diff --git a/documentation/5/.buildinfo b/documentation/5/.buildinfo index 955bbe69f..cd1b111cc 100644 --- a/documentation/5/.buildinfo +++ b/documentation/5/.buildinfo @@ -1,4 +1,4 @@ # Sphinx build info version 1 # This file hashes the configuration used when building these files. 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@@ -35,7 +35,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/documentation/5/_downloads/f57116a727438d4e47eb66f1b799aa48/superspike_demo.ipynb b/documentation/5/_downloads/f57116a727438d4e47eb66f1b799aa48/superspike_demo.ipynb index 8263a6d20..0c238c92e 100644 --- a/documentation/5/_downloads/f57116a727438d4e47eb66f1b799aa48/superspike_demo.ipynb +++ b/documentation/5/_downloads/f57116a727438d4e47eb66f1b799aa48/superspike_demo.ipynb @@ -35,7 +35,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.11.5" } }, "nbformat": 4, diff --git a/documentation/5/_images/tutorials_1_neurons_19_0.png b/documentation/5/_images/tutorials_1_neurons_19_0.png deleted file mode 100644 index 38e5b1c93..000000000 Binary files a/documentation/5/_images/tutorials_1_neurons_19_0.png and /dev/null differ diff --git a/documentation/5/_images/tutorials_2_synapses_28_0.png b/documentation/5/_images/tutorials_2_synapses_28_0.png deleted file mode 100644 index 8dcf88146..000000000 Binary files a/documentation/5/_images/tutorials_2_synapses_28_0.png and /dev/null differ diff --git a/documentation/5/_sources/source/pygenn.rst.txt b/documentation/5/_sources/source/pygenn.rst.txt index b2295566a..8e1e7a1e6 100644 --- a/documentation/5/_sources/source/pygenn.rst.txt +++ b/documentation/5/_sources/source/pygenn.rst.txt @@ -41,6 +41,14 @@ pygenn.custom\_update\_models module :undoc-members: :show-inheritance: +pygenn.deprecated module +------------------------ + +.. automodule:: pygenn.deprecated + :members: + :undoc-members: + :show-inheritance: + pygenn.genn\_groups module -------------------------- diff --git a/documentation/5/_sources/tutorials/1_neurons.ipynb.txt b/documentation/5/_sources/tutorials/1_neurons.ipynb.txt deleted file mode 100644 index 83164b1d9..000000000 --- a/documentation/5/_sources/tutorials/1_neurons.ipynb.txt +++ /dev/null @@ -1,334 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "lGa0_oLb61zz" - }, - "source": [ - "# Defining populations of neurons\n", - "In this tutorial we're going to define a population of Izhikevich neurons and configure individual neurons within it to operate in various regimes:\n", - "![image.png](data:image/png;base64,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)\n", - "\n", - "(Electronic version of the figure and reproduction permissions are freely available at www.izhikevich.com)\n", - "## Install PyGeNN wheel from Google Drive\n", - "Download wheel file" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t2ihZLXh5VD-", - "outputId": "510653d0-3172-4c5f-c101-1bfe66297121" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n", - "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n", - "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n", - "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n", - "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n", - "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n", - "Downloading...\n", - "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n", - "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n", - "100% 8.29M/8.29M [00:00<00:00, 118MB/s]\n", - "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n", - "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n", - "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n", - "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n", - "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n", - "env: CUDA_PATH=/usr/local/cuda\n" - ] - } - ], - "source": [ - "if \"google.colab\" in str(get_ipython()):\n", - " #import IPython\n", - " #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n", - " #%run \"../install_collab.ipynb\"\n", - " !pip install gdown --upgrade\n", - " !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n", - " !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n", - " %env CUDA_PATH=/usr/local/cuda" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8GngV4fThkhM" - }, - "source": [ - "## Build model\n", - "Import numpy, matplotlib and the main `GeNNModel` class from PyGeNN" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "q6WNelXsbjy1" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pygenn import GeNNModel" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "261uLnJsgyeE" - }, - "source": [ - "Create a new model called \"tutorial1\" with floating point precision and set the simulation timestep to 0.1ms" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "EDpiDOK0gkEz" - }, - "outputs": [], - "source": [ - "model = GeNNModel(\"float\", \"tutorial1\")\n", - "model.dt = 0.1" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LrfXpMqfjRBe" - }, - "source": [ - "Configure initial state for a population of Izhikevich neurons with a constant value for the `V` and `U` state variables and different values for the `a`, `b`, `c` and `d` parameters (because we are going to be using the `IzhikevichVariable` model, the parameters are also implemented as state variables so they can vary across the population of neurons)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "tU2M4MgFjRae" - }, - "outputs": [], - "source": [ - "izk_init = {\"V\": -65.0,\n", - " \"U\": -20.0,\n", - " \"a\": [0.02, 0.1, 0.02, 0.02],\n", - " \"b\": [0.2, 0.2, 0.2, 0.2],\n", - " \"c\": [-65.0, -65.0, -50.0, -55.0],\n", - " \"d\": [8.0, 2.0, 2.0, 4.0]}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YrOQPgYBjuym" - }, - "source": [ - "Add a population of 4 of these neurons (GeNN's built in models are selected by specifying model as a string)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "zc-e5Lu2j_Yq" - }, - "outputs": [], - "source": [ - "pop = model.add_neuron_population(\"Neurons\", 4, \"IzhikevichVariable\", {}, izk_init)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "u8wu06PZkBnS" - }, - "source": [ - "Add a DC (i.e. constant) current input to the population to inject a constant current into the neurons and make them spike\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "GNBjEGWPj_3Q" - }, - "outputs": [], - "source": [ - "model.add_current_source(\"CurrentSource\", \"DC\", pop, {\"amp\": 10.0}, {});" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IGKUIiaGkA0Z" - }, - "source": [ - "Generate code and load it into PyGeNN" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "d0mK72rYkiYe" - }, - "outputs": [], - "source": [ - "model.build()\n", - "model.load()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cNs18ywkkq6T" - }, - "source": [ - "# Simulate tutorial model\n", - "State variables in the GeNN model can be accessed directly using memory views. Create a memory view to access the membrane voltage of our neurons" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "nWFVfYfdkobN" - }, - "outputs": [], - "source": [ - "voltage = pop.vars[\"V\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wv-hDOIe3Hgy" - }, - "source": [ - "We want to record these voltages for each neuron every timestep so, after every we simulate each time step, we copy the membrane voltage back from the GPU and add a copy (because the memory view gives access to the actual simulator state we need to make a copy) to a list" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "99MBe7JKk5Ut" - }, - "outputs": [], - "source": [ - "voltages = []\n", - "while model.t < 200.0:\n", - " model.step_time()\n", - " voltage.pull_from_device()\n", - " voltages.append(voltage.values)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ug6S1h-z3k7v" - }, - "source": [ - "Plot the voltages over time in 4 seperate panels" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 718 - }, - "id": "RsVbAbIPlEO8", - "outputId": "731335aa-f7da-4490-fae4-daa33b98f92b", - "scrolled": true - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "# Stack voltages together into a 2000x4 matrix\n", - "voltages = np.vstack(voltages)\n", - "\n", - "# Create figure with 4 axes\n", - "fig, axes = plt.subplots(4, sharex=True, figsize=(15, 8))\n", - "\n", - "# Plot voltages of each neuron in\n", - "for i, t in enumerate([\"RS\", \"FS\", \"CH\", \"IB\"]):\n", - " axes[i].set_title(t)\n", - " axes[i].set_ylabel(\"V [mV]\")\n", - " axes[i].plot(np.arange(0.0, 200.0, 0.1), voltages[:,i])\n", - "\n", - "axes[-1].set_xlabel(\"Time [ms]\");" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "h4yw3JiNpXOM" - }, - "source": [ - "Exercises\n", - "---\n", - "1. Add three more neurons with the remaining neuron types: Thalamo-cortical, resonator, and low-threshold spiking.\n", - "2. Make a neuron that changes its type gradually from the beginning to the end of the simulation. Use a longer simulation time to make this meaningful." - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "name": "1_neurons", - "provenance": [], - "gpuType": "T4" - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/documentation/5/_sources/tutorials/2_synapses.ipynb.txt b/documentation/5/_sources/tutorials/2_synapses.ipynb.txt deleted file mode 100644 index 081dc4173..000000000 --- a/documentation/5/_sources/tutorials/2_synapses.ipynb.txt +++ /dev/null @@ -1,466 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "lGa0_oLb61zz" - }, - "source": [ - "# Tutorial 2 - synapses\n", - "This tutorial explains how to add synapses to connect the neuron populations we talked about in the previous tutorial into a balanced random network model.\n", - "\n", - "## Install PyGeNN wheel from Google Drive\n", - "Download wheel file" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t2ihZLXh5VD-", - "outputId": "462667f0-6335-4203-d1e1-7ca16b76806b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n", - "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n", - "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n", - "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n", - "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n", - "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n", - "Downloading...\n", - "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n", - "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n", - "100% 8.29M/8.29M [00:00<00:00, 98.5MB/s]\n", - "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n", - "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n", - "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n", - "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n", - "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n", - "env: CUDA_PATH=/usr/local/cuda\n" - ] - } - ], - "source": [ - "if \"google.colab\" in str(get_ipython()):\n", - " #import IPython\n", - " #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n", - " #%run \"../install_collab.ipynb\"\n", - " !pip install gdown --upgrade\n", - " !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n", - " !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n", - " %env CUDA_PATH=/usr/local/cuda" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8GngV4fThkhM" - }, - "source": [ - "Import numpy, matplotlib and the main `GeNNModel` class from PyGeNN" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "q6WNelXsbjy1" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pygenn import GeNNModel, init_postsynaptic, init_sparse_connectivity, init_var, init_weight_update" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "261uLnJsgyeE" - }, - "source": [ - "## Build model\n", - "Create a new model called \"tutorial2\" with floating point precision and set the simulation timestep to 1ms" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "EDpiDOK0gkEz" - }, - "outputs": [], - "source": [ - "model = GeNNModel(\"float\", \"tutorial2\")\n", - "model.dt = 1.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mki7b8R8xhAv" - }, - "source": [ - "For this tutorial were going to use Leaky-Integrate-and-Fire neurons which have the following dynamics:\n", - "\n", - "\\begin{align}\n", - " \\tau_{\\text{m}} \\frac{dV_{i}}{dt} = & (V_{\\text{rest}} - V_{i}) + R_{\\text{m}}I_{i}.\n", - "\\end{align}\n", - "\n", - "We configure these using the parameters from (Vogels & Abbott, 2005 [link text](https://doi.org/10.1523/JNEUROSCI.3508-05.2005)). Note that the resting voltage is **higher** than the reset to provide a constant current input **TODO** get rid of this" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "AkMk7Ml4tOxM" - }, - "outputs": [], - "source": [ - "lif_params = {\"C\": 1.0, \"TauM\": 20.0, \"Vrest\": -49.0, \"Vreset\": -60.0,\n", - " \"Vthresh\": -50.0, \"Ioffset\": 0.0, \"TauRefrac\": 5.0}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XboW6qxrxnok" - }, - "source": [ - "So that the network starts in a non-pathological state, we want to randomly initialise the neuron's membrane potentials so that they are between their threshold and resting potentials. GeNN provides [various](https://genn-team.github.io/genn/documentation/4/html/d4/dc6/sectVariableInitialisation.html) initialisation \"snippets\" which can be used to parallelise variable initialisation but, here we are going to use `Uniform` to sample values from a uniform distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "dWf4f4Bpxl7u" - }, - "outputs": [], - "source": [ - "lif_init = {\"V\": init_var(\"Uniform\", {\"min\": -60.0, \"max\": -50.0}),\n", - " \"RefracTime\": 0.0}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "B3hhcDILxeki" - }, - "source": [ - "For this tutorial we create an excitary and inhibitory population of these neurons and we enable spike recording for both" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "5AECcjzMs8Iz" - }, - "outputs": [], - "source": [ - "exc_pop = model.add_neuron_population(\"E\", 3200, \"LIF\", lif_params, lif_init)\n", - "inh_pop = model.add_neuron_population(\"I\", 800, \"LIF\", lif_params, lif_init)\n", - "\n", - "exc_pop.spike_recording_enabled = True\n", - "inh_pop.spike_recording_enabled = True" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QypcRqLi0hgq" - }, - "source": [ - "So this network sits in a asynchronous irregular state, we initialise the inhibitory weights as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "id": "WpmzQu0UuPky" - }, - "outputs": [], - "source": [ - "exc_synapse_init = {\"g\": 0.0008}\n", - "inh_synapse_init = {\"g\": -0.0102}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "58kevKNm0rfi" - }, - "source": [ - "We are going to use an exponential synapse model where a single time constant $\\tau_{\\text{syn}}$ to define it's dynamics:\n", - "\\begin{align}\n", - " \\tau_{\\text{syn}} \\frac{dI_{\\text{syn}_{i}}}{dt} = & -I_{\\text{syn}_{i}} + \\sum_{j=0}^{n} w_{ij} \\sum_{t_{j}} \\delta(t - t_{j}).\n", - "\\end{align}\n", - "To approximate biolological AMPA and GABA receptors, we pick different time constants for excitatory and inhibitory synapses." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "VnbedWiB0oAF" - }, - "outputs": [], - "source": [ - "exc_post_syn_params = {\"tau\": 5.0}\n", - "inh_post_syn_params = {\"tau\": 10.0}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nJ1JwSAO1qNi" - }, - "source": [ - "We want to connect these with a fixed probability of 0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "ciwtEyzB0nte" - }, - "outputs": [], - "source": [ - "fixed_prob = {\"prob\": 0.1}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HWvUT89Z106p" - }, - "source": [ - "Now we have defined the synaptic weights (in GeNN, this is the responsibility of the **weight update model**), the synapse dynamics (in GeNN this is the responsibility of the **postsynaptic model**) and the connectivity parameters we can add the synapse populations to the model.\n", - "Each of these synapse populations all configured with:\n", - "* `SPARSE` connectivity meaning that they are connected with a sparse weight matrix.\n", - "* The built in `StaticPulseConstantWeight` **weight update model** which is used for spiking synapses without any sort of learning. This has a single parameter `g` representing the synaptic weight used for all synapses.\n", - "* The build in `ExpCurr` **postsynaptic model** which implements the exponential synapses described previously\n", - "* The sparse connectivity is configured using the built in `FixedProbability` model described previosuly\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "id": "rD6K22qZtxId" - }, - "outputs": [], - "source": [ - "model.add_synapse_population(\"EE\", \"SPARSE\",\n", - " exc_pop, exc_pop,\n", - " init_weight_update(\"StaticPulseConstantWeight\", exc_synapse_init),\n", - " init_postsynaptic(\"ExpCurr\", exc_post_syn_params),\n", - " init_sparse_connectivity(\"FixedProbabilityNoAutapse\", fixed_prob))\n", - "\n", - "model.add_synapse_population(\"EI\", \"SPARSE\",\n", - " exc_pop, inh_pop,\n", - " init_weight_update(\"StaticPulseConstantWeight\", exc_synapse_init),\n", - " init_postsynaptic(\"ExpCurr\", exc_post_syn_params),\n", - " init_sparse_connectivity(\"FixedProbability\", fixed_prob))\n", - "\n", - "model.add_synapse_population(\"II\", \"SPARSE\",\n", - " inh_pop, inh_pop,\n", - " init_weight_update(\"StaticPulseConstantWeight\", inh_synapse_init),\n", - " init_postsynaptic(\"ExpCurr\", inh_post_syn_params),\n", - " init_sparse_connectivity(\"FixedProbabilityNoAutapse\", fixed_prob))\n", - "\n", - "model.add_synapse_population(\"IE\", \"SPARSE\",\n", - " inh_pop, exc_pop,\n", - " init_weight_update(\"StaticPulseConstantWeight\", inh_synapse_init),\n", - " init_postsynaptic(\"ExpCurr\", inh_post_syn_params),\n", - " init_sparse_connectivity(\"FixedProbability\", fixed_prob));" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FiAsrqRx5OgZ" - }, - "source": [ - "Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout our entire 1 second simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "id": "0I-7lZP4vWE2" - }, - "outputs": [], - "source": [ - "model.build()\n", - "model.load(num_recording_timesteps=1000)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1JLVx3u1281A" - }, - "source": [ - "## Simulate model\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8HhNMK4C4d6f" - }, - "source": [ - "Simulate the model for 1000 timesteps" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "id": "v0lT7gaIviev" - }, - "outputs": [], - "source": [ - "while model.timestep < 1000:\n", - " model.step_time()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SUzXrYxr4kO5" - }, - "source": [ - "Copy the recorded spike data back from the GPU and extract the spike times and IDs" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "bDJLu6Kwvn7W" - }, - "outputs": [], - "source": [ - "model.pull_recording_buffers_from_device()\n", - "\n", - "exc_spike_times, exc_spike_ids = exc_pop.spike_recording_data[0]\n", - "inh_spike_times, inh_spike_ids = inh_pop.spike_recording_data[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jS5OtCX15CCJ" - }, - "source": [ - "Plot spikes and rates" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 850 - }, - "id": "9rWE-Rvjvo5I", - "outputId": "3133a219-c0bb-4258-84fe-9bbb2fc2a415" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "fig, axes = plt.subplots(3, sharex=True, figsize=(20, 10))\n", - "\n", - "# Define some bins to calculate spike rates\n", - "bin_size = 20.0\n", - "rate_bins = np.arange(0, 1000.0, bin_size)\n", - "rate_bin_centres = rate_bins[:-1] + (bin_size / 2.0)\n", - "\n", - "# Plot excitatory and inhibitory spikes on first axis\n", - "axes[0].scatter(exc_spike_times, exc_spike_ids, s=1)\n", - "axes[0].scatter(inh_spike_times, inh_spike_ids + 3200, s=1)\n", - "\n", - "# Plot excitatory rates on second axis\n", - "exc_rate = np.histogram(exc_spike_times, bins=rate_bins)[0]\n", - "axes[1].plot(rate_bin_centres, exc_rate * (1000.0 / bin_size) * (1.0 / 3200.0))\n", - "\n", - "# Plot inhibitory rates on third axis\n", - "inh_rate = np.histogram(inh_spike_times, bins=rate_bins)[0]\n", - "axes[2].plot(rate_bin_centres, inh_rate * (1000.0 / bin_size) * (1.0 / 800.0))\n", - "\n", - "# Label axes\n", - "axes[0].set_ylabel(\"Neuron ID\")\n", - "axes[1].set_ylabel(\"Excitatory rate [Hz]\")\n", - "axes[2].set_ylabel(\"Inhibitory rate [Hz]\")\n", - "axes[2].set_xlabel(\"Time [ms]\");" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "lkZXMKuC42jG" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "name": "2_synapses", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/documentation/5/_sources/tutorials/comp_neuro_101/index.rst.txt b/documentation/5/_sources/tutorials/comp_neuro_101/index.rst.txt deleted file mode 100644 index 0555dc93e..000000000 --- a/documentation/5/_sources/tutorials/comp_neuro_101/index.rst.txt +++ /dev/null @@ -1,9 +0,0 @@ -CompNeuro 101 -============= -Building spiking neural network models in GeNN - -.. nbgallery:: - :name: rst-gallery - :glob: - - *.ipynb \ No newline at end of file diff --git a/documentation/5/_sources/tutorials/mnist_inference/index.rst.txt b/documentation/5/_sources/tutorials/mnist_inference/index.rst.txt deleted file mode 100644 index c4799e2aa..000000000 --- a/documentation/5/_sources/tutorials/mnist_inference/index.rst.txt +++ /dev/null @@ -1,9 +0,0 @@ -MNIST inference -=============== -Perform MNIST inference by converting a pre-trained ANN to an SNN - -.. nbgallery:: - :name: rst-gallery - :glob: - - *.ipynb \ No newline at end of file diff --git a/documentation/5/_sources/tutorials/mushroom_body/index.rst.txt b/documentation/5/_sources/tutorials/mushroom_body/index.rst.txt deleted file mode 100644 index bead1cbd3..000000000 --- a/documentation/5/_sources/tutorials/mushroom_body/index.rst.txt +++ /dev/null @@ -1,9 +0,0 @@ -Insect-inspired MNIST classification -==================================== -Train a model of the insect mushroom body using an STDP learning rule to classify MNIST. - -.. nbgallery:: - :name: rst-gallery - :glob: - - *.ipynb \ No newline at end of file diff --git a/documentation/5/_static/_sphinx_javascript_frameworks_compat.js b/documentation/5/_static/_sphinx_javascript_frameworks_compat.js index 81415803e..8549469dc 100644 --- a/documentation/5/_static/_sphinx_javascript_frameworks_compat.js +++ b/documentation/5/_static/_sphinx_javascript_frameworks_compat.js @@ -1,9 +1,20 @@ -/* Compatability shim for jQuery and underscores.js. +/* + * _sphinx_javascript_frameworks_compat.js + * ~~~~~~~~~~ + * + * Compatability shim for jQuery and underscores.js. + * + * WILL BE REMOVED IN Sphinx 6.0 + * xref RemovedInSphinx60Warning * - * Copyright Sphinx contributors - * Released under the two clause BSD licence */ +/** + * select a different prefix for underscore + */ +$u = _.noConflict(); + + /** * small helper function to urldecode strings * diff --git a/documentation/5/_static/basic.css b/documentation/5/_static/basic.css index 1506b6540..d64b53cc2 100644 --- a/documentation/5/_static/basic.css +++ b/documentation/5/_static/basic.css @@ -4,7 +4,7 @@ * * Sphinx stylesheet -- basic theme. * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ @@ -324,17 +324,17 @@ aside.sidebar { p.sidebar-title { font-weight: bold; } - nav.contents, aside.topic, + div.admonition, div.topic, blockquote { clear: left; } /* -- topics ---------------------------------------------------------------- */ - nav.contents, aside.topic, + div.topic { border: 1px solid #ccc; padding: 7px; @@ -375,6 +375,7 @@ div.sidebar > :last-child, aside.sidebar > :last-child, nav.contents > :last-child, aside.topic > :last-child, + div.topic > :last-child, div.admonition > :last-child { margin-bottom: 0; @@ -384,6 +385,7 @@ div.sidebar::after, aside.sidebar::after, nav.contents::after, aside.topic::after, + div.topic::after, div.admonition::after, blockquote::after { @@ -609,6 +611,25 @@ ul.simple p { margin-bottom: 0; } +/* Docutils 0.17 and older (footnotes & citations) */ +dl.footnote > dt, +dl.citation > dt { + float: left; + margin-right: 0.5em; +} + +dl.footnote > dd, +dl.citation > dd { + margin-bottom: 0em; +} + +dl.footnote > dd:after, +dl.citation > dd:after { + content: ""; + clear: both; +} + +/* Docutils 0.18+ (footnotes & citations) */ aside.footnote > span, div.citation > span { float: left; @@ -633,6 +654,8 @@ div.citation > p:last-of-type:after { clear: both; } +/* Footnotes & citations ends */ + dl.field-list { display: grid; grid-template-columns: fit-content(30%) auto; @@ -645,6 +668,10 @@ dl.field-list > dt { padding-right: 5px; } +dl.field-list > dt:after { + content: ":"; +} + dl.field-list > dd { padding-left: 0.5em; margin-top: 0em; @@ -670,16 +697,6 @@ dd { margin-left: 30px; } -.sig dd { - margin-top: 0px; - margin-bottom: 0px; -} - -.sig dl { - margin-top: 0px; - margin-bottom: 0px; -} - dl > dd:last-child, dl > dd:last-child > :last-child { margin-bottom: 0; @@ -748,14 +765,6 @@ abbr, acronym { cursor: help; } -.translated { - background-color: rgba(207, 255, 207, 0.2) -} - -.untranslated { - background-color: rgba(255, 207, 207, 0.2) -} - /* -- code displays --------------------------------------------------------- */ pre { diff --git a/documentation/5/_static/classic.css b/documentation/5/_static/classic.css deleted file mode 100644 index 564c5bccd..000000000 --- a/documentation/5/_static/classic.css +++ /dev/null @@ -1,269 +0,0 @@ -/* - * classic.css_t - * ~~~~~~~~~~~~~ - * - * Sphinx stylesheet -- classic theme. - * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. - * :license: BSD, see LICENSE for details. - * - */ - -@import url("basic.css"); - -/* -- page layout ----------------------------------------------------------- */ - -html { - /* CSS hack for macOS's scrollbar (see #1125) */ - background-color: #FFFFFF; -} - -body { - font-family: sans-serif; - font-size: 100%; - background-color: #11303d; - color: #000; - margin: 0; - padding: 0; -} - -div.document { - display: flex; - background-color: #1c4e63; -} - -div.documentwrapper { - float: left; - width: 100%; -} - -div.bodywrapper { - margin: 0 0 0 450px; -} - -div.body { - background-color: #ffffff; - color: #000000; - padding: 0 20px 30px 20px; -} - -div.footer { - color: #ffffff; - width: 100%; - padding: 9px 0 9px 0; - text-align: center; - font-size: 75%; -} - -div.footer a { - color: #ffffff; - text-decoration: underline; -} - -div.related { - background-color: #133f52; - line-height: 30px; - color: #ffffff; -} - -div.related a { - color: #ffffff; -} - -div.sphinxsidebar { -} - -div.sphinxsidebar h3 { - font-family: 'Trebuchet MS', sans-serif; - color: #ffffff; - font-size: 1.4em; - font-weight: normal; - margin: 0; - padding: 0; -} - -div.sphinxsidebar h3 a { - color: #ffffff; -} - -div.sphinxsidebar h4 { - font-family: 'Trebuchet MS', sans-serif; - color: #ffffff; - font-size: 1.3em; - font-weight: normal; - margin: 5px 0 0 0; - padding: 0; -} - -div.sphinxsidebar p { - color: #ffffff; -} - -div.sphinxsidebar p.topless { - margin: 5px 10px 10px 10px; -} - -div.sphinxsidebar ul { - margin: 10px; - padding: 0; - color: #ffffff; -} - -div.sphinxsidebar a { - color: #98dbcc; -} - -div.sphinxsidebar input { - border: 1px solid #98dbcc; - font-family: sans-serif; - font-size: 1em; -} - - - -/* -- hyperlink styles ------------------------------------------------------ */ - -a { - color: #355f7c; - text-decoration: none; -} - -a:visited { - color: #355f7c; - text-decoration: none; -} - -a:hover { - text-decoration: underline; -} - - - -/* -- body styles ----------------------------------------------------------- */ - -div.body h1, -div.body h2, -div.body h3, -div.body h4, -div.body h5, -div.body h6 { - font-family: 'Trebuchet MS', sans-serif; - background-color: #f2f2f2; - font-weight: normal; - color: #20435c; - border-bottom: 1px solid #ccc; - margin: 20px -20px 10px -20px; - padding: 3px 0 3px 10px; -} - -div.body h1 { margin-top: 0; font-size: 200%; } -div.body h2 { font-size: 160%; } -div.body h3 { font-size: 140%; } -div.body h4 { font-size: 120%; } -div.body h5 { font-size: 110%; } -div.body h6 { font-size: 100%; } - -a.headerlink { - color: #c60f0f; - font-size: 0.8em; - padding: 0 4px 0 4px; - text-decoration: none; -} - -a.headerlink:hover { - background-color: #c60f0f; - color: white; -} - -div.body p, div.body dd, div.body li, div.body blockquote { - text-align: justify; - line-height: 130%; -} - -div.admonition p.admonition-title + p { - display: inline; -} - -div.admonition p { - margin-bottom: 5px; -} - -div.admonition pre { - margin-bottom: 5px; -} - -div.admonition ul, div.admonition ol { - margin-bottom: 5px; -} - -div.note { - background-color: #eee; - border: 1px solid #ccc; -} - -div.seealso { - background-color: #ffc; - border: 1px solid #ff6; -} - -nav.contents, -aside.topic, -div.topic { - background-color: #eee; -} - -div.warning { - background-color: #ffe4e4; - border: 1px solid #f66; -} - -p.admonition-title { - display: inline; -} - -p.admonition-title:after { - content: ":"; -} - -pre { - padding: 5px; - background-color: unset; - color: unset; - line-height: 120%; - border: 1px solid #ac9; - border-left: none; - border-right: none; -} - -code { - background-color: #ecf0f3; - padding: 0 1px 0 1px; - font-size: 0.95em; -} - -th, dl.field-list > dt { - background-color: #ede; -} - -.warning code { - background: #efc2c2; -} - -.note code { - background: #d6d6d6; -} - -.viewcode-back { - font-family: sans-serif; -} - -div.viewcode-block:target { - background-color: #f4debf; - border-top: 1px solid #ac9; - border-bottom: 1px solid #ac9; -} - -div.code-block-caption { - color: #efefef; - background-color: #1c4e63; -} \ No newline at end of file diff --git a/documentation/5/_static/doctools.js b/documentation/5/_static/doctools.js index d06a71d75..c3db08d1c 100644 --- a/documentation/5/_static/doctools.js +++ b/documentation/5/_static/doctools.js @@ -4,19 +4,12 @@ * * Base JavaScript utilities for all Sphinx HTML documentation. * - * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS. + * :copyright: Copyright 2007-2022 by the Sphinx team, see AUTHORS. * :license: BSD, see LICENSE for details. * */ "use strict"; -const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([ - "TEXTAREA", - "INPUT", - "SELECT", - "BUTTON", -]); - const _ready = (callback) => { if (document.readyState !== "loading") { callback(); @@ -25,11 +18,73 @@ const _ready = (callback) => { } }; +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + parent.insertBefore( + span, + parent.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.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: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + /** * Small JavaScript module for the documentation. */ const Documentation = { init: () => { + Documentation.highlightSearchWords(); Documentation.initDomainIndexTable(); Documentation.initOnKeyListeners(); }, @@ -71,6 +126,51 @@ const Documentation = { Documentation.LOCALE = catalog.locale; }, + /** + * highlight the search words provided in the url in the text + */ + highlightSearchWords: () => { + const highlight = + new URLSearchParams(window.location.search).get("highlight") || ""; + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + Documentation.gettext("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + const url = new URL(window.location); + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + }, + /** * helper function to focus on search bar */ @@ -110,11 +210,15 @@ const Documentation = { ) return; + const blacklistedElements = new Set([ + "TEXTAREA", + "INPUT", + "SELECT", + "BUTTON", + ]); document.addEventListener("keydown", (event) => { - // bail for input elements - if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; - // bail with special keys - if (event.altKey || event.ctrlKey || event.metaKey) return; + if (blacklistedElements.has(document.activeElement.tagName)) return; // bail for input elements + if (event.altKey || event.ctrlKey || event.metaKey) return; // bail with special keys if (!event.shiftKey) { switch (event.key) { @@ -136,6 +240,10 @@ const Documentation = { event.preventDefault(); } break; + case "Escape": + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break; + Documentation.hideSearchWords(); + event.preventDefault(); } } diff --git a/documentation/5/_static/documentation_options.js b/documentation/5/_static/documentation_options.js index b57ae3b83..a750e4d5e 100644 --- a/documentation/5/_static/documentation_options.js +++ b/documentation/5/_static/documentation_options.js @@ -10,5 +10,5 @@ var DOCUMENTATION_OPTIONS = { SOURCELINK_SUFFIX: '.txt', NAVIGATION_WITH_KEYS: false, SHOW_SEARCH_SUMMARY: true, - ENABLE_SEARCH_SHORTCUTS: true, + ENABLE_SEARCH_SHORTCUTS: false, }; \ No newline at end of file diff --git a/documentation/5/_static/jquery-3.6.0.js b/documentation/5/_static/jquery-3.6.0.js new file mode 100644 index 000000000..fc6c299b7 --- /dev/null +++ b/documentation/5/_static/jquery-3.6.0.js @@ -0,0 +1,10881 @@ +/*! + * jQuery JavaScript Library v3.6.0 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright OpenJS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2021-03-02T17:08Z + */ +( 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. + // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 + // Plus for old WebKit, typeof returns "function" for HTML collections + // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) + return typeof obj === "function" && typeof obj.nodeType !== "number" && + typeof obj.item !== "function"; + }; + + +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 = "3.6.0", + + // 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 v2.3.6 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2021-02-16 + */ +( 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 && elem.namespaceURI, + docElem = elem && ( 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 primary Deferred + primary = 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 ) ) { + primary.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( primary.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return primary.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); + } + + return primary.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 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(); + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && 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: true +}, 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; + }, + + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + 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! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) + 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;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. + tr.style.height = "1px"; + trChild.style.height = "9px"; + + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; + + 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, parserErrorElem; + 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 ) {} + + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + 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 but not if jsonp + if ( !isSuccess && + jQuery.inArray( "script", s.dataTypes ) > -1 && + jQuery.inArray( "json", s.dataTypes ) < 0 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( " - - - - + + + + + + diff --git a/documentation/5/building_networks.html b/documentation/5/building_networks.html index c973f1b5c..5a99d9fab 100644 --- a/documentation/5/building_networks.html +++ b/documentation/5/building_networks.html @@ -1,28 +1,29 @@ - + Building networks — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + @@ -178,11 +179,11 @@

Variables
Parameters:
@@ -350,11 +351,11 @@

Neuron populations @@ -391,12 +392,12 @@

Synapse populationsweight_update_models) or an instance of WeightUpdateModelBase (for example returned by create_weight_update_model())

-

  • params (Dict[str, int | float]) – parameter values (see Parameters)

    • -

  • vars (Dict[str, VarInit | int | float | ndarray | Sequence]) – initial synaptic variable values or +

  • params (Dict[str, Union[int, float]]) – parameter values (see Parameters)

    • +

  • vars (Dict[str, Union[VarInit, int, float, ndarray, Sequence]]) – initial synaptic variable values or initialisers (see Variables)

    • -

  • pre_vars (Dict[str, VarInit | int | float | ndarray | Sequence]) – initial presynaptic variable values or +

  • pre_vars (Dict[str, Union[VarInit, int, float, ndarray, Sequence]]) – initial presynaptic variable values or initialisers (see Variables)

    • -

  • post_vars (Dict[str, VarInit | int | float | ndarray | Sequence]) – initial postsynaptic variable values or initialisers +

  • post_vars (Dict[str, Union[VarInit, int, float, ndarray, Sequence]]) – initial postsynaptic variable values or initialisers (see Variables)

  • pre_var_refs (Dict[str, VarReference]) – references to presynaptic neuron variables, typically created using create_var_ref() @@ -424,14 +425,14 @@

    Synapse populations
    Parameters:
    @@ -488,11 +489,11 @@

    Synapse populations
    Parameters:
    @@ -547,14 +548,14 @@

    Synapse populationsParameters:
    @@ -736,17 +725,17 @@

    Current source modelsParameters:

    • class_name (str) – name of the new class (only for debugging)

      • -

    • params (Sequence[str | Tuple[str, str | ResolvedType]] | None) – name and optional types of model parameters

      • -

    • vars (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccess]] | None) – names, types and optional variable access +

    • params (Optional[Sequence[Union[str, Tuple[str, Union[str, ResolvedType]]]]]) – name and optional types of model parameters

      • +

    • vars (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccess]]]]) – names, types and optional variable access modifiers of model variables

      • -

    • neuron_var_refs (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccessMode]] | None) – names, types and optional variable access +

    • neuron_var_refs (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccessMode]]]]) – names, types and optional variable access of references to be assigned to variables in neuron population current source is attached to

      • -

    • derived_params (Sequence[Tuple[str, Callable, str | ResolvedType]] | None) – names, types and callables to calculate +

    • derived_params (Optional[Sequence[Tuple[str, Callable, Union[str, ResolvedType]]]]) – names, types and callables to calculate derived parameter values from params

      • -

    • injection_code (str | None) – string containing the simulation code +

    • injection_code (Optional[str]) – string containing the simulation code statements to be run every timestep

      • -

    • extra_global_params (Sequence[Tuple[str, str | ResolvedType]] | None) – names and types of model +

    • extra_global_params (Optional[Sequence[Tuple[str, Union[str, ResolvedType]]]]) – names and types of model extra global parameters

    @@ -795,17 +784,17 @@

    Custom update modelsParameters:
    @@ -899,16 +882,16 @@

    Custom connectivity update modelsParameters:

    • class_name (str) – name of the new class (only for debugging)

      • -

    • params (Sequence[str | Tuple[str, str | ResolvedType]] | None) – name and optional types of model parameters

      • -

    • vars (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccess]] | None) – names, types and optional variable access +

    • params (Optional[Sequence[Union[str, Tuple[str, Union[str, ResolvedType]]]]]) – name and optional types of model parameters

      • +

    • vars (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccess]]]]) – names, types and optional variable access modifiers of per-synapse model variables

      • -

    • pre_vars (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccess]] | None) – names, types and optional variable access +

    • pre_vars (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccess]]]]) – names, types and optional variable access modifiers of per-presynaptic neuron model variables

    • names (post_vars) – modifiers of per-postsynaptic neuron model variables

    • access (types and optional variable) – modifiers of per-postsynaptic neuron model variables

      • -

    • derived_params (Sequence[Tuple[str, Callable, str | ResolvedType]] | None) – names, types and callables to calculate +

    • derived_params (Optional[Sequence[Tuple[str, Callable, Union[str, ResolvedType]]]]) – names, types and callables to calculate derived parameter values from params

      • -

    • var_refs (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccessMode]] | None) – names, types and optional variable access +

    • var_refs (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccessMode]]]]) – names, types and optional variable access of references to be assigned to synaptic variables

    • pre_neuron_var_refs – names, types and optional variable access of references to be assigned to presynaptic @@ -916,22 +899,20 @@

      Custom connectivity update modelsVarAccess]] | None) –

      • -

    • pre_var_refs (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccessMode]] | None) –

      • -

    • post_var_refs (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccessMode]] | None) –

      • +

    • post_vars (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccess]]]]) –

      • +

    • pre_var_refs (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccessMode]]]]) –

      • +

    • post_var_refs (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccessMode]]]]) –

    -
    -

    Parallel synapse iteration and removal

    The main GPU operation that custom connectivity updates expose is the ability to generate per-presynaptic neuron update code. This can be used to implement a very simple model which removes ‘diagonals’ from the connection matrix:

    remove_diagonal_model = pygenn.create_custom_connectivity_update_model(
     "remove_diagonal",
@@ -946,9 +927,6 @@ 
    Parallel synapse iteration and removal        """)
    -
    -
    -

    Parallel synapse creation

    Similarly you could implement a custom connectivity model which adds diagonals back into the connection matrix like this:

    add_diagonal_model = pygenn.create_custom_connectivity_update_model(
     "add_diagonal",
@@ -968,9 +946,6 @@ 
    Parallel synapse creation        """)
    -
    -
    -

    Host updates

    Some common connectivity update scenarios involve some computation which can’t be easily parallelized. If, for example you wanted to determine which elements on each row you wanted to remove on the host, you can include host_update_code which gets run before the row update code:

    remove_diagonal_model = pygenn.create_custom_connectivity_update_model(
     "remove_diagonal",
@@ -993,7 +968,6 @@ 
    Host updates        """)
    -

    diff --git a/documentation/5/genindex.html b/documentation/5/genindex.html index 52ee5f60e..31d4827bb 100644 --- a/documentation/5/genindex.html +++ b/documentation/5/genindex.html @@ -4,24 +4,25 @@ Index — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + @@ -281,6 +282,8 @@

    D

    • -
  • get_var_values() (pygenn.genn_groups.CustomConnectivityUpdateMixin method) - -
  • get_wu_post_var_location() (pygenn.SynapseGroup method) @@ -521,6 +516,8 @@

    M

  • pygenn.custom_connectivity_update_models
  • pygenn.custom_update_models +
    • +
  • pygenn.deprecated
  • pygenn.genn_groups
    • @@ -708,32 +705,16 @@

    P

  • ps_initialiser (pygenn.SynapseGroup property)
  • pull_connectivity_from_device() (pygenn.genn_groups.SynapseGroupMixin method) -
    • -
  • pull_extra_global_param_from_device() (pygenn.genn_groups.GroupMixin method)
  • pull_from_device() (pygenn.model_preprocessor.ArrayBase method) -
    • -
  • pull_in_syn_from_device() (pygenn.genn_groups.SynapseGroupMixin method) -
    • -
  • pull_psm_extra_global_param_from_device() (pygenn.genn_groups.SynapseGroupMixin method)
  • pull_recording_buffers_from_device() (pygenn.GeNNModel method)
    • -
  • pull_var_from_device() (pygenn.genn_groups.GroupMixin method) +
  • push_connectivity_to_device() (pygenn.genn_groups.SynapseGroupMixin method)
      • -
    • push_connectivity_to_device() (pygenn.genn_groups.SynapseGroupMixin method) -
      • -
    • push_extra_global_param_to_device() (pygenn.genn_groups.GroupMixin method) -
      • -
    • push_in_syn_to_device() (pygenn.genn_groups.SynapseGroupMixin method) -
      • -
    • push_psm_extra_global_param_to_device() (pygenn.genn_groups.SynapseGroupMixin method) -
    • push_to_device() (pygenn.model_preprocessor.ArrayBase method) -
      • -
    • push_var_to_device() (pygenn.genn_groups.GroupMixin method)
    • pygenn @@ -768,6 +749,13 @@

      P

      • +
    • + pygenn.deprecated + +
    • @@ -938,12 +926,6 @@

      S

    • set_sparse_connections() (pygenn.genn_groups.SynapseGroupMixin method)
      • -
    • set_values() (pygenn.model_preprocessor.ExtraGlobalParameter method) - -
    • set_var_location() (pygenn.CurrentSource method) - +
      • sparse_connectivity_initialiser (pygenn.SynapseGroup property)
      • sparse_connectivity_location (pygenn.SynapseGroup property) diff --git a/documentation/5/index.html b/documentation/5/index.html index fca226606..4446cd4f1 100644 --- a/documentation/5/index.html +++ b/documentation/5/index.html @@ -1,28 +1,29 @@ - + PyGeNN documentation — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + diff --git a/documentation/5/installation.html b/documentation/5/installation.html index f864ec003..8c8b7b58a 100644 --- a/documentation/5/installation.html +++ b/documentation/5/installation.html @@ -1,28 +1,29 @@ - + Installation — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + diff --git a/documentation/5/objects.inv b/documentation/5/objects.inv index d0e4e5976..0e5f8bc40 100644 Binary files a/documentation/5/objects.inv and b/documentation/5/objects.inv differ diff --git a/documentation/5/py-modindex.html b/documentation/5/py-modindex.html index b705f03b5..8c9e8fdda 100644 --- a/documentation/5/py-modindex.html +++ b/documentation/5/py-modindex.html @@ -4,24 +4,25 @@ Python Module Index — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + @@ -121,6 +122,11 @@

        Python Module Index

            pygenn.custom_update_models + + +     + pygenn.deprecated +     diff --git a/documentation/5/search.html b/documentation/5/search.html index 67ee63143..b8324fbaa 100644 --- a/documentation/5/search.html +++ b/documentation/5/search.html @@ -4,12 +4,12 @@ Search — PyGeNN documentation - - - - - - + + + + + + @@ -17,12 +17,13 @@ - - - - - + + + + + + diff --git a/documentation/5/searchindex.js b/documentation/5/searchindex.js index b2e25703a..45b69d27d 100644 --- a/documentation/5/searchindex.js +++ b/documentation/5/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["bibliography", "building_networks", "custom_models", "index", "installation", "sg_execution_times", "simulating_networks", "source/modules", "source/pygenn", "tutorials/comp_neuro_101/1_neurons", "tutorials/comp_neuro_101/2_synapses", "tutorials/index", "tutorials/mnist_inference/tutorial_1", "tutorials/mnist_inference/tutorial_2", "tutorials/mnist_inference/tutorial_3", 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3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "nbsphinx": 4, "sphinx": 56}}) \ No newline at end of file diff --git a/documentation/5/sg_execution_times.html b/documentation/5/sg_execution_times.html index ed35fd5da..a5bfdb61b 100644 --- a/documentation/5/sg_execution_times.html +++ b/documentation/5/sg_execution_times.html @@ -1,28 +1,29 @@ - + Computation times — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + diff --git a/documentation/5/simulating_networks.html b/documentation/5/simulating_networks.html index 076dbac78..8ddec00b6 100644 --- a/documentation/5/simulating_networks.html +++ b/documentation/5/simulating_networks.html @@ -1,28 +1,29 @@ - + Simulating networks — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + diff --git a/documentation/5/source/modules.html b/documentation/5/source/modules.html index 4efea2da5..fcae1b7dd 100644 --- a/documentation/5/source/modules.html +++ b/documentation/5/source/modules.html @@ -1,28 +1,29 @@ - + pygenn — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + @@ -88,57 +89,12 @@

        pygenn
        • pygenn package
            -
          • CurrentSource
            • -
          • CustomConnectivityUpdate
            • -
          • CustomUpdate
            • -
          • CustomUpdateBase
            • -
          • CustomUpdateVarAccess
            • -
          • CustomUpdateWU
            • -
          • GeNNModel
            • -
          • ModelSpec
            • -
          • NeuronGroup
            • -
          • ParallelismHint
            • -
          • PlogSeverity
            • -
          • SynapseGroup
            • -
          • SynapseMatrixConnectivity
            • -
          • SynapseMatrixType
            • -
          • SynapseMatrixWeight
            • -
          • VarAccess
            • -
          • VarAccessDim
            • -
          • VarAccessMode
            • -
          • VarAccessModeAttribute
            • -
          • VarLocation
            • -
          • VarLocationAttribute
            • -
          • create_current_source_model()
            • -
          • create_custom_connectivity_update_model()
            • -
          • create_custom_update_model()
            • -
          • create_egp_ref()
            • -
          • create_neuron_model()
            • -
          • create_post_var_ref()
            • -
          • create_postsynaptic_model()
            • -
          • create_pre_var_ref()
            • -
          • create_psm_egp_ref()
            • -
          • create_psm_var_ref()
            • -
          • create_sparse_connect_init_snippet()
            • -
          • create_toeplitz_connect_init_snippet()
            • -
          • create_var_init_snippet()
            • -
          • create_var_ref()
            • -
          • create_weight_update_model()
            • -
          • create_wu_egp_ref()
            • -
          • create_wu_post_var_ref()
            • -
          • create_wu_pre_var_ref()
            • -
          • create_wu_var_ref()
            • -
          • get_var_access_dim()
            • -
          • init_postsynaptic()
            • -
          • init_sparse_connectivity()
            • -
          • init_toeplitz_connectivity()
            • -
          • init_var()
            • -
          • init_weight_update()
          • Submodules
          • pygenn.cuda_backend module
          • pygenn.current_source_models module
          • pygenn.custom_connectivity_update_models module
          • pygenn.custom_update_models module
            • +
          • pygenn.deprecated module
          • pygenn.genn_groups module
          • pygenn.init_sparse_connectivity_snippets module
          • pygenn.init_toeplitz_connectivity_snippets module
            • diff --git a/documentation/5/source/pygenn.html b/documentation/5/source/pygenn.html index c72ac087e..eb2689ebe 100644 --- a/documentation/5/source/pygenn.html +++ b/documentation/5/source/pygenn.html @@ -1,28 +1,29 @@ - + pygenn package — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + @@ -369,10 +370,10 @@
            Parameters:
              -

            • precision (str | ResolvedType) – Data type to use for scalar variables

              • +

            • precision (Union[str, ResolvedType]) – Data type to use for scalar variables

            • model_name (str) – Name of the model

              • -

            • backend (str | None) – Name of backend module to use. Defaults to one to pick ‘best’ backend for your system

              • -

            • time_precision (str | ResolvedType | None) – data type to use for representing time

              • +

            • backend (Optional[str]) – Name of backend module to use. Defaults to one to pick ‘best’ backend for your system

              • +

            • time_precision (Optional[Union[str, ResolvedType]]) – data type to use for representing time

            • genn_log_level (PlogSeverity) – Log level for GeNN

            • code_gen_log_level (PlogSeverity) – Log level for GeNN code-generator

            • transpiler_log_level (PlogSeverity) – Log level for GeNN transpiler

              • @@ -390,12 +391,12 @@
              Parameters:

              • cs_name (str) – unique name

                • -

              • current_source_model (CurrentSourceModelBase | str) – current source model either as a string referencing a built-in model +

              • current_source_model (Union[CurrentSourceModelBase, str]) – current source model either as a string referencing a built-in model (see current_source_models) or an instance of CurrentSourceModelBase (for example returned by create_current_source_model())

              • pop (NeuronGroup) – neuron population to inject current into

                • -

              • params (Dict[str, int | float]) – parameter values for the current source model (see `Parameters`_)

                • -

              • vars (Dict[str, VarInit | int | float | ndarray | Sequence]) – initial variable values or initialisers +

              • params (Dict[str, Union[int, float]]) – parameter values for the current source model (see `Parameters`_)

                • +

              • vars (Dict[str, Union[VarInit, int, float, ndarray, Sequence]]) – initial variable values or initialisers for the current source model (see `Variables`_)

              • var_refs (Dict[str, VarReference]) – variables references to neuron variables in pop, typically created using create_var_ref() @@ -426,16 +427,16 @@

              • group_name (str) – name of the ‘custom update group’ to include this update in. All custom updates in the same group are executed simultaneously.

              • syn_group (SynapseGroup) – Synapse group to attach custom connectivity update to

                • -

              • custom_conn_update_model (CustomConnectivityUpdateModelBase | str) – custom connectivity update model either as a string referencing a built-in model +

              • custom_conn_update_model (Union[CustomConnectivityUpdateModelBase, str]) – custom connectivity update model either as a string referencing a built-in model (see custom_connectivity_update_models) or an instance of CustomConnectivityUpdateModelBaseUpdateModelBase (for example returned by create_custom_connectivity_update_model())

                • -

              • params (Dict[str, int | float]) – parameter values for the custom connectivity model (see `Parameters`_)

                • -

              • vars (Dict[str, VarInit | int | float | ndarray | Sequence]) – initial synaptic variable values or +

              • params (Dict[str, Union[int, float]]) – parameter values for the custom connectivity model (see `Parameters`_)

                • +

              • vars (Dict[str, Union[VarInit, int, float, ndarray, Sequence]]) – initial synaptic variable values or initialisers (see `Variables`_)

                • -

              • pre_vars (Dict[str, VarInit | int | float | ndarray | Sequence]) – initial presynaptic variable values or +

              • pre_vars (Dict[str, Union[VarInit, int, float, ndarray, Sequence]]) – initial presynaptic variable values or initialisers (see `Variables`_)

                • -

              • post_vars (Dict[str, VarInit | int | float | ndarray | Sequence]) – initial postsynaptic variable values or initialisers +

              • post_vars (Dict[str, Union[VarInit, int, float, ndarray, Sequence]]) – initial postsynaptic variable values or initialisers (see `Variables`_)

              • var_refs (Dict[str, WUVarReference]) – references to synaptic variables, typically created using create_wu_var_ref() @@ -464,14 +465,14 @@

              • cu_name (str) – unique name

              • group_name (str) – name of the ‘custom update group’ to include this update in. All custom updates in the same group are executed simultaneously.

                • -

              • custom_update_model (CustomUpdateModelBase | str) – custom update model either as a string referencing a built-in model +

              • custom_update_model (Union[CustomUpdateModelBase, str]) – custom update model either as a string referencing a built-in model (see custom_update_models) or an instance of CustomUpdateModelBase (for example returned by create_custom_update_model())

                • -

              • params (Dict[str, int | float]) – parameter values for the custom update model (see `Parameters`_)

                • -

              • vars (Dict[str, VarInit | int | float | ndarray | Sequence]) – initial variable values or initialisers +

              • params (Dict[str, Union[int, float]]) – parameter values for the custom update model (see `Parameters`_)

                • +

              • vars (Dict[str, Union[VarInit, int, float, ndarray, Sequence]]) – initial variable values or initialisers for the custom update model (see `Variables`_)

                • -

              • var_refs (Dict[str, VarReference] | Dict[str, WUVarReference]) – references to variables in other populations to +

              • var_refs (Union[Dict[str, VarReference], Dict[str, WUVarReference]]) – references to variables in other populations to access from this update, typically created using either create_var_ref() or create_wu_var_ref() (see `Variables references`_).

                • @@ -504,11 +505,11 @@

                • pop_name (str) – unique name

                • num_neurons (int) – number of neurons

                  • -

                • neuron (NeuronModelBase | str) – neuron model either as a string referencing a built-in model +

                • neuron (Union[NeuronModelBase, str]) – neuron model either as a string referencing a built-in model (see neuron_models) or an instance of NeuronModelBase (for example returned by create_neuron_model())

                  • -

                • params (Dict[str, int | float]) – parameter values for the neuron model (see `Parameters`_)

                  • -

                • vars (Dict[str, VarInit | int | float | ndarray | Sequence]) – initial variable values or initialisers +

                • params (Dict[str, Union[int, float]]) – parameter values for the neuron model (see `Parameters`_)

                  • +

                • vars (Dict[str, Union[VarInit, int, float, ndarray, Sequence]]) – initial variable values or initialisers for the neuron model (see `Variables`_)

                @@ -533,14 +534,14 @@
                Parameters:

                • pop_name (str) – unique name

                  • -

                • matrix_type (SynapseMatrixType | str) – type of connectivity to use

                  • +

                • matrix_type (Union[SynapseMatrixType, str]) – type of connectivity to use

                • source (NeuronGroup) – source neuron group

                • target (NeuronGroup) – target neuron group

                • weight_update_init – initialiser for weight update model, typically created using init_weight_update()

                • postsynaptic_init – initialiser for postsynaptic model, typically created using init_postsynaptic()

                  • -

                • connectivity_init (None | SparseConnectivityInit | ToeplitzConnectivityInit) – initialiser for connectivity, typically created +

                • connectivity_init (Union[None, SparseConnectivityInit, ToeplitzConnectivityInit]) – initialiser for connectivity, typically created using init_sparse_connectivity() when matrix_type is SynapseMatrixType.BITMASK, SynapseMatrixType.SPARSE, @@ -659,7 +660,7 @@

                  Load the previously built model into memory;

                  Parameters:
                  -

                  num_recording_timesteps (int | None) – Number of timesteps to record spikes +

                  num_recording_timesteps (Optional[int]) – Number of timesteps to record spikes for. pull_recording_buffers_from_device() must be called after this number of timesteps

                  @@ -1633,17 +1634,17 @@
                  Parameters:

                  • class_name (str) – name of the new class (only for debugging)

                    • -

                  • params (Sequence[str | Tuple[str, str | ResolvedType]] | None) – name and optional types of model parameters

                    • -

                  • vars (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccess]] | None) – names, types and optional variable access +

                  • params (Optional[Sequence[Union[str, Tuple[str, Union[str, ResolvedType]]]]]) – name and optional types of model parameters

                    • +

                  • vars (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccess]]]]) – names, types and optional variable access modifiers of model variables

                    • -

                  • neuron_var_refs (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccessMode]] | None) – names, types and optional variable access +

                  • neuron_var_refs (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccessMode]]]]) – names, types and optional variable access of references to be assigned to variables in neuron population current source is attached to

                    • -

                  • derived_params (Sequence[Tuple[str, Callable, str | ResolvedType]] | None) – names, types and callables to calculate +

                  • derived_params (Optional[Sequence[Tuple[str, Callable, Union[str, ResolvedType]]]]) – names, types and callables to calculate derived parameter values from params

                    • -

                  • injection_code (str | None) – string containing the simulation code +

                  • injection_code (Optional[str]) – string containing the simulation code statements to be run every timestep

                    • -

                  • extra_global_params (Sequence[Tuple[str, str | ResolvedType]] | None) – names and types of model +

                  • extra_global_params (Optional[Sequence[Tuple[str, Union[str, ResolvedType]]]]) – names and types of model extra global parameters

                  @@ -1688,16 +1689,16 @@
                  Parameters:

                  • class_name (str) – name of the new class (only for debugging)

                    • -

                  • params (Sequence[str | Tuple[str, str | ResolvedType]] | None) – name and optional types of model parameters

                    • -

                  • vars (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccess]] | None) – names, types and optional variable access +

                  • params (Optional[Sequence[Union[str, Tuple[str, Union[str, ResolvedType]]]]]) – name and optional types of model parameters

                    • +

                  • vars (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccess]]]]) – names, types and optional variable access modifiers of per-synapse model variables

                    • -

                  • pre_vars (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccess]] | None) – names, types and optional variable access +

                  • pre_vars (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccess]]]]) – names, types and optional variable access modifiers of per-presynaptic neuron model variables

                  • names (post_vars) – modifiers of per-postsynaptic neuron model variables

                  • access (types and optional variable) – modifiers of per-postsynaptic neuron model variables

                    • -

                  • derived_params (Sequence[Tuple[str, Callable, str | ResolvedType]] | None) – names, types and callables to calculate +

                  • derived_params (Optional[Sequence[Tuple[str, Callable, Union[str, ResolvedType]]]]) – names, types and callables to calculate derived parameter values from params

                    • -

                  • var_refs (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccessMode]] | None) – names, types and optional variable access +

                  • var_refs (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccessMode]]]]) – names, types and optional variable access of references to be assigned to synaptic variables

                  • pre_neuron_var_refs – names, types and optional variable access of references to be assigned to presynaptic @@ -1705,22 +1706,20 @@

                  • post_neuron_var_refs – names, types and optional variable access of references to be assigned to postsynaptic neuron variables

                    • -

                  • row_update_code (str | None) – string containing the code statements +

                  • row_update_code (Optional[str]) – string containing the code statements to be run when custom update is launched

                    • -

                  • host_update_code (str | None) – string containing the code statements to be run +

                  • host_update_code (Optional[str]) – string containing the code statements to be run on CPU when custom connectivity update is launched

                  • extra_global_params – names and types of model extra global parameters

                  • extra_global_param_refs – names and types of extra global parameter references

                    • -

                  • post_vars (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccess]] | None) –

                    • -

                  • pre_var_refs (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccessMode]] | None) –

                    • -

                  • post_var_refs (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccessMode]] | None) –

                    • +

                  • post_vars (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccess]]]]) –

                    • +

                  • pre_var_refs (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccessMode]]]]) –

                    • +

                  • post_var_refs (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccessMode]]]]) –

                  -
                  -

                  Parallel synapse iteration and removal

                  The main GPU operation that custom connectivity updates expose is the ability to generate per-presynaptic neuron update code. This can be used to implement a very simple model which removes ‘diagonals’ from the connection matrix:

                  remove_diagonal_model = pygenn.create_custom_connectivity_update_model(
     "remove_diagonal",
@@ -1735,9 +1734,6 @@ 
                  Parallel synapse iteration and removal        """)
                  -
                  -
                  -

                  Parallel synapse creation

                  Similarly you could implement a custom connectivity model which adds diagonals back into the connection matrix like this:

                  add_diagonal_model = pygenn.create_custom_connectivity_update_model(
     "add_diagonal",
@@ -1757,9 +1753,6 @@ 
                  Parallel synapse creation        """)
                  -
                  -
                  -

                  Host updates

                  Some common connectivity update scenarios involve some computation which can’t be easily parallelized. If, for example you wanted to determine which elements on each row you wanted to remove on the host, you can include host_update_code which gets run before the row update code:

                  remove_diagonal_model = pygenn.create_custom_connectivity_update_model(
     "remove_diagonal",
@@ -1782,7 +1775,6 @@ 
                  Host updates        """)
                  -
            @@ -1814,17 +1806,17 @@

            Host updatesParameters:

            @@ -1927,19 +1913,19 @@

            Neuron reductionParameters:

            • class_name (str) – name of the new class (only for debugging)

              • -

            • params (Sequence[str | Tuple[str, str | ResolvedType]] | None) – name and optional types of model parameters

              • -

            • vars (Sequence[Tuple[str, str | ResolvedType] | Tuple[str, str | ResolvedType, VarAccess]] | None) – names, types and optional variable access +

            • params (Optional[Sequence[Union[str, Tuple[str, Union[str, ResolvedType]]]]]) – name and optional types of model parameters

              • +

            • vars (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccess]]]]) – names, types and optional variable access modifiers of model variables

              • -

            • derived_params (Sequence[Tuple[str, Callable, str | ResolvedType]] | None) – names, types and callables to calculate +

            • derived_params (Optional[Sequence[Tuple[str, Callable, Union[str, ResolvedType]]]]) – names, types and callables to calculate derived parameter values from params

              • -

            • sim_code (str | None) – string containing the simulation code +

            • sim_code (Optional[str]) – string containing the simulation code statements to be run every timestep

              • -

            • threshold_condition_code (str | None) – string containing a threshold condition +

            • threshold_condition_code (Optional[str]) – string containing a threshold condition expression to test whether a spike should be emitted

              • -

            • reset_code (str | None) – string containing the reset code +

            • reset_code (Optional[str]) – string containing the reset code statements to run after emitting a spike

              • -

            • extra_global_params (Sequence[Tuple[str, str | ResolvedType]] | None) – names and types of model +

            • extra_global_params (Optional[Sequence[Tuple[str, Union[str, ResolvedType]]]]) – names and types of model extra global parameters

            • additional_input_vars – list of tuples with names and types as strings and initial values of additional @@ -1967,8 +1953,6 @@

              Neuron reductionvars=[("V", "scalar", pygenn.VarAccess.READ_WRITE)]) -
              -

              Additional input variables

              Normally, neuron models receive the linear sum of the inputs coming from all of their synaptic inputs through the Isyn variable. However neuron models can define additional input variables - allowing input from different synaptic inputs to be combined non-linearly. For example, if we wanted our leaky integrator to operate on the the product of two input currents, we could modify our model as follows:

              @@ -1980,7 +1964,6 @@

              Additional input variables """, -

            @@ -2012,14 +1995,14 @@

            Additional input variablesVarAccessMode]] | None) – names, types and optional variable access +

          • neuron_var_refs (Optional[Sequence[Union[Tuple[str, Union[str, ResolvedType]], Tuple[str, Union[str, ResolvedType], VarAccessMode]]]]) – names, types and optional variable access of references to be assigned to postsynaptic neuron variables

            • -

          • derived_params (Sequence[Tuple[str, Callable, str | ResolvedType]] | None) – names, types and callables to calculate +

          • derived_params (Optional[Sequence[Tuple[str, Callable, Union[str, ResolvedType]]]]) – names, types and callables to calculate derived parameter values from params

            • -

          • sim_code (str | None) – string containing the simulation code +

          • sim_code (Optional[str]) – string containing the simulation code statements to be run every timestep

            • -

          • extra_global_params (Sequence[Tuple[str, str | ResolvedType]] | None) – names and types of model +

          • extra_global_params (Optional[Sequence[Tuple[str, Union[str, ResolvedType]]]]) – names and types of model extra global parameters

          @@ -2076,17 +2059,17 @@

          Additional input variables

          • class_name (str) – name of the snippet (only for debugging)

          • params – name and optional types of model parameters

            • -

          • derived_params (Sequence[Tuple[str, Callable, str | ResolvedType]] | None) – names, types and callables to calculate +

          • derived_params (Optional[Sequence[Tuple[str, Callable, Union[str, ResolvedType]]]]) – names, types and callables to calculate derived parameter values from paramss

            • -

          • row_build_code (str | None) – code for building connectivity row by row

            • -

          • col_build_code (str | None) – code for building connectivity column by column

            • -

          • calc_max_row_len_func (Callable | None) – used to calculate the maximum +

          • row_build_code (Optional[str]) – code for building connectivity row by row

            • +

          • col_build_code (Optional[str]) – code for building connectivity column by column

            • +

          • calc_max_row_len_func (Optional[Callable]) – used to calculate the maximum row length of synaptic matrix created using this snippet

            • -

          • calc_max_col_len_func (Callable | None) – used to calculate the maximum +

          • calc_max_col_len_func (Optional[Callable]) – used to calculate the maximum column length of synaptic matrix created using this snippet

            • -

          • calc_kernel_size_func (Callable | None) – used to calculate the size of the kernel if snippet requires one

            • -

          • extra_global_params (Sequence[Tuple[str, str | ResolvedType]] | None) – names and types of snippet extra global parameters

            • -

          • param_names (Sequence[str | Tuple[str, str | ResolvedType]] | None) –

            • +

          • calc_kernel_size_func (Optional[Callable]) – used to calculate the size of the kernel if snippet requires one

            • +

          • extra_global_params (Optional[Sequence[Tuple[str, Union[str, ResolvedType]]]]) – names and types of snippet extra global parameters

            • +

          • param_names (Optional[Sequence[Union[str, Tuple[str, Union[str, ResolvedType]]]]]) –

          @@ -2141,14 +2124,14 @@

          Additional input variablesParameters:

          • class_name (str) – name of the snippet (only for debugging)

            • -

          • params (Sequence[str | Tuple[str, str | ResolvedType]] | None) – name and optional types of model parameters

            • -

          • derived_params (Sequence[Tuple[str, Callable, str | ResolvedType]] | None) – names, types and callables to calculate +

          • params (Optional[Sequence[Union[str, Tuple[str, Union[str, ResolvedType]]]]]) – name and optional types of model parameters

            • +

          • derived_params (Optional[Sequence[Tuple[str, Callable, Union[str, ResolvedType]]]]) – names, types and callables to calculate derived parameter values from paramss

            • -

          • diagonal_build_code (str | None) – code for building connectivity row by row

            • -

          • calc_max_row_len_func (Callable | None) – used to calculate the maximum +

          • diagonal_build_code (Optional[str]) – code for building connectivity row by row

            • +

          • calc_max_row_len_func (Optional[Callable]) – used to calculate the maximum row length of synaptic matrix created using this snippet

            • -

          • calc_kernel_size_func (Callable | None) – used to calculate the size of the kernel

            • -

          • extra_global_params (Sequence[Tuple[str, str | ResolvedType]] | None) – names and types of snippet extra global parameters

            • +

          • calc_kernel_size_func (Optional[Callable]) – used to calculate the size of the kernel

            • +

          • extra_global_params (Optional[Sequence[Tuple[str, Union[str, ResolvedType]]]]) – names and types of snippet extra global parameters

          @@ -2211,10 +2194,10 @@

          Additional input variablesParameters:
          @@ -2495,14 +2469,14 @@

          Spike-like events
          Parameters:
          @@ -2526,11 +2500,11 @@

          Spike-like events
          Parameters:

          @@ -2582,11 +2556,11 @@

          Spike-like events
          Parameters:
          @@ -2609,12 +2583,12 @@

          Spike-like eventsweight_update_models) or an instance of WeightUpdateModelBase (for example returned by create_weight_update_model())

          • -

        • params (Dict[str, int | float]) – parameter values (see `Parameters`_)

          • -

        • vars (Dict[str, VarInit | int | float | ndarray | Sequence]) – initial synaptic variable values or +

        • params (Dict[str, Union[int, float]]) – parameter values (see `Parameters`_)

          • +

        • vars (Dict[str, Union[VarInit, int, float, ndarray, Sequence]]) – initial synaptic variable values or initialisers (see `Variables`_)

          • -

        • pre_vars (Dict[str, VarInit | int | float | ndarray | Sequence]) – initial presynaptic variable values or +

        • pre_vars (Dict[str, Union[VarInit, int, float, ndarray, Sequence]]) – initial presynaptic variable values or initialisers (see `Variables`_)

          • -

        • post_vars (Dict[str, VarInit | int | float | ndarray | Sequence]) – initial postsynaptic variable values or initialisers +

        • post_vars (Dict[str, Union[VarInit, int, float, ndarray, Sequence]]) – initial postsynaptic variable values or initialisers (see `Variables`_)

        • pre_var_refs (Dict[str, VarReference]) – references to presynaptic neuron variables, typically created using create_var_ref() @@ -2814,6 +2788,14 @@

          Submodules +

          pygenn.deprecated module

          +
          +
          +pygenn.deprecated.deprecated(message)
          +
          +

          pygenn.genn_groups module

          @@ -2839,11 +2821,6 @@

          Submodules -
          -get_var_values(var_name)
          -
          -
          @@ -2858,11 +2835,6 @@

          Submodulesclass pygenn.genn_groups.CustomUpdateWUMixin

          Bases: GroupMixin

          Mixin added to custom update WU objects

          -
          -
          -get_var_values(var_name)
          -
          -

          @@ -2883,50 +2855,6 @@

          Submodules -
          -pull_extra_global_param_from_device(egp_name)
          -

          Pull extra global parameter from device

          -
          -
          Parameters:
          -

          egp_name – name of the extra global parameter

          -
          -
          -

          - -
          -
          -pull_var_from_device(var_name)
          -

          Pull variable from the device for a given population

          -
          -
          Parameters:
          -

          var_name – name of the variable

          -
          -
          -
          - -
          -
          -push_extra_global_param_to_device(egp_name)
          -

          Push extra global parameter to device

          -
          -
          Parameters:
          -

          egp_name – name of the extra global parameter

          -
          -
          -
          - -
          -
          -push_var_to_device(var_name)
          -

          Push population state variable to the device

          -
          -
          Parameters:
          -

          var_name – name of the variable

          -
          -
          -
          -
          set_dynamic_param_value(name, value)
          @@ -2935,7 +2863,7 @@

          SubmodulesParameters:

          • name (str) – name of the parameter

            • -

          • value (float | int) – numeric value to assign to parameters

            • +

          • value (Union[float, int]) – numeric value to assign to parameters

          @@ -3025,11 +2953,6 @@

          Submodules -
          -get_var_values(var_name)
          -
          -
          property post_spike_event_recording_data: List[Tuple[ndarray, ndarray]]
          @@ -3056,40 +2979,12 @@

          Submodules -
          -pull_in_syn_from_device()
          -

          Pull synaptic input current from device

          -

          - -
          -
          -pull_psm_extra_global_param_from_device(egp_name)
          -

          Wrapper around GeNNModel.pull_extra_global_param_from_device

          -

          Args: -egp_name – string with the name of the variable

          -
          -
          push_connectivity_to_device()

          Push connectivity to device

          -
          -
          -push_in_syn_to_device()
          -

          Push synaptic input current to device

          -
          - -
          -
          -push_psm_extra_global_param_to_device(egp_name)
          -

          Wrapper around GeNNModel.push_extra_global_param_to_device

          -

          Args: -egp_name – string with the name of the variable

          -
          -
          set_sparse_connections(pre_indices, post_indices)
          @@ -3097,8 +2992,8 @@

          Submodules
          Parameters:
            -

          • pre_indices (Sequence[int] | ndarray) – presynaptic indices

            • -

          • post_indices (Sequence[int] | ndarray) – postsynaptic indices

            • +

          • pre_indices (Union[Sequence[int], ndarray]) – presynaptic indices

            • +

          • post_indices (Union[Sequence[int], ndarray]) – postsynaptic indices

          @@ -3424,7 +3319,7 @@

          Submodules
          Parameters:
          -

          variable_type (ResolvedType | UnresolvedType) –

          +

          variable_type (Union[ResolvedType, UnresolvedType]) –

          @@ -3444,7 +3339,7 @@

          Submodules
          Parameters:
            -

          • variable_type (ResolvedType | UnresolvedType) – data type of array elements

            • +

          • variable_type (Union[ResolvedType, UnresolvedType]) – data type of array elements

          • group – group array belongs to

          @@ -3486,7 +3381,7 @@

          SubmodulesParameters:

          • variable_name (str) – name of the extra global parameter

            • -

          • variable_type (ResolvedType | UnresolvedType) – data type of the extra global parameter

            • +

          • variable_type (Union[ResolvedType, UnresolvedType]) – data type of the extra global parameter

          • group – group extra global parameter belongs to

          • init_values – values to initialise extra global parameter with

          @@ -3503,11 +3398,6 @@

          Submodules -
          -set_values(values)
          -

          -
          property values: ndarray
          @@ -3531,7 +3421,7 @@

          SubmodulesParameters:

          • variable_name (str) – name of the variable

            • -

          • variable_type (ResolvedType | UnresolvedType) – data type of the variable

            • +

          • variable_type (Union[ResolvedType, UnresolvedType]) – data type of the variable

          • init_values – values to initialise variable with

          • group – group variable belongs to

          @@ -3576,7 +3466,7 @@

          SubmodulesParameters:

          • variable_name (str) – name of the variable

            • -

          • variable_type (ResolvedType | UnresolvedType) – data type of the variable

            • +

          • variable_type (Union[ResolvedType, UnresolvedType]) – data type of the variable

          • init_values – values to initialise variable with

          • group – group variable belongs to

          @@ -3619,7 +3509,7 @@

          SubmodulesParameters:

          • variable_name (str) – name of the variable

            • -

          • variable_type (ResolvedType | UnresolvedType) – data type of the variable

            • +

          • variable_type (Union[ResolvedType, UnresolvedType]) – data type of the variable

          • init_values – values to initialise variable with

          • group – group variable belongs to

          @@ -3651,11 +3541,6 @@

          Submodules -
          -set_values(values)
          -

          -

          diff --git a/documentation/5/tutorials/1_neurons.html b/documentation/5/tutorials/1_neurons.html deleted file mode 100644 index 2038cfa23..000000000 --- a/documentation/5/tutorials/1_neurons.html +++ /dev/null @@ -1,305 +0,0 @@ - - - - - - - Defining populations of neurons — PyGeNN documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - -
          - - -
          - -
          -
          -
          - -
          -
          -
          -
          - -
          -

          Defining populations of neurons

          -

          In this tutorial we’re going to define a population of Izhikevich neurons and configure individual neurons within it to operate in various regimes: image.png

          -

          (Electronic version of the figure and reproduction permissions are freely available at www.izhikevich.com) ## Install PyGeNN wheel from Google Drive Download wheel file

          -
          -
          [1]:
-
          -
          -
          if "google.colab" in str(get_ipython()):
-    #import IPython
-    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a
-    #%run "../install_collab.ipynb"
-    !pip install gdown --upgrade
-    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
-    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
-    %env CUDA_PATH=/usr/local/cuda
-
          -
          -
          -
          -
          -
          -
          -
          -Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)
-Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)
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-Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)
-Downloading...
-From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
-To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
-100% 8.29M/8.29M [00:00<00:00, 118MB/s]
-Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
-Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)
-Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)
-Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)
-Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)
-pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.
-env: CUDA_PATH=/usr/local/cuda
-
          -
          -
          -

          Build model

          -

          Import numpy, matplotlib and the main GeNNModel class from PyGeNN

          -
          -
          [2]:
-
          -
          -
          import numpy as np
-import matplotlib.pyplot as plt
-
-from pygenn import GeNNModel
-
          -
          -
          -

          Create a new model called “tutorial1” with floating point precision and set the simulation timestep to 0.1ms

          -
          -
          [3]:
-
          -
          -
          model = GeNNModel("float", "tutorial1")
-model.dt = 0.1
-
          -
          -
          -

          Configure initial state for a population of Izhikevich neurons with a constant value for the V and U state variables and different values for the a, b, c and d parameters (because we are going to be using the IzhikevichVariable model, the parameters are also implemented as state variables so they can vary across the population of neurons)

          -
          -
          [4]:
-
          -
          -
          izk_init = {"V": -65.0,
-            "U": -20.0,
-            "a": [0.02,     0.1,    0.02,   0.02],
-            "b": [0.2,      0.2,    0.2,    0.2],
-            "c": [-65.0,    -65.0,  -50.0,  -55.0],
-            "d": [8.0,      2.0,    2.0,    4.0]}
-
          -
          -
          -

          Add a population of 4 of these neurons (GeNN’s built in models are selected by specifying model as a string)

          -
          -
          [5]:
-
          -
          -
          pop = model.add_neuron_population("Neurons", 4, "IzhikevichVariable", {}, izk_init)
-
          -
          -
          -

          Add a DC (i.e. constant) current input to the population to inject a constant current into the neurons and make them spike

          -
          -
          [6]:
-
          -
          -
          model.add_current_source("CurrentSource", "DC", pop, {"amp": 10.0}, {});
-
          -
          -
          -

          Generate code and load it into PyGeNN

          -
          -
          [7]:
-
          -
          -
          model.build()
-model.load()
-
          -
          -
          -
          -
          -
          -

          Simulate tutorial model

          -

          State variables in the GeNN model can be accessed directly using memory views. Create a memory view to access the membrane voltage of our neurons

          -
          -
          [8]:
-
          -
          -
          voltage = pop.vars["V"]
-
          -
          -
          -

          We want to record these voltages for each neuron every timestep so, after every we simulate each time step, we copy the membrane voltage back from the GPU and add a copy (because the memory view gives access to the actual simulator state we need to make a copy) to a list

          -
          -
          [10]:
-
          -
          -
          voltages = []
-while model.t < 200.0:
-    model.step_time()
-    voltage.pull_from_device()
-    voltages.append(voltage.values)
-
          -
          -
          -

          Plot the voltages over time in 4 seperate panels

          -
          -
          [11]:
-
          -
          -
          # Stack voltages together into a 2000x4 matrix
-voltages = np.vstack(voltages)
-
-# Create figure with 4 axes
-fig, axes = plt.subplots(4, sharex=True, figsize=(15, 8))
-
-# Plot voltages of each neuron in
-for i, t in enumerate(["RS", "FS", "CH", "IB"]):
-    axes[i].set_title(t)
-    axes[i].set_ylabel("V [mV]")
-    axes[i].plot(np.arange(0.0, 200.0, 0.1), voltages[:,i])
-
-axes[-1].set_xlabel("Time [ms]");
-
          -
          -
          -
          -
          -
          -
          -../_images/tutorials_1_neurons_19_0.png -
          -
          -
          -

          Exercises

          -
            -

          1. Add three more neurons with the remaining neuron types: Thalamo-cortical, resonator, and low-threshold spiking.

            2. -

          2. Make a neuron that changes its type gradually from the beginning to the end of the simulation. Use a longer simulation time to make this meaningful.

            3. -
          -
          -
          - - -
          -
          - -
          -
          -
          -
          - - - - \ No newline at end of file diff --git a/documentation/5/tutorials/1_neurons.ipynb b/documentation/5/tutorials/1_neurons.ipynb deleted file mode 100644 index 23107a370..000000000 --- a/documentation/5/tutorials/1_neurons.ipynb +++ /dev/null @@ -1,334 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "lGa0_oLb61zz" - }, - "source": [ - "# Defining populations of neurons\n", - "In this tutorial we're going to define a population of Izhikevich neurons and configure individual neurons within it to operate in various regimes:\n", - "![image.png](data:image/png;base64,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)\n", - "\n", - "(Electronic version of the figure and reproduction permissions are freely available at www.izhikevich.com)\n", - "## Install PyGeNN wheel from Google Drive\n", - "Download wheel file" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t2ihZLXh5VD-", - "outputId": "510653d0-3172-4c5f-c101-1bfe66297121" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n", - "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n", - "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n", - "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n", - "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n", - "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n", - "Downloading...\n", - "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n", - "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n", - "100% 8.29M/8.29M [00:00<00:00, 118MB/s]\n", - "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n", - "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n", - "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n", - "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n", - "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n", - "env: CUDA_PATH=/usr/local/cuda\n" - ] - } - ], - "source": [ - "if \"google.colab\" in str(get_ipython()):\n", - " #import IPython\n", - " #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n", - " #%run \"../install_collab.ipynb\"\n", - " !pip install gdown --upgrade\n", - " !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n", - " !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n", - " %env CUDA_PATH=/usr/local/cuda" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8GngV4fThkhM" - }, - "source": [ - "## Build model\n", - "Import numpy, matplotlib and the main `GeNNModel` class from PyGeNN" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "id": "q6WNelXsbjy1" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pygenn import GeNNModel" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "261uLnJsgyeE" - }, - "source": [ - "Create a new model called \"tutorial1\" with floating point precision and set the simulation timestep to 0.1ms" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "EDpiDOK0gkEz" - }, - "outputs": [], - "source": [ - "model = GeNNModel(\"float\", \"tutorial1\")\n", - "model.dt = 0.1" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "LrfXpMqfjRBe" - }, - "source": [ - "Configure initial state for a population of Izhikevich neurons with a constant value for the `V` and `U` state variables and different values for the `a`, `b`, `c` and `d` parameters (because we are going to be using the `IzhikevichVariable` model, the parameters are also implemented as state variables so they can vary across the population of neurons)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "tU2M4MgFjRae" - }, - "outputs": [], - "source": [ - "izk_init = {\"V\": -65.0,\n", - " \"U\": -20.0,\n", - " \"a\": [0.02, 0.1, 0.02, 0.02],\n", - " \"b\": [0.2, 0.2, 0.2, 0.2],\n", - " \"c\": [-65.0, -65.0, -50.0, -55.0],\n", - " \"d\": [8.0, 2.0, 2.0, 4.0]}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YrOQPgYBjuym" - }, - "source": [ - "Add a population of 4 of these neurons (GeNN's built in models are selected by specifying model as a string)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "zc-e5Lu2j_Yq" - }, - "outputs": [], - "source": [ - "pop = model.add_neuron_population(\"Neurons\", 4, \"IzhikevichVariable\", {}, izk_init)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "u8wu06PZkBnS" - }, - "source": [ - "Add a DC (i.e. constant) current input to the population to inject a constant current into the neurons and make them spike\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "GNBjEGWPj_3Q" - }, - "outputs": [], - "source": [ - "model.add_current_source(\"CurrentSource\", \"DC\", pop, {\"amp\": 10.0}, {});" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "IGKUIiaGkA0Z" - }, - "source": [ - "Generate code and load it into PyGeNN" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "d0mK72rYkiYe" - }, - "outputs": [], - "source": [ - "model.build()\n", - "model.load()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cNs18ywkkq6T" - }, - "source": [ - "# Simulate tutorial model\n", - "State variables in the GeNN model can be accessed directly using memory views. Create a memory view to access the membrane voltage of our neurons" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "nWFVfYfdkobN" - }, - "outputs": [], - "source": [ - "voltage = pop.vars[\"V\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wv-hDOIe3Hgy" - }, - "source": [ - "We want to record these voltages for each neuron every timestep so, after every we simulate each time step, we copy the membrane voltage back from the GPU and add a copy (because the memory view gives access to the actual simulator state we need to make a copy) to a list" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "id": "99MBe7JKk5Ut" - }, - "outputs": [], - "source": [ - "voltages = []\n", - "while model.t < 200.0:\n", - " model.step_time()\n", - " voltage.pull_from_device()\n", - " voltages.append(voltage.values)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Ug6S1h-z3k7v" - }, - "source": [ - "Plot the voltages over time in 4 seperate panels" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 718 - }, - "id": "RsVbAbIPlEO8", - "outputId": "731335aa-f7da-4490-fae4-daa33b98f92b", - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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          " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Stack voltages together into a 2000x4 matrix\n", - "voltages = np.vstack(voltages)\n", - "\n", - "# Create figure with 4 axes\n", - "fig, axes = plt.subplots(4, sharex=True, figsize=(15, 8))\n", - "\n", - "# Plot voltages of each neuron in\n", - "for i, t in enumerate([\"RS\", \"FS\", \"CH\", \"IB\"]):\n", - " axes[i].set_title(t)\n", - " axes[i].set_ylabel(\"V [mV]\")\n", - " axes[i].plot(np.arange(0.0, 200.0, 0.1), voltages[:,i])\n", - "\n", - "axes[-1].set_xlabel(\"Time [ms]\");" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "h4yw3JiNpXOM" - }, - "source": [ - "Exercises\n", - "---\n", - "1. Add three more neurons with the remaining neuron types: Thalamo-cortical, resonator, and low-threshold spiking.\n", - "2. Make a neuron that changes its type gradually from the beginning to the end of the simulation. Use a longer simulation time to make this meaningful." - ] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "gpuType": "T4", - "name": "1_neurons", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/documentation/5/tutorials/2_synapses.html b/documentation/5/tutorials/2_synapses.html deleted file mode 100644 index bb735486f..000000000 --- a/documentation/5/tutorials/2_synapses.html +++ /dev/null @@ -1,392 +0,0 @@ - - - - - - - Tutorial 2 - synapses — PyGeNN documentation - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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          Tutorial 2 - synapses

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          This tutorial explains how to add synapses to connect the neuron populations we talked about in the previous tutorial into a balanced random network model.

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          Install PyGeNN wheel from Google Drive

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          Download wheel file

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-    #import IPython
-    #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a
-    #%run "../install_collab.ipynb"
-    !pip install gdown --upgrade
-    !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW
-    !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl
-    %env CUDA_PATH=/usr/local/cuda
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          Import numpy, matplotlib and the main GeNNModel class from PyGeNN

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          Build model

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          Create a new model called “tutorial2” with floating point precision and set the simulation timestep to 1ms

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          For this tutorial were going to use Leaky-Integrate-and-Fire neurons which have the following dynamics:

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          tau_{text{m}} frac{dV_{i}}{dt} = & (V_{text{rest}} - V_{i}) + R_{text{m}}I_{i}.

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          end{align}`

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          We configure these using the parameters from (Vogels & Abbott, 2005 link text). Note that the resting voltage is higher than the reset to provide a constant current input TODO get rid of this

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          lif_params = {"C": 1.0, "TauM": 20.0, "Vrest": -49.0, "Vreset": -60.0,
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          So that the network starts in a non-pathological state, we want to randomly initialise the neuron’s membrane potentials so that they are between their threshold and resting potentials. GeNN provides various initialisation “snippets” which can be used to parallelise variable initialisation but, here we are going to use Uniform to sample values from a uniform distribution.

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          For this tutorial we create an excitary and inhibitory population of these neurons and we enable spike recording for both

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          exc_pop = model.add_neuron_population("E", 3200, "LIF", lif_params, lif_init)
-inh_pop = model.add_neuron_population("I", 800, "LIF", lif_params, lif_init)
-
-exc_pop.spike_recording_enabled = True
-inh_pop.spike_recording_enabled = True
-
          -
          -
          -

          So this network sits in a asynchronous irregular state, we initialise the inhibitory weights as follows:

          -
          -
          [18]:
-
          -
          -
          exc_synapse_init = {"g": 0.0008}
-inh_synapse_init = {"g": -0.0102}
-
          -
          -
          -
          -
          We are going to use an exponential synapse model where a single time constant \(\tau_{\text{syn}}\) to define it’s dynamics: :nbsphinx-math:`begin{align}

          tau_{text{syn}} frac{dI_{text{syn}_{i}}}{dt} = & -I_{text{syn}_{i}} + sum_{j=0}^{n} w_{ij} sum_{t_{j}} delta(t - t_{j}).

          -
          -
          -

          end{align}` To approximate biolological AMPA and GABA receptors, we pick different time constants for excitatory and inhibitory synapses.

          -
          -
          [19]:
-
          -
          -
          exc_post_syn_params = {"tau": 5.0}
-inh_post_syn_params = {"tau": 10.0}
-
          -
          -
          -

          We want to connect these with a fixed probability of 0.1

          -
          -
          [20]:
-
          -
          -
          fixed_prob = {"prob": 0.1}
-
          -
          -
          -

          Now we have defined the synaptic weights (in GeNN, this is the responsibility of the weight update model), the synapse dynamics (in GeNN this is the responsibility of the postsynaptic model) and the connectivity parameters we can add the synapse populations to the model. Each of these synapse populations all configured with: * SPARSE connectivity meaning that they are connected with a sparse weight matrix. * The built in StaticPulseConstantWeight weight update model which -is used for spiking synapses without any sort of learning. This has a single parameter g representing the synaptic weight used for all synapses. * The build in ExpCurr postsynaptic model which implements the exponential synapses described previously * The sparse connectivity is configured using the built in FixedProbability model described previosuly

          -
          -
          [21]:
-
          -
          -
          model.add_synapse_population("EE", "SPARSE",
-    exc_pop, exc_pop,
-    init_weight_update("StaticPulseConstantWeight", exc_synapse_init),
-    init_postsynaptic("ExpCurr", exc_post_syn_params),
-    init_sparse_connectivity("FixedProbabilityNoAutapse", fixed_prob))
-
-model.add_synapse_population("EI", "SPARSE",
-    exc_pop, inh_pop,
-    init_weight_update("StaticPulseConstantWeight", exc_synapse_init),
-    init_postsynaptic("ExpCurr", exc_post_syn_params),
-    init_sparse_connectivity("FixedProbability", fixed_prob))
-
-model.add_synapse_population("II", "SPARSE",
-    inh_pop, inh_pop,
-    init_weight_update("StaticPulseConstantWeight", inh_synapse_init),
-    init_postsynaptic("ExpCurr", inh_post_syn_params),
-    init_sparse_connectivity("FixedProbabilityNoAutapse", fixed_prob))
-
-model.add_synapse_population("IE", "SPARSE",
-    inh_pop, exc_pop,
-    init_weight_update("StaticPulseConstantWeight", inh_synapse_init),
-    init_postsynaptic("ExpCurr", inh_post_syn_params),
-    init_sparse_connectivity("FixedProbability", fixed_prob));
-
          -
          -
          -

          Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout our entire 1 second simulation

          -
          -
          [22]:
-
          -
          -
          model.build()
-model.load(num_recording_timesteps=1000)
-
          -
          -
          -
          -
          -

          Simulate model

          -

          Simulate the model for 1000 timesteps

          -
          -
          [23]:
-
          -
          -
          while model.timestep < 1000:
-    model.step_time()
-
          -
          -
          -

          Copy the recorded spike data back from the GPU and extract the spike times and IDs

          -
          -
          [24]:
-
          -
          -
          model.pull_recording_buffers_from_device()
-
-exc_spike_times, exc_spike_ids = exc_pop.spike_recording_data[0]
-inh_spike_times, inh_spike_ids = inh_pop.spike_recording_data[0]
-
          -
          -
          -

          Plot spikes and rates

          -
          -
          [25]:
-
          -
          -
          fig, axes = plt.subplots(3, sharex=True, figsize=(20, 10))
-
-# Define some bins to calculate spike rates
-bin_size = 20.0
-rate_bins = np.arange(0, 1000.0, bin_size)
-rate_bin_centres = rate_bins[:-1] + (bin_size / 2.0)
-
-# Plot excitatory and inhibitory spikes on first axis
-axes[0].scatter(exc_spike_times, exc_spike_ids, s=1)
-axes[0].scatter(inh_spike_times, inh_spike_ids + 3200, s=1)
-
-# Plot excitatory rates on second axis
-exc_rate = np.histogram(exc_spike_times, bins=rate_bins)[0]
-axes[1].plot(rate_bin_centres, exc_rate * (1000.0 / bin_size) * (1.0 / 3200.0))
-
-# Plot inhibitory rates on third axis
-inh_rate = np.histogram(inh_spike_times, bins=rate_bins)[0]
-axes[2].plot(rate_bin_centres, inh_rate * (1000.0 / bin_size) * (1.0 / 800.0))
-
-# Label axes
-axes[0].set_ylabel("Neuron ID")
-axes[1].set_ylabel("Excitatory rate [Hz]")
-axes[2].set_ylabel("Inhibitory rate [Hz]")
-axes[2].set_xlabel("Time [ms]");
-
          -
          -
          -
          -
          -
          -
          -../_images/tutorials_2_synapses_28_0.png -
          -
          -
          -
          [ ]:
-
          -
          -
          
-
          -
          -
          -
          -
          - - -
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          - -
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          - - - - \ No newline at end of file diff --git a/documentation/5/tutorials/2_synapses.ipynb b/documentation/5/tutorials/2_synapses.ipynb deleted file mode 100644 index 209799ec9..000000000 --- a/documentation/5/tutorials/2_synapses.ipynb +++ /dev/null @@ -1,466 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "lGa0_oLb61zz" - }, - "source": [ - "# Tutorial 2 - synapses\n", - "This tutorial explains how to add synapses to connect the neuron populations we talked about in the previous tutorial into a balanced random network model.\n", - "\n", - "## Install PyGeNN wheel from Google Drive\n", - "Download wheel file" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "t2ihZLXh5VD-", - "outputId": "462667f0-6335-4203-d1e1-7ca16b76806b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n", - "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n", - "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.13.1)\n", - "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.2)\n", - "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.6)\n", - "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n", - "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n", - "Downloading...\n", - "From: https://drive.google.com/uc?id=1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n", - "To: /content/pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n", - "100% 8.29M/8.29M [00:00<00:00, 98.5MB/s]\n", - "Processing ./pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n", - "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.25.2)\n", - "Requirement already satisfied: deprecated in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (1.2.14)\n", - "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pygenn==5.0.0) (5.9.5)\n", - "Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated->pygenn==5.0.0) (1.14.1)\n", - "pygenn is already installed with the same version as the provided wheel. Use --force-reinstall to force an installation of the wheel.\n", - "env: CUDA_PATH=/usr/local/cuda\n" - ] - } - ], - "source": [ - "if \"google.colab\" in str(get_ipython()):\n", - " #import IPython\n", - " #IPython.core.magics.execution.ExecutionMagics.run.func_defaults[2] = lambda a: a\n", - " #%run \"../install_collab.ipynb\"\n", - " !pip install gdown --upgrade\n", - " !gdown 1V_GzXUDzcFz9QDIpxAD8QNEglcSipssW\n", - " !pip install pygenn-5.0.0-cp310-cp310-linux_x86_64.whl\n", - " %env CUDA_PATH=/usr/local/cuda" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8GngV4fThkhM" - }, - "source": [ - "Import numpy, matplotlib and the main `GeNNModel` class from PyGeNN" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "q6WNelXsbjy1" - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from pygenn import GeNNModel, init_postsynaptic, init_sparse_connectivity, init_var, init_weight_update" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "261uLnJsgyeE" - }, - "source": [ - "## Build model\n", - "Create a new model called \"tutorial2\" with floating point precision and set the simulation timestep to 1ms" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "EDpiDOK0gkEz" - }, - "outputs": [], - "source": [ - "model = GeNNModel(\"float\", \"tutorial2\")\n", - "model.dt = 1.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "mki7b8R8xhAv" - }, - "source": [ - "For this tutorial were going to use Leaky-Integrate-and-Fire neurons which have the following dynamics:\n", - "\n", - "\\begin{align}\n", - " \\tau_{\\text{m}} \\frac{dV_{i}}{dt} = & (V_{\\text{rest}} - V_{i}) + R_{\\text{m}}I_{i}.\n", - "\\end{align}\n", - "\n", - "We configure these using the parameters from (Vogels & Abbott, 2005 [link text](https://doi.org/10.1523/JNEUROSCI.3508-05.2005)). Note that the resting voltage is **higher** than the reset to provide a constant current input **TODO** get rid of this" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "id": "AkMk7Ml4tOxM" - }, - "outputs": [], - "source": [ - "lif_params = {\"C\": 1.0, \"TauM\": 20.0, \"Vrest\": -49.0, \"Vreset\": -60.0,\n", - " \"Vthresh\": -50.0, \"Ioffset\": 0.0, \"TauRefrac\": 5.0}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XboW6qxrxnok" - }, - "source": [ - "So that the network starts in a non-pathological state, we want to randomly initialise the neuron's membrane potentials so that they are between their threshold and resting potentials. GeNN provides [various](https://genn-team.github.io/genn/documentation/4/html/d4/dc6/sectVariableInitialisation.html) initialisation \"snippets\" which can be used to parallelise variable initialisation but, here we are going to use `Uniform` to sample values from a uniform distribution." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "id": "dWf4f4Bpxl7u" - }, - "outputs": [], - "source": [ - "lif_init = {\"V\": init_var(\"Uniform\", {\"min\": -60.0, \"max\": -50.0}),\n", - " \"RefracTime\": 0.0}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "B3hhcDILxeki" - }, - "source": [ - "For this tutorial we create an excitary and inhibitory population of these neurons and we enable spike recording for both" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "id": "5AECcjzMs8Iz" - }, - "outputs": [], - "source": [ - "exc_pop = model.add_neuron_population(\"E\", 3200, \"LIF\", lif_params, lif_init)\n", - "inh_pop = model.add_neuron_population(\"I\", 800, \"LIF\", lif_params, lif_init)\n", - "\n", - "exc_pop.spike_recording_enabled = True\n", - "inh_pop.spike_recording_enabled = True" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QypcRqLi0hgq" - }, - "source": [ - "So this network sits in a asynchronous irregular state, we initialise the inhibitory weights as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "id": "WpmzQu0UuPky" - }, - "outputs": [], - "source": [ - "exc_synapse_init = {\"g\": 0.0008}\n", - "inh_synapse_init = {\"g\": -0.0102}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "58kevKNm0rfi" - }, - "source": [ - "We are going to use an exponential synapse model where a single time constant $\\tau_{\\text{syn}}$ to define it's dynamics:\n", - "\\begin{align}\n", - " \\tau_{\\text{syn}} \\frac{dI_{\\text{syn}_{i}}}{dt} = & -I_{\\text{syn}_{i}} + \\sum_{j=0}^{n} w_{ij} \\sum_{t_{j}} \\delta(t - t_{j}).\n", - "\\end{align}\n", - "To approximate biolological AMPA and GABA receptors, we pick different time constants for excitatory and inhibitory synapses." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "id": "VnbedWiB0oAF" - }, - "outputs": [], - "source": [ - "exc_post_syn_params = {\"tau\": 5.0}\n", - "inh_post_syn_params = {\"tau\": 10.0}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "nJ1JwSAO1qNi" - }, - "source": [ - "We want to connect these with a fixed probability of 0.1" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "id": "ciwtEyzB0nte" - }, - "outputs": [], - "source": [ - "fixed_prob = {\"prob\": 0.1}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "HWvUT89Z106p" - }, - "source": [ - "Now we have defined the synaptic weights (in GeNN, this is the responsibility of the **weight update model**), the synapse dynamics (in GeNN this is the responsibility of the **postsynaptic model**) and the connectivity parameters we can add the synapse populations to the model.\n", - "Each of these synapse populations all configured with:\n", - "* `SPARSE` connectivity meaning that they are connected with a sparse weight matrix.\n", - "* The built in `StaticPulseConstantWeight` **weight update model** which is used for spiking synapses without any sort of learning. This has a single parameter `g` representing the synaptic weight used for all synapses.\n", - "* The build in `ExpCurr` **postsynaptic model** which implements the exponential synapses described previously\n", - "* The sparse connectivity is configured using the built in `FixedProbability` model described previosuly\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "id": "rD6K22qZtxId" - }, - "outputs": [], - "source": [ - "model.add_synapse_population(\"EE\", \"SPARSE\",\n", - " exc_pop, exc_pop,\n", - " init_weight_update(\"StaticPulseConstantWeight\", exc_synapse_init),\n", - " init_postsynaptic(\"ExpCurr\", exc_post_syn_params),\n", - " init_sparse_connectivity(\"FixedProbabilityNoAutapse\", fixed_prob))\n", - "\n", - "model.add_synapse_population(\"EI\", \"SPARSE\",\n", - " exc_pop, inh_pop,\n", - " init_weight_update(\"StaticPulseConstantWeight\", exc_synapse_init),\n", - " init_postsynaptic(\"ExpCurr\", exc_post_syn_params),\n", - " init_sparse_connectivity(\"FixedProbability\", fixed_prob))\n", - "\n", - "model.add_synapse_population(\"II\", \"SPARSE\",\n", - " inh_pop, inh_pop,\n", - " init_weight_update(\"StaticPulseConstantWeight\", inh_synapse_init),\n", - " init_postsynaptic(\"ExpCurr\", inh_post_syn_params),\n", - " init_sparse_connectivity(\"FixedProbabilityNoAutapse\", fixed_prob))\n", - "\n", - "model.add_synapse_population(\"IE\", \"SPARSE\",\n", - " inh_pop, exc_pop,\n", - " init_weight_update(\"StaticPulseConstantWeight\", inh_synapse_init),\n", - " init_postsynaptic(\"ExpCurr\", inh_post_syn_params),\n", - " init_sparse_connectivity(\"FixedProbability\", fixed_prob));" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "FiAsrqRx5OgZ" - }, - "source": [ - "Run code generator to generate simulation code for model and load it into PyGeNN. Allocate a spike recording buffer large enough to store the spikes emitted throughout our entire 1 second simulation" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "id": "0I-7lZP4vWE2" - }, - "outputs": [], - "source": [ - "model.build()\n", - "model.load(num_recording_timesteps=1000)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1JLVx3u1281A" - }, - "source": [ - "## Simulate model\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "8HhNMK4C4d6f" - }, - "source": [ - "Simulate the model for 1000 timesteps" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "id": "v0lT7gaIviev" - }, - "outputs": [], - "source": [ - "while model.timestep < 1000:\n", - " model.step_time()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "SUzXrYxr4kO5" - }, - "source": [ - "Copy the recorded spike data back from the GPU and extract the spike times and IDs" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": { - "id": "bDJLu6Kwvn7W" - }, - "outputs": [], - "source": [ - "model.pull_recording_buffers_from_device()\n", - "\n", - "exc_spike_times, exc_spike_ids = exc_pop.spike_recording_data[0]\n", - "inh_spike_times, inh_spike_ids = inh_pop.spike_recording_data[0]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jS5OtCX15CCJ" - }, - "source": [ - "Plot spikes and rates" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 850 - }, - "id": "9rWE-Rvjvo5I", - "outputId": "3133a219-c0bb-4258-84fe-9bbb2fc2a415" - }, - "outputs": [ - { - "data": { - "image/png": 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          " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, axes = plt.subplots(3, sharex=True, figsize=(20, 10))\n", - "\n", - "# Define some bins to calculate spike rates\n", - "bin_size = 20.0\n", - "rate_bins = np.arange(0, 1000.0, bin_size)\n", - "rate_bin_centres = rate_bins[:-1] + (bin_size / 2.0)\n", - "\n", - "# Plot excitatory and inhibitory spikes on first axis\n", - "axes[0].scatter(exc_spike_times, exc_spike_ids, s=1)\n", - "axes[0].scatter(inh_spike_times, inh_spike_ids + 3200, s=1)\n", - "\n", - "# Plot excitatory rates on second axis\n", - "exc_rate = np.histogram(exc_spike_times, bins=rate_bins)[0]\n", - "axes[1].plot(rate_bin_centres, exc_rate * (1000.0 / bin_size) * (1.0 / 3200.0))\n", - "\n", - "# Plot inhibitory rates on third axis\n", - "inh_rate = np.histogram(inh_spike_times, bins=rate_bins)[0]\n", - "axes[2].plot(rate_bin_centres, inh_rate * (1000.0 / bin_size) * (1.0 / 800.0))\n", - "\n", - "# Label axes\n", - "axes[0].set_ylabel(\"Neuron ID\")\n", - "axes[1].set_ylabel(\"Excitatory rate [Hz]\")\n", - "axes[2].set_ylabel(\"Inhibitory rate [Hz]\")\n", - "axes[2].set_xlabel(\"Time [ms]\");" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "lkZXMKuC42jG" - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "accelerator": "GPU", - "colab": { - "name": "2_synapses", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3", - "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": "3.7.7" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/documentation/5/tutorials/comp_neuro_101/1_neurons.html b/documentation/5/tutorials/comp_neuro_101/1_neurons.html index b8f7cdd15..0fb5e64c7 100644 --- a/documentation/5/tutorials/comp_neuro_101/1_neurons.html +++ b/documentation/5/tutorials/comp_neuro_101/1_neurons.html @@ -1,29 +1,30 @@ - + Defining populations of neurons — PyGeNN documentation - - - - - - - + + + + + + + - - - - - + + + + + + diff --git a/documentation/5/tutorials/comp_neuro_101/2_synapses.html b/documentation/5/tutorials/comp_neuro_101/2_synapses.html index c10639d63..dc122bcf8 100644 --- a/documentation/5/tutorials/comp_neuro_101/2_synapses.html +++ b/documentation/5/tutorials/comp_neuro_101/2_synapses.html @@ -1,29 +1,30 @@ - + Adding synapses — PyGeNN documentation - - - - - - - + + + + + + + - - - - - + + + + + + diff --git a/documentation/5/tutorials/comp_neuro_101/index.html b/documentation/5/tutorials/comp_neuro_101/index.html deleted file mode 100644 index d5625dc6e..000000000 --- a/documentation/5/tutorials/comp_neuro_101/index.html +++ /dev/null @@ -1,137 +0,0 @@ - - - - - - - CompNeuro 101 — PyGeNN documentation - - - - - - - - - - - - - - - - - - - - - - - - - -
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          CompNeuro 101

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          Building spiking neural network models in GeNN

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          Perform MNIST inference by converting a pre-trained ANN to an SNN

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          - - - - \ No newline at end of file diff --git a/documentation/5/tutorials/mnist_inference/tutorial_1.html b/documentation/5/tutorials/mnist_inference/tutorial_1.html index 47cad16d7..35fcae5ba 100644 --- a/documentation/5/tutorials/mnist_inference/tutorial_1.html +++ b/documentation/5/tutorials/mnist_inference/tutorial_1.html @@ -1,29 +1,30 @@ - + Classification of a single digit — PyGeNN documentation - - - - - - - + + + + + + + - - - - - + + + + + + diff --git a/documentation/5/tutorials/mnist_inference/tutorial_2.html b/documentation/5/tutorials/mnist_inference/tutorial_2.html index 7337b3f74..7b05db591 100644 --- a/documentation/5/tutorials/mnist_inference/tutorial_2.html +++ b/documentation/5/tutorials/mnist_inference/tutorial_2.html @@ -1,29 +1,30 @@ - + Classification of the entire test set — PyGeNN documentation - - - - - - - + + + + + + + - - - - - + + + + + + diff --git a/documentation/5/tutorials/mnist_inference/tutorial_3.html b/documentation/5/tutorials/mnist_inference/tutorial_3.html index b92590680..1a1509c94 100644 --- a/documentation/5/tutorials/mnist_inference/tutorial_3.html +++ b/documentation/5/tutorials/mnist_inference/tutorial_3.html @@ -1,29 +1,30 @@ - + Faster classification of the whole test set — PyGeNN documentation - - - - - - - + + + + + + + - - - - - + + + + + + diff --git a/documentation/5/tutorials/mushroom_body/1_first_layer.html b/documentation/5/tutorials/mushroom_body/1_first_layer.html index d0b8ba7b1..228d07d88 100644 --- a/documentation/5/tutorials/mushroom_body/1_first_layer.html +++ b/documentation/5/tutorials/mushroom_body/1_first_layer.html @@ -1,29 +1,30 @@ - + Presenting latency-coded inputs — PyGeNN documentation - - - - - - - + + + + + + + - - - - - + + + + + + diff --git a/documentation/5/tutorials/mushroom_body/2_second_layer.html b/documentation/5/tutorials/mushroom_body/2_second_layer.html index 8839147a2..64c7d2bf7 100644 --- a/documentation/5/tutorials/mushroom_body/2_second_layer.html +++ b/documentation/5/tutorials/mushroom_body/2_second_layer.html @@ -1,29 +1,30 @@ - + Adding Kenyon Cells — PyGeNN documentation - - - - - - - + + + + + + + - - - - - + + + + + + diff --git a/documentation/5/tutorials/mushroom_body/3_second_layer_gain_control.html b/documentation/5/tutorials/mushroom_body/3_second_layer_gain_control.html index ef8ad80a3..804d63cdd 100644 --- a/documentation/5/tutorials/mushroom_body/3_second_layer_gain_control.html +++ b/documentation/5/tutorials/mushroom_body/3_second_layer_gain_control.html @@ -1,29 +1,30 @@ - + Feedback-inhibition based gain control — PyGeNN documentation - - - - - - - + + + + + + + - - - - - + + + + + + diff --git a/documentation/5/tutorials/mushroom_body/4_third_layer.html b/documentation/5/tutorials/mushroom_body/4_third_layer.html index 172a514d8..7f3b705e7 100644 --- a/documentation/5/tutorials/mushroom_body/4_third_layer.html +++ b/documentation/5/tutorials/mushroom_body/4_third_layer.html @@ -1,29 +1,30 @@ - + Output neurons and learning — PyGeNN documentation - - - - - - - + + + + + + + - - - - - + + + + + + diff --git a/documentation/5/tutorials/mushroom_body/5_testing.html b/documentation/5/tutorials/mushroom_body/5_testing.html index c69559ee2..75d6b9ea5 100644 --- a/documentation/5/tutorials/mushroom_body/5_testing.html +++ b/documentation/5/tutorials/mushroom_body/5_testing.html @@ -1,29 +1,30 @@ - + Testing — PyGeNN documentation - - - - - - - + + + + + + + - - - - - + + + + + + diff --git a/documentation/5/tutorials/mushroom_body/index.html b/documentation/5/tutorials/mushroom_body/index.html deleted file mode 100644 index 42fb8ff11..000000000 --- a/documentation/5/tutorials/mushroom_body/index.html +++ /dev/null @@ -1,137 +0,0 @@ - - - - - - - Insect-inspired MNIST classification — PyGeNN documentation - - - - - - - - - - - - - - - - - - - - - - - - - -
          - - -
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          Insect-inspired MNIST classification

          -

          Train a model of the insect mushroom body using an STDP learning rule to classify MNIST.

          - -
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          - - - - \ No newline at end of file diff --git a/documentation/5/upgrading.html b/documentation/5/upgrading.html index b2b77dd62..d88c383b6 100644 --- a/documentation/5/upgrading.html +++ b/documentation/5/upgrading.html @@ -1,28 +1,29 @@ - + Upgrading from GeNN 4 — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + diff --git a/documentation/5/userproject/index.html b/documentation/5/userproject/index.html index 2ed7e35a9..c86fea461 100644 --- a/documentation/5/userproject/index.html +++ b/documentation/5/userproject/index.html @@ -1,28 +1,29 @@ - + User projects — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + diff --git a/documentation/5/userproject/mnist_mb_classifier.html b/documentation/5/userproject/mnist_mb_classifier.html index 01aaf75f3..f72b0ff78 100644 --- a/documentation/5/userproject/mnist_mb_classifier.html +++ b/documentation/5/userproject/mnist_mb_classifier.html @@ -1,28 +1,29 @@ - + MNIST classification using an insect-inspired mushroom body model — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + diff --git a/documentation/5/userproject/potjans_microcircuit.html b/documentation/5/userproject/potjans_microcircuit.html index 311014236..4b30e1243 100644 --- a/documentation/5/userproject/potjans_microcircuit.html +++ b/documentation/5/userproject/potjans_microcircuit.html @@ -1,28 +1,29 @@ - + PyGeNN implementation of local cortical microcircuit model — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + @@ -104,8 +105,8 @@ with in vivo recordings in awake animals, including the low rate of layer 2/3 excitatory cells. This example can be used as follows:

          usage: potjans_microcircuit [-h] [--duration DURATION] [--neuron-scale NEURON_SCALE]
-                            [--connectivity-scale CONNECTIVITY_SCALE] [--kernel-profiling]
-                            [--procedural-connectivity] [--save-data]
+                            [--connectivity-scale CONNECTIVITY_SCALE] [--kernel-profiling] [--procedural-connectivity]
+                            [--save-data]
          diff --git a/documentation/5/userproject/sg_execution_times.html b/documentation/5/userproject/sg_execution_times.html index fb7b09b3d..a62f55684 100644 --- a/documentation/5/userproject/sg_execution_times.html +++ b/documentation/5/userproject/sg_execution_times.html @@ -1,28 +1,29 @@ - + Computation times — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + diff --git a/documentation/5/userproject/superspike_demo.html b/documentation/5/userproject/superspike_demo.html index fe417d673..f22244c75 100644 --- a/documentation/5/userproject/superspike_demo.html +++ b/documentation/5/userproject/superspike_demo.html @@ -1,28 +1,29 @@ - + PyGeNN implementation of SuperSpike — PyGeNN documentation - - - - - - + + + + + + - - - - - + + + + + + @@ -103,9 +104,8 @@ learning rule to learn the transformation between fixed spike trains of Poisson noise and a target spiking output (by default the Radcliffe Camera at Oxford).

          This example can be used as follows:

          -
          usage: superspike_demo [-h] --record-trial [RECORD_TRIAL [RECORD_TRIAL ...]]
-                       [--target-file TARGET_FILE] [--num-trials NUM_TRIALS] [--kernel-profiling]
-                       [--save-data]
+
          usage: superspike_demo [-h] --record-trial [RECORD_TRIAL ...] [--target-file TARGET_FILE] [--num-trials NUM_TRIALS]
+                       [--kernel-profiling] [--save-data]