be assigned resources in the form of a Dask cluster. The cluster consists of the following components:

+
    +

  • scheduler : A scheduler creates and manages task graphs and distributes tasks to workers.

    • +

  • workers : A worker is typically a separate Python process on either the local host or a remote machine. A Dask cluster usually consists of many workers. Basically, a worker is a Python interpretor which will perform work on a subset of our dataset.

    • +

  • client - A high-level interface that points to the scheduler (often local but not always). A client serves as the entry point for interacting with a Dask scheduler.

    • +
+

Dask Distributed Cluster

+

Image credit: Dask Contributors

+

We will learn more about Dask Collections and Dynamic Task Scheduling in the next tutorials.

+
+
+
+

Useful Resources

+ +
+
+ + + + +
+ +
+ + +
+

previous

+

How to Cite This Cookbook

+
+ + +
+

next

+

Dask Array

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
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+
+
+
+ NCAR +
+
+ Unidata +
+
+ UAlbany +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2023. + + By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk. + + Last updated on 19 October 2023. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/16/notebooks/01-dask-array.html b/_preview/16/notebooks/01-dask-array.html new file mode 100644 index 0000000..14a9b15 --- /dev/null +++ b/_preview/16/notebooks/01-dask-array.html @@ -0,0 +1,1727 @@ + + + + + + + + Dask Array — Dask Cookbook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
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+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +

Dask logo

+
+

Dask Array

+
+

In this tutorial, you learn:

+
    +

  • What is a Dask Array?

    • +

  • Basic concepts and features of Dask Arrays

    • +

  • Working with Dask arrays

    • +
+

Related Dask Array Documentation

+ +
+
+

Dask Arrays

+

Dask Arrays are basically parallelized version of NumPy arrays for processing larger-than-memory data sets.

+https://docs.dask.org/en/stable/_images/dask-array.svg +

Image credit: Dask Contributors

+

Dask Array can be used as a drop-in replacement for NumPy arrays, with a similar API and support for a subset of NumPy functions.

+

Dask effectively reduces the memory footprint of large array computations by dividing the arrays into smaller pieces (called chunks) that can fit into memory and stream the data from disk.

+

Dask Arrays are lazy: Unlike Numpy, operations on Dask arrays are not computed until you explicitly request them.

+
+

Lazy Evaluation: objects are evaluated just in time when the results are needed!

+

Lazy evaluation help us avoid having large pieces of memory resident on the workers and optimize the resource requirements.

+
+

Dask Arrays don’t directly hold any data. Instead, they provide a symbolic representation of the necessary computations to generate the data. We will explain this more below.

+

Let’s start exploring Dask Arrays:

+
+
+

Setup: Start a Dask Client

+

We will talk in-depth about Dask Cluster and Dask Clients later in this tutorial. Here we just created a local cluster and attached a client to it.

+
+
+
from dask.distributed import Client
+
+client = Client()
+client
+
+
+
+
+
+
+
+

Client

+

Client-7c05aba6-6ebc-11ee-89bc-6045bd783dc1

+ + + + + + + + + + + + + + + + +
Connection method: Cluster objectCluster type: distributed.LocalCluster
+ Dashboard: http://127.0.0.1:8787/status +
+ + + + + + +
+

Cluster Info

 + +
+ + +
+
+
+
+
+

Blocked Algorithms

+

Dask Arrays use blocked algorithms to split large computations into smaller computations which operate on subsets of the data (called chunks).

+

Let’s see what this means in an example:

+
+
+
import numpy as np
+import dask.array as da
+
+# A 4x4 numpy array that goes from 1 to 16 
+
+narr = np.array([
+        [ 1,  2,  3,  4],
+        [ 5,  6,  7,  8],
+        [ 9, 10, 11, 12],
+        [13, 14, 15, 16]
+        ])
+
+# -- convert numpy array to dask array with 4 chunks
+darr = da.from_array( narr,chunks=(2, 2))
+
+
+
+
+

Now we can calculate the sum of this array using darr.sum() similar to numpy. But how is it different from numpy?

+

When you take the sum of the Dask array, Dask first takes the sum of each chunk and only after each of those is completed, takes the sum of the results from each chunk.

+https://d33wubrfki0l68.cloudfront.net/f7bf6ca40c8f217386f83795b36e0c964c6a9d2b/ad6da/images/blog/what-is-dask-blockwise-sum.jpg +

Image adapted from saturncloud.io

+
+
+

Task Graph

+

The Dask Task Graph serves as a blueprint for executing the computations.

+

The Task Graph defines the (1) relationships between tasks, and (2) the order in which they should be executed.

+

In a task graph each node in the graph represents a task and lines represent the dependencies/relationships between tasks.

+

We can visualize the low-level task graph using .visualize() method.

+
+
+
darr.sum().visualize(rankdir="LR")
+
+
+
+
+../_images/13585ba5c7b942934e8b7bb4e873c0ad8ee067c823917fee80ffa0713fead5b1.png +
+
+

It is generally good practice to look at the task graph before executing the computation. By looking at the task graph, you can learn about potential bottlenecks where parallelism is not possible.

+
+

TIP: For big computations, low-level task graphs gets very confusing. An alternative that provides a more concise graph is using .dask.visualize().

+
+
+
+
#darr.sum().dask.visualize()
+
+
+
+
+
+

Now, let’s start with another example. Here we create a 2D array of ones using NumPy.

+
+
+
shape = (10000,12000)
+
+ones_np = np.ones(shape)
+ones_np
+
+
+
+
+
array([[1., 1., 1., ..., 1., 1., 1.],
+       [1., 1., 1., ..., 1., 1., 1.],
+       [1., 1., 1., ..., 1., 1., 1.],
+       ...,
+       [1., 1., 1., ..., 1., 1., 1.],
+       [1., 1., 1., ..., 1., 1., 1.],
+       [1., 1., 1., ..., 1., 1., 1.]])
+
+
+
+
+

Now, let’s create the same array using Dask:

+
+
+
ones_da = da.ones(shape)
+ones_da
+
+
+
+
+
+ + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Array Chunk
Bytes 915.53 MiB 128.00 MiB
Shape (10000, 12000) (4096, 4096)
Dask graph 9 chunks in 1 graph layer
Data type float64 numpy.ndarray
+ 		+ + + + + + + + + + + + + + + + + + + 12000 + 10000 + +
+
+

We see a Dask Array representation of the data. +This is a symbolic representation; no data has actually been generated yet.

+

As we discussed previously, this mode of operation is called “lazy”.

+

This allows the user to build up a series of computations or tasks before being passed to the scheduler for execution.

+
+
+

Chunks

+

When checking the Dask array, the symbolic representation illustrates the concept of chunks. Dask arrays split the data into sub-arrays (or chunks) to optimize computation with large arrays.

+
+

Chunking an array

+

The way that arrays are chunked can significantly affect total performance.

+

For specifying the chunking of an array, we use the chunks argument when creating our dask.array.

+
+

⚠️ WARNING: Please note that chunks argument stands for chunk shape rather than “number of chunks”.
+For example, chunks=1 means that you will have several chunks with one element.

+
+

There are several ways to define chunks. For example:

+
    +

  1. A uniform dimension size like 1000, meaning chunks of size 1000 in each dimension.

    2. +

  2. A uniform chunk shape like (1000, 2000, 3000), meaning chunks of size 1000 in the first axis, 2000 in the second axis, and 3000 in the third.

    3. +

  3. Fully explicit sizes of all blocks for all dimensions, like ((1000, 1000, 500), (400, 400), (5, 5, 5, 5, 5))

    4. +

  4. A dictionary specifying chunk size per dimension like {0: 1000, 1: 2000, 2: 3000}.

    5. +
+

Let’s recreate the above Dask array, but this time we will specify chunk sizes (a.k.a. shapes) using the argument chunks.

+
+
+
# -- remember what the shape of our data array was
+shape
+
+
+
+
+
(10000, 12000)
+
+
+
+
+
+
+
# create a dask array with 6 chunks
+chunk_shape = (5000,4000)
+ones_da = da.ones(shape,chunks=chunk_shape)
+ones_da
+
+
+
+
+
+ + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Array Chunk
Bytes 915.53 MiB 152.59 MiB
Shape (10000, 12000) (5000, 4000)
Dask graph 6 chunks in 1 graph layer
Data type float64 numpy.ndarray
+ 		+ + + + + + + + + + + + + + + + + + 12000 + 10000 + +
+
+

You can see in the above dask array representation that we now have 6 chunks, each of shape (5000,4000) and size of ~ 160.0 MiB.

+
+
+
+

Performance Comparison

+

To compare the performance between a NumPy array and an equivalent Dask array, let’s calculate the mean.

+
+
+
%%time
+# The %%time cell magic measures the execution time of the whole cell
+ones_np.mean()
+
+
+
+
+
CPU times: user 80.5 ms, sys: 0 ns, total: 80.5 ms
+Wall time: 78.3 ms
+
+
+
1.0
+
+
+
+
+
+
+
%%time
+# Remember, we are not doing any computation here, just constructing our task graph
+mean_of_ones_da = ones_da.mean()
+
+
+
+
+
CPU times: user 1.46 ms, sys: 0 ns, total: 1.46 ms
+Wall time: 1.45 ms
+
+
+
+
+

Remember :

+
+

Dask doesn’t do anything until you tell it… It is lazy!

+
+

So far we have just constructed our task graph but no computations yet!

+
+

NOTE: In order to generate the data, we need to call the .compute() method on the Dask Array to trigger our computation.

+
+

Let’s run the .compute() method to see how this works:

+
+
+
%%time
+mean_of_ones_da.compute()
+
+
+
+
+
CPU times: user 22.9 ms, sys: 4.86 ms, total: 27.7 ms
+Wall time: 398 ms
+
+
+
1.0
+
+
+
+
+

.compute() method convertes Dask Arrays to Numpy Arrays. Let’s check to see if this is true:

+
+
+
type (ones_da.compute())
+
+
+
+
+
numpy.ndarray
+
+
+
+
+
+

WARNING: Typically, when working with Dask arrays, we do not want to generate the data right away by calling .compute() on a large array.

+
+

We usually want to perform some computations that reduce the data size. For example, we might compute statistics like the mean or standard deviation.

+

Let’s look at an example of taking the mean and visualize the task graph. Remember, that no actual computation is taking place until we call .compute().

+
+
+
mean_of_ones_da = ones_da.mean()
+mean_of_ones_da.visualize(rankdir="LR")
+
+
+
+
+../_images/18ec61fa7fc878ea1494d4b8f09a869fe91f5d7685a339c801b700b94c95ea05.png +
+
+
+

What are the sizes of these arrays in memory?

+

First, let’s define a function that returns array size in MiB.

+
+
+
import sys
+
+# Define function to display variable size in MiB
+def var_size(in_var):
+    result = sys.getsizeof(in_var) / 1024/1024
+    return (result)
+
+
+
+
+
+
+
print("Shape of the numpy array : ", ones_np.shape) 
+print("Shape of the dask array  : ", ones_da.shape) 
+
+# memory size of numpy array in MiB
+print(f"Memory size of numpy array in MB : {var_size(ones_np):.2f} MiB")
+# memory size of dask array in MiB
+print(f"Memory size of dask array in MB  : {var_size(ones_da):.2f} MiB")
+
+
+
+
+
Shape of the numpy array :  (10000, 12000)
+Shape of the dask array  :  (10000, 12000)
+Memory size of numpy array in MB : 915.53 MiB
+Memory size of dask array in MB  : 0.00 MiB
+
+
+
+
+

Why memory size for the above Dask array is zero?

+

Remember, this variable is only a graph representation of the full array which will be split across workers.

+

However, Dask does give us ways to see the full size of the data (often much larger than your client machine can handle)!

+
+
+
print("Size of Dask dataset:  {:.2f} MiB".format(ones_da.nbytes / 1024/1024))
+
+
+
+
+
Size of Dask dataset:  915.53 MiB
+
+
+
+
+
+
+

Larger Data

+

The previous example illustrated how Dask works, but using Dask is not really necessary (nor advisable) for an array of size 915.53 MiB.
+Let’s try an example using bigger data and bigger calculations:

+
+
+
big_shape = (2000, 200, 2000)
+
+# -- this will make a big numpy array that might not fit on your machine
+#big_np = np.ones(big_shape)
+
+
+
+
+

Make a similar Dask Array with similar shape but specifying the chunks size:

+
+
+
big_da = da.ones(big_shape)
+big_da
+
+
+
+
+
+ + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Array Chunk
Bytes 5.96 GiB 127.44 MiB
Shape (2000, 200, 2000) (289, 200, 289)
Dask graph 49 chunks in 1 graph layer
Data type float64 numpy.ndarray
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 2000 + 200 + 2000 + +
+
+
+
+
# size of data
+#print("Memory size of NumPy dataset :  {:.2f} GiB".format(big_np.nbytes / 1024/1024/1024))
+print("Memory size of Dask dataset  :  {:.2f} GiB".format(big_da.nbytes / 1024/1024/1024))
+
+
+
+
+
Memory size of Dask dataset  :  5.96 GiB
+
+
+
+
+

This may be close to the available memory/RAM that you have in your computer.

+

Let’s try bigger calculations on this array:

+
+
+
#%%time 
+#z_np = (big_np + big_np.T)[::2,:].mean()
+
+
+
+
+
+
+
%%time
+
+z_da = (big_da + big_da.T)[::2,:].mean(axis=2)
+
+result = z_da.compute()
+
+
+
+
+
CPU times: user 234 ms, sys: 34.7 ms, total: 269 ms
+Wall time: 1.94 s
+
+
+
+
+
+
+
#-- warning : do not try low level visualization with big arrays
+
+#z_da.visualize()
+
+
+
+
+

All the usual NumPy functions work on dask arrays, though the computations will remain lazy until you either call .compute(), .load() or your want to plot the data.

+

As we discussed above, the way that Dask arrays are chunked can significantly affect the performance. In the remainder of this notebook, let’s do a similar calculation using a different chunks size.

+

We will learn more about best practices regarding chunk size later during the tutorial.

+
+
+

Supplementary Material: Rechunking Arrays

+

We can change the chunking of a Dask array, using the rechunk method. Please note that rechunking Dask arrays can be very expensive, so choosing an appropriate chunk size initially is ideal.

+
+
+
new_chunk_shape = (50,50,50)
+rechunked_big_da = big_da.rechunk(new_chunk_shape)
+rechunked_big_da
+
+
+
+
+
+ + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Array Chunk
Bytes 5.96 GiB 0.95 MiB
Shape (2000, 200, 2000) (50, 50, 50)
Dask graph 6400 chunks in 2 graph layers
Data type float64 numpy.ndarray
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 2000 + 200 + 2000 + +
+
+
+
+
%%time
+# perform big computation on chunked array
+
+z_da_rechunked = (rechunked_big_da + rechunked_big_da.T)[::2,:].mean(axis=2)
+
+result = z_da_rechunked.compute()
+
+
+
+
+
CPU times: user 16.7 s, sys: 743 ms, total: 17.4 s
+Wall time: 39.2 s
+
+
+
+
+

We can see how the choice of smaller chunks (more total chunks) significantly reduce the total performance of our computation.

+
+

TIP: As a rule of thumb, a chunk should be big enough so that the computation on that chunk take significantly longer than the overhead from Dask scheduler. The Dask scheduler takes roughly 1ms per task for scheduling.

+
+

Let’s try a bigger chunk size:

+
+
+
new_chunk_shape = (500, 100, 500)
+#big_chunk = 
+
+rechunked_big_da = big_da.rechunk(new_chunk_shape)
+rechunked_big_da
+
+
+
+
+
+ + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Array Chunk
Bytes 5.96 GiB 190.73 MiB
Shape (2000, 200, 2000) (500, 100, 500)
Dask graph 32 chunks in 2 graph layers
Data type float64 numpy.ndarray
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 2000 + 200 + 2000 + +
+
+
+
+
%%time
+# perform big computation on chunked array
+
+z_da_rechunked = (rechunked_big_da + rechunked_big_da.T)[::2,:].mean(axis=2)
+
+result = z_da_rechunked.compute()
+
+
+
+
+
CPU times: user 1.12 s, sys: 59.2 ms, total: 1.18 s
+Wall time: 13.1 s
+
+
+
+
+
+

TIP: As a rule of thumb, a chunk should be small enough to fit comfortably in the memory. Chunk sizes between 10MB-1GB are common, depending on your machine,

+
+
+
+
client.close()
+
+
+
+
+
+
+

Summary:

+
    +

  • Dask Array provides parallel computing capabilities by dividing arrays into smaller pieces called chunks.

    • +

  • Blocked algorithms split large computations into smaller computations which operate on subsets of the array.

    • +

  • Dask Array supports efficient computation on large arrays through a combination of lazy evaluation and task parallelism.

    • +

  • Dask Array can be used as a drop-in replacement for NumPy ndarray, with a similar API and support for a subset of NumPy functions.

    • +

  • The way that arrays are chunked can significantly affect total performance. Poor chunking can singifincantly worsen performance of Dask compared to NumPy.

    • +
+
+
+

Resources and references

+ +
+
+ + + + +
+ +
+ + +
+

previous

+

Dask Overview

+
+ + +
+

next

+

Dask DataFrame

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ NCAR +
+
+ Unidata +
+
+ UAlbany +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2023. + + By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk. + + Last updated on 19 October 2023. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/16/notebooks/02-dask-dataframe.html b/_preview/16/notebooks/02-dask-dataframe.html new file mode 100644 index 0000000..82206b3 --- /dev/null +++ b/_preview/16/notebooks/02-dask-dataframe.html @@ -0,0 +1,3743 @@ + + + + + + + + Dask DataFrame — Dask Cookbook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +

Dask logo

+
+

Dask DataFrame

+
+

In this tutorial, you learn:

+
    +

  • Basic concepts and features of Dask DataFrames

    • +

  • Applications of Dask DataFrames

    • +

  • Interacting with Dask DataFrames

    • +

  • Built-in operations with Dask DataFrames

    • +

  • Dask DataFrames Best Practices

    • +
+
+ +
+

Prerequisites

+ + + + + + + + + + + + + + + + + +

Concepts

Importance

Notes

Familiarity with Pandas DataFrame

Necessary

Dask Overview

Necessary

+
    +

  • Time to learn: 40 minutes

    • +
+
+
+
+

Introduction

+

Dask DataFrame is composed of pandas DataFrames

+

Image credit: Dask Contributors

+

pandas is a very popular tool for working with tabular datasets, but the dataset needs to fit into the memory.

+

pandas operates best with smaller datasets, and if you have a large dataset, you’ll receive an out of memory error using pandas. A general rule of thumb for pandas is:

+
+
+
“Have 5 to 10 times as much RAM as the size of your dataset”

Wes McKinney (2017) in 10 things I hate about pandas

+
+
+
+

But Dask DataFrame can be used to solve pandas performance issues with larger-than-memory datasets.

+
+

What is Dask DataFrame?

+
    +

  • A Dask DataFrame is a parallel DataFrame composed of smaller pandas DataFrames (also known as partitions).

    • +

  • Dask Dataframes look and feel like the pandas DataFrames on the surface.

    • +

  • Dask DataFrames partition the data into manageable partitions that can be processed in parallel and across multiple cores or computers.

    • +

  • Similar to Dask Arrays, Dask DataFrames are lazy!

    +

    Unlike pandas, operations on Dask DataFrames are not computed until you explicitly request them (e.g. by calling .compute).

    +
    • +
+
+
+
+

When to use Dask DataFrame and when to avoid it?

+

Dask DataFrames are used in situations where pandas fails or has poor performance due to data size.

+

Dask DataFrame is a good choice when doing parallalizeable computations.
+Some examples are:

+
    +

  • Element-wise operations such as df.x + df.y

    • +

  • Row-wise filtering such as df[df.x>0]

    • +

  • Common aggregations such as df.x.max()

    • +

  • Dropping duplicates such as df.x.drop_duplicate()

    • +
+

However, Dask is not great for operations that requires shuffling or re-indexing.
+Some examples are:

+
    +

  • Set index: df.set_index(df.x)

    • +
+
+WARNING: Although, Dask DataFrame has a very similar interface to the pandas DataFrame (as we will see in this tutorial), it does NOT include some of the pandas interface yet. +

See the Dask DataFrame API documentation for a compehnsive list of available functions.

+
+
+
+
+

Tutorial Dataset

+

In this tutorial, we are going to use the NOAA Global Historical Climatology Network Daily (GHCN-D) dataset.
+GHCN-D is a public available dataset that includes daily climate records from +100,000 surface observations around the world.
+This is an example of a real dataset that is used by NCAR scientists for their research. GHCN-D raw dataset for all stations is available through NOAA Climate Data Online.

+

To learn more about GHCNd dataset, please visit:

+ +
+

Download the data

+

For this example, we are going to look through a subset of data from the GHCN-D dataset.

+

First, we look at the daily observations from Denver International Airport, next we are going to look through selected stations in the US.

+

The access the preprocessed dataset for this tutorial, please run the following script:

+
+
+
!./get_data.sh
+
+
+
+
+
Downloading https://docs.google.com/uc?export=download&id=14doSRn8hT14QYtjZz28GKv14JgdIsbFF
+
+
+
USC00023160.csv
+USC00027281.csv
+
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USC00053038.csv
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USC00053146.csv
+
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+
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USC00053951.csv
+USC00054076.csv
+
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+
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+
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+USC00055722.csv
+
+
+
USC00057167.csv
+
+
+
USC00057337.csv
+
+
+
Downloading https://docs.google.com/uc?export=download&id=15rCwQUxxpH6angDhpXzlvbe1nGetYHrf
+
+
+
USC00057936.csv
+
+
+
USC00058204.csv
+USC00058429.csv
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+USC00080211.csv
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USC00088824.csv
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USC00100470.csv
+USC00105275.csv
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USC00106152.csv
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USC00107264.csv
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USC00108137.csv
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USC00110338.csv
+
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+
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+USC00112348.csv
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USC00112483.csv
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USC00113335.csv
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USC00114108.csv
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USC00114442.csv
+USC00114823.csv
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USC00115079.csv
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USC00115326.csv
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USC00115768.csv
+USC00115833.csv
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+
+
+
USC00115943.csv
+
+
+
USC00116446.csv
+
+
+
USW00003017.csv
+Downloading https://docs.google.com/uc?export=download&id=1Tbuom1KMCwHjy7-eexEQcOXSr51i6mae
+
+
+
data_for_cesm/
+data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.205501-206412.nc
+
+
+
data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.209501-210012.nc
+
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data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.202501-203412.nc
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data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.204501-205412.nc
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data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.207501-208412.nc
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data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.201501-202412.nc
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+
+
data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.208501-209412.nc
+
+
+
+
+

This script should save the preprocessed GHCN-D data in ../data path.

+
+
+
+
+

Pandas DataFrame Basics

+

Let’s start with an example using pandas DataFrame.

+

First, let’s read in the comma-seperated GHCN-D dataset for one station at Denver International Airport (DIA), CO (site ID : USW00003017).

+

To see the list of all available GHCN-D sites and their coordinates and IDs, please see this link.

+
+
+
import os
+import pandas as pd
+
+# DIA ghcnd id
+site = 'USW00003017'
+data_dir = '../data/'
+
+
+df = pd.read_csv(os.path.join(data_dir, site+'.csv'), parse_dates=['DATE'], index_col=0)
+
+
+
+
+
+
+
# Display the top five rows of the dataframe
+df.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
IDYEARMONTHDAYTMAXTMAX_FLAGSTMINTMIN_FLAGSPRCPPRCP_FLAGS...RHMN_FLAGSRHMXRHMX_FLAGSPSUNPSUN_FLAGSLATITUDELONGITUDEELEVATIONSTATESTATION
DATE
1994-07-20USW000030171994720316.0XXS150.0XXS20.0DXS...XXXNaNXXXNaNXXX39.8467-104.65611647.1CODENVER INTL AP
1994-07-23USW000030171994723355.0XXS166.0XXS0.0DXS...XXXNaNXXXNaNXXX39.8467-104.65611647.1CODENVER INTL AP
1994-07-24USW000030171994724333.0XXS155.0XXS81.0DXS...XXXNaNXXXNaNXXX39.8467-104.65611647.1CODENVER INTL AP
1994-07-25USW000030171994725327.0XXS172.0XXS0.0DXS...XXXNaNXXXNaNXXX39.8467-104.65611647.1CODENVER INTL AP
1994-07-26USW000030171994726327.0XXS155.0XXS0.0DXS...XXXNaNXXXNaNXXX39.8467-104.65611647.1CODENVER INTL AP
+

5 rows × 99 columns

+
+
+

Question: What variables are available?

+
+
+
df.columns
+
+
+
+
+
Index(['ID', 'YEAR', 'MONTH', 'DAY', 'TMAX', 'TMAX_FLAGS', 'TMIN',
+       'TMIN_FLAGS', 'PRCP', 'PRCP_FLAGS', 'TAVG', 'TAVG_FLAGS', 'SNOW',
+       'SNOW_FLAGS', 'SNWD', 'SNWD_FLAGS', 'AWND', 'AWND_FLAGS', 'FMTM',
+       'FMTM_FLAGS', 'PGTM', 'PGTM_FLAGS', 'WDF2', 'WDF2_FLAGS', 'WDF5',
+       'WDF5_FLAGS', 'WSF2', 'WSF2_FLAGS', 'WSF5', 'WSF5_FLAGS', 'WT01',
+       'WT01_FLAGS', 'WT02', 'WT02_FLAGS', 'WT08', 'WT08_FLAGS', 'WT16',
+       'WT16_FLAGS', 'WT17', 'WT17_FLAGS', 'WT18', 'WT18_FLAGS', 'WT03',
+       'WT03_FLAGS', 'WT05', 'WT05_FLAGS', 'WT19', 'WT19_FLAGS', 'WT10',
+       'WT10_FLAGS', 'WT09', 'WT09_FLAGS', 'WT06', 'WT06_FLAGS', 'WT07',
+       'WT07_FLAGS', 'WT11', 'WT11_FLAGS', 'WT13', 'WT13_FLAGS', 'WT21',
+       'WT21_FLAGS', 'WT14', 'WT14_FLAGS', 'WT15', 'WT15_FLAGS', 'WT22',
+       'WT22_FLAGS', 'WT04', 'WT04_FLAGS', 'WV03', 'WV03_FLAGS', 'TSUN',
+       'TSUN_FLAGS', 'WV01', 'WV01_FLAGS', 'WESD', 'WESD_FLAGS', 'ADPT',
+       'ADPT_FLAGS', 'ASLP', 'ASLP_FLAGS', 'ASTP', 'ASTP_FLAGS', 'AWBT',
+       'AWBT_FLAGS', 'RHAV', 'RHAV_FLAGS', 'RHMN', 'RHMN_FLAGS', 'RHMX',
+       'RHMX_FLAGS', 'PSUN', 'PSUN_FLAGS', 'LATITUDE', 'LONGITUDE',
+       'ELEVATION', 'STATE', 'STATION'],
+      dtype='object')
+
+
+
+
+

The description and units of the dataset is available here.

+
+

Operations on pandas DataFrame

+

pandas DataFrames has several features that give us flexibility to do different calculations and analysis on our dataset. Let’s check some out:

+
+

Simple Analysis

+

For example:

+
    +

  • When was the coldest day at this station during December of last year?

    • +
+
+
+
# use python slicing notation inside .loc 
+# use idxmin() to find the index of minimum valus
+df.loc['2022-12-01':'2022-12-31'].TMIN.idxmin()
+
+
+
+
+
Timestamp('2022-12-22 00:00:00')
+
+
+
+
+
+
+
# Here we easily plot the prior data using matplotlib from pandas
+# -- .loc for value based indexing
+df.loc['2022-12-01':'2022-12-31'].SNWD.plot(ylabel= 'Daily Average Snow Depth [mm]')
+
+
+
+
+
<Axes: xlabel='DATE', ylabel='Daily Average Snow Depth [mm]'>
+
+
+../_images/ef20c4e03ed57efe0aa75a5a8520c0e5c1a5729bdafea28942725cd9862bc0bb.png +
+
+
    +

  • How many snow days do we have each year at this station?

    • +
+

Pandas groupby is used for grouping the data according to the categories.

+
+
+
# 1- First select days with snow > 0
+# 2- Create a "groupby object" based on the selected columns
+# 3- use .size() to compute the size of each group
+# 4- sort the values descending 
+
+# we count days where SNOW>0, and sort them and show top 5 years:
+df[df['SNOW']>0].groupby('YEAR').size().sort_values(ascending=False).head()
+
+
+
+
+
YEAR
+2015    36
+2019    34
+2014    32
+2008    32
+2007    31
+dtype: int64
+
+
+
+
+

Or for a more complex analysis:

+

For example, we have heard that this could be Denver’s first January in 13 years with no 60-degree days.

+

News article showing that this January is abnormally warm

+

Below, we show all days with high temperature above 60°F (155.5°C/10) since 2010:

+
+
+
df[(df['MONTH']==1) & (df['YEAR']>=2010) & (df['TMAX']>155.5)].groupby(['YEAR']).size()
+
+
+
+
+
YEAR
+2011    1
+2012    6
+2013    4
+2014    3
+2015    6
+2016    1
+2017    4
+2018    5
+2019    3
+2020    2
+2021    2
+2022    3
+dtype: int64
+
+
+
+
+

This is great! But how big is this dataset for one station?

+

First, let’s check the file size:

+
+
+
!ls -lh ../data/USW00003017.csv
+
+
+
+
+
-rw-r--r-- 1 runner docker 3.6M Feb  5  2023 ../data/USW00003017.csv
+
+
+
+
+

Similar to the previous tutorial, we can use the following function to find the size of a variable on memory.

+
+
+
# Define function to display variable size in MB
+import sys
+def var_size(in_var):
+    result = sys.getsizeof(in_var) / 1e6
+    print(f"Size of variable: {result:.2f} MB")
+
+
+
+
+
+
+
var_size(df)
+
+
+
+
+
Size of variable: 33.21 MB
+
+
+
+
+

Remember, the above rule?

+
+
+
“Have 5 to 10 times as much RAM as the size of your dataset”

Wes McKinney (2017) in 10 things I hate about pandas

+
+
+
+

So far, we read in and analyzed data for one station. We have a total of +118,000 stations over the world and +4500 stations in Colorado alone!

+

What if we want to look at the larger dataset?

+
+
+
+
+

Scaling up to a larger dataset

+

Let’s start by reading data from selected stations. The downloaded data for this example includes the climatology observations from 66 selected sites in Colorado.

+

Pandas can concatenate data to load data spread across multiple files:

+
+
+
!du -csh ../data/*.csv |tail -n1
+
+
+
+
+
565M	total
+
+
+
+
+

Using a for loop with pandas.concat, we can read multiple files at the same time:

+
+
+
%%time
+import glob
+co_sites = glob.glob(os.path.join(data_dir, '*.csv'))
+df = pd.concat(pd.read_csv(f, index_col=0, parse_dates=['DATE']) for f in co_sites)
+
+
+
+
+
CPU times: user 14.4 s, sys: 2.76 s, total: 17.2 s
+Wall time: 17.2 s
+
+
+
+
+
    +

  • How many stations have we read in?

    • +
+
+
+
print ("Concatenated data for", len(df.ID.unique()), "unique sites.")
+
+
+
+
+
Concatenated data for 66 unique sites.
+
+
+
+
+

Now that we concatenated the data for all sites in one DataFrame, we can do similar analysis on it:

+
    +

  • Which site has recorded the most snow days in a year?

    • +
+
+
+
%%time
+# ~90s on 4GB RAM
+snowy_days = df[df['SNOW']>0].groupby(['ID','YEAR']).size()
+
+print ('This site has the highest number of snow days in a year : ')
+snowy_days.agg(['idxmax','max'])
+
+
+
+
+
This site has the highest number of snow days in a year : 
+CPU times: user 363 ms, sys: 0 ns, total: 363 ms
+Wall time: 363 ms
+
+
+
idxmax    (USC00052281, 1983)
+max                       102
+dtype: object
+
+
+
+
+

Excersise: Which Colorado site has recorded the most snow days in 2023?

+

Dask allows us to conceptualize all of these files as a single dataframe!

+
+
+
# Let's do a little cleanup
+del df, snowy_days
+
+
+
+
+
+
+

Computations on Dask DataFrame

+
+

Create a “LocalCluster” Client with Dask

+
+
+
from dask.distributed import Client, LocalCluster
+
+cluster = LocalCluster()
+client = Client(cluster)
+client
+
+
+
+
+
+
+
+

Client

+

Client-c11dcd37-6ebc-11ee-8a0c-6045bd783dc1

+ + + + + + + + + + + + + + + + +
Connection method: Cluster objectCluster type: distributed.LocalCluster
+ Dashboard: http://127.0.0.1:8787/status +
+ + + + + + +
+

Cluster Info

 + +
+ + +
+
+
+

☝️ Click the Dashboard link above.

+

👈 Or click the “Search” 🔍 button in the dask-labextension dashboard.

+
+
+

Dask DataFrame read_csv to read multiple files

+

dask.dataframe.read_csv function can be used in conjunction with glob to read multiple csv files at the same time.

+

Remember we can read one file with pandas.read_csv. For reading multiple files with pandas, we have to concatenate them with pd.concatenate. However, we can read many files at once just using dask.dataframe.read_csv.

+

Overall, Dask is designed to perform I/O in parallel and is more performant than pandas for operations with multiple files or large files.

+
+
+
%%time
+import dask
+import dask.dataframe as dd
+
+ddf = dd.read_csv(co_sites, parse_dates=['DATE'])
+ddf
+
+
+
+
+
CPU times: user 262 ms, sys: 19.8 ms, total: 282 ms
+Wall time: 331 ms
+
+
+
Dask DataFrame Structure:
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DATEIDYEARMONTHDAYTMAXTMAX_FLAGSPRCPPRCP_FLAGSSNOWSNOW_FLAGSSNWDSNWD_FLAGSTMINTMIN_FLAGSWT16WT16_FLAGSWT01WT01_FLAGSTOBSTOBS_FLAGSWT03WT03_FLAGSWT18WT18_FLAGSWT04WT04_FLAGSWT08WT08_FLAGSWT14WT14_FLAGSWT05WT05_FLAGSWT11WT11_FLAGSWT07WT07_FLAGSWT09WT09_FLAGSWT10WT10_FLAGSDAPRDAPR_FLAGSMDPRMDPR_FLAGSWT06WT06_FLAGSLATITUDELONGITUDEELEVATIONSTATESTATION
npartitions=66
datetime64[ns]stringint64int64int64float64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64stringfloat64float64float64stringstring
............................................................................................................................................................
...............................................................................................................................................................
............................................................................................................................................................
............................................................................................................................................................
+
+
Dask Name: to_pyarrow_string, 2 graph layers
+
+
+
+
ddf.TMAX.mean()
+
+
+
+
+
dd.Scalar<series-..., dtype=float64>
+
+
+
+
+

Notice that the representation of the DataFrame object contains no data just headers and datatypes. Why?

+
+

Lazy Evaluation

+

Similar to Dask Arrays, Dask DataFrames are lazy. Here the data has not yet been read into the dataframe yet (a.k.a. lazy evaluation).
+Dask just construct the task graph of the computation but it will “evaluate” them only when necessary.

+

So how does Dask know the name and dtype of each column?

+

Dask has just read the start of the first file and infers the column names and dtypes.

+

Unlike pandas.read_csv that reads in all files before inferring data types, dask.dataframe.read_csv only reads in a sample from the beginning of the file (or first file if using a glob). The column names and dtypes are then enforced when reading the specific partitions (Dask can make mistakes on these inferences if there is missing or misleading data in the early rows).

+

Let’s take a look at the start of our dataframe:

+
+
+
ddf.head()
+
+
+
+
+
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
DATEIDYEARMONTHDAYTMAXTMAX_FLAGSPRCPPRCP_FLAGSSNOW...DAPR_FLAGSMDPRMDPR_FLAGSWT06WT06_FLAGSLATITUDELONGITUDEELEVATIONSTATESTATION
01883-04-02USC00030936188342167.0XX60.0XX60.0...XXXNaNXXXNaNXXX34.8822-91.215356.4ARBRINKLEY
11883-04-03USC00030936188343172.0XX60.0XX60.0...XXXNaNXXXNaNXXX34.8822-91.215356.4ARBRINKLEY
21883-04-04USC00030936188344217.0XX60.0XX60.0...XXXNaNXXXNaNXXX34.8822-91.215356.4ARBRINKLEY
31883-04-05USC00030936188345261.0XX60.0XX60.0...XXXNaNXXXNaNXXX34.8822-91.215356.4ARBRINKLEY
41883-04-06USC00030936188346244.0XX638.0XX60.0...XXXNaNXXXNaNXXX34.8822-91.215356.4ARBRINKLEY
+

5 rows × 52 columns

+
+
+
+

NOTE: Whenever we operate on our dataframe we read through all of our CSV data so that we don’t fill up RAM. Dask will delete intermediate results (like the full pandas DataFrame for each file) as soon as possible. This enables you to handle larger than memory datasets but, repeated computations will have to load all of the data in each time.

+

Similar data manipulations as pandas.dataframe can be done for dask.dataframes.
+For example, let’s find the highest number of snow days in Colorado:

+
+
+
%%time
+print ('This site has the highest number of snow days in a year : ')
+snowy_days = ddf[ddf['SNOW']>0].groupby(['ID','YEAR']).size()
+snowy_days.compute().agg(['idxmax','max'])
+
+
+
+
+
This site has the highest number of snow days in a year : 
+
+
+
CPU times: user 477 ms, sys: 19.5 ms, total: 497 ms
+Wall time: 11.6 s
+
+
+
idxmax    (USC00052281, 1983)
+max                       102
+dtype: object
+
+
+
+
+
+
+

Nice, but what did Dask do?

+
+
+
# Requires ipywidgets
+
+snowy_days.dask
+
+
+
+
+
+
+
+ + + + + + + + + + +
+
+

HighLevelGraph

+

+ HighLevelGraph with 7 layers and 397 keys from all layers. +

+ +
+ + + + + + +
+ +

Layer1: read-csv

+ +

+ read-csv-80fcf0b29841c91e58f0d9c3b00fb30b +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeDataFrameIOLayer
is_materializedFalse
number of outputs66
npartitions66
columns['DATE', 'ID', 'YEAR', 'MONTH', 'DAY', 'TMAX', 'TMAX_FLAGS', 'PRCP', 'PRCP_FLAGS', 'SNOW', 'SNOW_FLAGS', 'SNWD', 'SNWD_FLAGS', 'TMIN', 'TMIN_FLAGS', 'WT16', 'WT16_FLAGS', 'WT01', 'WT01_FLAGS', 'TOBS', 'TOBS_FLAGS', 'WT03', 'WT03_FLAGS', 'WT18', 'WT18_FLAGS', 'WT04', 'WT04_FLAGS', 'WT08', 'WT08_FLAGS', 'WT14', 'WT14_FLAGS', 'WT05', 'WT05_FLAGS', 'WT11', 'WT11_FLAGS', 'WT07', 'WT07_FLAGS', 'WT09', 'WT09_FLAGS', 'WT10', 'WT10_FLAGS', 'DAPR', 'DAPR_FLAGS', 'MDPR', 'MDPR_FLAGS', 'WT06', 'WT06_FLAGS', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'STATE', 'STATION']
typedask.dataframe.core.DataFrame
dataframe_typepandas.core.frame.DataFrame
series_dtypes{'DATE': dtype('<M8[ns]'), 'ID': string[pyarrow], 'YEAR': dtype('int64'), 'MONTH': dtype('int64'), 'DAY': dtype('int64'), 'TMAX': dtype('float64'), 'TMAX_FLAGS': string[pyarrow], 'PRCP': dtype('float64'), 'PRCP_FLAGS': string[pyarrow], 'SNOW': dtype('float64'), 'SNOW_FLAGS': string[pyarrow], 'SNWD': dtype('float64'), 'SNWD_FLAGS': string[pyarrow], 'TMIN': dtype('float64'), 'TMIN_FLAGS': string[pyarrow], 'WT16': dtype('float64'), 'WT16_FLAGS': string[pyarrow], 'WT01': dtype('float64'), 'WT01_FLAGS': string[pyarrow], 'TOBS': dtype('float64'), 'TOBS_FLAGS': string[pyarrow], 'WT03': dtype('float64'), 'WT03_FLAGS': string[pyarrow], 'WT18': dtype('float64'), 'WT18_FLAGS': string[pyarrow], 'WT04': dtype('float64'), 'WT04_FLAGS': string[pyarrow], 'WT08': dtype('float64'), 'WT08_FLAGS': string[pyarrow], 'WT14': dtype('float64'), 'WT14_FLAGS': string[pyarrow], 'WT05': dtype('float64'), 'WT05_FLAGS': string[pyarrow], 'WT11': dtype('float64'), 'WT11_FLAGS': string[pyarrow], 'WT07': dtype('float64'), 'WT07_FLAGS': string[pyarrow], 'WT09': dtype('float64'), 'WT09_FLAGS': string[pyarrow], 'WT10': dtype('float64'), 'WT10_FLAGS': string[pyarrow], 'DAPR': dtype('float64'), 'DAPR_FLAGS': string[pyarrow], 'MDPR': dtype('float64'), 'MDPR_FLAGS': string[pyarrow], 'WT06': dtype('float64'), 'WT06_FLAGS': string[pyarrow], 'LATITUDE': dtype('float64'), 'LONGITUDE': dtype('float64'), 'ELEVATION': dtype('float64'), 'STATE': string[pyarrow], 'STATION': string[pyarrow]}
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer2: to_pyarrow_string

+ +

+ to_pyarrow_string-d61ff0e6aa014e703d079138d5d6bbc3 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs66
npartitions66
columns['DATE', 'ID', 'YEAR', 'MONTH', 'DAY', 'TMAX', 'TMAX_FLAGS', 'PRCP', 'PRCP_FLAGS', 'SNOW', 'SNOW_FLAGS', 'SNWD', 'SNWD_FLAGS', 'TMIN', 'TMIN_FLAGS', 'WT16', 'WT16_FLAGS', 'WT01', 'WT01_FLAGS', 'TOBS', 'TOBS_FLAGS', 'WT03', 'WT03_FLAGS', 'WT18', 'WT18_FLAGS', 'WT04', 'WT04_FLAGS', 'WT08', 'WT08_FLAGS', 'WT14', 'WT14_FLAGS', 'WT05', 'WT05_FLAGS', 'WT11', 'WT11_FLAGS', 'WT07', 'WT07_FLAGS', 'WT09', 'WT09_FLAGS', 'WT10', 'WT10_FLAGS', 'DAPR', 'DAPR_FLAGS', 'MDPR', 'MDPR_FLAGS', 'WT06', 'WT06_FLAGS', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'STATE', 'STATION']
typedask.dataframe.core.DataFrame
dataframe_typepandas.core.frame.DataFrame
series_dtypes{'DATE': dtype('<M8[ns]'), 'ID': string[pyarrow], 'YEAR': dtype('int64'), 'MONTH': dtype('int64'), 'DAY': dtype('int64'), 'TMAX': dtype('float64'), 'TMAX_FLAGS': string[pyarrow], 'PRCP': dtype('float64'), 'PRCP_FLAGS': string[pyarrow], 'SNOW': dtype('float64'), 'SNOW_FLAGS': string[pyarrow], 'SNWD': dtype('float64'), 'SNWD_FLAGS': string[pyarrow], 'TMIN': dtype('float64'), 'TMIN_FLAGS': string[pyarrow], 'WT16': dtype('float64'), 'WT16_FLAGS': string[pyarrow], 'WT01': dtype('float64'), 'WT01_FLAGS': string[pyarrow], 'TOBS': dtype('float64'), 'TOBS_FLAGS': string[pyarrow], 'WT03': dtype('float64'), 'WT03_FLAGS': string[pyarrow], 'WT18': dtype('float64'), 'WT18_FLAGS': string[pyarrow], 'WT04': dtype('float64'), 'WT04_FLAGS': string[pyarrow], 'WT08': dtype('float64'), 'WT08_FLAGS': string[pyarrow], 'WT14': dtype('float64'), 'WT14_FLAGS': string[pyarrow], 'WT05': dtype('float64'), 'WT05_FLAGS': string[pyarrow], 'WT11': dtype('float64'), 'WT11_FLAGS': string[pyarrow], 'WT07': dtype('float64'), 'WT07_FLAGS': string[pyarrow], 'WT09': dtype('float64'), 'WT09_FLAGS': string[pyarrow], 'WT10': dtype('float64'), 'WT10_FLAGS': string[pyarrow], 'DAPR': dtype('float64'), 'DAPR_FLAGS': string[pyarrow], 'MDPR': dtype('float64'), 'MDPR_FLAGS': string[pyarrow], 'WT06': dtype('float64'), 'WT06_FLAGS': string[pyarrow], 'LATITUDE': dtype('float64'), 'LONGITUDE': dtype('float64'), 'ELEVATION': dtype('float64'), 'STATE': string[pyarrow], 'STATION': string[pyarrow]}
depends on read-csv-80fcf0b29841c91e58f0d9c3b00fb30b
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer3: getitem

+ +

+ getitem-46337166a60f763157f33e8d280f2535 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs66
depends on to_pyarrow_string-d61ff0e6aa014e703d079138d5d6bbc3
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer4: gt

+ +

+ gt-571002c2824cf9b67469bf64891f95ca +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs66
depends on getitem-46337166a60f763157f33e8d280f2535
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer5: getitem

+ +

+ getitem-f57144ad07ddb75a1f85dd7db8e8366a +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs66
npartitions66
columns['DATE', 'ID', 'YEAR', 'MONTH', 'DAY', 'TMAX', 'TMAX_FLAGS', 'PRCP', 'PRCP_FLAGS', 'SNOW', 'SNOW_FLAGS', 'SNWD', 'SNWD_FLAGS', 'TMIN', 'TMIN_FLAGS', 'WT16', 'WT16_FLAGS', 'WT01', 'WT01_FLAGS', 'TOBS', 'TOBS_FLAGS', 'WT03', 'WT03_FLAGS', 'WT18', 'WT18_FLAGS', 'WT04', 'WT04_FLAGS', 'WT08', 'WT08_FLAGS', 'WT14', 'WT14_FLAGS', 'WT05', 'WT05_FLAGS', 'WT11', 'WT11_FLAGS', 'WT07', 'WT07_FLAGS', 'WT09', 'WT09_FLAGS', 'WT10', 'WT10_FLAGS', 'DAPR', 'DAPR_FLAGS', 'MDPR', 'MDPR_FLAGS', 'WT06', 'WT06_FLAGS', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'STATE', 'STATION']
typedask.dataframe.core.DataFrame
dataframe_typepandas.core.frame.DataFrame
series_dtypes{'DATE': dtype('<M8[ns]'), 'ID': string[pyarrow], 'YEAR': dtype('int64'), 'MONTH': dtype('int64'), 'DAY': dtype('int64'), 'TMAX': dtype('float64'), 'TMAX_FLAGS': string[pyarrow], 'PRCP': dtype('float64'), 'PRCP_FLAGS': string[pyarrow], 'SNOW': dtype('float64'), 'SNOW_FLAGS': string[pyarrow], 'SNWD': dtype('float64'), 'SNWD_FLAGS': string[pyarrow], 'TMIN': dtype('float64'), 'TMIN_FLAGS': string[pyarrow], 'WT16': dtype('float64'), 'WT16_FLAGS': string[pyarrow], 'WT01': dtype('float64'), 'WT01_FLAGS': string[pyarrow], 'TOBS': dtype('float64'), 'TOBS_FLAGS': string[pyarrow], 'WT03': dtype('float64'), 'WT03_FLAGS': string[pyarrow], 'WT18': dtype('float64'), 'WT18_FLAGS': string[pyarrow], 'WT04': dtype('float64'), 'WT04_FLAGS': string[pyarrow], 'WT08': dtype('float64'), 'WT08_FLAGS': string[pyarrow], 'WT14': dtype('float64'), 'WT14_FLAGS': string[pyarrow], 'WT05': dtype('float64'), 'WT05_FLAGS': string[pyarrow], 'WT11': dtype('float64'), 'WT11_FLAGS': string[pyarrow], 'WT07': dtype('float64'), 'WT07_FLAGS': string[pyarrow], 'WT09': dtype('float64'), 'WT09_FLAGS': string[pyarrow], 'WT10': dtype('float64'), 'WT10_FLAGS': string[pyarrow], 'DAPR': dtype('float64'), 'DAPR_FLAGS': string[pyarrow], 'MDPR': dtype('float64'), 'MDPR_FLAGS': string[pyarrow], 'WT06': dtype('float64'), 'WT06_FLAGS': string[pyarrow], 'LATITUDE': dtype('float64'), 'LONGITUDE': dtype('float64'), 'ELEVATION': dtype('float64'), 'STATE': string[pyarrow], 'STATION': string[pyarrow]}
depends on to_pyarrow_string-d61ff0e6aa014e703d079138d5d6bbc3
gt-571002c2824cf9b67469bf64891f95ca
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer6: dataframe-groupby-size-chunk

+ +

+ dataframe-groupby-size-chunk-fcf9abb994b4903f8a39e93a77c03b93-a36b0ba0e8c4dfec47ce7578a81dfb05 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs66
depends on getitem-f57144ad07ddb75a1f85dd7db8e8366a
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer7: dataframe-groupby-size-agg

+ +

+ dataframe-groupby-size-agg-fcf9abb994b4903f8a39e93a77c03b93 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeDataFrameTreeReduction
is_materializedFalse
number of outputs1
depends on dataframe-groupby-size-chunk-fcf9abb994b4903f8a39e93a77c03b93-a36b0ba0e8c4dfec47ce7578a81dfb05
+ 		+ +
+ +
+
+ +
+
+
+
+

You can also view the underlying task graph using .visualize():

+
+
+
#graph is too large
+snowy_days.visualize()
+
+
+
+
+../_images/b3a6bafd3383b6ab51e446ee855c4bb9dccc61a407e7556fe629295c84651aa6.png +
+
+
+
+
+

Use .compute wisely!

+
+

Share intermediate results

+

For most operations, dask.dataframe hashes the arguments, allowing duplicate computations to be shared, and only computed once.

+

For example, let’s compute the mean and standard deviation for Maximum daily temperature of all snow days.

+
+
+
snowy_days = ddf[ddf['SNOW']>0]
+mean_tmax = snowy_days.TMAX.mean()
+std_tmax = snowy_days.TMAX.std()
+
+
+
+
+
+
+
%%time
+
+mean_tmax_result = mean_tmax.compute()
+std_tmax_result = std_tmax.compute()
+
+
+
+
+
CPU times: user 951 ms, sys: 70.7 ms, total: 1.02 s
+Wall time: 22.6 s
+
+
+
+
+

But if we pass both arguments in a single .compute, we can share the intermediate results:

+
+
+
%%time
+mean_tmax_result, std_tmax_result = dask.compute(mean_tmax, std_tmax)
+
+
+
+
+
CPU times: user 558 ms, sys: 42.6 ms, total: 600 ms
+Wall time: 11.6 s
+
+
+
+
+

Here using dask.compute only one allowed sharing intermediate results between TMAX mean and median calculations and improved total performance.

+
+
+
mean_tmax.dask
+
+
+
+
+
+
+
+ + + + + + + + + + +
+
+

HighLevelGraph

+

+ HighLevelGraph with 11 layers and 531 keys from all layers. +

+ +
+ + + + + + +
+ +

Layer1: read-csv

+ +

+ read-csv-80fcf0b29841c91e58f0d9c3b00fb30b +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeDataFrameIOLayer
is_materializedTrue
number of outputs66
npartitions66
columns['DATE', 'ID', 'YEAR', 'MONTH', 'DAY', 'TMAX', 'TMAX_FLAGS', 'PRCP', 'PRCP_FLAGS', 'SNOW', 'SNOW_FLAGS', 'SNWD', 'SNWD_FLAGS', 'TMIN', 'TMIN_FLAGS', 'WT16', 'WT16_FLAGS', 'WT01', 'WT01_FLAGS', 'TOBS', 'TOBS_FLAGS', 'WT03', 'WT03_FLAGS', 'WT18', 'WT18_FLAGS', 'WT04', 'WT04_FLAGS', 'WT08', 'WT08_FLAGS', 'WT14', 'WT14_FLAGS', 'WT05', 'WT05_FLAGS', 'WT11', 'WT11_FLAGS', 'WT07', 'WT07_FLAGS', 'WT09', 'WT09_FLAGS', 'WT10', 'WT10_FLAGS', 'DAPR', 'DAPR_FLAGS', 'MDPR', 'MDPR_FLAGS', 'WT06', 'WT06_FLAGS', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'STATE', 'STATION']
typedask.dataframe.core.DataFrame
dataframe_typepandas.core.frame.DataFrame
series_dtypes{'DATE': dtype('<M8[ns]'), 'ID': string[pyarrow], 'YEAR': dtype('int64'), 'MONTH': dtype('int64'), 'DAY': dtype('int64'), 'TMAX': dtype('float64'), 'TMAX_FLAGS': string[pyarrow], 'PRCP': dtype('float64'), 'PRCP_FLAGS': string[pyarrow], 'SNOW': dtype('float64'), 'SNOW_FLAGS': string[pyarrow], 'SNWD': dtype('float64'), 'SNWD_FLAGS': string[pyarrow], 'TMIN': dtype('float64'), 'TMIN_FLAGS': string[pyarrow], 'WT16': dtype('float64'), 'WT16_FLAGS': string[pyarrow], 'WT01': dtype('float64'), 'WT01_FLAGS': string[pyarrow], 'TOBS': dtype('float64'), 'TOBS_FLAGS': string[pyarrow], 'WT03': dtype('float64'), 'WT03_FLAGS': string[pyarrow], 'WT18': dtype('float64'), 'WT18_FLAGS': string[pyarrow], 'WT04': dtype('float64'), 'WT04_FLAGS': string[pyarrow], 'WT08': dtype('float64'), 'WT08_FLAGS': string[pyarrow], 'WT14': dtype('float64'), 'WT14_FLAGS': string[pyarrow], 'WT05': dtype('float64'), 'WT05_FLAGS': string[pyarrow], 'WT11': dtype('float64'), 'WT11_FLAGS': string[pyarrow], 'WT07': dtype('float64'), 'WT07_FLAGS': string[pyarrow], 'WT09': dtype('float64'), 'WT09_FLAGS': string[pyarrow], 'WT10': dtype('float64'), 'WT10_FLAGS': string[pyarrow], 'DAPR': dtype('float64'), 'DAPR_FLAGS': string[pyarrow], 'MDPR': dtype('float64'), 'MDPR_FLAGS': string[pyarrow], 'WT06': dtype('float64'), 'WT06_FLAGS': string[pyarrow], 'LATITUDE': dtype('float64'), 'LONGITUDE': dtype('float64'), 'ELEVATION': dtype('float64'), 'STATE': string[pyarrow], 'STATION': string[pyarrow]}
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer2: to_pyarrow_string

+ +

+ to_pyarrow_string-d61ff0e6aa014e703d079138d5d6bbc3 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedTrue
number of outputs66
npartitions66
columns['DATE', 'ID', 'YEAR', 'MONTH', 'DAY', 'TMAX', 'TMAX_FLAGS', 'PRCP', 'PRCP_FLAGS', 'SNOW', 'SNOW_FLAGS', 'SNWD', 'SNWD_FLAGS', 'TMIN', 'TMIN_FLAGS', 'WT16', 'WT16_FLAGS', 'WT01', 'WT01_FLAGS', 'TOBS', 'TOBS_FLAGS', 'WT03', 'WT03_FLAGS', 'WT18', 'WT18_FLAGS', 'WT04', 'WT04_FLAGS', 'WT08', 'WT08_FLAGS', 'WT14', 'WT14_FLAGS', 'WT05', 'WT05_FLAGS', 'WT11', 'WT11_FLAGS', 'WT07', 'WT07_FLAGS', 'WT09', 'WT09_FLAGS', 'WT10', 'WT10_FLAGS', 'DAPR', 'DAPR_FLAGS', 'MDPR', 'MDPR_FLAGS', 'WT06', 'WT06_FLAGS', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'STATE', 'STATION']
typedask.dataframe.core.DataFrame
dataframe_typepandas.core.frame.DataFrame
series_dtypes{'DATE': dtype('<M8[ns]'), 'ID': string[pyarrow], 'YEAR': dtype('int64'), 'MONTH': dtype('int64'), 'DAY': dtype('int64'), 'TMAX': dtype('float64'), 'TMAX_FLAGS': string[pyarrow], 'PRCP': dtype('float64'), 'PRCP_FLAGS': string[pyarrow], 'SNOW': dtype('float64'), 'SNOW_FLAGS': string[pyarrow], 'SNWD': dtype('float64'), 'SNWD_FLAGS': string[pyarrow], 'TMIN': dtype('float64'), 'TMIN_FLAGS': string[pyarrow], 'WT16': dtype('float64'), 'WT16_FLAGS': string[pyarrow], 'WT01': dtype('float64'), 'WT01_FLAGS': string[pyarrow], 'TOBS': dtype('float64'), 'TOBS_FLAGS': string[pyarrow], 'WT03': dtype('float64'), 'WT03_FLAGS': string[pyarrow], 'WT18': dtype('float64'), 'WT18_FLAGS': string[pyarrow], 'WT04': dtype('float64'), 'WT04_FLAGS': string[pyarrow], 'WT08': dtype('float64'), 'WT08_FLAGS': string[pyarrow], 'WT14': dtype('float64'), 'WT14_FLAGS': string[pyarrow], 'WT05': dtype('float64'), 'WT05_FLAGS': string[pyarrow], 'WT11': dtype('float64'), 'WT11_FLAGS': string[pyarrow], 'WT07': dtype('float64'), 'WT07_FLAGS': string[pyarrow], 'WT09': dtype('float64'), 'WT09_FLAGS': string[pyarrow], 'WT10': dtype('float64'), 'WT10_FLAGS': string[pyarrow], 'DAPR': dtype('float64'), 'DAPR_FLAGS': string[pyarrow], 'MDPR': dtype('float64'), 'MDPR_FLAGS': string[pyarrow], 'WT06': dtype('float64'), 'WT06_FLAGS': string[pyarrow], 'LATITUDE': dtype('float64'), 'LONGITUDE': dtype('float64'), 'ELEVATION': dtype('float64'), 'STATE': string[pyarrow], 'STATION': string[pyarrow]}
depends on read-csv-80fcf0b29841c91e58f0d9c3b00fb30b
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer3: getitem

+ +

+ getitem-46337166a60f763157f33e8d280f2535 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs66
depends on to_pyarrow_string-d61ff0e6aa014e703d079138d5d6bbc3
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer4: gt

+ +

+ gt-571002c2824cf9b67469bf64891f95ca +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs66
depends on getitem-46337166a60f763157f33e8d280f2535
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer5: getitem

+ +

+ getitem-f57144ad07ddb75a1f85dd7db8e8366a +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs66
npartitions66
columns['DATE', 'ID', 'YEAR', 'MONTH', 'DAY', 'TMAX', 'TMAX_FLAGS', 'PRCP', 'PRCP_FLAGS', 'SNOW', 'SNOW_FLAGS', 'SNWD', 'SNWD_FLAGS', 'TMIN', 'TMIN_FLAGS', 'WT16', 'WT16_FLAGS', 'WT01', 'WT01_FLAGS', 'TOBS', 'TOBS_FLAGS', 'WT03', 'WT03_FLAGS', 'WT18', 'WT18_FLAGS', 'WT04', 'WT04_FLAGS', 'WT08', 'WT08_FLAGS', 'WT14', 'WT14_FLAGS', 'WT05', 'WT05_FLAGS', 'WT11', 'WT11_FLAGS', 'WT07', 'WT07_FLAGS', 'WT09', 'WT09_FLAGS', 'WT10', 'WT10_FLAGS', 'DAPR', 'DAPR_FLAGS', 'MDPR', 'MDPR_FLAGS', 'WT06', 'WT06_FLAGS', 'LATITUDE', 'LONGITUDE', 'ELEVATION', 'STATE', 'STATION']
typedask.dataframe.core.DataFrame
dataframe_typepandas.core.frame.DataFrame
series_dtypes{'DATE': dtype('<M8[ns]'), 'ID': string[pyarrow], 'YEAR': dtype('int64'), 'MONTH': dtype('int64'), 'DAY': dtype('int64'), 'TMAX': dtype('float64'), 'TMAX_FLAGS': string[pyarrow], 'PRCP': dtype('float64'), 'PRCP_FLAGS': string[pyarrow], 'SNOW': dtype('float64'), 'SNOW_FLAGS': string[pyarrow], 'SNWD': dtype('float64'), 'SNWD_FLAGS': string[pyarrow], 'TMIN': dtype('float64'), 'TMIN_FLAGS': string[pyarrow], 'WT16': dtype('float64'), 'WT16_FLAGS': string[pyarrow], 'WT01': dtype('float64'), 'WT01_FLAGS': string[pyarrow], 'TOBS': dtype('float64'), 'TOBS_FLAGS': string[pyarrow], 'WT03': dtype('float64'), 'WT03_FLAGS': string[pyarrow], 'WT18': dtype('float64'), 'WT18_FLAGS': string[pyarrow], 'WT04': dtype('float64'), 'WT04_FLAGS': string[pyarrow], 'WT08': dtype('float64'), 'WT08_FLAGS': string[pyarrow], 'WT14': dtype('float64'), 'WT14_FLAGS': string[pyarrow], 'WT05': dtype('float64'), 'WT05_FLAGS': string[pyarrow], 'WT11': dtype('float64'), 'WT11_FLAGS': string[pyarrow], 'WT07': dtype('float64'), 'WT07_FLAGS': string[pyarrow], 'WT09': dtype('float64'), 'WT09_FLAGS': string[pyarrow], 'WT10': dtype('float64'), 'WT10_FLAGS': string[pyarrow], 'DAPR': dtype('float64'), 'DAPR_FLAGS': string[pyarrow], 'MDPR': dtype('float64'), 'MDPR_FLAGS': string[pyarrow], 'WT06': dtype('float64'), 'WT06_FLAGS': string[pyarrow], 'LATITUDE': dtype('float64'), 'LONGITUDE': dtype('float64'), 'ELEVATION': dtype('float64'), 'STATE': string[pyarrow], 'STATION': string[pyarrow]}
depends on to_pyarrow_string-d61ff0e6aa014e703d079138d5d6bbc3
gt-571002c2824cf9b67469bf64891f95ca
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer6: getitem

+ +

+ getitem-1c4b145449101b8ba8dbc3358ddfc4f6 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs66
depends on getitem-f57144ad07ddb75a1f85dd7db8e8366a
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer7: series-count-chunk

+ +

+ series-count-chunk-ff432699653253bb3d63abef5b09b678-5dd62cdc20279f32c3a83e994573a9f2 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs66
depends on getitem-1c4b145449101b8ba8dbc3358ddfc4f6
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer8: series-count-agg

+ +

+ series-count-agg-ff432699653253bb3d63abef5b09b678 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeDataFrameTreeReduction
is_materializedFalse
number of outputs1
depends on series-count-chunk-ff432699653253bb3d63abef5b09b678-5dd62cdc20279f32c3a83e994573a9f2
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer9: series-sum-chunk

+ +

+ series-sum-chunk-4a21ab2b0d596b595d6e4eafe0430db5-0d0bb3c0d7f5f4985c6aa287f7db638c +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs66
depends on getitem-1c4b145449101b8ba8dbc3358ddfc4f6
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer10: series-sum-agg

+ +

+ series-sum-agg-4a21ab2b0d596b595d6e4eafe0430db5 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeDataFrameTreeReduction
is_materializedFalse
number of outputs1
depends on series-sum-chunk-4a21ab2b0d596b595d6e4eafe0430db5-0d0bb3c0d7f5f4985c6aa287f7db638c
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer11: series-mean

+ +

+ series-mean-7a21a6c5a07424ec4b2670c3ab27c484-91e5c34402287255ad6370e9cf5532c0 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs1
depends on series-count-agg-ff432699653253bb3d63abef5b09b678
series-sum-agg-4a21ab2b0d596b595d6e4eafe0430db5
+ 		+ +
+ +
+
+ +
+
+
+
+

Here some operations such as the calls to read the csv files, the filtering, and the grouping is exactly similar between both operations, so they can share intermediate results. Remember, Dask will delete intermediate results (like the full pandas DataFrame for each file) as soon as possible.

+
+
+
+

.persist or caching

+

Sometimes you might want your computers to keep intermediate results in memory, if it fits in the memory.

+

The .persist() method can be used to “cache” data and tell Dask what results to keep around. You should only use .persist() with any data or computation that fits in memory.

+

For example, if we want to only do analysis on a subset of data (for example snow days at Boulder site):

+
+
+
boulder_snow = ddf[(ddf['SNOW']>0)&(ddf['ID']=='USC00050848')]
+
+
+
+
+
+
+
%%time
+tmax = boulder_snow.TMAX.mean().compute()
+tmin = boulder_snow.TMIN.mean().compute()
+
+print (tmin, tmax)
+
+
+
+
+
-74.82074711099168 37.419103836866114
+CPU times: user 1.07 s, sys: 83.2 ms, total: 1.16 s
+Wall time: 22.9 s
+
+
+
+
+
+
+
boulder_snow = ddf[(ddf['SNOW']>0)&(ddf['ID']=='USC00050848')].persist()
+
+
+
+
+
+
+
%%time
+
+tmax = boulder_snow.TMAX.mean().compute()
+tmin = boulder_snow.TMIN.mean().compute()
+print (tmin, tmax)
+
+
+
+
+
-74.82074711099168 37.419103836866114
+CPU times: user 658 ms, sys: 55.3 ms, total: 713 ms
+Wall time: 11.7 s
+
+
+
+
+

As you can see the analysis on this persisted data is much faster because we are not repeating the loading and selecting.

+
+
+
+

Dask DataFrames Best Practices

+
+

Use pandas (when you can)

+

For data that fits into RAM, pandas can often be easier and more efficient to use than Dask DataFrame. However, Dask DataFrame is a powerful tool for larger-than-memory datasets.

+

When the data is still larger than memory, Dask DataFrame can be used to reduce the larger datasets to a manageable level that pandas can handle. Next, use pandas at that point.

+
+
+

Avoid Full-Data Shuffling

+

Some operations are more expensive to compute in a parallel setting than if they are in-memory on a single machine (for example, set_index or merge). In particular, shuffling operations that rearrange data can become very communication intensive.

+
+
+

pandas performance tips

+

pandas performance tips such as using vectorized operations also apply to Dask DataFrames. See Modern Pandas notebook for more tips on better performance with pandas.

+
+
+

Check Partition Size

+

Similar to chunks, partitions should be small enough that they fit in the memory, but large enough to avoid that the communication overhead.

+
+

blocksize

+
    +

  • The number of partitions can be set using the blocksize argument. +If none is given, the number of partitions/blocksize is calculated depending on the available memory and the number of cores on a machine up to a max of 64 MB. As we increase the blocksize, the number of partitions (calculated by Dask) will decrease. This is especially important when reading one large csv file.

    • +
+

As a good rule of thumb, you should aim for partitions that have around 100MB of data each.

+
+
+
+

Smart use of .compute()

+

Try avoiding running .compute() operation as long as possible. Dask works best when users avoid computation until results are needed. The .compute() command informs Dask to trigger computations on the Dask DataFrame.
+As shown in the above example, the intermediate results can also be shared by calling .compute() only once.

+
+
+

Close your local Dask Cluster

+

It is always a good practice to close the Dask cluster you created.

+
+
+
client.shutdown()
+
+
+
+
+
+
+
+
+

Summary

+

In this notebook, we have learned about:

+
    +

  • Dask DataFrame concept and component.

    • +

  • When to use and when to avoid Dask DataFrames?

    • +

  • How to use Dask DataFrame?

    • +

  • Some best practices around Dask DataFrames.

    • +
+
+
+

Resources and references

+ +
+
+ + + + +
+ +
+ + +
+

previous

+

Dask Array

+
+ + +
+

next

+

Dask Schedulers

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ NCAR +
+
+ Unidata +
+
+ UAlbany +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2023. + + By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk. + + Last updated on 19 October 2023. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/16/notebooks/03-dask-xarray.html b/_preview/16/notebooks/03-dask-xarray.html new file mode 100644 index 0000000..ffaef0c --- /dev/null +++ b/_preview/16/notebooks/03-dask-xarray.html @@ -0,0 +1,11176 @@ + + + + + + + + Parallelizing Xarray with Dask — Dask Cookbook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+ On this page +
+ +
+
+
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+ +
+ +

Dask logo +Xarray

+
+

Parallelizing Xarray with Dask

+
+

In this tutorial, you learn:

+
    +

  • Using Dask with Xarray

    • +

  • Read/write netCDF files with Dask

    • +

  • Dask backed Xarray objects and operations

    • +

  • Extract Dask arrays from Xarray objects and use Dask array directly.

    • +

  • Xarray built-in operations can transparently use dask

    • +
+
+
+

Prerequisites

+ + + + + + + + + + + + + + + + + + + + + +

Concepts

Importance

Notes

Intro to Xarray

Necessary

Dask Arrays

Necessary

Dask DataFrames

Necessary

+
    +

  • Time to learn: 40 minutes

    • +
+
+
+
+

Introduction

+
+

Xarray Quick Overview

+

Xarray is an open-source Python library designed for working with labelled multi-dimensional data. By multi-dimensional data (also often called N-dimensional), we mean data that has many independent dimensions or axes (e.g. latitude, longitude, time). By labelled we mean that these axes or dimensions are associated with coordinate names (like “latitude”) and coordinate labels like “30 degrees North”.

+

Xarray provides pandas-level convenience for working with this type of data.

+

Xarray

+

Image credit: Xarray Contributors

+

The dataset illustrated has two variables (temperature and precipitation) that have three dimensions. Coordinate vectors (e.g., latitude, longitude, time) that describe the data are also included.

+
+

Xarray Data Structures

+

Xarray has two fundamental data structures:

+
    +

  • DataArray : holds a single multi-dimensional variable and its coordinates

    • +

  • Dataset : holds multiple DataArrays that potentially share the same coordinates

    • +
+

Xarray DataArray

+

A DataArray has four essential attributes:

+
    +

  • data: a numpy.ndarray holding the values.

    • +

  • dims: dimension names for each axis (e.g., latitude, longitude, time).

    • +

  • coords: a dict-like container of arrays (coordinates) that label each point (e.g., 1-dimensional arrays of numbers, datetime objects or strings).

    • +

  • attrs: a dictionary to hold arbitrary metadata (attributes).

    • +
+

Xarray DataSet

+

A dataset is simply an object containing multiple Xarray DataArrays indexed by variable name.

+
+
+
+

Xarray can wrap many array types like Numpy and Dask.

+

Let’s start with a random 2D NumPy array, for example this can be SST (sea-surface temperature) values of a domain with dimension of 300x450 grid:

+
+
+
import numpy as np 
+import dask.array as da
+import xarray as xr
+
+xr.set_options(display_expand_data=False);
+
+
+
+
+
+
+
# -- numpy array 
+sst_np = np.random.rand(300,450)
+type(sst_np)
+
+
+
+
+
numpy.ndarray
+
+
+
+
+

As we saw in the previous tutorial, we can convert them to a Dask Array:

+
+
+
sst_da = da.from_array( sst_np)
+sst_da
+
+
+
+
+
+ + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Array Chunk
Bytes 1.03 MiB 1.03 MiB
Shape (300, 450) (300, 450)
Dask graph 1 chunks in 1 graph layer
Data type float64 numpy.ndarray
+ 		+ + + + + + + + + + + + + + + 450 + 300 + +
+
+

This is great and fast! BUT

+
    +

  • What if we want to attach coordinate values to this array?

    • +

  • What if we want to add metadata (e.g. units) to this array?

    • +
+
+
+
# similarly we can convert them to xarray datarray
+sst_xr = xr.DataArray(sst_da)
+sst_xr
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray 'array-4d6b36fc0ec0c63700c2e6e8de28efe1' (dim_0: 300,
+                                                            dim_1: 450)>
+dask.array<chunksize=(300, 450), meta=np.ndarray>
+Dimensions without coordinates: dim_0, dim_1
+
+

A simple DataArray without dimensions or coordinates isn’t much use.

+
+
+
# we can add dimension names to this:
+sst_xr = xr.DataArray(sst_da,dims=['lat','lon'])
+
+sst_xr.dims
+
+
+
+
+
('lat', 'lon')
+
+
+
+
+

We can add our coordinates with values to it :

+
+
+
# -- create some dummy values for lat and lon dimensions
+lat = np.random.uniform(low=-90, high=90, size=300)
+lon = np.random.uniform(low=-180, high=180, size=450)
+
+sst_xr = xr.DataArray(sst_da,
+                      dims=['lat','lon'],
+                      coords={'lat': lat, 'lon':lon},
+                      attrs=dict(
+                        description="Sea Surface Temperature.",
+                        units="degC")
+                     )
+sst_xr
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray 'array-4d6b36fc0ec0c63700c2e6e8de28efe1' (lat: 300, lon: 450)>
+dask.array<chunksize=(300, 450), meta=np.ndarray>
+Coordinates:
+  * lat      (lat) float64 19.99 -61.79 2.061 77.77 ... -74.11 59.84 -82.38
+  * lon      (lon) float64 -30.8 148.4 152.0 164.0 ... 2.168 -42.78 176.7 -32.27
+Attributes:
+    description:  Sea Surface Temperature.
+    units:        degC
+
+

Xarray data structures are a very powerful tool that allows us to use metadata to express different analysis patterns (slicing, selecting, groupby, averaging, and many other things).

+
+

Take Away

+

Xarray DataArray provides a wrapper around arrays, and uses labeled dimensions and coordinates to support metadata-aware operations (e.g. da.sum(dim="time") instead of array.sum(axis=-1))

+

Xarray can wrap dask arrays instead of numpy arrays.

+

This capability turns Xarray into an extremely useful tool for Big Data earth science.

+

With this introduction, let’s start our tutorial on features of Xarray and Dask:

+
+
+

Setup: Spinning up a cluster

+
+
+
from dask.distributed import LocalCluster, Client
+cluster = LocalCluster()
+client = Client(cluster)
+client
+
+
+
+
+
+
+
+

Client

+

Client-f8ab3816-6ebc-11ee-8aa3-6045bd783dc1

+ + + + + + + + + + + + + + + + +
Connection method: Cluster objectCluster type: distributed.LocalCluster
+ Dashboard: http://127.0.0.1:8787/status +
+ + + + + + +
+

Cluster Info

 + +
+ + +
+
+
+
+
+
+
+

Reading data with Dask and Xarray

+
+

Reading multiple netCDF files with open_mfdataset

+

Xarray provides a function called open_dataset function that allows us to load a netCDF dataset into a Python data structure. To read more about this function, please see xarray open_dataset API documentation.

+

Xarray also provides open_mfdataset, which open multiple files as a single xarray dataset. Passing the argument parallel=True will speed up reading multiple datasets by executing these tasks in parallel using Dask Delayed under the hood.

+

In this example, we are going to examine a subset of CESM2 Large Ensemble Data Sets (LENS). We will use 2m temperature (TREFHT) for this analysis.

+

To learn more about LENS dataset, please visit:

+ +

For this tutorial, we only look at a small subset of data. If you don’t have the data, running the following code enables you to download, prepare, and stage the required datasets (../data/ folder) for this cookbook.

+
+
+
!./get_data.sh notebook3
+
+
+
+
+
Downloading https://docs.google.com/uc?export=download&id=1Tbuom1KMCwHjy7-eexEQcOXSr51i6mae
+
+
+
data_for_cesm/
+data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.205501-206412.nc
+
+
+
data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.209501-210012.nc
+
+
+
data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.202501-203412.nc
+
+
+
data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.204501-205412.nc
+
+
+
data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.207501-208412.nc
+
+
+
data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.201501-202412.nc
+
+
+
data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.206501-207412.nc
+
+
+
data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.203501-204412.nc
+
+
+
data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.208501-209412.nc
+
+
+
+
+

We can open up multiple files using open_mfdataset function.

+
+
+

Constructing Xarray Datasets from files

+
+
+
import os
+import glob
+
+var = 'TREFHT'
+
+# find all LENS files for 1 ensemble
+data_dir = '../data/data_for_cesm'
+files = glob.glob(os.path.join(data_dir, 'b.e21.BSSP370smbb.f09_g17.LE2-1301.013*.nc'))
+
+print("All files: [", len(files), "files]")
+
+
+
+
+
All files: [ 9 files]
+
+
+
+
+
+
+
%%time
+ds = xr.open_mfdataset(
+    sorted(files),
+    # concatenate along this dimension
+    concat_dim="time",
+    # concatenate files in the order provided
+    combine="nested",
+    # parallelize the reading of individual files using dask
+    # This means the returned arrays will be dask arrays
+    parallel=True,
+    # these are netCDF4 files, use the h5netcdf package to read them
+    engine="h5netcdf",
+    # hold off on decoding time
+    decode_cf=False,
+    # specify that data should be automatically chunked
+    chunks="auto",
+)
+ds = xr.decode_cf(ds)
+ds
+
+
+
+
+
CPU times: user 406 ms, sys: 39 ms, total: 445 ms
+Wall time: 1.86 s
+
+
+
+ + + + + + + + + + + + + + +
<xarray.Dataset>
+Dimensions:       (lat: 192, zlon: 1, time: 1032, nbnd: 2, lon: 288, lev: 32,
+                   ilev: 33)
+Coordinates:
+  * lat           (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0
+  * zlon          (zlon) float64 0.0
+  * lon           (lon) float64 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8
+  * lev           (lev) float64 3.643 7.595 14.36 24.61 ... 957.5 976.3 992.6
+  * ilev          (ilev) float64 2.255 5.032 10.16 18.56 ... 967.5 985.1 1e+03
+  * time          (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
+Dimensions without coordinates: nbnd
+Data variables: (12/27)
+    zlon_bnds     (time, zlon, nbnd) float64 dask.array<chunksize=(120, 1, 2), meta=np.ndarray>
+    gw            (time, lat) float64 dask.array<chunksize=(120, 192), meta=np.ndarray>
+    hyam          (time, lev) float64 dask.array<chunksize=(120, 32), meta=np.ndarray>
+    hybm          (time, lev) float64 dask.array<chunksize=(120, 32), meta=np.ndarray>
+    P0            (time) float64 ...
+    hyai          (time, ilev) float64 dask.array<chunksize=(120, 33), meta=np.ndarray>
+    ...            ...
+    n2ovmr        (time) float64 dask.array<chunksize=(120,), meta=np.ndarray>
+    f11vmr        (time) float64 dask.array<chunksize=(120,), meta=np.ndarray>
+    f12vmr        (time) float64 dask.array<chunksize=(120,), meta=np.ndarray>
+    sol_tsi       (time) float64 dask.array<chunksize=(120,), meta=np.ndarray>
+    nsteph        (time) int32 dask.array<chunksize=(120,), meta=np.ndarray>
+    TREFHT        (time, lat, lon) float32 dask.array<chunksize=(120, 192, 288), meta=np.ndarray>
+Attributes:
+    Conventions:       CF-1.0
+    source:            CAM
+    case:              b.e21.BSSP370smbb.f09_g17.LE2-1301.013
+    logname:           sunseon
+    host:              mom1
+    initial_file:      b.e21.BHISTsmbb.f09_g17.LE2-1301.013.cam.i.2015-01-01-...
+    topography_file:   /mnt/lustre/share/CESM/cesm_input/atm/cam/topo/fv_0.9x...
+    model_doi_url:     https://doi.org/10.5065/D67H1H0V
+    time_period_freq:  month_1
+
+

For complex scenarios, you can access each file individually by utilizing the open_dataset function with the specified chunks and then combine the outputs into a single dataset later.

+

Note that the “real” values are not displayed, since that would trigger actual computation.

+
+

Xarray automatically wraps Dask Arrays and Dask is lazy, meaning that operations are not computed until we explicitly request them, for example by calling .compute().

+
+

Please see previous notebooks for more information on “lazy evaluation”.

+

The represntation of TREFHT DataArray shows details of chunks and chunk-sizes of Xarray DataArray:

+
+
+
tref = ds.TREFHT
+tref
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray 'TREFHT' (time: 1032, lat: 192, lon: 288)>
+dask.array<chunksize=(120, 192, 288), meta=np.ndarray>
+Coordinates:
+  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
+  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
+  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
+Attributes:
+    units:         K
+    long_name:     Reference height temperature
+    cell_methods:  time: mean
+
+
+
+
tref.chunks
+
+
+
+
+
((120, 120, 120, 120, 120, 120, 120, 120, 72), (192,), (288,))
+
+
+
+
+
    +

  • How many chunks do we have?

    • +

  • What is the size of each chunk size?

    • +
+

Here we can see that we have a total of 9 chunks - equal to the number of our netCDF files. In general open_mfdataset will return one chunk per netCDF file.

+
+

WARNING: The chunk structure within the file is important. When re-chunking the dataset after creation with ds.chunk() it is recommended to only use multiples of the on-file chunk shape.

+
+

We can check what that shape is by looking at the encoding:

+
+
+
tref.encoding
+
+
+
+
+
{'chunksizes': (1, 192, 288),
+ 'fletcher32': False,
+ 'shuffle': True,
+ 'preferred_chunks': {'time': 1, 'lat': 192, 'lon': 288},
+ 'zlib': True,
+ 'complevel': 1,
+ 'source': '/home/runner/work/dask-cookbook/dask-cookbook/data/data_for_cesm/b.e21.BSSP370smbb.f09_g17.LE2-1301.013.cam.h0.TREFHT.201501-202412.nc',
+ 'original_shape': (120, 192, 288),
+ 'dtype': dtype('float32')}
+
+
+
+
+
+

TIP: The chunks parameter can significantly affect total performance when using Dask Arrays. chunks should be small enough that each chunk fit in the memory, but large enough to avoid that the communication overhead.

+

A good rule of thumb is to create arrays with a minimum chunksize of at least one million elements. Here we have 120x192x288 elements in each chunk (except for the last chunk).
+With large arrays (10+ GB), the cost of queuing up Dask operations can be noticeable, and you may need even larger chunksizes.

+

Additional Reading

+ +
+
+

Xarray data structures are Dask collections.

+

This means you can call the following Dask-related functions on Xarray Data Arrays and Datasets:

+
    +

  • .visualize()

    • +

  • .compute()

    • +

  • .persist()

    • +
+

For more information about Dask Arrays, please see Dask Array chapter.

+
+
+
tref_mean = tref.mean('time')
+tref_mean.data.dask
+
+
+
+
+
+
+
+ + + + + + + + + + +
+
+

HighLevelGraph

+

+ HighLevelGraph with 22 layers and 40 keys from all layers. +

+ +
+ + + + + + +
+ +

Layer1: original

+ +

+ original-open_dataset-TREFHT-829692327d0c603dfb15dca2e3dcdc44 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs1
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer2: open_dataset-TREFHT

+ +

+ open_dataset-TREFHT-829692327d0c603dfb15dca2e3dcdc44 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs1
shape(72, 192, 288)
dtypefloat32
chunksize(72, 192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on original-open_dataset-TREFHT-829692327d0c603dfb15dca2e3dcdc44
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 72 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer3: original

+ +

+ original-open_dataset-TREFHT-f86d7279a6eb1e8b525fd433a35f8212 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs1
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer4: open_dataset-TREFHT

+ +

+ open_dataset-TREFHT-f86d7279a6eb1e8b525fd433a35f8212 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs1
shape(120, 192, 288)
dtypefloat32
chunksize(120, 192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on original-open_dataset-TREFHT-f86d7279a6eb1e8b525fd433a35f8212
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 120 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer5: original

+ +

+ original-open_dataset-TREFHT-5dc9996db723cb182026c4f1703afbb0 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs1
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer6: open_dataset-TREFHT

+ +

+ open_dataset-TREFHT-5dc9996db723cb182026c4f1703afbb0 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs1
shape(120, 192, 288)
dtypefloat32
chunksize(120, 192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on original-open_dataset-TREFHT-5dc9996db723cb182026c4f1703afbb0
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 120 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer7: original

+ +

+ original-open_dataset-TREFHT-1bec5697677600ec285823b8894b0aef +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs1
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer8: open_dataset-TREFHT

+ +

+ open_dataset-TREFHT-1bec5697677600ec285823b8894b0aef +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs1
shape(120, 192, 288)
dtypefloat32
chunksize(120, 192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on original-open_dataset-TREFHT-1bec5697677600ec285823b8894b0aef
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 120 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer9: original

+ +

+ original-open_dataset-TREFHT-b376d457af800028faa60fbb881274e7 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs1
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer10: open_dataset-TREFHT

+ +

+ open_dataset-TREFHT-b376d457af800028faa60fbb881274e7 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs1
shape(120, 192, 288)
dtypefloat32
chunksize(120, 192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on original-open_dataset-TREFHT-b376d457af800028faa60fbb881274e7
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 120 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer11: original

+ +

+ original-open_dataset-TREFHT-490e30721b157769a0556664ad692090 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs1
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer12: open_dataset-TREFHT

+ +

+ open_dataset-TREFHT-490e30721b157769a0556664ad692090 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs1
shape(120, 192, 288)
dtypefloat32
chunksize(120, 192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on original-open_dataset-TREFHT-490e30721b157769a0556664ad692090
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 120 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer13: original

+ +

+ original-open_dataset-TREFHT-971435323705299ff3d752a5ef032e55 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs1
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer14: open_dataset-TREFHT

+ +

+ open_dataset-TREFHT-971435323705299ff3d752a5ef032e55 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs1
shape(120, 192, 288)
dtypefloat32
chunksize(120, 192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on original-open_dataset-TREFHT-971435323705299ff3d752a5ef032e55
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 120 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer15: original

+ +

+ original-open_dataset-TREFHT-b78db7e389278ff8df22805186deb05f +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs1
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer16: open_dataset-TREFHT

+ +

+ open_dataset-TREFHT-b78db7e389278ff8df22805186deb05f +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs1
shape(120, 192, 288)
dtypefloat32
chunksize(120, 192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on original-open_dataset-TREFHT-b78db7e389278ff8df22805186deb05f
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 120 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer17: original

+ +

+ original-open_dataset-TREFHT-f5a6416afc1782a1e95d5966b17d38c7 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs1
+ 		+ +
+ +
+
+ +
+ + + + + + +
+ +

Layer18: open_dataset-TREFHT

+ +

+ open_dataset-TREFHT-f5a6416afc1782a1e95d5966b17d38c7 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs1
shape(120, 192, 288)
dtypefloat32
chunksize(120, 192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on original-open_dataset-TREFHT-f5a6416afc1782a1e95d5966b17d38c7
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 120 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer19: concatenate

+ +

+ concatenate-a6c5fea69b791b9738ccc6ae9cc7b754 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs9
shape(1032, 192, 288)
dtypefloat32
chunksize(120, 192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on open_dataset-TREFHT-490e30721b157769a0556664ad692090
open_dataset-TREFHT-829692327d0c603dfb15dca2e3dcdc44
open_dataset-TREFHT-b376d457af800028faa60fbb881274e7
open_dataset-TREFHT-5dc9996db723cb182026c4f1703afbb0
open_dataset-TREFHT-f5a6416afc1782a1e95d5966b17d38c7
open_dataset-TREFHT-1bec5697677600ec285823b8894b0aef
open_dataset-TREFHT-b78db7e389278ff8df22805186deb05f
open_dataset-TREFHT-f86d7279a6eb1e8b525fd433a35f8212
open_dataset-TREFHT-971435323705299ff3d752a5ef032e55
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 1032 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer20: mean_chunk

+ +

+ mean_chunk-ea9b6860fd613ff87e2d51c4fd9ddad3 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedFalse
number of outputs9
shape(1032, 192, 288)
dtypefloat32
chunksize(120, 192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on concatenate-a6c5fea69b791b9738ccc6ae9cc7b754
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 1032 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer21: mean_combine-partial

+ +

+ mean_combine-partial-29ebc4186cd8978740b8f6d09e980557 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs3
shape(3, 192, 288)
dtypefloat32
chunksize(1, 192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on mean_chunk-ea9b6860fd613ff87e2d51c4fd9ddad3
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 3 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer22: mean_agg-aggregate

+ +

+ mean_agg-aggregate-1b12ee8daaf71a9d05ebb4f0b5aeb961 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs1
shape(192, 288)
dtypefloat32
chunksize(192, 288)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on mean_combine-partial-29ebc4186cd8978740b8f6d09e980557
+ 		+ + + + + + + + + + + + + + + 288 + 192 + +
+ +
+
+ +
+
+
+
+

If we check Dask Task Graph for tref_mean, we can see all the steps required for calculating it (from opening the netcdf file to calculating mean and aggreagting it).

+
+
+

Getting concrete values

+

At some point, you will want to actually do the calculations and receive concrete values from Dask.

+

There are two ways to compute values on dask arrays.

+
    +

  1. compute() returns a new xarray object with the data now represented as a numpy array.

    2. +

  2. load() replaces the dask array in the xarray object with a numpy array. Equivalent to ds = ds.compute().

    3. +
+

.load() operates inplace and .compute() returns a new xarray object.

+
+

Distributed non-blocking concrete values

+

There is another option available third option : “persisting”. .persist() loads the values into distributed RAM. The values are computed but remain distributed across workers. So essentially persist turns a lazy Dask collection into a Dask collection where the results are either fully computed or actively computing in the background.

+

So ds.air.persist() is still backed by a Dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory.

+

Read more: Dask user guide

+
+
+
+
+

How to access underlying data in an Xarray object?

+

There are two basic ways to extract values from an Xarray object:

+
    +

  1. Using .data will return a Dask array. For example:

    2. +
+
+
+
tref.data
+
+
+
+
+
+ + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Array Chunk
Bytes 217.69 MiB 25.31 MiB
Shape (1032, 192, 288) (120, 192, 288)
Dask graph 9 chunks in 19 graph layers
Data type float32 numpy.ndarray
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 288 + 192 + 1032 + +
+
+

This means that for Dask-backed Xarray object, we can access the values using .compute

+
+
+
%%time
+
+tref.data.compute()
+
+
+
+
+
CPU times: user 94.3 ms, sys: 225 ms, total: 320 ms
+Wall time: 1.13 s
+
+
+
array([[[248.39987, 248.39989, 248.39992, ..., 248.39989, 248.39989,
+         248.39989],
+        [248.95004, 248.9094 , 248.75017, ..., 248.98384, 248.97466,
+         248.9626 ],
+        [249.20784, 249.17082, 249.15718, ..., 249.42188, 249.37703,
+         249.31105],
+        ...,
+        [251.5821 , 251.6182 , 251.65166, ..., 251.48357, 251.51555,
+         251.54646],
+        [251.34143, 251.35114, 251.36166, ..., 251.30699, 251.32008,
+         251.3312 ],
+        [251.35237, 251.35286, 251.35332, ..., 251.35059, 251.35123,
+         251.35182]],
+
+       [[237.44759, 237.44759, 237.44759, ..., 237.44759, 237.44759,
+         237.44759],
+        [238.10292, 238.05934, 237.8917 , ..., 238.16042, 238.14326,
+         238.12119],
+        [238.86865, 238.82155, 238.79092, ..., 239.1196 , 239.04976,
+         238.96754],
+        ...,
+        [246.23404, 246.25865, 246.28221, ..., 246.15912, 246.18312,
+         246.20715],
+        [246.70511, 246.7112 , 246.71793, ..., 246.68782, 246.69476,
+         246.69987],
+        [247.26158, 247.26067, 247.25984, ..., 247.26494, 247.2637 ,
+         247.26259]],
+
+       [[227.2174 , 227.2174 , 227.2174 , ..., 227.2174 , 227.2174 ,
+         227.2174 ],
+        [227.27689, 227.23364, 227.07358, ..., 227.32939, 227.3166 ,
+         227.2957 ],
+        [227.58324, 227.54382, 227.52094, ..., 227.81204, 227.76472,
+         227.69241],
+        ...,
+        [247.57649, 247.60806, 247.63834, ..., 247.49849, 247.52321,
+         247.54758],
+        [247.34538, 247.35608, 247.36736, ..., 247.30998, 247.32211,
+         247.33403],
+        [247.2048 , 247.20529, 247.20573, ..., 247.20308, 247.20372,
+         247.20428]],
+
+       ...,
+
+       [[230.82976, 230.82976, 230.82976, ..., 230.82976, 230.82976,
+         230.82976],
+        [231.51309, 231.47168, 231.3188 , ..., 231.55086, 231.54416,
+         231.53171],
+        [232.11208, 232.0665 , 232.04721, ..., 232.34473, 232.29143,
+         232.22214],
+        ...,
+        [273.3896 , 273.38998, 273.3906 , ..., 273.3897 , 273.3895 ,
+         273.3894 ],
+        [273.46066, 273.46008, 273.45963, ..., 273.4635 , 273.4624 ,
+         273.46143],
+        [273.5544 , 273.55417, 273.55392, ..., 273.5554 , 273.55505,
+         273.55472]],
+
+       [[240.62163, 240.62163, 240.62163, ..., 240.62163, 240.62163,
+         240.62163],
+        [241.24452, 241.20761, 241.05177, ..., 241.27812, 241.26988,
+         241.2583 ],
+        [241.4827 , 241.44122, 241.42111, ..., 241.70222, 241.65529,
+         241.58728],
+        ...,
+        [271.83112, 271.82843, 271.82672, ..., 271.84128, 271.83743,
+         271.8343 ],
+        [272.06815, 272.06625, 272.06448, ..., 272.07593, 272.07327,
+         272.07053],
+        [272.28296, 272.2825 , 272.2821 , ..., 272.28467, 272.28403,
+         272.28348]],
+
+       [[250.27185, 250.2721 , 250.27223, ..., 250.27199, 250.27199,
+         250.27194],
+        [251.08855, 251.05937, 250.90967, ..., 251.1056 , 251.10072,
+         251.09595],
+        [251.64214, 251.61182, 251.6042 , ..., 251.83585, 251.79689,
+         251.7379 ],
+        ...,
+        [271.2762 , 271.2781 , 271.28018, ..., 271.2714 , 271.27295,
+         271.27457],
+        [271.2889 , 271.28967, 271.2904 , ..., 271.28693, 271.28738,
+         271.28815],
+        [271.26477, 271.26483, 271.2649 , ..., 271.26456, 271.26465,
+         271.2647 ]]], dtype=float32)
+
+
+
+
+
    +

  1. We can also use .values to see the “real” values of Xarray object. Another option is using .to_numpy. Both of these option return the values of underlying Dask object in a numpy array.

    2. +
+
+
+
%%time
+tref.to_numpy()
+
+
+
+
+
CPU times: user 88.9 ms, sys: 229 ms, total: 318 ms
+Wall time: 1.08 s
+
+
+
array([[[248.39987, 248.39989, 248.39992, ..., 248.39989, 248.39989,
+         248.39989],
+        [248.95004, 248.9094 , 248.75017, ..., 248.98384, 248.97466,
+         248.9626 ],
+        [249.20784, 249.17082, 249.15718, ..., 249.42188, 249.37703,
+         249.31105],
+        ...,
+        [251.5821 , 251.6182 , 251.65166, ..., 251.48357, 251.51555,
+         251.54646],
+        [251.34143, 251.35114, 251.36166, ..., 251.30699, 251.32008,
+         251.3312 ],
+        [251.35237, 251.35286, 251.35332, ..., 251.35059, 251.35123,
+         251.35182]],
+
+       [[237.44759, 237.44759, 237.44759, ..., 237.44759, 237.44759,
+         237.44759],
+        [238.10292, 238.05934, 237.8917 , ..., 238.16042, 238.14326,
+         238.12119],
+        [238.86865, 238.82155, 238.79092, ..., 239.1196 , 239.04976,
+         238.96754],
+        ...,
+        [246.23404, 246.25865, 246.28221, ..., 246.15912, 246.18312,
+         246.20715],
+        [246.70511, 246.7112 , 246.71793, ..., 246.68782, 246.69476,
+         246.69987],
+        [247.26158, 247.26067, 247.25984, ..., 247.26494, 247.2637 ,
+         247.26259]],
+
+       [[227.2174 , 227.2174 , 227.2174 , ..., 227.2174 , 227.2174 ,
+         227.2174 ],
+        [227.27689, 227.23364, 227.07358, ..., 227.32939, 227.3166 ,
+         227.2957 ],
+        [227.58324, 227.54382, 227.52094, ..., 227.81204, 227.76472,
+         227.69241],
+        ...,
+        [247.57649, 247.60806, 247.63834, ..., 247.49849, 247.52321,
+         247.54758],
+        [247.34538, 247.35608, 247.36736, ..., 247.30998, 247.32211,
+         247.33403],
+        [247.2048 , 247.20529, 247.20573, ..., 247.20308, 247.20372,
+         247.20428]],
+
+       ...,
+
+       [[230.82976, 230.82976, 230.82976, ..., 230.82976, 230.82976,
+         230.82976],
+        [231.51309, 231.47168, 231.3188 , ..., 231.55086, 231.54416,
+         231.53171],
+        [232.11208, 232.0665 , 232.04721, ..., 232.34473, 232.29143,
+         232.22214],
+        ...,
+        [273.3896 , 273.38998, 273.3906 , ..., 273.3897 , 273.3895 ,
+         273.3894 ],
+        [273.46066, 273.46008, 273.45963, ..., 273.4635 , 273.4624 ,
+         273.46143],
+        [273.5544 , 273.55417, 273.55392, ..., 273.5554 , 273.55505,
+         273.55472]],
+
+       [[240.62163, 240.62163, 240.62163, ..., 240.62163, 240.62163,
+         240.62163],
+        [241.24452, 241.20761, 241.05177, ..., 241.27812, 241.26988,
+         241.2583 ],
+        [241.4827 , 241.44122, 241.42111, ..., 241.70222, 241.65529,
+         241.58728],
+        ...,
+        [271.83112, 271.82843, 271.82672, ..., 271.84128, 271.83743,
+         271.8343 ],
+        [272.06815, 272.06625, 272.06448, ..., 272.07593, 272.07327,
+         272.07053],
+        [272.28296, 272.2825 , 272.2821 , ..., 272.28467, 272.28403,
+         272.28348]],
+
+       [[250.27185, 250.2721 , 250.27223, ..., 250.27199, 250.27199,
+         250.27194],
+        [251.08855, 251.05937, 250.90967, ..., 251.1056 , 251.10072,
+         251.09595],
+        [251.64214, 251.61182, 251.6042 , ..., 251.83585, 251.79689,
+         251.7379 ],
+        ...,
+        [271.2762 , 271.2781 , 271.28018, ..., 271.2714 , 271.27295,
+         271.27457],
+        [271.2889 , 271.28967, 271.2904 , ..., 271.28693, 271.28738,
+         271.28815],
+        [271.26477, 271.26483, 271.2649 , ..., 271.26456, 271.26465,
+         271.2647 ]]], dtype=float32)
+
+
+
+
+
+
+

Computation

+

All built-in Xarray methods (.mean, .max, .rolling, .groupby etc.) support dask arrays.

+

Now, let’s do some computations on this Xarray dataset.

+
+

Single Point Calculations

+

To start out, let’s do the calculations on a single point first. First, we extract the time series data at a grid point and save it to a variable. Here we select the closest point using .sel and load the data.

+
+
+
tref_boulder = tref.sel(lat=40.0150, lon=-105.2705, method='nearest').load()
+
+
+
+
+
+

WARNING: Remember as soon as you call .load() you are telling Dask to trigger computation.

+
+
+
# -- take annual average
+tb = tref_boulder.resample(time='AS').mean()
+tb
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray 'TREFHT' (time: 87)>
+291.8 290.3 290.3 290.1 290.7 291.2 ... 294.3 293.5 293.5 293.4 293.9 289.8
+Coordinates:
+    lat      float64 40.05
+    lon      float64 0.0
+  * time     (time) object 2015-01-01 00:00:00 ... 2101-01-01 00:00:00
+Attributes:
+    units:         K
+    long_name:     Reference height temperature
+    cell_methods:  time: mean
+
+

We can either see the values of our DataArray in the text representation above or by plotting it:

+
+
+
tb.plot()
+
+
+
+
+
[<matplotlib.lines.Line2D at 0x7f0e68d89db0>]
+
+
+../_images/8bc4b912a3d759f27fd30727dd6bfca7f132caed4b92d2fe905854a33760d874.png +
+
+
+
+

Calculations over all grids

+
+
+
# change the unit from Kelvin to degree Celsius 
+tref_c = tref - 273.15
+tref_c
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray 'TREFHT' (time: 1032, lat: 192, lon: 288)>
+dask.array<chunksize=(120, 192, 288), meta=np.ndarray>
+Coordinates:
+  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
+  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
+  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
+
+
+
+
%%time 
+
+tref_c = tref_c.load()
+
+
+
+
+
CPU times: user 98.5 ms, sys: 235 ms, total: 333 ms
+Wall time: 1.13 s
+
+
+
+
+
+
+
%%time
+
+# Compute monthly anomaly
+
+# -- 1. calculate monthly average
+tref_grouped = tref.groupby('time.month')
+tmean = tref_grouped.mean(dim='time')
+
+#-- 2. calculate monthly anomaly
+tos_anom = tref_grouped - tmean
+tos_anom
+
+
+
+
+
CPU times: user 52.6 ms, sys: 0 ns, total: 52.6 ms
+Wall time: 52.6 ms
+
+
+
/usr/share/miniconda3/envs/dask-cookbook/lib/python3.10/site-packages/xarray/core/indexing.py:1446: PerformanceWarning: Slicing with an out-of-order index is generating 86 times more chunks
+  return self.array[key]
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray 'TREFHT' (time: 1032, lat: 192, lon: 288)>
+dask.array<chunksize=(1, 192, 288), meta=np.ndarray>
+Coordinates:
+  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
+  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
+  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
+    month    (time) int64 2 3 4 5 6 7 8 9 10 11 12 ... 3 4 5 6 7 8 9 10 11 12 1
+
+
+
+
%%time
+computed_anom = tos_anom.load()
+type(computed_anom)
+
+
+
+
+
CPU times: user 2.55 s, sys: 317 ms, total: 2.86 s
+Wall time: 5.89 s
+
+
+
xarray.core.dataarray.DataArray
+
+
+
+
+
+
+
tos_anom.sel(lon=310, lat=50, method='nearest').plot( size=4)
+
+
+
+
+
[<matplotlib.lines.Line2D at 0x7f0e60fb3310>]
+
+
+../_images/7399b91edaf79a827264bf510c3982b92ecd9683536efc4efd3bb51aaf10c3bc.png +
+
+
+
+
tos_anom.sel(time='2030-01-01').plot()
+
+
+
+
+
<matplotlib.collections.QuadMesh at 0x7f0e6082ed70>
+
+
+../_images/bb55cc8c334bd8d58adacbb04b3b14d8198d830abb3c613abc2d4b31a85ab222.png +
+
+
+

TIP: Using Xarray plotting functionality automatically triggers computations on the Dask Array, similar to .compute().

+

We can do more complex calculations too:

+
+
+
rolling_mean = tref.rolling(time=5).mean()
+rolling_mean  # contains dask array
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray 'TREFHT' (time: 1032, lat: 192, lon: 288)>
+dask.array<chunksize=(124, 192, 288), meta=np.ndarray>
+Coordinates:
+  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
+  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
+  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
+Attributes:
+    units:         K
+    long_name:     Reference height temperature
+    cell_methods:  time: mean
+
+
+
+
timeseries = rolling_mean.isel(lon=1, lat=20)  # no activity on dashboard
+timeseries  # contains dask array
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray 'TREFHT' (time: 1032)>
+dask.array<chunksize=(124,), meta=np.ndarray>
+Coordinates:
+    lat      float64 -71.15
+    lon      float64 1.25
+  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
+Attributes:
+    units:         K
+    long_name:     Reference height temperature
+    cell_methods:  time: mean
+
+
+
+
computed = rolling_mean.compute()  # activity on dashboard
+computed  # has real numpy values
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray 'TREFHT' (time: 1032, lat: 192, lon: 288)>
+nan nan nan nan nan nan nan nan ... 273.4 273.4 273.4 273.4 273.4 273.4 273.4
+Coordinates:
+  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
+  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
+  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
+Attributes:
+    units:         K
+    long_name:     Reference height temperature
+    cell_methods:  time: mean
+
+
+
+

Supplementary Material: Advanced workflows and automatic parallelization using apply_ufunc

+

Most of xarray’s built-in operations work on Dask arrays. If you want to use a function that isn’t wrapped by Xarray to work with Dask, one option is to extract Dask arrays from xarray objects (.data) and use Dask directly.

+

Another option is to use xarray’s apply_ufunc() function. xr.apply_ufunc() can automate embarrassingly parallel “map” type operations where a function written for processing NumPy arrays, but we want to apply it on our Xarray DataArray.

+

xr.apply_ufunc() give users capability to run custom-written functions such as parameter calculations in a parallel way. See the Xarray tutorial material on apply_ufunc for more.

+

In the example below, we calculate the saturation vapor pressure by using apply_ufunc() to apply this function to our Dask Array chunk by chunk.

+
+
+
import numpy as np
+
+def sat_p(t):
+    """Calculate saturation vapor pressure using Clausius-Clapeyron equation"""
+    return 0.611 * np.exp(17.67 * (t-273.15)*((t-29.65)**(-1)))
+
+
+
+
+
+
+
es = xr.apply_ufunc(sat_p, tref, dask="parallelized", output_dtypes=[float])
+es
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray 'TREFHT' (time: 1032, lat: 192, lon: 288)>
+dask.array<chunksize=(120, 192, 288), meta=np.ndarray>
+Coordinates:
+  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
+  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
+  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
+
+
+
+
es.compute()
+
+
+
+
+
+ + + + + + + + + + + + + + +
<xarray.DataArray 'TREFHT' (time: 1032, lat: 192, lon: 288)>
+0.08275 0.08275 0.08275 0.08275 0.08275 ... 0.5323 0.5323 0.5323 0.5323 0.5323
+Coordinates:
+  * lat      (lat) float64 -90.0 -89.06 -88.12 -87.17 ... 87.17 88.12 89.06 90.0
+  * lon      (lon) float64 0.0 1.25 2.5 3.75 5.0 ... 355.0 356.2 357.5 358.8
+  * time     (time) object 2015-02-01 00:00:00 ... 2101-01-01 00:00:00
+
+

The data used for this tutorial is from one ensemble member. What if we want to use multiple ensemble members? So far, we only run on one machine, what if we run an HPC cluster? We will go over this in the next tutorial.

+
+
+

Dask + Xarray Good Practices

+
+

Summary of Dask + Xarray Good Practices

+

The good practices regarding Dask + Xarray is the same as the good practices for Dask only.

+
    +

  • Similar to Dask DataFrames, it is more efficient to first do spatial and temporal indexing (e.g. .sel() or .isel()) and filter the dataset early in the pipeline, especially before calling resample() or groupby().

    • +

  • Chunk sizes should be small enough to fit into the memory at once but large enough to avoid the additional communication overhead. Good chunk size ~100 MB.

    • +

  • It is always better to chunk along the time dimension.

    • +

  • Avoid too many tasks since each task will introduce 1ms of overhead.

    • +

  • When possible, use xr.apply_ufunc to apply an unvectorized function to the Xarray object.

    • +
+
+
+
+

Close you local Dask Cluster

+

It is always a good practice to close the Dask cluster you created.

+
+
+
client.shutdown()
+
+
+
+
+
+
+
+

Summary

+

In this notebook, we have learned about:

+
    +

  • Using Dask with Xarray

    • +

  • Read/write netCDF files with Dask

    • +

  • Dask backed Xarray objects and operations

    • +

  • Extract Dask arrays from Xarray objects and use Dask array directly…

    • +

  • Customized workflows using apply_ufunc

    • +
+
+
+

Resources and references

+ +
+

Ask for help

+ +
+
+
+ + + + +
+ +
+ + +
+

previous

+

Dask Schedulers

+
+ +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ NCAR +
+
+ Unidata +
+
+ UAlbany +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2023. + + By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk. + + Last updated on 19 October 2023. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/16/notebooks/04-dask-cluster.html b/_preview/16/notebooks/04-dask-cluster.html new file mode 100644 index 0000000..990e273 --- /dev/null +++ b/_preview/16/notebooks/04-dask-cluster.html @@ -0,0 +1,1511 @@ + + + + + + + + Dask Schedulers — Dask Cookbook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
+ + + + + +
+
+
+
+ + + + +
+ + +
+ + + + + + +
+
+ + + +
+ +
+ + + +
+
+ + + + + + + +
+ +
+ + + + + + +
+
+ +
+ + +
+ +
+ On this page +
+ +
+
+
+
+
+ +
+ +

Dask logo

+
+

Dask Schedulers

+
+

In this tutorial, you learn:

+
    +

  • Components of Dask Schedulers

    • +

  • Types of Dask Schedulers

    • +

  • Single Machine Schedulers

    • +
+

Related Documentation

+ +
+
+

Introduction

+

As we mentioned in our Dask overview, Dask is composed of two main parts:

+
    +

  1. Dask Collections (APIs)

    2. +

  2. Dynamic Task Scheduling

    3. +
+

So far, we have talked about different Dask collections, but in this tutorial we are going to talk more about the second part.

+
+
+

The Dask scheduler - our task orchestrator

+

The Dask.distributed task scheduler is a centralized, dynamic system that coordinates the efforts of various dask worker processes spread accross different machines.

+

When a computational task is submitted, the Dask distributed scheduler sends it off to a Dask cluster - simply a collection of Dask workers. A worker is typically a separate Python process on either the local host or a remote machine.

+

To perform work, a scheduler must be assigned resources in the form of a Dask cluster. The cluster consists of the following components:

+
    +

  • scheduler : A scheduler creates and manages task graphs and distributes tasks to workers.

    • +

  • workers : A worker is typically a separate Python process on either the local host or a remote machine. A Dask cluster usually consists of many workers. Basically, a worker is a Python interpretor which will perform work on a subset of our dataset.

    • +

  • client - A high-level interface that points to the scheduler (often local but not always). A client serves as the entry point for interacting with a Dask scheduler.

    • +
+

Dask Distributed Cluster

+

Image credit: Dask Contributors

+
+
+

Schedulers

+

Dask essentially offers two types of schedulers:

+High level collections are used to generate task graphs which can be executed by schedulers on a single machine or a cluster. +

Image credit: Dask Contributors

+
+

1. Single machine scheduler

+
    +

  • The Single-machine Scheduler schedules tasks and manages the execution of those tasks on the same machine where the scheduler is running.

    • +

  • It is designed to be used in situations where the amount of data or the computational requirements are too large for a single process to handle, but not large enough to warrant the use of a cluster of machines.

    • +

  • It is relatively simple and cheap to use but it does not scale as it only runs on a single machine.

    • +
+

Single machine scheduler is the default choice used by Dask.

+

In Dask, there are several types of single machine schedulers that can be used to schedule computations on a single machine:

+
+

1.1. Single-threaded scheduler

+

This scheduler runs all tasks serially on a single thread.
+This is only useful for debugging and profiling, but does not have any parallelization.

+
+
+

1.2. Threaded scheduler

+

The threaded scheduler uses a pool of local threads to execute tasks concurrently.
+This is the default scheduler for Dask, and is suitable for most use cases on a single machine. Multithreading works well for Dask Array and Dask DataFrame.

+

To select one of the above scheduler for your computation, you can specify it when doing .compute():

+

For example:

+
this.compute(scheduler="single-threaded")  # for debugging and profiling only
+
+
+

As mentioned above the threaded scheduler is the default scheduler in Dask. But you can set the default scheduler to Single-threaded or multi-processing by:

+
import dask
+dask.config.set(scheduler='synchronous')  # overwrite default with single-threaded scheduler
+
+
+

Multi-processing works well for pure Python code - delayed functions and operations on Dask Bags.

+

Let’s compare the performance of each of these single-machine schedulers:

+
+
+
import numpy as np
+import dask.array as da
+
+
+
+
+
+
+
%%time
+## - numpy performance
+xn = np.random.normal(10, 0.1, size=(20_000, 20_000))
+yn = xn.mean(axis=0)
+yn
+
+
+
+
+
CPU times: user 12.1 s, sys: 356 ms, total: 12.5 s
+Wall time: 12.5 s
+
+
+
array([10.00004743, 10.00008111,  9.99959931, ...,  9.99941312,
+       10.00033013, 10.00073954])
+
+
+
+
+
+
+
%%time
+# -- dask array using the default
+xd = da.random.normal(10, 0.1, size=(20_000, 20_000), chunks=(2000, 2000))
+yd = xd.mean(axis=0)
+yd.compute()
+
+
+
+
+
CPU times: user 12.2 s, sys: 32 ms, total: 12.2 s
+Wall time: 6.5 s
+
+
+
array([ 9.99899053,  9.99985628, 10.00021032, ..., 10.00070013,
+        9.99904243,  9.99906142])
+
+
+
+
+
+
+
import time
+# -- dask testing different schedulers:
+for sch in ['threading', 'processes', 'sync']:
+    t0 = time.time()
+    r = yd.compute(scheduler=sch)
+    t1 = time.time()
+    print(f"{sch:>10} :  {t1 - t0:0.4f} s")
+
+
+
+
+
 threading :  6.1124 s
+
+
+
 processes :  6.7396 s
+
+
+
      sync :  12.0132 s
+
+
+
+
+
+
+
yd.dask
+
+
+
+
+
+
+
+ + + + + + + + + + +
+
+

HighLevelGraph

+

+ HighLevelGraph with 4 layers and 240 keys from all layers. +

+ +
+ + + + + + +
+ +

Layer1: normal

+ +

+ normal-a82fa71bd70c8bcb14e565ab76ef95fa +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs100
shape(20000, 20000)
dtypefloat64
chunksize(2000, 2000)
typedask.array.core.Array
chunk_typenumpy.ndarray
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 20000 + 20000 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer2: mean_chunk

+ +

+ mean_chunk-ad1cb98fda907dd881125f8dd7045fc8 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeBlockwise
is_materializedTrue
number of outputs100
shape(20000, 20000)
dtypefloat64
chunksize(2000, 2000)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on normal-a82fa71bd70c8bcb14e565ab76ef95fa
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 20000 + 20000 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer3: mean_combine-partial

+ +

+ mean_combine-partial-613b978cd49ee4d7c599d323b2b7276c +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs30
shape(3, 20000)
dtypefloat64
chunksize(1, 2000)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on mean_chunk-ad1cb98fda907dd881125f8dd7045fc8
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + + + 20000 + 3 + +
+ +
+
+ +
+ + + + + + +
+ +

Layer4: mean_agg-aggregate

+ +

+ mean_agg-aggregate-4576951a236ee4fc982da8779275bd67 +

+ + + + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
layer_typeMaterializedLayer
is_materializedTrue
number of outputs10
shape(20000,)
dtypefloat64
chunksize(2000,)
typedask.array.core.Array
chunk_typenumpy.ndarray
depends on mean_combine-partial-613b978cd49ee4d7c599d323b2b7276c
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + 20000 + 1 + +
+ +
+
+ +
+
+
+
+
+
+
yd
+
+
+
+
+
+ + + + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Array Chunk
Bytes 156.25 kiB 15.62 kiB
Shape (20000,) (2000,)
Dask graph 10 chunks in 4 graph layers
Data type float64 numpy.ndarray
+ 		+ + + + + + + + + + + + + + + + + + + + + + + + 20000 + 1 + +
+
+
    +

  • Notice how sync scheduler takes almost the same time as pure NumPy code.

    • +

  • Why is the multiprocessing scheduler so much slower?

    • +
+

If you use the multiprocessing backend, all communication between processes still needs to pass through the main process because processes are isolated from other processes. This introduces a large overhead.

+

The Dask developers recommend using the Dask Distributed Scheduler which we will cover now.

+
+
+
+

2. Distributed scheduler

+
    +

  • The Distributed scheduler or dask.distributed schedules tasks and manages the execution of those tasks on workers from a single or multiple machines.

    • +

  • This scheduler is more sophisticated and offers more features including a live diagnostic dashboard which provides live insight on performance and progress of the calculations.

    • +
+

In most cases, dask.distributed is preferred since it is very scalable, and provides and informative interactive dashboard and access to more complex Dask collections such as futures.

+
+

2.1. Local Cluster

+

A Dask Local Cluster refers to a group of worker processes that run on a single machine and are managed by a single Dask scheduler.

+

This is useful for situations where the computational requirements are not large enough to warrant the use of a full cluster of separate machines. It provides an easy way to run parallel computations on a single machine, without the need for complex cluster management or other infrastructure.

+
+
Let’s start by creating a Local Cluster
+

For this we need to set up a LocalCluster using dask.distributed and connect a client to it.

+
+
+
from dask.distributed import LocalCluster, Client
+
+cluster = LocalCluster()
+client = Client(cluster)
+client
+
+
+
+
+
+
+
+

Client

+

Client-228209b4-6ebd-11ee-8aec-6045bd783dc1

+ + + + + + + + + + + + + + + + +
Connection method: Cluster objectCluster type: distributed.LocalCluster
+ Dashboard: http://127.0.0.1:8787/status +
+ + + + + + +
+

Cluster Info

 + +
+ + +
+
+
+

☝️ Click the Dashboard link above.

+

👈 Or click the “Search” 🔍 button in the dask-labextension dashboard.

+

If no arguments are specified in LocalCluster it will automatically detect the number of CPU cores your system has and the amount of memory and create workers to appropriately fill that.

+

A LocalCluster will use the full resources of the current JupyterLab session. For example, if you used BinderHub, it will use the number of CPUs selected.

+

Note that LocalCluster() takes a lot of optional arguments, allowing you to configure the number of processes/threads, memory limits and other settings.

+

You can also find your cluster dashboard link using :

+
+
+
cluster.dashboard_link
+
+
+
+
+
'http://127.0.0.1:8787/status'
+
+
+
+
+
+
+
%%time
+# -- dask array using the default
+xd = da.random.normal(10, 0.1, size=(30_000, 30_000), chunks=(3000, 3000))
+yd = xd.mean(axis=0)
+yd.compute()
+
+
+
+
+
CPU times: user 676 ms, sys: 51.1 ms, total: 727 ms
+Wall time: 15.8 s
+
+
+
array([ 9.99983726, 10.00059396,  9.99900242, ...,  9.99956259,
+        9.99941681,  9.99957203])
+
+
+
+
+

Always remember to close your local Dask cluster:

+
+
+
client.shutdown()
+
+
+
+
+
+
+
+
+

Dask Distributed (Cluster Managers)

+

So far we have talked about running a job on a local machine.

+

Dask can be deployed on distributed infrastructure, such as a an HPC system or a cloud computing system.

+High level collections are used to generate task graphs which can be executed by schedulers on a single machine or a cluster. +

Image credit: Dask Contributors

+

Dask Cluster Managers have different names corresponding to different computing environments. Some examples are dask-jobqueue for your HPC systems (including PBSCluster) or Kubernetes Cluster for machines on the Cloud.

+

The NCAR tutorial series includes an in-depth exploration and practical use cases of Dask on HPC systems and best practices for Dask on HPC. For the complete set of NCAR tutorial materials, please refer to the main NCAR tutorial content available here.

+

For more information visit the Dask Docs.

+
+
+
+ + + + +
+ +
+ + +
+

previous

+

Dask DataFrame

+
+ + +
+

next

+

Parallelizing Xarray with Dask

+
+ + +
+
+
+
+ +
+
+ + + +
+
+ +
+ +
+
+
+
+ NCAR +
+
+ Unidata +
+
+ UAlbany +
+
+
+
+
+ +
+ +
+ +
+
+
+
+

+ © 2023. + + By Negin Sobhani, Brian Vanderwende, Deepak Cherian, and Ben Kirk. + + Last updated on 19 October 2023. +

+
+
+
+
+ +
+ +
+ +
+
+ + \ No newline at end of file diff --git a/_preview/16/notebooks/how-to-cite.html b/_preview/16/notebooks/how-to-cite.html new file mode 100644 index 0000000..b0cadc5 --- /dev/null +++ b/_preview/16/notebooks/how-to-cite.html @@ -0,0 +1,438 @@ + + + + + + + + How to Cite This Cookbook — Dask Cookbook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + +
+
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+ + +
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+ + + +
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+ + +
+ +
+
+
+
+
+ +
+ +
+

How to Cite This Cookbook

+

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

+

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

+

DOI

+
+ + + + +
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+ + \ No newline at end of file diff --git a/_preview/16/objects.inv b/_preview/16/objects.inv new file mode 100644 index 0000000..e9e82ec --- /dev/null +++ b/_preview/16/objects.inv @@ -0,0 +1,7 @@ +# Sphinx inventory version 2 +# Project: Python +# Version: +# The remainder of this file is compressed using zlib. +xڅN0D +NܪQˁ/U'lizl+6Cdfff+~X +9A1v"Wa/]d4phvQ^g'/Q= FH5v6/x)6YsK[ouGVQ}xUDoE4{OG +/Tܭfd.!D cͼ- 4ʈq@Y؊boƆbqeo.l@ҌgK煪 2 o \ No newline at end of file diff --git a/_preview/16/search.html b/_preview/16/search.html new file mode 100644 index 0000000..0aeb653 --- /dev/null +++ b/_preview/16/search.html @@ -0,0 +1,413 @@ + + + + + + + + Search — Dask Cookbook + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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